Literature DB >> 27565134

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

Ana Conesa1,2, Pedro Madrigal3,4, Sonia Tarazona5,6, David Gomez-Cabrero7,8,9,10, Alejandra Cervera11, Andrew McPherson12, Michal Wojciech Szcześniak13, Daniel J Gaffney14, Laura L Elo15, Xuegong Zhang16,17, Ali Mortazavi18,19.   

Abstract

Entities:  

Year:  2016        PMID: 27565134      PMCID: PMC5000515          DOI: 10.1186/s13059-016-1047-4

Source DB:  PubMed          Journal:  Genome Biol        ISSN: 1474-7596            Impact factor:   13.583


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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).
  1 in total

Review 1.  A survey of best practices for RNA-seq data analysis.

Authors:  Ana Conesa; Pedro Madrigal; Sonia Tarazona; David Gomez-Cabrero; Alejandra Cervera; Andrew McPherson; Michał Wojciech Szcześniak; Daniel J Gaffney; Laura L Elo; Xuegong Zhang; Ali Mortazavi
Journal:  Genome Biol       Date:  2016-01-26       Impact factor: 13.583

  1 in total
  49 in total

1.  The cryptic unstable transcripts are associated with developmentally regulated gene expression in blood-stage Plasmodium falciparum.

Authors:  Shigang Yin; Yanting Fan; Xiaohui He; Guiying Wei; Yuhao Wen; Yuemeng Zhao; Mingli Shi; Jieqiong Wei; Huiling Chen; Jiping Han; Lubin Jiang; Qingfeng Zhang
Journal:  RNA Biol       Date:  2020-02-27       Impact factor: 4.652

2.  De novo assembly of the transcriptome of scleractinian coral, Anomastraea irregularis and analyses of its response to thermal stress.

Authors:  Christine A Onyango; David Glassom; Angus MacDonald
Journal:  Mol Biol Rep       Date:  2021-03-03       Impact factor: 2.316

Review 3.  Genomics pipelines and data integration: challenges and opportunities in the research setting.

Authors:  Jeremy Davis-Turak; Sean M Courtney; E Starr Hazard; W Bailey Glen; Willian A da Silveira; Timothy Wesselman; Larry P Harbin; Bethany J Wolf; Dongjun Chung; Gary Hardiman
Journal:  Expert Rev Mol Diagn       Date:  2017-01-25       Impact factor: 5.225

Review 4.  Single-cell technologies in reproductive immunology.

Authors:  Jessica Vazquez; Irene M Ong; Aleksandar K Stanic
Journal:  Am J Reprod Immunol       Date:  2019-06-26       Impact factor: 3.886

5.  Hierarchical analysis of RNA-seq reads improves the accuracy of allele-specific expression.

Authors:  Narayanan Raghupathy; Kwangbom Choi; Matthew J Vincent; Glen L Beane; Keith S Sheppard; Steven C Munger; Ron Korstanje; Fernando Pardo-Manual de Villena; Gary A Churchill
Journal:  Bioinformatics       Date:  2018-07-01       Impact factor: 6.937

6.  RNA Seq analysis for transcriptome profiling in response to classical swine fever vaccination in indigenous and crossbred pigs.

Authors:  Shalu Kumari Pathak; Amit Kumar; G Bhuwana; Vaishali Sah; Vikramadiya Upmanyu; A K Tiwari; A P Sahoo; A R Sahoo; Sajjad A Wani; Manjit Panigrahi; N R Sahoo; Ravi Kumar
Journal:  Funct Integr Genomics       Date:  2017-03-30       Impact factor: 3.410

7.  MSIQ: JOINT MODELING OF MULTIPLE RNA-SEQ SAMPLES FOR ACCURATE ISOFORM QUANTIFICATION.

Authors:  Wei Vivian Li; Anqi Zhao; Shihua Zhang; Jingyi Jessica Li
Journal:  Ann Appl Stat       Date:  2018-03-09       Impact factor: 2.083

8.  Exploring the effect of library preparation on RNA sequencing experiments.

Authors:  Lei Wang; Sara J Felts; Virginia P Van Keulen; Larry R Pease; Yuji Zhang
Journal:  Genomics       Date:  2018-12-06       Impact factor: 5.736

Review 9.  Identification and expression profiling analysis of NBS-LRR genes involved in Fusarium oxysporum f.sp. conglutinans resistance in cabbage.

Authors:  Zeci Liu; Jianming Xie; Huiping Wang; Xionghui Zhong; Hailong Li; Jihua Yu; Jungen Kang
Journal:  3 Biotech       Date:  2019-05-04       Impact factor: 2.406

10.  Transcriptomics of manually isolated Amborella trichopoda egg apparatus cells.

Authors:  María Flores-Tornero; Sebastian Proost; Marek Mutwil; Charles P Scutt; Thomas Dresselhaus; Stefanie Sprunck
Journal:  Plant Reprod       Date:  2019-02-01       Impact factor: 3.767

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