Readme.md

In chernolab/ASpli: Analysis of Alternative Splicing Using RNA-Seq

ASpli

An integrative R package for analysing alternative splicing using RNAseq

Authors

Estefania Mancini, Andrés Rabinovich, Javier Iserte, Marcelo Yanovsky, Ariel Chernomoretz

Introduction

Alternative splicing (AS) is a common mechanism of post-transcriptional gene regulation in eukaryotic organisms that expands the functional and regulatory diversity of a single gene by generating multiple mRNA isoforms that encode structurally and functionally distinct proteins.

Genome-wide analysis of AS has been a very active field of research since the early days of NGS (Next generation sequencing) technologies. Since then, evergrowing data availability and the development of increasingly sophisticated analysis methods have uncovered the complexity of the general splicing repertoire.

ASpli was specifically designed to integrate several independent signals in order to deal with the complexity that might arise in splicing patterns. Taking into account genome annotation information, ASpli considers bin-based signals along with junction inclusion indexes in order to assess for statistically significant changes in read coverage. In addition, annotation-independent signals are estimated based on the complete set of experimentally detected splice junctions. ASpli makes use of a generalized linear model framework (as implemented in edgeR R-package) to assess for the statistical analysis of specific contrasts of interest. In this way, ASpli can provide a comprehensive description of genome-wide splicing alterations even for complex experimental designs.

A typical ASpli workflow involves: parsing the genome annotation into subgenic features called bins, overlapping read alignments against them, perform junction counting, fulfill inference tasks of differential bin and junction usage and, finally, report integrated splicing signals. At every step ASpli generates self-contained outcomes that, if required, can be easily exported and integrated into other processing pipelines.

Installation

if (!requireNamespace("BiocManager", quietly = TRUE))
  install.packages("BiocManager")

BiocManager::install("ASpli")

library(ASpli)

Note: samtools is also required for image creation when exporting integrated signals (reports can be generated without samtools if images are not required).

Quick start

ASpli provides toy BAM and GTF files to introduce the working pipeline. Here is an example for a pairwise comparison between 2 conditions (Control vs Treatment, 3 replicates each) using default parameters.

Extract features from genome, define targets data.frame with phenotype data, and mBAMs data.frame with phenotype data for merged BAMs:

library(ASpli)
library(GenomicFeatures)

# gtf preprocessing ----
gtfFileName <- aspliExampleGTF()
genomeTxDb  <- makeTxDbFromGFF( gtfFileName )

# feature extraction ----
features    <- binGenome( genomeTxDb )

#bams and target file ----
BAMFiles <- aspliExampleBamList()
targets  <- data.frame(row.names = paste0('Sample',c(1:6)),
                       bam = BAMFiles[1:6],
                       f1  = c( 'control','control','control','treatment','treatment','treatment'),
                       stringsAsFactors = FALSE)
mBAMs <- data.frame( bam = sub("_[012]","",targets$bam[c(1,4)]),
                     condition = c("control","treatment"))

Read counting against annotated features:

gbcounts <- gbCounts(features=features, targets=targets,
                     minReadLength = 100, maxISize = 50000)
gbcounts

Junction-based de-novo counting and splicing signal estimation:

asd <- jCounts(counts=gbcounts, features=features, minReadLength=100)
asd

Differential gene expression and bin usage signal estimation:

gb  <- gbDUreport(gbcounts, contrast = c(-1,1))
gb

Differential junction usage analysis:

jdur <- jDUreport(asd, contrast=c(-1,1))
jdur

Bin and junction signal integration:

sr <- splicingReport(gb, jdur, counts=gbcounts)

Summary of integration of splicing signals along genomic-regions.

is <- integrateSignals(sr,asd)

Export results:

exportIntegratedSignals(is,sr=sr,
                          output.dir = "aspliExample",
                          counts=gbcounts,features=features,asd=asd,
                          mergedBams = mBAMs)

Documentation and help

Entry point for ASpli documentation is ASpli vignette, available after installing ASpli from R:

browseVignettes("ASpli")

If user has a question not answered in ASpli vignette, ASpli has an issue board with previous issues available in https://github.com/chernolab/ASpli. If no previous issue answers the question, user can upload a new issue requesting help.

Citation

ASpli: Integrative analysis of splicing landscapes through RNA-seq assays, Mancini E, Rabinovich A, Iserte J, Yanovsky M, Chernomoretz A, Bioinformatics, March 2021,

chernolab/ASpli documentation built on March 11, 2021, 12:24 a.m.