1 Calculating error rates.

I wrote the function ‘create_matrices()’ to collect mutation counts. At least in theory the results from it should be able to address most/any question regarding the counts of mutations observed in the data.

1.1 Categorize the data with at least 3 indexes per mutant

devtools::load_all("errRt")

## Loading errRt

## Loading required package: dplyr

## 
## Attaching package: 'dplyr'

## The following object is masked from 'package:hpgltools':
## 
##     combine

## The following object is masked from 'package:Biobase':
## 
##     combine

## The following objects are masked from 'package:BiocGenerics':
## 
##     combine, intersect, setdiff, union

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

## Loading required package: tidyr

triples <-  create_matrices(sample_sheet="sample_sheets/all_samples.xlsx",
                            ident_column="identtable", mut_column="mutationtable",
                            min_reads=3, min_indexes=3, min_sequencer=10,
                            min_position=24, max_position=176,
                            prune_n=TRUE, verbose=TRUE)

## Starting sample: 1.

## Reading the file containing mutations: preprocessing/s1/step4.txt.xz

## Reading the file containing the identical reads: preprocessing/s1/step2_identical_reads.txt.xz

## Mutation data: removing any differences before position: 24.

## Mutation data: before pruning, there are: 1156535 reads.

## Mutation data: after min-position pruning, there are: 1037310 reads: 119225 lost or 10.31%.

## Mutation data: removing any differences after position: 176.

## Mutation data: before pruning, there are: 1037310 reads.

## Mutation data: after max-position pruning, there are: 968161 reads: 69149 lost or 6.67%.

## Mutation data: removing any reads with 'N' as the hit.

## Mutation data: after N pruning, there are: 953181 reads: 14980 lost or 1.55%.

## Mutation data: all filters removed 203354 reads, or 17.58%.

## All data: gathering information about the indexes observed, this is slow.

## Before reads/index pruning, there are: 1742165 indexes in all the data.

## After reads/index pruning, there are: 837608 indexes: 904557 lost or 51.92%.

## All data: removing indexes with fewer than 3 reads/index.

## All data: before reads/index pruning, there are: 953181 changed reads.

## All data: before reads/index pruning, there are: 4681501 identical reads.

## All data: after index pruning, there are: 491995 changed reads: 51.62%.

## All data: after index pruning, there are: 3663004 identical reads: 78.24%.

## Gathering identical, mutant, and sequencer reads/indexes.

## Before classification, there are 3663004 identical reads.

## Before classification, there are 491995 reads with mutations.

## After classification, there are 2738199 reads/indexes which are only identical.

## After classification, there are 11023 reads/indexes which are strictly sequencer.

## After classification, there are 26963 reads/indexes which are deemed from reverse transcriptase.

## Counted by direction: 7018785 forward reads and 7148314 reverse_reads.

## Subsetting based on mutations with at least 3 indexes.

## Classified mutation strings according to various queries.

## Starting sample: 2.

## Reading the file containing mutations: preprocessing/s2/step4.txt.xz

## Reading the file containing the identical reads: preprocessing/s2/step2_identical_reads.txt.xz

## Mutation data: removing any differences before position: 24.

## Mutation data: before pruning, there are: 3421203 reads.

## Mutation data: after min-position pruning, there are: 1758479 reads: 1662724 lost or 48.60%.

## Mutation data: removing any differences after position: 176.

## Mutation data: before pruning, there are: 1758479 reads.

## Mutation data: after max-position pruning, there are: 1667302 reads: 91177 lost or 5.18%.

## Mutation data: removing any reads with 'N' as the hit.

## Mutation data: after N pruning, there are: 1642969 reads: 24333 lost or 1.46%.

## Mutation data: all filters removed 1778234 reads, or 51.98%.

## All data: gathering information about the indexes observed, this is slow.

## Before reads/index pruning, there are: 1261478 indexes in all the data.

## After reads/index pruning, there are: 693725 indexes: 567753 lost or 45.01%.

## All data: removing indexes with fewer than 3 reads/index.

## All data: before reads/index pruning, there are: 1642969 changed reads.

## All data: before reads/index pruning, there are: 5230976 identical reads.

## All data: after index pruning, there are: 814407 changed reads: 49.57%.

## All data: after index pruning, there are: 4834092 identical reads: 92.41%.

## Gathering identical, mutant, and sequencer reads/indexes.

## Before classification, there are 4834092 identical reads.

## Before classification, there are 814407 reads with mutations.

## After classification, there are 2802107 reads/indexes which are only identical.

## After classification, there are 111708 reads/indexes which are strictly sequencer.

## After classification, there are 126921 reads/indexes which are deemed from reverse transcriptase.

## Counted by direction: 11803361 forward reads and 12275547 reverse_reads.

## Subsetting based on mutations with at least 3 indexes.

## Classified mutation strings according to various queries.

## Starting sample: 3.

## Reading the file containing mutations: preprocessing/s3/step4.txt.xz

## Reading the file containing the identical reads: preprocessing/s3/step2_identical_reads.txt.xz

## Mutation data: removing any differences before position: 24.

## Mutation data: before pruning, there are: 4309681 reads.

## Mutation data: after min-position pruning, there are: 1564155 reads: 2745526 lost or 63.71%.

## Mutation data: removing any differences after position: 176.

## Mutation data: before pruning, there are: 1564155 reads.

## Mutation data: after max-position pruning, there are: 1482559 reads: 81596 lost or 5.22%.

## Mutation data: removing any reads with 'N' as the hit.

## Mutation data: after N pruning, there are: 1452047 reads: 30512 lost or 2.06%.

## Mutation data: all filters removed 2857634 reads, or 66.31%.

## All data: gathering information about the indexes observed, this is slow.

## Before reads/index pruning, there are: 884042 indexes in all the data.

## After reads/index pruning, there are: 463445 indexes: 420597 lost or 47.58%.

## All data: removing indexes with fewer than 3 reads/index.

## All data: before reads/index pruning, there are: 1452047 changed reads.

## All data: before reads/index pruning, there are: 3583390 identical reads.

## All data: after index pruning, there are: 730397 changed reads: 50.30%.

## All data: after index pruning, there are: 3332136 identical reads: 92.99%.

## Gathering identical, mutant, and sequencer reads/indexes.

## Before classification, there are 3332136 identical reads.

## Before classification, there are 730397 reads with mutations.

## After classification, there are 1851177 reads/indexes which are only identical.

## After classification, there are 90341 reads/indexes which are strictly sequencer.

## After classification, there are 244494 reads/indexes which are deemed from reverse transcriptase.

## Counted by direction: 9104237 forward reads and 9257103 reverse_reads.

## Subsetting based on mutations with at least 3 indexes.

## Classified mutation strings according to various queries.

## Making a matrix of miss_reads_by_position.

## Making a matrix of miss_indexes_by_position.

## Making a matrix of miss_sequencer_by_position.

## Making a matrix of miss_reads_by_string.

## Making a matrix of miss_indexes_by_string.

## Making a matrix of miss_sequencer_by_string.

## Making a matrix of miss_reads_by_ref_nt.

## Making a matrix of miss_indexes_by_ref_nt.

## Making a matrix of miss_sequencer_by_ref_nt.

## Making a matrix of miss_reads_by_hit_nt.

## Making a matrix of miss_indexes_by_hit_nt.

## Making a matrix of miss_sequencer_by_hit_nt.

## Making a matrix of miss_reads_by_type.

## Making a matrix of miss_indexes_by_type.

## Making a matrix of miss_sequencer_by_type.

## Making a matrix of miss_reads_by_trans.

## Making a matrix of miss_indexes_by_trans.

## Making a matrix of miss_sequencer_by_trans.

## Making a matrix of miss_reads_by_strength.

## Making a matrix of miss_indexes_by_strength.

## Making a matrix of miss_sequencer_by_strength.

## Making a matrix of insert_reads_by_position.

## Making a matrix of insert_indexes_by_position.

## Making a matrix of insert_sequencer_by_position.

## Making a matrix of insert_reads_by_nt.

## Making a matrix of insert_indexes_by_nt.

## Making a matrix of insert_sequencer_by_nt.

## Making a matrix of delete_reads_by_position.

## Making a matrix of delete_indexes_by_position.

## Making a matrix of delete_sequencer_by_position.

## Making a matrix of delete_reads_by_nt.

## Making a matrix of delete_indexes_by_nt.

## Making a matrix of delete_sequencer_by_nt.

## Skipping table: miss_reads_by_ref_nt

## Skipping table: miss_indexes_by_ref_nt

## Skipping table: miss_sequencer_by_ref_nt

## Skipping table: miss_reads_by_hit_nt

## Skipping table: miss_indexes_by_hit_nt

## Skipping table: miss_sequencer_by_hit_nt

## Skipping table: delete_reads_by_position

## Skipping table: delete_indexes_by_position

## Skipping table: delete_sequencer_by_position

## Skipping table: delete_reads_by_nt

## Skipping table: delete_indexes_by_nt

## Skipping table: delete_sequencer_by_nt

triple_plots <- barplot_matrices(triples)
summary(triples)

##                      Length Class  Mode   
## samples               3     -none- list   
## reads_per_sample      3     -none- numeric
## indexes_per_sample    3     -none- numeric
## matrices             33     -none- list   
## matrices_by_counts   33     -none- list   
## normalized           33     -none- list   
## normalized_by_counts 33     -none- list

triples_tenmpr <- create_matrices(sample_sheet="sample_sheets/all_samples.xlsx",
                                  ident_column="identtable", mut_column="mutationtable",
                                  min_reads=3, min_indexes=3, min_sequencer=10,
                                  min_position=24, max_position=176,
                                  max_mutations_per_read=10,
                                  prune_n=TRUE, verbose=TRUE)

## Starting sample: 1.

## Reading the file containing mutations: preprocessing/s1/step4.txt.xz

## Reading the file containing the identical reads: preprocessing/s1/step2_identical_reads.txt.xz

## Mutation data: removing any differences before position: 24.

## Mutation data: before pruning, there are: 1156535 reads.

## Mutation data: after min-position pruning, there are: 1037310 reads: 119225 lost or 10.31%.

## Mutation data: removing any differences after position: 176.

## Mutation data: before pruning, there are: 1037310 reads.

## Mutation data: after max-position pruning, there are: 968161 reads: 69149 lost or 6.67%.

## Mutation data: removing any reads with 'N' as the hit.

## Mutation data: after N pruning, there are: 953181 reads: 14980 lost or 1.55%.

## Mutation data: removing reads with greater than 10 mutations.

## Mutation data: after max_mutation pruning, there are: 799403 reads: 153778 lost or 16.13%.

## Mutation data: all filters removed 357132 reads, or 30.88%.

## All data: gathering information about the indexes observed, this is slow.

## Before reads/index pruning, there are: 1733789 indexes in all the data.

## After reads/index pruning, there are: 836838 indexes: 896951 lost or 51.73%.

## All data: removing indexes with fewer than 3 reads/index.

## All data: before reads/index pruning, there are: 799403 changed reads.

## All data: before reads/index pruning, there are: 4681501 identical reads.

## All data: after index pruning, there are: 441562 changed reads: 55.24%.

## All data: after index pruning, there are: 3661605 identical reads: 78.21%.

## Gathering identical, mutant, and sequencer reads/indexes.

## Before classification, there are 3661605 identical reads.

## Before classification, there are 441562 reads with mutations.

## After classification, there are 2748736 reads/indexes which are only identical.

## After classification, there are 9916 reads/indexes which are strictly sequencer.

## After classification, there are 26403 reads/indexes which are deemed from reverse transcriptase.

## Counted by direction: 7049093 forward reads and 7175885 reverse_reads.

## Subsetting based on mutations with at least 3 indexes.

## Classified mutation strings according to various queries.

## Starting sample: 2.

## Reading the file containing mutations: preprocessing/s2/step4.txt.xz

## Reading the file containing the identical reads: preprocessing/s2/step2_identical_reads.txt.xz

## Mutation data: removing any differences before position: 24.

## Mutation data: before pruning, there are: 3421203 reads.

## Mutation data: after min-position pruning, there are: 1758479 reads: 1662724 lost or 48.60%.

## Mutation data: removing any differences after position: 176.

## Mutation data: before pruning, there are: 1758479 reads.

## Mutation data: after max-position pruning, there are: 1667302 reads: 91177 lost or 5.18%.

## Mutation data: removing any reads with 'N' as the hit.

## Mutation data: after N pruning, there are: 1642969 reads: 24333 lost or 1.46%.

## Mutation data: removing reads with greater than 10 mutations.

## Mutation data: after max_mutation pruning, there are: 1232741 reads: 410228 lost or 24.97%.

## Mutation data: all filters removed 2188462 reads, or 63.97%.

## All data: gathering information about the indexes observed, this is slow.

## Before reads/index pruning, there are: 1231605 indexes in all the data.

## After reads/index pruning, there are: 693381 indexes: 538224 lost or 43.70%.

## All data: removing indexes with fewer than 3 reads/index.

## All data: before reads/index pruning, there are: 1232741 changed reads.

## All data: before reads/index pruning, there are: 5230976 identical reads.

## All data: after index pruning, there are: 720963 changed reads: 58.48%.

## All data: after index pruning, there are: 4833605 identical reads: 92.40%.

## Gathering identical, mutant, and sequencer reads/indexes.

## Before classification, there are 4833605 identical reads.

## Before classification, there are 720963 reads with mutations.

## After classification, there are 2832509 reads/indexes which are only identical.

## After classification, there are 98387 reads/indexes which are strictly sequencer.

## After classification, there are 123178 reads/indexes which are deemed from reverse transcriptase.

## Counted by direction: 11930745 forward reads and 12406826 reverse_reads.

## Subsetting based on mutations with at least 3 indexes.

## Classified mutation strings according to various queries.

## Starting sample: 3.

## Reading the file containing mutations: preprocessing/s3/step4.txt.xz

## Reading the file containing the identical reads: preprocessing/s3/step2_identical_reads.txt.xz

## Mutation data: removing any differences before position: 24.

## Mutation data: before pruning, there are: 4309681 reads.

## Mutation data: after min-position pruning, there are: 1564155 reads: 2745526 lost or 63.71%.

## Mutation data: removing any differences after position: 176.

## Mutation data: before pruning, there are: 1564155 reads.

## Mutation data: after max-position pruning, there are: 1482559 reads: 81596 lost or 5.22%.

## Mutation data: removing any reads with 'N' as the hit.

## Mutation data: after N pruning, there are: 1452047 reads: 30512 lost or 2.06%.

## Mutation data: removing reads with greater than 10 mutations.

## Mutation data: after max_mutation pruning, there are: 1110089 reads: 341958 lost or 23.55%.

## Mutation data: all filters removed 3199592 reads, or 74.24%.

## All data: gathering information about the indexes observed, this is slow.

## Before reads/index pruning, there are: 857851 indexes in all the data.

## After reads/index pruning, there are: 463161 indexes: 394690 lost or 46.01%.

## All data: removing indexes with fewer than 3 reads/index.

## All data: before reads/index pruning, there are: 1110089 changed reads.

## All data: before reads/index pruning, there are: 3583390 identical reads.

## All data: after index pruning, there are: 662025 changed reads: 59.64%.

## All data: after index pruning, there are: 3331914 identical reads: 92.98%.

## Gathering identical, mutant, and sequencer reads/indexes.

## Before classification, there are 3331914 identical reads.

## Before classification, there are 662025 reads with mutations.

## After classification, there are 1873630 reads/indexes which are only identical.

## After classification, there are 79142 reads/indexes which are strictly sequencer.

## After classification, there are 237111 reads/indexes which are deemed from reverse transcriptase.

## Counted by direction: 9205882 forward reads and 9355117 reverse_reads.

## Subsetting based on mutations with at least 3 indexes.

## Classified mutation strings according to various queries.

## Making a matrix of miss_reads_by_position.

## Making a matrix of miss_indexes_by_position.

## Making a matrix of miss_sequencer_by_position.

## Making a matrix of miss_reads_by_string.

## Making a matrix of miss_indexes_by_string.

## Making a matrix of miss_sequencer_by_string.

## Making a matrix of miss_reads_by_ref_nt.

## Making a matrix of miss_indexes_by_ref_nt.

## Making a matrix of miss_sequencer_by_ref_nt.

## Making a matrix of miss_reads_by_hit_nt.

## Making a matrix of miss_indexes_by_hit_nt.

## Making a matrix of miss_sequencer_by_hit_nt.

## Making a matrix of miss_reads_by_type.

## Making a matrix of miss_indexes_by_type.

## Making a matrix of miss_sequencer_by_type.

## Making a matrix of miss_reads_by_trans.

## Making a matrix of miss_indexes_by_trans.

## Making a matrix of miss_sequencer_by_trans.

## Making a matrix of miss_reads_by_strength.

## Making a matrix of miss_indexes_by_strength.

## Making a matrix of miss_sequencer_by_strength.

## Making a matrix of insert_reads_by_position.

## Making a matrix of insert_indexes_by_position.

## Making a matrix of insert_sequencer_by_position.

## Making a matrix of insert_reads_by_nt.

## Making a matrix of insert_indexes_by_nt.

## Making a matrix of insert_sequencer_by_nt.

## Making a matrix of delete_reads_by_position.

## Making a matrix of delete_indexes_by_position.

## Making a matrix of delete_sequencer_by_position.

## Making a matrix of delete_reads_by_nt.

## Making a matrix of delete_indexes_by_nt.

## Making a matrix of delete_sequencer_by_nt.

## Skipping table: miss_reads_by_ref_nt

## Skipping table: miss_indexes_by_ref_nt

## Skipping table: miss_sequencer_by_ref_nt

## Skipping table: miss_reads_by_hit_nt

## Skipping table: miss_indexes_by_hit_nt

## Skipping table: miss_sequencer_by_hit_nt

## Skipping table: delete_reads_by_position

## Skipping table: delete_indexes_by_position

## Skipping table: delete_sequencer_by_position

## Skipping table: delete_reads_by_nt

## Skipping table: delete_indexes_by_nt

## Skipping table: delete_sequencer_by_nt

triple_tenmpr_plots <- barplot_matrices(triples_tenmpr)
summary(triples_tenmpr)

##                      Length Class  Mode   
## samples               3     -none- list   
## reads_per_sample      3     -none- numeric
## indexes_per_sample    3     -none- numeric
## matrices             33     -none- list   
## matrices_by_counts   33     -none- list   
## normalized           33     -none- list   
## normalized_by_counts 33     -none- list

triples_fivempr <- create_matrices(sample_sheet="sample_sheets/all_samples.xlsx",
                                  ident_column="identtable", mut_column="mutationtable",
                                  min_reads=3, min_indexes=3, min_sequencer=10,
                                  min_position=24, max_position=176,
                                  max_mutations_per_read=5,
                                  prune_n=TRUE, verbose=TRUE)

## Starting sample: 1.

## Reading the file containing mutations: preprocessing/s1/step4.txt.xz

## Reading the file containing the identical reads: preprocessing/s1/step2_identical_reads.txt.xz

## Mutation data: removing any differences before position: 24.

## Mutation data: before pruning, there are: 1156535 reads.

## Mutation data: after min-position pruning, there are: 1037310 reads: 119225 lost or 10.31%.

## Mutation data: removing any differences after position: 176.

## Mutation data: before pruning, there are: 1037310 reads.

## Mutation data: after max-position pruning, there are: 968161 reads: 69149 lost or 6.67%.

## Mutation data: removing any reads with 'N' as the hit.

## Mutation data: after N pruning, there are: 953181 reads: 14980 lost or 1.55%.

## Mutation data: removing reads with greater than 5 mutations.

## Mutation data: after max_mutation pruning, there are: 608429 reads: 344752 lost or 36.17%.

## Mutation data: all filters removed 548106 reads, or 47.39%.

## All data: gathering information about the indexes observed, this is slow.

## Before reads/index pruning, there are: 1713933 indexes in all the data.

## After reads/index pruning, there are: 834821 indexes: 879112 lost or 51.29%.

## All data: removing indexes with fewer than 3 reads/index.

## All data: before reads/index pruning, there are: 608429 changed reads.

## All data: before reads/index pruning, there are: 4681501 identical reads.

## All data: after index pruning, there are: 379603 changed reads: 62.39%.

## All data: after index pruning, there are: 3657910 identical reads: 78.14%.

## Gathering identical, mutant, and sequencer reads/indexes.

## Before classification, there are 3657910 identical reads.

## Before classification, there are 379603 reads with mutations.

## After classification, there are 2777271 reads/indexes which are only identical.

## After classification, there are 8544 reads/indexes which are strictly sequencer.

## After classification, there are 25485 reads/indexes which are deemed from reverse transcriptase.

## Counted by direction: 7127863 forward reads and 7254038 reverse_reads.

## Subsetting based on mutations with at least 3 indexes.

## Classified mutation strings according to various queries.

## Starting sample: 2.

## Reading the file containing mutations: preprocessing/s2/step4.txt.xz

## Reading the file containing the identical reads: preprocessing/s2/step2_identical_reads.txt.xz

## Mutation data: removing any differences before position: 24.

## Mutation data: before pruning, there are: 3421203 reads.

## Mutation data: after min-position pruning, there are: 1758479 reads: 1662724 lost or 48.60%.

## Mutation data: removing any differences after position: 176.

## Mutation data: before pruning, there are: 1758479 reads.

## Mutation data: after max-position pruning, there are: 1667302 reads: 91177 lost or 5.18%.

## Mutation data: removing any reads with 'N' as the hit.

## Mutation data: after N pruning, there are: 1642969 reads: 24333 lost or 1.46%.

## Mutation data: removing reads with greater than 5 mutations.

## Mutation data: after max_mutation pruning, there are: 807185 reads: 835784 lost or 50.87%.

## Mutation data: all filters removed 2614018 reads, or 76.41%.

## All data: gathering information about the indexes observed, this is slow.

## Before reads/index pruning, there are: 1179116 indexes in all the data.

## After reads/index pruning, there are: 692307 indexes: 486809 lost or 41.29%.

## All data: removing indexes with fewer than 3 reads/index.

## All data: before reads/index pruning, there are: 807185 changed reads.

## All data: before reads/index pruning, there are: 5230976 identical reads.

## All data: after index pruning, there are: 585835 changed reads: 72.58%.

## All data: after index pruning, there are: 4832196 identical reads: 92.38%.

## Gathering identical, mutant, and sequencer reads/indexes.

## Before classification, there are 4832196 identical reads.

## Before classification, there are 585835 reads with mutations.

## After classification, there are 2934376 reads/indexes which are only identical.

## After classification, there are 79902 reads/indexes which are strictly sequencer.

## After classification, there are 116271 reads/indexes which are deemed from reverse transcriptase.

## Counted by direction: 12365004 forward reads and 12844113 reverse_reads.

## Subsetting based on mutations with at least 3 indexes.

## Classified mutation strings according to various queries.

## Starting sample: 3.

## Reading the file containing mutations: preprocessing/s3/step4.txt.xz

## Reading the file containing the identical reads: preprocessing/s3/step2_identical_reads.txt.xz

## Mutation data: removing any differences before position: 24.

## Mutation data: before pruning, there are: 4309681 reads.

## Mutation data: after min-position pruning, there are: 1564155 reads: 2745526 lost or 63.71%.

## Mutation data: removing any differences after position: 176.

## Mutation data: before pruning, there are: 1564155 reads.

## Mutation data: after max-position pruning, there are: 1482559 reads: 81596 lost or 5.22%.

## Mutation data: removing any reads with 'N' as the hit.

## Mutation data: after N pruning, there are: 1452047 reads: 30512 lost or 2.06%.

## Mutation data: removing reads with greater than 5 mutations.

## Mutation data: after max_mutation pruning, there are: 746662 reads: 705385 lost or 48.58%.

## Mutation data: all filters removed 3563019 reads, or 82.67%.

## All data: gathering information about the indexes observed, this is slow.

## Before reads/index pruning, there are: 808995 indexes in all the data.

## After reads/index pruning, there are: 461997 indexes: 346998 lost or 42.89%.

## All data: removing indexes with fewer than 3 reads/index.

## All data: before reads/index pruning, there are: 746662 changed reads.

## All data: before reads/index pruning, there are: 3583390 identical reads.

## All data: after index pruning, there are: 555226 changed reads: 74.36%.

## All data: after index pruning, there are: 3330970 identical reads: 92.96%.

## Gathering identical, mutant, and sequencer reads/indexes.

## Before classification, there are 3330970 identical reads.

## Before classification, there are 555226 reads with mutations.

## After classification, there are 1957637 reads/indexes which are only identical.

## After classification, there are 63014 reads/indexes which are strictly sequencer.

## After classification, there are 223250 reads/indexes which are deemed from reverse transcriptase.

## Counted by direction: 9578873 forward reads and 9724531 reverse_reads.

## Subsetting based on mutations with at least 3 indexes.

## Classified mutation strings according to various queries.

## Making a matrix of miss_reads_by_position.

## Making a matrix of miss_indexes_by_position.

## Making a matrix of miss_sequencer_by_position.

## Making a matrix of miss_reads_by_string.

## Making a matrix of miss_indexes_by_string.

## Making a matrix of miss_sequencer_by_string.

## Making a matrix of miss_reads_by_ref_nt.

## Making a matrix of miss_indexes_by_ref_nt.

## Making a matrix of miss_sequencer_by_ref_nt.

## Making a matrix of miss_reads_by_hit_nt.

## Making a matrix of miss_indexes_by_hit_nt.

## Making a matrix of miss_sequencer_by_hit_nt.

## Making a matrix of miss_reads_by_type.

## Making a matrix of miss_indexes_by_type.

## Making a matrix of miss_sequencer_by_type.

## Making a matrix of miss_reads_by_trans.

## Making a matrix of miss_indexes_by_trans.

## Making a matrix of miss_sequencer_by_trans.

## Making a matrix of miss_reads_by_strength.

## Making a matrix of miss_indexes_by_strength.

## Making a matrix of miss_sequencer_by_strength.

## Making a matrix of insert_reads_by_position.

## Making a matrix of insert_indexes_by_position.

## Making a matrix of insert_sequencer_by_position.

## Making a matrix of insert_reads_by_nt.

## Making a matrix of insert_indexes_by_nt.

## Making a matrix of insert_sequencer_by_nt.

## Making a matrix of delete_reads_by_position.

## Making a matrix of delete_indexes_by_position.

## Making a matrix of delete_sequencer_by_position.

## Making a matrix of delete_reads_by_nt.

## Making a matrix of delete_indexes_by_nt.

## Making a matrix of delete_sequencer_by_nt.

## Skipping table: miss_reads_by_ref_nt

## Skipping table: miss_indexes_by_ref_nt

## Skipping table: miss_sequencer_by_ref_nt

## Skipping table: miss_reads_by_hit_nt

## Skipping table: miss_indexes_by_hit_nt

## Skipping table: miss_sequencer_by_hit_nt

## Skipping table: delete_reads_by_position

## Skipping table: delete_indexes_by_position

## Skipping table: delete_sequencer_by_position

## Skipping table: delete_reads_by_nt

## Skipping table: delete_indexes_by_nt

## Skipping table: delete_sequencer_by_nt

triple_fivempr_plots <- barplot_matrices(triples_fivempr)
summary(triples_fivempr)

##                      Length Class  Mode   
## samples               3     -none- list   
## reads_per_sample      3     -none- numeric
## indexes_per_sample    3     -none- numeric
## matrices             33     -none- list   
## matrices_by_counts   33     -none- list   
## normalized           33     -none- list   
## normalized_by_counts 33     -none- list

1.2 Categorize the data with at least 5 indexes per mutant

quints <- create_matrices(sample_sheet="sample_sheets/all_samples.xlsx",
                          ident_column="identtable", mut_column="mutationtable",
                          min_reads=3, min_indexes=5, min_sequencer=10,
                          min_position=24, max_position=176, prune_n=TRUE,
                          verbose=TRUE)

## Starting sample: 1.

## Reading the file containing mutations: preprocessing/s1/step4.txt.xz

## Reading the file containing the identical reads: preprocessing/s1/step2_identical_reads.txt.xz

## Mutation data: removing any differences before position: 24.

## Mutation data: before pruning, there are: 1156535 reads.

## Mutation data: after min-position pruning, there are: 1037310 reads: 119225 lost or 10.31%.

## Mutation data: removing any differences after position: 176.

## Mutation data: before pruning, there are: 1037310 reads.

## Mutation data: after max-position pruning, there are: 968161 reads: 69149 lost or 6.67%.

## Mutation data: removing any reads with 'N' as the hit.

## Mutation data: after N pruning, there are: 953181 reads: 14980 lost or 1.55%.

## Mutation data: all filters removed 203354 reads, or 17.58%.

## All data: gathering information about the indexes observed, this is slow.

## Before reads/index pruning, there are: 1742165 indexes in all the data.

## After reads/index pruning, there are: 837608 indexes: 904557 lost or 51.92%.

## All data: removing indexes with fewer than 3 reads/index.

## All data: before reads/index pruning, there are: 953181 changed reads.

## All data: before reads/index pruning, there are: 4681501 identical reads.

## All data: after index pruning, there are: 491995 changed reads: 51.62%.

## All data: after index pruning, there are: 3663004 identical reads: 78.24%.

## Gathering identical, mutant, and sequencer reads/indexes.

## Before classification, there are 3663004 identical reads.

## Before classification, there are 491995 reads with mutations.

## After classification, there are 2738199 reads/indexes which are only identical.

## After classification, there are 11023 reads/indexes which are strictly sequencer.

## After classification, there are 26963 reads/indexes which are deemed from reverse transcriptase.

## Counted by direction: 7018785 forward reads and 7148314 reverse_reads.

## Subsetting based on mutations with at least 5 indexes.

## Classified mutation strings according to various queries.

## Starting sample: 2.

## Reading the file containing mutations: preprocessing/s2/step4.txt.xz

## Reading the file containing the identical reads: preprocessing/s2/step2_identical_reads.txt.xz

## Mutation data: removing any differences before position: 24.

## Mutation data: before pruning, there are: 3421203 reads.

## Mutation data: after min-position pruning, there are: 1758479 reads: 1662724 lost or 48.60%.

## Mutation data: removing any differences after position: 176.

## Mutation data: before pruning, there are: 1758479 reads.

## Mutation data: after max-position pruning, there are: 1667302 reads: 91177 lost or 5.18%.

## Mutation data: removing any reads with 'N' as the hit.

## Mutation data: after N pruning, there are: 1642969 reads: 24333 lost or 1.46%.

## Mutation data: all filters removed 1778234 reads, or 51.98%.

## All data: gathering information about the indexes observed, this is slow.

## Before reads/index pruning, there are: 1261478 indexes in all the data.

## After reads/index pruning, there are: 693725 indexes: 567753 lost or 45.01%.

## All data: removing indexes with fewer than 3 reads/index.

## All data: before reads/index pruning, there are: 1642969 changed reads.

## All data: before reads/index pruning, there are: 5230976 identical reads.

## All data: after index pruning, there are: 814407 changed reads: 49.57%.

## All data: after index pruning, there are: 4834092 identical reads: 92.41%.

## Gathering identical, mutant, and sequencer reads/indexes.

## Before classification, there are 4834092 identical reads.

## Before classification, there are 814407 reads with mutations.

## After classification, there are 2802107 reads/indexes which are only identical.

## After classification, there are 111708 reads/indexes which are strictly sequencer.

## After classification, there are 126921 reads/indexes which are deemed from reverse transcriptase.

## Counted by direction: 11803361 forward reads and 12275547 reverse_reads.

## Subsetting based on mutations with at least 5 indexes.

## Classified mutation strings according to various queries.

## Starting sample: 3.

## Reading the file containing mutations: preprocessing/s3/step4.txt.xz

## Reading the file containing the identical reads: preprocessing/s3/step2_identical_reads.txt.xz

## Mutation data: removing any differences before position: 24.

## Mutation data: before pruning, there are: 4309681 reads.

## Mutation data: after min-position pruning, there are: 1564155 reads: 2745526 lost or 63.71%.

## Mutation data: removing any differences after position: 176.

## Mutation data: before pruning, there are: 1564155 reads.

## Mutation data: after max-position pruning, there are: 1482559 reads: 81596 lost or 5.22%.

## Mutation data: removing any reads with 'N' as the hit.

## Mutation data: after N pruning, there are: 1452047 reads: 30512 lost or 2.06%.

## Mutation data: all filters removed 2857634 reads, or 66.31%.

## All data: gathering information about the indexes observed, this is slow.

## Before reads/index pruning, there are: 884042 indexes in all the data.

## After reads/index pruning, there are: 463445 indexes: 420597 lost or 47.58%.

## All data: removing indexes with fewer than 3 reads/index.

## All data: before reads/index pruning, there are: 1452047 changed reads.

## All data: before reads/index pruning, there are: 3583390 identical reads.

## All data: after index pruning, there are: 730397 changed reads: 50.30%.

## All data: after index pruning, there are: 3332136 identical reads: 92.99%.

## Gathering identical, mutant, and sequencer reads/indexes.

## Before classification, there are 3332136 identical reads.

## Before classification, there are 730397 reads with mutations.

## After classification, there are 1851177 reads/indexes which are only identical.

## After classification, there are 90341 reads/indexes which are strictly sequencer.

## After classification, there are 244494 reads/indexes which are deemed from reverse transcriptase.

## Counted by direction: 9104237 forward reads and 9257103 reverse_reads.

## Subsetting based on mutations with at least 5 indexes.

## Classified mutation strings according to various queries.

## Making a matrix of miss_reads_by_position.

## Making a matrix of miss_indexes_by_position.

## Making a matrix of miss_sequencer_by_position.

## Making a matrix of miss_reads_by_string.

## Making a matrix of miss_indexes_by_string.

## Making a matrix of miss_sequencer_by_string.

## Making a matrix of miss_reads_by_ref_nt.

## Making a matrix of miss_indexes_by_ref_nt.

## Making a matrix of miss_sequencer_by_ref_nt.

## Making a matrix of miss_reads_by_hit_nt.

## Making a matrix of miss_indexes_by_hit_nt.

## Making a matrix of miss_sequencer_by_hit_nt.

## Making a matrix of miss_reads_by_type.

## Making a matrix of miss_indexes_by_type.

## Making a matrix of miss_sequencer_by_type.

## Making a matrix of miss_reads_by_trans.

## Making a matrix of miss_indexes_by_trans.

## Making a matrix of miss_sequencer_by_trans.

## Making a matrix of miss_reads_by_strength.

## Making a matrix of miss_indexes_by_strength.

## Making a matrix of miss_sequencer_by_strength.

## Making a matrix of insert_reads_by_position.

## Making a matrix of insert_indexes_by_position.

## Making a matrix of insert_sequencer_by_position.

## Making a matrix of insert_reads_by_nt.

## Making a matrix of insert_indexes_by_nt.

## Making a matrix of insert_sequencer_by_nt.

## Making a matrix of delete_reads_by_position.

## Making a matrix of delete_indexes_by_position.

## Making a matrix of delete_sequencer_by_position.

## Making a matrix of delete_reads_by_nt.

## Making a matrix of delete_indexes_by_nt.

## Making a matrix of delete_sequencer_by_nt.

## Skipping table: miss_reads_by_ref_nt

## Skipping table: miss_indexes_by_ref_nt

## Skipping table: miss_sequencer_by_ref_nt

## Skipping table: miss_reads_by_hit_nt

## Skipping table: miss_indexes_by_hit_nt

## Skipping table: miss_sequencer_by_hit_nt

## Skipping table: delete_reads_by_position

## Skipping table: delete_indexes_by_position

## Skipping table: delete_sequencer_by_position

## Skipping table: delete_reads_by_nt

## Skipping table: delete_indexes_by_nt

## Skipping table: delete_sequencer_by_nt

quint_plots <- barplot_matrices(quints)
summary(quints)

##                      Length Class  Mode   
## samples               3     -none- list   
## reads_per_sample      3     -none- numeric
## indexes_per_sample    3     -none- numeric
## matrices             33     -none- list   
## matrices_by_counts   33     -none- list   
## normalized           33     -none- list   
## normalized_by_counts 33     -none- list

quints_tenmpr <- create_matrices(sample_sheet="sample_sheets/all_samples.xlsx",
                                 ident_column="identtable", mut_column="mutationtable",
                                 min_reads=3, min_indexes=5, min_sequencer=10,
                                 min_position=24, max_position=176,
                                 max_mutations_per_read=10,
                                 prune_n=TRUE, verbose=TRUE)

## Starting sample: 1.

## Reading the file containing mutations: preprocessing/s1/step4.txt.xz

## Reading the file containing the identical reads: preprocessing/s1/step2_identical_reads.txt.xz

## Mutation data: removing any differences before position: 24.

## Mutation data: before pruning, there are: 1156535 reads.

## Mutation data: after min-position pruning, there are: 1037310 reads: 119225 lost or 10.31%.

## Mutation data: removing any differences after position: 176.

## Mutation data: before pruning, there are: 1037310 reads.

## Mutation data: after max-position pruning, there are: 968161 reads: 69149 lost or 6.67%.

## Mutation data: removing any reads with 'N' as the hit.

## Mutation data: after N pruning, there are: 953181 reads: 14980 lost or 1.55%.

## Mutation data: removing reads with greater than 10 mutations.

## Mutation data: after max_mutation pruning, there are: 799403 reads: 153778 lost or 16.13%.

## Mutation data: all filters removed 357132 reads, or 30.88%.

## All data: gathering information about the indexes observed, this is slow.

## Before reads/index pruning, there are: 1733789 indexes in all the data.

## After reads/index pruning, there are: 836838 indexes: 896951 lost or 51.73%.

## All data: removing indexes with fewer than 3 reads/index.

## All data: before reads/index pruning, there are: 799403 changed reads.

## All data: before reads/index pruning, there are: 4681501 identical reads.

## All data: after index pruning, there are: 441562 changed reads: 55.24%.

## All data: after index pruning, there are: 3661605 identical reads: 78.21%.

## Gathering identical, mutant, and sequencer reads/indexes.

## Before classification, there are 3661605 identical reads.

## Before classification, there are 441562 reads with mutations.

## After classification, there are 2748736 reads/indexes which are only identical.

## After classification, there are 9916 reads/indexes which are strictly sequencer.

## After classification, there are 26403 reads/indexes which are deemed from reverse transcriptase.

## Counted by direction: 7049093 forward reads and 7175885 reverse_reads.

## Subsetting based on mutations with at least 5 indexes.

## Classified mutation strings according to various queries.

## Starting sample: 2.

## Reading the file containing mutations: preprocessing/s2/step4.txt.xz

## Reading the file containing the identical reads: preprocessing/s2/step2_identical_reads.txt.xz

## Mutation data: removing any differences before position: 24.

## Mutation data: before pruning, there are: 3421203 reads.

## Mutation data: after min-position pruning, there are: 1758479 reads: 1662724 lost or 48.60%.

## Mutation data: removing any differences after position: 176.

## Mutation data: before pruning, there are: 1758479 reads.

## Mutation data: after max-position pruning, there are: 1667302 reads: 91177 lost or 5.18%.

## Mutation data: removing any reads with 'N' as the hit.

## Mutation data: after N pruning, there are: 1642969 reads: 24333 lost or 1.46%.

## Mutation data: removing reads with greater than 10 mutations.

## Mutation data: after max_mutation pruning, there are: 1232741 reads: 410228 lost or 24.97%.

## Mutation data: all filters removed 2188462 reads, or 63.97%.

## All data: gathering information about the indexes observed, this is slow.

## Before reads/index pruning, there are: 1231605 indexes in all the data.

## After reads/index pruning, there are: 693381 indexes: 538224 lost or 43.70%.

## All data: removing indexes with fewer than 3 reads/index.

## All data: before reads/index pruning, there are: 1232741 changed reads.

## All data: before reads/index pruning, there are: 5230976 identical reads.

## All data: after index pruning, there are: 720963 changed reads: 58.48%.

## All data: after index pruning, there are: 4833605 identical reads: 92.40%.

## Gathering identical, mutant, and sequencer reads/indexes.

## Before classification, there are 4833605 identical reads.

## Before classification, there are 720963 reads with mutations.

## After classification, there are 2832509 reads/indexes which are only identical.

## After classification, there are 98387 reads/indexes which are strictly sequencer.

## After classification, there are 123178 reads/indexes which are deemed from reverse transcriptase.

## Counted by direction: 11930745 forward reads and 12406826 reverse_reads.

## Subsetting based on mutations with at least 5 indexes.

## Classified mutation strings according to various queries.

## Starting sample: 3.

## Reading the file containing mutations: preprocessing/s3/step4.txt.xz

## Reading the file containing the identical reads: preprocessing/s3/step2_identical_reads.txt.xz

## Mutation data: removing any differences before position: 24.

## Mutation data: before pruning, there are: 4309681 reads.

## Mutation data: after min-position pruning, there are: 1564155 reads: 2745526 lost or 63.71%.

## Mutation data: removing any differences after position: 176.

## Mutation data: before pruning, there are: 1564155 reads.

## Mutation data: after max-position pruning, there are: 1482559 reads: 81596 lost or 5.22%.

## Mutation data: removing any reads with 'N' as the hit.

## Mutation data: after N pruning, there are: 1452047 reads: 30512 lost or 2.06%.

## Mutation data: removing reads with greater than 10 mutations.

## Mutation data: after max_mutation pruning, there are: 1110089 reads: 341958 lost or 23.55%.

## Mutation data: all filters removed 3199592 reads, or 74.24%.

## All data: gathering information about the indexes observed, this is slow.

## Before reads/index pruning, there are: 857851 indexes in all the data.

## After reads/index pruning, there are: 463161 indexes: 394690 lost or 46.01%.

## All data: removing indexes with fewer than 3 reads/index.

## All data: before reads/index pruning, there are: 1110089 changed reads.

## All data: before reads/index pruning, there are: 3583390 identical reads.

## All data: after index pruning, there are: 662025 changed reads: 59.64%.

## All data: after index pruning, there are: 3331914 identical reads: 92.98%.

## Gathering identical, mutant, and sequencer reads/indexes.

## Before classification, there are 3331914 identical reads.

## Before classification, there are 662025 reads with mutations.

## After classification, there are 1873630 reads/indexes which are only identical.

## After classification, there are 79142 reads/indexes which are strictly sequencer.

## After classification, there are 237111 reads/indexes which are deemed from reverse transcriptase.

## Counted by direction: 9205882 forward reads and 9355117 reverse_reads.

## Subsetting based on mutations with at least 5 indexes.

## Classified mutation strings according to various queries.

## Making a matrix of miss_reads_by_position.

## Making a matrix of miss_indexes_by_position.

## Making a matrix of miss_sequencer_by_position.

## Making a matrix of miss_reads_by_string.

## Making a matrix of miss_indexes_by_string.

## Making a matrix of miss_sequencer_by_string.

## Making a matrix of miss_reads_by_ref_nt.

## Making a matrix of miss_indexes_by_ref_nt.

## Making a matrix of miss_sequencer_by_ref_nt.

## Making a matrix of miss_reads_by_hit_nt.

## Making a matrix of miss_indexes_by_hit_nt.

## Making a matrix of miss_sequencer_by_hit_nt.

## Making a matrix of miss_reads_by_type.

## Making a matrix of miss_indexes_by_type.

## Making a matrix of miss_sequencer_by_type.

## Making a matrix of miss_reads_by_trans.

## Making a matrix of miss_indexes_by_trans.

## Making a matrix of miss_sequencer_by_trans.

## Making a matrix of miss_reads_by_strength.

## Making a matrix of miss_indexes_by_strength.

## Making a matrix of miss_sequencer_by_strength.

## Making a matrix of insert_reads_by_position.

## Making a matrix of insert_indexes_by_position.

## Making a matrix of insert_sequencer_by_position.

## Making a matrix of insert_reads_by_nt.

## Making a matrix of insert_indexes_by_nt.

## Making a matrix of insert_sequencer_by_nt.

## Making a matrix of delete_reads_by_position.

## Making a matrix of delete_indexes_by_position.

## Making a matrix of delete_sequencer_by_position.

## Making a matrix of delete_reads_by_nt.

## Making a matrix of delete_indexes_by_nt.

## Making a matrix of delete_sequencer_by_nt.

## Skipping table: miss_reads_by_ref_nt

## Skipping table: miss_indexes_by_ref_nt

## Skipping table: miss_sequencer_by_ref_nt

## Skipping table: miss_reads_by_hit_nt

## Skipping table: miss_indexes_by_hit_nt

## Skipping table: miss_sequencer_by_hit_nt

## Skipping table: delete_reads_by_position

## Skipping table: delete_indexes_by_position

## Skipping table: delete_sequencer_by_position

## Skipping table: delete_reads_by_nt

## Skipping table: delete_indexes_by_nt

## Skipping table: delete_sequencer_by_nt

quint_tenmpr_plots <- barplot_matrices(quints_tenmpr)
summary(quints_tenmpr)

##                      Length Class  Mode   
## samples               3     -none- list   
## reads_per_sample      3     -none- numeric
## indexes_per_sample    3     -none- numeric
## matrices             33     -none- list   
## matrices_by_counts   33     -none- list   
## normalized           33     -none- list   
## normalized_by_counts 33     -none- list

quints_fivempr <- create_matrices(sample_sheet="sample_sheets/all_samples.xlsx",
                                  ident_column="identtable", mut_column="mutationtable",
                                  min_reads=3, min_indexes=5, min_sequencer=10,
                                  min_position=24, max_position=176,
                                  max_mutations_per_read=5,
                                  prune_n=TRUE, verbose=TRUE)

## Starting sample: 1.

## Reading the file containing mutations: preprocessing/s1/step4.txt.xz

## Reading the file containing the identical reads: preprocessing/s1/step2_identical_reads.txt.xz

## Mutation data: removing any differences before position: 24.

## Mutation data: before pruning, there are: 1156535 reads.

## Mutation data: after min-position pruning, there are: 1037310 reads: 119225 lost or 10.31%.

## Mutation data: removing any differences after position: 176.

## Mutation data: before pruning, there are: 1037310 reads.

## Mutation data: after max-position pruning, there are: 968161 reads: 69149 lost or 6.67%.

## Mutation data: removing any reads with 'N' as the hit.

## Mutation data: after N pruning, there are: 953181 reads: 14980 lost or 1.55%.

## Mutation data: removing reads with greater than 5 mutations.

## Mutation data: after max_mutation pruning, there are: 608429 reads: 344752 lost or 36.17%.

## Mutation data: all filters removed 548106 reads, or 47.39%.

## All data: gathering information about the indexes observed, this is slow.

## Before reads/index pruning, there are: 1713933 indexes in all the data.

## After reads/index pruning, there are: 834821 indexes: 879112 lost or 51.29%.

## All data: removing indexes with fewer than 3 reads/index.

## All data: before reads/index pruning, there are: 608429 changed reads.

## All data: before reads/index pruning, there are: 4681501 identical reads.

## All data: after index pruning, there are: 379603 changed reads: 62.39%.

## All data: after index pruning, there are: 3657910 identical reads: 78.14%.

## Gathering identical, mutant, and sequencer reads/indexes.

## Before classification, there are 3657910 identical reads.

## Before classification, there are 379603 reads with mutations.

## After classification, there are 2777271 reads/indexes which are only identical.

## After classification, there are 8544 reads/indexes which are strictly sequencer.

## After classification, there are 25485 reads/indexes which are deemed from reverse transcriptase.

## Counted by direction: 7127863 forward reads and 7254038 reverse_reads.

## Subsetting based on mutations with at least 5 indexes.

## Classified mutation strings according to various queries.

## Starting sample: 2.

## Reading the file containing mutations: preprocessing/s2/step4.txt.xz

## Reading the file containing the identical reads: preprocessing/s2/step2_identical_reads.txt.xz

## Mutation data: removing any differences before position: 24.

## Mutation data: before pruning, there are: 3421203 reads.

## Mutation data: after min-position pruning, there are: 1758479 reads: 1662724 lost or 48.60%.

## Mutation data: removing any differences after position: 176.

## Mutation data: before pruning, there are: 1758479 reads.

## Mutation data: after max-position pruning, there are: 1667302 reads: 91177 lost or 5.18%.

## Mutation data: removing any reads with 'N' as the hit.

## Mutation data: after N pruning, there are: 1642969 reads: 24333 lost or 1.46%.

## Mutation data: removing reads with greater than 5 mutations.

## Mutation data: after max_mutation pruning, there are: 807185 reads: 835784 lost or 50.87%.

## Mutation data: all filters removed 2614018 reads, or 76.41%.

## All data: gathering information about the indexes observed, this is slow.

## Before reads/index pruning, there are: 1179116 indexes in all the data.

## After reads/index pruning, there are: 692307 indexes: 486809 lost or 41.29%.

## All data: removing indexes with fewer than 3 reads/index.

## All data: before reads/index pruning, there are: 807185 changed reads.

## All data: before reads/index pruning, there are: 5230976 identical reads.

## All data: after index pruning, there are: 585835 changed reads: 72.58%.

## All data: after index pruning, there are: 4832196 identical reads: 92.38%.

## Gathering identical, mutant, and sequencer reads/indexes.

## Before classification, there are 4832196 identical reads.

## Before classification, there are 585835 reads with mutations.

## After classification, there are 2934376 reads/indexes which are only identical.

## After classification, there are 79902 reads/indexes which are strictly sequencer.

## After classification, there are 116271 reads/indexes which are deemed from reverse transcriptase.

## Counted by direction: 12365004 forward reads and 12844113 reverse_reads.

## Subsetting based on mutations with at least 5 indexes.

## Classified mutation strings according to various queries.

## Starting sample: 3.

## Reading the file containing mutations: preprocessing/s3/step4.txt.xz

## Reading the file containing the identical reads: preprocessing/s3/step2_identical_reads.txt.xz

## Mutation data: removing any differences before position: 24.

## Mutation data: before pruning, there are: 4309681 reads.

## Mutation data: after min-position pruning, there are: 1564155 reads: 2745526 lost or 63.71%.

## Mutation data: removing any differences after position: 176.

## Mutation data: before pruning, there are: 1564155 reads.

## Mutation data: after max-position pruning, there are: 1482559 reads: 81596 lost or 5.22%.

## Mutation data: removing any reads with 'N' as the hit.

## Mutation data: after N pruning, there are: 1452047 reads: 30512 lost or 2.06%.

## Mutation data: removing reads with greater than 5 mutations.

## Mutation data: after max_mutation pruning, there are: 746662 reads: 705385 lost or 48.58%.

## Mutation data: all filters removed 3563019 reads, or 82.67%.

## All data: gathering information about the indexes observed, this is slow.

## Before reads/index pruning, there are: 808995 indexes in all the data.

## After reads/index pruning, there are: 461997 indexes: 346998 lost or 42.89%.

## All data: removing indexes with fewer than 3 reads/index.

## All data: before reads/index pruning, there are: 746662 changed reads.

## All data: before reads/index pruning, there are: 3583390 identical reads.

## All data: after index pruning, there are: 555226 changed reads: 74.36%.

## All data: after index pruning, there are: 3330970 identical reads: 92.96%.

## Gathering identical, mutant, and sequencer reads/indexes.

## Before classification, there are 3330970 identical reads.

## Before classification, there are 555226 reads with mutations.

## After classification, there are 1957637 reads/indexes which are only identical.

## After classification, there are 63014 reads/indexes which are strictly sequencer.

## After classification, there are 223250 reads/indexes which are deemed from reverse transcriptase.

## Counted by direction: 9578873 forward reads and 9724531 reverse_reads.

## Subsetting based on mutations with at least 5 indexes.

## Classified mutation strings according to various queries.

## Making a matrix of miss_reads_by_position.

## Making a matrix of miss_indexes_by_position.

## Making a matrix of miss_sequencer_by_position.

## Making a matrix of miss_reads_by_string.

## Making a matrix of miss_indexes_by_string.

## Making a matrix of miss_sequencer_by_string.

## Making a matrix of miss_reads_by_ref_nt.

## Making a matrix of miss_indexes_by_ref_nt.

## Making a matrix of miss_sequencer_by_ref_nt.

## Making a matrix of miss_reads_by_hit_nt.

## Making a matrix of miss_indexes_by_hit_nt.

## Making a matrix of miss_sequencer_by_hit_nt.

## Making a matrix of miss_reads_by_type.

## Making a matrix of miss_indexes_by_type.

## Making a matrix of miss_sequencer_by_type.

## Making a matrix of miss_reads_by_trans.

## Making a matrix of miss_indexes_by_trans.

## Making a matrix of miss_sequencer_by_trans.

## Making a matrix of miss_reads_by_strength.

## Making a matrix of miss_indexes_by_strength.

## Making a matrix of miss_sequencer_by_strength.

## Making a matrix of insert_reads_by_position.

## Making a matrix of insert_indexes_by_position.

## Making a matrix of insert_sequencer_by_position.

## Making a matrix of insert_reads_by_nt.

## Making a matrix of insert_indexes_by_nt.

## Making a matrix of insert_sequencer_by_nt.

## Making a matrix of delete_reads_by_position.

## Making a matrix of delete_indexes_by_position.

## Making a matrix of delete_sequencer_by_position.

## Making a matrix of delete_reads_by_nt.

## Making a matrix of delete_indexes_by_nt.

## Making a matrix of delete_sequencer_by_nt.

## Skipping table: miss_reads_by_ref_nt

## Skipping table: miss_indexes_by_ref_nt

## Skipping table: miss_sequencer_by_ref_nt

## Skipping table: miss_reads_by_hit_nt

## Skipping table: miss_indexes_by_hit_nt

## Skipping table: miss_sequencer_by_hit_nt

## Skipping table: delete_reads_by_position

## Skipping table: delete_indexes_by_position

## Skipping table: delete_sequencer_by_position

## Skipping table: delete_reads_by_nt

## Skipping table: delete_indexes_by_nt

## Skipping table: delete_sequencer_by_nt

quint_fivempr_plots <- barplot_matrices(quints_fivempr)
summary(quints_fivempr)

##                      Length Class  Mode   
## samples               3     -none- list   
## reads_per_sample      3     -none- numeric
## indexes_per_sample    3     -none- numeric
## matrices             33     -none- list   
## matrices_by_counts   33     -none- list   
## normalized           33     -none- list   
## normalized_by_counts 33     -none- list

2 Questions from Dr. DeStefano

I think what is best is to get the number of recovered mutations of each type from each data set. That would be A to T, A to G, A to C; T to A, T to G, T to C; G to A, G to C, G to T; and C to A, C to G, C to T; as well as deletions and insertions. I would then need the sum number of the reads that met all our criteria (i.e. at least 3 good recovered reads for that 14 nt index). Each set of 3 or more would ct as “1” read of that particular index so I would need the total with this in mind. I also need to know the total number of nucleotides that were in the region we decided to consider in the analysis. We may want to try this for 3 or more and 5 or more recovered indexes if it is not hard. This information does not include specific positions on the template where errors occurred but we can look at that latter. Right now I just want to get a general error rate and type of error. It would basically be calculated by dividing the number of recovered mutations of a particular type by sum number of the reads times the number of nucleotides screened in the template. As it ends up, this number does not really have a lot of meaning but it can be used to calculate the overall mutation rate as well as the rate for transversions, transitions, and deletions and insertions.

3 Answers

In order to address those queries, I invoked create_matrices() with a minimum index count of 3 and 5. It should be noted that this is not the same as requiring 3 or 5 reads per index. In both cases I require 3 reads per index.

3.1 Recovered mutations of each type

I am interpreting this question as the number of indexes recovered for each mutation type. I collect this information in 2 ways of interest: the indexes by type which are deemed to be from the RT and from the sequencer. In addition, I calculate a normalized (cpm) version of this information which may be used to look for changes across samples.

3.1.1 Mutations by RT index

This following block should print out tables of the numbers of mutant indexes observed for each type for the RT and the sequencer. One would hope that the sequencer will be consistent for all samples, but I think the results will instead suggest that my metric is not yet stringent enough.

knitr::kable(triples[["matrices"]][["miss_indexes_by_type"]])

	s1	s2	s3
A_C	1226	4078	8324
A_G	687	14428	50666
A_T	212	2050	4514
C_A	9115	28661	33332
C_G	329	3690	9533
C_T	2108	17340	59479
G_A	1617	29449	35634
G_C	268	1549	2843
G_T	9304	11694	14377
T_A	178	4752	7492
T_C	805	3995	8312
T_G	1044	5090	9203

knitr::kable(quints[["matrices"]][["miss_indexes_by_type"]])

	s1	s2	s3
A_C	1216	4078	8324
A_G	675	14428	50666
A_T	202	2050	4514
C_A	9115	28661	33332
C_G	305	3686	9533
C_T	2104	17340	59479
G_A	1613	29449	35634
G_C	243	1545	2839
G_T	9304	11694	14377
T_A	161	4752	7492
T_C	797	3995	8312
T_G	1044	5084	9203

knitr::kable(triples[["matrices"]][["miss_sequencer_by_type"]])

	s1	s2	s3
A_C	2265	17215	14189
A_G	623	7106	5588
A_T	170	2701	2256
C_A	1163	14161	11148
C_G	561	5632	4067
C_T	695	7037	5431
G_A	519	4839	3979
G_C	452	6119	5518
G_T	966	8307	6799
T_A	372	3370	2702
T_C	916	9795	8136
T_G	2227	25324	20436

knitr::kable(quints[["matrices"]][["miss_sequencer_by_type"]])

	s1	s2	s3
A_C	2258	17215	14189
A_G	623	7106	5588
A_T	148	2701	2256
C_A	1139	14161	11148
C_G	551	5632	4067
C_T	676	7037	5431
G_A	508	4839	3979
G_C	427	6119	5518
G_T	954	8307	6799
T_A	351	3370	2702
T_C	912	9795	8136
T_G	2214	25324	20436

Plots of this information

triple_plots[["matrices"]][["miss_indexes_by_type"]]

triple_plots[["normal"]][["miss_indexes_by_type"]]

quint_plots[["matrices"]][["miss_indexes_by_type"]]

quint_plots[["normal"]][["miss_indexes_by_type"]]

This suggests to me that this information needs to be normalized in some more sensible fashion. Thus the following:

3.1.2 Mutations by RT index, post normalization

The same numbers may be expressed in the context of the number of indexes observed / sample and/or as a cpm across samples. Thus in the first instance one can look at the apparent error rate for each sample, and in the second instance one may look for relative changes in apparent error rate across samples.

3.1.2.1 Rewriting the matrices as cpm to account for library sizes.

knitr::kable(triples[["normalized"]][["miss_indexes_by_type"]])

	s1	s2	s3
A_C	45588	32167	34155
A_G	25546	113807	207895
A_T	7883	16170	18522
C_A	338936	226076	136770
C_G	12234	29106	39116
C_T	78385	136777	244057
G_A	60127	232292	146215
G_C	9965	12218	11666
G_T	345964	92241	58992
T_A	6619	37483	30742
T_C	29933	31512	34106
T_G	38821	40150	37762

knitr::kable(quints[["normalized"]][["miss_indexes_by_type"]])

	s1	s2	s3
A_C	45409	32171	34156
A_G	25206	113820	207899
A_T	7543	16172	18522
C_A	340379	226101	136772
C_G	11390	29078	39117
C_T	78569	136792	244061
G_A	60234	232317	146218
G_C	9074	12188	11649
G_T	347436	92252	58993
T_A	6012	37488	30742
T_C	29762	31516	34107
T_G	38986	40107	37763

knitr::kable(triples[["normalized"]][["miss_sequencer_by_type"]])

	s1	s2	s3
A_C	207247	154248	157221
A_G	57004	63670	61918
A_T	15555	24201	24998
C_A	106414	126884	123525
C_G	51331	50463	45064
C_T	63592	63052	60178
G_A	47488	43358	44089
G_C	41358	54827	61142
G_T	88389	74431	75336
T_A	34038	30196	29939
T_C	83814	87764	90151
T_G	203770	226905	226440

knitr::kable(quints[["normalized"]][["miss_sequencer_by_type"]])

	s1	s2	s3
A_C	209832	154248	157221
A_G	57894	63670	61918
A_T	13753	24201	24998
C_A	105845	126884	123525
C_G	51203	50463	45064
C_T	62819	63052	60178
G_A	47208	43358	44089
G_C	39680	54827	61142
G_T	88653	74431	75336
T_A	32618	30196	29939
T_C	84750	87764	90151
T_G	205743	226905	226440

3.1.2.2 Rewriting the matrices by dividing by all indexes

This I think starts to address the later text in your query.

knitr::kable(triples[["matrices_by_counts"]][["miss_indexes_by_type"]])

	s1	s2	s3
A_C	0.0015	0.0049	0.0099
A_G	0.0010	0.0208	0.0730
A_T	0.0005	0.0044	0.0097
C_A	0.0109	0.0342	0.0398
C_G	0.0005	0.0053	0.0137
C_T	0.0045	0.0374	0.1283
G_A	0.0019	0.0352	0.0425
G_C	0.0004	0.0022	0.0041
G_T	0.0201	0.0252	0.0310
T_A	0.0002	0.0057	0.0089
T_C	0.0012	0.0058	0.0120
T_G	0.0023	0.0110	0.0199

knitr::kable(quints[["matrices_by_counts"]][["miss_indexes_by_type"]])

	s1	s2	s3
A_C	0.0015	0.0049	0.0099
A_G	0.0010	0.0208	0.0730
A_T	0.0004	0.0044	0.0097
C_A	0.0109	0.0342	0.0398
C_G	0.0004	0.0053	0.0137
C_T	0.0045	0.0374	0.1283
G_A	0.0019	0.0352	0.0425
G_C	0.0004	0.0022	0.0041
G_T	0.0201	0.0252	0.0310
T_A	0.0002	0.0057	0.0089
T_C	0.0011	0.0058	0.0120
T_G	0.0023	0.0110	0.0199

knitr::kable(triples[["matrices_by_counts"]][["miss_sequencer_by_type"]])

	s1	s2	s3
A_C	0.0027	0.0206	0.0169
A_G	0.0009	0.0102	0.0081
A_T	0.0004	0.0058	0.0049
C_A	0.0014	0.0169	0.0133
C_G	0.0008	0.0081	0.0059
C_T	0.0015	0.0152	0.0117
G_A	0.0006	0.0058	0.0048
G_C	0.0007	0.0088	0.0080
G_T	0.0021	0.0179	0.0147
T_A	0.0004	0.0040	0.0032
T_C	0.0013	0.0141	0.0117
T_G	0.0048	0.0546	0.0441

knitr::kable(quints[["matrices_by_counts"]][["miss_sequencer_by_type"]])

	s1	s2	s3
A_C	0.0027	0.0206	0.0169
A_G	0.0009	0.0102	0.0081
A_T	0.0003	0.0058	0.0049
C_A	0.0014	0.0169	0.0133
C_G	0.0008	0.0081	0.0059
C_T	0.0015	0.0152	0.0117
G_A	0.0006	0.0058	0.0048
G_C	0.0006	0.0088	0.0080
G_T	0.0021	0.0179	0.0147
T_A	0.0004	0.0040	0.0032
T_C	0.0013	0.0141	0.0117
T_G	0.0048	0.0546	0.0441

3.1.2.3 Rewriting the matrices by dividing by all indexes and cpm

I think this might prove to be where we get the most meaningful results.

The nicest thing in it is that after accounting for library sizes and total indexes observed, we finally see that the sequencer error is mostly consistent across all samples and mutation types – with a couple of notable exceptions.

By the same token, for the mutations which are identical for the sequencer, we have some which are decidedly different for the non-sequencer data. The most notable examples I think are A to G but _not G to A; and C to T.

knitr::kable(triples[["normalized_by_counts"]][["miss_indexes_by_type"]])

	s1	s2	s3
A_C	0.0544	0.0384	0.0408
A_G	0.0368	0.1641	0.2997
A_T	0.0170	0.0349	0.0400
C_A	0.4046	0.2699	0.1633
C_G	0.0176	0.0420	0.0564
C_T	0.1691	0.2951	0.5266
G_A	0.0718	0.2773	0.1746
G_C	0.0144	0.0176	0.0168
G_T	0.7465	0.1990	0.1273
T_A	0.0079	0.0448	0.0367
T_C	0.0431	0.0454	0.0492
T_G	0.0838	0.0866	0.0815

knitr::kable(quints[["normalized_by_counts"]][["miss_indexes_by_type"]])

	s1	s2	s3
A_C	0.0542	0.0384	0.0408
A_G	0.0363	0.1641	0.2997
A_T	0.0163	0.0349	0.0400
C_A	0.4064	0.2699	0.1633
C_G	0.0164	0.0419	0.0564
C_T	0.1695	0.2952	0.5266
G_A	0.0719	0.2774	0.1746
G_C	0.0131	0.0176	0.0168
G_T	0.7497	0.1991	0.1273
T_A	0.0072	0.0448	0.0367
T_C	0.0429	0.0454	0.0492
T_G	0.0841	0.0865	0.0815

knitr::kable(triples[["normalized_by_counts"]][["miss_sequencer_by_type"]])

	s1	s2	s3
A_C	0.2474	0.1842	0.1877
A_G	0.0822	0.0918	0.0893
A_T	0.0336	0.0522	0.0539
C_A	0.1270	0.1515	0.1475
C_G	0.0740	0.0727	0.0650
C_T	0.1372	0.1361	0.1298
G_A	0.0567	0.0518	0.0526
G_C	0.0596	0.0790	0.0881
G_T	0.1907	0.1606	0.1626
T_A	0.0406	0.0360	0.0357
T_C	0.1208	0.1265	0.1300
T_G	0.4397	0.4896	0.4886

knitr::kable(quints[["normalized_by_counts"]][["miss_sequencer_by_type"]])

	s1	s2	s3
A_C	0.2505	0.1842	0.1877
A_G	0.0835	0.0918	0.0893
A_T	0.0297	0.0522	0.0539
C_A	0.1264	0.1515	0.1475
C_G	0.0738	0.0727	0.0650
C_T	0.1355	0.1361	0.1298
G_A	0.0564	0.0518	0.0526
G_C	0.0572	0.0790	0.0881
G_T	0.1913	0.1606	0.1626
T_A	0.0389	0.0360	0.0357
T_C	0.1222	0.1265	0.1300
T_G	0.4439	0.4896	0.4886

3.1.3 Indels by RT index

The following blocks will repeat the above, but looking for insertions. This data does not observe sufficient deletions to make a proper count for them.

knitr::kable(triples[["matrices"]][["insert_indexes_by_nt"]])

	s2	s3
A	25	382
C	23	69
G	31	89
T	48	221

knitr::kable(quints[["matrices"]][["insert_indexes_by_nt"]])

	s2	s3
A	25	382
C	20	65
G	27	89
T	48	217

knitr::kable(triples[["matrices"]][["insert_sequencer_by_nt"]])

	s2	s3
A	3	8
C	24	25
G	14	16
T	0	3

knitr::kable(quints[["matrices"]][["insert_sequencer_by_nt"]])

	s2	s3
A	0	5
C	17	15
G	10	5

Plots of this information

triple_plots[["matrices"]][["insert_indexes_by_nt"]]

triple_plots[["normal"]][["insert_indexes_by_nt"]]

quint_plots[["matrices"]][["insert_indexes_by_nt"]]

quint_plots[["normal"]][["insert_indexes_by_nt"]]

quint_plots[["matrices"]][["insert_sequencer_by_nt"]]

quint_plots[["normal"]][["insert_sequencer_by_nt"]]

3.1.4 Insertions by RT index, post normalization

3.1.4.1 Rewriting the matrices as cpm to account for library sizes.

knitr::kable(triples[["normalized"]][["insert_indexes_by_nt"]])

	s2	s3
A	196850	501971
C	181102	90670
G	244094	116951
T	377953	290407

knitr::kable(quints[["normalized"]][["insert_indexes_by_nt"]])

	s2	s3
A	208333	507304
C	166667	86321
G	225000	118194
T	400000	288181

knitr::kable(triples[["normalized"]][["insert_sequencer_by_nt"]])

	s2	s3
A	73171	153846
C	585366	480769
G	341463	307692
T	0	57692

knitr::kable(quints[["normalized"]][["insert_sequencer_by_nt"]])

	s2	s3
A	0	2e+05
C	629630	6e+05
G	370370	2e+05

3.1.4.2 Rewriting the matrices by dividing by all indexes

I think that there are few enough insertion events that this gets a bit messed up. I will double check the logic of this, but that is my initial guess given how few insertions I was seeing when reading the outputs manually. Unfortunately, this means that for these I also cannot provide a cpm measurement.

knitr::kable(triples[["matrices_by_counts"]][["insert_indexes_by_nt"]])

	s2	s3
A	0e+00	8e-04
C	0e+00	1e-04
G	0e+00	1e-04
T	1e-04	5e-04

knitr::kable(quints[["matrices_by_counts"]][["insert_indexes_by_nt"]])

	s2	s3
A	0e+00	8e-04
C	0e+00	1e-04
G	0e+00	1e-04
T	1e-04	5e-04

knitr::kable(triples[["matrices_by_counts"]][["insert_sequencer_by_nt"]])

	s1	s2	s3
A	0	0e+00	0
C	0	1e-04	0
G	0	0e+00	0
T	0	0e+00	0

knitr::kable(quints[["matrices_by_counts"]][["insert_sequencer_by_nt"]])

	s1	s2	s3
A	0	0	0
C	0	0	0
G	0	0	0

The following is my previous writing of this worksheet which just dumped the various tables.

4 Print raw tables

for (t in 1:length(triples[["matrices"]])) {
  table_name <- names(triples[["matrices"]])[t]
  message("Raw table: ", table_name, ".")
  print(knitr::kable(triples[["matrices"]][t]))
}

## Raw table: miss_reads_by_position.

	s1	s2	s3
24	1082	6979	20734
25	1036	9832	11968
26	1450	16817	27753
27	443	4198	10754
28	540	6621	15877
29	1799	12450	17723
30	1454	7690	15468
31	91	1073	3704
32	130	592	1274
33	106	1073	5030
34	813	5641	17901
35	565	8568	25170
36	313	7470	18175
37	1137	6117	20732
38	1016	7700	20044
39	1240	14314	20064
40	375	3738	8456
41	867	4119	13956
42	1144	11299	13646
43	476	5246	10608
44	2066	9974	14404
45	529	6353	30134
46	471	12855	43166
47	832	7832	14989
48	1159	10159	17354
49	1561	15304	18566
50	924	12245	49944
51	594	10319	29285
52	65	6816	30179
53	115	8536	31608
54	2735	14347	28687
55	960	12186	23847
56	1486	6942	13026
57	2984	10992	13130
58	737	6308	10184
59	2875	13813	15245
60	3568	14694	15788
61	1305	9828	25278
62	1218	12168	13443
63	144	5547	11376
64	109	2523	4492
65	590	9448	28923
66	349	12021	45895
67	565	7534	13275
68	1645	7931	12231
69	189	2732	5172
70	199	3844	15874
71	506	7079	21714
72	45	386	2602
73	28	1294	2162
74	67	4837	12230
75	27	5764	22687
76	95	3776	6761
77	1187	7253	16357
78	2548	10360	14452
79	1145	7678	19605
80	700	4503	11510
81	72	834	3506
82	29	2132	4386
83	1890	8218	10263
84	1456	7461	24705
85	95	2086	7609
86	1963	10303	14311
87	1524	10514	29814
88	116	3152	7532
89	359	10550	24913
90	84	2810	13139
91	97	3903	5670
92	807	5340	10655
93	929	8459	16510
94	1509	10380	12361
95	1727	7188	9531
96	1409	8434	10615
97	81	2286	3281
98	64	1471	3522
99	66	802	968
100	1183	24779	35182
101	1375	8683	9226
102	1506	5100	12505
103	720	4335	6625
104	64	1989	5566
105	1906	9748	14310
106	1020	9894	22520
107	86	3640	4844
108	1115	7505	7765
109	1429	8309	11540
110	1879	9926	26871
111	2377	11160	10486
112	674	5170	10033
113	29	4037	15655
114	77	7363	30696
115	260	5975	10906
116	397	4640	15681
117	1251	9613	10307
118	1355	10120	23997
119	1144	9753	8994
120	90	3531	12273
121	157	7311	27801
122	318	5164	9712
123	140	5685	24221
124	744	6839	9596
125	2634	10293	11833
126	3091	11237	25877
127	1443	5818	9692
128	2031	10398	14631
129	2641	13560	14273
130	1339	12539	26197
131	528	5552	14707
132	747	7859	24694
133	1505	5726	12309
134	777	8638	6755
135	209	3494	11648
136	155	2082	5623
137	702	5866	11197
138	1776	7864	10086
139	852	8434	22446
140	641	4622	12870
141	1047	5841	10885
142	439	2996	6501
143	435	4604	8932
144	711	6053	14406
145	816	6542	10762
146	1558	6906	13467
147	65	1310	5359
148	114	3227	11528
149	695	8643	22241
150	147	3314	8504
151	1185	8854	9579
152	186	3960	8354
153	432	3825	8208
154	1085	7330	7948
155	1242	9835	20205
156	1701	10143	12898
157	1222	9790	25495
158	326	4564	11104
159	1326	7484	11549
160	948	7848	26028
161	845	4454	11056
162	849	4762	6670
163	992	9429	9065
164	62	2940	12958
165	55	5553	28490
166	344	5355	8892
167	1339	7364	9660
168	1495	9529	11232
169	1248	9124	18962
170	764	8787	10023
171	170	4598	13410
172	89	8436	36314
173	143	5059	7466
174	946	8408	10076
175	1482	11679	12357
176	2206	10122	21620

## Raw table: miss_indexes_by_position.

	s1	s2	s3
24	197	862	2070
25	219	1106	1296
26	309	1934	2902
27	105	568	1238
28	116	783	1742
29	335	1485	1860
30	311	947	1773
31	23	109	345
32	27	70	173
33	29	119	504
34	190	790	1972
35	128	997	2695
36	68	948	2177
37	223	770	2261
38	170	879	2088
39	269	1675	2198
40	88	523	997
41	178	502	1461
42	249	1285	1508
43	104	686	1298
44	345	1098	1536
45	120	770	3054
46	85	1400	4191
47	174	1012	1780
48	238	1201	1681
49	284	1666	1847
50	192	1465	5115
51	126	1252	3038
52	16	719	2991
53	24	916	3183
54	506	1513	2909
55	193	1482	2828
56	268	734	1313
57	598	1210	1358
58	164	959	1340
59	522	1457	1645
60	704	1534	1682
61	254	1129	2696
62	248	1326	1400
63	35	789	1478
64	23	293	490
65	141	1069	3054
66	72	1410	4672
67	118	829	1348
68	298	779	1218
69	31	319	565
70	36	476	1682
71	104	802	2305
72	12	55	273
73	5	132	232
74	14	470	1306
75	0	654	2267
76	19	407	713
77	213	814	1745
78	475	1129	1448
79	237	804	1911
80	131	499	1114
81	16	111	399
82	4	232	484
83	374	936	1115
84	234	834	2481
85	25	266	771
86	374	1189	1388
87	271	1175	2989
88	27	329	814
89	75	1208	2497
90	17	338	1424
91	16	406	616
92	163	585	1081
93	188	935	1594
94	297	1089	1241
95	308	859	1006
96	278	921	1199
97	14	240	353
98	16	193	372
99	12	95	136
100	233	2705	3681
101	289	991	1068
102	286	601	1384
103	149	527	759
104	17	219	561
105	364	1026	1417
106	190	1021	2382
107	20	385	538
108	225	836	806
109	272	906	1206
110	361	1185	2825
111	453	1205	1104
112	137	636	1191
113	7	493	1665
114	15	843	3184
115	61	719	1216
116	82	532	1620
117	253	1065	1105
118	263	1157	2456
119	230	1099	929
120	20	437	1286
121	29	826	2850
122	45	570	1004
123	31	605	2436
124	163	828	1026
125	510	1184	1272
126	533	1287	2651
127	285	655	1044
128	379	1151	1468
129	535	1393	1495
130	262	1395	2609
131	108	691	1671
132	141	802	2522
133	255	672	1276
134	142	1024	735
135	44	395	1229
136	35	260	655
137	131	654	1136
138	342	930	1118
139	156	910	2396
140	124	540	1284
141	202	651	1082
142	85	389	794
143	91	600	1080
144	156	676	1477
145	150	684	1131
146	263	766	1458
147	13	165	552
148	28	366	1222
149	132	997	2265
150	28	344	963
151	237	1001	970
152	36	488	865
153	93	501	1010
154	224	813	824
155	271	1110	2115
156	331	1170	1365
157	224	1198	2512
158	60	542	1201
159	270	944	1343
160	199	965	2664
161	160	505	1130
162	162	603	794
163	178	1009	970
164	15	356	1326
165	10	650	2990
166	81	712	1059
167	278	893	1092
168	314	1093	1261
169	252	1016	2093
170	157	1041	1068
171	37	529	1502
172	17	964	3778
173	29	598	811
174	149	914	1049
175	310	1377	1302
176	402	1234	2405

## Raw table: miss_sequencer_by_position.

	s1	s2	s3
24	14	186	159
25	46	347	269
26	78	1033	985
27	219	2125	1855
28	32	490	351
29	65	528	426
30	47	2471	2096
31	8	157	116
32	9	115	111
33	35	244	195
34	283	1593	1349
35	74	773	644
36	134	3379	3063
37	294	1762	1529
38	26	781	621
39	166	2396	2135
40	278	2564	2089
41	22	355	254
42	46	1171	1027
43	299	3013	2510
44	51	764	653
45	421	2281	1844
46	29	483	397
47	313	3270	2837
48	61	578	486
49	30	517	410
50	97	493	450
51	65	1176	977
52	37	348	268
53	67	385	319
54	195	1300	1019
55	27	2693	2144
56	25	142	122
57	43	198	165
58	115	1940	1667
59	60	1335	1124
60	360	1518	1332
61	35	295	270
62	72	845	711
63	48	3080	2415
64	46	298	243
65	359	2225	1903
66	34	704	537
67	20	251	206
68	86	478	387
69	30	196	164
70	52	478	375
71	13	120	115
72	19	159	127
73	14	154	137
74	22	280	223
75	18	175	128
76	30	288	258
77	14	111	88
78	27	258	212
79	10	124	115
80	14	105	87
81	17	250	192
82	17	200	170
83	28	259	198
84	16	220	193
85	25	269	213
86	20	225	188
87	22	135	97
88	46	458	398
89	9	115	103
90	28	334	279
91	38	279	251
92	13	90	82
93	3	165	128
94	8	93	68
95	8	71	78
96	18	176	130
97	10	163	144
98	20	217	190
99	25	188	169
100	13	95	80
101	42	333	273
102	29	268	221
103	11	123	110
104	17	172	154
105	11	132	132
106	6	122	118
107	32	246	239
108	7	145	148
109	29	205	180
110	132	696	518
111	28	250	217
112	311	2730	2175
113	16	212	166
114	16	210	178
115	198	1817	1432
116	65	590	505
117	15	158	131
118	77	812	555
119	26	166	136
120	47	516	403
121	50	558	419
122	14	164	125
123	39	480	349
124	69	608	540
125	116	857	644
126	159	1693	839
127	55	675	491
128	119	907	715
129	20	374	322
130	117	1151	952
131	310	2815	2087
132	53	527	468
133	31	321	245
134	30	228	203
135	63	681	569
136	107	803	659
137	46	444	325
138	35	361	300
139	85	740	591
140	95	867	761
141	40	438	339
142	202	1788	1347
143	320	2896	2223
144	31	439	339
145	74	669	559
146	87	926	670
147	22	274	207
148	41	549	396
149	39	346	274
150	53	613	439
151	26	217	179
152	78	690	540
153	258	2334	1873
154	20	207	166
155	90	803	607
156	27	274	218
157	53	604	489
158	103	1154	857
159	75	613	403
160	54	615	473
161	50	594	423
162	118	1235	942
163	38	357	256
164	21	266	210
165	32	324	265
166	192	1967	1414
167	20	240	214
168	35	461	388
169	64	726	558
170	21	245	156
171	70	703	581
172	17	266	197
173	92	808	658
174	11	191	156
175	61	559	472
176	148	1528	1116

## Raw table: miss_reads_by_string.

	s1	s2	s3
024_mis_C_A	844	4608	6360
024_mis_C_G	0	789	2634
024_mis_C_T	238	1582	11740
025_mis_C_A	795	5897	6698
025_mis_C_G	36	766	1059
025_mis_C_T	205	3169	4211
026_mis_G_A	194	12570	21387
026_mis_G_C	72	1115	2213
026_mis_G_T	1184	3132	4153
027_mis_T_A	4	622	3382
027_mis_T_C	400	1726	3953
027_mis_T_G	39	1850	3419
028_mis_C_A	368	4659	4860
028_mis_C_G	14	241	847
028_mis_C_T	158	1721	10170
029_mis_G_A	133	10100	13500
029_mis_G_C	114	448	780
029_mis_G_T	1552	1902	3443
030_mis_T_A	11	49	568
030_mis_T_C	172	1561	4099
030_mis_T_G	1271	6080	10801
031_mis_T_A	4	509	1691
031_mis_T_C	33	458	1747
031_mis_T_G	54	106	266
032_mis_T_A	4	115	174
032_mis_T_C	107	412	1006
032_mis_T_G	19	65	94
033_mis_T_A	37	190	3092
033_mis_T_C	69	833	1654
033_mis_T_G	0	50	284
034_mis_A_C	319	1068	2041
034_mis_A_G	275	2888	12792
034_mis_A_T	219	1685	3068
035_mis_C_A	443	3545	4489
035_mis_C_G	0	0	626
035_mis_C_T	122	5023	20055
036_mis_A_C	196	4220	10776
036_mis_A_G	114	2894	6296
036_mis_A_T	3	356	1103
037_mis_A_C	639	2757	4637
037_mis_A_G	493	2984	15390
037_mis_A_T	5	376	705
038_mis_C_A	823	4598	7374
038_mis_C_G	31	162	975
038_mis_C_T	162	2940	11695
039_mis_G_A	241	9069	10200
039_mis_G_C	65	2626	5073
039_mis_G_T	934	2619	4791
040_mis_T_A	14	115	1347
040_mis_T_C	335	1544	3586
040_mis_T_G	26	2079	3523
041_mis_C_A	810	3049	7087
041_mis_C_G	18	101	1720
041_mis_C_T	39	969	5149
042_mis_G_A	263	8181	9118
042_mis_G_C	49	961	2618
042_mis_G_T	832	2157	1910
043_mis_T_A	19	665	2204
043_mis_T_C	53	1096	1720
043_mis_T_G	404	3485	6684
044_mis_G_A	78	5629	9357
044_mis_G_C	7	128	489
044_mis_G_T	1981	4217	4558
045_mis_A_C	489	2137	4698
045_mis_A_G	23	3588	24743
045_mis_A_T	17	628	693
046_mis_C_A	352	4953	4779
046_mis_C_G	8	1167	4388
046_mis_C_T	111	6735	33999
047_mis_T_A	66	2770	4198
047_mis_T_C	203	3710	8165
047_mis_T_G	563	1352	2626
048_mis_G_A	103	7501	14034
048_mis_G_C	0	37	64
048_mis_G_T	1056	2621	3256
049_mis_G_A	178	12468	13989
049_mis_G_C	78	385	436
049_mis_G_T	1305	2451	4141
050_mis_G_A	386	11312	48113
050_mis_G_C	47	306	300
050_mis_G_T	491	627	1531
051_mis_A_C	47	418	868
051_mis_A_G	526	9689	28151
051_mis_A_T	21	212	266
052_mis_A_C	45	280	1430
052_mis_A_G	20	6200	28234
052_mis_A_T	0	336	515
053_mis_A_C	86	583	2153
053_mis_A_G	18	7904	28341
053_mis_A_T	11	49	1114
054_mis_A_C	2720	7444	5736
054_mis_A_G	15	6895	22055
054_mis_A_T	0	8	896
055_mis_C_A	818	9437	11999
055_mis_C_G	0	881	973
055_mis_C_T	142	1868	10875
056_mis_C_A	1342	4438	7017
056_mis_C_G	105	347	986
056_mis_C_T	39	2157	5023
057_mis_C_A	1305	6854	7430
057_mis_C_G	0	256	1248
057_mis_C_T	1679	3882	4452
058_mis_T_A	19	1796	3200
058_mis_T_C	614	4060	6044
058_mis_T_G	104	452	940
059_mis_G_A	1200	9563	9193
059_mis_G_C	24	242	409
059_mis_G_T	1651	4008	5643
060_mis_G_A	15	7782	6123
060_mis_G_C	283	1349	2342
060_mis_G_T	3270	5563	7323
061_mis_C_A	1213	5880	5427
061_mis_C_G	54	964	3419
061_mis_C_T	38	2984	16432
062_mis_G_A	184	9013	8614
062_mis_G_C	7	707	1521
062_mis_G_T	1027	2448	3308
063_mis_T_A	3	434	1656
063_mis_T_C	33	652	1996
063_mis_T_G	108	4461	7724
064_mis_T_A	32	1423	1903
064_mis_T_C	71	1072	2301
064_mis_T_G	6	28	288
065_mis_A_C	436	1510	3604
065_mis_A_G	19	5397	19060
065_mis_A_T	135	2541	6259
066_mis_C_A	108	3149	4658
066_mis_C_G	0	717	2783
066_mis_C_T	241	8155	38454
067_mis_C_A	354	4324	5460
067_mis_C_G	0	347	508
067_mis_C_T	211	2863	7307
068_mis_C_A	1462	5865	7218
068_mis_C_G	0	469	1233
068_mis_C_T	183	1597	3780
069_mis_A_C	65	752	830
069_mis_A_G	124	993	2561
069_mis_A_T	0	987	1781
070_mis_A_C	122	261	1022
070_mis_A_G	61	3304	14393
070_mis_A_T	16	279	459
071_mis_C_A	324	2347	3971
071_mis_C_G	0	825	1716
071_mis_C_T	182	3907	16027
072_mis_T_A	0	176	862
072_mis_T_C	24	161	1618
072_mis_T_G	21	49	122
073_mis_T_A	10	819	743
073_mis_T_C	18	399	1078
073_mis_T_G	0	76	341
074_mis_A_C	20	1065	732
074_mis_A_G	16	3322	9921
074_mis_A_T	31	450	1577
075_mis_A_C	18	439	1844
075_mis_A_G	6	4766	20196
075_mis_A_T	3	559	647
076_mis_T_A	58	2701	3142
076_mis_T_C	37	1002	3331
076_mis_T_G	0	73	288
077_mis_C_A	988	5817	8314
077_mis_C_G	6	316	857
077_mis_C_T	193	1120	7186
078_mis_G_A	72	6326	7546
078_mis_G_C	14	77	314
078_mis_G_T	2462	3957	6592
079_mis_C_A	1107	3922	3769
079_mis_C_G	0	471	2223
079_mis_C_T	38	3285	13613
080_mis_C_A	594	2396	3981
080_mis_C_G	14	34	312
080_mis_C_T	92	2073	7217
081_mis_T_A	27	388	1513
081_mis_T_C	45	327	1419
081_mis_T_G	0	119	574
082_mis_T_A	7	1239	2735
082_mis_T_C	14	789	1496
082_mis_T_G	8	104	155
083_mis_G_A	71	5565	5732
083_mis_G_C	32	99	171
083_mis_G_T	1787	2554	4360
084_mis_C_A	1291	2971	3850
084_mis_C_G	15	158	1139
084_mis_C_T	150	4332	19716
085_mis_A_C	0	165	1047
085_mis_A_G	95	1145	5416
085_mis_A_T	0	776	1146
086_mis_G_A	745	8158	10255
086_mis_G_C	45	149	498
086_mis_G_T	1173	1996	3558
087_mis_C_A	1450	6956	7791
087_mis_C_G	9	1610	3644
087_mis_C_T	65	1948	18379
088_mis_A_C	43	408	859
088_mis_A_G	40	2204	5469
088_mis_A_T	33	540	1204
089_mis_C_A	210	4123	6744
089_mis_C_G	0	947	1948
089_mis_C_T	149	5480	16221
090_mis_A_C	29	658	1980
090_mis_A_G	37	1961	9294
090_mis_A_T	18	191	1865
091_mis_T_A	15	3160	2929
091_mis_T_C	75	694	2587
091_mis_T_G	7	49	154
092_mis_C_A	655	3178	3459
092_mis_C_G	0	476	779
092_mis_C_T	152	1686	6417
093_mis_C_A	794	5822	10877
093_mis_C_G	25	1018	1073
093_mis_C_T	110	1619	4560
094_mis_C_A	1430	9140	6174
094_mis_C_G	9	178	916
094_mis_C_T	70	1062	5271
095_mis_C_A	1664	6189	6716
095_mis_C_G	0	297	775
095_mis_C_T	63	702	2040
096_mis_C_A	1270	6443	4804
096_mis_C_G	0	97	202
096_mis_C_T	139	1894	5609
097_mis_T_A	9	1048	2740
097_mis_T_C	72	928	321
097_mis_T_G	0	310	220
098_mis_T_A	20	983	2079
098_mis_T_C	21	446	1320
098_mis_T_G	23	42	123
099_mis_T_A	3	117	260
099_mis_T_C	55	669	652
099_mis_T_G	8	16	56
100_mis_C_A	919	3809	3851
100_mis_C_G	13	22	99
100_mis_C_T	251	20948	31232
101_mis_G_A	168	5676	6210
101_mis_G_C	60	170	339
101_mis_G_T	1147	2837	2677
102_mis_C_A	1241	3002	2928
102_mis_C_G	13	130	112
102_mis_C_T	252	1968	9465
103_mis_C_A	567	2986	3044
103_mis_C_G	0	88	296
103_mis_C_T	153	1261	3285
104_mis_A_C	15	508	462
104_mis_A_G	34	878	3289
104_mis_A_T	15	603	1815
105_mis_G_A	65	5986	7992
105_mis_G_C	16	17	39
105_mis_G_T	1825	3745	6279
106_mis_C_A	809	6757	7189
106_mis_C_G	0	810	2374
106_mis_C_T	211	2327	12957
107_mis_T_A	11	2944	3039
107_mis_T_C	40	205	558
107_mis_T_G	35	491	1247
108_mis_G_A	77	5417	5172
108_mis_G_C	28	188	55
108_mis_G_T	1010	1900	2538
109_mis_G_A	67	4493	7106
109_mis_G_C	9	183	92
109_mis_G_T	1353	3633	4342
110_mis_C_A	1239	5192	5671
110_mis_C_G	204	963	1640
110_mis_C_T	436	3771	19560
111_mis_G_A	856	8459	7017
111_mis_G_C	8	182	207
111_mis_G_T	1513	2519	3262
112_mis_T_A	28	1159	2150
112_mis_T_C	247	1217	2129
112_mis_T_G	399	2794	5754
113_mis_A_C	0	109	1046
113_mis_A_G	26	3162	12927
113_mis_A_T	3	766	1682
114_mis_A_C	15	819	2261
114_mis_A_G	24	5879	26398
114_mis_A_T	38	665	2037
115_mis_T_A	89	3886	4065
115_mis_T_C	13	638	4153
115_mis_T_G	158	1451	2688
116_mis_A_C	71	806	1587
116_mis_A_G	279	3709	12060
116_mis_A_T	47	125	2034
117_mis_G_A	16	5290	6530
117_mis_G_C	26	196	296
117_mis_G_T	1209	4127	3481
118_mis_C_A	1168	6115	7334
118_mis_C_G	55	1336	3559
118_mis_C_T	132	2669	13104
119_mis_G_A	138	6777	6278
119_mis_G_C	6	339	227
119_mis_G_T	1000	2637	2489
120_mis_A_C	47	247	911
120_mis_A_G	32	3099	11136
120_mis_A_T	11	185	226
121_mis_A_C	91	663	1979
121_mis_A_G	66	6531	25010
121_mis_A_T	0	117	812
122_mis_G_A	236	4991	8666
122_mis_G_C	5	63	94
122_mis_G_T	77	110	952
123_mis_A_C	36	65	712
123_mis_A_G	101	5525	23391
123_mis_A_T	3	95	118
124_mis_G_A	37	5078	6670
124_mis_G_C	0	38	55
124_mis_G_T	707	1723	2871
125_mis_G_A	165	6237	5565
125_mis_G_C	9	98	150
125_mis_G_T	2460	3958	6118
126_mis_C_A	2554	5317	6169
126_mis_C_G	80	2823	6225
126_mis_C_T	457	3097	13483
127_mis_C_A	1069	4241	5479
127_mis_C_G	52	721	1089
127_mis_C_T	322	856	3124
128_mis_C_A	1534	8150	8639
128_mis_C_G	152	915	2104
128_mis_C_T	345	1333	3888
129_mis_G_A	1076	10251	9501
129_mis_G_C	35	215	300
129_mis_G_T	1530	3094	4472
130_mis_C_A	1231	8503	10864
130_mis_C_G	77	895	3784
130_mis_C_T	31	3141	11549
131_mis_A_C	284	2674	5639
131_mis_A_G	181	2590	7598
131_mis_A_T	63	288	1470
132_mis_C_A	598	2066	4807
132_mis_C_G	8	833	3153
132_mis_C_T	141	4960	16734
133_mis_C_A	1285	3999	5861
133_mis_C_G	34	213	1055
133_mis_C_T	186	1514	5393
134_mis_G_A	232	7298	5458
134_mis_G_C	17	90	249
134_mis_G_T	528	1250	1048
135_mis_A_C	98	439	1571
135_mis_A_G	92	2419	9632
135_mis_A_T	19	636	445
136_mis_T_A	20	834	2702
136_mis_T_C	66	764	2059
136_mis_T_G	69	484	862
137_mis_C_A	562	4253	4481
137_mis_C_G	52	392	1088
137_mis_C_T	88	1221	5628
138_mis_G_A	175	4174	5952
138_mis_G_C	23	270	621
138_mis_G_T	1578	3420	3513
139_mis_C_A	747	3724	4762
139_mis_C_G	36	765	2633
139_mis_C_T	69	3945	15051
140_mis_C_A	429	2529	6239
140_mis_C_G	113	663	1664
140_mis_C_T	99	1430	4967
141_mis_C_A	918	3962	4990
141_mis_C_G	26	451	335
141_mis_C_T	103	1428	5560
142_mis_T_A	73	279	1818
142_mis_T_C	72	970	1737
142_mis_T_G	294	1747	2946
143_mis_T_A	51	1475	2302
143_mis_T_C	58	366	1203
143_mis_T_G	326	2763	5427
144_mis_C_A	596	4292	4671
144_mis_C_G	31	378	1207
144_mis_C_T	84	1383	8528
145_mis_C_A	761	4693	4329
145_mis_C_G	42	249	644
145_mis_C_T	13	1600	5789
146_mis_C_A	1460	6155	9621
146_mis_C_G	3	62	577
146_mis_C_T	95	689	3269
147_mis_A_C	27	134	348
147_mis_A_G	38	1050	3411
147_mis_A_T	0	126	1600
148_mis_A_C	35	341	699
148_mis_A_G	52	2638	10310
148_mis_A_T	27	248	519
149_mis_C_A	482	4253	6288
149_mis_C_G	36	143	458
149_mis_C_T	177	4247	15495
150_mis_A_C	67	414	1795
150_mis_A_G	55	2337	5770
150_mis_A_T	25	563	939
151_mis_G_A	47	6380	6390
151_mis_G_C	3	59	47
151_mis_G_T	1135	2415	3142
152_mis_T_A	31	2646	4108
152_mis_T_C	60	595	1914
152_mis_T_G	95	719	2332
153_mis_T_A	9	1129	2249
153_mis_T_C	124	395	1258
153_mis_T_G	299	2301	4701
154_mis_G_A	90	5380	5839
154_mis_G_C	8	59	165
154_mis_G_T	987	1891	1944
155_mis_C_A	829	4503	6418
155_mis_C_G	55	1292	3240
155_mis_C_T	358	4040	10547
156_mis_G_A	396	7943	8183
156_mis_G_C	9	164	316
156_mis_G_T	1296	2036	4399
157_mis_C_A	1113	7168	7573
157_mis_C_G	24	1216	4373
157_mis_C_T	85	1406	13549
158_mis_A_C	57	518	1662
158_mis_A_G	196	3103	7402
158_mis_A_T	73	943	2040
159_mis_G_A	25	5094	6014
159_mis_G_C	4	80	190
159_mis_G_T	1297	2310	5345
160_mis_C_A	753	4302	6998
160_mis_C_G	68	781	3301
160_mis_C_T	127	2765	15729
161_mis_C_A	775	2636	5358
161_mis_C_G	12	82	496
161_mis_C_T	58	1736	5202
162_mis_T_A	85	2046	2809
162_mis_T_C	494	1411	1761
162_mis_T_G	270	1305	2100
163_mis_G_A	77	6316	6115
163_mis_G_C	0	95	267
163_mis_G_T	915	3018	2683
164_mis_A_C	0	146	1085
164_mis_A_G	30	2632	11229
164_mis_A_T	32	162	644
165_mis_A_C	11	335	1073
165_mis_A_G	40	4841	26971
165_mis_A_T	4	377	446
166_mis_T_A	59	2739	3037
166_mis_T_C	45	731	2056
166_mis_T_G	240	1885	3799
167_mis_G_A	162	5204	7219
167_mis_G_C	0	70	212
167_mis_G_T	1177	2090	2229
168_mis_G_A	170	6806	7798
168_mis_G_C	9	36	268
168_mis_G_T	1316	2687	3166
169_mis_C_A	961	5690	5368
169_mis_C_G	62	743	1310
169_mis_C_T	225	2691	12284
170_mis_G_A	98	6932	7629
170_mis_G_C	79	54	214
170_mis_G_T	587	1801	2180
171_mis_A_C	112	758	1591
171_mis_A_G	33	3560	11259
171_mis_A_T	25	280	560
172_mis_A_C	11	325	3007
172_mis_A_G	54	7833	32140
172_mis_A_T	24	278	1167
173_mis_T_A	56	3262	2361
173_mis_T_C	3	1047	3268
173_mis_T_G	84	750	1837
174_mis_G_A	51	6663	7739
174_mis_G_C	0	73	214
174_mis_G_T	895	1672	2123
175_mis_G_A	139	6844	8053
175_mis_G_C	20	61	66
175_mis_G_T	1323	4774	4238
176_mis_C_A	1486	6337	7239
176_mis_C_G	84	971	3639
176_mis_C_T	636	2814	10742

## Raw table: miss_indexes_by_string.

	s1	s2	s3
024_mis_C_A	157	562	620
024_mis_C_G	0	105	291
024_mis_C_T	40	195	1159
025_mis_C_A	166	675	680
025_mis_C_G	8	104	123
025_mis_C_T	45	327	493
026_mis_G_A	40	1413	2162
026_mis_G_C	22	169	330
026_mis_G_T	247	352	410
027_mis_T_A	0	89	319
027_mis_T_C	96	223	453
027_mis_T_G	9	256	466
028_mis_C_A	70	533	548
028_mis_C_G	3	33	103
028_mis_C_T	43	217	1091
029_mis_G_A	22	1178	1363
029_mis_G_C	24	62	98
029_mis_G_T	289	245	399
030_mis_T_A	3	8	65
030_mis_T_C	32	177	375
030_mis_T_G	276	762	1333
031_mis_T_A	0	34	125
031_mis_T_C	9	61	187
031_mis_T_G	14	14	33
032_mis_T_A	0	18	19
032_mis_T_C	22	43	143
032_mis_T_G	5	9	11
033_mis_T_A	10	25	290
033_mis_T_C	19	87	187
033_mis_T_G	0	7	27
034_mis_A_C	66	130	227
034_mis_A_G	61	338	1328
034_mis_A_T	63	322	417
035_mis_C_A	100	425	526
035_mis_C_G	0	0	70
035_mis_C_T	28	572	2099
036_mis_A_C	42	591	1427
036_mis_A_G	26	312	634
036_mis_A_T	0	45	116
037_mis_A_C	125	328	533
037_mis_A_G	98	400	1655
037_mis_A_T	0	42	73
038_mis_C_A	130	508	772
038_mis_C_G	7	22	84
038_mis_C_T	33	349	1232
039_mis_G_A	50	980	1010
039_mis_G_C	21	377	700
039_mis_G_T	198	318	488
040_mis_T_A	3	21	143
040_mis_T_C	79	208	389
040_mis_T_G	6	294	465
041_mis_C_A	165	375	731
041_mis_C_G	4	11	165
041_mis_C_T	9	116	565
042_mis_G_A	45	882	913
042_mis_G_C	11	145	354
042_mis_G_T	193	258	241
043_mis_T_A	4	69	212
043_mis_T_C	11	125	207
043_mis_T_G	89	492	879
044_mis_G_A	18	661	985
044_mis_G_C	0	14	45
044_mis_G_T	327	423	506
045_mis_A_C	108	256	520
045_mis_A_G	7	449	2463
045_mis_A_T	5	65	71
046_mis_C_A	61	562	568
046_mis_C_G	0	133	378
046_mis_C_T	24	705	3245
047_mis_T_A	12	315	398
047_mis_T_C	46	530	1083
047_mis_T_G	116	167	299
048_mis_G_A	24	857	1279
048_mis_G_C	0	7	9
048_mis_G_T	214	337	393
049_mis_G_A	30	1342	1380
049_mis_G_C	16	41	47
049_mis_G_T	238	283	420
050_mis_G_A	73	1337	4891
050_mis_G_C	8	35	37
050_mis_G_T	111	93	187
051_mis_A_C	9	53	99
051_mis_A_G	112	1173	2903
051_mis_A_T	5	26	36
052_mis_A_C	11	44	144
052_mis_A_G	5	646	2792
052_mis_A_T	0	29	55
053_mis_A_C	19	67	235
053_mis_A_G	5	839	2824
053_mis_A_T	0	10	124
054_mis_A_C	502	799	667
054_mis_A_G	4	714	2158
054_mis_A_T	0	0	84
055_mis_C_A	162	1179	1548
055_mis_C_G	0	84	108
055_mis_C_T	31	219	1172
056_mis_C_A	245	500	681
056_mis_C_G	14	33	102
056_mis_C_T	9	201	530
057_mis_C_A	273	747	806
057_mis_C_G	0	25	92
057_mis_C_T	325	438	460
058_mis_T_A	6	211	306
058_mis_T_C	136	690	927
058_mis_T_G	22	58	107
059_mis_G_A	219	951	945
059_mis_G_C	7	40	53
059_mis_G_T	296	466	647
060_mis_G_A	5	771	654
060_mis_G_C	62	168	271
060_mis_G_T	637	595	757
061_mis_C_A	231	658	633
061_mis_C_G	14	137	365
061_mis_C_T	9	334	1698
062_mis_G_A	40	940	840
062_mis_G_C	0	99	193
062_mis_G_T	208	287	367
063_mis_T_A	0	38	155
063_mis_T_C	9	82	232
063_mis_T_G	26	669	1091
064_mis_T_A	8	176	218
064_mis_T_C	15	114	240
064_mis_T_G	0	3	32
065_mis_A_C	101	210	419
065_mis_A_G	4	511	1910
065_mis_A_T	36	348	725
066_mis_C_A	27	392	539
066_mis_C_G	0	72	286
066_mis_C_T	45	946	3847
067_mis_C_A	69	460	548
067_mis_C_G	0	47	54
067_mis_C_T	49	322	746
068_mis_C_A	255	596	744
068_mis_C_G	0	42	119
068_mis_C_T	43	141	355
069_mis_A_C	11	84	93
069_mis_A_G	20	126	299
069_mis_A_T	0	109	173
070_mis_A_C	19	33	124
070_mis_A_G	14	405	1489
070_mis_A_T	3	38	69
071_mis_C_A	76	278	422
071_mis_C_G	0	79	202
071_mis_C_T	28	445	1681
072_mis_T_A	0	19	92
072_mis_T_C	5	28	164
072_mis_T_G	7	8	17
073_mis_T_A	0	78	83
073_mis_T_C	5	44	111
073_mis_T_G	0	10	38
074_mis_A_C	3	96	85
074_mis_A_G	4	323	1049
074_mis_A_T	7	51	172
075_mis_A_C	0	60	181
075_mis_A_G	0	533	2019
075_mis_A_T	0	61	67
076_mis_T_A	12	285	312
076_mis_T_C	7	111	362
076_mis_T_G	0	11	39
077_mis_C_A	174	618	858
077_mis_C_G	0	40	103
077_mis_C_T	39	156	784
078_mis_G_A	17	684	773
078_mis_G_C	4	12	44
078_mis_G_T	454	433	631
079_mis_C_A	227	426	394
079_mis_C_G	0	58	244
079_mis_C_T	10	320	1273
080_mis_C_A	108	269	375
080_mis_C_G	4	4	29
080_mis_C_T	19	226	710
081_mis_T_A	5	55	191
081_mis_T_C	11	40	148
081_mis_T_G	0	16	60
082_mis_T_A	0	131	300
082_mis_T_C	4	88	164
082_mis_T_G	0	13	20
083_mis_G_A	15	619	647
083_mis_G_C	9	16	23
083_mis_G_T	350	301	445
084_mis_C_A	200	304	401
084_mis_C_G	0	27	101
084_mis_C_T	34	503	1979
085_mis_A_C	0	27	99
085_mis_A_G	25	156	570
085_mis_A_T	0	83	102
086_mis_G_A	120	892	972
086_mis_G_C	12	22	59
086_mis_G_T	242	275	357
087_mis_C_A	252	770	825
087_mis_C_G	3	180	384
087_mis_C_T	16	225	1780
088_mis_A_C	9	60	112
088_mis_A_G	11	224	580
088_mis_A_T	7	45	122
089_mis_C_A	43	484	654
089_mis_C_G	0	109	171
089_mis_C_T	32	615	1672
090_mis_A_C	5	79	235
090_mis_A_G	8	233	994
090_mis_A_T	4	26	195
091_mis_T_A	0	311	313
091_mis_T_C	16	88	283
091_mis_T_G	0	7	20
092_mis_C_A	132	335	360
092_mis_C_G	0	59	83
092_mis_C_T	31	191	638
093_mis_C_A	161	653	1071
093_mis_C_G	7	111	118
093_mis_C_T	20	171	405
094_mis_C_A	280	944	631
094_mis_C_G	3	28	115
094_mis_C_T	14	117	495
095_mis_C_A	294	736	671
095_mis_C_G	0	35	97
095_mis_C_T	14	88	238
096_mis_C_A	248	677	558
096_mis_C_G	0	17	40
096_mis_C_T	30	227	601
097_mis_T_A	0	121	285
097_mis_T_C	14	86	43
097_mis_T_G	0	33	25
098_mis_T_A	6	122	220
098_mis_T_C	5	65	134
098_mis_T_G	5	6	18
099_mis_T_A	0	15	33
099_mis_T_C	12	77	92
099_mis_T_G	0	3	11
100_mis_C_A	179	455	428
100_mis_C_G	3	5	11
100_mis_C_T	51	2245	3242
101_mis_G_A	40	637	702
101_mis_G_C	16	23	43
101_mis_G_T	233	331	323
102_mis_C_A	235	333	311
102_mis_C_G	0	12	20
102_mis_C_T	51	256	1053
103_mis_C_A	113	357	309
103_mis_C_G	0	12	37
103_mis_C_T	36	158	413
104_mis_A_C	4	52	50
104_mis_A_G	8	96	342
104_mis_A_T	5	71	169
105_mis_G_A	17	615	827
105_mis_G_C	4	0	4
105_mis_G_T	343	411	586
106_mis_C_A	149	669	738
106_mis_C_G	0	65	240
106_mis_C_T	41	287	1404
107_mis_T_A	0	291	323
107_mis_T_C	11	32	78
107_mis_T_G	9	62	137
108_mis_G_A	15	570	540
108_mis_G_C	6	22	8
108_mis_G_T	204	244	258
109_mis_G_A	13	467	729
109_mis_G_C	0	20	14
109_mis_G_T	259	419	463
110_mis_C_A	223	653	634
110_mis_C_G	41	96	199
110_mis_C_T	97	436	1992
111_mis_G_A	147	877	728
111_mis_G_C	0	18	22
111_mis_G_T	306	310	354
112_mis_T_A	7	135	244
112_mis_T_C	49	115	216
112_mis_T_G	81	386	731
113_mis_A_C	0	17	107
113_mis_A_G	7	401	1381
113_mis_A_T	0	75	177
114_mis_A_C	3	79	269
114_mis_A_G	6	683	2705
114_mis_A_T	6	81	210
115_mis_T_A	20	445	458
115_mis_T_C	4	94	409
115_mis_T_G	37	180	349
116_mis_A_C	17	96	200
116_mis_A_G	57	419	1251
116_mis_A_T	8	17	169
117_mis_G_A	4	623	677
117_mis_G_C	4	24	27
117_mis_G_T	245	418	401
118_mis_C_A	224	691	708
118_mis_C_G	11	158	383
118_mis_C_T	28	308	1365
119_mis_G_A	27	750	634
119_mis_G_C	0	35	31
119_mis_G_T	203	314	264
120_mis_A_C	10	37	116
120_mis_A_G	7	378	1136
120_mis_A_T	3	22	34
121_mis_A_C	14	67	169
121_mis_A_G	15	741	2594
121_mis_A_T	0	18	87
122_mis_G_A	33	543	880
122_mis_G_C	0	9	13
122_mis_G_T	12	18	111
123_mis_A_C	6	9	81
123_mis_A_G	25	580	2334
123_mis_A_T	0	16	21
124_mis_G_A	11	607	676
124_mis_G_C	0	7	9
124_mis_G_T	152	214	341
125_mis_G_A	37	675	588
125_mis_G_C	3	15	20
125_mis_G_T	470	494	664
126_mis_C_A	433	603	672
126_mis_C_G	17	322	657
126_mis_C_T	83	362	1322
127_mis_C_A	213	472	580
127_mis_C_G	12	74	125
127_mis_C_T	60	109	339
128_mis_C_A	295	868	837
128_mis_C_G	27	116	273
128_mis_C_T	57	167	358
129_mis_G_A	215	1022	1009
129_mis_G_C	9	27	41
129_mis_G_T	311	344	445
130_mis_C_A	238	929	1027
130_mis_C_G	15	117	399
130_mis_C_T	9	349	1183
131_mis_A_C	63	359	710
131_mis_A_G	34	299	816
131_mis_A_T	11	33	145
132_mis_C_A	108	227	502
132_mis_C_G	0	101	317
132_mis_C_T	33	474	1703
133_mis_C_A	200	464	582
133_mis_C_G	8	26	129
133_mis_C_T	47	182	565
134_mis_G_A	40	864	584
134_mis_G_C	3	12	33
134_mis_G_T	99	148	118
135_mis_A_C	20	61	176
135_mis_A_G	19	256	996
135_mis_A_T	5	78	57
136_mis_T_A	4	99	299
136_mis_T_C	16	97	249
136_mis_T_G	15	64	107
137_mis_C_A	101	475	478
137_mis_C_G	11	44	133
137_mis_C_T	19	135	525
138_mis_G_A	36	493	663
138_mis_G_C	5	36	70
138_mis_G_T	301	401	385
139_mis_C_A	134	391	550
139_mis_C_G	7	96	292
139_mis_C_T	15	423	1554
140_mis_C_A	75	287	590
140_mis_C_G	22	67	166
140_mis_C_T	27	186	528
141_mis_C_A	173	429	499
141_mis_C_G	5	41	40
141_mis_C_T	24	181	543
142_mis_T_A	13	43	216
142_mis_T_C	14	114	201
142_mis_T_G	58	232	377
143_mis_T_A	9	179	249
143_mis_T_C	15	52	155
143_mis_T_G	67	369	676
144_mis_C_A	129	462	475
144_mis_C_G	6	40	137
144_mis_C_T	21	174	865
145_mis_C_A	137	481	521
145_mis_C_G	9	37	63
145_mis_C_T	4	166	547
146_mis_C_A	239	676	1073
146_mis_C_G	0	9	50
146_mis_C_T	24	81	335
147_mis_A_C	5	19	46
147_mis_A_G	8	125	365
147_mis_A_T	0	21	141
148_mis_A_C	7	43	88
148_mis_A_G	15	296	1067
148_mis_A_T	6	27	67
149_mis_C_A	102	502	656
149_mis_C_G	7	23	66
149_mis_C_T	23	472	1543
150_mis_A_C	13	54	219
150_mis_A_G	10	236	645
150_mis_A_T	5	54	99
151_mis_G_A	8	733	656
151_mis_G_C	0	8	7
151_mis_G_T	229	260	307
152_mis_T_A	7	302	405
152_mis_T_C	11	84	200
152_mis_T_G	18	102	260
153_mis_T_A	3	132	242
153_mis_T_C	27	48	163
153_mis_T_G	63	321	605
154_mis_G_A	17	590	571
154_mis_G_C	0	8	20
154_mis_G_T	207	215	233
155_mis_C_A	172	496	642
155_mis_C_G	13	153	344
155_mis_C_T	86	461	1129
156_mis_G_A	71	866	891
156_mis_G_C	0	18	43
156_mis_G_T	260	286	431
157_mis_C_A	199	846	785
157_mis_C_G	4	154	454
157_mis_C_T	21	198	1273
158_mis_A_C	11	69	170
158_mis_A_G	36	370	825
158_mis_A_T	13	103	206
159_mis_G_A	7	623	711
159_mis_G_C	0	12	28
159_mis_G_T	263	309	604
160_mis_C_A	153	534	639
160_mis_C_G	12	103	350
160_mis_C_T	34	328	1675
161_mis_C_A	143	315	541
161_mis_C_G	0	12	58
161_mis_C_T	17	178	531
162_mis_T_A	19	242	311
162_mis_T_C	92	183	208
162_mis_T_G	51	178	275
163_mis_G_A	14	690	667
163_mis_G_C	0	11	29
163_mis_G_T	164	308	274
164_mis_A_C	0	17	116
164_mis_A_G	8	321	1126
164_mis_A_T	7	18	84
165_mis_A_C	0	37	131
165_mis_A_G	10	564	2799
165_mis_A_T	0	49	60
166_mis_T_A	14	372	385
166_mis_T_C	13	96	212
166_mis_T_G	54	244	462
167_mis_G_A	37	616	822
167_mis_G_C	0	10	22
167_mis_G_T	241	267	248
168_mis_G_A	48	767	878
168_mis_G_C	3	4	32
168_mis_G_T	263	322	351
169_mis_C_A	192	622	630
169_mis_C_G	14	94	170
169_mis_C_T	46	300	1293
170_mis_G_A	20	822	791
170_mis_G_C	15	7	26
170_mis_G_T	122	212	251
171_mis_A_C	23	98	196
171_mis_A_G	8	400	1238
171_mis_A_T	6	31	68
172_mis_A_C	0	47	280
172_mis_A_G	10	881	3379
172_mis_A_T	7	36	119
173_mis_T_A	13	371	281
173_mis_T_C	0	113	297
173_mis_T_G	16	114	233
174_mis_G_A	11	702	786
174_mis_G_C	0	8	28
174_mis_G_T	138	204	235
175_mis_G_A	31	790	810
175_mis_G_C	4	8	10
175_mis_G_T	275	579	482
176_mis_C_A	250	758	761
176_mis_C_G	18	118	393
176_mis_C_T	134	358	1251

## Raw table: miss_sequencer_by_string.

	s1	s2	s3
024_mis_C_A	5	50	43
024_mis_C_G	0	53	44
024_mis_C_T	9	83	72
025_mis_C_A	14	79	59
025_mis_C_G	16	131	87
025_mis_C_T	16	137	123
026_mis_G_A	9	142	130
026_mis_G_C	11	554	618
026_mis_G_T	58	337	237
027_mis_T_A	4	75	67
027_mis_T_C	210	881	813
027_mis_T_G	5	1169	975
028_mis_C_A	3	36	57
028_mis_C_G	20	146	100
028_mis_C_T	9	308	194
029_mis_G_A	7	74	66
029_mis_G_C	4	187	157
029_mis_G_T	54	267	203
030_mis_T_A	5	25	18
030_mis_T_C	14	155	123
030_mis_T_G	28	2291	1955
031_mis_T_A	0	21	13
031_mis_T_C	8	100	74
031_mis_T_G	0	36	29
032_mis_T_A	0	19	18
032_mis_T_C	4	71	67
032_mis_T_G	5	25	26
033_mis_T_A	26	107	85
033_mis_T_C	9	120	82
033_mis_T_G	0	17	28
034_mis_A_C	260	1051	915
034_mis_A_G	6	100	78
034_mis_A_T	17	442	356
035_mis_C_A	66	698	560
035_mis_C_G	0	7	14
035_mis_C_T	8	68	70
036_mis_A_C	115	3241	2953
036_mis_A_G	16	114	93
036_mis_A_T	3	24	17
037_mis_A_C	284	1652	1443
037_mis_A_G	10	85	64
037_mis_A_T	0	25	22
038_mis_C_A	4	660	506
038_mis_C_G	10	53	52
038_mis_C_T	12	68	63
039_mis_G_A	18	134	111
039_mis_G_C	13	1583	1485
039_mis_G_T	135	679	539
040_mis_T_A	0	64	51
040_mis_T_C	267	1211	987
040_mis_T_G	11	1289	1051
041_mis_C_A	5	54	42
041_mis_C_G	4	66	55
041_mis_C_T	13	235	157
042_mis_G_A	8	117	87
042_mis_G_C	8	806	715
042_mis_G_T	30	248	225
043_mis_T_A	14	86	99
043_mis_T_C	29	231	175
043_mis_T_G	256	2696	2236
044_mis_G_A	33	225	183
044_mis_G_C	6	38	31
044_mis_G_T	12	501	439
045_mis_A_C	400	1928	1541
045_mis_A_G	13	262	229
045_mis_A_T	8	91	74
046_mis_C_A	4	367	298
046_mis_C_G	0	11	17
046_mis_C_T	25	105	82
047_mis_T_A	3	47	37
047_mis_T_C	36	2287	1936
047_mis_T_G	274	936	864
048_mis_G_A	8	107	75
048_mis_G_C	4	30	22
048_mis_G_T	49	441	389
049_mis_G_A	9	128	97
049_mis_G_C	6	39	27
049_mis_G_T	15	350	286
050_mis_G_A	57	303	274
050_mis_G_C	3	39	20
050_mis_G_T	37	151	156
051_mis_A_C	32	264	231
051_mis_A_G	22	844	694
051_mis_A_T	11	68	52
052_mis_A_C	25	217	177
052_mis_A_G	12	115	81
052_mis_A_T	0	16	10
053_mis_A_C	61	309	264
053_mis_A_G	6	63	47
053_mis_A_T	0	13	8
054_mis_A_C	184	1228	963
054_mis_A_G	5	56	40
054_mis_A_T	6	16	16
055_mis_C_A	21	2637	2108
055_mis_C_G	0	0	5
055_mis_C_T	6	56	31
056_mis_C_A	15	79	61
056_mis_C_G	0	11	5
056_mis_C_T	10	52	56
057_mis_C_A	7	34	43
057_mis_C_G	0	11	5
057_mis_C_T	36	153	117
058_mis_T_A	10	58	58
058_mis_T_C	29	1512	1268
058_mis_T_G	76	370	341
059_mis_G_A	19	123	108
059_mis_G_C	16	103	69
059_mis_G_T	25	1109	947
060_mis_G_A	7	54	54
060_mis_G_C	283	1177	1032
060_mis_G_T	70	287	246
061_mis_C_A	5	29	38
061_mis_C_G	16	138	121
061_mis_C_T	14	128	111
062_mis_G_A	7	71	53
062_mis_G_C	5	505	459
062_mis_G_T	60	269	199
063_mis_T_A	10	78	74
063_mis_T_C	17	242	166
063_mis_T_G	21	2760	2175
064_mis_T_A	32	133	127
064_mis_T_C	11	148	104
064_mis_T_G	3	17	12
065_mis_A_C	328	1468	1232
065_mis_A_G	13	80	67
065_mis_A_T	18	677	604
066_mis_C_A	27	614	463
066_mis_C_G	0	10	12
066_mis_C_T	7	80	62
067_mis_C_A	9	165	126
067_mis_C_G	0	17	15
067_mis_C_T	11	69	65
068_mis_C_A	76	375	307
068_mis_C_G	0	16	15
068_mis_C_T	10	87	65
069_mis_A_C	19	100	83
069_mis_A_G	11	79	70
069_mis_A_T	0	17	11
070_mis_A_C	37	298	228
070_mis_A_G	7	109	81
070_mis_A_T	8	71	66
071_mis_C_A	6	78	69
071_mis_C_G	0	5	6
071_mis_C_T	7	37	40
072_mis_T_A	3	25	20
072_mis_T_C	7	98	70
072_mis_T_G	9	36	37
073_mis_T_A	4	45	39
073_mis_T_C	7	86	69
073_mis_T_G	3	23	29
074_mis_A_C	4	127	93
074_mis_A_G	5	62	63
074_mis_A_T	13	91	67
075_mis_A_C	6	68	38
075_mis_A_G	7	62	44
075_mis_A_T	5	45	46
076_mis_T_A	8	133	111
076_mis_T_C	19	99	96
076_mis_T_G	3	56	51
077_mis_C_A	7	36	22
077_mis_C_G	0	8	11
077_mis_C_T	7	67	55
078_mis_G_A	8	129	89
078_mis_G_C	7	72	63
078_mis_G_T	12	57	60
079_mis_C_A	6	63	67
079_mis_C_G	0	23	13
079_mis_C_T	4	38	35
080_mis_C_A	8	37	29
080_mis_C_G	0	8	7
080_mis_C_T	6	60	51
081_mis_T_A	0	54	51
081_mis_T_C	13	110	88
081_mis_T_G	4	86	53
082_mis_T_A	4	40	23
082_mis_T_C	13	126	116
082_mis_T_G	0	34	31
083_mis_G_A	6	80	62
083_mis_G_C	9	58	38
083_mis_G_T	13	121	98
084_mis_C_A	0	35	28
084_mis_C_G	3	25	28
084_mis_C_T	13	160	137
085_mis_A_C	7	108	56
085_mis_A_G	18	143	142
085_mis_A_T	0	18	15
086_mis_G_A	3	104	87
086_mis_G_C	10	47	47
086_mis_G_T	7	74	54
087_mis_C_A	18	58	44
087_mis_C_G	0	16	7
087_mis_C_T	4	61	46
088_mis_A_C	20	236	198
088_mis_A_G	14	115	117
088_mis_A_T	12	107	83
089_mis_C_A	3	40	39
089_mis_C_G	0	9	8
089_mis_C_T	6	66	56
090_mis_A_C	3	165	144
090_mis_A_G	17	92	82
090_mis_A_T	8	77	53
091_mis_T_A	6	87	73
091_mis_T_C	17	150	133
091_mis_T_G	15	42	45
092_mis_C_A	6	31	26
092_mis_C_G	3	17	6
092_mis_C_T	4	42	50
093_mis_C_A	3	72	58
093_mis_C_G	0	18	18
093_mis_C_T	0	75	52
094_mis_C_A	4	41	26
094_mis_C_G	0	13	13
094_mis_C_T	4	39	29
095_mis_C_A	0	23	36
095_mis_C_G	0	14	7
095_mis_C_T	8	34	35
096_mis_C_A	5	40	27
096_mis_C_G	0	6	5
096_mis_C_T	13	130	98
097_mis_T_A	5	33	34
097_mis_T_C	5	107	99
097_mis_T_G	0	23	11
098_mis_T_A	3	61	46
098_mis_T_C	8	121	105
098_mis_T_G	9	35	39
099_mis_T_A	0	23	15
099_mis_T_C	19	129	121
099_mis_T_G	6	36	33
100_mis_C_A	3	16	12
100_mis_C_G	0	7	0
100_mis_C_T	10	72	68
101_mis_G_A	25	202	178
101_mis_G_C	7	74	59
101_mis_G_T	10	57	36
102_mis_C_A	6	48	42
102_mis_C_G	0	21	24
102_mis_C_T	23	199	155
103_mis_C_A	0	28	18
103_mis_C_G	0	12	19
103_mis_C_T	11	83	73
104_mis_A_C	0	41	47
104_mis_A_G	12	108	81
104_mis_A_T	5	23	26
105_mis_G_A	4	48	37
105_mis_G_C	0	14	15
105_mis_G_T	7	70	80
106_mis_C_A	6	71	72
106_mis_C_G	0	16	13
106_mis_C_T	0	35	33
107_mis_T_A	5	22	27
107_mis_T_C	9	104	79
107_mis_T_G	18	120	133
108_mis_G_A	7	68	75
108_mis_G_C	0	21	21
108_mis_G_T	0	56	52
109_mis_G_A	14	131	107
109_mis_G_C	0	9	17
109_mis_G_T	15	65	56
110_mis_C_A	25	115	77
110_mis_C_G	78	315	226
110_mis_C_T	29	266	215
111_mis_G_A	16	150	116
111_mis_G_C	0	33	26
111_mis_G_T	12	67	75
112_mis_T_A	25	208	162
112_mis_T_C	8	85	61
112_mis_T_G	278	2437	1952
113_mis_A_C	0	16	12
113_mis_A_G	13	157	119
113_mis_A_T	3	39	35
114_mis_A_C	0	21	17
114_mis_A_G	12	110	110
114_mis_A_T	4	79	51
115_mis_T_A	60	568	437
115_mis_T_C	9	103	87
115_mis_T_G	129	1146	908
116_mis_A_C	26	276	270
116_mis_A_G	30	267	203
116_mis_A_T	9	47	32
117_mis_G_A	4	74	58
117_mis_G_C	7	48	39
117_mis_G_T	4	36	34
118_mis_C_A	28	277	180
118_mis_C_G	40	415	273
118_mis_C_T	9	120	102
119_mis_G_A	14	85	68
119_mis_G_C	3	44	44
119_mis_G_T	9	37	24
120_mis_A_C	17	218	142
120_mis_A_G	27	250	206
120_mis_A_T	3	48	55
121_mis_A_C	0	37	37
121_mis_A_G	47	484	360
121_mis_A_T	3	37	22
122_mis_G_A	7	76	65
122_mis_G_C	0	32	22
122_mis_G_T	7	56	38
123_mis_A_C	0	26	17
123_mis_A_G	39	428	315
123_mis_A_T	0	26	17
124_mis_G_A	11	112	79
124_mis_G_C	0	18	17
124_mis_G_T	58	478	444
125_mis_G_A	15	78	72
125_mis_G_C	5	48	39
125_mis_G_T	96	731	533
126_mis_C_A	24	242	101
126_mis_C_G	62	691	379
126_mis_C_T	73	760	359
127_mis_C_A	20	208	146
127_mis_C_G	7	163	128
127_mis_C_T	28	304	217
128_mis_C_A	53	291	242
128_mis_C_G	39	357	273
128_mis_C_T	27	259	200
129_mis_G_A	16	258	228
129_mis_G_C	4	69	57
129_mis_G_T	0	47	37
130_mis_C_A	73	723	630
130_mis_C_G	37	330	245
130_mis_C_T	7	98	77
131_mis_A_C	263	2356	1742
131_mis_A_G	44	393	291
131_mis_A_T	3	66	54
132_mis_C_A	33	345	294
132_mis_C_G	12	123	111
132_mis_C_T	8	59	63
133_mis_C_A	10	120	67
133_mis_C_G	10	139	102
133_mis_C_T	11	62	76
134_mis_G_A	12	77	74
134_mis_G_C	4	38	30
134_mis_G_T	14	113	99
135_mis_A_C	22	328	258
135_mis_A_G	34	268	243
135_mis_A_T	7	85	68
136_mis_T_A	23	183	129
136_mis_T_C	30	271	212
136_mis_T_G	54	349	318
137_mis_C_A	19	191	132
137_mis_C_G	13	144	113
137_mis_C_T	14	109	80
138_mis_G_A	19	170	152
138_mis_G_C	12	130	98
138_mis_G_T	4	61	50
139_mis_C_A	61	502	386
139_mis_C_G	19	166	138
139_mis_C_T	5	72	67
140_mis_C_A	71	519	454
140_mis_C_G	13	246	220
140_mis_C_T	11	102	87
141_mis_C_A	19	175	138
141_mis_C_G	5	73	53
141_mis_C_T	16	190	148
142_mis_T_A	10	80	73
142_mis_T_C	19	206	166
142_mis_T_G	173	1502	1108
143_mis_T_A	23	167	123
143_mis_T_C	32	304	196
143_mis_T_G	265	2425	1904
144_mis_C_A	15	186	169
144_mis_C_G	10	124	76
144_mis_C_T	6	129	94
145_mis_C_A	59	531	435
145_mis_C_G	5	51	47
145_mis_C_T	10	87	77
146_mis_C_A	76	802	564
146_mis_C_G	0	31	23
146_mis_C_T	11	93	83
147_mis_A_C	12	104	58
147_mis_A_G	10	139	116
147_mis_A_T	0	31	33
148_mis_A_C	21	243	164
148_mis_A_G	15	195	148
148_mis_A_T	5	111	84
149_mis_C_A	30	267	202
149_mis_C_G	6	27	20
149_mis_C_T	3	52	52
150_mis_A_C	35	321	236
150_mis_A_G	18	266	180
150_mis_A_T	0	26	23
151_mis_G_A	15	93	85
151_mis_G_C	0	14	10
151_mis_G_T	11	110	84
152_mis_T_A	5	60	43
152_mis_T_C	10	92	108
152_mis_T_G	63	538	389
153_mis_T_A	6	55	31
153_mis_T_C	23	267	196
153_mis_T_G	229	2012	1646
154_mis_G_A	11	87	56
154_mis_G_C	0	36	37
154_mis_G_T	9	84	73
155_mis_C_A	17	140	115
155_mis_C_G	33	304	205
155_mis_C_T	40	359	287
156_mis_G_A	12	130	105
156_mis_G_C	10	85	65
156_mis_G_T	5	59	48
157_mis_C_A	33	424	350
157_mis_C_G	15	109	89
157_mis_C_T	5	71	50
158_mis_A_C	27	204	162
158_mis_A_G	73	906	649
158_mis_A_T	3	44	46
159_mis_G_A	15	74	45
159_mis_G_C	5	28	27
159_mis_G_T	55	511	331
160_mis_C_A	18	143	145
160_mis_C_G	28	348	226
160_mis_C_T	8	124	102
161_mis_C_A	36	408	267
161_mis_C_G	8	101	80
161_mis_C_T	6	85	76
162_mis_T_A	37	363	250
162_mis_T_C	18	192	168
162_mis_T_G	63	680	524
163_mis_G_A	19	186	124
163_mis_G_C	0	14	14
163_mis_G_T	19	157	118
164_mis_A_C	0	19	19
164_mis_A_G	16	161	125
164_mis_A_T	5	86	66
165_mis_A_C	0	17	11
165_mis_A_G	26	271	212
165_mis_A_T	6	36	42
166_mis_T_A	25	303	238
166_mis_T_C	9	98	98
166_mis_T_G	158	1566	1078
167_mis_G_A	20	191	175
167_mis_G_C	0	13	10
167_mis_G_T	0	36	29
168_mis_G_A	32	380	311
168_mis_G_C	3	26	28
168_mis_G_T	0	55	49
169_mis_C_A	23	247	190
169_mis_C_G	21	191	140
169_mis_C_T	20	288	228
170_mis_G_A	10	156	91
170_mis_G_C	7	27	26
170_mis_G_T	4	62	39
171_mis_A_C	57	501	410
171_mis_A_G	8	128	103
171_mis_A_T	5	74	68
172_mis_A_C	0	27	28
172_mis_A_G	17	194	135
172_mis_A_T	0	45	34
173_mis_T_A	16	147	130
173_mis_T_C	7	89	73
173_mis_T_G	69	572	455
174_mis_G_A	11	109	92
174_mis_G_C	0	34	22
174_mis_G_T	0	48	42
175_mis_G_A	11	113	110
175_mis_G_C	0	26	12
175_mis_G_T	50	420	350
176_mis_C_A	78	711	538
176_mis_C_G	28	296	168
176_mis_C_T	42	521	410

## Raw table: miss_reads_by_ref_nt.

## Warning in kable_markdown(x, padding = padding, ...): The table should have a
## header (column names)

|| || || ||

## Raw table: miss_indexes_by_ref_nt.

## Warning in kable_markdown(x, padding = padding, ...): The table should have a
## header (column names)

|| || || ||

## Raw table: miss_sequencer_by_ref_nt.

## Warning in kable_markdown(x, padding = padding, ...): The table should have a
## header (column names)

|| || || ||

## Raw table: miss_reads_by_hit_nt.

## Warning in kable_markdown(x, padding = padding, ...): The table should have a
## header (column names)

|| || || ||

## Raw table: miss_indexes_by_hit_nt.

## Warning in kable_markdown(x, padding = padding, ...): The table should have a
## header (column names)

|| || || ||

## Raw table: miss_sequencer_by_hit_nt.

## Warning in kable_markdown(x, padding = padding, ...): The table should have a
## header (column names)

|| || || ||

## Raw table: miss_reads_by_type.

	s1	s2	s3
A_C	6251	33466	71685
A_G	3215	127920	492245
A_T	921	16430	41852
C_A	48900	255194	317477
C_G	1676	31571	89438
C_T	10105	153993	581712
G_A	8426	266926	346257
G_C	1211	11429	21911
G_T	47570	99899	134058
T_A	874	41718	71058
T_C	3673	30878	72189
T_G	4930	37536	72375

## Raw table: miss_indexes_by_type.

	s1	s2	s3
A_C	1226	4078	8324
A_G	687	14428	50666
A_T	212	2050	4514
C_A	9115	28661	33332
C_G	329	3690	9533
C_T	2108	17340	59479
G_A	1617	29449	35634
G_C	268	1549	2843
G_T	9304	11694	14377
T_A	178	4752	7492
T_C	805	3995	8312
T_G	1044	5090	9203

## Raw table: miss_sequencer_by_type.

	s1	s2	s3
A_C	2265	17215	14189
A_G	623	7106	5588
A_T	170	2701	2256
C_A	1163	14161	11148
C_G	561	5632	4067
C_T	695	7037	5431
G_A	519	4839	3979
G_C	452	6119	5518
G_T	966	8307	6799
T_A	372	3370	2702
T_C	916	9795	8136
T_G	2227	25324	20436

## Raw table: miss_reads_by_trans.

	s1	s2	s3
transition	25419	579717	1492403
transversion	112333	527243	819854

## Raw table: miss_indexes_by_trans.

	s1	s2	s3
transition	5217	65212	154091
transversion	21676	61564	89618

## Raw table: miss_sequencer_by_trans.

	s1	s2	s3
transition	2753	28777	23134
transversion	8176	82829	67115

## Raw table: miss_reads_by_strength.

	s1	s2	s3
strong_strong	2887	43000	111349
strong_weak	115001	776012	1379504
weak_strong	18069	229800	708494
weak_weak	1795	58148	112910

## Raw table: miss_indexes_by_strength.

	s1	s2	s3
strong_strong	597	5239	12376
strong_weak	22144	87144	142822
weak_strong	3762	27591	76505
weak_weak	390	6802	12006

## Raw table: miss_sequencer_by_strength.

	s1	s2	s3
strong_strong	1013	11751	9585
strong_weak	3343	34344	27357
weak_strong	6031	59440	48349
weak_weak	542	6071	4958

## Raw table: insert_reads_by_position.

	s1	s2	s3
24	0	0	13
25	0	7	0
26	0	4	0
27	0	12	0
28	0	0	13
29	0	0	4
30	0	14	78
35	0	0	23
36	0	7	8
42	0	14	0
44	0	0	5
45	0	0	13
46	6	9	36
47	0	5	3
48	0	156	829
50	0	0	20
51	4	194	3465
52	0	0	144
55	0	0	256
58	0	0	3
59	0	0	7
61	0	0	3
62	0	3	0
69	0	0	5
72	0	0	483
81	0	0	64
91	0	72	698
92	6	191	216
93	0	0	36
97	0	260	753
100	0	23	0
105	0	0	8
110	0	0	4
120	0	0	4
123	0	0	320
124	0	0	4
126	7	0	0
130	0	0	5
132	0	10	0
134	4	0	0
137	0	0	10
138	0	0	4
142	0	0	31
144	0	0	90
148	0	0	20
149	0	12	0
151	0	12	0
154	0	64	0
156	0	6	0
171	7	0	4
174	7	0	0

## Raw table: insert_indexes_by_position.

	s2	s3
30	0	8
46	0	4
48	19	77
51	25	358
52	0	12
55	0	16
72	0	39
81	0	8
91	8	72
92	20	34
93	0	6
97	40	83
100	3	0
123	0	24
142	0	7
144	0	9
148	0	4
151	4	0
154	8	0

## Raw table: insert_sequencer_by_position.

	s2	s3
29	4	4
34	0	3
35	3	0
42	0	4
46	0	3
48	5	5
51	3	5
55	0	3
66	4	0
92	17	15
97	0	3
149	0	4
151	0	3
156	5	0

## Raw table: insert_reads_by_nt.

	s1	s2	s3
A	11	206	3839
C	19	239	709
G	11	266	996
T	0	364	2138

## Raw table: insert_indexes_by_nt.

	s2	s3
A	25	382
C	23	69
G	31	89
T	48	221

## Raw table: insert_sequencer_by_nt.

	s2	s3
A	3	8
C	24	25
G	14	16
T	0	3

## Raw table: delete_reads_by_position.

s.x	s.y	s

## Raw table: delete_indexes_by_position.

s.x	s.y	s

## Raw table: delete_sequencer_by_position.

s.x	s.y	s

## Raw table: delete_reads_by_nt.

s.x	s.y	s

## Raw table: delete_indexes_by_nt.

s.x	s.y	s

## Raw table: delete_sequencer_by_nt.

s.x	s.y	s

5 Print raw plots

for (t in 1:length(triples[["matrices"]])) {
  message("Raw table: ", table_name, ".")
  print(triplet_plots[["matrices"]][t])
}

## Raw table: delete_sequencer_by_nt.

## Error in print(triplet_plots[["matrices"]][t]): object 'triplet_plots' not found

6 Print normalized tables

for (t in 1:length(triplets[["normalized"]])) {
  table_name <- names(triples[["normalized"]])[t]
  message("Normalized table: ", table_name, ".")
  print(knitr::kable(triples[["normalized"]][t]))
}

## Error in eval(expr, envir, enclos): object 'triplets' not found

7 Print normalized plots

for (t in 1:length(triples[["normalized"]])) {
  message("Normalized table: ", table_name, ".")
  print(triplet_plots[["normal"]][t])
}

## Normalized table: delete_sequencer_by_nt.

## Error in print(triplet_plots[["normal"]][t]): object 'triplet_plots' not found

pander::pander(sessionInfo())

R version 3.6.1 (2019-07-05)

Platform: x86_64-pc-linux-gnu (64-bit)

locale: LC_CTYPE=en_US.UTF-8, LC_NUMERIC=C, LC_TIME=en_US.UTF-8, LC_COLLATE=en_US.UTF-8, LC_MONETARY=en_US.UTF-8, LC_MESSAGES=en_US.UTF-8, LC_PAPER=en_US.UTF-8, LC_NAME=C, LC_ADDRESS=C, LC_TELEPHONE=C, LC_MEASUREMENT=en_US.UTF-8 and LC_IDENTIFICATION=C

attached base packages: parallel, stats, graphics, grDevices, utils, datasets, methods and base

other attached packages: errRt(v.1.0), tidyr(v.1.0.0), dplyr(v.0.8.3), hpgltools(v.1.0), Biobase(v.2.46.0) and BiocGenerics(v.0.32.0)

loaded via a namespace (and not attached): tidyselect(v.0.2.5), lme4(v.1.1-21), RSQLite(v.2.1.4), AnnotationDbi(v.1.48.0), grid(v.3.6.1), BiocParallel(v.1.20.0), devtools(v.2.2.1), munsell(v.0.5.0), codetools(v.0.2-16), withr(v.2.1.2), colorspace(v.1.4-1), GOSemSim(v.2.12.0), highr(v.0.8), knitr(v.1.26), rstudioapi(v.0.10), stats4(v.3.6.1), DOSE(v.3.12.0), labeling(v.0.3), urltools(v.1.7.3), GenomeInfoDbData(v.1.2.2), polyclip(v.1.10-0), bit64(v.0.9-7), farver(v.2.0.1), rprojroot(v.1.3-2), vctrs(v.0.2.1), xfun(v.0.11), BiocFileCache(v.1.10.2), R6(v.2.4.1), doParallel(v.1.0.15), GenomeInfoDb(v.1.22.0), graphlayouts(v.0.5.0), locfit(v.1.5-9.1), bitops(v.1.0-6), fgsea(v.1.12.0), gridGraphics(v.0.4-1), DelayedArray(v.0.12.0), assertthat(v.0.2.1), scales(v.1.1.0), ggraph(v.2.0.0), enrichplot(v.1.6.0), gtable(v.0.3.0), sva(v.3.34.0), processx(v.3.4.1), tidygraph(v.1.1.2), rlang(v.0.4.2), zeallot(v.0.1.0), genefilter(v.1.68.0), splines(v.3.6.1), rtracklayer(v.1.46.0), lazyeval(v.0.2.2), europepmc(v.0.3), BiocManager(v.1.30.10), yaml(v.2.2.0), reshape2(v.1.4.3), GenomicFeatures(v.1.38.0), backports(v.1.1.5), qvalue(v.2.18.0), clusterProfiler(v.3.14.0), tools(v.3.6.1), usethis(v.1.5.1), ggplotify(v.0.0.4), ggplot2(v.3.2.1), ellipsis(v.0.3.0), gplots(v.3.0.1.1), RColorBrewer(v.1.1-2), sessioninfo(v.1.1.1), ggridges(v.0.5.1), Rcpp(v.1.0.3), plyr(v.1.8.5), base64enc(v.0.1-3), progress(v.1.2.2), zlibbioc(v.1.32.0), purrr(v.0.3.3), RCurl(v.1.95-4.12), ps(v.1.3.0), prettyunits(v.1.0.2), openssl(v.1.4.1), viridis(v.0.5.1), cowplot(v.1.0.0), S4Vectors(v.0.24.1), SummarizedExperiment(v.1.16.0), ggrepel(v.0.8.1), colorRamps(v.2.3), fs(v.1.3.1), variancePartition(v.1.16.0), magrittr(v.1.5), data.table(v.1.12.8), DO.db(v.2.9), openxlsx(v.4.1.4), triebeard(v.0.3.0), matrixStats(v.0.55.0), pkgload(v.1.0.2), hms(v.0.5.2), evaluate(v.0.14), xtable(v.1.8-4), pbkrtest(v.0.4-7), XML(v.3.98-1.20), IRanges(v.2.20.1), gridExtra(v.2.3), testthat(v.2.3.1), compiler(v.3.6.1), biomaRt(v.2.42.0), tibble(v.2.1.3), KernSmooth(v.2.23-16), crayon(v.1.3.4), minqa(v.1.2.4), htmltools(v.0.4.0), mgcv(v.1.8-31), DBI(v.1.0.0), tweenr(v.1.0.1), dbplyr(v.1.4.2), MASS(v.7.3-51.4), rappdirs(v.0.3.1), boot(v.1.3-23), Matrix(v.1.2-18), readr(v.1.3.1), cli(v.2.0.0), gdata(v.2.18.0), igraph(v.1.2.4.2), GenomicRanges(v.1.38.0), pkgconfig(v.2.0.3), rvcheck(v.0.1.7), GenomicAlignments(v.1.22.1), xml2(v.1.2.2), foreach(v.1.4.7), annotate(v.1.64.0), XVector(v.0.26.0), stringr(v.1.4.0), callr(v.3.4.0), digest(v.0.6.23), Biostrings(v.2.54.0), rmarkdown(v.1.18), fastmatch(v.1.1-0), edgeR(v.3.28.0), curl(v.4.3), Rsamtools(v.2.2.1), gtools(v.3.8.1), nloptr(v.1.2.1), lifecycle(v.0.1.0), nlme(v.3.1-143), jsonlite(v.1.6), desc(v.1.2.0), viridisLite(v.0.3.0), askpass(v.1.1), limma(v.3.42.0), fansi(v.0.4.0), pillar(v.1.4.3), lattice(v.0.20-38), httr(v.1.4.1), pkgbuild(v.1.0.6), survival(v.3.1-8), GO.db(v.3.10.0), glue(v.1.3.1), remotes(v.2.1.0), zip(v.2.0.4), iterators(v.1.0.12), pander(v.0.6.3), bit(v.1.1-14), ggforce(v.0.3.1), stringi(v.1.4.3), blob(v.1.2.0), caTools(v.1.17.1.3) and memoise(v.1.1.0)

message(paste0("This is hpgltools commit: ", get_git_commit()))

## If you wish to reproduce this exact build of hpgltools, invoke the following:

## > git clone http://github.com/abelew/hpgltools.git

## > git reset defea68c4df789830e6d759243e1f973d2d9dca7

## This is hpgltools commit: Fri Dec 27 17:07:39 2019 -0500: defea68c4df789830e6d759243e1f973d2d9dca7

this_save <- paste0(gsub(pattern="\\.Rmd", replace="", x=rmd_file), "-v", ver, ".rda.xz")
message(paste0("Saving to ", this_save))

## Saving to error_quant-v20191201.rda.xz

tmp <- sm(saveme(filename=this_save))

loadme(filename=this_save)

---
title: "Counting RT mutations from illumina sequencing data."
author: "atb abelew@gmail.com"
date: "`r Sys.Date()`"
output:
  html_document:
    code_download: true
    code_folding: show
    fig_caption: true
    fig_height: 7
    fig_width: 7
    highlight: tango
    keep_md: false
    mode: selfcontained
    number_sections: true
    self_contained: true
    theme: readable
    toc: true
    toc_float:
      collapsed: false
      smooth_scroll: false
  rmdformats::readthedown:
    code_download: true
    code_folding: show
    df_print: paged
    fig_caption: true
    fig_height: 7
    fig_width: 7
    highlight: tango
    width: 300
    keep_md: false
    mode: selfcontained
    toc_float: true
  BiocStyle::html_document:
    code_download: true
    code_folding: show
    fig_caption: true
    fig_height: 7
    fig_width: 7
    highlight: tango
    keep_md: false
    mode: selfcontained
    toc_float: true
---

<style type="text/css">
body, td {
  font-size: 16px;
}
code.r{
  font-size: 16px;
}
pre {
 font-size: 16px
}
</style>

```{r options, include=FALSE}
library("hpgltools")
tt <- devtools::load_all("/data/hpgltools")
knitr::opts_knit$set(width=120,
                     progress=TRUE,
                     verbose=TRUE,
                     echo=TRUE)
knitr::opts_chunk$set(error=TRUE,
                      dpi=96)
old_options <- options(digits=4,
                       stringsAsFactors=FALSE,
                       knitr.duplicate.label="allow")
ggplot2::theme_set(ggplot2::theme_bw(base_size=10))
rundate <- format(Sys.Date(), format="%Y%m%d")
previous_file <- "index.Rmd"
ver <- "20191201"

##tmp <- sm(loadme(filename=paste0(gsub(pattern="\\.Rmd", replace="", x=previous_file), "-v", ver, ".rda.xz")))
rmd_file <- "error_quant.Rmd"
```

# Calculating error rates.

I wrote the function 'create_matrices()' to collect mutation counts.  At least
in theory the results from it should be able to address most/any question
regarding the counts of mutations observed in the data.

## Categorize the data with at least 3 indexes per mutant

```{r triples}
devtools::load_all("errRt")

triples <-  create_matrices(sample_sheet="sample_sheets/all_samples.xlsx",
                            ident_column="identtable", mut_column="mutationtable",
                            min_reads=3, min_indexes=3, min_sequencer=10,
                            min_position=24, max_position=176,
                            prune_n=TRUE, verbose=TRUE)
triple_plots <- barplot_matrices(triples)
summary(triples)

triples_tenmpr <- create_matrices(sample_sheet="sample_sheets/all_samples.xlsx",
                                  ident_column="identtable", mut_column="mutationtable",
                                  min_reads=3, min_indexes=3, min_sequencer=10,
                                  min_position=24, max_position=176,
                                  max_mutations_per_read=10,
                                  prune_n=TRUE, verbose=TRUE)
triple_tenmpr_plots <- barplot_matrices(triples_tenmpr)
summary(triples_tenmpr)
triples_fivempr <- create_matrices(sample_sheet="sample_sheets/all_samples.xlsx",
                                  ident_column="identtable", mut_column="mutationtable",
                                  min_reads=3, min_indexes=3, min_sequencer=10,
                                  min_position=24, max_position=176,
                                  max_mutations_per_read=5,
                                  prune_n=TRUE, verbose=TRUE)
triple_fivempr_plots <- barplot_matrices(triples_fivempr)
summary(triples_fivempr)
```

## Categorize the data with at least 5 indexes per mutant

```{r quints}
quints <- create_matrices(sample_sheet="sample_sheets/all_samples.xlsx",
                          ident_column="identtable", mut_column="mutationtable",
                          min_reads=3, min_indexes=5, min_sequencer=10,
                          min_position=24, max_position=176, prune_n=TRUE,
                          verbose=TRUE)
quint_plots <- barplot_matrices(quints)
summary(quints)
quints_tenmpr <- create_matrices(sample_sheet="sample_sheets/all_samples.xlsx",
                                 ident_column="identtable", mut_column="mutationtable",
                                 min_reads=3, min_indexes=5, min_sequencer=10,
                                 min_position=24, max_position=176,
                                 max_mutations_per_read=10,
                                 prune_n=TRUE, verbose=TRUE)
quint_tenmpr_plots <- barplot_matrices(quints_tenmpr)
summary(quints_tenmpr)
quints_fivempr <- create_matrices(sample_sheet="sample_sheets/all_samples.xlsx",
                                  ident_column="identtable", mut_column="mutationtable",
                                  min_reads=3, min_indexes=5, min_sequencer=10,
                                  min_position=24, max_position=176,
                                  max_mutations_per_read=5,
                                  prune_n=TRUE, verbose=TRUE)
quint_fivempr_plots <- barplot_matrices(quints_fivempr)
summary(quints_fivempr)
```

# Questions from Dr. DeStefano

I think what is best is to get the number of recovered mutations of each type
from each data set.  That would be A to T, A to G, A to C; T to A, T to G, T to
C; G to A, G to C, G to T; and C to A, C to G, C to T; as well as deletions and
insertions.  I would then need the sum number of the reads that met all our
criteria (i.e. at least 3 good recovered reads for that 14 nt index).  Each set
of 3 or more would ct as "1" read of that particular index so I would need the
total with this in mind.  I also need to know the total number of nucleotides
that were in the region we decided to consider in the analysis.  We may want to
try this for 3 or more and 5 or more recovered indexes if it is not hard.  This
information does not include specific positions on the template where errors
occurred but we can look at that latter.  Right now I just want to get a general
error rate and type of error.  It would basically be calculated by dividing the
number of recovered mutations of a particular type by sum number of the reads
times the number of nucleotides screened in the template.  As it ends up, this
number does not really have a lot of meaning but it can be used to calculate the
overall mutation rate as well as the rate for transversions, transitions, and
deletions and insertions.

# Answers

In order to address those queries, I invoked create_matrices() with a minimum
index count of 3 and 5.  It should be noted that this is not the same as
requiring 3 or 5 reads per index.  In both cases I require 3 reads per index.

## Recovered mutations of each type

I am interpreting this question as the number of indexes recovered for each
mutation type.  I collect this information in 2 ways of interest: the indexes by
type which are deemed to be from the RT and from the sequencer.  In addition, I
calculate a normalized (cpm) version of this information which may be used to look for
changes across samples.

### Mutations by RT index

This following block should print out tables of the numbers of mutant indexes
observed for each type for the RT and the sequencer.  One would hope that the
sequencer will be consistent for all samples, but I think the results will
instead suggest that my metric is not yet stringent enough.

```{r mutation_index_count, results='asis'}
knitr::kable(triples[["matrices"]][["miss_indexes_by_type"]])
knitr::kable(quints[["matrices"]][["miss_indexes_by_type"]])

knitr::kable(triples[["matrices"]][["miss_sequencer_by_type"]])
knitr::kable(quints[["matrices"]][["miss_sequencer_by_type"]])
```

Plots of this information

```{r mutation_index_count_plots}
triple_plots[["matrices"]][["miss_indexes_by_type"]]
triple_plots[["normal"]][["miss_indexes_by_type"]]

quint_plots[["matrices"]][["miss_indexes_by_type"]]
quint_plots[["normal"]][["miss_indexes_by_type"]]
```

This suggests to me that this information needs to be normalized in some more
sensible fashion.  Thus the following:

### Mutations by RT index, post normalization

The same numbers may be expressed in the context of the number of indexes
observed / sample and/or as a cpm across samples.  Thus in the first instance
one can look at the apparent error rate for each sample, and in the second
instance one may look for relative changes in apparent error rate across
samples.

#### Rewriting the matrices as cpm to account for library sizes.

```{r mutation_index_normalized, results='asis'}
knitr::kable(triples[["normalized"]][["miss_indexes_by_type"]])
knitr::kable(quints[["normalized"]][["miss_indexes_by_type"]])

knitr::kable(triples[["normalized"]][["miss_sequencer_by_type"]])
knitr::kable(quints[["normalized"]][["miss_sequencer_by_type"]])
```

#### Rewriting the matrices by dividing by all indexes

This I think starts to address the later text in your query.

```{r mutation_index_normalized_by_counts, results='asis'}
knitr::kable(triples[["matrices_by_counts"]][["miss_indexes_by_type"]])
knitr::kable(quints[["matrices_by_counts"]][["miss_indexes_by_type"]])

knitr::kable(triples[["matrices_by_counts"]][["miss_sequencer_by_type"]])
knitr::kable(quints[["matrices_by_counts"]][["miss_sequencer_by_type"]])
```

#### Rewriting the matrices by dividing by all indexes and cpm

I think this might prove to be where we get the most meaningful results.

The nicest thing in it is that after accounting for library sizes and total
indexes observed, we finally see that the sequencer error is mostly consistent
across all samples and mutation types -- with a couple of notable exceptions.

By the same token, for the mutations which _are_ identical for the sequencer, we
have some which are decidedly different for the non-sequencer data.  The most
notable examples I think are A to G but _not G to A; and C to T.

```{r mutation_index_cpm_by_counts, results='asis'}
knitr::kable(triples[["normalized_by_counts"]][["miss_indexes_by_type"]])
knitr::kable(quints[["normalized_by_counts"]][["miss_indexes_by_type"]])

knitr::kable(triples[["normalized_by_counts"]][["miss_sequencer_by_type"]])
knitr::kable(quints[["normalized_by_counts"]][["miss_sequencer_by_type"]])
```

### Indels by RT index

The following blocks will repeat the above, but looking for insertions.
This data does not observe sufficient deletions to make a proper count for them.

```{r insert_index_count, results='asis'}
knitr::kable(triples[["matrices"]][["insert_indexes_by_nt"]])
knitr::kable(quints[["matrices"]][["insert_indexes_by_nt"]])

knitr::kable(triples[["matrices"]][["insert_sequencer_by_nt"]])
knitr::kable(quints[["matrices"]][["insert_sequencer_by_nt"]])
```

Plots of this information

```{r insert_index_count_plots}
triple_plots[["matrices"]][["insert_indexes_by_nt"]]
triple_plots[["normal"]][["insert_indexes_by_nt"]]

quint_plots[["matrices"]][["insert_indexes_by_nt"]]
quint_plots[["normal"]][["insert_indexes_by_nt"]]

quint_plots[["matrices"]][["insert_sequencer_by_nt"]]
quint_plots[["normal"]][["insert_sequencer_by_nt"]]
```

### Insertions by RT index, post normalization

#### Rewriting the matrices as cpm to account for library sizes.

```{r insert_index_normalized, results='asis'}
knitr::kable(triples[["normalized"]][["insert_indexes_by_nt"]])
knitr::kable(quints[["normalized"]][["insert_indexes_by_nt"]])

knitr::kable(triples[["normalized"]][["insert_sequencer_by_nt"]])
knitr::kable(quints[["normalized"]][["insert_sequencer_by_nt"]])
```

#### Rewriting the matrices by dividing by all indexes

I think that there are few enough insertion events that this gets a bit messed
up.  I will double check the logic of this, but that is my initial guess given
how few insertions I was seeing when reading the outputs manually.
Unfortunately, this means that for these I also cannot provide a cpm measurement.

```{r insert_index_normalized_by_counts, results='asis'}
knitr::kable(triples[["matrices_by_counts"]][["insert_indexes_by_nt"]])
knitr::kable(quints[["matrices_by_counts"]][["insert_indexes_by_nt"]])

knitr::kable(triples[["matrices_by_counts"]][["insert_sequencer_by_nt"]])
knitr::kable(quints[["matrices_by_counts"]][["insert_sequencer_by_nt"]])
```

The following is my previous writing of this worksheet which just dumped the
various tables.

# Print raw tables

```{r raw, results='asis'}
for (t in 1:length(triples[["matrices"]])) {
  table_name <- names(triples[["matrices"]])[t]
  message("Raw table: ", table_name, ".")
  print(knitr::kable(triples[["matrices"]][t]))
}
```

# Print raw plots

```{r raw_plots}
for (t in 1:length(triples[["matrices"]])) {
  message("Raw table: ", table_name, ".")
  print(triplet_plots[["matrices"]][t])
}
```

# Print normalized tables

```{r norm, results='asis'}
for (t in 1:length(triplets[["normalized"]])) {
  table_name <- names(triples[["normalized"]])[t]
  message("Normalized table: ", table_name, ".")
  print(knitr::kable(triples[["normalized"]][t]))
}
```

# Print normalized plots

```{r norm_plots}
for (t in 1:length(triples[["normalized"]])) {
  message("Normalized table: ", table_name, ".")
  print(triplet_plots[["normal"]][t])
}
```

```{r saveme}
pander::pander(sessionInfo())
message(paste0("This is hpgltools commit: ", get_git_commit()))
this_save <- paste0(gsub(pattern="\\.Rmd", replace="", x=rmd_file), "-v", ver, ".rda.xz")
message(paste0("Saving to ", this_save))
tmp <- sm(saveme(filename=this_save))
```


```{r loadme, eval=FALSE}
loadme(filename=this_save)
```


Counting RT mutations from illumina sequencing data.

atb abelew@gmail.com

2020-01-09

1 Calculating error rates.

1.1 Categorize the data with at least 3 indexes per mutant

1.2 Categorize the data with at least 5 indexes per mutant

2 Questions from Dr. DeStefano

3 Answers

3.1 Recovered mutations of each type

3.1.1 Mutations by RT index

3.1.2 Mutations by RT index, post normalization

3.1.2.1 Rewriting the matrices as cpm to account for library sizes.

3.1.2.2 Rewriting the matrices by dividing by all indexes

3.1.2.3 Rewriting the matrices by dividing by all indexes and cpm

3.1.3 Indels by RT index

3.1.4 Insertions by RT index, post normalization

3.1.4.1 Rewriting the matrices as cpm to account for library sizes.

3.1.4.2 Rewriting the matrices by dividing by all indexes

4 Print raw tables

5 Print raw plots

6 Print normalized tables

7 Print normalized plots