Alignments anchored on genomic landmarks can aid in the identification of regulatory elements.
BACKGROUND
The transcription start site (TSS) has been located for an increasing number of genes across several organisms. Statistical tests have shown that some cis-acting regulatory elements have positional preferences with respect to the TSS, but few strategies have emerged for locating elements by their positional preferences. This paper elaborates such a strategy. First, we align promoter regions without gaps, anchoring the alignment on each promoter's TSS. Second, we apply a novel word-specific mask. Third, we apply a clustering test related to gapless BLAST statistics. The test examines whether any specific word is placed unusually consistently with respect to the TSS. Finally, our program A-GLAM, an extension of the GLAM program, uses significant word positions as new 'anchors' to realign the sequences. A Gibbs sampling algorithm then locates putative cis-acting regulatory elements. Usually, Gibbs sampling requires a preliminary masking step, to avoid convergence onto a dominant but uninteresting signal from a DNA repeat. However, since the positional anchors focus A-GLAM on the motif of interest, masking DNA repeats during Gibbs sampling becomes unnecessary.


RESULTS
In a set of human DNA sequences with experimentally characterized TSSs, the placement of 791 octonucleotide words was unusually consistent (multiple test corrected P < 0.05). Alignments anchored on these words sometimes located statistically significant motifs inaccessible to GLAM or AlignACE.


BACKGROUND
The A-GLAM program and a list of statistically significant words are available at ftp://ftp.ncbi.nih.gov/pub/spouge/papers/archive/AGLAM/.
Tharakaraman
Kannan
K
Computational Biology Branch, National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health Building 38A, 8600 Rockville Pike, Bethesda, MD 20894-6075, USA.
Mariño-Ramírez
Leonardo
L
Sheetlin
Sergey
S
Landsman
David
D
Spouge
John L
JL
D020816:Amino Acid Motifs; D001483:Base Sequence; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods*; D004247:DNA / Q000737:chemistry; D030562:Databases, Protein; D023281:Genomics / Q000379:methods*; D006801:Humans; D015233:Models, Statistical; D008969:Molecular Sequence Data; D009711:Nucleotides / Q000737:chemistry; D011401:Promoter Regions, Genetic; D012045:Regulatory Sequences, Nucleic Acid; D016415:Sequence Alignment; D012984:Software; D024363:Transcription Initiation Site
D009711:Nucleotides; D004247:DNA