1 Introduction

This document seeks to lay out my process in poking at the DNAsequencing results of a series of Pseudomonas aeruginosa PA14 and PAK strains.

If I understand Dr. Lee and co.’s goal, they wish to ensure that these strains are still reasonably close to the associated reference strains. I therefore am running my default trimming/mapping/variant search methods.

I have a single command that can run all of these commands at the same time, but I have been actively breaking my tools recently; so I decided to run them one at a time with the assumption that something would not work (but everything did work on the first try, so that was nice).

1.1 Downloading and sorting the data

I downloaded the .zip archive file using the link in Dr. Lee’s email. I did not save it though, so if we need to download the data again, we will have to go to him. I created my usual work directory ‘preprocessing/’ within this tree and moved it there. I unzipped it and moved each pair of reads to a directory which follows Dr. Lee’s desired naming convention.

I then created the directories: ‘reference/’ and ‘sample_sheets/’. The sample_sheets remained empty for a while, but I immediately downloaded the full genbank flat file for the Pseudomonas PAK strain from NCBI, found here:

https://www.ncbi.nlm.nih.gov/nuccore/NZ_CP020659

Note, that when downloading, one must hit the ‘customize view’ button on the right and ensure that the entire sequence and all annotations are included. Then hit the ‘send to’ button and send it to a file. This file I copied to reference/paeruginosa_pak.gb.

1.2 Creation of the pak reference

Given the full PAK genbank file, I converted it to the expected fasta/gff file for mapping:

cd reference
cyoa --method gb2gff --input paeruginosa_pak.gb

This command created a series of fasta and gff files which provide the coordinates for the various annotations (genes/cds/rRNA/intercds) and sequence for the genome, CDS nucleotides, and amino acids. I then copied the genome/gff files to my global reference directory and prepared it for usage by my favorite mapper:

cd ~/libraries/genome
cyoa --method indexhisat --species paeruginosa_pak

Now all of the pieces are in place for me to play. Each of the following steps was performed twice, once for the PA14 samples, once for the PAK samples. The only difference in the invocations was due to the fact that the PAK annotations provide different tags. E.g. I used the ‘Alias’ tag for PA14 and the ‘locus_tag’ tag for PAK. As a result I am only going to write down in this document the PA14 invocations and assume the reader can figure out the difference.

1.3 Trimming

I have a couple of trimming methods, in this instance I just used the default and will operate under the assumption that it is sufficient until I see otherwise.

cd preprocessing
start=$(pwd)
for i in $(/bin/ls -d PA14*); do
    cd $i
    cyoa --method trim --input $(/bin/ls *.fastq.gz | tr '\n' ':' | sed 's/:$//g')
    cd $start
done

The above command line invocation produced a series of trimming jobs which when examined look like this (I am only showing examples from PA14_exoUTY, and am leaving off the beginning and end).

1.3.1 Resulting trimmer script

## This is a portion of file:
##  preprocessing/PA14_exoUTY/scripts/01trim_7_UTY_S138_R1_001.sh

module add trimomatic
mkdir -p outputs/01trimomatic
## Note that trimomatic prints all output and errors to STDERR, so send both to output
trimmomatic PE \
  -threads 1 \
  -phred33 \
  7_UTY_S138_R1_001.fastq.gz 7_UTY_S138_R2_001.fastq.gz \
  7_UTY_S138_R1_001-trimmed_paired.fastq 7_UTY_S138_R1_001-trimmed_unpaired.fastq \
  7_UTY_S138_R2_001-trimmed_paired.fastq 7_UTY_S138_R2_001-trimmed_unpaired.fastq \
   ILLUMINACLIP:/fs/cbcb-software/RedHat-8-x86_64/local/cyoa/202302/prefix/lib/perl5/auto/share/dist/Bio-Adventure/genome/adapters.fa:2:20:10:2:keepBothReads  \
  SLIDINGWINDOW:4:20 MINLEN:50 \
  1>outputs/01trimomatic/7_UTY_S138_R1_001-trimomatic.stdout \
  2>outputs/01trimomatic/7_UTY_S138_R1_001-trimomatic.stderr
excepted=$( { grep "Exception" "outputs/01trimomatic/7_UTY_S138_R1_001-trimomatic.stdout" || test $? = 1; } )

One thing I did not include in the above: upon completion, the script aggressively compresses the trimmed output and symbolically links it to r1_trimmed.fastq.xz and r2_trimmed.fastq.xz. Thus any following steps can use the same input name (r1_trimmed.fastq.xz:r2_trimmed.fastq.xz).

1.4 Mapping

My default mappers run the actual alignment, convert it to a compressed/indexed bam, and count it against the reference genome. In this context, the counting is a little silly, but does have the potential to help find duplications and such.

cd preprocessing
start=$(pwd)
for i in $(/bin/ls -d PA14*); do
    cd $i
    cyoa --method hisat --input r1_trimmed.fastq.xz:r2_trimmed.fastq.xz \
         --stranded no --species paeruginosa_pa14 --gff_type gene --gff_tag Alias
    cd $start
done

## Here is what I ran for PAK
cd preprocessing
start=$(pwd)
for i in $(/bin/ls -d PAK*); do
    cd $i
    cyoa --method hisat --input r1_trimmed.fastq.xz:r2_trimmed.fastq.xz \
         --stranded no --species paeruginosa_pa01 --gff_type gene --gff_tag locus_tag
    cd $start
done

1.4.1 The resulting mapper script run by the cluster

Similarly, I am just putting the meaty part.

module add hisat2 samtools htseq bamtools
mkdir -p outputs/40hisat2_paeruginosa_pa14
hisat2 -x ${HOME}/libraries/genome/indexes/paeruginosa_pa14  \
  -p 8 \
  -q   -1 <(less /home/trey/sshfs/scratch/atb/dnaseq/paeruginosa_strains_202304/preprocessing/PA14_exoUTY/r1_trimmed.fastq.xz) -2 <(less /home/trey/sshfs/scratch/atb/dnaseq/paeruginosa_strains_202304/preprocessing/PA14_exoUTY/r2_trimmed.fastq.xz)  \
  --phred33 \
  --un outputs/40hisat2_paeruginosa_pa14/unaldis_paeruginosa_pa14_genome.fastq \
  --al outputs/40hisat2_paeruginosa_pa14/aldis_paeruginosa_pa14_genome.fastq \
  --un-conc outputs/40hisat2_paeruginosa_pa14/unalcon_paeruginosa_pa14_genome.fastq \
  --al-conc outputs/40hisat2_paeruginosa_pa14/alcon_paeruginosa_pa14_genome.fastq \
  -S outputs/40hisat2_paeruginosa_pa14/paeruginosa_pa14_genome.sam \
  2>outputs/40hisat2_paeruginosa_pa14/hisat2_paeruginosa_pa14_genome_PA14_exoUTY.stderr \
  1>outputs/40hisat2_paeruginosa_pa14/hisat2_paeruginosa_pa14_genome_PA14_exoUTY.stdout

1.4.2 Conversion to bam script

The above cyoa invocation also creates this script. It is a little long because it does some checks and creates a couple of filtered versions of the output.

module add samtools bamtools

echo "Starting samtools"
if [[ -f "outputs/40hisat2_paeruginosa_pa14/paeruginosa_pa14_genome.bam" && -f "outputs/40hisat2_paeruginosa_pa14/paeruginosa_pa14_genome.sam" ]]; then
  echo "Both the bam and sam files exist, rerunning."
elif [[ -f "outputs/40hisat2_paeruginosa_pa14/paeruginosa_pa14_genome.bam" ]]; then
  echo "The output file exists, quitting."
  exit 0
elif [[ ! -f "outputs/40hisat2_paeruginosa_pa14/paeruginosa_pa14_genome.sam" ]]; then
  echo "Could not find the samtools input file."
  exit 1
fi

## If a previous sort file exists due to running out of memory,
## then we need to get rid of them first.
## hg38_100_genome-sorted.bam.tmp.0000.bam
if [[ -f "outputs/40hisat2_paeruginosa_pa14/paeruginosa_pa14_genome.bam.tmp.000.bam" ]]; then
  rm -f outputs/40hisat2_paeruginosa_pa14/paeruginosa_pa14_genome.bam.tmp.*.bam
fi
samtools view -u -t ${HOME}/libraries/genome/paeruginosa_pa14.fasta \
  -S outputs/40hisat2_paeruginosa_pa14/paeruginosa_pa14_genome.sam -o outputs/40hisat2_paeruginosa_pa14/paeruginosa_pa14_genome.bam  \
  2>outputs/40hisat2_paeruginosa_pa14/paeruginosa_pa14_genome.bam_samtools.stderr \
  1>outputs/40hisat2_paeruginosa_pa14/paeruginosa_pa14_genome.bam_samtools.stdout

echo "First samtools command finished with $?"
samtools sort -l 9 outputs/40hisat2_paeruginosa_pa14/paeruginosa_pa14_genome.bam \
  -o outputs/40hisat2_paeruginosa_pa14/paeruginosa_pa14_genome-sorted.bam \
  2>>outputs/40hisat2_paeruginosa_pa14/paeruginosa_pa14_genome.bam_samtools.stderr \
  1>>outputs/40hisat2_paeruginosa_pa14/paeruginosa_pa14_genome.bam_samtools.stdout
rm outputs/40hisat2_paeruginosa_pa14/paeruginosa_pa14_genome.bam
rm outputs/40hisat2_paeruginosa_pa14/paeruginosa_pa14_genome.sam
mv outputs/40hisat2_paeruginosa_pa14/paeruginosa_pa14_genome-sorted.bam outputs/40hisat2_paeruginosa_pa14/paeruginosa_pa14_genome.bam
samtools index outputs/40hisat2_paeruginosa_pa14/paeruginosa_pa14_genome.bam \
  2>>outputs/40hisat2_paeruginosa_pa14/paeruginosa_pa14_genome.bam_samtools.stderr \
  1>>outputs/40hisat2_paeruginosa_pa14/paeruginosa_pa14_genome.bam_samtools.stdout
echo "Second samtools command finished with $?"
bamtools stats -in outputs/40hisat2_paeruginosa_pa14/paeruginosa_pa14_genome.bam \
  2>>outputs/40hisat2_paeruginosa_pa14/paeruginosa_pa14_genome.bam_samtools.stats 1>&2
echo "Bamtools finished with $?"

## The following will fail if this is single-ended.
samtools view -b -f 2 \
  -o outputs/40hisat2_paeruginosa_pa14/paeruginosa_pa14_genome-paired.bam \
  outputs/40hisat2_paeruginosa_pa14/paeruginosa_pa14_genome.bam \
  2>>outputs/40hisat2_paeruginosa_pa14/paeruginosa_pa14_genome.bam_samtools.stderr \
  1>>outputs/40hisat2_paeruginosa_pa14/paeruginosa_pa14_genome.bam_samtools.stdout
samtools index outputs/40hisat2_paeruginosa_pa14/paeruginosa_pa14_genome-paired.bam \
  2>>outputs/40hisat2_paeruginosa_pa14/paeruginosa_pa14_genome.bam_samtools.stderr \
  1>>outputs/40hisat2_paeruginosa_pa14/paeruginosa_pa14_genome.bam_samtools.stdout
bamtools stats -in outputs/40hisat2_paeruginosa_pa14/paeruginosa_pa14_genome-paired.bam \
  2>>outputs/40hisat2_paeruginosa_pa14/paeruginosa_pa14_genome.bam_samtools.stats 1>&2

bamtools filter -tag XM:0 \
  -in outputs/40hisat2_paeruginosa_pa14/paeruginosa_pa14_genome.bam \
  -out outputs/40hisat2_paeruginosa_pa14/paeruginosa_pa14_genome-sorted_nomismatch.bam \
  2>>outputs/40hisat2_paeruginosa_pa14/paeruginosa_pa14_genome.bam_samtools.stats 1>&2
echo "bamtools filter finished with: $?"
samtools index \
  outputs/40hisat2_paeruginosa_pa14/paeruginosa_pa14_genome-sorted_nomismatch.bam \
  2>>outputs/40hisat2_paeruginosa_pa14/paeruginosa_pa14_genome.bam_samtools.stderr \
  1>>outputs/40hisat2_paeruginosa_pa14/paeruginosa_pa14_genome.bam_samtools.stdout
echo "final samtools index finished with: $?"

1.4.3 Counting against the genome

Note that this step is not really useful for a dnaseq dataset in most instances. I also have the default orientation set to reverse because most of the samples off our sequencer are reversed; but that is likely not true for this dataset. If it turns out we actually care about these counts, I may need to come back and rerun these.

module add htseq

htseq-count \
  -q -f bam \
  -s reverse -a 0 \
   --type all  --idattr Alias \
  outputs/40hisat2_paeruginosa_pa14/paeruginosa_pa14_genome-paired.bam \
  /home/trey/libraries/genome/paeruginosa_pa14.gff \
  2>outputs/40hisat2_paeruginosa_pa14/paeruginosa_pa14_genome-paired_sreverse_all_Alias.stderr \
  1>outputs/40hisat2_paeruginosa_pa14/paeruginosa_pa14_genome-paired_sreverse_all_Alias.count
xz -f -9e outputs/40hisat2_paeruginosa_pa14/paeruginosa_pa14_genome-paired_sreverse_all_Alias.count

2 Creating a sample sheet

In order to play further with the data, I will need a sample sheet. So I will start out by creating a blank one in excel (libreoffice) which contains only the samplenames in the same format as my directories in preprocessing/.

Once completed, I can use it as the input for my hpgltools package and it should extract the interesting information from the preprocessing logs and fill out the sample sheet accordingly. Lets see if it works!

Here is the before:

knitr::kable(extract_metadata("sample_sheets/all_samples.xlsx"))
## Did not find the condition column in the sample sheet.
## Filling it in as undefined.
## Did not find the batch column in the sample sheet.
## Filling it in as undefined.
sampleid parent condition batch
PA14_exoUTY PA14_exoUTY PA14 undefined undefined
PA14_JC PA14_JC PA14 undefined undefined
PA14_lux PA14_lux PA14 undefined undefined
PA14_NBH PA14_NBH PA14 undefined undefined
PA14_pscD_A5 PA14_pscD_A5 PA14 undefined undefined
PA14_pscD_E4 PA14_pscD_E4 PA14 undefined undefined
PA14_xcp PA14_xcp PA14 undefined undefined
PA14_xcp_pscD PA14_xcp_pscD PA14 undefined undefined
PAK PAK PAK undefined undefined
PAK_pscC PAK_pscC PAK undefined undefined
PAK_xcp PAK_xcp PAK undefined undefined
PAK_xcp_pscC PAK_xcp_pscC PAK undefined undefined

Like I said, not much going on. Lets see what it looks like after I run the gatherer on it… (Note, I have been meaning to change this to drop the unused columns, but not yet).

spec <- make_dnaseq_spec()
queried_species <- c("paeruginosa_pak", "paeruginosa_pa01", "paeruginosa_pa14")
modified <- sm(gather_preprocessing_metadata("sample_sheets/all_samples.xlsx",
  species = queried_species, verbose = FALSE,
  specification = spec))
knitr::kable(extract_metadata("sample_sheets/all_samples_modified.xlsx"))
rownames sampleid parent condition batch trimomaticinput trimomaticoutput trimomaticpercent fastqcpctgc hisatgenomesingleconcordantpaeruginosapak hisatgenomesingleconcordantpaeruginosapa01 hisatgenomesingleconcordantpaeruginosapa14 hisatgenomemulticoncordantpaeruginosapak hisatgenomemulticoncordantpaeruginosapa01 hisatgenomemulticoncordantpaeruginosapa14 hisatgenomesingleallpaeruginosapak hisatgenomesingleallpaeruginosapa01 hisatgenomesingleallpaeruginosapa14 hisatgenomemultiallpaeruginosapak hisatgenomemultiallpaeruginosapa01 hisatgenomemultiallpaeruginosapa14 hisatgenomepercentlogpaeruginosapak hisatgenomepercentlogpaeruginosapa01 hisatgenomepercentlogpaeruginosapa14 gatkunpairedpaeruginosapak gatkunpairedpaeruginosapa01 gatkunpairedpaeruginosapa14 gatkpairedpaeruginosapak gatkpairedpaeruginosapa01 gatkpairedpaeruginosapa14 gatksupplementarypaeruginosapak gatksupplementarypaeruginosapa01 gatksupplementarypaeruginosapa14 gatkunmappedpaeruginosapak gatkunmappedpaeruginosapa01 gatkunmappedpaeruginosapa14 gatkunpairedduplicatespaeruginosapak gatkunpairedduplicatespaeruginosapa01 gatkunpairedduplicatespaeruginosapa14 gatkpairedduplicatespaeruginosapak gatkpairedduplicatespaeruginosapa01 gatkpairedduplicatespaeruginosapa14 gatkpairedoptduplicatespaeruginosapak gatkpairedoptduplicatespaeruginosapa01 gatkpairedoptduplicatespaeruginosapa14 gatkduplicatepctpaeruginosapak gatkduplicatepctpaeruginosapa01 gatkduplicatepctpaeruginosapa14 gatklibsizepaeruginosapak gatklibsizepaeruginosapa01 gatklibsizepaeruginosapa14 freebayesobservedpaeruginosapak freebayesobservedpaeruginosapa01 freebayesobservedpaeruginosapa14 freebayesobservedfilepaeruginosapak freebayesobservedfilepaeruginosapa01 freebayesobservedfilepaeruginosapa14 hisatcounttablepaeruginosapak hisatcounttablepaeruginosapa01 hisatcounttablepaeruginosapa14 deduplicationstatspaeruginosapak deduplicationstatspaeruginosapa01 deduplicationstatspaeruginosapa14 freebayesvariantsbygenepaeruginosapak freebayesvariantsbygenepaeruginosapa01 freebayesvariantsbygenepaeruginosapa14 freebayesvariantstablepaeruginosapak freebayesvariantstablepaeruginosapa01 freebayesvariantstablepaeruginosapa14 freebayesmodifiedgenomepaeruginosapak freebayesmodifiedgenomepaeruginosapa14 freebayesbcffilepaeruginosapak freebayesbcffilepaeruginosapa01 freebayesbcffilepaeruginosapa14 freebayespenetrancefilepaeruginosapak freebayespenetrancefilepaeruginosapa01 freebayespenetrancefilepaeruginosapa14
PA14_exoUTY PA14_exoUTY PA14_exoUTY PA14 undefined undefined 4706372 4350712 0.924 NA 3690737 NA 4280240 664 NA 25722 307644 NA 34468 513 NA 562 88.56 NA 99.60 NA NA 0 NA NA 4305962 NA NA 83320 NA NA 0 NA NA 0 NA NA 823232 NA NA 81874 NA NA 0.1912 NA NA 10580352 NA NA 199 preprocessing/PA14_exoUTY/outputs/50freebayes_paeruginosa_pa14/all_tags.txt.xz preprocessing/PA14_exoUTY/outputs/40hisat2_paeruginosa_pak/paeruginosa_pak_genome-paired_sno_gene_locus_tag.count.xz preprocessing/PA14_exoUTY/outputs/40hisat2_paeruginosa_pa14/paeruginosa_pa14_genome-paired_sreverse_all_Alias.count.xz preprocessing/PA14_exoUTY/outputs/50freebayes_paeruginosa_pa14/deduplication_stats.txt preprocessing/PA14_exoUTY/outputs/50freebayes_paeruginosa_pa14/variants_by_gene.txt.xz preprocessing/PA14_exoUTY/outputs/50freebayes_paeruginosa_pa14/all_tags.txt.xz preprocessing/PA14_exoUTY/outputs/50freebayes_paeruginosa_pa14/paeruginosa_pa14-PA14_exoUTY.fasta preprocessing/PA14_exoUTY/outputs/50freebayes_paeruginosa_pa14/paeruginosa_pa14.bcf preprocessing/PA14_exoUTY/outputs/50freebayes_paeruginosa_pa14/variants_penetrance.txt.xz
PA14_JC PA14_JC PA14_JC PA14 undefined undefined 5786839 5336197 0.922 66 4550108 NA 5275687 1028 NA 32239 330047 NA 23521 586 NA 421 88.46 NA 99.77 NA NA 0 NA NA 5307926 NA NA 107378 NA NA 0 NA NA 0 NA NA 1127763 NA NA 103875 NA NA 0.2125 NA NA 11426698 NA NA 196 preprocessing/PA14_JC/outputs/50freebayes_paeruginosa_pa14/all_tags.txt.xz preprocessing/PA14_JC/outputs/40hisat2_paeruginosa_pak/paeruginosa_pak_genome-paired_sno_gene_locus_tag.count.xz preprocessing/PA14_JC/outputs/40hisat2_paeruginosa_pa14/paeruginosa_pa14_genome-paired_sreverse_all_Alias.count.xz preprocessing/PA14_JC/outputs/50freebayes_paeruginosa_pa14/deduplication_stats.txt preprocessing/PA14_JC/outputs/50freebayes_paeruginosa_pa14/variants_by_gene.txt.xz preprocessing/PA14_JC/outputs/50freebayes_paeruginosa_pa14/all_tags.txt.xz preprocessing/PA14_JC/outputs/50freebayes_paeruginosa_pa14/paeruginosa_pa14-PA14_JC.fasta preprocessing/PA14_JC/outputs/50freebayes_paeruginosa_pa14/paeruginosa_pa14.bcf preprocessing/PA14_JC/outputs/50freebayes_paeruginosa_pa14/variants_penetrance.txt.xz
PA14_lux PA14_lux PA14_lux PA14 undefined undefined 6622570 6099776 0.921 NA 5205608 NA 6028065 999 NA 36827 354183 NA 24917 645 NA 461 88.33 NA 99.70 NA NA 0 NA NA 6064892 NA NA 121596 NA NA 0 NA NA 0 NA NA 1432296 NA NA 127776 NA NA 0.2362 NA NA 11448973 NA NA 197 preprocessing/PA14_lux/outputs/50freebayes_paeruginosa_pa14/all_tags.txt.xz preprocessing/PA14_lux/outputs/40hisat2_paeruginosa_pak/paeruginosa_pak_genome-paired_sno_gene_locus_tag.count.xz preprocessing/PA14_lux/outputs/40hisat2_paeruginosa_pa14/paeruginosa_pa14_genome-paired_sreverse_all_Alias.count.xz preprocessing/PA14_lux/outputs/50freebayes_paeruginosa_pa14/deduplication_stats.txt preprocessing/PA14_lux/outputs/50freebayes_paeruginosa_pa14/variants_by_gene.txt.xz preprocessing/PA14_lux/outputs/50freebayes_paeruginosa_pa14/all_tags.txt.xz preprocessing/PA14_lux/outputs/50freebayes_paeruginosa_pa14/paeruginosa_pa14-PA14_lux.fasta preprocessing/PA14_lux/outputs/50freebayes_paeruginosa_pa14/paeruginosa_pa14.bcf preprocessing/PA14_lux/outputs/50freebayes_paeruginosa_pa14/variants_penetrance.txt.xz
PA14_NBH PA14_NBH PA14_NBH PA14 undefined undefined 5151127 4581433 0.889 NA 3883544 NA 4516421 711 NA 26915 313829 NA 32560 547 NA 460 88.31 NA 99.64 NA NA 0 NA NA 4543336 NA NA 88454 NA NA 0 NA NA 0 NA NA 896720 NA NA 83669 NA NA 0.1974 NA NA 10694127 NA NA 196 preprocessing/PA14_NBH/outputs/50freebayes_paeruginosa_pa14/all_tags.txt.xz preprocessing/PA14_NBH/outputs/40hisat2_paeruginosa_pak/paeruginosa_pak_genome-paired_sno_gene_locus_tag.count.xz preprocessing/PA14_NBH/outputs/40hisat2_paeruginosa_pa14/paeruginosa_pa14_genome-paired_sreverse_all_Alias.count.xz preprocessing/PA14_NBH/outputs/50freebayes_paeruginosa_pa14/deduplication_stats.txt preprocessing/PA14_NBH/outputs/50freebayes_paeruginosa_pa14/variants_by_gene.txt.xz preprocessing/PA14_NBH/outputs/50freebayes_paeruginosa_pa14/all_tags.txt.xz preprocessing/PA14_NBH/outputs/50freebayes_paeruginosa_pa14/paeruginosa_pa14-PA14_NBH.fasta preprocessing/PA14_NBH/outputs/50freebayes_paeruginosa_pa14/paeruginosa_pa14.bcf preprocessing/PA14_NBH/outputs/50freebayes_paeruginosa_pa14/variants_penetrance.txt.xz
PA14_pscD_A5 PA14_pscD_A5 PA14_pscD_A5 PA14 undefined undefined 5898210 5417082 0.918 NA 4579807 NA 5359077 989 NA 32852 338340 NA 21806 664 NA 440 87.75 NA 99.79 NA NA 0 NA NA 5391929 NA NA 110988 NA NA 0 NA NA 0 NA NA 1134595 NA NA 110041 NA NA 0.2104 NA NA 11790543 NA NA 204 preprocessing/PA14_pscD_A5/outputs/50freebayes_paeruginosa_pa14/all_tags.txt.xz preprocessing/PA14_pscD_A5/outputs/40hisat2_paeruginosa_pak/paeruginosa_pak_genome-paired_sno_gene_locus_tag.count.xz preprocessing/PA14_pscD_A5/outputs/40hisat2_paeruginosa_pa14/paeruginosa_pa14_genome-paired_sreverse_all_Alias.count.xz preprocessing/PA14_pscD_A5/outputs/50freebayes_paeruginosa_pa14/deduplication_stats.txt preprocessing/PA14_pscD_A5/outputs/50freebayes_paeruginosa_pa14/variants_by_gene.txt.xz preprocessing/PA14_pscD_A5/outputs/50freebayes_paeruginosa_pa14/all_tags.txt.xz preprocessing/PA14_pscD_A5/outputs/50freebayes_paeruginosa_pa14/paeruginosa_pa14-PA14_pscD_A5.fasta preprocessing/PA14_pscD_A5/outputs/50freebayes_paeruginosa_pa14/paeruginosa_pa14.bcf preprocessing/PA14_pscD_A5/outputs/50freebayes_paeruginosa_pa14/variants_penetrance.txt.xz
PA14_pscD_E4 PA14_pscD_E4 PA14_pscD_E4 PA14 undefined undefined 5854559 5418227 0.925 NA 4589839 NA 5361935 920 NA 33424 325823 NA 19561 572 NA 387 87.80 NA 99.81 NA NA 0 NA NA 5395359 NA NA 112228 NA NA 0 NA NA 0 NA NA 1204336 NA NA 118910 NA NA 0.2232 NA NA 10998073 NA NA 203 preprocessing/PA14_pscD_E4/outputs/50freebayes_paeruginosa_pa14/all_tags.txt.xz preprocessing/PA14_pscD_E4/outputs/40hisat2_paeruginosa_pak/paeruginosa_pak_genome-paired_sno_gene_locus_tag.count.xz preprocessing/PA14_pscD_E4/outputs/40hisat2_paeruginosa_pa14/paeruginosa_pa14_genome-paired_sreverse_all_Alias.count.xz preprocessing/PA14_pscD_E4/outputs/50freebayes_paeruginosa_pa14/deduplication_stats.txt preprocessing/PA14_pscD_E4/outputs/50freebayes_paeruginosa_pa14/variants_by_gene.txt.xz preprocessing/PA14_pscD_E4/outputs/50freebayes_paeruginosa_pa14/all_tags.txt.xz preprocessing/PA14_pscD_E4/outputs/50freebayes_paeruginosa_pa14/paeruginosa_pa14-PA14_pscD_E4.fasta preprocessing/PA14_pscD_E4/outputs/50freebayes_paeruginosa_pa14/paeruginosa_pa14.bcf preprocessing/PA14_pscD_E4/outputs/50freebayes_paeruginosa_pa14/variants_penetrance.txt.xz
PA14_xcp PA14_xcp PA14_xcp PA14 undefined undefined 5683132 5214875 0.918 NA 4430035 NA 5134054 1300 NA 30352 356590 NA 39479 588 NA 589 88.55 NA 99.62 NA NA 0 NA NA 5164406 NA NA 97676 NA NA 0 NA NA 0 NA NA 1092613 NA NA 103935 NA NA 0.2116 NA NA 11202466 NA NA 195 preprocessing/PA14_xcp/outputs/50freebayes_paeruginosa_pa14/all_tags.txt.xz preprocessing/PA14_xcp/outputs/40hisat2_paeruginosa_pak/paeruginosa_pak_genome-paired_sno_gene_locus_tag.count.xz preprocessing/PA14_xcp/outputs/40hisat2_paeruginosa_pa14/paeruginosa_pa14_genome-paired_sreverse_all_Alias.count.xz preprocessing/PA14_xcp/outputs/50freebayes_paeruginosa_pa14/deduplication_stats.txt preprocessing/PA14_xcp/outputs/50freebayes_paeruginosa_pa14/variants_by_gene.txt.xz preprocessing/PA14_xcp/outputs/50freebayes_paeruginosa_pa14/all_tags.txt.xz preprocessing/PA14_xcp/outputs/50freebayes_paeruginosa_pa14/paeruginosa_pa14-PA14_xcp.fasta preprocessing/PA14_xcp/outputs/50freebayes_paeruginosa_pa14/paeruginosa_pa14.bcf preprocessing/PA14_xcp/outputs/50freebayes_paeruginosa_pa14/variants_penetrance.txt.xz
PA14_xcp_pscD PA14_xcp_pscD PA14_xcp_pscD PA14 undefined undefined 2026150 1514509 0.747 NA 1223766 NA 1471930 191 NA 10238 137765 NA 27094 182 NA 367 85.66 NA 99.16 NA NA 0 NA NA 1482168 NA NA 41804 NA NA 0 NA NA 0 NA NA 188223 NA NA 20007 NA NA 0.1270 NA NA 5857317 NA NA 216 preprocessing/PA14_xcp_pscD/outputs/50freebayes_paeruginosa_pa14/all_tags.txt.xz preprocessing/PA14_xcp_pscD/outputs/40hisat2_paeruginosa_pak/paeruginosa_pak_genome-paired_sno_gene_locus_tag.count.xz preprocessing/PA14_xcp_pscD/outputs/40hisat2_paeruginosa_pa14/paeruginosa_pa14_genome-paired_sreverse_all_Alias.count.xz preprocessing/PA14_xcp_pscD/outputs/50freebayes_paeruginosa_pa14/deduplication_stats.txt preprocessing/PA14_xcp_pscD/outputs/50freebayes_paeruginosa_pa14/variants_by_gene.txt.xz preprocessing/PA14_xcp_pscD/outputs/50freebayes_paeruginosa_pa14/all_tags.txt.xz preprocessing/PA14_xcp_pscD/outputs/50freebayes_paeruginosa_pa14/paeruginosa_pa14-PA14_xcp_pscD.fasta preprocessing/PA14_xcp_pscD/outputs/50freebayes_paeruginosa_pa14/paeruginosa_pa14.bcf preprocessing/PA14_xcp_pscD/outputs/50freebayes_paeruginosa_pa14/variants_penetrance.txt.xz
PAK PAK PAK PAK undefined undefined 4779558 4318745 0.904 NA 4049183 3925784 3731781 1093 22836 19482 179265 179736 308170 455 2255 2452 96.71 94.27 91.10 168494 0 NA 4092362 3948620 NA 3393 99172 NA 284272 0 NA 126668 0 NA 902830 873059 NA 98051 94715 NA 0.2313 0.2211 NA 8530650 8207871 NA 333 26786 NA preprocessing/PAK/outputs/50freebayes_paeruginosa_pak/all_tags.txt.xz preprocessing/PAK/outputs/50freebayes_paeruginosa_pa01/all_tags.txt.xz preprocessing/PAK/outputs/40hisat2_paeruginosa_pak/paeruginosa_pak_genome-paired_sreverse_all_locus_tag.count.xz preprocessing/PAK/outputs/40hisat2_paeruginosa_pa01/paeruginosa_pa01_genome-paired_sno_gene_locus_tag.count.xz preprocessing/PAK/outputs/40hisat2_paeruginosa_pa14/paeruginosa_pa14_genome-paired_sno_gene_Alias.count.xz preprocessing/PAK/outputs/50freebayes_paeruginosa_pak/deduplication_stats.txt preprocessing/PAK/outputs/50freebayes_paeruginosa_pa01/deduplication_stats.txt preprocessing/PAK/outputs/50freebayes_paeruginosa_pak/variants_by_gene.txt.xz preprocessing/PAK/outputs/50freebayes_paeruginosa_pa01/variants_by_gene.txt.xz preprocessing/PAK/outputs/50freebayes_paeruginosa_pak/all_tags.txt.xz preprocessing/PAK/outputs/50freebayes_paeruginosa_pa01/all_tags.txt.xz preprocessing/PAK/outputs/50freebayes_paeruginosa_pak/paeruginosa_pak-PAK.fasta preprocessing/PAK/outputs/50freebayes_paeruginosa_pak/paeruginosa_pak.bcf preprocessing/PAK/outputs/50freebayes_paeruginosa_pa01/paeruginosa_pa01.bcf preprocessing/PAK/outputs/50freebayes_paeruginosa_pak/variants_penetrance.txt.xz preprocessing/PAK/outputs/50freebayes_paeruginosa_pa01/variants_penetrance.txt.xz
PAK_pscC PAK_pscC PAK_pscC PAK undefined undefined 5734960 5271470 0.919 NA 5090759 4853631 4638113 1343 29403 25963 116625 126483 266811 475 1937 2341 97.78 93.94 91.15 115014 0 NA 5097071 4883034 NA 4081 142224 NA 233784 0 NA 92389 0 NA 1102959 1056234 NA 106532 102207 NA 0.2229 0.2163 NA 10771706 10325724 NA 373 26856 NA preprocessing/PAK_pscC/outputs/50freebayes_paeruginosa_pak/all_tags.txt.xz preprocessing/PAK_pscC/outputs/50freebayes_paeruginosa_pa01/all_tags.txt.xz preprocessing/PAK_pscC/outputs/40hisat2_paeruginosa_pak/paeruginosa_pak_genome-paired_sreverse_gene_locus_tag.count.xz preprocessing/PAK_pscC/outputs/40hisat2_paeruginosa_pa01/paeruginosa_pa01_genome-paired_sno_gene_locus_tag.count.xz preprocessing/PAK_pscC/outputs/40hisat2_paeruginosa_pa14/paeruginosa_pa14_genome-paired_sno_gene_Alias.count.xz preprocessing/PAK_pscC/outputs/50freebayes_paeruginosa_pak/deduplication_stats.txt preprocessing/PAK_pscC/outputs/50freebayes_paeruginosa_pa01/deduplication_stats.txt preprocessing/PAK_pscC/outputs/50freebayes_paeruginosa_pak/variants_by_gene.txt.xz preprocessing/PAK_pscC/outputs/50freebayes_paeruginosa_pa01/variants_by_gene.txt.xz preprocessing/PAK_pscC/outputs/50freebayes_paeruginosa_pak/all_tags.txt.xz preprocessing/PAK_pscC/outputs/50freebayes_paeruginosa_pa01/all_tags.txt.xz preprocessing/PAK_pscC/outputs/50freebayes_paeruginosa_pak/paeruginosa_pak-PAK_pscC.fasta preprocessing/PAK_pscC/outputs/50freebayes_paeruginosa_pak/paeruginosa_pak.bcf preprocessing/PAK_pscC/outputs/50freebayes_paeruginosa_pa01/paeruginosa_pa01.bcf preprocessing/PAK_pscC/outputs/50freebayes_paeruginosa_pak/variants_penetrance.txt.xz preprocessing/PAK_pscC/outputs/50freebayes_paeruginosa_pa01/variants_penetrance.txt.xz
PAK_xcp PAK_xcp PAK_xcp PAK undefined undefined 4843414 4443669 0.917 NA 4293814 4088322 3904688 977 24341 21592 96933 110085 229330 394 1787 2140 97.82 93.90 91.07 95387 0 NA 4299065 4112663 NA 3145 116582 NA 193821 0 NA 73985 0 NA 889293 849827 NA 90548 86772 NA 0.2131 0.2066 NA 9634785 9231062 NA 363 26756 NA preprocessing/PAK_xcp/outputs/50freebayes_paeruginosa_pak/all_tags.txt.xz preprocessing/PAK_xcp/outputs/50freebayes_paeruginosa_pa01/all_tags.txt.xz preprocessing/PAK_xcp/outputs/40hisat2_paeruginosa_pak/paeruginosa_pak_genome-paired_sreverse_gene_locus_tag.count.xz preprocessing/PAK_xcp/outputs/40hisat2_paeruginosa_pa01/paeruginosa_pa01_genome-paired_sno_gene_locus_tag.count.xz preprocessing/PAK_xcp/outputs/40hisat2_paeruginosa_pa14/paeruginosa_pa14_genome-paired_sno_gene_Alias.count.xz preprocessing/PAK_xcp/outputs/50freebayes_paeruginosa_pak/deduplication_stats.txt preprocessing/PAK_xcp/outputs/50freebayes_paeruginosa_pa01/deduplication_stats.txt preprocessing/PAK_xcp/outputs/50freebayes_paeruginosa_pak/variants_by_gene.txt.xz preprocessing/PAK_xcp/outputs/50freebayes_paeruginosa_pa01/variants_by_gene.txt.xz preprocessing/PAK_xcp/outputs/50freebayes_paeruginosa_pak/all_tags.txt.xz preprocessing/PAK_xcp/outputs/50freebayes_paeruginosa_pa01/all_tags.txt.xz preprocessing/PAK_xcp/outputs/50freebayes_paeruginosa_pak/paeruginosa_pak-PAK_xcp.fasta preprocessing/PAK_xcp/outputs/50freebayes_paeruginosa_pak/paeruginosa_pak.bcf preprocessing/PAK_xcp/outputs/50freebayes_paeruginosa_pa01/paeruginosa_pa01.bcf preprocessing/PAK_xcp/outputs/50freebayes_paeruginosa_pak/variants_penetrance.txt.xz preprocessing/PAK_xcp/outputs/50freebayes_paeruginosa_pa01/variants_penetrance.txt.xz
PAK_xcp_pscC PAK_xcp_pscC PAK_xcp_pscC PAK undefined undefined 5195158 4611474 0.888 NA 4344138 4220150 4008749 1070 23899 20629 177601 174592 308658 400 2279 2409 96.74 94.46 91.21 168983 0 NA 4376720 4244049 NA 3164 102714 NA 300525 0 NA 129907 0 NA 943947 917470 NA 92772 89839 NA 0.2261 0.2162 NA 9299455 8989454 NA 384 26949 NA preprocessing/PAK_xcp_pscC/outputs/50freebayes_paeruginosa_pak/all_tags.txt.xz preprocessing/PAK_xcp_pscC/outputs/50freebayes_paeruginosa_pa01/all_tags.txt.xz preprocessing/PAK_xcp_pscC/outputs/40hisat2_paeruginosa_pak/paeruginosa_pak_genome-paired_sreverse_gene_locus_tag.count.xz preprocessing/PAK_xcp_pscC/outputs/40hisat2_paeruginosa_pa01/paeruginosa_pa01_genome-paired_sno_gene_locus_tag.count.xz preprocessing/PAK_xcp_pscC/outputs/40hisat2_paeruginosa_pa14/paeruginosa_pa14_genome-paired_sno_gene_Alias.count.xz preprocessing/PAK_xcp_pscC/outputs/50freebayes_paeruginosa_pak/deduplication_stats.txt preprocessing/PAK_xcp_pscC/outputs/50freebayes_paeruginosa_pa01/deduplication_stats.txt preprocessing/PAK_xcp_pscC/outputs/50freebayes_paeruginosa_pak/variants_by_gene.txt.xz preprocessing/PAK_xcp_pscC/outputs/50freebayes_paeruginosa_pa01/variants_by_gene.txt.xz preprocessing/PAK_xcp_pscC/outputs/50freebayes_paeruginosa_pak/all_tags.txt.xz preprocessing/PAK_xcp_pscC/outputs/50freebayes_paeruginosa_pa01/all_tags.txt.xz preprocessing/PAK_xcp_pscC/outputs/50freebayes_paeruginosa_pak/paeruginosa_pak-PAK_xcp_pscC.fasta preprocessing/PAK_xcp_pscC/outputs/50freebayes_paeruginosa_pak/paeruginosa_pak.bcf preprocessing/PAK_xcp_pscC/outputs/50freebayes_paeruginosa_pa01/paeruginosa_pa01.bcf preprocessing/PAK_xcp_pscC/outputs/50freebayes_paeruginosa_pak/variants_penetrance.txt.xz preprocessing/PAK_xcp_pscC/outputs/50freebayes_paeruginosa_pa01/variants_penetrance.txt.xz

I reran the missing PAK samples and looked into the logs. It may be the case that the PAK genome I downloaded is of somewhat lower quality than the PA14 and that is skewing the results somewhat.

Lets go one small step further. I have a series of modified genomes as well as the reference. We can do a quickie tree of them: First I will copy each modified genome to the tree/ directory and rename them to the sampleID.

start=$(pwd)
mkdir tree
cd preprocessing
for i in $(/bin/ls -d PA*); do
    cp $i/outputs/50*/paeruginosa_pak-*.fasta ${start}/tree/
    cp $i/outputs/50*/paeruginosa_pa14-*.fasta ${start}/tree/
done
cd $start
cp ~/libraries/genome/paeruginosa_pa14.fa ${start}/tree
cp ~/libraries/genome/paeruginosa_pak.fa ${start}/tree

Oh, it turns out that at the time of this writing, I forgot to run 3 samples, so this section will need to be redone. But I can at least run it for the samples that I didn’t forget.

funkytown <- genomic_sequence_phylo("tree", root = "paeruginosa_pa14")
## Error in genomic_sequence_phylo("tree", root = "paeruginosa_pa14"): could not find function "genomic_sequence_phylo"
plot(funkytown$phy)
## Error in eval(expr, envir, enclos): object 'funkytown' not found

3 Create an expressionset

The counts from hisat in theory are not very interesting for DNAseq data, except in this instance we want to see the coverage of the knockouts.

pa14_annot <- load_gff_annotations("~/libraries/genome/paeruginosa_pa14.gff",
                                   type = "gene", id_col = "Alias")
## Returning a df with 16 columns and 5979 rows.
rownames(pa14_annot) <- pa14_annot[["Alias"]]

pa14_expt <- create_expt("sample_sheets/all_samples_modified.xlsx", gene_info = pa14_annot,
                         file_column = "hisatcounttablepaeruginosapa14")
## Reading the sample metadata.
## The sample definitions comprises: 12 rows(samples) and 77 columns(metadata fields).
## Matched 5979 annotations and counts.
## Bringing together the count matrix and gene information.
## Some annotations were lost in merging, setting them to 'undefined'.
## Saving the expressionset to 'expt.rda'.
## The final expressionset has 5979 features and 12 samples.
pa14_write <- write_expt(pa14_expt, excel = "excel/pa14_strains.xlsx", batch = "raw")
## Deleting the file excel/pa14_strains.xlsx before writing the tables.
## Writing the first sheet, containing a legend and some summary data.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## 1758 entries are 0.  We are on a log scale, adding 1 to the data.
## 
## Changed 1758 zero count features.
## 
## Naively calculating coefficient of variation/dispersion with respect to condition.
## 
## Finished calculating dispersion estimates.
## 
## `geom_smooth()` using formula = 'y ~ x'
## The expressionset has a minimal or missing set of conditions/batches.
## 
## `geom_smooth()` using formula = 'y ~ x'
pak_annot <- load_gff_annotations("~/libraries/genome/paeruginosa_pak.gff", type = "gene", id_col = "locus_tag")
## Returning a df with 35 columns and 5871 rows.
rownames(pak_annot) <- pak_annot[["locus_tag"]]
pak_expt <- create_expt("sample_sheets/all_samples_modified.xlsx", file_column = "hisatcounttablepaeruginosapak")
## Reading the sample metadata.
## The sample definitions comprises: 12 rows(samples) and 77 columns(metadata fields).
## Matched 5871 annotations and counts.
## Bringing together the count matrix and gene information.
## Saving the expressionset to 'expt.rda'.
## The final expressionset has 5871 features and 12 samples.
pak_write <- write_expt(pak_expt, excel = "excel/pak_strains.xlsx", batch = "raw")
## Writing the first sheet, containing a legend and some summary data.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.2527 entries are 0.  We are on a log scale, adding 1 to the data.
## Changed 2527 zero count features.
## Naively calculating coefficient of variation/dispersion with respect to condition.
## Finished calculating dispersion estimates.
## `geom_smooth()` using formula = 'y ~ x'The expressionset has a minimal or missing set of conditions/batches.
## `geom_smooth()` using formula = 'y ~ x'

4 Write variants by sample

pa14_variants <- pData(pa14_expt)[["variantspenetrancefilepaeruginosapa14"]]
names(pa14_variants) <- rownames(pData(pa14_expt))
## Error in names(pa14_variants) <- rownames(pData(pa14_expt)): attempt to set an attribute on NULL
start <- init_xlsx(excel = "excel/pa14_variants.xlsx")
wb <- start[["wb"]]
for (s in seq_len(length(pa14_variants))) {
  sample_name <- names(pa14_variants)[[s]]
  if (pa14_variants[[s]] == "") {
    next
  }
  sample_data <- readr::read_tsv(pa14_variants[[s]])
  if (nrow(sample_data) == 0) {
    next
  }
  written <- write_xlsx(data = sample_data, sheet = sample_name, wb = wb)
}
saved <- openxlsx::saveWorkbook(written[["workbook"]], file = "excel/pa14_variants.xlsx")
## Error in eval(expr, envir, enclos): object 'written' not found
pak_variants <- pData(pak_expt)[["variantspenetrancefilepaeruginosapak"]]
names(pak_variants) <- rownames(pData(pak_expt))
## Error in names(pak_variants) <- rownames(pData(pak_expt)): attempt to set an attribute on NULL
start <- init_xlsx(excel = "excel/pak_variants.xlsx")
wb <- start[["wb"]]
for (s in seq_len(length(pak_variants))) {
  sample_name <- names(pak_variants)[[s]]
  if (pak_variants[[s]] == "") {
    next
  }
  sample_data <- readr::read_tsv(pak_variants[[s]])
  if (nrow(sample_data) == 0) {
    next
  }
  written <- write_xlsx(data = sample_data, sheet = sample_name, wb = wb)
}
saved <- openxlsx::saveWorkbook(written[["workbook"]], file = "excel/pak_variants.xlsx")
## Error in eval(expr, envir, enclos): object 'written' not found

5 Write mutations by sample

In this following block we will instead write out the nt/aa mutations of CDS/proteins.

pa14_mutations <- pData(pa14_expt)[["variantsbygenefilepaeruginosapa14"]]
names(pa14_mutations) <- rownames(pData(pa14_expt))
## Error in names(pa14_mutations) <- rownames(pData(pa14_expt)): attempt to set an attribute on NULL
start <- init_xlsx(excel = "excel/pa14_mutations.xlsx")
wb <- start[["wb"]]
for (s in seq_len(length(pa14_mutations))) {
  sample_name <- names(pa14_mutations)[[s]]
  if (pa14_mutations[[s]] == "") {
    next
  }
  sample_data <- readr::read_tsv(pa14_mutations[[s]])
  if (nrow(sample_data) == 0) {
    next
  }
  written <- write_xlsx(data = sample_data, sheet = sample_name, wb = wb)
}
saved <- openxlsx::saveWorkbook(written[["workbook"]], file = "excel/pa14_mutations.xlsx")
## Error in eval(expr, envir, enclos): object 'written' not found
pak_mutations <- pData(pak_expt)[["variantsbygenefilepaeruginosapak"]]
names(pak_mutations) <- rownames(pData(pak_expt))
## Error in names(pak_mutations) <- rownames(pData(pak_expt)): attempt to set an attribute on NULL
start <- init_xlsx(excel = "excel/pak_mutations.xlsx")
wb <- start[["wb"]]
for (s in seq_len(length(pak_mutations))) {
  sample_name <- names(pak_mutations)[[s]]
  if (pak_mutations[[s]] == "") {
    next
  }
  sample_data <- readr::read_tsv(pak_mutations[[s]])
  if (nrow(sample_data) == 0) {
    next
  }
  written <- write_xlsx(data = sample_data, sheet = sample_name, wb = wb)
}
saved <- openxlsx::saveWorkbook(written[["workbook"]], file = "excel/pak_mutations.xlsx")
## Error in eval(expr, envir, enclos): object 'written' not found

6 Comparing reads to some assemblies

spec <- make_dnaseq_spec()
modified_exoUTY <- gather_preprocessing_metadata("sample_sheets/exoUTY_224_samples.xlsx",
  species = "paeruginosa_exoUTY_224", verbose = FALSE,
  specification = spec)
## Error in `colnames<-`(`*tmp*`, value = tolower(gsub(pattern = "[[:punct:]]", : attempt to set 'colnames' on an object with less than two dimensions
modified_pscd <- sm(gather_preprocessing_metadata("sample_sheets/pscd_222_samples.xlsx",
  species = "paeruginosa_pscd_222", verbose = FALSE,
  specification = spec))
## Error in `colnames<-`(`*tmp*`, value = tolower(gsub(pattern = "[[:punct:]]", : attempt to set 'colnames' on an object with less than two dimensions
modified_wt <- sm(gather_preprocessing_metadata("sample_sheets/wt_221_samples.xlsx",
  species = "paeruginosa_wt_221", verbose = FALSE,
  specification = spec))
## Error in `colnames<-`(`*tmp*`, value = tolower(gsub(pattern = "[[:punct:]]", : attempt to set 'colnames' on an object with less than two dimensions
samples_exo <- create_expt(modified_exoUTY, file_column = "hisatcounttable")
## Error in eval(expr, envir, enclos): object 'modified_exoUTY' not found
samples_pscd <- create_expt(modified_pscd, file_column = "hisatcounttable")
## Error in eval(expr, envir, enclos): object 'modified_pscd' not found
samples_wt <- create_expt(modified_wt, file_column = "hisatcounttable")
## Error in eval(expr, envir, enclos): object 'modified_wt' not found

7 Write out the off-strain mappings

As the above suggests but does not explicitly state, I mapped some of the samples against multiple potential parental strains in an attempt to make it clear that some specific genes are or are not observed. Thus these last three expressionsets. Now write them out so that Vince can check out the mapping results with respect to these non-standard references.

exo_written <- write_expt(samples_exo, excel = "excel/samples_vs_exoUTY_reference.xlsx")
## Error in h(simpleError(msg, call)): error in evaluating the argument 'object' in selecting a method for function 'exprs': object 'samples_exo' not found
pscd_written <- write_expt(samples_pscd, excel = "excel/samples_vs_pscd_reference.xlsx")
## Error in h(simpleError(msg, call)): error in evaluating the argument 'object' in selecting a method for function 'exprs': object 'samples_pscd' not found
wt_written <- write_expt(samples_wt, excel = "excel/samples_vs_wt_reference.xlsx")
## Deleting the file excel/samples_vs_wt_reference.xlsx before writing the tables.
## Error in h(simpleError(msg, call)): error in evaluating the argument 'object' in selecting a method for function 'exprs': object 'samples_wt' not found

8 Compare two WT samples

nbh_tags <- "preprocessing/PA14_NBH/outputs/50freebayes_paeruginosa_pa14/all_tags.txt.xz"
jc_tags <- "preprocessing/PA14_JC/outputs/50freebayes_paeruginosa_pa14/all_tags.txt.xz"
a5_tags <- "preprocessing/PA14_pscD_A5/outputs/50freebayes_paeruginosa_pa14/all_tags.txt.xz"
e4_tags <- "preprocessing/PA14_pscD_E4/outputs/50freebayes_paeruginosa_pa14/all_tags.txt.xz"

nbh_in <- as.data.frame(readr::read_tsv(nbh_tags)[, c(1,2,3)])
## New names:
## Rows: 194 Columns: 47
## ── Column specification
## ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── Delimiter: "\t" chr
## (13): position, AF, PAO, PQA, SAP, AB, ABP, RPP, RPR, EPP, DPRA, TYPE, CIGAR dbl (21): NS, DP...3, DPB, AN, RO...8, PRO, QR...12, PQR, SRF, SRR,
## SRP, RPPR, EPPR, ODDS, GTI, NUMALT, MQMR, PAIREDR, DP...42, RO...43, QR...44 num (13): AC, AO...9, QA...13, SAF, SAR, RUN, RPL, LEN, MQM, PAIRED,
## AO...45, QA...46, MEANALT
## ℹ Use `spec()` to retrieve the full column specification for this data. ℹ Specify the column types or set `show_col_types = FALSE` to quiet this
## message.
## • `DP` -> `DP...3`
## • `RO` -> `RO...8`
## • `AO` -> `AO...9`
## • `QR` -> `QR...12`
## • `QA` -> `QA...13`
## • `DP` -> `DP...42`
## • `RO` -> `RO...43`
## • `QR` -> `QR...44`
## • `AO` -> `AO...45`
## • `QA` -> `QA...46`
jc_in <- as.data.frame(readr::read_tsv(jc_tags)[, c(1,2,3)])
## New names:
## Rows: 194 Columns: 47
## ── Column specification
## ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── Delimiter: "\t" chr
## (13): position, AF, PAO, PQA, SAP, AB, ABP, RPP, RPR, EPP, DPRA, TYPE, CIGAR dbl (21): NS, DP...3, DPB, AN, RO...8, PRO, QR...12, PQR, SRF, SRR,
## SRP, RPPR, EPPR, ODDS, GTI, NUMALT, MQMR, PAIREDR, DP...42, RO...43, QR...44 num (13): AC, AO...9, QA...13, SAF, SAR, RUN, RPL, LEN, MQM, PAIRED,
## AO...45, QA...46, MEANALT
## ℹ Use `spec()` to retrieve the full column specification for this data. ℹ Specify the column types or set `show_col_types = FALSE` to quiet this
## message.
## • `DP` -> `DP...3`
## • `RO` -> `RO...8`
## • `AO` -> `AO...9`
## • `QR` -> `QR...12`
## • `QA` -> `QA...13`
## • `DP` -> `DP...42`
## • `RO` -> `RO...43`
## • `QR` -> `QR...44`
## • `AO` -> `AO...45`
## • `QA` -> `QA...46`
a5_in <- as.data.frame(readr::read_tsv(a5_tags)[, c(1,2,3)])
## New names:
## Rows: 202 Columns: 47
## ── Column specification
## ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── Delimiter: "\t" chr
## (13): position, AF, PAO, PQA, SAP, AB, ABP, RPP, RPR, EPP, DPRA, TYPE, CIGAR dbl (21): NS, DP...3, DPB, AN, RO...8, PRO, QR...12, PQR, SRF, SRR,
## SRP, RPPR, EPPR, ODDS, GTI, NUMALT, MQMR, PAIREDR, DP...42, RO...43, QR...44 num (13): AC, AO...9, QA...13, SAF, SAR, RUN, RPL, LEN, MQM, PAIRED,
## AO...45, QA...46, MEANALT
## ℹ Use `spec()` to retrieve the full column specification for this data. ℹ Specify the column types or set `show_col_types = FALSE` to quiet this
## message.
## • `DP` -> `DP...3`
## • `RO` -> `RO...8`
## • `AO` -> `AO...9`
## • `QR` -> `QR...12`
## • `QA` -> `QA...13`
## • `DP` -> `DP...42`
## • `RO` -> `RO...43`
## • `QR` -> `QR...44`
## • `AO` -> `AO...45`
## • `QA` -> `QA...46`
e4_in <- as.data.frame(readr::read_tsv(e4_tags)[, c(1,2,3)])
## New names:
## Rows: 201 Columns: 47
## ── Column specification
## ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── Delimiter: "\t" chr
## (3): position, TYPE, CIGAR dbl (44): NS, DP...3, DPB, AC, AN, AF, RO...8, AO...9, PRO, PAO, QR...12, QA...13, PQR, PQA, SRF, SRR, SAF, SAR, SRP,
## SAP, AB, ABP, RUN, RPP, R...
## ℹ Use `spec()` to retrieve the full column specification for this data. ℹ Specify the column types or set `show_col_types = FALSE` to quiet this
## message.
## • `DP` -> `DP...3`
## • `RO` -> `RO...8`
## • `AO` -> `AO...9`
## • `QR` -> `QR...12`
## • `QA` -> `QA...13`
## • `DP` -> `DP...42`
## • `RO` -> `RO...43`
## • `QR` -> `QR...44`
## • `AO` -> `AO...45`
## • `QA` -> `QA...46`
dim(nbh_in)
## [1] 194   3
dim(jc_in)
## [1] 194   3
dim(a5_in)
## [1] 202   3
dim(e4_in)
## [1] 201   3
shared_nbh <- nbh_in[["position"]] %in% jc_in[["position"]]
shared_jc <- jc_in[["position"]] %in% nbh_in[["position"]]
nbh_in[!shared_nbh, "position"]
## [1] "chr_Pseudomonas_aeruginosa_UCBPP_PA14_pos_732792_ref_A_alt_G"  "chr_Pseudomonas_aeruginosa_UCBPP_PA14_pos_732857_ref_A_alt_C" 
## [3] "chr_Pseudomonas_aeruginosa_UCBPP_PA14_pos_4952996_ref_A_alt_C"
jc_in[!shared_jc, "position"]
## [1] "chr_Pseudomonas_aeruginosa_UCBPP_PA14_pos_807679_ref_A_alt_G"  "chr_Pseudomonas_aeruginosa_UCBPP_PA14_pos_5536163_ref_G_alt_A"
## [3] "chr_Pseudomonas_aeruginosa_UCBPP_PA14_pos_6070084_ref_T_alt_C"
together <- merge(nbh_in, jc_in, by = "position", all = TRUE)


shared_a5 <- a5_in[["position"]] %in% e4_in[["position"]]
shared_e4 <- e4_in[["position"]] %in% a5_in[["position"]]
a5_in[!shared_a5, "position"]
## [1] "chr_Pseudomonas_aeruginosa_UCBPP_PA14_pos_807679_ref_A_alt_G"                              
## [2] "chr_Pseudomonas_aeruginosa_UCBPP_PA14_pos_1640196_ref_G_alt_T"                             
## [3] "chr_Pseudomonas_aeruginosa_UCBPP_PA14_pos_2787819_ref_CAG_alt_CAAG,CAA"                    
## [4] "chr_Pseudomonas_aeruginosa_UCBPP_PA14_pos_3515863_ref_TTCA_alt_CTCA"                       
## [5] "chr_Pseudomonas_aeruginosa_UCBPP_PA14_pos_4952882_ref_T_alt_G"                             
## [6] "chr_Pseudomonas_aeruginosa_UCBPP_PA14_pos_4957228_ref_T_alt_C"                             
## [7] "chr_Pseudomonas_aeruginosa_UCBPP_PA14_pos_5028194_ref_CGGGTGGGTTCTTCCC_alt_CGGTCGCGTCTTCCC"
## [8] "chr_Pseudomonas_aeruginosa_UCBPP_PA14_pos_5460283_ref_A_alt_G"
e4_in[!shared_e4, "position"]
## [1] "chr_Pseudomonas_aeruginosa_UCBPP_PA14_pos_732792_ref_A_alt_G"                
## [2] "chr_Pseudomonas_aeruginosa_UCBPP_PA14_pos_732857_ref_A_alt_C"                
## [3] "chr_Pseudomonas_aeruginosa_UCBPP_PA14_pos_2787819_ref_CAG_alt_CAAG"          
## [4] "chr_Pseudomonas_aeruginosa_UCBPP_PA14_pos_3515863_ref_T_alt_C"               
## [5] "chr_Pseudomonas_aeruginosa_UCBPP_PA14_pos_3558713_ref_T_alt_C"               
## [6] "chr_Pseudomonas_aeruginosa_UCBPP_PA14_pos_3832569_ref_T_alt_A"               
## [7] "chr_Pseudomonas_aeruginosa_UCBPP_PA14_pos_5028194_ref_CGGGTGGGT_alt_CGGTCGCG"
together <- merge(a5_in, e4_in, by = "position", all = TRUE)

9 CupABCD and PelA-G samples

I keep forgetting to send Vince a sheet describing the state of the cup/pel and vibrio samples. Let us fix that now. I did process them and create a sample sheet, so it should at least be pretty easy.

Oh crap I used the gene# instead of PA# when mapping. My previous IDs are invalid for these samples.

pa14_annot <- load_gff_annotations("~/libraries/genome/paeruginosa_pa14.gff",
                                   type = "gene", id_col = "gene_id")
## Returning a df with 16 columns and 5979 rows.
rownames(pa14_annot) <- pa14_annot[["gene_id"]]
cup_expt <- create_expt("sample_sheets/all_samples_pa14_202308_modified.xlsx",
                        gene_info = pa14_annot, file_column = "hisatcounttable")
## Reading the sample metadata.
## The sample definitions comprises: 6 rows(samples) and 27 columns(metadata fields).
## Matched 5979 annotations and counts.
## Bringing together the count matrix and gene information.
## Some annotations were lost in merging, setting them to 'undefined'.
## Saving the expressionset to 'expt.rda'.
## The final expressionset has 5979 features and 6 samples.
written <- write_expt(cup_expt, excel = glue("excel/cup_pel_expt-v{ver}.xlsx"))
## Writing the first sheet, containing a legend and some summary data.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## 67 entries are 0.  We are on a log scale, adding 1 to the data.
## 
## Changed 67 zero count features.
## 
## Naively calculating coefficient of variation/dispersion with respect to condition.
## 
## Finished calculating dispersion estimates.
## 
## `geom_smooth()` using formula = 'y ~ x'
## The expressionset has a minimal or missing set of conditions/batches.
## Error in `contrasts<-`(`*tmp*`, value = contr.funs[1 + isOF[nn]]) : 
##   contrasts can be applied only to factors with 2 or more levels
## `geom_smooth()` using formula = 'y ~ x'

10 Vibrio samples

10.1 Preprocessing

I do not think I ever wrote down the commands used to preprocess the vibrio samples, likely because I just did them with another set?

I just added a job to give per-base coverage stats, lets run that. Note, if I use this new cyoa version, a lot of things will break horribly because I reorganized my reference data directory but have not yet finished the process.

module purge
module add cyoa/202302
cd preprocessing/202308_vibrio/
start=$(pwd)
for i in $(/bin/ls -d A*); do
    cd $i
    echo $i
    input=$(find unprocessed -type f | tr '\n' ':')
    cyoa --method pdnaseq --species vibrio_cholerae_a1552 \
         --introns 0 --gff_type CDS --gff_tag locus_tag \
         --input $input
    cd $start
done

module purge
module add cyoa/202402
cd preprocessing/202308_vibrio/
start=$(pwd)
for i in $(/bin/ls -d A*); do
    cd $i
    echo $i
    input=$(/bin/ls outputs/02hisat2_vibrio_cholerae_a1552/vibrio_cholerae_a1552_genome.bam)
    cyoa --method bam2cov --input ${input}
    cd $start
done

I am going to change my metadata collector to accept a non-existant sample sheet.

queried_species <- "vibrio_cholerae_a1552"
modified <- gather_preprocessing_metadata(basedir = "preprocessing/202308_vibrio", verbose = TRUE,
                                          species = queried_species, specification = make_dnaseq_spec(),
                                          new_metadata = "sample_sheets/202308_vibrio.xlsx")
## Using provided specification
## Starting trimomatic_input: 1.
## Checking input_file_spec: {basedir}/{meta[['sampleid']]}/outputs/*trimomatic/*-trimomatic.stderr.
## Example regex filename: preprocessing/202308_vibrio/A1552/outputs/*trimomatic/*-trimomatic.stderr.
## Found the correct line:
## Input Read Pairs: 2512423 Both Surviving: 2265635 (90.18%) Forward Only Surviving: 138802 (5.52%) Reverse Only Surviving: 36832 (1.47%) Dropped: 71154 (2.83%)
## Found the correct line:
## Input Read Pairs: 2966181 Both Surviving: 2726646 (91.92%) Forward Only Surviving: 122563 (4.13%) Reverse Only Surviving: 44966 (1.52%) Dropped: 72006 (2.43%)
## Found the correct line:
## Input Read Pairs: 2716935 Both Surviving: 2468715 (90.86%) Forward Only Surviving: 136092 (5.01%) Reverse Only Surviving: 39967 (1.47%) Dropped: 72161 (2.66%)
## Found the correct line:
## Input Read Pairs: 2506738 Both Surviving: 2262819 (90.27%) Forward Only Surviving: 87915 (3.51%) Reverse Only Surviving: 80945 (3.23%) Dropped: 75059 (2.99%)
## Starting trimomatic_output: 2.
## Checking input_file_spec: {basedir}/{meta[['sampleid']]}/outputs/*trimomatic/*-trimomatic.stderr.
## Example regex filename: preprocessing/202308_vibrio/A1552/outputs/*trimomatic/*-trimomatic.stderr.
## Found the correct line:
## Input Read Pairs: 2512423 Both Surviving: 2265635 (90.18%) Forward Only Surviving: 138802 (5.52%) Reverse Only Surviving: 36832 (1.47%) Dropped: 71154 (2.83%)
## Found the correct line:
## Input Read Pairs: 2966181 Both Surviving: 2726646 (91.92%) Forward Only Surviving: 122563 (4.13%) Reverse Only Surviving: 44966 (1.52%) Dropped: 72006 (2.43%)
## Found the correct line:
## Input Read Pairs: 2716935 Both Surviving: 2468715 (90.86%) Forward Only Surviving: 136092 (5.01%) Reverse Only Surviving: 39967 (1.47%) Dropped: 72161 (2.66%)
## Found the correct line:
## Input Read Pairs: 2506738 Both Surviving: 2262819 (90.27%) Forward Only Surviving: 87915 (3.51%) Reverse Only Surviving: 80945 (3.23%) Dropped: 75059 (2.99%)
## Starting trimomatic_ratio: 3.
## Checking input_file_spec: .
## The numerator column is: trimomatic_output.
## The denominator column is: trimomatic_input.
## Starting fastqc_pct_gc: 4.
## Checking input_file_spec: {basedir}/{meta[['sampleid']]}/outputs/*fastqc/*_fastqc/fastqc_data.txt.
## Example regex filename: preprocessing/202308_vibrio/A1552/outputs/*fastqc/*_fastqc/fastqc_data.txt.
## Found the correct line:
## %GC  47
## Found the correct line:
## %GC  47
## Found the correct line:
## %GC  48
## Found the correct line:
## %GC  47
## Starting fastqc_most_overrepresented: 5.
## Checking input_file_spec: {basedir}/{meta[['sampleid']]}/outputs/*fastqc/*_fastqc/fastqc_data.txt.
## Not including new entries for: fastqc_most_overrepresented, it is empty.
## Starting hisat_genome_single_concordant: 6.
## Checking input_file_spec: {basedir}/{meta[['sampleid']]}/outputs/*hisat*_{species}/hisat*_*genome*.stderr.
## Example regex filename: preprocessing/202308_vibrio/A1552/outputs/*hisat*_vibrio_cholerae_a1552/hisat*_*genome*.stderr.
## Found the correct line:
##     2220685 (98.02%) aligned concordantly exactly 1 time
## Found the correct line:
##     2666383 (97.79%) aligned concordantly exactly 1 time
## Found the correct line:
##     2428374 (98.37%) aligned concordantly exactly 1 time
## Found the correct line:
##     2214393 (97.86%) aligned concordantly exactly 1 time
## Starting hisat_genome_multi_concordant: 7.
## Checking input_file_spec: {basedir}/{meta[['sampleid']]}/outputs/*hisat*_{species}/hisat*_*genome*.stderr.
## Example regex filename: preprocessing/202308_vibrio/A1552/outputs/*hisat*_vibrio_cholerae_a1552/hisat*_*genome*.stderr.
## Found the correct line:
##     40157 (1.77%) aligned concordantly >1 times
## Found the correct line:
##     56552 (2.07%) aligned concordantly >1 times
## Found the correct line:
##     35989 (1.46%) aligned concordantly >1 times
## Found the correct line:
##     45388 (2.01%) aligned concordantly >1 times
## Starting hisat_genome_single_all: 8.
## Checking input_file_spec: {basedir}/{meta[['sampleid']]}/outputs/*hisat*_{species}/hisat*_*genome*.stderr.
## Example regex filename: preprocessing/202308_vibrio/A1552/outputs/*hisat*_vibrio_cholerae_a1552/hisat*_*genome*.stderr.
## Found the correct line:
##         2538 (50.70%) aligned exactly 1 time
## Found the correct line:
##         2086 (49.81%) aligned exactly 1 time
## Found the correct line:
##         2364 (49.35%) aligned exactly 1 time
## Found the correct line:
##         1855 (50.77%) aligned exactly 1 time
## Starting hisat_genome_multi_all: 9.
## Checking input_file_spec: {basedir}/{meta[['sampleid']]}/outputs/*hisat*_{species}/hisat*_*genome*.stderr.
## Example regex filename: preprocessing/202308_vibrio/A1552/outputs/*hisat*_vibrio_cholerae_a1552/hisat*_*genome*.stderr.
## Found the correct line:
##         172 (3.44%) aligned >1 times
## Found the correct line:
##         117 (2.79%) aligned >1 times
## Found the correct line:
##         112 (2.34%) aligned >1 times
## Found the correct line:
##         112 (3.07%) aligned >1 times
## Starting hisat_genome_percent_log: 10.
## Checking input_file_spec: {basedir}/{meta[['sampleid']]}/outputs/*hisat*_{species}/hisat*_*{type}*.stderr.
## Example regex filename: preprocessing/202308_vibrio/A1552/outputs/*hisat*_vibrio_cholerae_a1552/hisat*_*genome*.stderr.
## Found the correct line:
## 99.95% overall alignment rate
## Found the correct line:
## 99.96% overall alignment rate
## Found the correct line:
## 99.95% overall alignment rate
## Found the correct line:
## 99.96% overall alignment rate
## Starting hisat_genome_pct_vs_trimmed: 11.
## Warning in gather_preprocessing_metadata(basedir = "preprocessing/202308_vibrio", : Column: hisat_genome_percent_log already exists, replacing it.
## Checking input_file_spec: .
## Starting gatk_unpaired: 12.
## Checking input_file_spec: {basedir}/{meta[['sampleid']]}/outputs/*freebayes_{species}/deduplication_stats.txt.
## Example regex filename: preprocessing/202308_vibrio/A1552/outputs/*freebayes_vibrio_cholerae_a1552/deduplication_stats.txt.
## Found the correct line:
## Unknown Library  0   2260842 257414  0   0   205928  100039  0.091085    21320690
## Found the correct line:
## Unknown Library  0   2722935 327740  0   0   256760  103550  0.094295    21509499
## Found the correct line:
## Unknown Library  0   2464363 229232  0   0   233245  103907  0.094647    20745281
## Found the correct line:
## Unknown Library  0   2259781 263980  0   0   202776  86705   0.089733    19611117
## Not including new entries for: gatk_unpaired, it is empty.
## Starting gatk_paired: 13.
## Checking input_file_spec: {basedir}/{meta[['sampleid']]}/outputs/*freebayes_{species}/deduplication_stats.txt.
## Example regex filename: preprocessing/202308_vibrio/A1552/outputs/*freebayes_vibrio_cholerae_a1552/deduplication_stats.txt.
## Found the correct line:
## Unknown Library  0   2260842 257414  0   0   205928  100039  0.091085    21320690
## Found the correct line:
## Unknown Library  0   2722935 327740  0   0   256760  103550  0.094295    21509499
## Found the correct line:
## Unknown Library  0   2464363 229232  0   0   233245  103907  0.094647    20745281
## Found the correct line:
## Unknown Library  0   2259781 263980  0   0   202776  86705   0.089733    19611117
## Starting gatk_supplementary: 14.
## Checking input_file_spec: {basedir}/{meta[['sampleid']]}/outputs/*freebayes_{species}/deduplication_stats.txt.
## Example regex filename: preprocessing/202308_vibrio/A1552/outputs/*freebayes_vibrio_cholerae_a1552/deduplication_stats.txt.
## Found the correct line:
## Unknown Library  0   2260842 257414  0   0   205928  100039  0.091085    21320690
## Found the correct line:
## Unknown Library  0   2722935 327740  0   0   256760  103550  0.094295    21509499
## Found the correct line:
## Unknown Library  0   2464363 229232  0   0   233245  103907  0.094647    20745281
## Found the correct line:
## Unknown Library  0   2259781 263980  0   0   202776  86705   0.089733    19611117
## Starting gatk_unmapped: 15.
## Checking input_file_spec: {basedir}/{meta[['sampleid']]}/outputs/*freebayes_{species}/deduplication_stats.txt.
## Example regex filename: preprocessing/202308_vibrio/A1552/outputs/*freebayes_vibrio_cholerae_a1552/deduplication_stats.txt.
## Found the correct line:
## Unknown Library  0   2260842 257414  0   0   205928  100039  0.091085    21320690
## Found the correct line:
## Unknown Library  0   2722935 327740  0   0   256760  103550  0.094295    21509499
## Found the correct line:
## Unknown Library  0   2464363 229232  0   0   233245  103907  0.094647    20745281
## Found the correct line:
## Unknown Library  0   2259781 263980  0   0   202776  86705   0.089733    19611117
## Not including new entries for: gatk_unmapped, it is empty.
## Starting gatk_unpaired_duplicates: 16.
## Checking input_file_spec: {basedir}/{meta[['sampleid']]}/outputs/*freebayes_{species}/deduplication_stats.txt.
## Example regex filename: preprocessing/202308_vibrio/A1552/outputs/*freebayes_vibrio_cholerae_a1552/deduplication_stats.txt.
## Found the correct line:
## Unknown Library  0   2260842 257414  0   0   205928  100039  0.091085    21320690
## Found the correct line:
## Unknown Library  0   2722935 327740  0   0   256760  103550  0.094295    21509499
## Found the correct line:
## Unknown Library  0   2464363 229232  0   0   233245  103907  0.094647    20745281
## Found the correct line:
## Unknown Library  0   2259781 263980  0   0   202776  86705   0.089733    19611117
## Not including new entries for: gatk_unpaired_duplicates, it is empty.
## Starting gatk_paired_duplicates: 17.
## Checking input_file_spec: {basedir}/{meta[['sampleid']]}/outputs/*freebayes_{species}/deduplication_stats.txt.
## Example regex filename: preprocessing/202308_vibrio/A1552/outputs/*freebayes_vibrio_cholerae_a1552/deduplication_stats.txt.
## Found the correct line:
## Unknown Library  0   2260842 257414  0   0   205928  100039  0.091085    21320690
## Found the correct line:
## Unknown Library  0   2722935 327740  0   0   256760  103550  0.094295    21509499
## Found the correct line:
## Unknown Library  0   2464363 229232  0   0   233245  103907  0.094647    20745281
## Found the correct line:
## Unknown Library  0   2259781 263980  0   0   202776  86705   0.089733    19611117
## Starting gatk_paired_opt_duplicates: 18.
## Checking input_file_spec: {basedir}/{meta[['sampleid']]}/outputs/*freebayes_{species}/deduplication_stats.txt.
## Example regex filename: preprocessing/202308_vibrio/A1552/outputs/*freebayes_vibrio_cholerae_a1552/deduplication_stats.txt.
## Found the correct line:
## Unknown Library  0   2260842 257414  0   0   205928  100039  0.091085    21320690
## Found the correct line:
## Unknown Library  0   2722935 327740  0   0   256760  103550  0.094295    21509499
## Found the correct line:
## Unknown Library  0   2464363 229232  0   0   233245  103907  0.094647    20745281
## Found the correct line:
## Unknown Library  0   2259781 263980  0   0   202776  86705   0.089733    19611117
## Starting gatk_duplicate_pct: 19.
## Checking input_file_spec: {basedir}/{meta[['sampleid']]}/outputs/*freebayes_{species}/deduplication_stats.txt.
## Example regex filename: preprocessing/202308_vibrio/A1552/outputs/*freebayes_vibrio_cholerae_a1552/deduplication_stats.txt.
## Found the correct line:
## Unknown Library  0   2260842 257414  0   0   205928  100039  0.091085    21320690
## Found the correct line:
## Unknown Library  0   2722935 327740  0   0   256760  103550  0.094295    21509499
## Found the correct line:
## Unknown Library  0   2464363 229232  0   0   233245  103907  0.094647    20745281
## Found the correct line:
## Unknown Library  0   2259781 263980  0   0   202776  86705   0.089733    19611117
## Starting gatk_libsize: 20.
## Checking input_file_spec: {basedir}/{meta[['sampleid']]}/outputs/*freebayes_{species}/deduplication_stats.txt.
## Example regex filename: preprocessing/202308_vibrio/A1552/outputs/*freebayes_vibrio_cholerae_a1552/deduplication_stats.txt.
## Found the correct line:
## Unknown Library  0   2260842 257414  0   0   205928  100039  0.091085    21320690
## Found the correct line:
## Unknown Library  0   2722935 327740  0   0   256760  103550  0.094295    21509499
## Found the correct line:
## Unknown Library  0   2464363 229232  0   0   233245  103907  0.094647    20745281
## Found the correct line:
## Unknown Library  0   2259781 263980  0   0   202776  86705   0.089733    19611117
## Starting freebayes_observed: 21.
## Checking input_file_spec: {basedir}/{meta[['sampleid']]}/outputs/*freebayes_{species}/all_tags*.
## Example count filename: preprocessing/202308_vibrio/A1552/outputs/*freebayes_vibrio_cholerae_a1552/all_tags*.
## Starting input_r1: 22.
## Checking input_file_spec: {basedir}/{meta[['sampleid']]}/scripts/*trim_*.sh.
## Example regex filename: preprocessing/202308_vibrio/A1552/scripts/*trim_*.sh.
## Found the correct line:
##   <(less unprocessed/A1552_S52_R1_001.fastq.xz) <(less unprocessed/A1552_S52_R2_001.fastq.xz) \
## Found the correct line:
##   <(less unprocessed/A1552_capV_S53_R1_001.fastq.xz) <(less unprocessed/A1552_capV_S53_R2_001.fastq.xz) \
## Found the correct line:
##   <(less unprocessed/A1552_capV_dncV_S55_R1_001.fastq.xz) <(less unprocessed/A1552_capV_dncV_S55_R2_001.fastq.xz) \
## Found the correct line:
##   <(less unprocessed/A1552_dncV_S54_R1_001.fastq.xz) <(less unprocessed/A1552_dncV_S54_R2_001.fastq.xz) \
## Starting input_r2: 23.
## Checking input_file_spec: {basedir}/{meta[['sampleid']]}/scripts/*trim_*.sh.
## Example regex filename: preprocessing/202308_vibrio/A1552/scripts/*trim_*.sh.
## Found the correct line:
##   <(less unprocessed/A1552_S52_R1_001.fastq.xz) <(less unprocessed/A1552_S52_R2_001.fastq.xz) \
## Found the correct line:
##   <(less unprocessed/A1552_capV_S53_R1_001.fastq.xz) <(less unprocessed/A1552_capV_S53_R2_001.fastq.xz) \
## Found the correct line:
##   <(less unprocessed/A1552_capV_dncV_S55_R1_001.fastq.xz) <(less unprocessed/A1552_capV_dncV_S55_R2_001.fastq.xz) \
## Found the correct line:
##   <(less unprocessed/A1552_dncV_S54_R1_001.fastq.xz) <(less unprocessed/A1552_dncV_S54_R2_001.fastq.xz) \
## Starting freebayes_observed_file: 24.
## Checking input_file_spec: {basedir}/{meta[['sampleid']]}/outputs/*freebayes_{species}/all_tags*.
## Example filename: preprocessing/202308_vibrio/A1552/outputs/*freebayes_vibrio_cholerae_a1552/all_tags*.
## Starting hisat_count_table: 25.
## Checking input_file_spec: {basedir}/{meta[['sampleid']]}/outputs/*hisat*_{species}/{species}_*{type}*.count.xz.
## Example filename: preprocessing/202308_vibrio/A1552/outputs/*hisat*_vibrio_cholerae_a1552/vibrio_cholerae_a1552_*genome*.count.xz.
## Starting deduplication_stats: 26.
## Checking input_file_spec: {basedir}/{meta[['sampleid']]}/outputs/*freebayes_{species}/deduplication_stats.txt.
## Example filename: preprocessing/202308_vibrio/A1552/outputs/*freebayes_vibrio_cholerae_a1552/deduplication_stats.txt.
## Starting freebayes_variants_by_gene: 27.
## Checking input_file_spec: {basedir}/{meta[['sampleid']]}/outputs/*freebayes_{species}/variants_by_gene*.
## Example filename: preprocessing/202308_vibrio/A1552/outputs/*freebayes_vibrio_cholerae_a1552/variants_by_gene*.
## Starting freebayes_variants_table: 28.
## Checking input_file_spec: {basedir}/{meta[['sampleid']]}/outputs/*freebayes_{species}/all_tags*.
## Example filename: preprocessing/202308_vibrio/A1552/outputs/*freebayes_vibrio_cholerae_a1552/all_tags*.
## Starting freebayes_modified_genome: 29.
## Checking input_file_spec: {basedir}/{meta[['sampleid']]}/outputs/*freebayes_{species}/{species}*.fasta.
## Example filename: preprocessing/202308_vibrio/A1552/outputs/*freebayes_vibrio_cholerae_a1552/vibrio_cholerae_a1552*.fasta.
## Starting freebayes_bcf_file: 30.
## Checking input_file_spec: {basedir}/{meta[['sampleid']]}/outputs/*freebayes_{species}/{species}*.bcf.
## Example filename: preprocessing/202308_vibrio/A1552/outputs/*freebayes_vibrio_cholerae_a1552/vibrio_cholerae_a1552*.bcf.
## Starting freebayes_penetrance_file: 31.
## Checking input_file_spec: {basedir}/{meta[['sampleid']]}/outputs/*freebayes_{species}/variants_penetrance*.
## Example filename: preprocessing/202308_vibrio/A1552/outputs/*freebayes_vibrio_cholerae_a1552/variants_penetrance*.
## Starting bedtools_coverage_file: 32.
## Checking input_file_spec: {basedir}/{meta[['sampleid']]}/outputs/*bedtools_coverage_{species}/*.bed.
## Example filename: preprocessing/202308_vibrio/A1552/outputs/*bedtools_coverage_vibrio_cholerae_a1552/*.bed.
## Not including new entries for: bedtools_coverage_file, it is empty.
## Starting bbmap_coverage_stats: 33.
## Checking input_file_spec: {basedir}/{meta[['sampleid']]}/outputs/*bam2coverage*/coverage.tsv.xz.
## Example filename: preprocessing/202308_vibrio/A1552/outputs/*bam2coverage*/coverage.tsv.xz.
## Not including new entries for: bbmap_coverage_stats, it is empty.
## Starting bbmap_coverage_per_nt: 34.
## Checking input_file_spec: {basedir}/{meta[['sampleid']]}/outputs/*bam2coverage*/base_coverage.tsv.xz.
## Example filename: preprocessing/202308_vibrio/A1552/outputs/*bam2coverage*/base_coverage.tsv.xz.
## Not including new entries for: bbmap_coverage_per_nt, it is empty.
## Writing new metadata to: sample_sheets/202308_vibrio.xlsx
## Deleting the file sample_sheets/202308_vibrio.xlsx before writing the tables.
knitr::kable(extract_metadata("sample_sheets/202308_vibrio.xlsx"))
rownames sampleid condition batch trimomaticinput trimomaticoutput trimomaticpercent fastqcpctgc hisatgenomesingleconcordant hisatgenomemulticoncordant hisatgenomesingleall hisatgenomemultiall hisatgenomepercentlog gatkpaired gatksupplementary gatkpairedduplicates gatkpairedoptduplicates gatkduplicatepct gatklibsize freebayesobserved inputr1 inputr2 freebayesobservedfile hisatcounttable deduplicationstats freebayesvariantsbygene freebayesvariantstable freebayesmodifiedgenome freebayesbcffile freebayespenetrancefile
A1552 A1552 A1552 undefined undefined 2512423 2265635 0.902 47 2220685 40157 2538 172 0.980 2260842 257414 205928 100039 0.0911 21320690 10 unprocessed/A1552_S52_R1_001.fastq.xz unprocessed/A1552_S52_R2_001.fastq.xz preprocessing/202308_vibrio/A1552/outputs/03freebayes_vibrio_cholerae_a1552/all_tags.txt.xz preprocessing/202308_vibrio/A1552/outputs/02hisat2_vibrio_cholerae_a1552/vibrio_cholerae_a1552_genome-paired_sreverse_CDS_locus_tag.count.xz preprocessing/202308_vibrio/A1552/outputs/03freebayes_vibrio_cholerae_a1552/deduplication_stats.txt preprocessing/202308_vibrio/A1552/outputs/03freebayes_vibrio_cholerae_a1552/variants_by_gene.txt.xz preprocessing/202308_vibrio/A1552/outputs/03freebayes_vibrio_cholerae_a1552/all_tags.txt.xz preprocessing/202308_vibrio/A1552/outputs/03freebayes_vibrio_cholerae_a1552/vibrio_cholerae_a1552-A1552.fasta preprocessing/202308_vibrio/A1552/outputs/03freebayes_vibrio_cholerae_a1552/vibrio_cholerae_a1552.bcf preprocessing/202308_vibrio/A1552/outputs/03freebayes_vibrio_cholerae_a1552/variants_penetrance.txt.xz
A1552_capV A1552_capV A1552_capV undefined undefined 2966181 2726646 0.919 47 2666383 56552 2086 117 0.978 2722935 327740 256760 103550 0.0943 21509499 13 unprocessed/A1552_capV_S53_R1_001.fastq.xz unprocessed/A1552_capV_S53_R2_001.fastq.xz preprocessing/202308_vibrio/A1552_capV/outputs/03freebayes_vibrio_cholerae_a1552/all_tags.txt.xz preprocessing/202308_vibrio/A1552_capV/outputs/02hisat2_vibrio_cholerae_a1552/vibrio_cholerae_a1552_genome-paired_sreverse_CDS_locus_tag.count.xz preprocessing/202308_vibrio/A1552_capV/outputs/03freebayes_vibrio_cholerae_a1552/deduplication_stats.txt preprocessing/202308_vibrio/A1552_capV/outputs/03freebayes_vibrio_cholerae_a1552/variants_by_gene.txt.xz preprocessing/202308_vibrio/A1552_capV/outputs/03freebayes_vibrio_cholerae_a1552/all_tags.txt.xz preprocessing/202308_vibrio/A1552_capV/outputs/03freebayes_vibrio_cholerae_a1552/vibrio_cholerae_a1552-A1552_capV.fasta preprocessing/202308_vibrio/A1552_capV/outputs/03freebayes_vibrio_cholerae_a1552/vibrio_cholerae_a1552.bcf preprocessing/202308_vibrio/A1552_capV/outputs/03freebayes_vibrio_cholerae_a1552/variants_penetrance.txt.xz
A1552_capV_dncV A1552_capV_dncV A1552_capV_dncV undefined undefined 2716935 2468715 0.909 48 2428374 35989 2364 112 0.984 2464363 229232 233245 103907 0.0946 20745281 11 unprocessed/A1552_capV_dncV_S55_R1_001.fastq.xz unprocessed/A1552_capV_dncV_S55_R2_001.fastq.xz preprocessing/202308_vibrio/A1552_capV_dncV/outputs/03freebayes_vibrio_cholerae_a1552/all_tags.txt.xz preprocessing/202308_vibrio/A1552_capV_dncV/outputs/02hisat2_vibrio_cholerae_a1552/vibrio_cholerae_a1552_genome-paired_sreverse_CDS_locus_tag.count.xz preprocessing/202308_vibrio/A1552_capV_dncV/outputs/03freebayes_vibrio_cholerae_a1552/deduplication_stats.txt preprocessing/202308_vibrio/A1552_capV_dncV/outputs/03freebayes_vibrio_cholerae_a1552/variants_by_gene.txt.xz preprocessing/202308_vibrio/A1552_capV_dncV/outputs/03freebayes_vibrio_cholerae_a1552/all_tags.txt.xz preprocessing/202308_vibrio/A1552_capV_dncV/outputs/03freebayes_vibrio_cholerae_a1552/vibrio_cholerae_a1552-A1552_capV_dncV.fasta preprocessing/202308_vibrio/A1552_capV_dncV/outputs/03freebayes_vibrio_cholerae_a1552/vibrio_cholerae_a1552.bcf preprocessing/202308_vibrio/A1552_capV_dncV/outputs/03freebayes_vibrio_cholerae_a1552/variants_penetrance.txt.xz
A1552_dncV A1552_dncV A1552_dncV undefined undefined 2506738 2262819 0.903 47 2214393 45388 1855 112 0.979 2259781 263980 202776 86705 0.0897 19611117 14 unprocessed/A1552_dncV_S54_R1_001.fastq.xz unprocessed/A1552_dncV_S54_R2_001.fastq.xz preprocessing/202308_vibrio/A1552_dncV/outputs/03freebayes_vibrio_cholerae_a1552/all_tags.txt.xz preprocessing/202308_vibrio/A1552_dncV/outputs/02hisat2_vibrio_cholerae_a1552/vibrio_cholerae_a1552_genome-paired_sreverse_CDS_locus_tag.count.xz preprocessing/202308_vibrio/A1552_dncV/outputs/03freebayes_vibrio_cholerae_a1552/deduplication_stats.txt preprocessing/202308_vibrio/A1552_dncV/outputs/03freebayes_vibrio_cholerae_a1552/variants_by_gene.txt.xz preprocessing/202308_vibrio/A1552_dncV/outputs/03freebayes_vibrio_cholerae_a1552/all_tags.txt.xz preprocessing/202308_vibrio/A1552_dncV/outputs/03freebayes_vibrio_cholerae_a1552/vibrio_cholerae_a1552-A1552_dncV.fasta preprocessing/202308_vibrio/A1552_dncV/outputs/03freebayes_vibrio_cholerae_a1552/vibrio_cholerae_a1552.bcf preprocessing/202308_vibrio/A1552_dncV/outputs/03freebayes_vibrio_cholerae_a1552/variants_penetrance.txt.xz 
vibrio_annot <- load_gff_annotations("~/libraries/genome/vibrio_cholerae_a1552.gff", id_col = "locus_tag",
                                     type = "CDS")
## Returning a df with 39 columns and 3833 rows.
rownames(vibrio_annot) <- make.names(vibrio_annot[["locus_tag"]], unique = TRUE)
vibrio_expt <- create_expt("sample_sheets/202308_vibrio.xlsx",
                           file_column = "hisatcounttable", gene_info = vibrio_annot)
## Reading the sample metadata.
## The sample definitions comprises: 4 rows(samples) and 30 columns(metadata fields).
## Matched 3825 annotations and counts.
## Bringing together the count matrix and gene information.
## Some annotations were lost in merging, setting them to 'undefined'.
## Saving the expressionset to 'expt.rda'.
## The final expressionset has 3825 features and 4 samples.
written <- write_expt(vibrio_expt, excel = glue("excel/vibrio_expt-v{ver}.xlsx"))
## Writing the first sheet, containing a legend and some summary data.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Warning in plot_pca(..., pc_method = "tsne"): TSNE: Attempting to auto-detect perplexity failed, setting it to 1.
## Naively calculating coefficient of variation/dispersion with respect to condition.
## Finished calculating dispersion estimates.
## `geom_smooth()` using formula = 'y ~ x'
## Warning in plot_pca(..., pc_method = "tsne"): TSNE: Attempting to auto-detect perplexity failed, setting it to 1.
## The expressionset has a minimal or missing set of conditions/batches.
## Error in `contrasts<-`(`*tmp*`, value = contr.funs[1 + isOF[nn]]) : 
##   contrasts can be applied only to factors with 2 or more levels
## `geom_smooth()` using formula = 'y ~ x'

11 Write mutations by sample

Copy/pasted from above and modified to match the vibrio samples.

colnames(pData(vibrio_expt))
##  [1] "rownames"                    "sampleid"                    "condition"                   "batch"                      
##  [5] "trimomaticinput"             "trimomaticoutput"            "trimomaticpercent"           "fastqcpctgc"                
##  [9] "hisatgenomesingleconcordant" "hisatgenomemulticoncordant"  "hisatgenomesingleall"        "hisatgenomemultiall"        
## [13] "hisatgenomepercentlog"       "gatkpaired"                  "gatksupplementary"           "gatkpairedduplicates"       
## [17] "gatkpairedoptduplicates"     "gatkduplicatepct"            "gatklibsize"                 "freebayesobserved"          
## [21] "inputr1"                     "inputr2"                     "freebayesobservedfile"       "hisatcounttable"            
## [25] "deduplicationstats"          "freebayesvariantsbygene"     "freebayesvariantstable"      "freebayesmodifiedgenome"    
## [29] "freebayesbcffile"            "freebayespenetrancefile"     "file"
gene_mutations <- pData(vibrio_expt)[["freebayesvariantsbygene"]]
names(gene_mutations) <- rownames(pData(vibrio_expt))
start <- init_xlsx(excel = "excel/vibrio_mutations.xlsx")
## Deleting the file excel/vibrio_mutations.xlsx before writing the tables.
wb <- start[["wb"]]
for (s in seq_len(length(gene_mutations))) {
  sample_name <- names(gene_mutations)[[s]]
  if (gene_mutations[[s]] == "") {
    next
  }
  sample_data <- readr::read_tsv(gene_mutations[[s]])
  if (nrow(sample_data) == 0) {
    next
  }
  written <- write_xlsx(data = sample_data, sheet = sample_name, wb = wb)
}
## Rows: 6 Columns: 5
## ── Column specification ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Delimiter: "\t"
## chr (4): gene, chromosome, from_to, aa_subst
## dbl (1): position
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
## Rows: 7 Columns: 5
## ── Column specification ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Delimiter: "\t"
## chr (4): gene, chromosome, from_to, aa_subst
## dbl (1): position
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
## Rows: 8 Columns: 5
## ── Column specification ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Delimiter: "\t"
## chr (4): gene, chromosome, from_to, aa_subst
## dbl (1): position
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
## Rows: 7 Columns: 5
## ── Column specification ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Delimiter: "\t"
## chr (4): gene, chromosome, from_to, aa_subst
## dbl (1): position
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
saved <- openxlsx::saveWorkbook(written[["workbook"]], file = "excel/vibrio_mutations.xlsx")

12 Samples received in 202309

couple of notes to myself: I think these newest samples are seeking to understand the evolutionary pressures which resulted in plasmid mutations to the yciV promoter. Thus, I think they primary place to look is at that locus, both in the genome and plasmid.

In PA01 it is PA3200 and located at approximately location 3592500 as the first ORF of a reverse strand operon.

13 Samples received in 202401

Repeat the printing of counts/gene for a series of samples which have deleted operons for combinations of cupA/cupB/cupC,cupD, orn, pel, pilA and fliC.

I just created a blank sample sheet with appropriate IDs. Let us take a peek!

Caveat: I have yet to run the full freebayes variant caller. That is running now, but I think is not particularly desired for the purposes of this query.

spec <- make_dnaseq_spec()
meta_202401 <- gather_preprocessing_metadata("sample_sheets/202401_samples.xlsx",
  species = "paeruginosa_pa14", verbose = FALSE,
  specification = spec, basedir = "preprocessing/202401")
## Did not find the condition column in the sample sheet.
## Filling it in as undefined.
## Did not find the batch column in the sample sheet.
## Filling it in as undefined.
## Warning in gather_preprocessing_metadata("sample_sheets/202401_samples.xlsx", : Column: hisat_genome_percent_log already exists, replacing it.
## Writing new metadata to: sample_sheets/202401_samples_modified.xlsx
## Deleting the file sample_sheets/202401_samples_modified.xlsx before writing the tables.
expt_202401 <- create_expt(meta_202401[["new_meta"]], file_column = "hisatcounttable",
                           gene_info = pa14_annot)
## Reading the sample metadata.
## The sample definitions comprises: 13 rows(samples) and 22 columns(metadata fields).
## Warning in create_expt(meta_202401[["new_meta"]], file_column = "hisatcounttable", : Even after changing the rownames in gene info, they do not
## match the count table.
## Even after changing the rownames in gene info, they do not match the count table.
## Here are the first few rownames from the count tables:
## PA14_00010, PA14_00020, PA14_00030, PA14_00050, PA14_00060, PA14_00070
## Here are the first few rownames from the gene information table:
## gene1650835, gene1650837, gene1650839, gene1650841, gene1650843, gene1650845
## Bringing together the count matrix and gene information.
## Some annotations were lost in merging, setting them to 'undefined'.
## Saving the expressionset to 'expt.rda'.
## The final expressionset has 5979 features and 13 samples.
written_202401 <- write_expt(expt_202401, excel = "excel/cup_and_friends.xlsx")
## Deleting the file excel/cup_and_friends.xlsx before writing the tables.
## Writing the first sheet, containing a legend and some summary data.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## 182 entries are 0.  We are on a log scale, adding 1 to the data.
## 
## Changed 182 zero count features.
## 
## Naively calculating coefficient of variation/dispersion with respect to condition.
## 
## Finished calculating dispersion estimates.
## 
## `geom_smooth()` using formula = 'y ~ x'
## The expressionset has a minimal or missing set of conditions/batches.
## Error in `contrasts<-`(`*tmp*`, value = contr.funs[1 + isOF[nn]]) : 
##   contrasts can be applied only to factors with 2 or more levels
## `geom_smooth()` using formula = 'y ~ x'

14 Some new samples 202406

I am basically going to copy/paste the previous block and assume all goes well.

spec <- make_dnaseq_spec()
meta_202406 <- gather_preprocessing_metadata("sample_sheets/202406_samples.xlsx",
  species = "paeruginosa_pa14", verbose = FALSE,
  specification = spec, basedir = "preprocessing/202406")
## Did not find the batch column in the sample sheet.
## Filling it in as undefined.
## Warning in gather_preprocessing_metadata("sample_sheets/202406_samples.xlsx", : Column: hisat_genome_percent_log already exists, replacing it.
## Writing new metadata to: sample_sheets/202406_samples_modified.xlsx
## Deleting the file sample_sheets/202406_samples_modified.xlsx before writing the tables.
expt_202406 <- create_expt(meta_202406[["new_meta"]], file_column = "hisatcounttable",
                           gene_info = pa14_annot)
## Reading the sample metadata.
## The sample definitions comprises: 9 rows(samples) and 29 columns(metadata fields).
## Warning in create_expt(meta_202406[["new_meta"]], file_column = "hisatcounttable", : Even after changing the rownames in gene info, they do not
## match the count table.
## Even after changing the rownames in gene info, they do not match the count table.
## Here are the first few rownames from the count tables:
## PA14_00010, PA14_00020, PA14_00030, PA14_00050, PA14_00060, PA14_00070
## Here are the first few rownames from the gene information table:
## gene1650835, gene1650837, gene1650839, gene1650841, gene1650843, gene1650845
## Bringing together the count matrix and gene information.
## Some annotations were lost in merging, setting them to 'undefined'.
## Saving the expressionset to 'expt.rda'.
## The final expressionset has 5979 features and 9 samples.
written_202406 <- write_expt(expt_202406, excel = "excel/cup_and_friends_202406.xlsx")
## Deleting the file excel/cup_and_friends_202406.xlsx before writing the tables.
## Writing the first sheet, containing a legend and some summary data.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## 199 entries are 0.  We are on a log scale, adding 1 to the data.
## 
## Changed 199 zero count features.
## 
## Naively calculating coefficient of variation/dispersion with respect to condition.
## 
## Finished calculating dispersion estimates.
## 
## `geom_smooth()` using formula = 'y ~ x'
## The expressionset has a minimal or missing set of conditions/batches.
## Error in dstat0[, i] : subscript out of bounds
## `geom_smooth()` using formula = 'y ~ x'

15 Query sequencing library state via unicycler assembly

Vince made a query about the libraries: could the strange ‘dippyness’ of these new libraries be the reason some of the previous samples had so many contigs when we attempted to assemble them. Therefore he is curious to see the assembly state of a few of the original samples.

To that end, I am going to invoke an assembly of a couple of the earliest samples.

While I am at it, I will remap these two to get rid of the junctions. In addition, I loaded them alongside one of the new samples.

NBH,JC, and a dippy sample
NBH,JC, and a dippy sample
cd preprocessing
cd PA14_JC
cyoa --method unicycler --input r1_trimmed.fastq.xz:r2_trimmed.fastq.xz
cyoa --method hisat --species paeruginosa_pa14 --intron 0 \
     --input r1_trimmed.fastq.xz:r2_trimmed.fastq.xz \
     --gff_tag Alias

cd ../PA14_NBH
cyoa --method unicycler --input r1_trimmed.fastq.xz:r2_trimmed.fastq.xz
cyoa --method hisat --species paeruginosa_pa14 --intron 0 \
     --input r1_trimmed.fastq.xz:r2_trimmed.fastq.xz \
     --gff_tag Alias

16 Resequenced samples 202408

The previous samples had some problems vis a vis duplicate reads and were resequenced. I downloaded the resequenced samples into a new directory (202408_pa14). I was away for a couple weeks and seem to be having a little difficulty getting my head on straight, so I will hopefully get this correctly done on the first pass by doing the following:

  1. running my default dnaseq pipeline using the newest cyoa, if I recall correctly this now includes the GATK duplication marker step.
  2. I will add an explicit fastp check for duplication in order to (hopefully) recapitulate the duplication statistics from the seqcenter.
  3. I will use their sample sheet as the input for my sample sheet curation, so it should be pretty easy to check #2.
  4. Collect a few IGV pictures with Nour’s and Jared’s early samples as a control.
module add cyoa/202404_hack
cd preprocessing/202408_pa14
start=$(pwd)
for i in $(/bin/ls -d PA14*); do
    cd $i
    echo $i
    cyoa --method pdnaseq --species paeruginosa_pa14 --intron 0 \
         --input $(/bin/ls *.gz | tr '\n' ':') \
         --gff_tag Alias
    cd $start
done

Upon completion, I will collect the various stats using the following block, which I copy/pasted from above, with the caveat that I copied the sample sheet from the seqcenter to ‘202408_samples.xlsx’ and so all the metadata collected will get added as new columns to it.

spec <- make_dnaseq_spec()
meta_202408 <- gather_preprocessing_metadata("sample_sheets/202408_samples.xlsx",
  species = "paeruginosa_pa14", verbose = FALSE,
  specification = spec, basedir = "preprocessing/202408_pa14")
## Did not find the column: sampleid.
## Setting the ID column to the first column.
## Did not find the condition column in the sample sheet.
## Filling it in as undefined.
## Did not find the batch column in the sample sheet.
## Filling it in as undefined.
## Warning in gather_preprocessing_metadata("sample_sheets/202408_samples.xlsx", : Column: hisat_genome_percent_log already exists, replacing it.
## Writing new metadata to: sample_sheets/202408_samples_modified.xlsx
## Deleting the file sample_sheets/202408_samples_modified.xlsx before writing the tables.
expt_202408 <- create_expt(meta_202408[["new_meta"]], file_column = "hisatcounttable",
                           gene_info = pa14_annot)
## Reading the sample metadata.
## The sample definitions comprises: 6 rows(samples) and 35 columns(metadata fields).
## Warning in create_expt(meta_202408[["new_meta"]], file_column = "hisatcounttable", : Even after changing the rownames in gene info, they do not
## match the count table.
## Even after changing the rownames in gene info, they do not match the count table.
## Here are the first few rownames from the count tables:
## PA14_00010, PA14_00020, PA14_00030, PA14_00050, PA14_00060, PA14_00070
## Here are the first few rownames from the gene information table:
## gene1650835, gene1650837, gene1650839, gene1650841, gene1650843, gene1650845
## Bringing together the count matrix and gene information.
## Some annotations were lost in merging, setting them to 'undefined'.
## Saving the expressionset to 'expt.rda'.
## The final expressionset has 5979 features and 6 samples.
written_202408 <- write_expt(expt_202408, excel = "excel/cup_and_friends_202408_reseq.xlsx")
## Deleting the file excel/cup_and_friends_202408_reseq.xlsx before writing the tables.
## Writing the first sheet, containing a legend and some summary data.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## 106 entries are 0.  We are on a log scale, adding 1 to the data.
## 
## Changed 106 zero count features.
## 
## Naively calculating coefficient of variation/dispersion with respect to condition.
## 
## Finished calculating dispersion estimates.
## 
## `geom_smooth()` using formula = 'y ~ x'
## The expressionset has a minimal or missing set of conditions/batches.
## Error in `contrasts<-`(`*tmp*`, value = contr.funs[1 + isOF[nn]]) : 
##   contrasts can be applied only to factors with 2 or more levels
## `geom_smooth()` using formula = 'y ~ x'
pander::pander(sessionInfo())

R version 4.3.1 (2023-06-16)

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: stats4, stats, graphics, grDevices, utils, datasets, methods and base

other attached packages: ruv(v.0.9.7.1), hpgltools(v.1.0), testthat(v.3.2.1), reticulate(v.1.38.0), Matrix(v.1.6-5), glue(v.1.7.0), SummarizedExperiment(v.1.32.0), GenomicRanges(v.1.54.1), GenomeInfoDb(v.1.38.6), IRanges(v.2.36.0), S4Vectors(v.0.40.2), MatrixGenerics(v.1.14.0), matrixStats(v.1.2.0), Biobase(v.2.62.0) and BiocGenerics(v.0.48.1)

loaded via a namespace (and not attached): fs(v.1.6.3), bitops(v.1.0-7), enrichplot(v.1.22.0), devtools(v.2.4.5), HDO.db(v.0.99.1), httr(v.1.4.7), RColorBrewer(v.1.1-3), numDeriv(v.2016.8-1.1), profvis(v.0.3.8), tools(v.4.3.1), backports(v.1.4.1), utf8(v.1.2.4), R6(v.2.5.1), lazyeval(v.0.2.2), mgcv(v.1.9-1), urlchecker(v.1.0.1), withr(v.3.0.0), prettyunits(v.1.2.0), gridExtra(v.2.3), cli(v.3.6.2), scatterpie(v.0.2.1), labeling(v.0.4.3), sass(v.0.4.8), mvtnorm(v.1.2-4), readr(v.2.1.5), genefilter(v.1.84.0), Rsamtools(v.2.18.0), yulab.utils(v.0.1.4), gson(v.0.1.0), DOSE(v.3.28.2), sessioninfo(v.1.2.2), limma(v.3.58.1), rstudioapi(v.0.15.0), RSQLite(v.2.3.5), generics(v.0.1.3), gridGraphics(v.0.5-1), BiocIO(v.1.12.0), vroom(v.1.6.5), gtools(v.3.9.5), zip(v.2.3.1), dplyr(v.1.1.4), GO.db(v.3.18.0), fansi(v.1.0.6), abind(v.1.4-5), lifecycle(v.1.0.4), yaml(v.2.3.8), edgeR(v.4.0.16), Rtsne(v.0.17), gplots(v.3.1.3.1), qvalue(v.2.34.0), SparseArray(v.1.2.4), BiocFileCache(v.2.10.1), grid(v.4.3.1), blob(v.1.2.4), promises(v.1.2.1), crayon(v.1.5.2), miniUI(v.0.1.1.1), lattice(v.0.22-5), cowplot(v.1.1.3), GenomicFeatures(v.1.54.3), annotate(v.1.80.0), KEGGREST(v.1.42.0), pillar(v.1.9.0), knitr(v.1.45), varhandle(v.2.0.6), fgsea(v.1.28.0), rjson(v.0.2.21), boot(v.1.3-29), corpcor(v.1.6.10), codetools(v.0.2-19), fastmatch(v.1.1-4), ggfun(v.0.1.4), data.table(v.1.15.0), remotes(v.2.4.2.1), treeio(v.1.26.0), vctrs(v.0.6.5), png(v.0.1-8), Rdpack(v.2.6), gtable(v.0.3.4), cachem(v.1.0.8), openxlsx(v.4.2.5.2), xfun(v.0.42), rbibutils(v.2.2.16), S4Arrays(v.1.2.0), mime(v.0.12), tidygraph(v.1.3.1), survival(v.3.5-8), iterators(v.1.0.14), statmod(v.1.5.0), directlabels(v.2024.1.21), ellipsis(v.0.3.2), nlme(v.3.1-164), pbkrtest(v.0.5.2), ggtree(v.3.10.0), usethis(v.2.2.3), bit64(v.4.0.5), progress(v.1.2.3), EnvStats(v.2.8.1), filelock(v.1.0.3), rprojroot(v.2.0.4), bslib(v.0.6.1), KernSmooth(v.2.23-22), colorspace(v.2.1-0), DBI(v.1.2.2), tidyselect(v.1.2.0), bit(v.4.0.5), compiler(v.4.3.1), curl(v.5.2.0), graph(v.1.80.0), xml2(v.1.3.6), desc(v.1.4.3), DelayedArray(v.0.28.0), plotly(v.4.10.4), shadowtext(v.0.1.3), rtracklayer(v.1.62.0), scales(v.1.3.0), caTools(v.1.18.2), remaCor(v.0.0.18), quadprog(v.1.5-8), rappdirs(v.0.3.3), stringr(v.1.5.1), digest(v.0.6.34), minqa(v.1.2.6), variancePartition(v.1.32.5), rmarkdown(v.2.25), aod(v.1.3.3), XVector(v.0.42.0), RhpcBLASctl(v.0.23-42), htmltools(v.0.5.7), pkgconfig(v.2.0.3), lme4(v.1.1-35.1), dbplyr(v.2.4.0), fastmap(v.1.1.1), rlang(v.1.1.3), htmlwidgets(v.1.6.4), shiny(v.1.8.0), farver(v.2.1.1), jquerylib(v.0.1.4), jsonlite(v.1.8.8), BiocParallel(v.1.36.0), GOSemSim(v.2.28.1), RCurl(v.1.98-1.14), magrittr(v.2.0.3), GenomeInfoDbData(v.1.2.11), ggplotify(v.0.1.2), patchwork(v.1.2.0), munsell(v.0.5.0), Rcpp(v.1.0.12), ape(v.5.7-1), viridis(v.0.6.5), stringi(v.1.8.3), ggraph(v.2.1.0), brio(v.1.1.4), zlibbioc(v.1.48.0), MASS(v.7.3-60.0.1), plyr(v.1.8.9), pkgbuild(v.1.4.3), parallel(v.4.3.1), ggrepel(v.0.9.5), forcats(v.1.0.0), Biostrings(v.2.70.2), graphlayouts(v.1.1.0), splines(v.4.3.1), pander(v.0.6.5), hms(v.1.1.3), locfit(v.1.5-9.8), fastcluster(v.1.2.6), igraph(v.2.0.2), reshape2(v.1.4.4), biomaRt(v.2.58.2), pkgload(v.1.3.4), XML(v.3.99-0.16.1), evaluate(v.0.23), tzdb(v.0.4.0), nloptr(v.2.0.3), PROPER(v.1.34.0), foreach(v.1.5.2), tweenr(v.2.0.2), httpuv(v.1.6.14), tidyr(v.1.3.1), purrr(v.1.0.2), polyclip(v.1.10-6), ggplot2(v.3.5.0), ggforce(v.0.4.2), broom(v.1.0.5), xtable(v.1.8-4), restfulr(v.0.0.15), tidytree(v.0.4.6), fANCOVA(v.0.6-1), later(v.1.3.2), viridisLite(v.0.4.2), tibble(v.3.2.1), lmerTest(v.3.1-3), clusterProfiler(v.4.10.0), aplot(v.0.2.2), memoise(v.2.0.1), AnnotationDbi(v.1.64.1), GenomicAlignments(v.1.38.2), sva(v.3.50.0) and GSEABase(v.1.64.0)

message("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 5779a65508e724127c3394e5c0b4c56d3b650901
## This is hpgltools commit: Fri Jun 28 10:56:42 2024 -0400: 5779a65508e724127c3394e5c0b4c56d3b650901
this_save <- paste0(gsub(pattern = "\\.Rmd", replace = "", x = rmd_file), "-v", ver, ".rda.xz")
#message("Saving to ", this_save)
#tmp <- sm(saveme(filename = this_save))
---
title: "Querying a set of Pseudomonas strains, and vibrio later"
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: zenburn
    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: zenburn
    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: zenburn
    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)
library(reticulate)
tt <- devtools::load_all("~/hpgltools")
knitr::opts_knit$set(
  width = 120, progress = TRUE, verbose = TRUE, echo = TRUE)
knitr::opts_chunk$set(error = TRUE, dpi = 96)
lua_filters <- rmarkdown::pandoc_lua_filter_args("pandoc-zotxt.lua")
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 <- ""
ver <- format(Sys.Date(), "%Y%m%d")

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

# Introduction

This document seeks to lay out my process in poking at the
DNAsequencing results of a series of Pseudomonas aeruginosa PA14 and
PAK strains.

If I understand Dr. Lee and co.'s goal, they wish to ensure that these
strains are still reasonably close to the associated reference
strains.  I therefore am running my default trimming/mapping/variant
search methods.

I have a single command that can run all of these commands at the same
time, but I have been actively breaking my tools recently; so I
decided to run them one at a time with the assumption that something
would not work (but everything did work on the first try, so that was nice).

## Downloading and sorting the data

I downloaded the .zip archive file using the link in Dr. Lee's email.
I did not save it though, so if we need to download the data again, we
will have to go to him.  I created my usual work directory
'preprocessing/' within this tree and moved it there.  I unzipped it
and moved each pair of reads to a directory which follows Dr. Lee's
desired naming convention.

I then created the directories: 'reference/' and 'sample_sheets/'.
The sample_sheets remained empty for a while, but I immediately
downloaded the _full_ genbank flat file for the Pseudomonas PAK strain
from NCBI, found here:

https://www.ncbi.nlm.nih.gov/nuccore/NZ_CP020659

Note, that when downloading, one must hit the 'customize view' button
on the right and ensure that the entire sequence and all annotations
are included.  Then hit the 'send to' button and send it to a file.
This file I copied to reference/paeruginosa_pak.gb.

## Creation of the pak reference

Given the full PAK genbank file, I converted it to the expected
fasta/gff file for mapping:

```{bash convert_gb, eval=FALSE}
cd reference
cyoa --method gb2gff --input paeruginosa_pak.gb
```

This command created a series of fasta and gff files which provide the
coordinates for the various annotations (genes/cds/rRNA/intercds) and
sequence for the genome, CDS nucleotides, and amino acids.  I then
copied the genome/gff files to my global reference directory and
prepared it for usage by my favorite mapper:

```{bash index_pak, eval=FALSE}
cd ~/libraries/genome
cyoa --method indexhisat --species paeruginosa_pak
```

Now all of the pieces are in place for me to play.  Each of the
following steps was performed twice, once for the PA14 samples, once
for the PAK samples.  The only difference in the invocations was due
to the fact that the PAK annotations provide different tags.  E.g. I
used the 'Alias' tag for PA14 and the 'locus_tag' tag for PAK.  As a
result I am only going to write down in this document the PA14
invocations and assume the reader can figure out the difference.

## Trimming

I have a couple of trimming methods, in this instance I just used the
default and will operate under the assumption that it is sufficient
until I see otherwise.

```{bash cyoa_trim, eval=FALSE}
cd preprocessing
start=$(pwd)
for i in $(/bin/ls -d PA14*); do
    cd $i
    cyoa --method trim --input $(/bin/ls *.fastq.gz | tr '\n' ':' | sed 's/:$//g')
    cd $start
done
```

The above command line invocation produced a series of trimming jobs
which when examined look like this (I am only showing examples from
PA14_exoUTY, and am leaving off the beginning and end).

### Resulting trimmer script

```{bash trimomatic, eval=FALSE}
## This is a portion of file:
##  preprocessing/PA14_exoUTY/scripts/01trim_7_UTY_S138_R1_001.sh

module add trimomatic
mkdir -p outputs/01trimomatic
## Note that trimomatic prints all output and errors to STDERR, so send both to output
trimmomatic PE \
  -threads 1 \
  -phred33 \
  7_UTY_S138_R1_001.fastq.gz 7_UTY_S138_R2_001.fastq.gz \
  7_UTY_S138_R1_001-trimmed_paired.fastq 7_UTY_S138_R1_001-trimmed_unpaired.fastq \
  7_UTY_S138_R2_001-trimmed_paired.fastq 7_UTY_S138_R2_001-trimmed_unpaired.fastq \
   ILLUMINACLIP:/fs/cbcb-software/RedHat-8-x86_64/local/cyoa/202302/prefix/lib/perl5/auto/share/dist/Bio-Adventure/genome/adapters.fa:2:20:10:2:keepBothReads  \
  SLIDINGWINDOW:4:20 MINLEN:50 \
  1>outputs/01trimomatic/7_UTY_S138_R1_001-trimomatic.stdout \
  2>outputs/01trimomatic/7_UTY_S138_R1_001-trimomatic.stderr
excepted=$( { grep "Exception" "outputs/01trimomatic/7_UTY_S138_R1_001-trimomatic.stdout" || test $? = 1; } )
```

One thing I did not include in the above: upon completion, the script
aggressively compresses the trimmed output and symbolically links it
to r1_trimmed.fastq.xz and r2_trimmed.fastq.xz.  Thus any following
steps can use the same input name (r1_trimmed.fastq.xz:r2_trimmed.fastq.xz).

## Mapping

My default mappers run the actual alignment, convert it to a
compressed/indexed bam, and count it against the reference genome.  In
this context, the counting is a little silly, but does have the
potential to help find duplications and such.


```{bash hisat_cyoa, eval=FALSE}
cd preprocessing
start=$(pwd)
for i in $(/bin/ls -d PA14*); do
    cd $i
    cyoa --method hisat --input r1_trimmed.fastq.xz:r2_trimmed.fastq.xz \
         --stranded no --species paeruginosa_pa14 --gff_type gene --gff_tag Alias
    cd $start
done

## Here is what I ran for PAK
cd preprocessing
start=$(pwd)
for i in $(/bin/ls -d PAK*); do
    cd $i
    cyoa --method hisat --input r1_trimmed.fastq.xz:r2_trimmed.fastq.xz \
         --stranded no --species paeruginosa_pa01 --gff_type gene --gff_tag locus_tag
    cd $start
done
```

### The resulting mapper script run by the cluster

Similarly, I am just putting the meaty part.

```{bash hisat_mapper, eval=FALSE}
module add hisat2 samtools htseq bamtools
mkdir -p outputs/40hisat2_paeruginosa_pa14
hisat2 -x ${HOME}/libraries/genome/indexes/paeruginosa_pa14  \
  -p 8 \
  -q   -1 <(less /home/trey/sshfs/scratch/atb/dnaseq/paeruginosa_strains_202304/preprocessing/PA14_exoUTY/r1_trimmed.fastq.xz) -2 <(less /home/trey/sshfs/scratch/atb/dnaseq/paeruginosa_strains_202304/preprocessing/PA14_exoUTY/r2_trimmed.fastq.xz)  \
  --phred33 \
  --un outputs/40hisat2_paeruginosa_pa14/unaldis_paeruginosa_pa14_genome.fastq \
  --al outputs/40hisat2_paeruginosa_pa14/aldis_paeruginosa_pa14_genome.fastq \
  --un-conc outputs/40hisat2_paeruginosa_pa14/unalcon_paeruginosa_pa14_genome.fastq \
  --al-conc outputs/40hisat2_paeruginosa_pa14/alcon_paeruginosa_pa14_genome.fastq \
  -S outputs/40hisat2_paeruginosa_pa14/paeruginosa_pa14_genome.sam \
  2>outputs/40hisat2_paeruginosa_pa14/hisat2_paeruginosa_pa14_genome_PA14_exoUTY.stderr \
  1>outputs/40hisat2_paeruginosa_pa14/hisat2_paeruginosa_pa14_genome_PA14_exoUTY.stdout
```

### Conversion to bam script

The above cyoa invocation also creates this script.  It is a little
long because it does some checks and creates a couple of filtered
versions of the output.

```{bash sam2bam_script, eval=FALSE}
module add samtools bamtools

echo "Starting samtools"
if [[ -f "outputs/40hisat2_paeruginosa_pa14/paeruginosa_pa14_genome.bam" && -f "outputs/40hisat2_paeruginosa_pa14/paeruginosa_pa14_genome.sam" ]]; then
  echo "Both the bam and sam files exist, rerunning."
elif [[ -f "outputs/40hisat2_paeruginosa_pa14/paeruginosa_pa14_genome.bam" ]]; then
  echo "The output file exists, quitting."
  exit 0
elif [[ ! -f "outputs/40hisat2_paeruginosa_pa14/paeruginosa_pa14_genome.sam" ]]; then
  echo "Could not find the samtools input file."
  exit 1
fi

## If a previous sort file exists due to running out of memory,
## then we need to get rid of them first.
## hg38_100_genome-sorted.bam.tmp.0000.bam
if [[ -f "outputs/40hisat2_paeruginosa_pa14/paeruginosa_pa14_genome.bam.tmp.000.bam" ]]; then
  rm -f outputs/40hisat2_paeruginosa_pa14/paeruginosa_pa14_genome.bam.tmp.*.bam
fi
samtools view -u -t ${HOME}/libraries/genome/paeruginosa_pa14.fasta \
  -S outputs/40hisat2_paeruginosa_pa14/paeruginosa_pa14_genome.sam -o outputs/40hisat2_paeruginosa_pa14/paeruginosa_pa14_genome.bam  \
  2>outputs/40hisat2_paeruginosa_pa14/paeruginosa_pa14_genome.bam_samtools.stderr \
  1>outputs/40hisat2_paeruginosa_pa14/paeruginosa_pa14_genome.bam_samtools.stdout

echo "First samtools command finished with $?"
samtools sort -l 9 outputs/40hisat2_paeruginosa_pa14/paeruginosa_pa14_genome.bam \
  -o outputs/40hisat2_paeruginosa_pa14/paeruginosa_pa14_genome-sorted.bam \
  2>>outputs/40hisat2_paeruginosa_pa14/paeruginosa_pa14_genome.bam_samtools.stderr \
  1>>outputs/40hisat2_paeruginosa_pa14/paeruginosa_pa14_genome.bam_samtools.stdout
rm outputs/40hisat2_paeruginosa_pa14/paeruginosa_pa14_genome.bam
rm outputs/40hisat2_paeruginosa_pa14/paeruginosa_pa14_genome.sam
mv outputs/40hisat2_paeruginosa_pa14/paeruginosa_pa14_genome-sorted.bam outputs/40hisat2_paeruginosa_pa14/paeruginosa_pa14_genome.bam
samtools index outputs/40hisat2_paeruginosa_pa14/paeruginosa_pa14_genome.bam \
  2>>outputs/40hisat2_paeruginosa_pa14/paeruginosa_pa14_genome.bam_samtools.stderr \
  1>>outputs/40hisat2_paeruginosa_pa14/paeruginosa_pa14_genome.bam_samtools.stdout
echo "Second samtools command finished with $?"
bamtools stats -in outputs/40hisat2_paeruginosa_pa14/paeruginosa_pa14_genome.bam \
  2>>outputs/40hisat2_paeruginosa_pa14/paeruginosa_pa14_genome.bam_samtools.stats 1>&2
echo "Bamtools finished with $?"

## The following will fail if this is single-ended.
samtools view -b -f 2 \
  -o outputs/40hisat2_paeruginosa_pa14/paeruginosa_pa14_genome-paired.bam \
  outputs/40hisat2_paeruginosa_pa14/paeruginosa_pa14_genome.bam \
  2>>outputs/40hisat2_paeruginosa_pa14/paeruginosa_pa14_genome.bam_samtools.stderr \
  1>>outputs/40hisat2_paeruginosa_pa14/paeruginosa_pa14_genome.bam_samtools.stdout
samtools index outputs/40hisat2_paeruginosa_pa14/paeruginosa_pa14_genome-paired.bam \
  2>>outputs/40hisat2_paeruginosa_pa14/paeruginosa_pa14_genome.bam_samtools.stderr \
  1>>outputs/40hisat2_paeruginosa_pa14/paeruginosa_pa14_genome.bam_samtools.stdout
bamtools stats -in outputs/40hisat2_paeruginosa_pa14/paeruginosa_pa14_genome-paired.bam \
  2>>outputs/40hisat2_paeruginosa_pa14/paeruginosa_pa14_genome.bam_samtools.stats 1>&2

bamtools filter -tag XM:0 \
  -in outputs/40hisat2_paeruginosa_pa14/paeruginosa_pa14_genome.bam \
  -out outputs/40hisat2_paeruginosa_pa14/paeruginosa_pa14_genome-sorted_nomismatch.bam \
  2>>outputs/40hisat2_paeruginosa_pa14/paeruginosa_pa14_genome.bam_samtools.stats 1>&2
echo "bamtools filter finished with: $?"
samtools index \
  outputs/40hisat2_paeruginosa_pa14/paeruginosa_pa14_genome-sorted_nomismatch.bam \
  2>>outputs/40hisat2_paeruginosa_pa14/paeruginosa_pa14_genome.bam_samtools.stderr \
  1>>outputs/40hisat2_paeruginosa_pa14/paeruginosa_pa14_genome.bam_samtools.stdout
echo "final samtools index finished with: $?"
```

### Counting against the genome

Note that this step is not really useful for a dnaseq dataset in most
instances.  I also have the default orientation set to reverse because
most of the samples off our sequencer are reversed; but that is likely
not true for this dataset.  If it turns out we actually care about
these counts, I may need to come back and rerun these.

```{bash counting_script, eval=FALSE}
module add htseq

htseq-count \
  -q -f bam \
  -s reverse -a 0 \
   --type all  --idattr Alias \
  outputs/40hisat2_paeruginosa_pa14/paeruginosa_pa14_genome-paired.bam \
  /home/trey/libraries/genome/paeruginosa_pa14.gff \
  2>outputs/40hisat2_paeruginosa_pa14/paeruginosa_pa14_genome-paired_sreverse_all_Alias.stderr \
  1>outputs/40hisat2_paeruginosa_pa14/paeruginosa_pa14_genome-paired_sreverse_all_Alias.count
xz -f -9e outputs/40hisat2_paeruginosa_pa14/paeruginosa_pa14_genome-paired_sreverse_all_Alias.count
```

## Variant search

I tend to like to use freebayes for this.  It is a little
conservative, but I think it seems to work quite well.  I can also use
mpileup and snippy.  freebayes and mpileup are setup to feed a
post-processing script which I think is kind of fun and will be
decribed momentarily.

```{bash cyoa_freebayes, eval=FALSE}
cd preprocessing
start=$(pwd)
for i in $(/bin/ls -d PA14*); do
    cd $i
    cyoa --method freebayes \
         --input outputs/40hisat2_paeruginosa_pa14/paeruginosa_pa14_genome-paired.bam \
         --species paeruginosa_pa14 --gff_type gene --gff_tag Alias --intron 0
    cd $start
done

## Here is what I ran for PAK
for i in $(/bin/ls -d PAK*); do
    cd $i
    cyoa --method freebayes \
         --input outputs/40hisat2_paeruginosa_pa01/paeruginosa_pa01_genome-paired.bam \
         --species paeruginosa_pa01 --gff_type gene --gff_tag locus_tag --intron 0
    cd $start
done
```

### Freebayes script

Unlike hisat, I include the conversion to the
binary/compressed/indexed format with the invocation of the variant
search.  I also include the duplicate search functionality from gatk.

```{bash freebayes_script, eval=FALSE}
module add gatk freebayes libgsl libhts samtools bcftools vcftools

mkdir -p outputs/50freebayes_paeruginosa_pa14
gatk MarkDuplicates \
  -I outputs/40hisat2_paeruginosa_pa14/paeruginosa_pa14_genome.bam \
  -O outputs/50freebayes_paeruginosa_pa14/paeruginosa_pa14_genome_deduplicated.bam \
  -M outputs/50freebayes_paeruginosa_pa14/deduplication_stats.txt --REMOVE_DUPLICATES true --COMPRESSION_LEVEL 9 \
  2>outputs/50freebayes_paeruginosa_pa14/deduplication.stderr \
  1>outputs/50freebayes_paeruginosa_pa14/deduplication.stdout
echo "Finished gatk deduplication." >> outputs/50freebayes_paeruginosa_pa14/paeruginosa_pa14.stdout
samtools index outputs/50freebayes_paeruginosa_pa14/paeruginosa_pa14_genome_deduplicated.bam
echo "Finished samtools index." >> outputs/50freebayes_paeruginosa_pa14/paeruginosa_pa14.stdout
freebayes -f /home/trey/libraries/genome/paeruginosa_pa14.fasta \
  -v outputs/50freebayes_paeruginosa_pa14/paeruginosa_pa14.vcf \
  outputs/50freebayes_paeruginosa_pa14/paeruginosa_pa14_genome_deduplicated.bam \
  1>>outputs/50freebayes_paeruginosa_pa14/paeruginosa_pa14.stdout \
  2>>outputs/50freebayes_paeruginosa_pa14/paeruginosa_pa14.stderr
echo "Finished freebayes." >> outputs/50freebayes_paeruginosa_pa14/paeruginosa_pa14.stdout
bcftools convert outputs/50freebayes_paeruginosa_pa14/paeruginosa_pa14.vcf \
  -Ob -o outputs/50freebayes_paeruginosa_pa14/paeruginosa_pa14.bcf \
  2>>outputs/50freebayes_paeruginosa_pa14/paeruginosa_pa14.stderr \
  1>>outputs/50freebayes_paeruginosa_pa14/paeruginosa_pa14.stdout
echo "Finished bcftools convert." >> outputs/50freebayes_paeruginosa_pa14/paeruginosa_pa14.stdout
bcftools index outputs/50freebayes_paeruginosa_pa14/paeruginosa_pa14.bcf \
  2>>outputs/50freebayes_paeruginosa_pa14/paeruginosa_pa14.stderr \
  1>>outputs/50freebayes_paeruginosa_pa14/paeruginosa_pa14.stdout
echo "Finished bcftools index." >> outputs/50freebayes_paeruginosa_pa14/paeruginosa_pa14.stdout
rm outputs/50freebayes_paeruginosa_pa14/paeruginosa_pa14.vcf
```

The result from the above freebayes script is a bcf containing the
high-quality observed variants.  The cyoa invocation also creates the
following script, which will require a bit of explanation.

```{perl create_variant_fun, eval=FALSE}
use Bio::Adventure::SNP;
my $result = $h->Bio::Adventure::SNP::SNP_Ratio_Worker(
  input => 'outputs/50freebayes_paeruginosa_pa14/paeruginosa_pa14.bcf',
  species => 'paeruginosa_pa14',
  vcf_method => 'freebayes',
  vcf_cutoff => '5',
  vcf_minpct => '0.8',
  gff_tag => 'Alias',
  gff_type => 'gene',
  output_dir => 'outputs/50freebayes_paeruginosa_pa14',
  output => 'outputs/50freebayes_paeruginosa_pa14/all_tags.txt',
  output_count => 'outputs/50freebayes_paeruginosa_pa14/count.txt',
  output_genome => 'outputs/50freebayes_paeruginosa_pa14/modified.fasta',
  output_by_gene => 'outputs/50freebayes_paeruginosa_pa14/variants_by_gene.txt',
  output_pkm => 'outputs/50freebayes_paeruginosa_pa14/pkm.txt',
);
```

The function 'SNP_Ratio_Worker()' reads the reference genome, the set
of variants, and the genome annotations in order to create a new copy
of the genome (modified.fasta) which should be equivalent to the input
reads.  It also rewrites the bcf data into a matrix which is easier to
play with in R/python (all_tags.txt).  Finally, it uses the annotation
information to explicitly show the amino acid substitions observed in
every ORF (variants_by_gene.txt).  In theory it should also give a
rpkm-esque copy of the variants observed / ORF, but I turned that off
because it doesn't seem very useful and it is a little tricky to get
right.

# Creating a sample sheet

In order to play further with the data, I will need a sample sheet.
So I will start out by creating a blank one in excel (libreoffice)
which contains only the samplenames in the same format as my
directories in preprocessing/.

Once completed, I can use it as the input for my hpgltools package and
it should extract the interesting information from the preprocessing
logs and fill out the sample sheet accordingly.  Lets see if it works!

Here is the before:

```{r sample_sheet_start}
knitr::kable(extract_metadata("sample_sheets/all_samples.xlsx"))
```

Like I said, not much going on.  Lets see what it looks like after I
run the gatherer on it... (Note, I have been meaning to change this to
drop the unused columns, but not yet).

```{r sample_sheet_post}
spec <- make_dnaseq_spec()
queried_species <- c("paeruginosa_pak", "paeruginosa_pa01", "paeruginosa_pa14")
modified <- sm(gather_preprocessing_metadata("sample_sheets/all_samples.xlsx",
  species = queried_species, verbose = FALSE,
  specification = spec))
knitr::kable(extract_metadata("sample_sheets/all_samples_modified.xlsx"))
```

I reran the missing PAK samples and looked into the logs.  It may be
the case that the PAK genome I downloaded is of somewhat lower quality
than the PA14 and that is skewing the results somewhat.

Lets go one small step further.  I have a series of modified genomes
as well as the reference.  We can do a quickie tree of them:
First I will copy each modified genome to the tree/ directory and
rename them to the sampleID.

```{bash copy_modified, eval=FALSE}
start=$(pwd)
mkdir tree
cd preprocessing
for i in $(/bin/ls -d PA*); do
    cp $i/outputs/50*/paeruginosa_pak-*.fasta ${start}/tree/
    cp $i/outputs/50*/paeruginosa_pa14-*.fasta ${start}/tree/
done
cd $start
cp ~/libraries/genome/paeruginosa_pa14.fa ${start}/tree
cp ~/libraries/genome/paeruginosa_pak.fa ${start}/tree
```

Oh, it turns out that at the time of this writing, I forgot to run 3
samples, so this section will need to be redone.  But I can at least
run it for the samples that I didn't forget.

```{r quick_tree}
funkytown <- genomic_sequence_phylo("tree", root = "paeruginosa_pa14")
plot(funkytown$phy)
```

# Create an expressionset

The counts from hisat in theory are not very interesting for DNAseq
data, except in this instance we want to see the coverage of the knockouts.

```{r}
pa14_annot <- load_gff_annotations("~/libraries/genome/paeruginosa_pa14.gff",
                                   type = "gene", id_col = "Alias")
rownames(pa14_annot) <- pa14_annot[["Alias"]]

pa14_expt <- create_expt("sample_sheets/all_samples_modified.xlsx", gene_info = pa14_annot,
                         file_column = "hisatcounttablepaeruginosapa14")
pa14_write <- write_expt(pa14_expt, excel = "excel/pa14_strains.xlsx", batch = "raw")

pak_annot <- load_gff_annotations("~/libraries/genome/paeruginosa_pak.gff", type = "gene", id_col = "locus_tag")
rownames(pak_annot) <- pak_annot[["locus_tag"]]
pak_expt <- create_expt("sample_sheets/all_samples_modified.xlsx", file_column = "hisatcounttablepaeruginosapak")
pak_write <- write_expt(pak_expt, excel = "excel/pak_strains.xlsx", batch = "raw")
```

# Write variants by sample

```{r write_variants}
pa14_variants <- pData(pa14_expt)[["variantspenetrancefilepaeruginosapa14"]]
names(pa14_variants) <- rownames(pData(pa14_expt))
start <- init_xlsx(excel = "excel/pa14_variants.xlsx")
wb <- start[["wb"]]
for (s in seq_len(length(pa14_variants))) {
  sample_name <- names(pa14_variants)[[s]]
  if (pa14_variants[[s]] == "") {
    next
  }
  sample_data <- readr::read_tsv(pa14_variants[[s]])
  if (nrow(sample_data) == 0) {
    next
  }
  written <- write_xlsx(data = sample_data, sheet = sample_name, wb = wb)
}
saved <- openxlsx::saveWorkbook(written[["workbook"]], file = "excel/pa14_variants.xlsx")

pak_variants <- pData(pak_expt)[["variantspenetrancefilepaeruginosapak"]]
names(pak_variants) <- rownames(pData(pak_expt))
start <- init_xlsx(excel = "excel/pak_variants.xlsx")
wb <- start[["wb"]]
for (s in seq_len(length(pak_variants))) {
  sample_name <- names(pak_variants)[[s]]
  if (pak_variants[[s]] == "") {
    next
  }
  sample_data <- readr::read_tsv(pak_variants[[s]])
  if (nrow(sample_data) == 0) {
    next
  }
  written <- write_xlsx(data = sample_data, sheet = sample_name, wb = wb)
}
saved <- openxlsx::saveWorkbook(written[["workbook"]], file = "excel/pak_variants.xlsx")
```


# Write mutations by sample

In this following block we will instead write out the nt/aa mutations of CDS/proteins.

```{r write_mutations}
pa14_mutations <- pData(pa14_expt)[["variantsbygenefilepaeruginosapa14"]]
names(pa14_mutations) <- rownames(pData(pa14_expt))
start <- init_xlsx(excel = "excel/pa14_mutations.xlsx")
wb <- start[["wb"]]
for (s in seq_len(length(pa14_mutations))) {
  sample_name <- names(pa14_mutations)[[s]]
  if (pa14_mutations[[s]] == "") {
    next
  }
  sample_data <- readr::read_tsv(pa14_mutations[[s]])
  if (nrow(sample_data) == 0) {
    next
  }
  written <- write_xlsx(data = sample_data, sheet = sample_name, wb = wb)
}
saved <- openxlsx::saveWorkbook(written[["workbook"]], file = "excel/pa14_mutations.xlsx")

pak_mutations <- pData(pak_expt)[["variantsbygenefilepaeruginosapak"]]
names(pak_mutations) <- rownames(pData(pak_expt))
start <- init_xlsx(excel = "excel/pak_mutations.xlsx")
wb <- start[["wb"]]
for (s in seq_len(length(pak_mutations))) {
  sample_name <- names(pak_mutations)[[s]]
  if (pak_mutations[[s]] == "") {
    next
  }
  sample_data <- readr::read_tsv(pak_mutations[[s]])
  if (nrow(sample_data) == 0) {
    next
  }
  written <- write_xlsx(data = sample_data, sheet = sample_name, wb = wb)
}
saved <- openxlsx::saveWorkbook(written[["workbook"]], file = "excel/pak_mutations.xlsx")
```

# Comparing reads to some assemblies

```{r compare_to_assemblies}
spec <- make_dnaseq_spec()
modified_exoUTY <- gather_preprocessing_metadata("sample_sheets/exoUTY_224_samples.xlsx",
  species = "paeruginosa_exoUTY_224", verbose = FALSE,
  specification = spec)
modified_pscd <- sm(gather_preprocessing_metadata("sample_sheets/pscd_222_samples.xlsx",
  species = "paeruginosa_pscd_222", verbose = FALSE,
  specification = spec))
modified_wt <- sm(gather_preprocessing_metadata("sample_sheets/wt_221_samples.xlsx",
  species = "paeruginosa_wt_221", verbose = FALSE,
  specification = spec))

samples_exo <- create_expt(modified_exoUTY, file_column = "hisatcounttable")
samples_pscd <- create_expt(modified_pscd, file_column = "hisatcounttable")
samples_wt <- create_expt(modified_wt, file_column = "hisatcounttable")
```

# Write out the off-strain mappings

As the above suggests but does not explicitly state, I mapped some of
the samples against multiple potential parental strains in an attempt
to make it clear that some specific genes are or are not observed.
Thus these last three expressionsets. Now write them out so that Vince
can check out the mapping results with respect to these non-standard
references.

```{r write_off_species_mappings}
exo_written <- write_expt(samples_exo, excel = "excel/samples_vs_exoUTY_reference.xlsx")
pscd_written <- write_expt(samples_pscd, excel = "excel/samples_vs_pscd_reference.xlsx")
wt_written <- write_expt(samples_wt, excel = "excel/samples_vs_wt_reference.xlsx")
```

# Compare two WT samples

```{r compare_two_wt}
nbh_tags <- "preprocessing/PA14_NBH/outputs/50freebayes_paeruginosa_pa14/all_tags.txt.xz"
jc_tags <- "preprocessing/PA14_JC/outputs/50freebayes_paeruginosa_pa14/all_tags.txt.xz"
a5_tags <- "preprocessing/PA14_pscD_A5/outputs/50freebayes_paeruginosa_pa14/all_tags.txt.xz"
e4_tags <- "preprocessing/PA14_pscD_E4/outputs/50freebayes_paeruginosa_pa14/all_tags.txt.xz"

nbh_in <- as.data.frame(readr::read_tsv(nbh_tags)[, c(1,2,3)])
jc_in <- as.data.frame(readr::read_tsv(jc_tags)[, c(1,2,3)])

a5_in <- as.data.frame(readr::read_tsv(a5_tags)[, c(1,2,3)])
e4_in <- as.data.frame(readr::read_tsv(e4_tags)[, c(1,2,3)])

dim(nbh_in)
dim(jc_in)

dim(a5_in)
dim(e4_in)

shared_nbh <- nbh_in[["position"]] %in% jc_in[["position"]]
shared_jc <- jc_in[["position"]] %in% nbh_in[["position"]]
nbh_in[!shared_nbh, "position"]
jc_in[!shared_jc, "position"]
together <- merge(nbh_in, jc_in, by = "position", all = TRUE)


shared_a5 <- a5_in[["position"]] %in% e4_in[["position"]]
shared_e4 <- e4_in[["position"]] %in% a5_in[["position"]]
a5_in[!shared_a5, "position"]
e4_in[!shared_e4, "position"]
together <- merge(a5_in, e4_in, by = "position", all = TRUE)
```

# CupABCD and PelA-G samples

I keep forgetting to send Vince a sheet describing the state of the
cup/pel and vibrio samples.  Let us fix that now.  I did process them
and create a sample sheet, so it should at least be pretty easy.

Oh crap I used the gene# instead of PA# when mapping.  My previous IDs
are invalid for these samples.

```{r}
pa14_annot <- load_gff_annotations("~/libraries/genome/paeruginosa_pa14.gff",
                                   type = "gene", id_col = "gene_id")
rownames(pa14_annot) <- pa14_annot[["gene_id"]]
cup_expt <- create_expt("sample_sheets/all_samples_pa14_202308_modified.xlsx",
                        gene_info = pa14_annot, file_column = "hisatcounttable")
written <- write_expt(cup_expt, excel = glue("excel/cup_pel_expt-v{ver}.xlsx"))
```

# Vibrio samples

## Preprocessing

I do not think I ever wrote down the commands used to preprocess the
vibrio samples, likely because I just did them with another set?

I just added a job to give per-base coverage stats, lets run
that. Note, if I use this new cyoa version, a lot of things will break
horribly because I reorganized my reference data directory but have
not yet finished the process.

```{bash, eval=FALSE}
module purge
module add cyoa/202302
cd preprocessing/202308_vibrio/
start=$(pwd)
for i in $(/bin/ls -d A*); do
    cd $i
    echo $i
    input=$(find unprocessed -type f | tr '\n' ':')
    cyoa --method pdnaseq --species vibrio_cholerae_a1552 \
         --introns 0 --gff_type CDS --gff_tag locus_tag \
         --input $input
    cd $start
done

module purge
module add cyoa/202402
cd preprocessing/202308_vibrio/
start=$(pwd)
for i in $(/bin/ls -d A*); do
    cd $i
    echo $i
    input=$(/bin/ls outputs/02hisat2_vibrio_cholerae_a1552/vibrio_cholerae_a1552_genome.bam)
    cyoa --method bam2cov --input ${input}
    cd $start
done

```

I am going to change my metadata collector to accept a non-existant sample sheet.

```{r sample_sheet_post}
queried_species <- "vibrio_cholerae_a1552"
modified <- gather_preprocessing_metadata(basedir = "preprocessing/202308_vibrio", verbose = TRUE,
                                          species = queried_species, specification = make_dnaseq_spec(),
                                          new_metadata = "sample_sheets/202308_vibrio.xlsx")
knitr::kable(extract_metadata("sample_sheets/202308_vibrio.xlsx"))
```

```{r}
vibrio_annot <- load_gff_annotations("~/libraries/genome/vibrio_cholerae_a1552.gff", id_col = "locus_tag",
                                     type = "CDS")
rownames(vibrio_annot) <- make.names(vibrio_annot[["locus_tag"]], unique = TRUE)
vibrio_expt <- create_expt("sample_sheets/202308_vibrio.xlsx",
                           file_column = "hisatcounttable", gene_info = vibrio_annot)

written <- write_expt(vibrio_expt, excel = glue("excel/vibrio_expt-v{ver}.xlsx"))
```

# Write mutations by sample

Copy/pasted from above and modified to match the vibrio samples.

```{r}
colnames(pData(vibrio_expt))

gene_mutations <- pData(vibrio_expt)[["freebayesvariantsbygene"]]
names(gene_mutations) <- rownames(pData(vibrio_expt))
start <- init_xlsx(excel = "excel/vibrio_mutations.xlsx")
wb <- start[["wb"]]
for (s in seq_len(length(gene_mutations))) {
  sample_name <- names(gene_mutations)[[s]]
  if (gene_mutations[[s]] == "") {
    next
  }
  sample_data <- readr::read_tsv(gene_mutations[[s]])
  if (nrow(sample_data) == 0) {
    next
  }
  written <- write_xlsx(data = sample_data, sheet = sample_name, wb = wb)
}
saved <- openxlsx::saveWorkbook(written[["workbook"]], file = "excel/vibrio_mutations.xlsx")
```

# Samples received in 202309

 couple of notes to myself: I think these newest samples are seeking
to understand the evolutionary pressures which resulted in plasmid
mutations to the yciV promoter.  Thus, I think they primary place to
look is at that locus, both in the genome and plasmid.

In PA01 it is PA3200 and located at approximately location 3592500 as
the first ORF of a reverse strand operon.

# Samples received in 202401

Repeat the printing of counts/gene for a series of samples which have
deleted operons for combinations of cupA/cupB/cupC,cupD, orn, pel,
pilA and fliC.

I just created a blank sample sheet with appropriate IDs.  Let us take
a peek!

Caveat: I have yet to run the full freebayes variant caller.  That is
running now, but I think is not particularly desired for the purposes
of this query.

```{r}
spec <- make_dnaseq_spec()
meta_202401 <- gather_preprocessing_metadata("sample_sheets/202401_samples.xlsx",
  species = "paeruginosa_pa14", verbose = FALSE,
  specification = spec, basedir = "preprocessing/202401")
expt_202401 <- create_expt(meta_202401[["new_meta"]], file_column = "hisatcounttable",
                           gene_info = pa14_annot)
written_202401 <- write_expt(expt_202401, excel = "excel/cup_and_friends.xlsx")
```

# Some new samples 202406

I am basically going to copy/paste the previous block and assume all goes well.

```{r}
spec <- make_dnaseq_spec()
meta_202406 <- gather_preprocessing_metadata("sample_sheets/202406_samples.xlsx",
  species = "paeruginosa_pa14", verbose = FALSE,
  specification = spec, basedir = "preprocessing/202406")
expt_202406 <- create_expt(meta_202406[["new_meta"]], file_column = "hisatcounttable",
                           gene_info = pa14_annot)
written_202406 <- write_expt(expt_202406, excel = "excel/cup_and_friends_202406.xlsx")
```

# Query sequencing library state via unicycler assembly

Vince made a query about the libraries: could the strange 'dippyness'
of these new libraries be the reason some of the previous samples had
so many contigs when we attempted to assemble them.  Therefore he is
curious to see the assembly state of a few of the original samples.

To that end, I am going to invoke an assembly of a couple of the
earliest samples.

While I am at it, I will remap these two to get rid of the junctions.
In addition, I loaded them alongside one of the new samples.

![NBH,JC, and a dippy sample](igv/earliest_vs_dippy.png "Early samples vs new.")

```{bash, eval=FALSE}
cd preprocessing
cd PA14_JC
cyoa --method unicycler --input r1_trimmed.fastq.xz:r2_trimmed.fastq.xz
cyoa --method hisat --species paeruginosa_pa14 --intron 0 \
     --input r1_trimmed.fastq.xz:r2_trimmed.fastq.xz \
     --gff_tag Alias

cd ../PA14_NBH
cyoa --method unicycler --input r1_trimmed.fastq.xz:r2_trimmed.fastq.xz
cyoa --method hisat --species paeruginosa_pa14 --intron 0 \
     --input r1_trimmed.fastq.xz:r2_trimmed.fastq.xz \
     --gff_tag Alias
```

# Resequenced samples 202408

The previous samples had some problems vis a vis duplicate reads and
were resequenced.  I downloaded the resequenced samples into a new
directory (202408_pa14).  I was away for a couple weeks and seem to be
having a little difficulty getting my head on straight, so I will
hopefully get this correctly done on the first pass by doing the
following:

1.  running my default dnaseq pipeline using the newest cyoa, if I
    recall correctly this now includes the GATK duplication marker
    step.
2.  I will add an explicit fastp check for duplication in order to
    (hopefully) recapitulate the duplication statistics from the
    seqcenter.
3.  I will use their sample sheet as the input for my sample sheet
    curation, so it should be pretty easy to check #2.
4.  Collect a few IGV pictures with Nour's and Jared's early samples
    as a control.

```{bash, eval=FALSE}
module add cyoa/202404_hack
cd preprocessing/202408_pa14
start=$(pwd)
for i in $(/bin/ls -d PA14*); do
    cd $i
    echo $i
    cyoa --method pdnaseq --species paeruginosa_pa14 --intron 0 \
         --input $(/bin/ls *.gz | tr '\n' ':') \
         --gff_tag Alias
    cd $start
done
```

Upon completion, I will collect the various stats using the following
block, which I copy/pasted from above, with the caveat that I copied
the sample sheet from the seqcenter to '202408_samples.xlsx' and so
all the metadata collected will get added as new columns to it.

```{r}
spec <- make_dnaseq_spec()
meta_202408 <- gather_preprocessing_metadata("sample_sheets/202408_samples.xlsx",
  species = "paeruginosa_pa14", verbose = FALSE,
  specification = spec, basedir = "preprocessing/202408_pa14")
expt_202408 <- create_expt(meta_202408[["new_meta"]], file_column = "hisatcounttable",
                           gene_info = pa14_annot)
written_202408 <- write_expt(expt_202408, excel = "excel/cup_and_friends_202408_reseq.xlsx")
```


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