Liping Li1, Enguo Chen2, Chun Yang2, Jun Zhu2, Pushkala Jayaraman2, Jeffrey De Pons2, Catherine C Kaczorowski2, Howard J Jacob1, Andrew S Greene2, Matthew R Hodges2, Allen W Cowley2, Mingyu Liang2, Haiming Xu2, Pengyuan Liu1, Yan Lu1. 1. Department of Gynecologic Oncology, The Affiliated Women's Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang 310006, Institute of Bioinformatics, Zhejiang University, Hangzhou, Zhejiang 310058, China, Division of Respiratory Medicine, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang 310058, China, Department of Physiology and the Cancer Center, Medical College of Wisconsin, Milwaukee, WI 53226, USA and Human Molecular and Genetics Center, Medical College of Wisconsin, Milwaukee, WI 53226, USA Department of Gynecologic Oncology, The Affiliated Women's Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang 310006, Institute of Bioinformatics, Zhejiang University, Hangzhou, Zhejiang 310058, China, Division of Respiratory Medicine, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang 310058, China, Department of Physiology and the Cancer Center, Medical College of Wisconsin, Milwaukee, WI 53226, USA and Human Molecular and Genetics Center, Medical College of Wisconsin, Milwaukee, WI 53226, USA. 2. Department of Gynecologic Oncology, The Affiliated Women's Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang 310006, Institute of Bioinformatics, Zhejiang University, Hangzhou, Zhejiang 310058, China, Division of Respiratory Medicine, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang 310058, China, Department of Physiology and the Cancer Center, Medical College of Wisconsin, Milwaukee, WI 53226, USA and Human Molecular and Genetics Center, Medical College of Wisconsin, Milwaukee, WI 53226, USA.
Abstract
MOTIVATION: RNA-Seq (also called whole-transcriptome sequencing) is an emerging technology that uses the capabilities of next-generation sequencing to detect and quantify entire transcripts. One of its important applications is the improvement of existing genome annotations. RNA-Seq provides rapid, comprehensive and cost-effective tools for the discovery of novel genes and transcripts compared with expressed sequence tag (EST), which is instrumental in gene discovery and gene sequence determination. The rat is widely used as a laboratory disease model, but has a less well-annotated genome as compared with humans and mice. In this study, we incorporated deep RNA-Seq data from three rat tissues-bone marrow, brain and kidney-with EST data to improve the annotation of the rat genome. RESULTS: Our analysis identified 32 197 transcripts, including 13 461 known transcripts, 13 934 novel isoforms and 4802 new genes, which almost doubled the numbers of transcripts in the current public rat genome database (rn5). Comparisons of our predicted protein-coding gene sets with those in public datasets suggest that RNA-Seq significantly improves genome annotation and identifies novel genes and isoforms in the rat. Importantly, the large majority of novel genes and isoforms are supported by direct evidence of RNA-Seq experiments. These predicted genes were integrated into the Rat Genome Database (RGD) and can serve as an important resource for functional studies in the research community. AVAILABILITY AND IMPLEMENTATION: The predicted genes are available at http://rgd.mcw.edu.
MOTIVATION: RNA-Seq (also called whole-transcriptome sequencing) is an emerging technology that uses the capabilities of next-generation sequencing to detect and quantify entire transcripts. One of its important applications is the improvement of existing genome annotations. RNA-Seq provides rapid, comprehensive and cost-effective tools for the discovery of novel genes and transcripts compared with expressed sequence tag (EST), which is instrumental in gene discovery and gene sequence determination. The rat is widely used as a laboratory disease model, but has a less well-annotated genome as compared with humans and mice. In this study, we incorporated deep RNA-Seq data from three rat tissues-bone marrow, brain and kidney-with EST data to improve the annotation of the rat genome. RESULTS: Our analysis identified 32 197 transcripts, including 13 461 known transcripts, 13 934 novel isoforms and 4802 new genes, which almost doubled the numbers of transcripts in the current public rat genome database (rn5). Comparisons of our predicted protein-coding gene sets with those in public datasets suggest that RNA-Seq significantly improves genome annotation and identifies novel genes and isoforms in the rat. Importantly, the large majority of novel genes and isoforms are supported by direct evidence of RNA-Seq experiments. These predicted genes were integrated into the Rat Genome Database (RGD) and can serve as an important resource for functional studies in the research community. AVAILABILITY AND IMPLEMENTATION: The predicted genes are available at http://rgd.mcw.edu.
Authors: Timothy J Aitman; John K Critser; Edwin Cuppen; Anna Dominiczak; Xose M Fernandez-Suarez; Jonathan Flint; Dominique Gauguier; Aron M Geurts; Michael Gould; Peter C Harris; Rikard Holmdahl; Norbert Hubner; Zsuzsanna Izsvák; Howard J Jacob; Takashi Kuramoto; Anne E Kwitek; Anna Marrone; Tomoji Mashimo; Carol Moreno; John Mullins; Linda Mullins; Tomas Olsson; Michal Pravenec; Lela Riley; Kathrin Saar; Tadao Serikawa; James D Shull; Claude Szpirer; Simon N Twigger; Birger Voigt; Kim Worley Journal: Nat Genet Date: 2008-05 Impact factor: 38.330
Authors: Carol Moreno; Jan M Williams; Limin Lu; Mingyu Liang; Jozef Lazar; Howard J Jacob; Allen W Cowley; Richard J Roman Journal: Am J Physiol Heart Circ Physiol Date: 2011-01-21 Impact factor: 4.733
Authors: Andrew I Su; Tim Wiltshire; Serge Batalov; Hilmar Lapp; Keith A Ching; David Block; Jie Zhang; Richard Soden; Mimi Hayakawa; Gabriel Kreiman; Michael P Cooke; John R Walker; John B Hogenesch Journal: Proc Natl Acad Sci U S A Date: 2004-04-09 Impact factor: 11.205
Authors: France Denoeud; Jean-Marc Aury; Corinne Da Silva; Benjamin Noel; Odile Rogier; Massimo Delledonne; Michele Morgante; Giorgio Valle; Patrick Wincker; Claude Scarpelli; Olivier Jaillon; François Artiguenave Journal: Genome Biol Date: 2008-12-16 Impact factor: 13.583