Discovering Sequence Motifs with Arbitrary Insertions and Deletions
By Martin C. Frith, Neil F. W. Saunders, Bostjan Kobe, Timothy L. Bailey. PLoS Comput. Biol., 2008
376 citations recorded by the source corpus.
Biology is encoded in molecular sequences: deciphering this encoding remains a grand scientific challenge. Functional regions of DNA, RNA, and protein sequences often exhibit characteristic but subtle motifs; thus, computational discovery of motifs in sequences is a fundamental and much-studied problem. However, most current algorithms do not allow for insertions or deletions (indels) within motifs, and the few that do have other limitations. We present a method, GLAM2 (Gapped Local Alignment of Motifs), for discovering motifs allowing indels in a fully general manner, and a companion method GLAM2SCAN for searching sequence databases using such motifs. glam2 is a generalization of the gapless Gibbs sampling algorithm. It re-discovers variable-width protein motifs from the PROSITE database significantly more accurately than the alternative methods PRATT and SAM-T2K. Furthermore, it usefully refines protein motifs from the ELM database: in some cases, the refined motifs make orders of magnitude fewer overpredictions than the original ELM regular expressions. GLAM2 performs respectably on the BAliBASE multiple alignment benchmark, and may be superior to leading multiple alignment methods for “motif-like” alignments with N- and C-terminal extensions. Finally, we demonstrate the use of GLAM2 to discover protein kinase substrate motifs and a gapped DNA motif for the LIM-only transcriptional regulatory complex: using GLAM2SCAN, we identify promising targets for the latter. GLAM2 is especially promising for short protein motifs, and it should improve our ability to identify the protein cleavage sites, interaction sites, post-translational modification attachment sites, etc., that underlie much of biology. It may be equally useful for arbitrarily gapped motifs in DNA and RNA, although fewer examples of such motifs are known at present. GLAM2 is public domain software, available for download at http://bioinformatics.org.au/glam2.
Subfields: Molecular Biology
View Paper: https://www.semanticscholar.org/paper/2cd313c7eb352ee16dd766865308821162272db8
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In Molecular Biology
- Charles E. Grant
- Timothy L. Bailey
- William Stafford Noble
- James Johnson
- Chris P. Ponting
- Manuela Helmer-Citterich
- Mikael Bodén
- Andreas Heger
Citations in the corpus
- A ChIP-seq defined genome-wide map of vitamin D receptor binding: associations with disease and evolution. (2010, methodology): “This was confirmed using GLAM2 and BioProspector (Liu et al. 2001; Frith et al. 2008).”
- Revealing protein–lncRNA interaction (2015, methodology): “Motif finding algorithms such as MEME [47] or GLAM2 [48] are often used to identify primary sequence preferences of binding.”
- The MEME Suite (2015, methodology): “The web-based version of the MEME Suite includes 13 tools (1,2,3,4,5,6,7,8,9,10,11,12,13) for performing motif discovery, motif enrichment analysis, motif scanning and motif–motif comparisons (Figure 1).”
- MEME Suite: tools for motif discovery and searching (2009, methodology): “To overcome this limitation, we have incorporated a recent algorithm for gapped motif discovery—GLAM2 (3)—into the MEME suite.”