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Open Access

Erratum to: A survey of best practices for RNA-seq data analysis

Genome Biology201617:181

https://doi.org/10.1186/s13059-016-1047-4

Received: 17 August 2016

Accepted: 17 August 2016

Published: 26 August 2016

The original article was published in Genome Biology 2016 17:13

Erratum

During editing of the article by Conesa et al. [1], an error was introduced to some of the citations, such that incorrect references were provided for some articles the second time they were cited. The following sentences are affected:

Algorithms that quantify expression from transcriptome mappings include RSEM (RNA-Seq by Expectation Maximization) [40], eXpress [41], Sailfish [35] and kallisto [42] among others. These methods allocate multi-mapping reads among transcript and output within-sample normalized values corrected for sequencing biases [35, 41, 43].

The citation for Sailfish should be [34] (Patro et al., Nat Biotechnol. 2014;32:463–4) in both sentences.

Additional factors that interfere with intra-sample comparisons include changes in transcript length across samples or conditions [50], positional biases in coverage along the transcript (which are accounted for in Cufflinks), average fragment size [43], and the GC contents of genes (corrected in the EDAseq package [21]).

The citation for EDAseq should be [20] (Risso et al. BMC Bioinformatics. 2011;12:480)

The NOISeq R package [20] contains a wide variety of diagnostic plots to identify sources of biases in RNA-seq data and to apply appropriate normalization procedures in each case.

The citation for NOISeq should be [19] (Tarazona et al. Nucleic Acids Res. 2015;43:e140)

These effects can be minimized by appropriate experimental design [51] or, alternatively, removed by batch-correction methods such as COMBAT [52] or ARSyN [20, 53].

The citations for ARSyN should be [19, 53] (Tarazona et al. Nucleic Acids Res. 2015;43:e140, Nueda et al. Biostatistics. 2012;13:553–66).

All these approaches are generally hampered by the intrinsic limitations of short-read sequencing for accurate identification at the isoform level, as discussed in the RNA-seq Genome Annotation Assessment Project paper [30].

The citation for the RGASP article should be [29] (Engström et al. Nat Methods. 2013;10:1185–91).

We refer the reader to [30] for a comprehensive comparison of RNA-seq mappers.

This citation should be [29] (Engström et al. Nat Methods. 2013;10:1185–91).

Notes

Declarations

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.

Authors’ Affiliations

(1)
Institute for Food and Agricultural Sciences, Department of Microbiology and Cell Science, University of Florida
(2)
Centro de Investigación Príncipe Felipe, Genomics of Gene Expression Laboratory
(3)
Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus
(4)
Wellcome Trust-Medical Research Council Cambridge Stem Cell Institute, Anne McLaren Laboratory for Regenerative Medicine, Department of Surgery, University of Cambridge
(5)
Department of Applied Statistics, Operations Research and Quality, Universidad Politécnica de Valencia
(6)
Unit of Computational Medicine, Department of Medicine, Karolinska Institutet, Karolinska University Hospital
(7)
Center for Molecular Medicine, Karolinska Institutet
(8)
Unit of Clinical Epidemiology, Department of Medicine, Karolinska University Hospital
(9)
Science for Life Laboratory
(10)
Systems Biology Laboratory, Institute of Biomedicine and Genome-Scale Biology Research Program, University of Helsinki
(11)
School of Computing Science, Simon Fraser University
(12)
Department of Bioinformatics, Institute of Molecular Biology and Biotechnology, Adam Mickiewicz University in Poznań
(13)
Turku Centre for Biotechnology, University of Turku and Åbo Akademi University
(14)
Key Lab of Bioinformatics/Bioinformatics Division, TNLIST and Department of Automation, Tsinghua University
(15)
School of Life Sciences, Tsinghua University
(16)
Department of Developmental and Cell Biology, University of California, Irvine
(17)
Center for Complex Biological Systems, University of California, Irvine

References

  1. Conesa A, Madrigal P, Tarazona S, Gomez-Cabrero D, Cervera A, McPherson A, et al. Genome Biol. 2016;17:13.View ArticlePubMedPubMed CentralGoogle Scholar

Copyright

© The Author(s). 2016

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