General guidelines

The goals of your presentation should be:

1. to distil key points of the paper, including salient aspects of the model and of its biological validation (experimental and/or computational);
2. to identify strengths and limitations of the method, with particular attention to "realism" (pros, cons, improvements, omissions...);
3. to stimulate discussion, in particular of where this paper sits in relation to the "state-of-the-art", and of areas that could yet be explored.

Powerpoint presentations are mildly discouraged---use them sparingly if you must, but be aware that they tend to stultify discussion a little bit. (Using the projector to highlight sections of the paper is an appropriate use of visual aids.) Whiteboard-based presentations are much better since they are more flexible and tend to encourage interruptions.

As a general rule you should aim to present at most one paper per session. (This is somewhat flexible, though.)

Papers for class discussion

Please add your initials in front of papers you opt to present. (To edit this page & add your initials, click on the pencil icon at the top right.)

1. SUBSTITUTION: Point, codon, nearest-neighbor and latent substitution models
   • (JA) Nielsen R, Yang Z.  Likelihood models for detecting positively selected amino acid sites and applications to the HIV-1 envelope gene.  Genetics. 1998 Mar;148(3):929-36.
   • (SR) Soyer O, Dimmic MW, Neubig RR, Goldstein RA.  Using evolutionary methods to study G-protein coupled receptors.  Pac Symp Biocomput. 2002;:625-36.
   • (DS) Lunter G, Hein J.  A nucleotide substitution model with nearest-neighbour interactions.  Bioinformatics. 2004 Aug 4;20 Suppl 1:i216-23.
   • (IH) Siepel A, Haussler D.  Phylogenetic estimation of context-dependent substitution rates by maximum likelihood.  Mol Biol Evol. 2004 Mar;21(3):468-88. Epub 2003 Dec 5.
2. ONTOLOGIES: Modeling on ontologies
   • (OW) Barutcuoglu Z, Schapire RE, Troyanskaya OG.  Hierarchical multi-label prediction of gene function.  Bioinformatics. 2006 Apr 1;22(7):830-6. Epub 2006 Jan 12.
   • (PB) Carroll S, Pavlovic V.  Protein classification using probabilistic chain graphs and the Gene Ontology structure.  Bioinformatics. 2006 Aug 1;22(15):1871-8. Epub 2006 May 16.
   • King OD, Foulger RE, Dwight SS, White JV, Roth FP.  Predicting gene function from patterns of annotation.  Genome Res. 2003 May;13(5):896-904. Epub 2003 Apr 14.
   • (SM) Engelhardt BE, Jordan MI, Muratore KE, Brenner SE.  Protein molecular function prediction by Bayesian phylogenomics.  PLoS Comput Biol. 2005 Oct;1(5):e45. Epub 2005 Oct 7.
3. HMM: Hidden Markov models for homology detection, gene- & signal-finding
   • Kall L, Krogh A, Sonnhammer EL.  A combined transmembrane topology and signal peptide prediction method.  J Mol Biol. 2004 May 14;338(5):1027-36.
   • (RH) Burge C, Karlin S.  Prediction of complete gene structures in human genomic DNA.  J Mol Biol. 1997 Apr 25;268(1):78-94.
   • Goldman N, Thorne JL, Jones DT.  Using evolutionary trees in protein secondary structure prediction and other comparative sequence analyses.  J Mol Biol. 1996 Oct 25;263(2):196-208.
4. FAMILIES: Gene/species tree reconciliation and gene family birth-death models
   • Zmasek CM, Eddy SR.  A simple algorithm to infer gene duplication and speciation events on a gene tree.  Bioinformatics. 2001 Sep;17(9):821-8.
   • Hahn MW, De Bie T, Stajich JE, Nguyen C, Cristianini N.  Estimating the tempo and mode of gene family evolution from comparative genomic data.  Genome Res. 2005 Aug;15(8):1153-60.
   • Arvestad L, Berglund AC, Lagergren J, Sennblad B.  Bayesian gene/species tree reconciliation and orthology analysis using MCMC.  Bioinformatics. 2003;19 Suppl 1:i7-15.
5. TREES: Phylogenetic likelihood models and algorithms
   • Pollard KS et al.  Forces shaping the fastest evolving regions in the human genome.  PLoS Genet. 2006 Oct 13;2(10):e168. Epub 2006 Aug 23.
   • (OW) Friedman N, Ninio M, Pe'er I, Pupko T.  A structural EM algorithm for phylogenetic inference.  J Comput Biol. 2002;9(2):331-53.
   • Husmeier D, Wright F.  Detection of recombination in DNA multiple alignments with hidden Markov models.  J Comput Biol. 2001;8(4):401-27.
6. HIV: Models of the Human Immunodeficiency Virus fitness landscape
   • (JH) Beerenwinkel N, Daumer M, Sing T, Rahnenfuhrer J, Lengauer T, Selbig J, Hoffmann D, Kaiser R.  Estimating HIV evolutionary pathways and the genetic barrier to drug resistance.  J Infect Dis. 2005 Jun 1;191(11):1953-60. Epub 2005 Apr 28.
   • Beerenwinkel N, Rahnenfuhrer J, Daumer M, Hoffmann D, Kaiser R, Selbig J, Lengauer T.  Learning multiple evolutionary pathways from cross-sectional data.  J Comput Biol. 2005 Jul-Aug;12(6):584-98.
   • (OW) Schultz AK, Zhang M, Leitner T, Kuiken C, Korber B, Morgenstern B, Stanke M.  A jumping profile Hidden Markov Model and applications to recombination sites in HIV and HCV genomes.  BMC Bioinformatics. 2006 May 22;7:265.
7. PALEOGENOMICS: Phylo-alignment; reconstruction of ancestral genotypes
   • Blanchette M, Green ED, Miller W, Haussler D.  Reconstructing large regions of an ancestral mammalian genome in silico.  Genome Res. 2004 Dec;14(12):2412-23.
   • Ma J, Zhang L, Suh BB, Raney BJ, Burhans RC, Kent WJ, Blanchette M, Haussler D, Miller W.  Reconstructing contiguous regions of an ancestral genome.  Genome Res. 2006 Dec;16(12):1557-65. Epub 2006 Sep 18.
   • Elias I, Tuller T.  Reconstruction of ancestral genomic sequences using likelihood.  J Comput Biol. 2007 Mar;14(2):216-37.
   • Williams PD, Pollock DD, Blackburne BP, Goldstein RA.  Assessing the accuracy of ancestral protein reconstruction methods.  PLoS Comput Biol. 2006 Jun 23;2(6):e69. Epub 2006 Jun 23.
8. FIELDS: Markov random fields and microarray analysis
   • Gottardo R, Besag J, Stephens M, Murua A.  Probabilistic segmentation and intensity estimation for microarray images.  Biostatistics. 2006 Jan;7(1):85-99. Epub 2005 Jul 27.
   • 15840703
   • Katzer M, Kummert F, Sagerer G.  Methods for automatic microarray image segmentation.  IEEE Trans Nanobioscience. 2003 Dec;2(4):202-14.
9. INDELS: Birth-death models of nucleotide insertion and deletion
   • Lunter G, Ponting CP, Hein J.  Genome-wide identification of human functional DNA using a neutral indel model.  PLoS Comput Biol. 2006 Jan;2(1):e5. Epub 2006 Jan 13.
   • (DS) Kim J, Sinha S.  Indelign: a probabilistic framework for annotation of insertions and deletions in a multiple alignment.  Bioinformatics. 2007 Feb 1;23(3):289-97. Epub 2006 Nov 15.
   • Diallo AB, Makarenkov V, Blanchette M.  Exact and heuristic algorithms for the Indel Maximum Likelihood Problem.  J Comput Biol. 2007 May;14(4):446-61.
10. VIRUS: Evolutionary bioinformatics of viral genomes
    • (RH)Pedersen JS, Forsberg R, Meyer IM, Hein J.  An evolutionary model for protein-coding regions with conserved RNA structure.  Mol Biol Evol. 2004 Oct;21(10):1913-22. Epub 2004 Jun 30.
    • (JH)Lemey P, Kosakovsky Pond SL, Drummond AJ, Pybus OG, Shapiro B, Barroso H, Taveira N, Rambaut A.  Synonymous substitution rates predict HIV disease progression as a result of underlying replication dynamics.  PLoS Comput Biol. 2007 Feb 16;3(2):e29. Epub 2007 Jan 2.
    • (JA) de Groot S, Mailund T, Hein J.  Comparative annotation of viral genomes with non-conserved gene structure.  Bioinformatics. 2007 May 1;23(9):1080-9. Epub 2007 Mar 6.
11. SCFG: Stochastic context-free grammars
    • (PB) Bockhorst J, Qiu Y, Glasner J, Liu M, Blattner F, Craven M.  Predicting bacterial transcription units using sequence and expression data.  Bioinformatics. 2003;19 Suppl 1:i34-43.
    • Dowell RD, Eddy SR.  Evaluation of several lightweight stochastic context-free grammars for RNA secondary structure prediction.  BMC Bioinformatics. 2004 Jun 4;5:71.
    • (IH) Holmes I.  Accelerated probabilistic inference of RNA structure evolution.  BMC Bioinformatics. 2005 Mar 24;6:73.
    • (JA) Do CB, Woods DA, Batzoglou S.  CONTRAfold: RNA secondary structure prediction without physics-based models.  Bioinformatics. 2006 Jul 15;22(14):e90-8.
12. SYSTEMS: Stochastic systems biology
    • (JH) Samoilov M, Plyasunov S, Arkin AP.  Stochastic amplification and signaling in enzymatic futile cycles through noise-induced bistability with oscillations.  Proc Natl Acad Sci U S A. 2005 Feb 15;102(7):2310-5. Epub 2005 Feb 8.
    • (JH) Wolf DM, Vazirani VV, Arkin AP.  Diversity in times of adversity: probabilistic strategies in microbial survival games.  J Theor Biol. 2005 May 21;234(2):227-53. Epub 2005 Jan 24.
    • (RH) Elowitz MB, Levine AJ, Siggia ED, Swain PS.  Stochastic gene expression in a single cell.  Science. 2002 Aug 16;297(5584):1183-6.
13. POPULATIONS: Populations and fitness
    • Kimura M, Ohta T.  The Average Number of Generations until Fixation of a Mutant Gene in a Finite Population.  Genetics. 1969 Mar;61(3):763-771.
    • Berg J, Willmann S, Lassig M.  Adaptive evolution of transcription factor binding sites.  BMC Evol Biol. 2004 Oct 28;4(1):42.

-- IanHolmes - 23 Aug 2007