Needleman-Wunsch algorithm: A
general method applicable to the search for similarities in the amino acid
sequence of two proteins, J Mol Biol. 48(3):443-53.
Smith-Waterman algorithm: Identification of Common Molecular Subsequences, Journal of Molecular Biology, 147:195-197, 1981
Altschul, S.F., Gish, W., Miller, W., Myers, E.W. & Lipman, D.J. (1990) "Basic local alignment search tool." J. Mol. Biol. 215:403-410. PubMed
Altschul, S.F., Madden, T.L., Schäffer, A.A., Zhang, J., Zhang, Z., Miller, W. & Lipman, D.J. (1997) "Gapped BLAST and PSI-BLAST: a new generation of protein database search programs." Nucleic Acids Res. 25:3389-3402. PubMed
Dayhoff,
M.O., Schwartz, R.M. & Orcutt, B.C. (1978)
"A model of evolutionary change in proteins." In "Atlas of Protein Sequence and Structure, vol. 5, suppl. 3." M.O. Dayhoff
(ed.), pp. 345-352, Natl. Biomed. Res.
Found.,
Schwartz, R.M. & Dayhoff,
M.O. (1978) "Matrices for detecting distant relationships." In "Atlas of Protein Sequence and Structure, vol. 5, suppl. 3." M.O. Dayhoff
(ed.), pp. 353-358, Natl. Biomed. Res.
Found.,
Altschul, S.F. (1991) "Amino acid substitution matrices from an information theoretic perspective." J. Mol. Biol. 219:555-565. PubMed
States, D.J., Gish, W., Altschul, S.F. (1991) "Improved sensitivity of nucleic acid database searches using application-specific scoring matrices." Methods 3:66-70.
Henikoff, S. & Henikoff, J.G. (1992) "Amino acid substitution matrices from protein blocks." Proc. Natl. Acad. Sci. USA 89:10915-10919. PubMed
Altschul, S.F. (1993) "A protein alignment scoring system sensitive at all evolutionary distances." J. Mol. Evol. 36:290-300. PubMed
Karlin, S. & Altschul, S.F. (1990) "Methods for assessing the statistical significance of molecular sequence features by using general scoring schemes." Proc. Natl. Acad. Sci. USA 87:2264-2268. PubMed
Karlin, S. & Altschul, S.F. (1993) "Applications and statistics for multiple high-scoring segments in molecular sequences." Proc. Natl. Acad. Sci. USA 90:5873-5877. PubMed
Dembo, A., Karlin, S. & Zeitouni, O. (1994) "Limit distribution of maximal non-aligned two-sequence segmental score." Ann. Prob. 22:2022-2039.
Altschul, S.F. (1997) "Evaluating the statistical significance of
multiple distinct local alignments." In
"Theoretical and Computational Methods in Genome Research." (S. Suhai, ed.), pp. 1-14, Plenum,
Schaffer AA, Aravind L, Madden TL, Shavirin S, Spouge JL, Wolf YI, Koonin EV, Altschul SF. (2001) "Improving the accuracy of PSI-BLAST protein database searches with composition-based statistics and other refinements." Nucleic Acids Res. 2001 Jul 15;29(14):2994-3005. PubMed
Bin Ma, John Tromp, Ming Li. PatternHunter: faster and more sensitive homology search. Bioinformatics, 18(3):440-445. March 2002. [ PubMed ]
Ming Li, Bin Ma, Derek Kisman, John Tromp. PatternHunter II: Highly Sensitive and Fast Homology Search. Journal of Bioinformatics and Computational Biology, 2(3):417-439. 2004.
Jinbo Xu, Daniel G. Brown, Ming Li, Bin Ma. Optimizing multiple spaced seeds for homology search. Journal of Computational Biology, 2005. Accepted November 2004.
Daniel G. Brown. Optimizing Multiple Seed for Protein Homology Search. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2(1):29-38. 2005.
Brona Brejova, Daniel G. Brown, Tomas Vinar. Vector seeds: an extension to spaced seeds. Journal of Computer and System Sciences, 70(3):364--380. 2005.
Daniel G. Brown, Ming Li, Bin Ma. A tutorial of recent developments in the seeding of local alignment. Journal of Bioinformatics and Computational Biology, 2(4):819-842. 2004.
C. Notredame, D. G. Higgins and J. Heringa. T-Coffee: A Novel Method for Fast and Accurate Multiple Sequence Alignment. J. Mol Biol (2000) 302, 205-217
Wallace IM, O'Sullivan O, Higgins DG and Notredame C. M-Coffee: combining multiple sequence alignment methods with T-Coffee. Nucleic Acids Res. 2006 Mar 23;34(6):1692-9.
Edgar RC and S. Batzoglou. Multiple sequence alignment. Curr Opin Struct Biol. 2006 Jun;16(3):368-73. Epub 2006 May 5.
J. D. Thompson, D. G. Higgins and T. J. Gibson. CLUSTAL W: improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice. Nucleic Acids Research, 1994, Vol. 22, No. 22, 4673-4680
R. C. Edgar. MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Research, 2004, Vol. 32, No. 5, 1792-1797
D. T. Jones. Protein Secondary Structure Prediction Based on Position-specific Scoring Matrices. J. Mol. Biol. (1999) 292, 195-202.
Bryson K, McGuffin LJ, Marsden
RL, Ward JJ, Sodhi JS. & Jones
DT. (2005) Protein
structure prediction servers at University College London. Nucl.
Acids Res. 33(Web Server issue): W36-38.
Meiler J., Mueller M., Zeidler A., Schmaeschke F. Generation and Evaluation of Dimension Reduced Amino Acid Parameter Representations by Artificial Neural Networks. J. Mol. Model., 7, (9), 360-369. ; 2001
Meiler J., Baker D. Coupled Prediction of Protein Secondary and Tertiary Structure. PNAS, 100, (21), 12105-12110. ; 2003
Y. Zhang, J. Skolnick,
Scoring
function for automated assessment of protein structure template quality, Proteins, 2004 57: 702-710 (download the
PDF file and Correction).
Y. Zhang, J. Skolnick,
TM-align: A
protein structure alignment algorithm based on TM-score, Nucleic Acids Research, 2005 33: 2302-2309
(download the
PDF file).
Matthew Menke, Bonnie Berger and Lenore Cowen. Matt: Local Flexibility Aids Protein Multiple Structure Alignment
Profile HMM: http://bioinformatics.oxfordjournals.org/cgi/reprint/14/9/755
HHpred: Protein homology detection by HMM-HMM comparison.
BoostThreader: Boosting protein threading accuracy. RECOMB 2009.
Thomas Hamelryck, John T. Kent and Anders Krogh. Sampling Realistic Protein Conformations Using Local Structural Bias.
Zhao F, Li S, Sterner BW and Xu J. Discriminative learning for protein conformation sampling.
Feng Zhao, Jian Peng, Joe DeBartolo, Karl F. Freed, Tobin R. Sosnick and Jinbo Xu. A Probabilistic Graphical Model for Ab Initio Folding
Covariance model of RNA: RNA sequence analysis using covariance models.
E. P. Nawrocki, D. L. Kolbe and S. R. Eddy. Infernal
1.0: Inference of RNA Alignments.
Bioinformatics, 25:1335-1337, 2009.
CONTRAfold: RNA secondary structure prediction without
physics-based models
Chuong B. Do, Chuan-Sheng Foo and Serafim Batzoglou: A max-margin model for efficient simultaneous alignment and folding of RNA sequences. ISMB 2008: 68-76
A Probabilistic Model of RNA Conformational Space
Automated de novo prediction of native-like RNA tertiary structures
The MC-Fold and MC-Sym pipeline infers RNA structure from sequence data
Pairwise Global Alignment of Protein Interaction Networks
by Matching Neighborhood Topology
IsoRankN: spectral methods for global alignment of multiple
protein networks
Jason Flannick, Antal F. Novak, Chuong B. Do, Balaji S. Srinivasan, Serafim Batzoglou: Automatic Parameter Learning for Multiple Network Alignment. RECOMB 2008: 214-231