A survey of DNA motif finding algorithms
By Modan K. Das, Ho-Kwok Dai. BMC Bioinformatics, 2007
483 citations recorded by the source corpus.
BackgroundUnraveling the mechanisms that regulate gene expression is a major challenge in biology. An important task in this challenge is to identify regulatory elements, especially the binding sites in deoxyribonucleic acid (DNA) for transcription factors. These binding sites are short DNA segments that are called motifs. Recent advances in genome sequence availability and in high-throughput gene expression analysis technologies have allowed for the development of computational methods for motif finding. As a result, a large number of motif finding algorithms have been implemented and applied to various motif models over the past decade. This survey reviews the latest developments in DNA motif finding algorithms.ResultsEarlier algorithms use promoter sequences of coregulated genes from single genome and search for statistically overrepresented motifs. Recent algorithms are designed to use phylogenetic footprinting or orthologous sequences and also an integrated approach where promoter sequences of coregulated genes and phylogenetic footprinting are used. All the algorithms studied have been reported to correctly detect the motifs that have been previously detected by laboratory experimental approaches, and some algorithms were able to find novel motifs. However, most of these motif finding algorithms have been shown to work successfully in yeast and other lower organisms, but perform significantly worse in higher organisms.ConclusionDespite considerable efforts to date, DNA motif finding remains a complex challenge for biologists and computer scientists. Researchers have taken many different approaches in developing motif discovery tools and the progress made in this area of research is very encouraging. Performance comparison of different motif finding tools and identification of the best tools have proven to be a difficult task because tools are designed based on algorithms and motif models that are diverse and complex and our incomplete understanding of the biology of regulatory mechanism does not always provide adequate evaluation of underlying algorithms over motif models.
Subfields: Genetics, Molecular Biology
View Paper: https://www.semanticscholar.org/paper/5effd92c9e52af1b8c6b926347b0db42ca20ce5a
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Citations in the corpus
- Genome-wide prediction of transcription factor binding sites using an integrated model (2010, methodology): “Many computational methods [6-15] (for a survey, see [16]) have been developed to identify DNA segments recognized by TFs.”
- Computational pan-genomics: status, promises and challenges (2016, background): “Computational tasks related to understanding gene regulation therefore include detecting a given motif or to find all its occurrences in certain genomic context, or in a discovery mode, to extract or infer such over-represented motifs from positive and negative examples; refer to [116, 29] for revie”
- Non-coding nuclear DNA markers in phylogenetic reconstruction (2009, methodology): “There are several software tools available that are capable of helping, to a certain extent, in the process of finding conserved regions within promoter sequences (e.g. MEME, PhyloGibbs, PhyME and others, Das and Dai 2007; Gertz et al. 2006).”
- A Convex Atomic-Norm Approach to Multiple Sequence Alignment and Motif Discovery (2016, unknown): “Note that the MD problem, in its simplest form, does not consider insertion-deletion noise presumably due to its intrinsic difficulty, and therefore, finding alignment a becomes equivalent to finding the start positions of motifs on the data sequences (Das & Dai, 2007).”