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Bioinformatics Advance Access originally published online on December 10, 2004
Bioinformatics 2005 21(8):1393-1400; doi:10.1093/bioinformatics/bti207
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© The Author 2004. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions{at}oupjournals.org

LongSAGE analysis significantly improves genome annotation: identifications of novel genes and alternative transcripts in the mouse

Matthias B. Wahl 1,{dagger}, Ulrich Heinzmann 2 and Kenji Imai 1,*

1Institute of Developmental Genetics, GSF-National Research Center for Environment and Health Ingolstädter Landstrasse 1, D-85764 Neuherberg, Germany
2Institute of Pathology, GSF-National Research Center for Environment and Health Ingolstädter Landstrasse 1, D-85764 Neuherberg, Germany

*To whom correspondence should be addressed.

Motivation: Owing to its increased tag length, LongSAGE tags are expected to be more reliable in direct assignment to genome sequences. Therefore, we evaluated the use of LongSAGE data in genome annotation by using our LongSAGE dataset of 202 015 tags (consisting of 41 718 unique tags), experimentally generated from mouse embryonic tail libraries.

Results: A fraction of LongSAGE tags could not be unambiguously assigned to its gene, due to the presence of widely conserved sequences downstream of particular CATG anchor sites. The presence of alternative forms of transcripts was confirmed in 45% of all detected genes. Surprisingly, a large fraction of LongSAGE tags with hits to the genome (66%) could not be assigned to any gene annotated in EnsEMBL. Among such cases, 2098 LongSAGE tags fell into a region containing a putative gene predicted by GenScan, providing experimental evidence for the presence of real genes, while 9112 genes were found out to be left out or wrongly annotated by the EnsEMBL pipeline.

Conclusions: LongSAGE transcriptome data can significantly improve the genome annotation by identifying novel genes and alternative transcripts, even in the case of thus far best-characterized organisms like the mouse.

Contact: imai{at}gsf.de


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