Focus
Focus on RNA sequencing quality control (SEQC)
- Focus
- September 2014 Volume 32, No 9
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.
In This Issue
Focus on RNA sequencing quality control (SEQC) - pvii
doi:10.1038/nbt.3025
Full Text - Focus on RNA sequencing quality control (SEQC) | PDF (223 KB) - Focus on RNA sequencing quality control (SEQC)
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.
Abstract - | Full Text - Honing our reading skills | PDF (167 KB) - Honing our reading skills
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.
Full Text - The devil in the details of RNA-seq | PDF (331 KB) - The devil in the details 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.
Full Text - Bringing RNA-seq closer to the clinic | PDF (200 KB) - Bringing RNA-seq closer to the clinic
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.
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.
Abstract - | Full Text - Detecting and correcting systematic variation in large-scale RNA sequencing data | PDF (1,651 KB) - Detecting and correcting systematic variation in large-scale RNA sequencing data | Supplementary information
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.
Abstract - | Full Text - Normalization of RNA-seq data using factor analysis of control genes or samples | PDF (1,888 KB) - Normalization of RNA-seq data using factor analysis of control genes or samples | Supplementary information
See also: News and Views by Kratz & Carninci | News and Views by Van Keuren-Jensen et al.
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.
Abstract - | Full Text - A comprehensive assessment of RNA-seq accuracy, reproducibility and information content by the Sequencing Quality Control Consortium | PDF (1,811 KB) - A comprehensive assessment of RNA-seq accuracy, reproducibility and information content by the Sequencing Quality Control Consortium | Supplementary information
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.
Abstract - | Full Text - Multi-platform assessment of transcriptome profiling using RNA-seq in the ABRF next-generation sequencing study | PDF (2,015 KB) - Multi-platform assessment of transcriptome profiling using RNA-seq in the ABRF next-generation sequencing study | Supplementary information
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.
Abstract - | Full Text - The concordance between RNA-seq and microarray data depends on chemical treatment and transcript abundance | PDF (1,466 KB) - The concordance between RNA-seq and microarray data depends on chemical treatment and transcript abundance | Supplementary information
See also: News and Views by Kratz & Carninci | News and Views by Van Keuren-Jensen et al.
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