Alternative splicing identification software tools | Single-cell RNA sequencing data analysis
Single cell RNA-seq experiments provide valuable insight into cellular heterogeneity but suffer from low coverage, 3' bias and technical noise. These unique properties of single cell RNA-seq data make study of alternative splicing difficult, and thus most single cell studies have restricted analysis of transcriptome variation to the gene level.
An easy-to-use application for microarray, RNA-Seq and metabolomics analysis. For splicing sensitive platforms (RNA-Seq or Affymetrix Exon, Gene and Junction arrays), AltAnalyze will assess alternative exon (known and novel) expression along protein isoforms, domain composition and microRNA targeting. In addition to splicing-sensitive platforms, AltAnalyze provides comprehensive methods for the analysis of other data (RMA summarization, batch-effect removal, QC, statistics, annotation, clustering, network creation, lineage characterization, alternative exon visualization, gene-set enrichment and more).
Uses junction reads from RNA seq data, and a graph database to create a de novo alternative splicing annotation with a graph database. Outrigger is a Python package and an RNA-seq analysis software that quantify percent spliced-in (Psi) of the events. It finds novel splicing events, including novel exons and was developed to help user to be confident in alternative exon inclusion calculations. It is recommended to use the Anaconda Python Distribution and creates an environment to install outrigger.
Allows users to visualize and census splicing distribution changes. bonvoyage provides a package leaning on an approach able to generate a 2-dimensional space from Ψ distribution. This application primarily discretizes the distribution of Ψ values of each alternative splicing (AS) events and lastly reduces them by using a non-negative matrix factorization (NMF). It aims to assist users in detecting AS events that change across populations.
Quantifies splicing in individual single cells. BRIE is a flexible framework that detects differential splicing between individual cells from scRNA-seq data. This method was developed for modelling and, while sequence features are particularly appealing due to their ease of usage and availability, additional side information, such as DNA methylation and chromatin accessibility, could easily be incorporated.
A tool for studying alternative splicing using single cell RNA-seq data. SingleSplice uses a statistical model to detect genes whose isoform usage varies more than expected from the effects of technical noise alone. Importantly, SingleSplice detects such isoform usage differences without attempting to infer expression levels for full-length transcripts. To the best of our knowledge, SingleSplice is the first method that can detect genes whose isoform usage shows significant variation across a set of single cells.
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