-
Function: Filters and/or converts GFF3/GTF2 records
Usage: gffread "input_gff" [-g "genomic_seqs_fasta" | "dir"][-s "seq_info.fsize"] [-o "outfile.gff"] [-t "tname"] [-r [["strand"]"chr":]"start".."end" [-R]] [-CTVNJMKQAFGUBHZWTOLE] [-w "exons.fa"] [-x "cds.fa"] [-y "tr_cds.fa"] [-i "maxintron"]
-
Function: Check if the current sequencing depth was saturated or not (or if the RPKM values were stable or not) in terms of genes’ expression estimation by resampling a series of subsets from total RNA reads and then calculate RPKM value using each subset
Usage: RPKM_saturation.py [options] -r REFGENE_BED -i INPUT_BAM -o OUTPUT_PREFIX
-
Function: Cuffquant provides pre-calculation of gene expression levels.
Usage: cuffquant [options]* <annotation.(gtf/gff)> <aligned_reads.(sam/bam)>
-
Function: The main goal of this tol is to count the number of reads/fragments per exon of each gene in RNA-seq sample. In addition it also prepares your annotation gtf file compatible for counting.
Usage: counts(object,normalized=FALSE)
-
Function: Cufflinks assembles transcripts, estimates their abundances, and tests for differential expression and regulation in RNA-Seq samples. It accepts aligned RNA-Seq reads and assembles the alignments into a parsimonious set of transcripts. Cufflinks then estimates the relative abundances of these transcripts based on how many reads support each one.
Usage: cufflinks [options] <aligned_reads.(sam/bam)>
-
Function: It produces a number of output files that contain expression levels and normalized fragment counts at the level of transcripts, primary transcripts, and genes. It also tracks changes in the relative abundance of transcripts sharing a common transcription start site, and in the relative abundances of the primary transcripts of each gene.
Usage: cuffnorm [options] <transcripts.gtf> <sample1_replicate1.sam[,…,sample1_replicateM.sam]> <sample2_replicate1.sam[,…,sample2_replicateM.sam]>… [sampleN.sam_replicate1.sam[,…,sample2_replicateM.sam]]
-
Function: The burden tool provides a set of utilities to perform burden summaries on a per-gene, per sample basis.
Usage: gemini burden test.burden.db
-
Function: Calculate fragment size for each gene/transcript. For each transcript, it will report :
1) Number of fragment that was used to estimate mean, median, std (see below).
2) mean of fragment size
3) median of fragment size
4) stdev of fragment size
Usage: RNA_fragment_size.py -r hg19.RefSeq.union.bed -i SRR873822_RIN10.bam > SRR873822_RIN10.fragSize
-
Function: Cuffdiff estimates the number of fragments that originated from each transcript, primary transcript, and gene in each sample. Primary transcript and gene counts are computed by summing the counts of transcripts in each primary transcript group or gene group.
Usage: cuffdiff [options]* <transcripts.gtf> <sample1_replicate1.sam[,…,sample1_replicateM.sam]> <sample2_replicate1.sam[,…,sample2_replicateM.sam]> … [sampleN.sam_replicate1.sam[,…,sample2_replicateM.sam]]
-
Function: Building an index for kallisto
Usage: kallisto index -i transcripts.idx transcripts.fasta.gz
-
Function: Quantify abundances of the transcripts using the two read files reads_1.fastq.gz and reads_2.fastq.gz (the .gz suffix means the read files have been gzipped; kallisto can read in either plain-text or gzipped read files).
Usage: kallisto quant -i transcripts.idx -o output -b 100 --single -l 180 -s 20 reads_1.fastq.gz
-
Function: Quantify cell subpopulation proportions in bulk tissue expression profiles by utilizing predefined signature genes or automatically extracted from single-cell transcriptomes or sorted cell populations.
Usage: docker run <bind_mounts> cibersortxfractions [Options]
Supported input format:
TSV, TXT
-
Function: Inference of differential exon usage in RNA-Seq.
Usage: DEXSeq(object, fullModel=design(object), reducedModel = ~ sample + exon, BPPARAM=MulticoreParam(workers=1), fitExpToVar="condition", quiet=TRUE )
-
Function: Quantify abundances of the transcripts using the two read files reads_1.fastq.gz and reads_2.fastq.gz (the .gz suffix means the read files have been gzipped; kallisto can read in either plain-text or gzipped read files).
Usage: kallisto quant -i transcripts.idx -o output -b 100 reads_1.fastq.gz reads_2.fastq.gz
-
Function: Estimate gene and isoform expression from RNA-Seq data
Usage: rsem-calculate-expression [options] --alignments [--paired-end] input reference_name sample_name