Commands in RumBall

4. Commands in RumBall#

4.1. download_genomedata.sh#

download_genomedata.sh downloads the genome and gene annotation files of the genome build specified. RumBall assumes the reference data is downloaded by this command.

download_genomedata.sh <build> <outputdir>
  build:
         human (GRCh38, GRCh37)
         mouse (GRCm39, GRCm38)
         rat (mRatBN7.2)
         fly (BDGP6)
         zebrafish (GRCz11)
         chicken (GRCg6a)
         African clawed frog (xenLae2)
         C. elegans (WBcel235)
         S. cerevisiae (R64-1-1)
         S. pombe (SPombe)
  Example:
         download_genomedata.sh GRCh38 Ensembl-GRCh38

4.2. build-index-RNAseq.sh: build index for RNA-seq#

(From the RumBall version 0.4.2, build-index.sh is renamed to build-index-RNAseq.sh.)

build-index-RNAseq.sh builds index files of the tools specified. <odir> should be the same with <outputdir> directory provided in download_genomedata.sh. The <odir> is used in the RumBall commands below.

build-index-RNAseq.sh [Options] <program> <build> <odir>
  <program>: rsem-star, rsem-bowtie2, hisat2, kallisto, salmon
  <build> (only for hisat2):
         human (GRCh38, GRCh37)
         mouse (GRCm39, GRCm38)
         rat (mRatBN7.2)
         fly (BDGP6)
         zebrafish (GRCz11)
         C. elegans (WBcel235)
         S. cerevisiae (R64-1-1)
         S. pombe (SPombe)
  <odir>: outout directory
   Options:
      -a: consider all scaffolds (default: chromosomes only)
      -p: number of CPUs (default: 4)
  Example:
         build-index-RNAseq.sh -p 12 rsem-star GRCh38 Ensembl-GRCh38

When specifying hisat2, build-index-RNAseq.sh downloads prebuilt indexes instead of building them to reduce the computational time. Therefore build-index-RNAseq.sh allows the genome build shown in the help abobe for hisat2, while any genome data is acceptable for the other programs.

In default, build-index-RNAseq.sh considers chromosomes only. If you want to include all scaffolds, add -a option.

4.3. star.sh: execute STAR and RSEM#

star.sh [Options] <single|paired> <prefix> <fastq> <Ddir> <strandedness>
   <single|paired>: single-end or paired-end reads
   <prefix>: prefix of output files
   <fastq>: fastq files (should be quoted if paired-end)
   <Ddir>: directory of index and gtf files
   <strandedness [none|forward|reverse]>: strandedness of input fastq files ("reverse" in the most cases)
  Options:
      -d outputdir: Output directory (default: "star/")
      -p ncore: number of CPUs (default: 12, note that large number (e.g., 64) may cause an error in STAR)
   Example:
      star.sh single HeLa_rep1 HeLa_rep1.fastq.gz Ensembl-GRCh38 reverse
      star.sh paired HeLa_rep1 "HeLa_rep1_1.fastq.gz HeLa_rep1_2.fastq.gz" Ensembl-GRCh38 reverse

  • Output

    • mapfile for a genome (star/*.Aligned.sortedByCoord.out.bam)

    • mapfile for genes (star/*.Aligned.toTranscriptome.out.bam)

    • gene expression data (star/*.genes.results)

    • transcript expression data (star/*.isoforms.results)

    • mapping stats (log/star-*.txt)

log example:

Sequenced

Uniquely mapped

(%)

Mapped to multiple loci

(%)

Mapped to too many loci

(%)

Unmapped (too many mismatches)

Unmapped (too short)

Unmapped (other)

chimeric reads

(%)

Splices total

Annotated

(%)

Non-canonical

(%)

Mismatch rate per base (%)

Deletion rate per base (%)

Insertion rate per base (%)

29446992

27430449

93.15

1012811

3.44

5253

0.02

0%

3%

0%

0

0

18960488

18725703

98.76

30590

0.16

0.19

0.01

0.01

4.4. rsem_merge.sh: merge expression data of multiple samples#

rsem_merge.sh [-s <strings for sed>] <inputdirs> <prefix> <Ddir>
    <inputdirs>: directories of samples (should be quoted)
    <prefix>: prefix of output files
    <Ddir>: directory of index and gtf files
    Options:
        -s <strings for sed>: specify strings that you want to remove from sample labels (e.g., "HeLa_", multiple strings should be separated by spaces)
    Example:
        rsem_merge.sh "star/Ctrl1 star/Ctrl2 star/siCTCF1 star/siCTCF2" Matrix_edgeR/HEK293

  • Output

    • gene expression data: *.genes.<TPM|count>.txt

    • transcript expression data: *.isoforms.<TPM|count>.txt

    • merged xlsx file: *.xlsx

4.5. DESeq2.sh: differential expression analysis for two groups by DESeq2#

DESeq2.sh [Options] <inputfile> <num of reps> <groupname> <species>
    <inputfile>: prefix of input matrix file
    <Ddir>: directory of gene annotation files
    <num of reps>: number of replicates (quated by ":")
    <group name>: labels of two groups compared (quated by ":")
    <species>: Species for the GO analysis [Human|Mouse|Rat|Fly|Celegans]
    Options:
        -l <float>: log2 fold change threshold (default: 1)
        -c <int>: column position for gene symbol (default: 1)
        -t <float>: FDR threshould for DEG identification (default: 0.05)
        -n <int>: number of top-ranked DEGs for GO analysis (default: 500)
        -k: Supply when the input file is generated by "kallisto_merge.sh" (default: "rsem_merge.sh")
    Example:
        DESeq2.sh star/Matrix 2:2 WT:KD Human

  • Output

    • Matrix.*.count.DESeq2.all.tsv … list of all genes

    • Matrix.*.count.DESeq2.DEGs.tsv … list of all DEGs

    • Matrix.*.count.DESeq2.upDEGs.tsv … list of all upregulated DEGs

    • Matrix.*.count.DESeq2.downDEGs.tsv … list of all upregulated DEGs

    • Matrix.*.count.DESeq2.xlsx … xlsx file that include all .tsv files above

    • Matrix.*.count.DEGs.bed … BED file of DEGs

    • Matrix.*.count.DEGs.bed6 … BED6 file of DEGs that contain gene name, length and strand information

    • Matrix.*.count.DESeq2.Dispersionplot.pdf … Dispersion plot of log-scale gene expression before and after dispersion fitting

    • Matrix.*.count.DESeq2.MAplot.pdf … MA plot of all genes. Significantly differential genes are highlighted in red. “shrunken apeglm” removes the high variance of low expression genes.

    • Matrix.*.count.DESeq2.Volcano.pdf … Volcano plot of all genes. Top-ranked genes are labeled.

    • Matrix.*.count.DESeq2.HighlyExpressedGenes.pdf … Heatmap of top-ranked DEGs

    • Matrix.*.count.DESeq2.sampleClustering.pdf … Clustering results of sample-wide comparison

    • Matrix.*.count.DESeq2.samplePCA.pdf … PCA plot of samples based on gene expression level

4.6. edgeR.sh: differential expression analysis for two groups by edgeR#

edgeR.sh [Options] <inputfile> <num of reps> <groupname> <species>
    <inputfile>: prefix of input matrix file
    <Ddir>: directory of gene annotation files
    <num of reps>: number of replicates (quated by ":")
    <group name>: labels of two groups compared (quated by ":")
    <species>: [Human|Mouse|Rat|Fly|Celegans]
    Options:
        -t <float>: FDR threshould for DEG identification (default: 0.05)
        -n <int>: number of top-ranked DEGs for GO analysis (default: 500)
        -k: Supply whe the input file is generated by "kallisto_merge.sh" (default: "rsem_merge.sh")
    Example:
    edgeR.sh Matrix 2:2 WT:KD Human

  • Output

    • Matrix.*.count.edgeR.all.tsv … list of all genes

    • Matrix.*.count.edgeR.DEGs.tsv … list of all DEGs

    • Matrix.*.count.edgeR.upDEGs.tsv … list of all upregulated DEGs

    • Matrix.*.count.edgeR.downDEGs.tsv … list of all downregulated DEGs

    • Matrix.*.count.edgeR.xlsx … xlsx file that include all .tsv files above

    • Matrix.*.count.DEGs.bed … BED file of DEGs

    • Matrix.*.count.DEGs.bed6 … BED6 file of DEGs that contain gene name, length and strand information

    • Matrix.*.count.density.png … Gene expression distribution (log scale)

    • Matrix.*.count.QQplot.1stSample.pdf … QQplot of the 1st sample

    • Matrix.*.count.edgeR.BCV-MDS.pdf … BCV and MDS plots for estimating variance among input samples

    • Matrix.*.count.edgeR.MAplot.pdf … MA plot of all genes. Significantly differential genes are highlighted in red. “shrunken apeglm” removes the high variance of low expression genes.

    • Matrix.*.count.heatmap.0.01.png … Heatmap of DEGs

    • Matrix.*.count.samplesCluster.inDEGs.pdf … Hierarchical tree of samples obtained the heatmap above

    • Matrix.*.count.edgeR.Volcano.pdf … Volcano plot of all genes. Top-ranked genes are labeled.

    • Matrix.*.count.samplePCA.pdf … PCA plot of samples based on gene expression level

  • Matrix.\*.count.edgeR.\*.tsv contains the value fitted by the glmQLFit function in edgeR, which is then normalized by the total number of reads. It does not contain the variance among replicates. edgeR (and DESeq2) assumes no variance in the same group, so the fitted value will be the same.

  • Matrix.\*.count.heatmap.0.01.png uses the normalized fitted values.

  • Note:

    • While the previous version of edgeR.sh filtered genes with 0 expression in all samples, the current version uses the filterByExpr function provided by edgeR. This results in more genes being filtered than before, and the FDR value changes accordingly, so more genes become non-significant.

    • The current version allows -lfcthre if you want to filter DEGs by log2foldchange in addition to the FDR threshold. Setting -lfcthre=1 will output only those genes that vary more than 2-fold (not strictly) between groups as DEGs.

    • Fixed an error on drawing heatmaps when the number of DEGs is zero.

4.7. check_stranded.sh#

In case that it is not clear whether the input samples are stranded or not, use check_stranded.sh for the quick check.

check_stranded.sh [human|mouse] <fastq>

This command runs bowtie to map reads onto the mRNA sequences obtained from NCBI. If the samples are reverse-straned, the most reads will be mapped to the reverse strand. If fifty-fifty, the samples are unstranded.

4.8. parsebowtielog2.pl#

parsebowtielog2.pl parses the log file of bowtie2 and outputs the number of mapped reads. This script is useful to merge the stats for all samples into a single .tsv file. Add -p option if the input files are paired-end reads.

parsebowtielog2.pl [--pair|-p] <file> <label>

4.9. csv2xlsx.pl#

This command merges csv/tsv files to a single xlsx file.

csv2xlsx.pl -i file1.tsv -n tabname1 [-i file2.tsv -n tabname2 ...] -o output.xlsx
Options:
      -d --delim=<str>: delimiter of input files (default:\t)
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