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Post-Transcriptional Gene Regulation

Volume 419 of the series Methods In Molecular Biology™ pp 39-52

Bioinformatic Tools for Studying Post-Transcriptional Gene Regulation: The UAlbany TUTR Collection and Other Informatic Resources

  • Francis DoyleAffiliated withGen*NY*Sis Center for Excellence in Cancer Genomics, Department of Biomedical Sciences, University at Albany-SUNY, School of Public Health
  • , Christopher ZaleskiAffiliated withGen*NY*Sis Center for Excellence in Cancer Genomics, Department of Biomedical Sciences, University at Albany-SUNY, School of Public Health
  • , Ajish D. GeorgeAffiliated withGen*NY*Sis Center for Excellence in Cancer Genomics, Department of Biomedical Sciences, University at Albany-SUNY, School of Public Health
  • , Erin K. StensonAffiliated withGen*NY*Sis Center for Excellence in Cancer Genomics, Department of Biomedical Sciences, University at Albany-SUNY, School of Public Health
  • , Adele RicciardiAffiliated withGen*NY*Sis Center for Excellence in Cancer Genomics, Department of Biomedical Sciences, University at Albany-SUNY, School of Public Health
  • , Scott A. TenenbaumAffiliated withGen*NY*Sis Center for Excellence in Cancer Genomics, Department of Biomedical Sciences, University at Albany-SUNY, School of Public Health

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Summary

The untranslated regions (UTRs) of many mRNAs contain sequence and structural motifs that are used to regulate the stability, localization, and translatability of the mRNA. It should be possible to discover previously unidentified RNA regulatory motifs by examining many related nucleotide sequences, which are assumed to contain a common motif. This is a general practice for discovery of DNA-based sequence-based patterns, in which alignment tools are heavily exploited. However, because of the complexity of sequential and structural components of RNA-based motifs, simple-alignment tools are frequently inadequate. The consensus sequences they find frequently have the potential for significant variability at any given position and are only loosely characterized. The development of RNA-motif discovery tools that infer and integrate structural information into motif discovery is both necessary and expedient. Here, we provide a selected list of existing web-accessible algorithms for the discovery of RNA motifs, which, although not exhaustive, represents the current state of the art. To facilitate the development, evaluation, and training of new software programs that identify RNA motifs, we created the UAlbany training UTR (TUTR) database, which is a collection of validated sets of sequences containing experimentally defined regulatory motifs. Presently, eleven training sets have been generated with associated indexes and “answer sets” provided that identify where the previously characterized RNA motif [the iron responsive element (IRE), AU-rich class-2 element (ARE), selenocysteine insertion sequence (SECIS), etc.] resides in each sequence. The UAlbany TUTR collection is a shared resource that is available to researchers for software development and as a research aid.

Key Words

mRNA RNA motif RNA binding RNA consensus RNA folding post-transcription bioinformatics informatics