SuperTranscripts: a data driven reference for analysis and visualisation of transcriptomes

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Abstract

Numerous methods have been developed to analyse RNA sequencing (RNA-seq) data, but most rely on the availability of a reference genome, making them unsuitable for non-model organisms. Here we present superTranscripts, a substitute for a reference genome, where each gene with multiple transcripts is represented by a single sequence. The Lace software is provided to construct superTranscripts from any set of transcripts, including de novo assemblies. We demonstrate how superTranscripts enable visualisation, variant detection and differential isoform detection in non-model organisms. We further use Lace to combine reference and assembled transcriptomes for chicken and recover hundreds of gaps in the reference genome.

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The online version of this article (doi:10.1186/s13059-017-1284-1) contains supplementary material, which is available to authorized users.

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featureCounts: An efficient general-purpose program for assigning sequence reads to genomic features

, , (2013)

Next-generation sequencing technologies generate millions of short sequence reads, which are usually aligned to a reference genome. In many applications, the key information required for downstream analysis is the number of reads mapping to each genomic feature, for example to each exon or each gene. The process of counting reads is called read summarization. Read summarization is required for a great variety of genomic analyses but has so far received relatively little attention in the literature. We present featureCounts, a read summarization program suitable for counting reads generated from either RNA or genomic DNA sequencing experiments. featureCounts implements highly efficient chromosome hashing and feature blocking techniques. It is considerably faster than existing methods (by an order of magnitude for gene-level summarization) and requires far less computer memory. It works with either single or paired-end reads and provides a wide range of options appropriate for different sequencing applications. featureCounts is available under GNU General Public License as part of the Subread (http://subread.sourceforge.net) or Rsubread (http://www.bioconductor.org) software packages.

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Next-generation transcriptome assembly.

Jeffrey A Martin, Zhong Wang (2011)

Transcriptomics studies often rely on partial reference transcriptomes that fail to capture the full catalogue of transcripts and their variations. Recent advances in sequencing technologies and assembly algorithms have facilitated the reconstruction of the entire transcriptome by deep RNA sequencing (RNA-seq), even without a reference genome. However, transcriptome assembly from billions of RNA-seq reads, which are often very short, poses a significant informatics challenge. This Review summarizes the recent developments in transcriptome assembly approaches - reference-based, de novo and combined strategies - along with some perspectives on transcriptome assembly in the near future.

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Understanding alternative splicing: towards a cellular code.

Arianne J. Matlin, Francis Clark, Christopher Smith (2005)

In violation of the 'one gene, one polypeptide' rule, alternative splicing allows individual genes to produce multiple protein isoforms - thereby playing a central part in generating complex proteomes. Alternative splicing also has a largely hidden function in quantitative gene control, by targeting RNAs for nonsense-mediated decay. Traditional gene-by-gene investigations of alternative splicing mechanisms are now being complemented by global approaches. These promise to reveal details of the nature and operation of cellular codes that are constituted by combinations of regulatory elements in pre-mRNA substrates and by cellular complements of splicing regulators, which together determine regulated splicing pathways.

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Author and article information

Contributors

Nadia M. Davidson: nadia.davidson@mcri.edu.au

Alicia Oshlack:

ORCID: http://orcid.org/0000-0001-9788-5690

alicia.oshlack@mcri.edu.au

Journal

Journal ID (nlm-ta): Genome Biol

Journal ID (iso-abbrev): Genome Biol

Title: Genome Biology

Publisher: BioMed Central (London )

ISSN (Print): 1474-7596

ISSN (Electronic): 1474-760X

Publication date (Electronic): 4 August 2017

Publication date PMC-release: 4 August 2017

Publication date Collection: 2017

Volume: 18

Electronic Location Identifier: 148

Affiliations

[1 ]Murdoch Children’s Research Institute, Royal Children’s Hospital, Melbourne, VIC Australia

[2 ]ISNI 0000 0001 2179 088X, GRID grid.1008.9, , School of BioSciences, University of Melbourne, ; Melbourne, VIC Australia

Author information

Alicia Oshlack http://orcid.org/0000-0001-9788-5690

Article

Publisher ID: 1284

DOI: 10.1186/s13059-017-1284-1

PMC ID: 5543425

PubMed ID: 28778180

SO-VID: ab7780ee-61a1-4d55-87cc-12444cf63d20

License:

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

History

Date received : 13 April 2017

Date accepted : 21 July 2017

Funding

Funded by: FundRef http://dx.doi.org/10.13039/501100000925, National Health and Medical Research Council;

Award ID: APP1051481

Award Recipient : Alicia Oshlack

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SuperTranscripts: a data driven reference for analysis and visualisation of transcriptomes

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Most cited references 14

featureCounts: An efficient general-purpose program for assigning sequence reads to genomic features

Next-generation transcriptome assembly.

Understanding alternative splicing: towards a cellular code.

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