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GffCompare gffcompare [options]* <stringtie_asm_1.gtf> [stringtie_asm_2.gtf] … [stringtie_asm_N.gtf] GffCompare 1. compare and evaluate the accuracy of RNA-Seq transcript assemblers (Cufflinks, Stringtie). 2. collapse (merge) duplicate transcripts from multiple GTF/GFF3 files (e.g. resulted from assembly of different samples) * classify transcripts from one or multiple GTF/GFF3 files as they relate to reference transcripts provided in a annotation file (also in GTF/GFF3 format) Show
Cuffnorm cuffnorm [options] <transcripts.gtf> <sample1_replicate1.sam[,…,sample1_replicateM.sam]> <sample2_replicate1.sam[,…,sample2_replicateM.sam]>… [sampleN.sam_replicate1.sam[,…,sample2_replicateM.sam]] 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. Show
gffread 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"] Filters and/or converts GFF3/GTF2 records Show
RPKM_saturation.py RPKM_saturation.py -r hg19.refseq.bed12 -d '1++,1--,2+-,2-+' -i Pairend_StrandSpecific_51mer_Human_hg19.bam -o output The precision of any sample statitics (RPKM) is affected by sample size (sequencing depth); “resampling” or “jackknifing” is a method to estimate the precision of sample statistics by using subsets of available data. This module will resample a series of subsets from total RNA reads and then calculate RPKM value using each subset. By doing this we are able to 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. If sequencing depth was saturated, the estimated RPKM value will be stationary or reproducible. By default, this module will calculate 20 RPKM values (using 5%, 10%, ... , 95%,100% of total reads) for each transcripts. Show
Cuffquant cuffquant [options]* <annotation.(gtf/gff)> <aligned_reads.(sam/bam)> Cuffquant provides pre-calculation of gene expression levels. Show
Cufflinks cufflinks [options] <aligned_reads.(sam/bam)> 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. Show
DEXSeq-Count counts(object,normalized=FALSE) 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. Show
GEMINI burden gemini burden test.burden.db The burden tool provides a set of utilities to perform burden summaries on a per-gene, per sample basis. Show
kallisto kallisto quant -i transcripts.idx -o output -b 100 --single -l 180 -s 20 reads_1.fastq.gz 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). Show
RNA_fragment_size.py $ python2.7 RNA_fragment_size.py -r hg19.RefSeq.union.bed -i SRR873822_RIN10.bam > SRR873822_RIN10.fragSize 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 Show
DEXSeq DEXSeq(object, fullModel=design(object), reducedModel = ~ sample + exon, BPPARAM=MulticoreParam(workers=1), fitExpToVar="condition", quiet=TRUE ) Inference of differential exon usage in RNA-Seq. Show
kallisto kallisto quant -i transcripts.idx -o output -b 100 reads_1.fastq.gz reads_2.fastq.gz 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). Show
kallisto kallisto index -i transcripts.idx transcripts.fasta.gz Building an index for kallisto Show
Cuffdiff cuffdiff [options]* <transcripts.gtf> <sample1_replicate1.sam[,…,sample1_replicateM.sam]> <sample2_replicate1.sam[,…,sample2_replicateM.sam]> … [sampleN.sam_replicate1.sam[,…,sample2_replicateM.sam]] 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. Show