OneStopRNAseq

Software and parameter settings used by OneStopRNAseq v1.0.0

Tools Version Parameter for users
(and default values)		 Parameters Link (reference)
Snakemake 5.17 None snakemake -p -k --jobs 999
--use-envmodules
--use-singularity
--use-conda --conda-prefix $PATH
--latency-wait 300
--ri --restart-times 1
--cluster 'bsub -q long -o lsf.log -R "rusage[mem={resources.mem_mb}]" -n {threads} -R span[hosts=1] -W 72:00' 		https://snakemake.readthedocs.io/en/stable/
FastQC 0.11.5 None Default https://www.bioinformatics.babraham.ac.uk/
projects/fastqc/
MultiQC 1.6 None Default https://multiqc.info
(Ewels et al., 2016)
STAR 2.7.5a Reference Genome1 STAR
--runThreadN {threads}
--genomeDir {INDEX}
--sjdbGTFfile {gtf}
--readFilesCommand zcat
--readFilesIn {reads}
--outFileNamePrefix {name}
--outFilterType BySJout
--outMultimapperOrder Random
--outFilterMultimapNmax 200
--alignSJoverhangMin 8
--alignSJDBoverhangMin 3
--outFilterMismatchNmax 999
--outFilterMismatchNoverReadLmax 0.05
--alignIntronMin 20
--alignIntronMax 1000000
--outFilterIntronMotifs RemoveNoncanonicalUnannotated
--outSAMstrandField None
--outSAMtype BAM Unsorted
--quantMode GeneCounts
--outReadsUnmapped Fastx 		https://github.com/alexdobin/STAR
(Dobin et al., 2013)
QoRTs 1.3.6 None Default https://hartleys.github.io/QoRTs/
(Hartley & Mullikin, 2015)
Samtools 1.9 None Default with more RAM and threads http://www.htslib.org/
(Li et al., 2009)
featureCounts 2.0.0 strandness (auto)
MODE2 (strict)		 MODE strict paired-end:
-Q 20 --minOverlap 1
--fracOverlap 0 -p -B -C

MODE liberal paired-end:
-M --primary -Q 0
--minOverlap 1 --fracOverlap 0 -p

MODE strict single-end:
-Q 20 --minOverlap 1
--fracOverlap 0

MODE liberal single-end:
-M --primary -Q 0
--minOverlap 1 --fracOverlap 0 		http://bioinf.wehi.edu.au/featureCounts/
(Liao et al., 2014)
SalmonTE 0.4 None python SalmonTE.py quant
--reference={ref} --exprtype=count
--num_threads={threads}
{read1} {read2} 		https://github.com/LiuzLab/SalmonTE
(Jeong et al., 2018)
DESeq2 1.28.1 MAX_FDR (0.05)
MIN_LFC (0.585)
cooksCutoff3 (TRUE)
independentFiltering4 (FALSE) 		With batch effect:
design = ~ 0 + group + batch

Without batch effect:
design = ~ 0 + group 		https://bioconductor.org/packages/release/
bioc/html/DESeq2.html
(Love et al., 2014)
DEXSeq 1.34.0 None default https://bioconductor.org/packages/release/
bioc/html/DEXSeq.html
(Anders et al., 2012)
rMATS 4.1.0 None python rmats.py
--b1 b1.txt --b2 b2.txt
--gtf {gtf} -t {type}
--readLength {length}
--variable-read-length
--libType {strandness}
--nthread {threads}
--tstat {threads}
--cstat 0.2
--od output
--tmp tmp 		http://rnaseq-mats.sourceforge.net/rmats4.1.0/
(Shen et al., 2014)
GSEA 4.0.3 NPLOTS5 (100) gsea-cli.sh GSEAPreranked
-gmx {db} -rpt_label {db}
-rnk {rnk}
-norm meandiv -nperm 1000
-scoring_scheme classic
-create_svgs {svg}
-make_sets true
-rnd_seed timestamp
-zip_report false
-set_max 15000 -set_min 15
-plot_top_x {GSEA_NPLOTS}
-out ./gsea/{contrast} 		https://www.gsea-msigdb.org/gsea/index.jsp
(Subramanian et al., 2005)
deepTools 3.1.3 MODE (strict) MODE strict:
bamCoverage --bam {input.bam}
-o {output}
--numberOfProcessors {threads}
--outFileFormat bigwig
--normalizeUsing CPM --binSize 10
--minMappingQuality 20

MODE liberal:
bamCoverage --bam {input.bam}
-o {output}
--numberOfProcessors {threads}
--outFileFormat bigwig
--normalizeUsing CPM --binSize 10 		https://deeptools.readthedocs.io/en/develop/index.html
(Ram et al., 2016)

1. Available reference genomes and annotations for users to select.

Species Genome Annotation
Human hg38 gencode.v34.primary_assembly
Mouse mm10 gencode.vM25.primary_assembly
Worm (C. elegans) WBcel235 WBcel235.90
Yeast (S. cerevisiae) R64-1-1 R64-1-1.90
Fruit fly (D. melanogaster) BDGP6 BDGP6.22.96
Zebra fish (D. rerio) danRer11 V4.3.2 (Lawson et al., 2020)

2. The MODE parameter in featureCounts: default to strict. The corresponding parameter in the web interface is “ Include only uniquely mapped reads (Yes)”.

strict: only uniquely mapped reads are included in the gene quantification.
liberal: reads that are mapped equally well to multiple locations on the genome will also be quantified and assigned to one of the locations randomly. This setting is useful if you know some of the genes of your interest have multiple copies on the genome, e.g. histone genes.

3. The cooksCutoff parameter in DEseq2: default to TRUE.

Source: https://bioconductor.org/packages/release/bioc/vignettes/DESeq2/inst/doc/DESeq2.html

TRUE: The p values and adjusted p values are set to NA for genes that contain a Cook’s distance above a cutoff for samples which have at least three replicates. Cook’s distance measures the magnitude of the influence of a single sample on the fitted coefficients for a gene, and a large value of Cook’s distance indicates an outlier count. For more detailed information, please refer to the section “Approach to count outliers“ at https://bioconductor.org/packages/release/bioc/vignettes/DESeq2/inst/doc/DESeq2.html
FALSE: No genes will be flagged with NA p values or adjusted p values because of a large Cook’s distance.

4. The independentFilter parameter in DESeq2: default to FALSE.

Source: https://bioconductor.org/packages/release/bioc/vignettes/DESeq2/inst/doc/DESeq2.html#independent-filtering-of-results

TURE: Exclude those tests that have very little chance of showing significant evidence using test statistic independent filtering statistics such as the mean of normalized counts. This will result in increased detection power at the same experiment-wide type I error. For more detailed information, please refer to https://bioconductor.org/packages/release/bioc/vignettes/DESeq2/inst/doc/DESeq2.html#independent-filtering-of-results.
FALSE: No independent filtering will be performed.

5. The NPLOTS parameter in GSEA: default to 100. The corresponding parameter in the web interface is “Please specify the number of top gene sets to be plotted (100)”.

Number of top gene sets (ranked by p-value) for which the enrichment plots will be created. For more information on available gene sets, please refer to https://www.gsea-msigdb.org/gsea/msigdb. An example of enrichment plot is available at https://www.gsea-msigdb.org/gsea/doc/GSEAUserGuideTEXT.fld/image009.jpg. To properly interpret the GSEA results, please refer to the section “Interpreting GSEA Results“ at https://www.gsea-msigdb.org/gsea/doc/GSEAUserGuideFrame.html.

Details of gene sets. Provides the following information for each of the analyzed gene sets and its leading edge subset:

         # members. Number of genes in the gene set.

         # members in signal. Number of genes in the leading edge subset.

         Tag %. The percentage of gene hits before (for positive ES) or after (for negative ES) the peak in the running enrichment score. This gives an indication of the percentage of genes contributing to the enrichment score.

         List %. The percentage of genes in the ranked gene list before (for positive ES) or after (for negative ES) the peak in the running enrichment score. This gives an indication of where in the list the enrichment score is attained.

         Signal strength. The enrichment signal strength that combines the two previous statistics:

where N is the number of genes in the list and Nh is the number of genes in the gene set. If the gene set is entirely within the first Nh positions in the list, then the signal strength is maximal or 100%. If the gene set is spread throughout the list, then the signal strength decreases towards 0%.

References

Li, R.; Hu, K.; Liu, H.; Green, M.R.; Zhu, L.J. OneStopRNAseq: A Web Application for Comprehensive and Efficient Analyses of RNA-Seq Data. Genes 2020, 11, 1165. https://doi.org/10.3390/genes11101165

Anders, S., Reyes, A., & Huber, W. (2012). Detecting differential usage of exons from RNA-seq data. Genome Research, 22(10), 2008–2017. https://doi.org/10.1101/gr.133744.111

Dobin, A., Davis, C. A., Schlesinger, F., Drenkow, J., Zaleski, C., Jha, S., Batut, P., Chaisson, M., & Gingeras, T. R. (2013). STAR: Ultrafast universal RNA-seq aligner. Bioinformatics. https://doi.org/10.1093/bioinformatics/bts635

Ewels, P., Magnusson, M., Lundin, S., & Käller, M. (2016). MultiQC: Summarize analysis results for multiple tools and samples in a single report. Bioinformatics. https://doi.org/10.1093/bioinformatics/btw354

Hartley, S. W., & Mullikin, J. C. (2015). QoRTs: A comprehensive toolset for quality control and data processing of RNA-Seq experiments. BMC Bioinformatics. https://doi.org/10.1186/s12859-015-0670-5

Jeong, H. H., Yalamanchili, H. K., Guo, C., Shulman, J. M., & Liu, Z. (2018). An ultra-fast and scalable quantification pipeline for transposable elements from next generation sequencing data. Pacific Symposium on Biocomputing, 0(212669), 168–179. https://doi.org/10.1142/9789813235533_0016

Li, H., Handsaker, B., Wysoker, A., Fennell, T., Ruan, J., Homer, N., Marth, G., Abecasis, G., & Durbin, R. (2009). The Sequence Alignment/Map format and SAMtools. Bioinformatics. https://doi.org/10.1093/bioinformatics/btp352

Liao, Y., Smyth, G. K., & Shi, W. (2014). FeatureCounts: An efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics. https://doi.org/10.1093/bioinformatics/btt656

Love, M. I., Huber, W., Anders, S., Love, M. I., Huber, W., & Anders, S. (2014). Moderated estimation of fold change and dispersion for RNA-Seq data with DESeq2 Moderated estimation of fold change and dispersion for RNA-Seq data with DESeq2.

Shen, S., Park, J. W., Lu, Z. X., Lin, L., Henry, M. D., Wu, Y. N., Zhou, Q., & Xing, Y. (2014). rMATS: Robust and flexible detection of differential alternative splicing from replicate RNA-Seq data. Proceedings of the National Academy of Sciences of the United States of America. https://doi.org/10.1073/pnas.1419161111

Subramanian, A., Tamayo, P., Mootha, V. K., Mukherjee, S., Ebert, B. L., Gillette, M. A., Paulovich, A., Pomeroy, S. L., Golub, T. R., Lander, E. S., & Mesirov, J. P. (2005). Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. Proceedings of the National Academy of Sciences. https://doi.org/10.1073/pnas.0506580102

Ou J, Liu H, Nirala NK, Stukalov A, Acharya U, Green MR, et al. (2020) dagLogo: An R/Bioconductor package for identifying and visualizing differential amino acid group usage in proteomics data. PLoS ONE 15(11): e0242030. https://doi.org/10.1371/journal.pone.0242030

Release Notes

Release 1.0.0 (09/16/2020)

Software and parameter settings used by OneStopRNAseq v1.0.0