BMI-203 --- Journal References

Listed below are journal references that may provide useful background reading. Many of these references come from a list compiled by Russ Altman for his MIS-214 course at Stanford. Some of these references are available in journals at the UCSF library, but others are from more obscure publications. If you have trouble finding an article you are interested in, please speak to one of the instructors.

• Altschul, S.F., Gish, W., Miller, W., Myers, E.W., and Lipman, D.J. Basic Local Alignment Search Tool. J. Mol. Biol., 215, pp. 403-410, 1990.

  The paper introducing the BLAST algorithm.

• Arun, K.S., Huang, T.S., and Blostein, S.D. Least-Squares Fitting of Two 3-D Point Sets. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. PAMI-I 9, No. 5, pp. 698-700, 1987.

  A nice paper introducing a method for computing optimal alignment of two sets of points, given their correspondences.

• Bork, P., Dandekar, T., Diaz-Lazcoz, Y., Eisenhaber, F., Huynen, M., and Yuan, Y. Predicting Function: From Genes to Genomes and Back. J. Mol. Biol., 283, pp. 707-725, 1998.

  An excellent review of the problem of predicting the function of genes in the genome age.

• Bowie, J.U., Luthy, R., and Eisenberg, D. A Method to Identify Protein Sequences that Fold into a Known Three-Dimensional Structure. Science, 253, pp.164-170, 1991.

  A classic paper introducing the idea of fold recognition, using dynamic programming and an environmental description of structure.

• Bryant, S.H. and Altschul, S.F. Statistics of Sequence-Structure Threading. Current Opinion in Structural Biology, 5, pp. 236-244, 1995.

  A good review about how to evaluate protein threading performance.

• Burge, C. and Karlin, S. Prediction of Complete Gene Structures in Human Genomic DNA. J. Mol. Biol., 268, pp. 78-94, 1997.

  An excellent method for finding genes using probabilistic models.

• Dayhoff, M.O., Schwartz, R.M., and Orcutt, B.C. A Model of Evolutionary Change in Proteins. Atlas of Protein Sequence and Structure, v. 5 suppl. 3, pp. 345-352, 1978.

  A classic paper introducing the PAM matrix.

• Doolittle, R.F., Feng, D-F, Tsang, S., Cho, G, and Little, E. Determining Divergence Times of the Major Kingdoms of Living Organisms With a Protein Clock. Science, 271, pp. 470-477, 1996.

  A classic paper discussing how to correlate divergence in sequence with chronological time.

• Friedman, R. Lesson 5: Sequence Comparison and Alignment. Columbia University, 2000.

  Lecture notes from Dr. Richard Friedman's class on sequence analysis. These notes may prove useful as supplementary material for the lecture on dynamic programming. The notes are available here.

• Gerstein, M. and Levitt, M. Using Iterative Dynamic Programming to Obtain Accurate Pairwise and Multiple Alignments of Protein Structures. Proc. of ISMB-96, pp. 59-67, 1996.

  A nice paper introducing a method, based on dynamic programming, for aligning two 3D structures and finding correspondences between points.

• Gotoh, O. An Improved Algorithm for Matching Biological Sequences. J. Mol. Biol., 162, pp. 705-708, 1982.

  An follow-on paper to Needleman-Wunsch that shows how sequence alignment can be done in O(N^2).

• Gribskov, M., McLachlan, A.D., and Eisenberg, D. Protein Analysis: Detection of Distantly Related Proteins. Proc. Natl. Acad. Sci. USA, 84, pp. 4355-4358, 1987.

  A classic exposition of the PROFILE method for characterizing protein sequence multiple alignments.

• Gribskov, M. and Devereux, J., eds. Sequence Analysis Primer, pp. 124-137, 1994.

  A very useful introduction to dynamic programming sequence alignment.

• Henikoff, S. and Henikoff, Jorja G. Amino Acid Substitution Matrices from Protein Blocks. Proc. Natl. Acad. Sci. USA, 89, pp. 10915-10919, 1992.

  Introduction of the BLOSUM matrix from the BLOCKS database.

• Karlin, S. and Altschul, S.F. Methods for Assessing the Statistical Significance of Molecular Sequence Features by Using General Scoring Schemes. Proc. Natl. Acad. Sci. USA, 87, pp. 2264-2268, 1990.

  An exposition of the statistical theory behind the BLAST significance scores.

• Karp, P.D. and Riley, M. EcoCyc: The Resource and the Lessons Learned. SRI Report, January 21, 1999.

  An introduction to the EcoCYC knowledge base of E. Coli metabolism.

• Koza, J.R. Evolution of a Computer Program for Classifying Protein Segments asTransmembrane Domains Using Genetic Programming. Proc. of ISMB-94, pp. 244-252, 1994.

  An excellent example of the use of Genetic Programming to solve a biological problem.

• Krogh, A., Brown, M., Mian, I.S., Sjolander, K., and Haussler, D. Hidden Markov Models in Computational Biology. J. Mol. Biol., 235, pp. 1501-1531, 1994.

  A landmark paper showing the use of Hidden Markov Models as applied to the globin family.

• Lathrop, R.H. The Protein Threading Problem With Sequence Amino Acid Interaction Preferences is NP-Complete. Protein Engineering, 7:9, pp. 1059-1068, 1994.

  A useful paper showing that protein threading is NP-complete.

• Lawrence, C.E., Altschul, S.F., Boguski, M.S., Liu, J.S., Neuwald, A.F., Wootton, J.C. Detecting Subtle Sequence Signals: A Gibbs Sampling Strategy for Multiple Alignment. Science, 262, pp. 208-214, 1993.

  A classic paper showing the applicability of Gibbs sampling methods to multiple sequence alignment.

• McClure, M.A., Vasi, T.K., and Fitch, W.M. Comparative Analysis of Multiple Protein-Sequence Alignment Methods. Mol. Biol. Evol., 11:4, pp. 571-592, 1994.

  A very useful review and comparison of different multiple alignment strategies, tested on some hard problems.

• Needleman, S.B. and Wunsch, C.D. A General Method Applicable to the Search for Similarities in Amino Acid Sequence of Two Proteins. J. Mol. Biol., 48, pp. 443-453, 1970.

  The classic introduction of dynamic programming for sequence alignment.

• Nussinov, R. and Wolfson, H.J. Efficient Detection of Three-dimensional Structural Motifs in Biological Macromolecules by Computer Vision Techniques. Proc. Natl. Acad. Sci. USA, 88, pp. 10495-10499, 1991.

  A paper introducing computer vision techniques for processing 3D structure.

• Orengo C.A. and Taylor, W.R. SSAP: Sequential Structure Alignment Program for Protein Structure Comparison. Methods in Enzymology, 266, pp. 617-635, 1996.

  A nice description of a method for comparing 3D structures and identifying correspondences.

• Prevelige. P. and Fasman, G.D. Chou-Fasman Prediction of the Secondary Structure of Proteins: The Chou--Fasman-Prevelige Algorithm. In Prediction of Protein Structure and the Principles of Protein Conformation (G.D. Fasman, ed) Plenum Publishing, N.Y. pp. 391-416, 1989.

  A description of the classic secondary structure prediction algorithm by Chou and Fasman.

• Richards, F.M. Calculation of Molecular Volumes and Areas for Structures of Known Geometry. Methods in Enzymology, 115, pp. 440-464, 1996.

  A classic paper on how to compute important 3D properties of volume and surface for macromolecules.

• Richards, F.M. The Protein Folding Problem. Scientific American, pp. 54-63, January 1991.

  A readable summary of the protein folding problem.

• Sippl, M.J. Knowledge-based Potentials for Proteins. Current Opinion in Structural Biology, 5, pp. 229-235, 1995.

  A good description of the use of distance based potentials for protein threading.

• Smith, T.F. The History of the Genetic Sequence Databases. Genomics, 6, pp. 701-707, 1990.

  A very useful, informal history of sequence databases, offering a glimpse into the high stakes world of biological sequence analysis.

• Smith, T.F. and Waterman, M.S. Identification of Common Molecular Subsequences. J. Mol. Biol., 147, pp. 195-197, 1981.

  The classic follow-on to the Needleman & Wunsch paper describing the use of a similar algorithm for local alignment.

• Subbiah, S., Laurents, D.V., and Levitt, M. Structural Similarity of DNA-binding Domains of Bacteriophage Repressors and the Globin Core. Current Biology, 3:3, pp. 141-148, 1993.

  A short paper describing a method for detecting 3D protein similarities, and some unexpected discoveries when this method is applied to the globins.

• Thorne, J.L., Kishino, H, and Felsenstein, J. An Evolutionary Model for Maximum Likelihood Alignment of DNA Sequences. J. Mol. Evol., 33, pp. 114-124, 1991.

  A classic paper on using a model for evolution to compute multiple sequence alignments.

• Unger, R. and Moult, J. Genetic Algorithms for Protein Folding Simulations. J. Mol. Biol., 231, pp. 75-81, 1993.

  One of the first applications of genetic algorithms to problems in molecular biology.

• Zuker, M. On Finding All Suboptimal Foldings of an RNA Molecule. Science, 244, pp. 48-52, 1989.

  Classic paper on the folding of RNA secondary structure, based on experimental measurements of interaction energies.