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

ABSTRACT: The concordance of RNA-sequencing (RNA-seq) with microarrays for genome-wide analysis of differential gene expression has not been rigorously assessed using a range of chemical treatment conditions. Here we use a comprehensive study design to generate Illumina RNA-seq and Affymetrix microarray data from the same liver samples of rats exposed in triplicate to varying degrees of perturbation by 27 chemicals representing multiple modes of action (MOAs). The cross-platform concordance in terms of differentially expressed genes (DEGs) or enriched pathways is linearly correlated with treatment effect size (R(2)?0.8). Furthermore, the concordance is also affected by transcript abundance and biological complexity of the MOA. RNA-seq outperforms microarray (93% versus 75%) in DEG verification as assessed by quantitative PCR, with the gain mainly due to its improved accuracy for low-abundance transcripts. Nonetheless, classifiers to predict MOAs perform similarly when developed using data from either platform. Therefore, the endpoint studied and its biological complexity, transcript abundance and the genomic application are important factors in transcriptomic research and for clinical and regulatory decision making.

SUBMITTER: Wang C

PROVIDER: S-EPMC4243706 | biostudies-literature | 2014 Sep

REPOSITORIES: biostudies-literature

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

Wang Charles C Gong Binsheng B Bushel Pierre R PR Thierry-Mieg Jean J Thierry-Mieg Danielle D Xu Joshua J Fang Hong H Hong Huixiao H Shen Jie J Su Zhenqiang Z Meehan Joe J Li Xiaojin X Yang Lu L Li Haiqing H Łabaj Paweł P PP Kreil David P DP Megherbi Dalila D Gaj Stan S Caiment Florian F van Delft Joost J Kleinjans Jos J Scherer Andreas A Devanarayan Viswanath V Wang Jian J Yang Yong Y Qian Hui-Rong HR Lancashire Lee J LJ Bessarabova Marina M Nikolsky Yuri Y Furlanello Cesare C Chierici Marco M Albanese Davide D Jurman Giuseppe G Riccadonna Samantha S Filosi Michele M Visintainer Roberto R Zhang Ke K KK Li Jianying J Hsieh Jui-Hua JH Svoboda Daniel L DL Fuscoe James C JC Deng Youping Y Shi Leming L Paules Richard S RS Auerbach Scott S SS Tong Weida W

Nature biotechnology 20140824 9

The concordance of RNA-sequencing (RNA-seq) with microarrays for genome-wide analysis of differential gene expression has not been rigorously assessed using a range of chemical treatment conditions. Here we use a comprehensive study design to generate Illumina RNA-seq and Affymetrix microarray data from the same liver samples of rats exposed in triplicate to varying degrees of perturbation by 27 chemicals representing multiple modes of action (MOAs). The cross-platform concordance in terms of di ...[more]

PMID: 25150839

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Project description:Alternative splicing leverages genomic content by allowing the synthesis of multiple transcripts and, by implication, protein isoforms, from a single gene. However, estimating the abundance of transcripts produced in a given tissue from short sequencing reads is difficult and can result in both the construction of transcripts that do not exist, and the failure to identify true transcripts. An alternative approach is to catalog the events that make up isoforms (splice junctions and exons). We present here the Event Analysis (EA) approach, where we project transcripts onto the genome and identify overlapping/unique regions and junctions. In addition, all possible logical junctions are assembled into a catalog. Transcripts are filtered before quantitation based on simple measures: the proportion of the events detected, and the coverage. We find that mapping to a junction catalog is more efficient at detecting novel junctions than mapping in a splice aware manner. We identify 99.8% of true transcripts while iReckon identifies 82% of the true transcripts and creates more transcripts not included in the simulation than were initially used in the simulation. Using PacBio Iso-seq data from a mouse neural progenitor cell model, EA detects 60% of the novel junctions that are combinations of existing exons while only 43% are detected by STAR. EA further detects ?5,000 annotated junctions missed by STAR. Filtering transcripts based on the proportion of the transcript detected and the number of reads on average supporting that transcript captures 95% of the PacBio transcriptome. Filtering the reference transcriptome before quantitation, results in is a more stable estimate of isoform abundance, with improved correlation between replicates. This was particularly evident when EA is applied to an RNA-seq study of type 1 diabetes (T1D), where the coefficient of variation among subjects (n = 81) in the transcript abundance estimates was substantially reduced compared to the estimation using the full reference. EA focuses on individual transcriptional events. These events can be quantitate and analyzed directly or used to identify the probable set of expressed transcripts. Simple rules based on detected events and coverage used in filtering result in a dramatic improvement in isoform estimation without the use of ancillary data (e.g., ChIP, long reads) that may not be available for many studies.

Dataset Information

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

Publications

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

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