Splicing QTL identification software tools | RNA sequencing data analysis
Identification of genetic variants affecting splicing in RNA sequencing population studies is still in its infancy. Splicing phenotype is more complex than gene expression and ought to be treated as a multivariate phenotype to be recapitulated completely.
A robust statistical method for detecting splicing quantitative trait loci (sQTLs) from RNA-seq data. GLiMMPS takes into account the individual variation in sequencing coverage and the noise prevalent in RNA-seq data. Analyses of simulated and real RNA-seq datasets demonstrate that GLiMMPS outperforms competing statistical models. As population-scale RNA-seq studies become increasingly affordable and popular, GLiMMPS provides a useful tool for elucidating the genetic variation of alternative splicing in humans and model organisms. GLiMMPS provides a useful tool for genome-wide identification of sQTLs from population-scale RNA-seq datasets.
A method for relative quantification of splicing events to be used in population genetics studies in discovery of alternative splicing quantitative trait loci (asQTLs). Because the phenotype is splicing ratios of exon links calculated from mapping of RNA-sequencing reads without modeling of transcript structure, it is a more direct estimation of splicing. We show that it is capable of identifying thousands of asQTLs, many of which are missed by other methods. We believe it will prove useful in the search for alternative splicing QTLs in population genetics studies.
An R package to detect splicing QTLs, which are variants associated with change in the splicing pattern of a gene. Here, splicing patterns are modeled by the relative expression of the transcripts of a gene. We use it to analyze RNA-Seq data from the Geuvadis project in 465 individuals. We identify hundreds of single nucleotide polymorphisms (SNPs) as splicing QTLs (sQTLs), including some falling in genome-wide association study SNPs. By developing the appropriate metrics, we show that sQTLseekeR compares favorably with existing methods that rely on univariate approaches, predicting variants that behave as expected from mutations affecting splicing.
Detects and quantifies novel and existing alternative splicing (AS) events by focusing on intron excisions. LeafCutter identifies variable intron splicing events from short-read RNA-seq data and finds AS events of high complexity. It obviates the need for transcript annotations and overcomes the challenges in determining relative isoform or exon usage in complex splicing events. This tool can be used to discover differential splicing between sample groups, and to map splicing quantitative trait loci (sQTLs).
Performs gene-level tests and returns p-values per gene only. DRIMSeq provides two frameworks. One for the differential splicing analysis between different conditions and one for the sQTL analysis. Both are based on modeling the counts of genomic features (i.e., transcripts, exons or exonic bins) with Dirichlet-multinomial distribution. The package also makes available functions for visualization and exploration of the data and results.
Detects genomic variants affecting the alternative splicing using genotypic and gene expression data (RNA-seq). IVAS is an R package available on Bioconductor. It provides functions to find alternative exons of a gene, to estimate relative expression ratio, to separate a TxDb object based on a chromosome, to calculate significance SNPs, to find single-nucleotide polymorphism (SNPs) which belong to alternative exons and flanking introns of them and many others.
Allows ultra-fast composite splicing quantitative trait loci (sQTL) analysis. ulfasQTL transforms vectors of splicing ratios to a spherical coordinate system. It uses a matrix-based computation to test multiple genes and variants at the same time. The tool is able to detect composite sQTLs for all gene-variant pairs on the whole genome efficiently. It is helpful to investigate both cis- and trans- factors that can be associated with splicing composite variation.