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• W2221226238 abstract "<ns4:p>Transcription factor (TF) binding site prediction remains a challenge in gene regulatory research due to degeneracy and potential variability in binding sites in the genome. Dozens of algorithms designed to learn binding models (motifs) have generated many motifs available in research papers with a subset making it to databases like JASPAR, UniPROBE and Transfac. The presence of many versions of motifs from the various databases for a single TF and the lack of a standardized assessment technique makes it difficult for biologists to make an appropriate choice of binding model and for algorithm developers to benchmark, test and improve on their models. In this study, we review and evaluate the approaches in use, highlight differences and demonstrate the difficulty of defining a standardized motif assessment approach. We review scoring functions, motif length, test data and the type of performance metrics used in prior studies as some of the factors that influence the outcome of a motif assessment. We show that the scoring functions and statistics used in motif assessment influence ranking of motifs in a TF-specific manner. We also show that TF binding specificity can vary by source of genomic binding data. We also demonstrate that information content of a motif is not in isolation a measure of motif quality but is influenced by TF binding behaviour. We conclude that there is a need for an easy-to-use tool that presents all available evidence for a comparative analysis.</ns4:p>" @default.
• W2221226238 created "2016-06-24" @default.
• W2221226238 creator A5008809486 @default.
• W2221226238 creator A5016033031 @default.
• W2221226238 date "2016-03-14" @default.
• W2221226238 modified "2023-10-15" @default.
• W2221226238 title "Transcription factor motif quality assessment requires systematic comparative analysis" @default.
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• W2221226238 doi "https://doi.org/10.12688/f1000research.7408.2" @default.
• W2221226238 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/4821295" @default.
• W2221226238 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/27110356" @default.
• W2221226238 hasPublicationYear "2016" @default.
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