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An Exploration Into Improving DNA Motif Inference by Looking for Highly Conserved Core Regions.

ABSTRACT: Although most verified functional elements in noncoding DNA contain a highly conserved core region, this concept is not generally incorporated into de novo motif inference systems. In this work, we explore the utility of adding the notion of conserved core regions into a comparative genomics approach for the search for putative functional elements in noncoding DNA. By modifying the scoring function for GAMI, Genetic Algorithms for Motif Inference, we investigate tradeoffs between the strength of conservation of the full motif vs. the strength of conservation of a core region. This work illustrates that incorporating information about the structure of transcription factor binding sites can be helpful in identifying biologically functional elements.

SUBMITTER: Thompson JA

PROVIDER: S-EPMC6474685 | biostudies-literature | 2013 Apr

REPOSITORIES: biostudies-literature

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An Exploration Into Improving DNA Motif Inference by Looking for Highly Conserved Core Regions.

Thompson Jeffrey A JA Congdon Clare Bates CB

IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology proceedings. IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology 20130401

Although most verified functional elements in noncoding DNA contain a highly conserved core region, this concept is not generally incorporated into <i>de novo</i> motif inference systems. In this work, we explore the utility of adding the notion of conserved core regions into a comparative genomics approach for the search for putative functional elements in noncoding DNA. By modifying the scoring function for GAMI, Genetic Algorithms for Motif Inference, we investigate tradeoffs between the stre ...[more]

PMID: 31008453

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Project description:Background: Long non-coding RNAs (lncRNAs), which are generally less functionally characterized or less annotated, evolve more rapidly than mRNAs and substantially possess fewer sequence conservation patterns than protein-coding genes across divergent species. People assume that the functional inference could be conducted on the evolutionarily conserved long non-coding RNAs as they are most likely to be functional. In the past decades, substantial progress has been made in discussions on the evolutionary conservation of non-coding genomic regions from multiple perspectives. However, understanding their conservation and the functions associated with sequence conservation in relation to further corresponding phenotypic variability or disorders still remains incomplete. Results: Accordingly, we determined a highly conserved region (HCR) to verify the sequence conservation among long non-coding RNAs and systematically profiled homologous long non-coding RNA clusters in humans and mice based on the detection of highly conserved regions. Moreover, according to homolog clustering, we explored the potential function inference via highly conserved regions on representative long non-coding RNAs. On lncRNA XACT, we investigated the potential functional competence between XACT and lncRNA XIST by recruiting miRNA-29a, regulating the downstream target genes. In addition, on lncRNA LINC00461, we examined the interaction relationship between LINC00461 and SND1. This interaction or association may be perturbed during the progression of glioma. In addition, we have constructed a website with user-friendly web interfaces for searching, analyzing, and downloading to present the homologous clusters of humans and mice. Conclusion: Collectively, homolog clustering via the highly conserved region definition and detection on long non-coding RNAs, as well as the functional explorations on representative sequences in our research, would provide new evidence for the potential function of long non-coding RNAs. Our results on the remarkable roles of long non-coding RNAs would presumably provide a new theoretical basis and candidate diagnostic indicators for tumors.

Dataset Information

An Exploration Into Improving DNA Motif Inference by Looking for Highly Conserved Core Regions.

Publications

An Exploration Into Improving DNA Motif Inference by Looking for Highly Conserved Core Regions.

Similar Datasets

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