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The mouse genome: Experimental examination of gene predictions and transcriptional start sites
Author(s) -
Sujit Dike,
Vivekanand S. Balija,
Lidia U. Nascimento,
Zhenyu Xuan,
Jacqueline Ou,
Theresa Zutavern,
Lance E. Palmer,
Greg Han,
Michael Q. Zhang,
W. Richard McCombie
Publication year - 2004
Publication title -
genome research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 9.556
H-Index - 297
eISSN - 1549-5469
pISSN - 1088-9051
DOI - 10.1101/gr.3158304
Subject(s) - biology , gene , genome , genetics , gene prediction , computational biology , genome project , human genome , exon , gene annotation
The completion of the mouse and other mammalian genome sequences will provide necessary, but not sufficient, knowledge for an understanding of much of mouse biology at the molecular level. As a requisite next step in this process, the genes in mouse and their structure must be elucidated. In particular, knowledge of the transcriptional start site of these genes will be necessary for further study of their regulatory regions. To assess the current state of mouse genome annotation to support this activity, we identified several hundred gene predictions in mouse with varying levels of supporting evidence and tested them using RACE-PCR. Modifications were made to the procedure allowing pooling of RNA samples, resulting in a scaleable procedure. The results illustrate potential errors or omissions in the current 5' end annotations in 58% of the genes detected. In testing experimentally unsupported gene predictions, we were able to identify 58 that are not usually annotated as genes but produced spliced transcripts (approximately 25% success rate). In addition, in many genes we were able to detect novel exons not predicted by any gene prediction algorithms. In 19.8% of the genes detected in this study, multiple transcript species were observed. These data show an urgent need to provide direct experimental validation of gene annotations. Moreover, these results show that direct validation using RACE-PCR can be an important component of genome-wide validation. This approach can be a useful tool in the ongoing efforts to increase the quality of gene annotations, especially transcriptional start sites, in complex genomes.

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