GS 541, Sp '05: The RNA Guest Lectures, 5/26 &5/31

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Lecture Slides

•  Noncoding RNA

1. Download the Infernal software package (infernal.tar.gz, version 0.55) and its "User's Guide" from here.
2. Read sections 1-3 of the user guide (possibly skipping "local alignments" on page 12).
3. Build and install it following the instructions in section 2 of the manual.
4. Follow the tutorial steps outlined in section 3 "Getting Started". Note that the files tutorial.sto/db/fa referred to there seem to have been removed or renamed in this release. Use trna.sto, trna_yeast_phe.fa for the first two of these; either use this for the cmalign input or make your own as described on the bottom of page 10.
5. The cmbuild example builds a model ("my.cm") for tRNA based on 5 or so yeast tRNAs. With so few sequences and such closely related ones, it shouldn't surprise you that it's not a very good model. But it's not useless. I've extracted a handful of tRNA sequences from the Genbank records for Pyrococcus furiosus (an anaerobic archaeon found in 100°C sediments near sea floor vents, presumably not a close relative of S. cerevisiae). The sequences are here. Run cmsearch on this data.
6. Email me (ruzzo at cs) the output produced by cmsearch in the step above.
7. Optional, open-ended extension: Note that the scores produced by this search are not convincingly high, even given the small size of the "data base" you just searched. And it missed some of the tRNAs (they have negative scores). See if you can do better. I.e., add some additional tRNA sequences to the samples in trna.sto, refine the structure annotation as needed, build a new model via cmbuild, and rescan the P. furiosus sequences to see if you can find more of the tRNAs and/or attain higher scores. This would be most convincing as a strategy for RNA annotation if the new sequences you add to the model training set are not closely related to P. furiosus, but even using a few of those to find the rest would be interesting. cmalign might be useful for helping you add new sequences to the alignment, but it's quite possible that you can do better manually. If you have the time and patience, scan more of the genome to see how it does, in terms of false positives, for example. Perhaps use cmbuild again to align all your P. furiosus hits. Email me (a) the refined .sto file you created, (b) the results of using it (or, rather, using the covariance model built from it) to rescan my small P. furiosus data set, and (c) a paragraph or two sketching how you went about refining the alignment.

• Storz, G.: An expanding universe of noncoding RNAs. 296 (2002) 1260--1263
• Eddy, S.R.: Computational genomics of noncoding RNA genes. Cell 109 (2002) 137--140
• Huttenhofer A, Schattner P, Polacek N. Non-coding RNAs: hope or hype? Trends Genet. 2005 May;21(5):289-97.

• Eddy SR, Durbin R. RNA sequence analysis using covariance models. Nucleic Acids Res. 1994 Jun 11;22(11):2079-88.
• Durbin, Richard and Eddy, Sean R. and Krogh, Anders and Mitchison, Graeme, "Biological Sequence Analysis: Probabilistic models of proteins and nucleic acids, Cambridge,1998. Sections 9.5-9.7 and Chapter 10.

• Eddy SR. A memory-efficient dynamic programming algorithm for optimal alignment of a sequence to an RNA secondary structure. BMC Bioinformatics. 2002 Jul 2;3(1):18.

• Griffiths-Jones, S., Moxon, S., Marshall, M., Khanna, A., Eddy, S.R., Bateman, A.: Rfam: annotating non-coding RNAs in complete genomes. Nucleic Acids Res 33 (2005) 121--124

• Paul P Gardner and Robert Giegerich, A comprehensive comparison of comparative RNA structure prediction approaches, BMC Bioinformatics 2004, 5:140, doi:10.1186/1471-2105-5-140

• Weinberg, Z. and Ruzzo, W.L. Faster Genome Annotation of Non-coding RNA Families Without Loss of Accuracy. Eighth Annual International Conference on Research in Computational Molecular Biology (RECOMB 2004) , pp 243-251, March 2004, San Diego, CA. Preprint.
• Weinberg, Z. and Ruzzo, W.L. Exploiting Conserved Structure for Faster Annotation of Non-coding RNAs Without Loss of Accuracy. Bioinformatics, 20 (suppl_1) i334-i341, 2004 and 12th International Conference on Intelligent Systems for Molecular Biology (ISMB 2004) , July 2004, Glasgow, Scottland. Preprint.

• Mandal, Lee, Barrick, Weinberg, Emilsson, Ruzzo, and Breaker: A Glycine-dependent Riboswitch that Uses Cooperative Binding to Control Gene Expression in Bacteria. Science, 2004 Oct 8;306(5694):275-9. Correction.
  • Science Perspectives: "RNAs turn on in tandem" by Michael Famulok Science. 2004 Oct 8;306(5694):233-4.
  • "A Sophisticated Riboswitch", Sci. STKE 2004 (254), tw369. [DOI: 10.1126/stke.2542004tw369]
• Barrick, Sudarsan, Weinberg, Ruzzo and Breaker: 6S RNA is a widespread regulator of eubacterial RNA polymerase that resembles an open promoter. RNA. 2005 May;11(5):774-84. Epub 2005 Apr 5.
• Trotochaud, A.E., Wassarman, K.M.: A highly conserved 6S RNA structure is required for regulation of transcription. Nat Struct Mol Biol 12 (2005) 313--319.
• Willkomm DK, Minnerup J, Huttenhofer A, Hartmann RK. Experimental RNomics in Aquifex aeolicus: identification of small non-coding RNAs and the putative 6S RNA homolog. Nucleic Acids Res. 2005 Apr 6;33(6):1949-60.

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