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A third approach to gene prediction suggests thousands of additional human transcribed regions.

ABSTRACT: The identification and characterization of the complete ensemble of genes is a main goal of deciphering the digital information stored in the human genome. Many algorithms for computational gene prediction have been described, ultimately derived from two basic concepts: (1) modeling gene structure and (2) recognizing sequence similarity. Successful hybrid methods combining these two concepts have also been developed. We present a third orthogonal approach to gene prediction, based on detecting the genomic signatures of transcription, accumulated over evolutionary time. We discuss four algorithms based on this third concept: Greens and CHOWDER, which quantify mutational strand biases caused by transcription-coupled DNA repair, and ROAST and PASTA, which are based on strand-specific selection against polyadenylation signals. We combined these algorithms into an integrated method called FEAST, which we used to predict the location and orientation of thousands of putative transcription units not overlapping known genes. Many of the newly predicted transcriptional units do not appear to code for proteins. The new algorithms are particularly apt at detecting genes with long introns and lacking sequence conservation. They therefore complement existing gene prediction methods and will help identify functional transcripts within many apparent "genomic deserts."

SUBMITTER: Glusman G

PROVIDER: S-EPMC1391917 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundMicro (mi)RNAs comprise a large family of small non-coding RNAs that are thought to regulate a large fraction of protein-coding genes. Generally, miRNAs downregulate messenger (m)RNA expression by binding to the 3' untranslated regions (UTRs) of the RNA molecules. An important factor for binding specificity is the matching in the seed region. In addition, target site accessibility is thought to be crucial for efficient repression of miRNA targets. Several recent studies indicated that miRNA repression can be facilitated by RNA-binding proteins. In this study, we examine the conjecture that RNA-binding proteins are involved in ushering miRNAs to bind targets that are initially less accessible.ResultsWe analyzed human 3'-UTR sequences containing potential binding sites of 153 conserved miRNA families, and ranked sequences around the sites according to their miRNA accessibility. By applying a rank-based motif search tool to these miRNA targets, we found motifs that are enriched among less accessible targets. As expected from our ranking method, most of the significant motifs were GC-rich. However, one AU-rich motif was found to be enriched among miR-410 less accessible targets. This motif resembles the Pumilio homolog 1 (PUM1) consensus binding site. We observed a stronger enrichment of the PUM1 motif in conserved targets than in non-conserved targets; moreover, the enrichment of this motif was found to be conserved in a subset of placental mammals. Further, we analyzed publicly available gene expression data, and found that the mutual expression of PUM1 and miR-410 has a greater negative influence on the expression of low accessibility targets than on other targets, an effect that was stronger than when considering both miR-410 and PUM1 separately.ConclusionsTaken together, our findings suggest a cooperative relationship between miR-410 and PUM1 in regulating human highly structured 3'-UTRs. This kind of cooperation can allow a second level of regulation of such targets. Considering cases in which miRNAs bind low accessibility targets may help to improve current miRNA prediction tools and to obtain a better understanding of the mechanisms underlying miRNA regulation activity.

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A third approach to gene prediction suggests thousands of additional human transcribed regions.

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