Literature DB >> 22468815

Exome versus transcriptome sequencing in identifying coding region variants.

Chee-Seng Ku1, Mengchu Wu, David N Cooper, Nasheen Naidoo, Yudi Pawitan, Brendan Pang, Barry Iacopetta, Richie Soong.   

Abstract

The advent of next-generation sequencing technologies has revolutionized the study of genetic variation in the human genome. Whole-genome sequencing currently represents the most comprehensive strategy for variant detection genome-wide but is costly for large sample sizes, and variants detected in noncoding regions remain largely uninterpretable. By contrast, whole-exome sequencing has been widely applied in the identification of germline mutations underlying Mendelian disorders, somatic mutations in various cancers and de novo mutations in neurodevelopmental disorders. Since whole-exome sequencing focuses upon the entire set of exons in the genome (the exome), it requires additional exome-enrichment steps compared with whole-genome sequencing. Although the availability of multiple commercial exome-enrichment kits has made whole-exome sequencing technically feasible, it has also added to the overall cost. This has led to the emergence of transcriptome (or RNA) sequencing as a potential alternative approach to variant detection within protein coding regions, since the transcriptome of a given tissue represents a quasi-complete set of transcribed genes (mRNAs) and other noncoding RNAs. A further advantage of this approach is that it bypasses the need for exome enrichment. Here we discuss the relative merits and limitations of these approaches as they are applied in the context of variant detection within gene coding regions.

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Year:  2012        PMID: 22468815     DOI: 10.1586/erm.12.10

Source DB:  PubMed          Journal:  Expert Rev Mol Diagn        ISSN: 1473-7159            Impact factor:   5.225


  18 in total

1.  Systems pharmacology identifies drug targets for Stargardt disease-associated retinal degeneration.

Authors:  Yu Chen; Grazyna Palczewska; Debarshi Mustafi; Marcin Golczak; Zhiqian Dong; Osamu Sawada; Tadao Maeda; Akiko Maeda; Krzysztof Palczewski
Journal:  J Clin Invest       Date:  2013-11-15       Impact factor: 14.808

Review 2.  Exome sequencing greatly expedites the progressive research of Mendelian diseases.

Authors:  Xuejun Zhang
Journal:  Front Med       Date:  2014-01-03       Impact factor: 4.592

Review 3.  Targeted capture in evolutionary and ecological genomics.

Authors:  Matthew R Jones; Jeffrey M Good
Journal:  Mol Ecol       Date:  2015-07-30       Impact factor: 6.185

4.  Inconsistency and features of single nucleotide variants detected in whole exome sequencing versus transcriptome sequencing: A case study in lung cancer.

Authors:  Timothy D O'Brien; Peilin Jia; Junfeng Xia; Uma Saxena; Hailing Jin; Huy Vuong; Pora Kim; Qingguo Wang; Martin J Aryee; Mari Mino-Kenudson; Jeffrey A Engelman; Long P Le; A John Iafrate; Rebecca S Heist; William Pao; Zhongming Zhao
Journal:  Methods       Date:  2015-04-23       Impact factor: 3.608

Review 5.  [Application of RNA sequencing in clinical diagnosis of Mendelian disease].

Authors:  Hui Xiao; Wen-Hao Zhou
Journal:  Zhongguo Dang Dai Er Ke Za Zhi       Date:  2020-10

6.  Integrated RNA and DNA sequencing improves mutation detection in low purity tumors.

Authors:  Matthew D Wilkerson; Christopher R Cabanski; Wei Sun; Katherine A Hoadley; Vonn Walter; Lisle E Mose; Melissa A Troester; Peter S Hammerman; Joel S Parker; Charles M Perou; D Neil Hayes
Journal:  Nucleic Acids Res       Date:  2014-06-26       Impact factor: 16.971

7.  Structural variant identification and characterization.

Authors:  Parithi Balachandran; Christine R Beck
Journal:  Chromosome Res       Date:  2020-01-06       Impact factor: 5.239

8.  RADIA: RNA and DNA integrated analysis for somatic mutation detection.

Authors:  Amie J Radenbaugh; Singer Ma; Adam Ewing; Joshua M Stuart; Eric A Collisson; Jingchun Zhu; David Haussler
Journal:  PLoS One       Date:  2014-11-18       Impact factor: 3.240

9.  Limitations of Detecting Genetic Variants from the RNA Sequencing Data in Tissue and Fine-Needle Aspiration Samples.

Authors:  Cihan Kaya; Princesca Dorsaint; Stephanie Mercurio; Alexander M Campbell; Kenneth Wha Eng; Marina N Nikiforova; Olivier Elemento; Yuri E Nikiforov; Andrea Sboner
Journal:  Thyroid       Date:  2020-10-13       Impact factor: 6.506

10.  Informatics for RNA Sequencing: A Web Resource for Analysis on the Cloud.

Authors:  Malachi Griffith; Jason R Walker; Nicholas C Spies; Benjamin J Ainscough; Obi L Griffith
Journal:  PLoS Comput Biol       Date:  2015-08-06       Impact factor: 4.475

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