ArticlePlant methods2013
Using an ensemble of statistical metrics to quantify large sets of plant transcription factor binding sites.
Article in Plant methods, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed, 4 citations in OpenAlex.
- Methodological approach for predicting and mapping the phenological adaptation of tropical maize (Plant methods · 2018Article
- Regulatory interplay between soybean root and soybean cyst nematode during a resistant and susceptible reaction.BMC plant biology · 2014Article
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundFrom initial seed germination through reproduction, plants continuously reprogram their transcriptional repertoire to facilitate growth and development. This dynamic is mediated by a diverse but inextricably-linked catalog of regulatory proteins called transcription factors (TFs). Statistically quantifying TF binding site (TFBS) abundance in promoters of differentially expressed genes can be used to identify binding site patterns in promoters that are closely related to stress-response. Output from today's transcriptomic assays necessitates statistically-oriented software to handle large promoter-sequence sets in a computationally tractable fashion.
resultsWe present Marina, an open-source software for identifying over-represented TFBSs from amongst large sets of promoter sequences, using an ensemble of 7 statistical metrics and binding-site profiles. Through software comparison, we show that Marina can identify considerably more over-represented plant TFBSs compared to a popular software alternative.
conclusionsMarina was used to identify over-represented TFBSs in a two time-point RNA-Seq study exploring the transcriptomic interplay between soybean (Glycine max) and soybean rust (Phakopsora pachyrhizi). Marina identified numerous abundant TFBSs recognized by transcription factors that are associated with defense-response such as WRKY, HY5 and MYB2. Comparing results from Marina to that of a popular software alternative suggests that regardless of the number of promoter-sequences, Marina is able to identify significantly more over-represented TFBSs.
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Registered trials
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