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SplAdder: identification, quantification and testing of alternative splicing events from RNA-Seq data
Motivation: Understanding the occurrence and regulation of alternative splicing (AS) is a key task towards explaining the regulatory processes that shape the complex transcriptomes of higher eukaryotes. With the advent of high-throughput sequencing of RNA (RNA-Seq), the diversity of AS transcripts c...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4908322/ https://www.ncbi.nlm.nih.gov/pubmed/26873928 http://dx.doi.org/10.1093/bioinformatics/btw076 |
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author | Kahles, André Ong, Cheng Soon Zhong, Yi Rätsch, Gunnar |
author_facet | Kahles, André Ong, Cheng Soon Zhong, Yi Rätsch, Gunnar |
author_sort | Kahles, André |
collection | PubMed |
description | Motivation: Understanding the occurrence and regulation of alternative splicing (AS) is a key task towards explaining the regulatory processes that shape the complex transcriptomes of higher eukaryotes. With the advent of high-throughput sequencing of RNA (RNA-Seq), the diversity of AS transcripts could be measured at an unprecedented depth. Although the catalog of known AS events has grown ever since, novel transcripts are commonly observed when working with less well annotated organisms, in the context of disease, or within large populations. Whereas an identification of complete transcripts is technically challenging and computationally expensive, focusing on single splicing events as a proxy for transcriptome characteristics is fruitful and sufficient for a wide range of analyses. Results: We present SplAdder, an alternative splicing toolbox, that takes RNA-Seq alignments and an annotation file as input to (i) augment the annotation based on RNA-Seq evidence, (ii) identify alternative splicing events present in the augmented annotation graph, (iii) quantify and confirm these events based on the RNA-Seq data and (iv) test for significant quantitative differences between samples. Thereby, our main focus lies on performance, accuracy and usability. Availability: Source code and documentation are available for download at http://github.com/ratschlab/spladder. Example data, introductory information and a small tutorial are accessible via http://bioweb.me/spladder. Contacts: andre.kahles@ratschlab.org or gunnar.ratsch@ratschlab.org Supplementary information: Supplementary data are available at Bioinformatics online. |
format | Online Article Text |
id | pubmed-4908322 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-49083222016-06-17 SplAdder: identification, quantification and testing of alternative splicing events from RNA-Seq data Kahles, André Ong, Cheng Soon Zhong, Yi Rätsch, Gunnar Bioinformatics Original Papers Motivation: Understanding the occurrence and regulation of alternative splicing (AS) is a key task towards explaining the regulatory processes that shape the complex transcriptomes of higher eukaryotes. With the advent of high-throughput sequencing of RNA (RNA-Seq), the diversity of AS transcripts could be measured at an unprecedented depth. Although the catalog of known AS events has grown ever since, novel transcripts are commonly observed when working with less well annotated organisms, in the context of disease, or within large populations. Whereas an identification of complete transcripts is technically challenging and computationally expensive, focusing on single splicing events as a proxy for transcriptome characteristics is fruitful and sufficient for a wide range of analyses. Results: We present SplAdder, an alternative splicing toolbox, that takes RNA-Seq alignments and an annotation file as input to (i) augment the annotation based on RNA-Seq evidence, (ii) identify alternative splicing events present in the augmented annotation graph, (iii) quantify and confirm these events based on the RNA-Seq data and (iv) test for significant quantitative differences between samples. Thereby, our main focus lies on performance, accuracy and usability. Availability: Source code and documentation are available for download at http://github.com/ratschlab/spladder. Example data, introductory information and a small tutorial are accessible via http://bioweb.me/spladder. Contacts: andre.kahles@ratschlab.org or gunnar.ratsch@ratschlab.org Supplementary information: Supplementary data are available at Bioinformatics online. Oxford University Press 2016-06-15 2016-02-11 /pmc/articles/PMC4908322/ /pubmed/26873928 http://dx.doi.org/10.1093/bioinformatics/btw076 Text en © The Author 2016. Published by Oxford University Press. http://creativecommons.org/licenses/by/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Original Papers Kahles, André Ong, Cheng Soon Zhong, Yi Rätsch, Gunnar SplAdder: identification, quantification and testing of alternative splicing events from RNA-Seq data |
title | SplAdder: identification, quantification and testing of alternative splicing events from RNA-Seq data |
title_full | SplAdder: identification, quantification and testing of alternative splicing events from RNA-Seq data |
title_fullStr | SplAdder: identification, quantification and testing of alternative splicing events from RNA-Seq data |
title_full_unstemmed | SplAdder: identification, quantification and testing of alternative splicing events from RNA-Seq data |
title_short | SplAdder: identification, quantification and testing of alternative splicing events from RNA-Seq data |
title_sort | spladder: identification, quantification and testing of alternative splicing events from rna-seq data |
topic | Original Papers |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4908322/ https://www.ncbi.nlm.nih.gov/pubmed/26873928 http://dx.doi.org/10.1093/bioinformatics/btw076 |
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