usage: pyseqrna input_file, samples_path, reference_genome, feature_file [options]
pyseqrna 0.0.2 : a python based RNAseq data analysis package
optional arguments:
-h, --help show this help message and exit
--version Show version information and exit
Required positional arguments:
input_file Tab-delimited file containing sample information
samples_path Directory for raw reads
reference_genome Path to the reference genome file.
feature_file Path to the GTF/GFF file
Internal arguments:
--source SOURCE Provide the source database of reference and feature file
[default: ENSEMBL]
--taxid TAXID Provide ncbi taxonomy id of the species
--species SPECIES Provide ncbi taxonomy id of the species
--organismType SPECIESTYPE
Provide Organism class
--outdir OUTDIR create output directory name to write results.
[default: pySeqRNA_results]
--paired Enable paired end functionality in pySeqRNA
[default:False]
--fastqc Enable initial quality check on raw reads with fastqc
[default:True]
--fastqcTrim Enable quality check on trimmed reads with fastqc
[default:False]
--ribosomal Enable removal of ribosomal RNA from reads
[default:False]
--rnadb Enable removal of ribosomal RNA from reads
[default:False]
--multimappedGroups Enable multimapped gene group quantification
[default:False]
--minReadCounts Minimum number of reads to consider per sample for MMG
[default:100]
--percentSample Minimum number of reads to consider in percent of samples for MMG
[default:0.5]
--combination COMBINATION [COMBINATION ...]
Provide space separated combination of samples to
be compared for differential expresion.
For example M1-A1 M1-V1 Z1-M1
[Default:all]
--fdr FDR False Discovery Rate threshold
[default:0.05]
--fold FOLD FOLD change value for filtering DEGs.
Remember pyseqrna performs log2 of the given value
[default:2]
--noreplicate Execute Differential gene expression with no replicate
--normalizeCount {RPKM,TPM,CPM,medianRatiocount}
Convert raw read counts to normalized counts
[default:RPKM]
--heatmap Create heatmap
[default:False]
--heatmapType {counts,degs}
Create heatmap based on selected choice
[default: counts]
--maPlot Create MA plot
[default:True]
--volcanoPlot Create Volcano plot
[default:True]
--vennPlot Enables venplot of differentially expressed genes.
[default: False]
--vennCombinations VENNCOMBINATION [VENNCOMBINATION ...]
Provide space separated 2-4 combination of samples to
be compared for differential expresion.For example M1-A1 M1-V1 Z1-M1
[Default is to make random vennplot of 4 combinations].
--cluster Cluster samples to find dissimilarities in data
Functional annotation arguments:
--geneOntology Enables gene ontology functional enrichment using BioMart
--keggPathway Enables kegg pathway functional enrichment using KEGG
External tool arguments:
--trimming {flexbar,trimmomatic,trim_galore}
Select a tool for quality based read trimming.
[default: trim_galore]
--aligner {STAR,hisat2}
Select a read alignment tool.
[default: STAR]
--quantTool {featureCounts,Htseq}
Select a feature quantification tool.
[default:featureCounts]
--deTool {DESeq2,edgeR}
Select a tool for differential expression.
[default:DESeq2]
Computation arguments:
--slurm Enable SLURM job scheduling on HPC
[default:False]
--threads THREADS Number of processors/threads to use
[default:80% of available CPU]
--memory MEMORY Max memory to use (in GB)
[default:16]
--resume {trimming,alignment,differential,functional}
Written by Naveen Duhan (naveen.duhan@usu.edu),
Kaundal Bioinformatics Lab, Utah State University,
Released under the terms of GNU General Public Licence v3