There are a few methods of importing annotation data into R. I will attempt some of them in preparation for loading them into the S.cerevisiae RNASeq data.
AnnotationHub is a newer service and has promise to be an excellent top-level resource for gathering annotation data. Its organization is peculiar, one connects to the database with AnnotationHub() and downloads lists of sqlite databases via query(). The primary problem with AnnotationHub is that it seems difficult to keep up with the changes in their database – so one must double-check the downloaded data to be sure that it is still actually data and not just a message saying ‘Public’.
Once it does download data, one may use normal orgdb functions to query it.
tmp <- sm(library(AnnotationHub))
ah <- sm(AnnotationHub())
orgdbs <- sm(query(ah, "OrgDb"))
sc_orgdb <- sm(query(ah, c("OrgDB", "Saccharomyces"))) ## AH49589 | org.Sc.sgd.db.sqlite
sc_orgdb <- sc_orgdb[[1]]## downloading 0 resources## loading from cache
## '/home/trey//.AnnotationHub/68530'## Loading required package: AnnotationDbi## Loading required package: stats4## Loading required package: Biobase## Welcome to Bioconductor
##
## Vignettes contain introductory material; view with
## 'browseVignettes()'. To cite Bioconductor, see
## 'citation("Biobase")', and for packages 'citation("pkgname")'.##
## Attaching package: 'Biobase'## The following object is masked from 'package:AnnotationHub':
##
## cache## Loading required package: IRanges## Loading required package: S4Vectors##
## Attaching package: 'S4Vectors'## The following object is masked from 'package:base':
##
## expand.gridsc_orgdb## OrgDb object:
## | DBSCHEMAVERSION: 2.1
## | Db type: OrgDb
## | Supporting package: AnnotationDbi
## | DBSCHEMA: YEAST_DB
## | ORGANISM: Saccharomyces cerevisiae
## | SPECIES: Yeast
## | YGSOURCENAME: Yeast Genome
## | YGSOURCEURL: http://downloads.yeastgenome.org/
## | YGSOURCEDATE: 14-Jan-2017
## | CENTRALID: ORF
## | TAXID: 559292
## | KEGGSOURCENAME: KEGG GENOME
## | KEGGSOURCEURL: ftp://ftp.genome.jp/pub/kegg/genomes
## | KEGGSOURCEDATE: 2011-Mar15
## | GOSOURCENAME: Gene Ontology
## | GOSOURCEURL: ftp://ftp.geneontology.org/pub/go/godatabase/archive/latest-lite/
## | GOSOURCEDATE: 2018-Mar28
## | EGSOURCEDATE: 2018-Apr4
## | EGSOURCENAME: Entrez Gene
## | EGSOURCEURL: ftp://ftp.ncbi.nlm.nih.gov/gene/DATA
## | ENSOURCEDATE: 2017-Dec04
## | ENSOURCENAME: Ensembl
## | ENSOURCEURL: ftp://ftp.ensembl.org/pub/current_fasta
## | UPSOURCENAME: Uniprot
## | UPSOURCEURL: http://www.UniProt.org/
## | UPSOURCEDATE: Mon Apr 9 21:11:24 2018##
## Please see: help('select') for usage information## Holy crap it worked!
sc_annotv1 <- load_orgdb_annotations(
sc_orgdb,
fields=c("alias", "description", "entrezid", "genename", "sgd"))## Unable to find CDSNAME, setting it to GENENAME.## Unable to find GENE_TYPE in the db, removing it.## Unable to find CDSCHROM in the db, removing it.## Unable to find CDSSTRAND in the db, removing it.## Unable to find CDSSTART in the db, removing it.## Unable to find CDSEND in the db, removing it.## Extracted all gene ids.## Attempting to select: GENENAME, ALIAS, DESCRIPTION, ENTREZID, GENENAME, SGD## 'select()' returned 1:many mapping between keys and columnssc_annotv1 <- sc_annotv1[["genes"]]In yeast, the transcript database is not super-useful, given that there are only 80 some genes wit introns, but it does provide a quick way to get transcript lengths.
tt <- please_install("TxDb.Scerevisiae.UCSC.sacCer3.sgdGene")
tmp <- sm(library(TxDb.Scerevisiae.UCSC.sacCer3.sgdGene))
sc_txdb <- TxDb.Scerevisiae.UCSC.sacCer3.sgdGeneThere is a non-zero chance we will want to use the actual genome sequence along with these annotations. The BSGenome packages provide that functionality.
tt <- sm(please_install("BSgenome.Scerevisiae.UCSC.sacCer3"))A completely separate and competing annotation source is biomart. Biomart provides programmatic access to the tremendous quantity of ensembl data. Their data is organized in some weird ways, including tables with thousands of available columns; but man is it awesome to have so much available stuff to sift through and learn about.
sc_annotv2 <- sm(load_biomart_annotations("scerevisiae"))
sc_annotv2 <- sc_annotv2[["annotation"]]
head(sc_annotv2)## transcriptID geneID
## X15S_rRNA 15S_rRNA 15S_rRNA
## X21S_rRNA 21S_rRNA 21S_rRNA
## HRA1 HRA1 HRA1
## ICR1 ICR1 ICR1
## LSR1 LSR1 LSR1
## NME1 NME1 NME1
## Description
## X15S_rRNA Ribosomal RNA of the small mitochondrial ribosomal subunit; MSU1 allele suppresses ochre stop mutations in mitochondrial protein-coding genes [Source:SGD;Acc:S000007287]
## X21S_rRNA Mitochondrial 21S rRNA; intron encodes the I-SceI DNA endonuclease [Source:SGD;Acc:S000007288]
## HRA1 Non-protein-coding RNA; substrate of RNase P, possibly involved in rRNA processing, specifically maturation of 20S precursor into the mature 18S rRNA [Source:SGD;Acc:S000119380]
## ICR1 Long intergenic regulatory ncRNA; has a key role in regulating transcription of the nearby protein-coding ORF FLO11; initiated far upstream from FLO11 and transcribed across much of the large promoter of FLO11, repressing FLO11 transcription in cis [Source:SGD;Acc:S000132612]
## LSR1 U2 spliceosomal RNA (U2 snRNA), component of the spliceosome; pairs with the branchpoint sequence; functionally equivalent to mammalian U2 snRNA; stress-induced pseudouridylations at positions 56 and 93 may contribute to regulation of splicing [Source:SGD;Acc:S000006478]
## NME1 RNA component of RNase MRP; RNase MRP cleaves pre-rRNA and has a role in cell cycle-regulated degradation of daughter cell-specific mRNAs; human ortholog is implicated in cartilage-hair hypoplasia (CHH) [Source:SGD;Acc:S000007436]
## Type length chromosome strand start end
## X15S_rRNA rRNA NA Mito 1 6546 8194
## X21S_rRNA rRNA NA Mito 1 58009 62447
## HRA1 ncRNA NA I 1 99305 99868
## ICR1 ncRNA NA IX -1 393884 397082
## LSR1 snRNA NA II -1 680688 681862
## NME1 snoRNA NA XIV 1 585587 585926sc_ontology <- sm(load_biomart_go("scerevisiae"))
sc_ontology <- sc_ontology[["go"]]
head(sc_ontology)## ID GO
## 1 YHR055C GO:0046872
## 2 YHR055C GO:0005829
## 3 YHR055C GO:0016209
## 4 YHR055C GO:0004784
## 5 YHR055C GO:0019430
## 6 YHR055C GO:0005507In contrast, it is possible to load most annotations of interest directly from the gff files used in the alignments. The main problem with gff data is that the format is incredibly inconsistent; but it is often the most direct way to go from the IDs of the genome to some immediately useful data.
## The old way of getting genome/annotation data
sc_gff <- "reference/scerevisiae.gff.gz"
sc_gff_annotations <- load_gff_annotations(sc_gff, type="gene")## Trying attempt: rtracklayer::import.gff3(gff, sequenceRegionsAsSeqinfo=TRUE)## Trying attempt: rtracklayer::import.gff3(gff, sequenceRegionsAsSeqinfo=FALSE)## Trying attempt: rtracklayer::import.gff2(gff, sequenceRegionsAsSeqinfo=TRUE)## Had a successful gff import with rtracklayer::import.gff2(gff, sequenceRegionsAsSeqinfo=TRUE)## Returning a df with 18 columns and 7050 rows.rownames(sc_gff_annotations) <- make.names(sc_gff_annotations$transcript_name, unique=TRUE)
head(sc_gff_annotations)## seqnames start end width strand source type score
## YAL069W I 335 646 312 + protein_coding gene NA
## YAL068W.A I 538 789 252 + protein_coding gene NA
## PAU8 I 1810 2169 360 - protein_coding gene NA
## YAL067W.A I 2480 2704 225 + protein_coding gene NA
## SEO1 I 7238 9016 1779 - protein_coding gene NA
## YAL066W I 10091 10396 306 + protein_coding gene NA
## phase exon_number gene_id ID p_id protein_id
## YAL069W 0 1 YAL069W YAL069W P3633 YAL069W
## YAL068W.A 0 1 YAL068W-A YAL068W-A P5377 YAL068W-A
## PAU8 0 1 YAL068C PAU8 P6023 YAL068C
## YAL067W.A 0 1 YAL067W-A YAL067W-A P4547 YAL067W-A
## SEO1 0 1 YAL067C SEO1 P5747 YAL067C
## YAL066W 0 1 YAL066W YAL066W P1766 YAL066W
## transcript_id transcript_name tss_id seqedit
## YAL069W YAL069W YAL069W TSS1128 <NA>
## YAL068W.A YAL068W-A YAL068W-A TSS5439 <NA>
## PAU8 YAL068C PAU8 TSS249 <NA>
## YAL067W.A YAL067W-A YAL067W-A TSS1248 <NA>
## SEO1 YAL067C SEO1 TSS5464 <NA>
## YAL066W YAL066W YAL066W TSS2674 <NA>In the following block we create an expressionset using the sample sheet and the annotations.
Annoyingly, the gff annotations are keyed in a peculiar fashion. Therefore I need to do a little work to merge them.
In the following block, I spend a little time setting up locations by chromosome/start/end and using those to merge the gff data and biomart data, thus solving the problem of inconsistent IDs. It is worth noting that I split this process between the genes on the + strand and those on the - strand because the definitions of ‘beginning of gene’ and ‘start’ mean different things: ‘beginning of gene’ refers to the location with respect to the start codon and is used by biomart, ‘start’ refers to the location with respect to the beginning of the chromosome and is used by the gff data.
## Start by making locations for the biomart data
sc_annotv2[["fwd_location"]] <- paste0(sc_annotv2[["chromosome"]], "_", sc_annotv2[["start"]])
sc_annotv2[["rev_location"]] <- paste0(sc_annotv2[["chromosome"]], "_", sc_annotv2[["end"]])
## Do the same for the gff annotations
sc_gff_annotations[["fwd_location"]] <- paste0(sc_gff_annotations[["seqnames"]], "_",
sc_gff_annotations[["start"]])
sc_gff_annotations[["rev_location"]] <- paste0(sc_gff_annotations[["seqnames"]], "_",
sc_gff_annotations[["end"]])
sc_gff_annotations[["gff_rowname"]] <- rownames(sc_gff_annotations)
## Now merge them.
sc_fwd_annotations <- merge(sc_annotv2, sc_gff_annotations, by="fwd_location")
sc_rev_annotations <- merge(sc_annotv2, sc_gff_annotations, by="rev_location")
colnames(sc_fwd_annotations) <- c("location","transcriptID","geneID", "Description",
"Type", "length", "chromosome", "strand.x", "start.x",
"end.x", "location.x", "seqnames",
"start.y", "end.y", "width", "strand.y", "source", "type",
"score", "phase", "exon_number", "gene_id", "ID", "p_id",
"protein_id", "transcript_id", "transcript_name", "tss_id",
"seqedit", "location.y", "gff_rowname")
colnames(sc_rev_annotations) <- colnames(sc_fwd_annotations)
sc_all_annotations <- rbind(sc_fwd_annotations, sc_rev_annotations)
rownames(sc_all_annotations) <- make.names(sc_all_annotations[["gff_rowname"]], unique=TRUE)
sc_all_annotations <- sc_all_annotations[, c("transcriptID", "geneID", "Description", "Type",
"length", "chromosome", "strand.x", "start.x", "end.x",
"tss_id")]
colnames(sc_all_annotations) <- c("transcriptID", "geneID", "Description", "Type", "length",
"chromosome", "strand", "start", "end", "tss_id")
sc_all_annotations[["location"]] <- paste0(sc_all_annotations[["chromosome"]], "_",
sc_all_annotations[["start"]], "_",
sc_all_annotations[["end"]])The function ’create_expt() gathers the annotation data, metadata, and counts, and invokes the various R functions to create an expressionset. This function is more complex than it should be, primarily because I go to some effort to accept a large array of inputs including: raw data frames of counts vs. sets of filenames of counts/hdf5/tpm/etc, data frames of metadata vs. excel sheets with varying columns, and arbitrary annotation data which may include all, some, or none of the included genes.
sc2_expt <- create_expt(
metadata="sample_sheets/all_samples.xlsx",
gene_info=sc_all_annotations,
file_column="bt2file")## Reading the sample metadata.## The sample definitions comprises: 28, 19 rows, columns.## Reading count tables.## Reading count tables with read.table().## /cbcb/nelsayed-scratch/atb/rnaseq/scerevisiae_cbf5_2017/preprocessing/v2/hpgl0774/outputs/bowtie2_scerevisiae/hpgl0774_forward-trimmed.count.xz contains 7131 rows.## /cbcb/nelsayed-scratch/atb/rnaseq/scerevisiae_cbf5_2017/preprocessing/v2/hpgl0775/outputs/bowtie2_scerevisiae/hpgl0775_forward-trimmed.count.xz contains 7131 rows and merges to 7131 rows.## /cbcb/nelsayed-scratch/atb/rnaseq/scerevisiae_cbf5_2017/preprocessing/v2/hpgl0776/outputs/bowtie2_scerevisiae/hpgl0776_forward-trimmed.count.xz contains 7131 rows and merges to 7131 rows.## /cbcb/nelsayed-scratch/atb/rnaseq/scerevisiae_cbf5_2017/preprocessing/v2/hpgl0777/outputs/bowtie2_scerevisiae/hpgl0777_forward-trimmed.count.xz contains 7131 rows and merges to 7131 rows.## /cbcb/nelsayed-scratch/atb/rnaseq/scerevisiae_cbf5_2017/preprocessing/v2/hpgl0778/outputs/bowtie2_scerevisiae/hpgl0778_forward-trimmed.count.xz contains 7131 rows and merges to 7131 rows.## /cbcb/nelsayed-scratch/atb/rnaseq/scerevisiae_cbf5_2017/preprocessing/v2/hpgl0779/outputs/bowtie2_scerevisiae/hpgl0779_forward-trimmed.count.xz contains 7131 rows and merges to 7131 rows.## /cbcb/nelsayed-scratch/atb/rnaseq/scerevisiae_cbf5_2017/preprocessing/v2/hpgl0780/outputs/bowtie2_scerevisiae/hpgl0780_forward-trimmed.count.xz contains 7131 rows and merges to 7131 rows.## /cbcb/nelsayed-scratch/atb/rnaseq/scerevisiae_cbf5_2017/preprocessing/v2/hpgl0781/outputs/bowtie2_scerevisiae/hpgl0781_forward-trimmed.count.xz contains 7131 rows and merges to 7131 rows.## /cbcb/nelsayed-scratch/atb/rnaseq/scerevisiae_cbf5_2017/preprocessing/v2/hpgl0782/outputs/bowtie2_scerevisiae/hpgl0782_forward-trimmed.count.xz contains 7131 rows and merges to 7131 rows.## /cbcb/nelsayed-scratch/atb/rnaseq/scerevisiae_cbf5_2017/preprocessing/v2/hpgl0783/outputs/bowtie2_scerevisiae/hpgl0783_forward-trimmed.count.xz contains 7131 rows and merges to 7131 rows.## /cbcb/nelsayed-scratch/atb/rnaseq/scerevisiae_cbf5_2017/preprocessing/v2/hpgl0784/outputs/bowtie2_scerevisiae/hpgl0784_forward-trimmed.count.xz contains 7131 rows and merges to 7131 rows.## /cbcb/nelsayed-scratch/atb/rnaseq/scerevisiae_cbf5_2017/preprocessing/v2/hpgl0785/outputs/bowtie2_scerevisiae/hpgl0785_forward-trimmed.count.xz contains 7131 rows and merges to 7131 rows.## /cbcb/nelsayed-scratch/atb/rnaseq/scerevisiae_cbf5_2017/preprocessing/v2/hpgl0786/outputs/bowtie2_scerevisiae/hpgl0786_forward-trimmed.count.xz contains 7131 rows and merges to 7131 rows.## /cbcb/nelsayed-scratch/atb/rnaseq/scerevisiae_cbf5_2017/preprocessing/v2/hpgl0787/outputs/bowtie2_scerevisiae/hpgl0787_forward-trimmed.count.xz contains 7131 rows and merges to 7131 rows.## /cbcb/nelsayed-scratch/atb/rnaseq/scerevisiae_cbf5_2017/preprocessing/v2/hpgl0788/outputs/bowtie2_scerevisiae/hpgl0788_forward-trimmed.count.xz contains 7131 rows and merges to 7131 rows.## /cbcb/nelsayed-scratch/atb/rnaseq/scerevisiae_cbf5_2017/preprocessing/v2/hpgl0789/outputs/bowtie2_scerevisiae/hpgl0789_forward-trimmed.count.xz contains 7131 rows and merges to 7131 rows.## Finished reading count tables.## Matched 6540 annotations and counts.## Bringing together the count matrix and gene information.## Some annotations were lost in merging, setting them to 'undefined'.knitr::kable(head(exprs(sc2_expt$expressionset)))| hpgl0774 | hpgl0775 | hpgl0776 | hpgl0777 | hpgl0778 | hpgl0779 | hpgl0780 | hpgl0781 | hpgl0782 | hpgl0783 | hpgl0784 | hpgl0785 | hpgl0786 | hpgl0787 | hpgl0788 | hpgl0789 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| X15S_rRNA | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| X21S_rRNA | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| AAC1 | 536 | 477 | 743 | 443 | 634 | 188 | 763 | 414 | 175 | 145 | 140 | 237 | 124 | 142 | 141 | 181 |
| AAC3 | 126 | 216 | 93 | 765 | 152 | 154 | 102 | 738 | 295 | 119 | 341 | 542 | 210 | 118 | 438 | 1071 |
| AAD10 | 1784 | 1928 | 2327 | 3869 | 2172 | 994 | 2472 | 3551 | 365 | 589 | 1476 | 1593 | 352 | 542 | 1782 | 2082 |
| AAD14 | 1054 | 901 | 1222 | 1863 | 1106 | 836 | 1307 | 1588 | 542 | 766 | 1580 | 1814 | 439 | 795 | 1924 | 2333 |
knitr::kable(head(fData(sc2_expt$expressionset)))| transcriptID | geneID | Description | Type | length | chromosome | strand | start | end | tss_id | location | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| X15S_rRNA | undefined | undefined | undefined | undefined | undefined | undefined | undefined | undefined | undefined | undefined | undefined |
| X21S_rRNA | undefined | undefined | undefined | undefined | undefined | undefined | undefined | undefined | undefined | undefined | undefined |
| AAC1 | YMR056C | YMR056C | Mitochondrial inner membrane ADP/ATP translocator; exchanges cytosolic ADP for mitochondrially synthesized ATP; phosphorylated; Aac1p is a minor isoform while Pet9p is the major ADP/ATP translocator; relocalizes from mitochondrion to cytoplasm upon DNA replication stress [Source:SGD;Acc:S000004660] | protein_coding | 930 | XIII | -1 | 387315 | 388244 | TSS5132 | XIII_387315_388244 |
| AAC3 | YBR085W | YBR085W | Mitochondrial inner membrane ADP/ATP translocator; exchanges cytosolic ADP for mitochondrially synthesized ATP; expressed under anaerobic conditions; similar to Aac1p; has roles in maintenance of viability and in respiration; AAC3 has a paralog, PET9, that arose from the whole genome duplication [Source:SGD;Acc:S000000289] | protein_coding | 924 | II | 1 | 415983 | 416906 | TSS1609 | II_415983_416906 |
| AAD10 | YJR155W | YJR155W | Putative aryl-alcohol dehydrogenase; similar to P. chrysosporium aryl-alcohol dehydrogenase; mutational analysis has not yet revealed a physiological role; members of the AAD gene family comprise three pairs (AAD3 + AAD15, AAD6/AAD16 + AAD4, AAD10 + AAD14) whose two genes are more related to one another than to other members of the family [Source:SGD;Acc:S000003916] | protein_coding | 867 | X | 1 | 727405 | 728271 | TSS5024 | X_727405_728271 |
| AAD14 | YNL331C | YNL331C | Putative aryl-alcohol dehydrogenase; similar to P. chrysosporium aryl-alcohol dehydrogenase; mutational analysis has not yet revealed a physiological role; members of the AAD gene family comprise three pairs (AAD3 + AAD15, AAD6/AAD16 + AAD4, AAD10 + AAD14) whose two genes are more related to one another than to other members of the family [Source:SGD;Acc:S000005275] | protein_coding | 1131 | XIV | -1 | 16118 | 17248 | TSS6941 | XIV_16118_17248 |
knitr::kable(head(pData(sc2_expt$expressionset)))| sampleid | strain | condition | batch | originalbatch | tube | cbf5igv | upf1igv | incubationtime | genotype | conc | bttotalreads | bttotalmapped | btleftmapped | btrightmapped | bowtiefile | bt2file | intronfile | allfile | file | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| hpgl0774 | hpgl0774 | yJD1524 | WT | E2B1 | E2B1 | f | wt | wt | 18h | wt ade2-1 can1-100 his3-11 leu2-3, 112 trp1-1 ura3-1 cbf5::TRP1 + CBF5 on pRS313 | NA | NA | NA | NA | NA | NA | preprocessing/v2/hpgl0774/outputs/bowtie2_scerevisiae/hpgl0774_forward-trimmed.count.xz | preprocessing/v2/hpgl0774/outputs/bowtie2_scerevisiae/introns.count.xz | preprocessing/v2/hpgl0774/outputs/bowtie2_scerevisiae/hpgl0774_forward-trimmed.count.xz | null |
| hpgl0775 | hpgl0775 | yJD1525 | cbf5_D95A | E2B1 | E2B1 | f | mut | wt | 18h | d95a ade2-1 can1-100 his3-11 leu2-3, 112 trp1-1 ura3-1 cbf5::TRP1 + CBF5 D95A on pRS313 | NA | NA | NA | NA | NA | NA | preprocessing/v2/hpgl0775/outputs/bowtie2_scerevisiae/hpgl0775_forward-trimmed.count.xz | preprocessing/v2/hpgl0775/outputs/bowtie2_scerevisiae/introns.count.xz | preprocessing/v2/hpgl0775/outputs/bowtie2_scerevisiae/hpgl0775_forward-trimmed.count.xz | null |
| hpgl0776 | hpgl0776 | yJD1745 | upf1d | E2B1 | E2B1 | f | wt | mut | 18h | wt ade2-1 can1-100 his3-11 leu2-3, 112 trp1-1 ura3-1 cbf5::TRP1 upf1::LEU2 + CBF5 on pRS313 (yJD1524 upf1Δ) | NA | NA | NA | NA | NA | NA | preprocessing/v2/hpgl0776/outputs/bowtie2_scerevisiae/hpgl0776_forward-trimmed.count.xz | preprocessing/v2/hpgl0776/outputs/bowtie2_scerevisiae/introns.count.xz | preprocessing/v2/hpgl0776/outputs/bowtie2_scerevisiae/hpgl0776_forward-trimmed.count.xz | null |
| hpgl0777 | hpgl0777 | yJD1746 | cbf5_D95A upf1d | E2B1 | E2B1 | f | mut | mut | 18h | d95a ade2-1 can1-100 his3-11 leu2-3, 112 trp1-1 ura3-1 cbf5::TRP1 upf1::LEU2 + CBF5 D95A on pRS313 (yJD1525 upf1Δ) | NA | NA | NA | NA | NA | NA | preprocessing/v2/hpgl0777/outputs/bowtie2_scerevisiae/hpgl0777_forward-trimmed.count.xz | preprocessing/v2/hpgl0777/outputs/bowtie2_scerevisiae/introns.count.xz | preprocessing/v2/hpgl0777/outputs/bowtie2_scerevisiae/hpgl0777_forward-trimmed.count.xz | null |
| hpgl0778 | hpgl0778 | yJD1524 | WT | E2B1 | E2B2 | g | wt | wt | 18h | wt ade2-1 can1-100 his3-11 leu2-3, 112 trp1-1 ura3-1 cbf5::TRP1 + CBF5 on pRS313 | NA | NA | NA | NA | NA | NA | preprocessing/v2/hpgl0778/outputs/bowtie2_scerevisiae/hpgl0778_forward-trimmed.count.xz | preprocessing/v2/hpgl0778/outputs/bowtie2_scerevisiae/introns.count.xz | preprocessing/v2/hpgl0778/outputs/bowtie2_scerevisiae/hpgl0778_forward-trimmed.count.xz | null |
| hpgl0779 | hpgl0779 | yJD1525 | cbf5_D95A | E2B1 | E2B2 | g | mut | wt | 18h | d95a ade2-1 can1-100 his3-11 leu2-3, 112 trp1-1 ura3-1 cbf5::TRP1 + CBF5 D95A on pRS313 | NA | NA | NA | NA | NA | NA | preprocessing/v2/hpgl0779/outputs/bowtie2_scerevisiae/hpgl0779_forward-trimmed.count.xz | preprocessing/v2/hpgl0779/outputs/bowtie2_scerevisiae/introns.count.xz | preprocessing/v2/hpgl0779/outputs/bowtie2_scerevisiae/hpgl0779_forward-trimmed.count.xz | null |
if (!isTRUE(get0("skip_load"))) {
pander::pander(sessionInfo())
message(paste0("This is hpgltools commit: ", get_git_commit()))
this_save <- paste0(gsub(pattern="\\.Rmd", replace="", x=rmd_file), "-v", ver, ".rda.xz")
tmp <- sm(saveme(filename=this_save))
}## If you wish to reproduce this exact build of hpgltools, invoke the following:## > git clone http://github.com/abelew/hpgltools.git## > git reset 0fd517d16f810afd23cc836b4ed74dafdf5274ba## R> packrat::restore()## This is hpgltools commit: Tue Jun 5 16:40:52 2018 -0400: 0fd517d16f810afd23cc836b4ed74dafdf5274ba