A comprehensive quality-control and quantification RNA-seq pipeline
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An open-source, reproducible, and scalable solution for analyzing RNA-seq data.
Table of Contents
-
Overview
2.1 RNA-seek Pipeline
2.2 Reference Genomes
2.3 Dependencies
2.4 Installation -
Run RNA-seek pipeline
3.1 Using Singularity
3.2 Using Docker
3.3 Biowulf
1. Introduction
RNA-sequencing ( RNA-seq ) has a wide variety of applications. This popular transcriptome profiling technique can be used to quantify gene and isoform expression, detect alternative splicing events, predict gene-fusions, call variants and much more.
RNA-seek is a comprehensive, open-source RNA-seq pipeline that relies on technologies like Docker20 and Singularity21 to maintain the highest-level of reproducibility. The pipeline consists of a series of data processing and quality-control steps orchestrated by Snakemake19 , a flexible and scalable workflow management system, to submit jobs to a cluster or cloud provider.
Fig 1. Run locally on a compute instance, on-premise using a cluster, or on the cloud using AWS.
A user can define the method or mode of execution. The pipeline can submit jobs to a cluster using a job scheduler like SLURM, or run on AWS using Tibanna (feature coming soon!). A hybrid approach ensures the pipeline is accessible to all users. As an optional step, relevelant output files and metadata can be stored in object storage using HPC DME (NIH users) or Amazon S3 for archival purposes (coming soon!).
2. Overview
2.1 RNA-seek Pipeline
A bioinformatics pipeline is more than the sum of its data processing steps. A pipeline without quality-control steps provides a myopic view of the potential sources of variation within your data (i.e., biological verses technical sources of variation). RNA-seek pipeline is composed of a series of quality-control and data processing steps.
The accuracy of the downstream interpretations made from transcriptomic data are highly dependent on initial sample library. Unwanted sources of technical variation, which if not accounted for properly, can influence the results. RNA-seek's comprehensive quality-control helps ensure your results are reliable and reproducible across experiments . In the data processing steps, RNA-seek quantifies gene and isoform expression and predicts gene fusions. Please note that the detection of alternative splicing events and variant calling will be incorporated in a later release.
Fig 2. An Overview of RNA-seek Pipeline.
Gene and isoform counts are quantified and a series of QC-checks are performed to assess the quality of the data. This pipeline stops at the generation of a raw counts matrix and gene-fusion calling. To run the pipeline, a user must select their raw data, a reference genome, and output directory (i.e., the location where the pipeline performs the analysis). Quality-control information is summarized across all samples in a MultiQC report.
Quality Control
FastQC
2
is used to assess the sequencing quality. FastQC is run twice, before and after adapter trimming. It generates a set of basic statistics to identify problems that can arise during sequencing or library preparation. FastQC will summarize per base and per read QC metrics such as quality scores and GC content. It will also summarize the distribution of sequence lengths and will report the presence of adapter sequences.
Kraken2 14 and FastQ Screen 17 are used to screen for various sources of contamination. During the process of sample collection to library preparation, there is a risk for introducing wanted sources of DNA. FastQ Screen compares your sequencing data to a set of different reference genomes to determine if there is contamination. It allows a user to see if the composition of your library matches what you expect. Also, if there are high levels of microbial contamination, Kraken can provide an estimation of the taxonomic composition. Kraken can be used in conjunction with Krona 15 to produce interactive reports.
Preseq 1 is used to estimate the complexity of a library for each samples. If the duplication rate is very high, the overall library complexity will be low. Low library complexity could signal an issue with library preparation where very little input RNA was over-amplified or the sample may be degraded.
Picard 10 can be used to estimate the duplication rate, and it has another particularly useful sub-command called CollectRNAseqMetrics which reports the number and percentage of reads that align to various regions: such as coding, intronic, UTR, intergenic and ribosomal regions. This is particularly useful as you would expect a library constructed with ploy(A)-selection to have a high percentage of reads that map to coding regions. Picard CollectRNAseqMetrics will also report the uniformity of coverage across all genes, which is useful for determining whether a sample has a 3' bias (observed in ploy(A)-selection libraries containing degraded RNA).
RSeQC 9 is another particularity useful package that is tailored for RNA-seq data. It is used to calculate the inner distance between paired-end reads and calculate TIN values for a set of canonical protein-coding transcripts. A median TIN value is calucated for each sample, which analogous to a computationally derived RIN.
MultiQC11 is used to aggreate the results of each tool into a single interactive report.
Quantification
Cutadapt
3
is used to remove adapter sequences, perform quality trimming, and remove very short sequences that would otherwise multi-map all over the genome prior to alignment.
STAR 4 is used to align reads to the reference genome. The RNA-seek pipeline runs STAR in a two-passes where splice-junctions are collected and aggregated across all samples and provided to the second-pass of STAR. In the second pass of STAR, the splice-junctions detected in the first pass are inserted into the genome indices prior to alignment.
RSEM 5 is used to quantify gene and isoform expression. The expected counts from RSEM are merged across samples to create a two counts matrices for gene counts and isoform counts.
Arriba 22 is used to predict gene-fusion events. The pre-built human and mouse reference genomes use Arriba blacklists to reduce the false-positive rate.
2.2 Reference Genomes
Reference files are pulled from an S3 bucket to the compute instance or local filesystem prior to execution.
RNA-seek comes bundled with pre-built reference files for the following genomes:
Name | Species | Genome | Annotation |
---|---|---|---|
hg38_30 | Homo sapiens (human) | GRCh38 | Gencode6 Release 30 |
mm10_M21 | Mus musculus (mouse) | GRCm38 | Gencode Release M21 |
Warning: This section contains FTP links for downloading each reference file. Open the link in a new tab to start a download.
2.3 Dependencies
Requires:
singularity>=3.5
snakemake>=6.0
Snakemake and singularity must be installed on the target system. Snakemake orchestrates the execution of each step in the pipeline. To guarantee reproducibility, each step relies on pre-built images from DockerHub . Snakemake uses singaularity to pull these images onto the local filesystem prior to job execution, and as so, snakemake and singularity are the only two dependencies.
2.4 Installation
Please clone this repository to your local filesystem using the following command:
# Clone Repository from Github
git clone https://github.com/skchronicles/RNA-seek.git
# Change your working directory to the RNA-seek repo
cd RNA-seek/
3. Run RNA-seek pipeline
3.1 Using Singularity
# Coming Soon!
3.2 Using Docker
# Coming Soon!
3.3 Biowulf
# rna-seek is configured to use different execution backends: local or slurm
# view the help page for more information
./rna-seek run --help
# @local: uses local singularity execution method
# The local MODE will run serially on compute
# instance. This is useful for testing, debugging,
# or when a users does not have access to a high
# performance computing environment.
# Please note that you can dry-run the command below
# by providing the --dry-run flag
# Do not run this on the head node!
# Grab an interactive node
sinteractive --mem=110g --cpus-per-task=12 --gres=lscratch:200
module purge
module load singularity snakemake
./rna-seek run --input .tests/*.R?.fastq.gz --output /data/$USER/RNA_hg38 --genome hg38_30 --mode local
# @slurm: uses slurm and singularity execution method
# The slurm MODE will submit jobs to the cluster.
# It is recommended running rna-seek in this mode.
module purge
module load singularity snakemake
./rna-seek run --input .tests/*.R?.fastq.gz --output /data/$USER/RNA_hg38 --genome hg38_30 --mode slurm
4. Contribute
This section is for new developers working with the RNA-seek pipeline. If you have added new features or adding new changes, please consider contributing them back to the original repository:
-
Fork the original repo to a personal or org account.
-
Clone the fork to your local filesystem.
-
Copy the modified files to the cloned fork.
-
Commit and push your changes to your fork.
-
Create a pull request to this repository.
5. References
1.
Daley, T. and A.D. Smith, Predicting the molecular complexity of sequencing libraries. Nat Methods, 2013. 10(4): p. 325-7.
2.
Andrews, S. (2010). FastQC: a quality control tool for high throughput sequence data.
3.
Martin, M. (2011). "Cutadapt removes adapter sequences from high-throughput sequencing reads." EMBnet 17(1): 10-12.
4.
Dobin, A., et al., STAR: ultrafast universal RNA-seq aligner. Bioinformatics, 2013. 29(1): p. 15-21.
5.
Li, B. and C.N. Dewey, RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC Bioinformatics, 2011. 12: p. 323.
6.
Harrow, J., et al., GENCODE: the reference human genome annotation for The ENCODE Project. Genome Res, 2012. 22(9): p. 1760-74.
7.
Law, C.W., et al., voom: Precision weights unlock linear model analysis tools for RNA-seq read counts. Genome Biol, 2014. 15(2): p. R29.
8.
Smyth, G.K., Linear models and empirical bayes methods for assessing differential expression in microarray experiments. Stat Appl Genet Mol Biol, 2004. 3: p. Article3.
9.
Wang, L., et al. (2012). "RSeQC: quality control of RNA-seq experiments." Bioinformatics 28(16): 2184-2185.
10.
The Picard toolkit. https://broadinstitute.github.io/picard/.
11.
Ewels, P., et al. (2016). "MultiQC: summarize analysis results for multiple tools and samples in a single report." Bioinformatics 32(19): 3047-3048.
12.
R Core Team (2018). R: A Language and Environment for Statistical Computing. Vienna, Austria, R Foundation for Statistical Computing.
13.
Li, H., et al. (2009). "The Sequence Alignment/Map format and SAMtools." Bioinformatics 25(16): 2078-2079.
14.
Wood, D. E. and S. L. Salzberg (2014). "Kraken: ultrafast metagenomic sequence classification using exact alignments." Genome Biol 15(3): R46.
15.
Ondov, B. D., et al. (2011). "Interactive metagenomic visualization in a Web browser." BMC Bioinformatics 12(1): 385.
16.
Okonechnikov, K., et al. (2015). "Qualimap 2: advanced multi-sample quality control for high-throughput sequencing data." Bioinformatics 32(2): 292-294.
17.
Wingett, S. and S. Andrews (2018). "FastQ Screen: A tool for multi-genome mapping and quality control." F1000Research 7(2): 1338.
18.
Robinson, M. D., et al. (2009). "edgeR: a Bioconductor package for differential expression analysis of digital gene expression data." Bioinformatics 26(1): 139-140.
19.
Koster, J. and S. Rahmann (2018). "Snakemake-a scalable bioinformatics workflow engine." Bioinformatics 34(20): 3600.
20.
Merkel, D. (2014). Docker: lightweight linux containers for consistent development and deployment. Linux Journal, 2014(239), 2.
21.
Kurtzer GM, Sochat V, Bauer MW (2017). Singularity: Scientific containers for mobility of compute. PLoS ONE 12(5): e0177459.
22.
Haas, B. J., et al. (2019). "Accuracy assessment of fusion transcript detection via read-mapping and de novo fusion transcript assembly-based methods." Genome Biology 20(1): 213.
Code Snippets
36 37 38 | shell: """ python {params.get_flowcell_lanes} {input.R1} {wildcards.name} > {output.fqinfo} """ |
63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 | shell: """ # Setups temporary directory for # intermediate files with built-in # mechanism for deletion on exit if [ ! -d "{params.tmpdir}" ]; then mkdir -p "{params.tmpdir}"; fi tmp=$(mktemp -d -p "{params.tmpdir}") trap 'rm -rf "${{tmp}}"' EXIT java -Xmx{params.memory}g -XX:ParallelGCThreads={threads} -jar ${{PICARDJARPATH}}/picard.jar AddOrReplaceReadGroups \ I={input.file1} O=${{tmp}}/{params.sampleName}.star_rg_added.sorted.bam \ TMP_DIR=${{tmp}} RGID=id RGLB=library RGPL=illumina RGPU=machine RGSM=sample VALIDATION_STRINGENCY=SILENT; java -Xmx{params.memory}g -XX:ParallelGCThreads={threads} -jar ${{PICARDJARPATH}}/picard.jar MarkDuplicates \ I=${{tmp}}/{params.sampleName}.star_rg_added.sorted.bam \ O=${{tmp}}/{params.sampleName}.star_rg_added.sorted.dmark.bam \ TMP_DIR=${{tmp}} CREATE_INDEX=true VALIDATION_STRINGENCY=SILENT METRICS_FILE={output.metrics}; mv ${{tmp}}/{params.sampleName}.star_rg_added.sorted.dmark.bam {output.bam}; mv ${{tmp}}/{params.sampleName}.star_rg_added.sorted.dmark.bai {output.bai}; sed -i 's/MarkDuplicates/picard.sam.MarkDuplicates/g' {output.metrics}; """ |
103 104 105 | shell:""" preseq c_curve -B -o {output.ccurve} {input.bam} """ |
130 131 132 133 134 | shell: """ unset DISPLAY; qualimap bamqc -bam {input.bamfile} --feature-file {params.gtfFile} \ -outdir {params.outdir} -nt {threads} --java-mem-size={params.memory}G """ |
168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 | shell: """ # Setups temporary directory for # intermediate files with built-in # mechanism for deletion on exit if [ ! -d "{params.tmpdir}" ]; then mkdir -p "{params.tmpdir}"; fi tmp=$(mktemp -d -p "{params.tmpdir}") trap 'rm -rf "${{tmp}}"' EXIT java -Xmx{params.memory}g -jar ${{PICARDJARPATH}}/picard.jar CollectRnaSeqMetrics \ REF_FLAT={params.refflat} I={input.file1} O={output.outstar1} \ RIBOSOMAL_INTERVALS={params.rrnalist} \ STRAND_SPECIFICITY=SECOND_READ_TRANSCRIPTION_STRAND \ TMP_DIR=${{tmp}} VALIDATION_STRINGENCY=SILENT; sed -i 's/CollectRnaSeqMetrics/picard.analysis.CollectRnaSeqMetrics/g' {output.outstar1} samtools flagstat {input.file1} > {output.outstar2}; python3 {params.statscript} {input.file1} >> {output.outstar2} """ |
210 211 212 213 214 | shell: """ python {params.pythonscript} {params.annotate} {params.inputdir} {params.inputdir} sed 's/\\t/|/1' {output.gene_counts_matrix} | \ sed '1 s/^gene_id|GeneName/symbol/' > {output.reformatted} """ |
236 237 238 239 | shell: """ infer_experiment.py -r {params.bedref} -i {input.file1} -s 1000000 > {output.out1} read_distribution.py -i {input.file1} -r {params.bedref} > {output.out2} """ |
266 267 268 269 270 | shell: """ # tin.py writes to current working directory cd {params.outdir} tin.py -i {input.bam} -r {params.bedref} """ |
291 292 293 | shell: """ python {params.create_matrix} {input.tins} > {output.matrix} """ |
30 31 32 33 34 | shell: """ mkdir -p {params.outdir} fastQValidator --noeof --file {input.R1} > {output.out1} fastQValidator --noeof --file {input.R2} > {output.out2} """ |
60 61 62 | shell: """ fastqc {input.R1} {input.R2} -t {threads} -o {params.outdir}; """ |
93 94 95 96 97 98 99 | shell: """ cutadapt --pair-filter=any --nextseq-trim=2 --trim-n \ -n 5 -O 5 -q {params.leadingquality},{params.trailingquality} \ -m {params.minlen}:{params.minlen} \ -b file:{params.fastawithadaptersetd} -B file:{params.fastawithadaptersetd} \ -j {threads} -o {output.out1} -p {output.out2} {input.file1} {input.file2} """ |
126 127 128 | shell: """ fastqc {input.R1} {input.R2} -t {threads} -o {params.outdir}; """ |
154 155 156 157 158 159 160 161 | shell: """ # Get encoding of Phred Quality Scores encoding=$(python {params.encoding} {input.R1}) echo "Detected Phred+${{encoding}} ASCII encoding" bbtools bbmerge-auto in1={input.R1} in2={input.R2} qin=${{encoding}} \ ihist={output} k=62 extend2=200 rem ecct -Xmx{params.memory}G """ |
201 202 203 204 205 206 207 208 209 | shell: """ fastq_screen --conf {params.fastq_screen_config} --outdir {params.outdir} \ --threads {threads} --subset 1000000 \ --aligner bowtie2 --force {input.file1} {input.file2} fastq_screen --conf {params.fastq_screen_config2} --outdir {params.outdir2} \ --threads {threads} --subset 1000000 \ --aligner bowtie2 --force {input.file1} {input.file2} """ |
240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 | shell: """ # Setups temporary directory for # intermediate files with built-in # mechanism for deletion on exit if [ ! -d "{params.tmpdir}" ]; then mkdir -p "{params.tmpdir}"; fi tmp=$(mktemp -d -p "{params.tmpdir}") trap 'rm -rf "${{tmp}}"' EXIT # Copy kraken2 db to /lscratch or temp # location to reduce filesystem strain cp -rv {params.bacdb} ${{tmp}}/; kdb_base=$(basename {params.bacdb}) kraken2 --db ${{tmp}}/${{kdb_base}} \ --threads {threads} --report {output.krakentaxa} \ --output {output.krakenout} \ --gzip-compressed \ --paired {input.fq1} {input.fq2} # Generate Krona Report cut -f2,3 {output.krakenout} | \ ktImportTaxonomy - -o {output.kronahtml} """ |
331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 | shell: """ # Setups temporary directory for # intermediate files with built-in # mechanism for deletion on exit if [ ! -d "{params.tmpdir}" ]; then mkdir -p "{params.tmpdir}"; fi tmp=$(mktemp -d -p "{params.tmpdir}") trap 'rm -rf "${{tmp}}"' EXIT # Optimal readlength for sjdbOverhang = max(ReadLength) - 1 [Default: 100] readlength=$( zcat {input.file1} | \ awk -v maxlen=100 'NR%4==2 {{if (length($1) > maxlen+0) maxlen=length($1)}}; \ END {{print maxlen-1}}' ) echo "sjdbOverhang for STAR: ${{readlength}}" STAR --genomeDir {params.stardir} \ --outFilterIntronMotifs {params.filterintronmotifs} \ --outSAMstrandField {params.samstrandfield} \ --outFilterType {params.filtertype} \ --outFilterMultimapNmax {params.filtermultimapnmax} \ --alignSJoverhangMin {params.alignsjoverhangmin} \ --alignSJDBoverhangMin {params.alignsjdboverhangmin} \ --outFilterMismatchNmax {params.filtermismatchnmax} \ --outFilterMismatchNoverLmax {params.filtermismatchnoverlmax} \ --alignIntronMin {params.alignintronmin} \ --alignIntronMax {params.alignintronmax} \ --alignMatesGapMax {params.alignmatesgapmax} \ --clip3pAdapterSeq {params.adapter1} {params.adapter2} \ --readFilesIn {input.file1} {input.file2} \ --readFilesCommand zcat \ --runThreadN {threads} \ --outFileNamePrefix {params.prefix}. \ --outSAMunmapped {params.outsamunmapped} \ --outWigType {params.wigtype} \ --outWigStrand {params.wigstrand} \ --twopassMode Basic \ --sjdbGTFfile {params.gtffile} \ --limitSjdbInsertNsj {params.nbjuncs} \ --quantMode TranscriptomeSAM GeneCounts \ --outSAMtype BAM Unsorted \ --alignEndsProtrude 10 ConcordantPair \ --peOverlapNbasesMin 10 \ --outTmpDir=${{tmp}}/STARtmp_{wildcards.name} \ --sjdbOverhang ${{readlength}} # SAMtools sort (uses less memory than STAR SortedByCoordinate) samtools sort -@ {threads} \ -m 2G -T ${{tmp}}/SORTtmp_{wildcards.name} \ -O bam {params.prefix}.Aligned.out.bam \ > {output.out1} rm {params.prefix}.Aligned.out.bam mv {params.prefix}.Aligned.toTranscriptome.out.bam {workpath}/{bams_dir}; mv {params.prefix}.Log.final.out {workpath}/{log_dir} """ |
433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 | shell: """ # Setups temporary directory for # intermediate files with built-in # mechanism for deletion on exit if [ ! -d "{params.tmpdir}" ]; then mkdir -p "{params.tmpdir}"; fi tmp=$(mktemp -d -p "{params.tmpdir}") trap 'rm -rf "${{tmp}}"' EXIT # Optimal readlength for sjdbOverhang = max(ReadLength) - 1 [Default: 100] readlength=$( zcat {input.file1} | \ awk -v maxlen=100 'NR%4==2 {{if (length($1) > maxlen+0) maxlen=length($1)}}; \ END {{print maxlen-1}}' ) echo "sjdbOverhang for STAR: ${{readlength}}" STAR --genomeDir {params.stardir} \ --outFilterIntronMotifs {params.filterintronmotifs} \ --outSAMstrandField {params.samstrandfield} \ --outFilterType {params.filtertype} \ --outFilterMultimapNmax {params.filtermultimapnmax} \ --alignSJoverhangMin {params.alignsjoverhangmin} \ --alignSJDBoverhangMin {params.alignsjdboverhangmin} \ --outFilterMismatchNmax {params.filtermismatchnmax} \ --outFilterMismatchNoverLmax {params.filtermismatchnoverlmax} \ --alignIntronMin {params.alignintronmin} \ --alignIntronMax {params.alignintronmax} \ --alignMatesGapMax {params.alignmatesgapmax} \ --clip3pAdapterSeq {params.adapter1} {params.adapter2} \ --readFilesIn {input.file1} {input.file2} \ --readFilesCommand zcat \ --runThreadN {threads} \ --outFileNamePrefix {params.prefix}. \ --outSAMtype BAM Unsorted \ --alignEndsProtrude 10 ConcordantPair \ --peOverlapNbasesMin 10 \ --sjdbGTFfile {params.gtffile} \ --outTmpDir=${{tmp}}/STARtmp_{wildcards.name} \ --sjdbOverhang ${{readlength}} """ |
492 493 494 495 496 497 498 499 | shell: """ cat {input.files} | \ sort | \ uniq | \ awk -F \"\\t\" '{{if ($5>0 && $6==1) {{print}}}}'| \ cut -f1-4 | sort | uniq | \ grep \"^chr\" | grep -v \"^chrM\" > {output.out1} """ |
554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 | shell: """ # Setups temporary directory for # intermediate files with built-in # mechanism for deletion on exit if [ ! -d "{params.tmpdir}" ]; then mkdir -p "{params.tmpdir}"; fi tmp=$(mktemp -d -p "{params.tmpdir}") trap 'rm -rf "${{tmp}}"' EXIT # Optimal readlength for sjdbOverhang = max(ReadLength) - 1 [Default: 100] readlength=$( zcat {input.file1} | \ awk -v maxlen=100 'NR%4==2 {{if (length($1) > maxlen+0) maxlen=length($1)}}; \ END {{print maxlen-1}}' ) echo "sjdbOverhang for STAR: ${{readlength}}" STAR --genomeDir {params.stardir} \ --outFilterIntronMotifs {params.filterintronmotifs} \ --outSAMstrandField {params.samstrandfield} \ --outFilterType {params.filtertype} \ --outFilterMultimapNmax {params.filtermultimapnmax} \ --alignSJoverhangMin {params.alignsjoverhangmin} \ --alignSJDBoverhangMin {params.alignsjdboverhangmin} \ --outFilterMismatchNmax {params.filtermismatchnmax} \ --outFilterMismatchNoverLmax {params.filtermismatchnoverlmax} \ --alignIntronMin {params.alignintronmin} \ --alignIntronMax {params.alignintronmax} \ --alignMatesGapMax {params.alignmatesgapmax} \ --clip3pAdapterSeq {params.adapter1} {params.adapter2} \ --readFilesIn {input.file1} {input.file2} \ --readFilesCommand zcat \ --runThreadN {threads} \ --outFileNamePrefix {params.prefix}. \ --outSAMunmapped {params.outsamunmapped} \ --outWigType {params.wigtype} \ --outWigStrand {params.wigstrand} \ --sjdbFileChrStartEnd {input.tab} \ --sjdbGTFfile {params.gtffile} \ --limitSjdbInsertNsj {params.nbjuncs} \ --quantMode TranscriptomeSAM GeneCounts \ --outSAMtype BAM Unsorted \ --alignEndsProtrude 10 ConcordantPair \ --peOverlapNbasesMin 10 \ --outTmpDir=${{tmp}}/STARtmp_{wildcards.name} \ --sjdbOverhang ${{readlength}} # SAMtools sort (uses less memory than STAR SortedByCoordinate) samtools sort -@ {threads} \ -m 2G -T ${{tmp}}/SORTtmp_{wildcards.name} \ -O bam {params.prefix}.Aligned.out.bam \ > {output.out1} rm {params.prefix}.Aligned.out.bam mv {params.prefix}.Aligned.toTranscriptome.out.bam {workpath}/{bams_dir}; mv {params.prefix}.Log.final.out {workpath}/{log_dir} """ |
652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 | shell: """ # Setups temporary directory for # intermediate files with built-in # mechanism for deletion on exit if [ ! -d "{params.tmpdir}" ]; then mkdir -p "{params.tmpdir}"; fi tmp=$(mktemp -d -p "{params.tmpdir}") trap 'rm -rf "${{tmp}}"' EXIT # Optimal readlength for sjdbOverhang = max(ReadLength) - 1 [Default: 100] readlength=$( zcat {input.R1} | \ awk -v maxlen=100 'NR%4==2 {{if (length($1) > maxlen+0) maxlen=length($1)}}; \ END {{print maxlen-1}}' ) # Avoids inheriting $R_LIBS_SITE # from local env variables R_LIBS_SITE=/usr/local/lib/R/site-library STAR --runThreadN {threads} \ --sjdbGTFfile {params.gtffile} \ --sjdbOverhang ${{readlength}} \ --genomeDir {params.stardir} \ --genomeLoad NoSharedMemory \ --readFilesIn {input.R1} {input.R2} \ --readFilesCommand zcat \ --outStd BAM_Unsorted \ --outSAMtype BAM Unsorted \ --outSAMunmapped Within \ --outFilterMultimapNmax 50 \ --peOverlapNbasesMin 10 \ --alignSplicedMateMapLminOverLmate 0.5 \ --alignSJstitchMismatchNmax 5 -1 5 5 \ --chimSegmentMin 10 \ --chimOutType WithinBAM HardClip \ --chimJunctionOverhangMin 10 \ --chimScoreDropMax 30 \ --chimScoreJunctionNonGTAG 0 \ --chimScoreSeparation 1 \ --chimSegmentReadGapMax 3 \ --chimMultimapNmax 50 \ --twopassMode Basic \ --outTmpDir=${{tmp}}/STARtmp_{wildcards.name} \ --outFileNamePrefix {params.prefix}. \ | tee ${{tmp}}/{params.chimericbam} | \ arriba -x /dev/stdin \ -o {output.fusions} \ -O {output.discarded} \ -a {params.reffa} \ -g {params.gtffile} \ -b {input.blacklist} \ # Sorting and Indexing BAM files is required for Arriba's Visualization samtools sort -@ {threads} \ -m 2G -T ${{tmp}}/SORTtmp_{wildcards.name} \ -O bam ${{tmp}}/{params.chimericbam} \ > {output.bam} samtools index {output.bam} {output.bai} rm ${{tmp}}/{params.chimericbam} # Generate Gene Fusions Visualization draw_fusions.R \ --fusions={output.fusions} \ --alignments={output.bam} \ --output={output.figure} \ --annotation={params.gtffile} \ --cytobands={input.cytoband} \ --proteinDomains={input.protdomain} """ |
750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 | shell: """ # Setups temporary directory for # intermediate files with built-in # mechanism for deletion on exit if [ ! -d "{params.tmpdir}" ]; then mkdir -p "{params.tmpdir}"; fi tmp=$(mktemp -d -p "{params.tmpdir}") trap 'rm -rf "${{tmp}}"' EXIT # Get strandedness to calculate Forward Probability fp=$(tail -n1 {input.file2} | awk '{{if($NF > 0.75) print "0.0"; else if ($NF<0.25) print "1.0"; else print "0.5";}}') echo "Forward Probability Passed to RSEM: $fp" rsem-calculate-expression --no-bam-output --calc-ci --seed 12345 \ --bam --paired-end -p {threads} {input.file1} {params.rsemref} {params.prefix} --time \ --temporary-folder ${{tmp}} --keep-intermediate-files --forward-prob=${{fp}} --estimate-rspd """ |
786 787 788 789 | shell: """ inner_distance.py -i {input.bam} -r {params.genemodel} \ -k 10000000 -o {params.prefix} """ |
818 819 820 821 822 823 824 825 826 827 828 | shell: """ bash {params.bashscript} {input.bam} {params.outprefix} # reverse files if method is not dUTP/NSR/NNSR ... ie, R1 in the direction of RNA strand. strandinfo=`tail -n1 {input.strandinfo} | awk '{{print $NF}}'` if [ `echo "$strandinfo < 0.25"|bc` -eq 1 ];then mv {output.fbw} {output.fbw}.tmp mv {output.rbw} {output.fbw} mv {output.fbw}.tmp {output.rbw} fi """ |
871 872 873 874 875 876 877 878 879 880 881 882 883 884 885 886 887 888 889 890 891 892 893 894 | shell: """ multiqc --ignore '*/.singularity/*' -f -c {params.qcconfig} --interactive --outdir {params.outdir} {params.workdir} # Parse RSeQC Inner Distance Maximas echo -e "Sample\\tInner_Dist_Maxima" > {output.maximas} for f in $(find {params.workdir} -iname '*.inner_distance_freq.txt'); do sample=$(basename "${{f}}"); inner_dist_maxima=$(sort -k3,3nr "${{f}}" | awk -F '\\t' 'NR==1{{print $1}}'); echo -e "${{sample}}\\t${{inner_dist_maxima}}"; done >> {output.maximas} # Parse RSeQC Median TINs echo -e "Sample\\tmedian_tin" > {output.medtins} find {params.workdir} -name '*.star_rg_added.sorted.dmark.summary.txt' -exec cut -f1,3 {{}} \\; | \ grep -v '^Bam_file' | \ awk -F '\\t' '{{printf "%s\\t%.3f\\n", $1,$2}}' >> {output.medtins} # Parse Flowcell and Lane information echo -e "Sample\\tflowcell_lanes" > {output.fclanes} find {params.workdir} -name '*.fastq.info.txt' -exec awk -F '\\t' -v OFS='\\t' 'NR==2 {{print $1,$5}}' {{}} \\; \ >> {output.fclanes} python3 {params.pyparser} {params.logfiles} {params.outdir} """ |
926 927 928 929 930 931 932 933 934 935 936 937 938 | shell: """ # Avoids inheriting $R_LIBS_SITE # from local env variables R_LIBS_SITE=/usr/local/lib/R/site-library # Generate RNA QC Dashboard {params.rwrapper} \ -m {params.rmarkdown} \ -r {input.counts} \ -t {input.tins} \ -q {input.qc} \ -o {params.odir} \ -f RNA_Report.html """ |
28 29 30 31 | shell: """ mkdir -p {params.outdir} fastQValidator --noeof --file {input.R1} > {output.out1} """ |
55 56 57 | shell: """ fastqc {input.R1} -t {threads} -o {params.outdir}; """ |
86 87 88 89 90 91 | shell: """ cutadapt --nextseq-trim=2 --trim-n \ -n 5 -O 5 -q {params.leadingquality},{params.trailingquality} \ -m 16 -b file:{params.fastawithadaptersetd} -j {threads} \ -o {output.outfq} {input.infq} """ |
118 119 120 121 122 123 | shell: """ cutadapt --nextseq-trim=2 --trim-n \ -n 5 -O 5 -q {params.leadingquality},{params.trailingquality} \ -m {params.minlen} -b file:{params.fastawithadaptersetd} -j {threads} \ -o {output.outfq} {input.infq} """ |
148 149 150 | shell: """ fastqc {input} -t {threads} -o {params.outdir}; """ |
184 185 186 187 188 189 190 | shell: """ fastq_screen --conf {params.fastq_screen_config} --outdir {params.outdir} \ --threads {threads} --subset 1000000 --aligner bowtie2 --force {input.file1} fastq_screen --conf {params.fastq_screen_config2} --outdir {params.outdir2} \ --threads {threads} --subset 1000000 --aligner bowtie2 --force {input.file1} """ |
220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 | shell: """ # Setups temporary directory for # intermediate files with built-in # mechanism for deletion on exit if [ ! -d "{params.tmpdir}" ]; then mkdir -p "{params.tmpdir}"; fi tmp=$(mktemp -d -p "{params.tmpdir}") trap 'rm -rf "${{tmp}}"' EXIT # Copy kraken2 db to /lscratch or temp # location to reduce filesytem strain cp -rv {params.bacdb} ${{tmp}}/; kdb_base=$(basename {params.bacdb}) kraken2 --db ${{tmp}}/${{kdb_base}} \ --threads {threads} --report {output.krakentaxa} \ --output {output.krakenout} \ --gzip-compressed \ {input.fq} # Generate Krona Report cut -f2,3 {output.krakenout} | \ ktImportTaxonomy - -o {output.kronahtml} """ |
309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 | shell: """ # Setups temporary directory for # intermediate files with built-in # mechanism for deletion on exit if [ ! -d "{params.tmpdir}" ]; then mkdir -p "{params.tmpdir}"; fi tmp=$(mktemp -d -p "{params.tmpdir}") trap 'rm -rf "${{tmp}}"' EXIT # Optimal readlength for sjdbOverhang = max(ReadLength) - 1 [Default: 100] readlength=$(zcat {input.file1} | awk -v maxlen=100 'NR%4==2 {{if (length($1) > maxlen+0) maxlen=length($1)}}; END {{print maxlen-1}}') echo "sjdbOverhang for STAR: ${{readlength}}" STAR --genomeDir {params.stardir} \ --outFilterIntronMotifs {params.filterintronmotifs} \ --outSAMstrandField {params.samstrandfield} \ --outFilterType {params.filtertype} \ --outFilterMultimapNmax {params.filtermultimapnmax} \ --alignSJoverhangMin {params.alignsjoverhangmin} \ --alignSJDBoverhangMin {params.alignsjdboverhangmin} \ --outFilterMismatchNmax {params.filtermismatchnmax} \ --outFilterMismatchNoverLmax {params.filtermismatchnoverlmax} \ --alignIntronMin {params.alignintronmin} \ --alignIntronMax {params.alignintronmax} \ --alignMatesGapMax {params.alignmatesgapmax} \ --clip3pAdapterSeq {params.adapter1} {params.adapter2} \ --readFilesIn {input.file1} --readFilesCommand zcat \ --runThreadN {threads} \ --outFileNamePrefix {params.prefix}. \ --outSAMunmapped {params.outsamunmapped} \ --outWigType {params.wigtype} \ --outWigStrand {params.wigstrand} \ --twopassMode Basic \ --sjdbGTFfile {params.gtffile} \ --limitSjdbInsertNsj {params.nbjuncs} \ --quantMode TranscriptomeSAM GeneCounts \ --outSAMtype BAM SortedByCoordinate \ --alignEndsProtrude 10 ConcordantPair \ --peOverlapNbasesMin 10 \ --outTmpDir=${{tmp}}/STARtmp_{wildcards.name} \ --sjdbOverhang ${{readlength}} mv {params.prefix}.Aligned.toTranscriptome.out.bam {workpath}/{bams_dir}; mv {params.prefix}.Log.final.out {workpath}/{log_dir} """ |
403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 | shell: """ # Setups temporary directory for # intermediate files with built-in # mechanism for deletion on exit if [ ! -d "{params.tmpdir}" ]; then mkdir -p "{params.tmpdir}"; fi tmp=$(mktemp -d -p "{params.tmpdir}") trap 'rm -rf "${{tmp}}"' EXIT # Optimal readlength for sjdbOverhang = max(ReadLength) - 1 [Default: 100] readlength=$(zcat {input.file1} | awk -v maxlen=100 'NR%4==2 {{if (length($1) > maxlen+0) maxlen=length($1)}}; END {{print maxlen-1}}') echo "sjdbOverhang for STAR: ${{readlength}}" STAR --genomeDir {params.stardir} \ --outFilterMultimapNmax {params.filtermultimapnmax} \ --alignSJDBoverhangMin 1000 \ --outFilterScoreMinOverLread 0 \ --outFilterMatchNminOverLread 0 \ --outFilterMatchNmin 16 \ --outFilterMismatchNoverLmax {params.filtermismatchnoverlmax} \ --alignIntronMax 1 \ --clip3pAdapterSeq {params.adapter1} {params.adapter2} \ --readFilesIn {input.file1} --readFilesCommand zcat \ --runThreadN {threads} \ --outFileNamePrefix {params.prefix}. \ --outSAMunmapped Within \ --sjdbGTFfile {params.gtffile} \ --limitSjdbInsertNsj {params.nbjuncs} \ --quantMode TranscriptomeSAM GeneCounts \ --outSAMtype BAM Unsorted \ --outTmpDir=${{tmp}}/STARtmp_{wildcards.name} \ --sjdbOverhang ${{readlength}} # SAMtools sort (uses less memory than STAR SortedByCoordinate) samtools sort -@ {threads} \ -m 2G -T ${{tmp}}/SORTtmp_{wildcards.name} \ -O bam {params.prefix}.Aligned.out.bam \ > {output.out1} rm {params.prefix}.Aligned.out.bam mv {params.prefix}.Aligned.toTranscriptome.out.bam {workpath}/{bams_dir}; mv {params.prefix}.Log.final.out {workpath}/{log_dir} """ |
489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 | shell: """ # Setups temporary directory for # intermediate files with built-in # mechanism for deletion on exit if [ ! -d "{params.tmpdir}" ]; then mkdir -p "{params.tmpdir}"; fi tmp=$(mktemp -d -p "{params.tmpdir}") trap 'rm -rf "${{tmp}}"' EXIT # Optimal readlength for sjdbOverhang = max(ReadLength) - 1 [Default: 100] readlength=$(zcat {input.file1} | awk -v maxlen=100 'NR%4==2 {{if (length($1) > maxlen+0) maxlen=length($1)}}; END {{print maxlen-1}}') echo "sjdbOverhang for STAR: ${{readlength}}" STAR --genomeDir {params.stardir} \ --outFilterIntronMotifs {params.filterintronmotifs} \ --outSAMstrandField {params.samstrandfield} \ --outFilterType {params.filtertype} \ --outFilterMultimapNmax {params.filtermultimapnmax} \ --alignSJoverhangMin {params.alignsjoverhangmin} \ --alignSJDBoverhangMin {params.alignsjdboverhangmin} \ --outFilterMismatchNmax {params.filtermismatchnmax} \ --outFilterMismatchNoverLmax {params.filtermismatchnoverlmax} \ --alignIntronMin {params.alignintronmin} \ --alignIntronMax {params.alignintronmax} \ --alignMatesGapMax {params.alignmatesgapmax} \ --clip3pAdapterSeq {params.adapter1} {params.adapter2} \ --readFilesIn {input.file1} --readFilesCommand zcat \ --runThreadN {threads} \ --outFileNamePrefix {params.prefix}. \ --outSAMtype BAM Unsorted \ --alignEndsProtrude 10 ConcordantPair \ --peOverlapNbasesMin 10 \ --sjdbGTFfile {params.gtffile} \ --outTmpDir=${{tmp}}/STARtmp_{wildcards.name} \ --sjdbOverhang ${{readlength}} """ |
542 543 544 545 546 547 548 | shell: """ cat {input.files} | \ sort | uniq | \ awk -F '\\t' '{{if ($5>0 && $6==1) {{print}}}}'| \ cut -f1-4 | sort | uniq | \ grep \"^chr\"|grep -v \"^chrM\" > {output.out1} """ |
602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 | shell: """ # Setups temporary directory for # intermediate files with built-in # mechanism for deletion on exit if [ ! -d "{params.tmpdir}" ]; then mkdir -p "{params.tmpdir}"; fi tmp=$(mktemp -d -p "{params.tmpdir}") trap 'rm -rf "${{tmp}}"' EXIT # Optimal readlength for sjdbOverhang = max(ReadLength) - 1 [Default: 100] readlength=$(zcat {input.file1} | awk -v maxlen=100 'NR%4==2 {{if (length($1) > maxlen+0) maxlen=length($1)}}; END {{print maxlen-1}}') echo "sjdbOverhang for STAR: ${{readlength}}" STAR --genomeDir {params.stardir} \ --outFilterIntronMotifs {params.filterintronmotifs} \ --outSAMstrandField {params.samstrandfield} \ --outFilterType {params.filtertype} \ --outFilterMultimapNmax {params.filtermultimapnmax} \ --alignSJoverhangMin {params.alignsjoverhangmin} \ --alignSJDBoverhangMin {params.alignsjdboverhangmin} \ --outFilterMismatchNmax {params.filtermismatchnmax} \ --outFilterMismatchNoverLmax {params.filtermismatchnoverlmax} \ --alignIntronMin {params.alignintronmin} \ --alignIntronMax {params.alignintronmax} \ --alignMatesGapMax {params.alignmatesgapmax} \ --clip3pAdapterSeq {params.adapter1} {params.adapter2} \ --readFilesIn {input.file1} --readFilesCommand zcat \ --runThreadN {threads} \ --outFileNamePrefix {params.prefix}. \ --outSAMunmapped {params.outsamunmapped} \ --outWigType {params.wigtype} \ --outWigStrand {params.wigstrand} \ --sjdbFileChrStartEnd {input.tab} \ --sjdbGTFfile {params.gtffile} \ --limitSjdbInsertNsj {params.nbjuncs} \ --quantMode TranscriptomeSAM GeneCounts \ --outSAMtype BAM SortedByCoordinate \ --alignEndsProtrude 10 ConcordantPair \ --peOverlapNbasesMin 10 \ --outTmpDir=${{tmp}}/STARtmp_{wildcards.name} \ --sjdbOverhang ${{readlength}} mv {params.prefix}.Aligned.toTranscriptome.out.bam {workpath}/{bams_dir}; mv {params.prefix}.Log.final.out {workpath}/{log_dir} """ |
674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 | shell: """ # Setups temporary directory for # intermediate files with built-in # mechanism for deletion on exit if [ ! -d "{params.tmpdir}" ]; then mkdir -p "{params.tmpdir}"; fi tmp=$(mktemp -d -p "{params.tmpdir}") trap 'rm -rf "${{tmp}}"' EXIT # Get strandedness to calculate Forward Probability fp=$(tail -n1 {input.file2} | awk '{{if($NF > 0.75) print "0.0"; else if ($NF<0.25) print "1.0"; else print "0.5";}}') echo "Forward Probability Passed to RSEM: $fp" rsem-calculate-expression --no-bam-output --calc-ci --seed 12345 \ --bam -p {threads} {input.file1} {params.rsemref} {params.prefix} --time \ --temporary-folder ${{tmp}} --keep-intermediate-files --forward-prob=${{fp}} --estimate-rspd """ |
718 719 720 721 722 723 724 725 726 727 728 | shell: """ bash {params.bashscript} {input.bam} {params.outprefix} # reverse files if method is not dUTP/NSR/NNSR ... ie, R1 in the direction of RNA strand. strandinfo=`tail -n1 {input.strandinfo} | awk '{{print $NF}}'` if [ `echo "$strandinfo < 0.25"|bc` -eq 1 ]; then mv {output.fbw} {output.fbw}.tmp mv {output.rbw} {output.fbw} mv {output.fbw}.tmp {output.rbw} fi """ |
770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 | shell: """ multiqc --ignore '*/.singularity/*' -f -c {params.qcconfig} --interactive --outdir {params.outdir} {params.workdir} # Parse RSeQC Median TINs echo -e "Sample\\tmedian_tin" > {output.medtins} find {params.workdir} -name '*.star_rg_added.sorted.dmark.summary.txt' -exec cut -f1,3 {{}} \\; | \ grep -v '^Bam_file' | \ awk -F '\\t' '{{printf "%s\\t%.3f\\n", $1,$2}}' >> {output.medtins} # Parse Flowcell and Lane information echo -e "Sample\\tflowcell_lanes" > {output.fclanes} find {params.workdir} -name '*.fastq.info.txt' -exec awk -F '\\t' -v OFS='\\t' 'NR==2 {{print $1,$5}}' {{}} \\; \ >> {output.fclanes} python3 {params.pyparser} {params.logfiles} {params.outdir} """ |
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