Bioi 7711 readings

Introduction

• The Computational Bioscience Program educational mission statement.
• Sample preliminary exam short essay questions.

Molecular Biology Databases

• The Molecular Biology Database Collection: 2007 update. Nucleic Acids Research 2007 35(Database Issue):D3-D4
• The NCBI tutorial, Entrez: Making Use of Its Power

Database Search and Sequence Alignment

• A textbook chapter on dynamic programming alignments.
• Per Kraulis' Lecture Notes on Dynamic Programming alignments, particularly the simple example and the more complex example
• A textbook chapter on BLAST.
• The NCBI BLAST education pages (all sections).
• Altschul, et al on PSI-BLAST and on improving PSI-BLAST (using ROC curves)

Multiple Sequence Alignment

• Edgar & Batzoglou, Multiple Sequence Alignment, Current Opinion in Structural Biology 16(3):368-373, June 2006
• Thompson, et al, CLUSTAL W: Improving the Sensitivity of Progressive Multiple Sequence Alignment Through Sequence Weighting, Position-Specific Gap Penalties and Weight Matrix Choice. Nucleic Acids Res. 1994 November 11; 22(22): 4673
• The ClustalW FAQ
• Wrabi & Grishin, Gaps in structurally similar proteins: Towards improvement of multiple sequence alignment Proteins: Structure, Function, and Bioinformatics 2003 54(1): 71 - 87
• Sauder, et al, Large-scale comparison of protein sequence alignment algorithms with structure alignments.Proteins. 2000 Jul 1;40(1):6-22.

Hidden Markov Models

• The HMM chapter in a textbook (The Ewans and Grant book is particularly good here).
• Sean Eddy's Review article on HMMs, Bioinformatics 14:755-763 (1998).
• A comparison of profile hidden Markov model procedures for remote homology detection. Nucleic Acids Research, 2002, Vol. 30, No. 19 4321-4328
• Rachel Karchin's ISMB '99 HMM tutorial "Making the Most of your HMM"
• The PFAM article in the 2006 NAR Database issue.

Sequence Assembly

• Gene Myers, Whole Genome DNA Sequencing, IEEE Computational Engineering and Science 3, 1 (1999), 33-43. [Only available to UCHSC IP addresses.]
• Huang, et al. PCAP: A Whole-Genome Assembly Program. Genome Research, 13: 2164-2170 (2003)
• Warren, et al, Physical map-assisted whole-genome shotgun sequence assemblies. Genome Research 16(6):768-75. (2006)
• Gordon, et al. Consed: a graphical tool for sequence finishing. Genome Research 8:195-202 (1998).

Gene Finding

• Burg & Karlin, Prediction of complete gene structures in human genomic DNA Journal of Molecular Biology Volume 268, Issue 1 , 25 April 1997, Pages 78-94
• Reese, et al, Genie: Gene Finding in Drosophila melanogaster Genome Research Vol. 10, Issue 4, 529-538, April 2000
• Ashbruner, M, A Biologist's View of the Drosophila Genome Annotation Assessment Project Genome Research Vol. 10, Issue 4, 391-393, April 2000. [NB: The entire special issue on the GRASP competition is of interest.]
• The EGRASP 2006 gene finding evaluation. The entire special issue is worth reading, but you must read at least:
  • The overall review and
  • The JIGSAW entry, which has results for exon finders. You may also be interested in the original JIGSAW paper.
• Korf, et al,'s Twinscan ISMB 2001 paper
• Wu and Haussler, Coding exon detection using comparative sequences (ShortHMM), J. Computational Biology Jul 2006, Vol. 13, No. 6: 1148-1164
• Ohler, Shomron and Burge, Recognition of unknown conserved alternatively spliced exons. PLoS Computational Biology 2005 Jul;1(2):113-22.
• Optional readings
  • Arumugam, et al, Pairagon+N-SCAN_EST: a model-based gene annotation pipeline Genome Biology 2006, 7(Suppl 1):S5
  • Crollius, et al Estimate of human gene number provided by genome-wide analysis using Tetraodon nigroviridis DNA sequence (ExoFish) Nature Genetics 25, 235 - 238 (2000)
  • Yao, et al, Evaluation of five ab initio gene prediction programs for the discovery of maize genes Plant Mol Biol. 2005 Feb;57(3):445-60.
  • Fossiac and Schiex, Integrating alternative splicing detection into gene prediction BMC Bioinformatics. 2005; 6: 25.
  • Cawley and Pachter, HMM sampling and applications to gene finding and alternative splicing. Bioinformatics. 2003 Oct;19 Suppl 2:II36-II41.
  • Carter, Dubchak and Holbrook A computational approach to identify genes for functional RNAs in genomic sequences Nucleic Acids Res. 2001 October 1; 29(19): 3928-3938
  • Ohler, et al, Patterns of flanking sequence conservation and a characteristic upstream motif for microRNA gene identification. RNA. 2004 September; 10(9): 1309-1322.

Protein Structure Prediction

• Simons, et al, Assembly of protein tertiary structures from fragments with similar local sequences using simulated annealing and Bayesian scoring functions. J Mol Biol. 1997 Apr 25;268(1):209-25.
• Ginalski, et al 3D-Jury: a simple approach to improve protein structure predictions. Bioinformatics. 2003 May 22;19(8):1015-8.
• Vincent, et al, Assessment of CASP6 predictions for new and nearly new fold targets [NB: you may find the whole special issue on CASP6 to be of interest. Proteins: Structure, Function, and Bioinformatics Volume 61, Issue S7, Pages 67-83
• Chivian, et al, Prediction of CASP6 structures using automated robetta protocols Proteins: Structure, Function, and Bioinformatics Volume 61, Issue S7, Pages 157-166
• D. Fisher, Servers for protein structure prediction Current Opinion in Structural Biology Volume 16, Issue 2 , April 2006, Pages 178-182
• Karplus, et al, SAM-T04: What is new in protein-structure prediction for CASP6 Proteins: Structure, Function, and Bioinformatics Volume 61, Issue S7, Pages 135-142
• You might also be interested in the preliminary CASP7 results.

Mechanics, Dynamics & Docking

• Karplus & McCammon's Molecular dynamics simulations of biomolecules Nature: Structural Biology 2002 Sep;9(9):646-52.
• Nussinov, et al, Principles of docking: An overview of search algorithms and a guide to scoring functions. Proteins 2002 Jun 1;47(4):409-43
• Gerstein & Echols' Exploring the range of protein flexibility, from a structural proteomics perspective. Curr Opin Chem Biol. 2004 Feb;8(1):14-9.

Computational Phylogeny

• Kim & Warnow's ISMB '99 tutorial on phylogenetic inference.
• Huelsenbeck, et al, Bayesian Inference of Phylogeny and Its Impact on Evolutionary Biology Science 14 December 2001: Vol. 294. no. 5550, pp. 2310 - 2314 DOI: 10.1126/science.1065889
• Edgar and Sjolander SATCHMO: Sequence alignment and tree construction using hidden Markov models, Bioinformatics 19(11), 1404-1411. 2003

Genetic Analysis

• Short and opinionated overview of linkage and association analysis by Robert Elston, one of the founders of the field. Genetic Epidemiology 15(6):565-576, 1998. This was the 1997 International Genetic Epidemiology Society Presidential Address [NB: only works from UCHSC IP address.]
• The slides and notes, from the first lecture in Gil McVean's outstanding course on population genetics at Oxford (slides from 2004, notes from 2002 version). I strongly recommend reviewing the rest of the notes and slides from the population genetics course. Kent Holsinger also has produced a good set of lecture notes in population genetics (the PDFs have amusing footnotes missing in the HTML versions). The Holsinger notes are from an undergraduate course, and are easier to follow if you've never had any population genetics before.
• Slides from Terry Speed's outstanding 2007 ISMB keynote
• Lin and Zou, Assessing genomewide statistical significance in linkage studies. Genet Epidemiol. 2004 Nov;27(3):202-14.
• Marchini, et al, Genome-wide strategies for detecting multiple loci that influence complex diseases, Nature Genetics 37:413 - 417 (2005)
• The Introduction to the 14th Genetic Analysis Workshop. You can review the entire collection of associated papers for ones of particular interest to you. GAW 15 happened in 2006, but the papers aren't published yet; some results are available.
• Mailund, et al, Whole genome association mapping by incompatibilities and local perfect phylogeniesBMC Bioinformatics 2006, 7:454
• Wang, et al., Genome-wide Association Studies: Theoretical and Practical Concerns Nature Reviews Genetics 6, 109-118 (2005)
• The International HapMap Consortium, A second generation human haplotype map of over 3.1 million SNPs. Nature 449, 851-861. 2007.
• Barrett, et al, Haploview: analysis and visualization of LD and haplotype maps Bioinformatics 21:2(263-265), 2005.
• Optional:
  
  • Kruglyak, et al's Parametric and nonparametric linkage analysis: a unified multipoint approach. American Journal of Human Genetics 58(6):1347 (1996). You have to go to the library to get a copy.
  • The lectures on the coalescent, either from the 2002 Oxford population genetics course or from Holsinger's course
  • Simone Tavaré, et al report on a Markov Chain Monte Carlo method finding disease genes by coalescence, I see dead people: Gene Mapping Via Ancestral Inference from the 12th Genetic Analysis Workshop
  • Nicolas, et al, A model-based approach to selection of tag SNPs BMC Bioinformatics. 2006; 7: 303.

Microarrays

• Kozarova, A. et al., Array of informatics: Applications in modern research. J Proteome Res. 2006 May;5(5):1051-9.
• Allison, DB, et al., Microarray data analysis: from disarray to consolidation and consensus Nature Reviews Genetics 7, 55-65 (January 2006)
• Kreil, et al, Microarray Oligonucleotide Probes Methods in Enzymology Volume 410 , 2006, Pages 73-98
• Quackenbush, Microarray data normalization and transformation Nat Genet. 2002 Dec;32 Suppl:496-501.
• Irizarry, Wu and Jaffee, Comparison of Affymetrix GeneChip expression measures Bioinformatics 22 (7): 789. Be sure to review the supplementary material, particularly the table of more recent results, and the related links on the course page.
• Kuo, et al., A sequence-oriented comparison of gene expression measurements across different hybridization-based technologies. Nat Biotechnol. 2006 Jul;24(7):832-40.
• The Microarray Quality Control consortium issue of Nature Biotechology.
• Review article by UCHSC's Douglas Curran-Everett, Multiple comparisons: philosophies and illustrations American Journal of Physiology - Regulatory, Integrative and Comparative Physiology Vol. 279, Issue 1, R1-R8, July 2000
• Patrik D'haeseleer How does gene expression clustering work? Nature Biotechnology 23, 1499 - 1501 (2005)
• Subramanian, et al., Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles PNAS | October 25, 2005 | vol. 102 | no. 43 | 15545-15550

  Optional:

  The original GCRMA paper, Wu, et al, A Model Based Background Adjustment for Oligonucleotide Expression Arrays (May 2004). Johns Hopkins University, Dept. of Biostatistics Working Papers. Working Paper 1.
  • Durbin, et al, A variance-stabilizing transformation for gene-expression microarray data Bioinformatics Vol. 18 no. 9 2002 Pages S105-S110
  • Golub, et al, Molecular Classification of Cancer: Class Discovery and Class Prediction by Gene Expression Monitoring Science 1999 Oct 15;286(5439):531-7
  • Brown, et al Knowledge-based analysis of microarray gene expression data by using support vector machines Proceedings of the National Academy of Sciences USA 97(1):262-267, January 4, 2000
  • MacKay & Mishkin, Latent Variable Models for Gene Expression Data Technical report.
  • Brazhnik, et al Gene networks: how to put the function in genomics Trends in Biotechnology Volume 20, Issue 11 , 1 November 2002, Pages 467-472

Proteomics

• Wysocki, et al., Mass spectrometry of peptides and proteins Methods Volume 35, Issue 3 , March 2005, Pages 211-222
• Chalkley, et al., Bioinformatic Methods to Exploit Mass Spectrometric Data for Proteomic Applications Methods in Enzymology Volume 402 , 2005, Pages 289-312
• Li, et al., Data mining techniques for cancer detection using serum proteomic profiling Artificial Intelligence in Medicine Volume 32, Issue 2 , October 2004, Pages 71-83
• optional review of instruments and relevant biophysics: Lane, Mass spectrometry-based proteomics in the life sciences Cell Mol Life Sci. 2005 Apr;62(7-8):848-69.

Biomedical Language Processing

• Hunter and Cohen, Biomedical Language Processing: What's Beyond PubMed? Molecular Cell 2006 Mar 3;21(5):589-94.
• Draft of our OpenDMAP manuscript.
• Shah, et al., Extraction of Transcript Diversity from Scientific Literature PLoS Comput Biol 1(1): e10
• Horn, et al., Automated extraction of mutation data from the literature: application of MuteXt to G protein-coupled receptors and nuclear hormone receptors. Bioinformatics. 2004 Mar 1;20(4):557-68.
• Hersh, et al., TREC Genomics 2006 Overview from the TREC Genomics site.
• Hirschman, et al., Overview of BioCreAtIvE: critical assessment of information extraction for biology BMC Bioinformatics 2005, 6(Suppl 1):S1. You may also be interested in the other articles from the BMC Bioinformatics special issue on the first BioCreative competition. The issue on the second BioCreative isn't out yet, but the BioCreative web site has some additional information.

Everything Else

• Transcription Factor Binding Sites
  • Vavouri and Elgar, Prediction of cis-regulatory elements using binding site matrices - the successes, the failures and the reasons for both Current Opinion in Genetics & Development Volume 15, Issue 4 , August 2005, Pages 395-402
  • Huber and Bulyk, Meta-analysis discovery of tissue-specific DNA sequence motifs from mammalian gene expression data BMC Bioinformatics. 2006; 7: 229.
• Whole genome comparisons
  • Eichler and Sankoff, Structural Dynamics of Eukaryotic Chromosome Evolution Science 8 August 2003: Vol. 301. no. 5634, pp. 793 - 797
  • Pennacchio, Insights from human/mouse genome comparisons Mammalian Genome Volume 14, Number 7 / July, 2003
• Pathways, simulation and systems biology informatics
  • Caspi, et al., MetaCyc: a multiorganism database of metabolic pathways and enzymes Nucleic Acids Res. 2006 January 1; 34(Database issue): D511\u2013D516.
  • Sveiczer, et al., Modelling the fission yeast cell cycle. Brief Funct Genomic Proteomic. 2004 Feb;2(4):298-307.
  • Klamt, et al., A methodology for the structural and functional analysis of signaling and regulatory networks BMC Bioinformatics. 2006; 7: 56.
• Network analysis
  • Kashtan and Alon, Spontaneous evolution of modularity and network motifs Proc Natl Acad Sci U S A. 2005 September 27; 102(39): 13773\u201313778.
  • Gunsalas, et al., Predictive models of molecular machines involved in Caenorhabditis elegans early embryogenesis Nature 436, 861-865 (11 August 2005)
  • Coveney and Fowler, Modelling biological complexity: a physical scientist's perspective Journal of The Royal Society: Interface Volume 2, Number 4 / September 22, 2005
• Pharmacoinformatics
  • Claus and Underwood, Discovery informatics: its evolving role in drug discovery Drug Discovery Today Volume 7, Issue 18 , 15 September 2002, Pages 957-966
  • Louie, et al., Data Integration and Genomic Medicine Journal of Biomedical Informatics Volume 40, Issue 1 , February 2007, Pages 5-16
  • Dix, et al., The ToxCast Program for Prioritizing Toxicity Testing of Environmental Chemicals. Toxicol Sci. 2006 Sep 8;
• Metabolomics
  • Shulaev, Metabolomics technology and bioinformatics Briefings in Bioinformatics 2006 7(2):128-139
  • Wishart, Metabolomics: The Principles and Potential Applications to Transplantation American Journal of Transplantation Volume 5 Issue 12 Page 2814 - December 2005
• Neuroinformatics
  • The journal Neuroinformatics
  • Amari, et al., Neuroinformatics: the integration of shared databases and tools towards integrative neuroscience. J Integr Neurosci. 2002 Dec;1(2):117-28.
  • Martone, et al., E-neuroscience: challenges and triumphs in integrating distributed data from molecules to brains. Nat Neurosci. 2004 May;7(5):467-72.
• Forensic Bioinformatics
  • Lin, et al., Interpreting anonymous DNA samples from mass disasters: probabilistic forensic inference using genetic markers. Bioinformatics. 2006 Jul 15;22(14):e298-306.
  • Bill, et al., PENDULUM-a guideline-based approach to the interpretation of STR mixtures. Forensic Sci Int. 2005 Mar 10;148(2-3):181-9.