Yb Biblio

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Non-coding RNA

Bartel &: MicroRNAs: genomics, biogenesis, mechanism, and function. Cell 2004;116:281-97. Review

Do et al.: CONTRAfold: RNA secondary structure prediction without physics-based models. Bioinformatics 2006;22:e90-8.

Eddy &: Computational genomics of noncoding RNA genes. Cell 2002;109:137-40. Review

Eddy &: Noncoding RNA genes. Curr. Opin. Genet. Dev. 1999;9:695-9. Review

Hertel & Stadler: Hairpins in a Haystack: recognizing microRNA precursors in comparative genomics data. Bioinformatics 2006;22:e197-202. Uses RNAz genome screen.

Hüttenhofer et al.: Non-coding RNAs: hope or hype?. Trends Genet. 2005;21:289-97.

Lai et al.: Computational identification of Drosophila microRNA genes. Genome Biol. 2003;4:R42.

Moulton &: Tracking down noncoding RNAs. Proc. Natl. Acad. Sci. U.S.A. 2005;102:2269-70.

Pedersen et al.: Identification and classification of conserved RNA secondary structures in the human genome. PLoS Comput. Biol. 2006;2:e33. Evofold.

Rivas & Eddy: Noncoding RNA gene detection using comparative sequence analysis. BMC Bioinformatics 2001;2:8. QRNA.

Rivas & Eddy: Secondary structure alone is generally not statistically significant for the detection of noncoding RNAs. Bioinformatics 2000;16:583-605.

Washietl et al.: Fast and reliable prediction of noncoding RNAs. Proc. Natl. Acad. Sci. U.S.A. 2005;102:2454-9. RNAz.

Washietl et al.: Mapping of conserved RNA secondary structures predicts thousands of functional noncoding RNAs in the human genome. Nat. Biotechnol. 2005;23:1383-90.


Doudna &: Structural genomics of RNA. Nat. Struct. Biol. 2000;7 Suppl:954-6.

Dowell & Eddy: Evaluation of several lightweight stochastic context-free grammars for RNA secondary structure prediction. BMC Bioinformatics 2004;5:71.

Gardner & Giegerich: A comprehensive comparison of comparative RNA structure prediction approaches. BMC Bioinformatics 2004;5:140.

Hofacker et al.: Conserved RNA secondary structures in viral genomes: a survey. Bioinformatics 2004;20:1495-9.

Hofacker et al.: Prediction of locally stable RNA secondary structures for genome-wide surveys. Bioinformatics 2004;20:186-90.

Holmes &: Accelerated probabilistic inference of RNA structure evolution. BMC Bioinformatics 2005;6:73. Stemloc

Leontis et al.: The RNA Ontology Consortium: an open invitation to the RNA community. RNA 2006;12:533-41.

Mandal & Breaker: Gene regulation by riboswitches. Nat. Rev. Mol. Cell Biol. 2004;5:451-63. Review

Vicens & Westhof: RNA as a drug target: the case of aminoglycosides. Chembiochem 2003;4:1018-23. Review

Protein coding gene

Guigo R, Wiehe T. Gene prediction accuracy in large DNA sequences. Frontiers in Computational Genomics. Functional Genomics Series, Vol 3. Caister Academic Press, 2003, p. 1-33.

Munch & Krogh: Automatic generation of gene finders for eukaryotic species. BMC Bioinformatics 2006;7:263. Agene.

Pedersen & Hein: Gene finding with a hidden Markov model of genome structure and evolution. Bioinformatics 2003;19:219-27. Evogene.

Siepel A, Haussler D. Computational identification of evolutionarily conserved exons. RECOMB 2004: Proceedings of the Eighth Annual International Conference on Computational Molecular Biology,2004 pg:177 -186. ExoniPhy.

Siepel & Haussler: Combining phylogenetic and hidden Markov models in biosequence analysis. J. Comput. Biol. 2004;11:413-28.

Wasserman & Sandelin: Applied bioinformatics for the identification of regulatory elements. Nat. Rev. Genet. 2004;5:276-87. Review

Zhang &: Computational prediction of eukaryotic protein-coding genes. Nat. Rev. Genet. 2002;3:698-709. Review

Sequence Analysis

Bray & Pachter: MAVID: constrained ancestral alignment of multiple sequences. Genome Res. 2004;14:693-9.

Chatterji & Pachter: Reference based annotation with GeneMapper. Genome Biol. 2006;7:R29.

Sys Bio

Aron, Uri. Intro to Sys Bio Circuits. CRC Press.

Kahlem & Birney: Dry work in a wet world: computation in systems biology. Mol. Syst. Biol. 2006;2:40.

Wilkinson, Darren. Stochastic Modelling in Systems Biology. CRC Press.

Syn Bio

Arkin & Fletcher: Fast, cheap and somewhat in control. Genome Biol. 2006;7:114. Opinion

Breaker &: Natural and engineered nucleic acids as tools to explore biology. Nature 2004;432:838-45.

Isaacs et al.: RNA synthetic biology. Nat. Biotechnol. 2006;24:545-54.

Khosla & Keasling: Metabolic engineering for drug discovery and development. Nat Rev Drug Discov 2003;2:1019-25. Review

Voigt &: Genetic parts to program bacteria. Curr. Opin. Biotechnol. 2006;17:548-57. Review

iGem Competition: http://parts2.mit.edu/wiki/index.php/Main_Page

CS/Machine Learning

K. Lari and S. J. Young. The estimation of stochastic context-free grammars using the inside–outside algorithm. Computer Speech and Language, 4:35–56, 1990.

Ng AY and Jordan MI. "On Discriminative vs. Generative classifiers: A comparison of logistic regression and naive Bayes." Advances in Neural Information Processing Systems, 2001.


Eddy, Sean. What is a hidden Markov model?

Eddy, Sean. What is Bayesian statistics?

Eddy, Sean. What is dynamic programming?

Eddy, Sean. How do RNA folding algorithms work?

Eddy, Sean. Where did the BLOSUM62 alignment score matrix come from?

-- Yuri Bendana - 19 May 2006