Focus


Focus on RNA sequencing quality control (SEQC)

This focus presents the results of the RNA Sequencing Quality Control (SEQC) project of the MicroArray Quality Control (MAQC) Consortium that sought to evaluate the comparability of RNA-seq data from many different laboratories and of assessing different sequencing platforms and data analysis approaches and their performance compared with DNA microarrays. Ultimately, these multi-platform, cross-site studies will enable RNA-seq to be applied more broadly in analyzing large cohorts for discovery research and clinical use.

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In This Issue


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Editorial

Focus on RNA sequencing quality control (SEQC)

Honing our reading skills p845

doi:10.1038/nbt.3021

Studies from the RNA Sequencing Quality Control (SEQC) initiative exemplify the kind of experimental groundwork needed to expand RNA-seq into a broader array of basic and translational applications.


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News and Views

Focus on RNA sequencing quality control (SEQC)

The devil in the details of RNA-seq pp882 - 884

Anton Kratz & Piero Carninci

doi:10.1038/nbt.3015

Large-scale consortium efforts provide a thorough understanding of RNA-seq.

See also: Computational Biology by Li et al. | Computational Biology by Risso et al. | Research by SEQC/MAQC-III Consortium | Research by Li et al. | Research by Wang et al.


Focus on RNA sequencing quality control (SEQC)

Bringing RNA-seq closer to the clinic pp884 - 885

Kendall Van Keuren-Jensen, Jonathan J Keats & David W Craig

doi:10.1038/nbt.3017

Several multicenter benchmark data sets represent valuable steps toward using RNA-seq as a diagnostic tool with clinical utility.

See also: Computational Biology by Li et al. | Computational Biology by Risso et al. | Research by SEQC/MAQC-III Consortium | Research by Li et al. | Research by Wang et al.


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Computational Biology

Analysis

Focus on RNA sequencing quality control (SEQC)

Detecting and correcting systematic variation in large-scale RNA sequencing data pp888 - 895

Sheng Li, Paweł P Łabaj, Paul Zumbo, Peter Sykacek, Wei Shi, Leming Shi, John Phan, Po-Yen Wu, May Wang, Charles Wang, Danielle Thierry-Mieg, Jean Thierry-Mieg, David P Kreil & Christopher E Mason

doi:10.1038/nbt.3000

Li et al. identify the top-performing methods to improve cross-site differential gene expression analysis with RNA-seq.

See also: News and Views by Kratz & Carninci | News and Views by Van Keuren-Jensen et al.


Focus on RNA sequencing quality control (SEQC)

Normalization of RNA-seq data using factor analysis of control genes or samples pp896 - 902

Davide Risso, John Ngai, Terence P Speed & Sandrine Dudoit

doi:10.1038/nbt.2931

Remove unwanted variation (RUV) is a new statistical method for RNA-seq data normalization that uses control genes or samples to improve differential expression analysis.

See also: News and Views by Kratz & Carninci | News and Views by Van Keuren-Jensen et al.


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Research

Articles

Focus on RNA sequencing quality control (SEQC)

A comprehensive assessment of RNA-seq accuracy, reproducibility and information content by the Sequencing Quality Control Consortium pp903 - 914

SEQC/MAQC-III Consortium

doi:10.1038/nbt.2957

The Sequencing Quality Control (SEQC) consortium shows that junction discovery and differential gene expression profiling with RNA-seq can be robust but transcript-level and absolute measurements remain challenging.

See also: News and Views by Kratz & Carninci | News and Views by Van Keuren-Jensen et al.


Focus on RNA sequencing quality control (SEQC)

Multi-platform assessment of transcriptome profiling using RNA-seq in the ABRF next-generation sequencing study pp915 - 925

Sheng Li, Scott W Tighe, Charles M Nicolet, Deborah Grove, Shawn Levy, William Farmerie, Agnes Viale, Chris Wright, Peter A Schweitzer, Yuan Gao, Dewey Kim, Joe Boland, Belynda Hicks, Ryan Kim, Sagar Chhangawala, Nadereh Jafari, Nalini Raghavachari, Jorge Gandara, Natàlia Garcia-Reyero, Cynthia Hendrickson, David Roberson, Jeffrey Rosenfeld, Todd Smith, Jason G Underwood, May Wang, Paul Zumbo, Don A Baldwin, George S Grills & Christopher E Mason

doi:10.1038/nbt.2972

For intact RNA, gene expression profiles from rRNA-depletion and poly-A enrichment are similar. In addition, rRNA- depletion enables effective analysis of degraded RNA samples.

See also: News and Views by Kratz & Carninci | News and Views by Van Keuren-Jensen et al.


Focus on RNA sequencing quality control (SEQC)

The concordance between RNA-seq and microarray data depends on chemical treatment and transcript abundance pp926 - 932

Charles Wang, Binsheng Gong, Pierre R Bushel, Jean Thierry-Mieg, Danielle Thierry-Mieg, Joshua Xu, Hong Fang, Huixiao Hong, Jie Shen, Zhenqiang Su, Joe Meehan, Xiaojin Li, Lu Yang, Haiqing Li, Paweł P Łabaj, David P Kreil, Dalila Megherbi, Stan Gaj, Florian Caiment, Joost van Delft, Jos Kleinjans, Andreas Scherer, Viswanath Devanarayan, Jian Wang, Yong Yang, Hui-Rong Qian, Lee J Lancashire, Marina Bessarabova, Yuri Nikolsky, Cesare Furlanello, Marco Chierici, Davide Albanese, Giuseppe Jurman, Samantha Riccadonna, Michele Filosi, Roberto Visintainer, Ke K Zhang, Jianying Li, Jui-Hua Hsieh, Daniel L Svoboda, James C Fuscoe, Youping Deng, Leming Shi, Richard S Paules, Scott S Auerbach & Weida Tong

doi:10.1038/nbt.3001

A comparison of RNA-seq and microarray data from samples treated with diverse drugs highlights a dependency of cross-platform concordance on treatment effect.

See also: News and Views by Kratz & Carninci | News and Views by Van Keuren-Jensen et al.


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