LASAGNA: A novel algorithm for transcription factor binding site alignment

BACKGROUND: Scientists routinely scan DNA sequences for transcription factor (TF) binding sites (TFBSs). Most of the available tools rely on position-specific scoring matrices (PSSMs) constructed from aligned binding sites. Because of the resolutions of assays used to obtain TFBSs, databases such as...

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Julkaistu: BioMed Central 2013
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Methodology Article
Linkit:https://ncbi.nlm.nih.gov/pmc/articles/PMC3747862/
https://ncbi.nlm.nih.gov/pubmed/23522376
https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1186/1471-2105-14-108
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id pubmed-3747862
record_format dspace
institution US National Library of Medicine
collection PubMed Central
language en
format Article
topic Methodology Article
spellingShingle Methodology Article
LASAGNA: A novel algorithm for transcription factor binding site alignment
topic_facet Methodology Article
description BACKGROUND: Scientists routinely scan DNA sequences for transcription factor (TF) binding sites (TFBSs). Most of the available tools rely on position-specific scoring matrices (PSSMs) constructed from aligned binding sites. Because of the resolutions of assays used to obtain TFBSs, databases such as TRANSFAC, ORegAnno and PAZAR store unaligned variable-length DNA segments containing binding sites of a TF. These DNA segments need to be aligned to build a PSSM. While the TRANSFAC database provides scoring matrices for TFs, nearly 78% of the TFs in the public release do not have matrices available. As work on TFBS alignment algorithms has been limited, it is highly desirable to have an alignment algorithm tailored to TFBSs. RESULTS: We designed a novel algorithm named LASAGNA, which is aware of the lengths of input TFBSs and utilizes position dependence. Results on 189 TFs of 5 species in the TRANSFAC database showed that our method significantly outperformed ClustalW2 and MEME. We further compared a PSSM method dependent on LASAGNA to an alignment-free TFBS search method. Results on 89 TFs whose binding sites can be located in genomes showed that our method is significantly more precise at fixed recall rates. Finally, we described LASAGNA-ChIP, a more sophisticated version for ChIP (Chromatin immunoprecipitation) experiments. Under the one-per-sequence model, it showed comparable performance with MEME in discovering motifs in ChIP-seq peak sequences. CONCLUSIONS: We conclude that the LASAGNA algorithm is simple and effective in aligning variable-length binding sites. It has been integrated into a user-friendly webtool for TFBS search and visualization called LASAGNA-Search. The tool currently stores precomputed PSSM models for 189 TFs and 133 TFs built from TFBSs in the TRANSFAC Public database (release 7.0) and the ORegAnno database (08Nov10 dump), respectively. The webtool is available at http://biogrid.engr.uconn.edu/lasagna_search/.
author_sort Lee, Chih
title LASAGNA: A novel algorithm for transcription factor binding site alignment
title_short LASAGNA: A novel algorithm for transcription factor binding site alignment
title_full LASAGNA: A novel algorithm for transcription factor binding site alignment
title_fullStr LASAGNA: A novel algorithm for transcription factor binding site alignment
title_full_unstemmed LASAGNA: A novel algorithm for transcription factor binding site alignment
title_sort lasagna: a novel algorithm for transcription factor binding site alignment
publisher BioMed Central
publisher_facet BioMed Central
publishDate 2013
url https://ncbi.nlm.nih.gov/pmc/articles/PMC3747862/
https://ncbi.nlm.nih.gov/pubmed/23522376
https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1186/1471-2105-14-108
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