What can we learn from noncoding regions of similarity between genomes?

BMC Bioinformatics
Thomas A Down, Tim J P Hubbard

Abstract

In addition to known protein-coding genes, large amounts of apparently non-coding sequence are conserved between the human and mouse genomes. It seems reasonable to assume that these conserved regions are more likely to contain functional elements than less-conserved portions of the genome. Here we used a motif-oriented machine learning method based on the Relevance Vector Machine algorithm to extract the strongest signal from a set of non-coding conserved sequences. We successfully fitted models to reflect the non-coding sequences, and showed that the results were quite consistent for repeated training runs. Using the learned models to scan genomic sequence, we found that they often made predictions close to the start of annotated genes. We compared this method with other published promoter-prediction systems, and showed that the set of promoters which are detected by this method is substantially similar to that detected by existing methods. The results presented here indicate that the promoter signal is the strongest single motif-based signal in the non-coding functional fraction of the genome. They also lend support to the belief that there exists a substantial subset of promoter regions which share several common features i...Continue Reading

References

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Mar 5, 2002·Genome Research·Thomas A Down, Tim J P Hubbard
Mar 28, 2002·Nucleic Acids Research·Ramesh RamakrishnanAbhijit Mazumder
Dec 6, 2002·Nature·UNKNOWN Mouse Genome Sequencing ConsortiumEric S Lander
Jan 17, 2003·Genome Research·John E CollinsIan Dunham
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Citations

Mar 4, 2011·Interdisciplinary Sciences, Computational Life Sciences·A MeeraSavithri Bhat
Oct 6, 2005·Physical Biology·András SzilágyiJeffrey Skolnick
May 11, 2007·Annual Review of Genomics and Human Genetics·Mathieu Blanchette

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Software Mentioned

biojava
ensembl
trf
EponineTSS
Genscan
Ensembl genome browser Annotation System
Eponine
RepeatMasker
EAS
PromoterInspector

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