Scanning sequences after Gibbs sampling to find multiple occurrences of functional elementsReport as inadecuate

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BMC Bioinformatics

, 7:408

First Online: 08 September 2006Received: 14 June 2006Accepted: 08 September 2006


BackgroundMany DNA regulatory elements occur as multiple instances within a target promoter. Gibbs sampling programs for finding DNA regulatory elements de novo can be prohibitively slow in locating all instances of such an element in a sequence set.

ResultsWe describe an improvement to the A-GLAM computer program, which predicts regulatory elements within DNA sequences with Gibbs sampling. The improvement adds an optional -scanning step- after Gibbs sampling. Gibbs sampling produces a position specific scoring matrix PSSM. The new scanning step resembles an iterative PSI-BLAST search based on the PSSM. First, it assigns an -individual score- to each subsequence of appropriate length within the input sequences using the initial PSSM. Second, it computes an E-value from each individual score, to assess the agreement between the corresponding subsequence and the PSSM. Third, it permits subsequences with E-values falling below a threshold to contribute to the underlying PSSM, which is then updated using the Bayesian calculus. A-GLAM iterates its scanning step to convergence, at which point no new subsequences contribute to the PSSM. After convergence, A-GLAM reports predicted regulatory elements within each sequence in order of increasing E-values, so users have a statistical evaluation of the predicted elements in a convenient presentation. Thus, although the Gibbs sampling step in A-GLAM finds at most one regulatory element per input sequence, the scanning step can now rapidly locate further instances of the element in each sequence.

ConclusionDatasets from experiments determining the binding sites of transcription factors were used to evaluate the improvement to A-GLAM. Typically, the datasets included several sequences containing multiple instances of a regulatory motif. The improvements to A-GLAM permitted it to predict the multiple instances.

AbbreviationsA-GLAMAnchored Gapless Local Alignment of Multiple Sequences.

PSSMPosition-Specific Scoring Matrix

Electronic supplementary materialThe online version of this article doi:10.1186-1471-2105-7-408 contains supplementary material, which is available to authorized users.

Kannan Tharakaraman, Leonardo Mariño-Ramírez contributed equally to this work.

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Author: Kannan Tharakaraman - Leonardo Mariño-Ramírez - Sergey L Sheetlin - David Landsman - John L Spouge



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