Skip to Main content Skip to Navigation
Journal articles

Transcriptome Annotation using Tandem SAGE Tags

Abstract : Analysis of several million expressed gene signatures (tags) revealed an increasing number of different sequences, largely exceeding that of annotated genes in mammalian genomes. Serial analysis of gene expression (SAGE) can reveal new Poly(A) RNAs transcribed from previously unrecognized chromosomal regions. However, conventional SAGE tags are too short to identify unambiguously unique sites in large genomes. Here, we design a novel strategy with tags anchored on two different restrictions sites of cDNAs. New transcripts are then tentatively defined by the two SAGE tags in tandem and by the spanning sequence read on the genome between these tagged sites. Having developed a new algorithm to locate these tag-delimited genomic sequences (TDGS), we first validated its capacity to recognize known genes and its ability to reveal new transcripts with two SAGE libraries built in parallel from a single RNA sample. Our algorithm proves fast enough to experiment this strategy at a large scale. We then collected and processed the complete sets of human SAGE tags to predict yet unknown transcripts. A cross-validation with tiling arrays data shows that 47% of these TDGS overlap transcriptional active regions. Our method provides a new and complementary approach for complex transcriptome annotation.
Complete list of metadatas

Cited literature [31 references]  Display  Hide  Download

https://hal-lirmm.ccsd.cnrs.fr/lirmm-00193291
Contributor : Eric Rivals <>
Submitted on : Monday, December 3, 2007 - 11:28:48 AM
Last modification on : Tuesday, March 10, 2020 - 11:10:03 AM
Document(s) archivé(s) le : Monday, April 12, 2010 - 5:49:59 AM

Identifiers

Collections

Citation

Eric Rivals, Anthony Boureux, Mireille Lejeune, Florence Ottones, Oscar Pecharromàn Pérez, et al.. Transcriptome Annotation using Tandem SAGE Tags. Nucleic Acids Research, Oxford University Press, 2007, 35 (17), pp.e108. ⟨10.1093/nar/gkm495⟩. ⟨lirmm-00193291⟩

Share

Metrics

Record views

1391

Files downloads

1539