ReviewBMC bioinformatics2007
A survey of DNA motif finding algorithms.
Review in BMC bioinformatics, 2007. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 140 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
140 citing papers in PubMed, 430 citations in OpenAlex.
- Motif caller for sequence reconstruction in motif-based DNA storage.Scientific reports · 2025Article
- Identifying DNA methylation types and methylated base positions from bacteria using nanopore sequencing with multi-scale neural network.Bioinformatics (Oxford, England) · 2025Article
- SEQSIM: A novel bioinformatics tool for comparisons of promoter regions-a case study of calcium binding protein spermatid associated 1 (CABS1).BMC bioinformatics · 2025Article
- Integrative transcriptomic analysis reveals alternative splicing complexity and transcriptomic diversity in porcine placentas across altitudes.DNA research : an international journal for rapid publication of reports on genes and genomes · 2025Article
- De-motif sampling: an approach to decompose hierarchical motifs with applications in T cell recognition.Briefings in bioinformatics · 2025Article
- Optimizing resource utilization for large scale problems through architecture aware scheduling.Scientific reports · 2024Article
- Discovering DNA shape motifs with multiple DNA shape features: generalization, methods, and validation.Nucleic acids research · 2024Article
- MMGAT: a graph attention network framework for ATAC-seq motifs finding.BMC bioinformatics · 2024Article
- A survey on algorithms to characterize transcription factor binding sites.Briefings in bioinformatics · 2023Review
- Prediction of mammalian tissue-specific CLOCK-BMAL1 binding to E-box DNA motifs.Scientific reports · 2023Article
- Article
- Discovering molecular features of intrinsically disordered regions by using evolution for contrastive learning.PLoS computational biology · 2022Article
- In silico analysis of promoter region and regulatory elements of glucan endo-1,3-beta-glucosidase encoding genes in Solanum tuberosum: cultivar DM 1-3 516 R44.Journal, genetic engineering & biotechnology · 2021Article
- A novel artificial intelligence-based approach for identification of deoxynucleotide aptamers.PLoS computational biology · 2021Article
- Whole-Genome Differentially Hydroxymethylated DNA Regions among Twins Discordant for Cardiovascular Death.Genes · 2021Article
- Interpretation of deep learning in genomics and epigenomics.Briefings in bioinformatics · 2021Review
- In silico analysis of promoter regions and regulatory elements (motifs and CpG islands) of the genes encoding for alcohol production in Saccharomyces cerevisiaea S288C and Schizosaccharomyces pombe 972h.Journal, genetic engineering & biotechnology · 2021Article
- Set cover-based methods for motif selection.Bioinformatics (Oxford, England) · 2020Article
- Identification and Characterization of Cis-Regulatory Elements for Photoreceptor-Type-Specific Transcription in ZebraFish.Methods in molecular biology (Clifton, N.J.) · 2020Article
- A new statistic for efficient detection of repetitive sequences.Bioinformatics (Oxford, England) · 2019Article
80 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.