Most information preliminary.

Lecturer: Dr. Matej Lexa, FI C505.
Office hours: Thu 1PM - 3PM, or other time by appointment
Fall 2006. Will meet 2hrs every week (Tue 12:00-12:50 B204, Tue 13:00-13:50 B116).
Discussion forum

This course is a new applied subject available at the Faculty of Informatics. It will lead the students into the world of algorithms for biological sequence analysis.

Official description: IV108 (IS MU)

TEXTBOOK: Michael S. Waterman (1995). Introduction to computational biology. Maps, sequences and genomes. Chapman & Hall/CRC, Boca Raton, 431 pp.

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RX - reserved for students of Bioinformatics
DO - reserved for students of Prof. Dokulil
xerox - a copy of the original text is available at the library
B306 - catalog number in the FI Library
WEB - WWW resource, follow the link

REQUIRED READING
(you should read these as soon as possible)

1) R.Dawkins (1998). Sobecky gen. Praha:Mlada Fronta, 319p. -DO-B306- (Ch.1,2,3,11)
2) S.Pinker (200X). Words and Rules. Weidelberg and Nicolson:London, 348p. (pages 4-9) -RX-xerox-
3) J.Glasgow, I.Jurisica, B.Rost (2004). AI and Bioinformatics. AI Magazine, Spring, 7-8. -RX-xerox-
4) J.Barker and J.Thornton (2004). Software engineering challenges in bioinformatics. Proceedings of ICSE 2004
5) B.Rost et al. (2003). Automatic prediction of protein function. CMLS 60, 2637-2650. -RX-xerox-

LECTURE AND BACKGROUND MATERIALS
(these are here for your convenience if you would like to review the lecture subjects and/or know more)

6) Starr and Taggart (1992). Biology: The Unity and Diversity of Life. Belmont:WPC, 921p. -RX-A225-
7) L.Hunter 2004. Life and its molecules. A brief introduction. AI Magazine Spring 2004, 9-22. -RX-xerox-
8) Chapter 5 - Chemistry and physiology of the cell. -RX-xerox-
9) A set of schemata and electron microscope micrographs illustrating the cellular structures of plants. -RX-xerox- 
10) Z.Storchova (200X). Molekuly na povel I. Jak muzeme molekuly DNA strihat a zase spojovat. Vesmir 77(5). -RX-xerox-
11) Z.Storchova (200X). Molekuly na povel II. I jedina molekula DNA se hleda mnohem lepe nez jehla v kupce sena. Vesmir 77(6). -RX-xerox-
12) Z.Storchova (200X). Molekuly na povel III. Jak to udelat, aby molekula byla dobre viditelna. Vesmir 77(7). -RX-xerox-
13) Z.Storchova (200X). Molekuly na povel IV. Z mala mnoho neni totez jako z komara velbloud. Vesmir 77(9). -RX-xerox-
14) Z.Storchova (200X). Molekuly na povel V. Cteni (genomu) na dobrou noc. Vesmir 77(10). -RX-xerox-
15) W.W.Gibbs (2004). Synthetic life. Scientific American, May, 49-55. -RX-xerox-
16) S.J.Freland, L.D.Hurst (2004). Evolution encoded. Scientific American, April, 56-63. -RX-xerox-
17) G.Stix (2004). Making proteins without DNA. Scientific American, April, 20-21. -RX-xerox-
18) C.Choi (2004). Making and unmaking memories. Scientific American, March, 16-16. -RX-xerox-
19) T.Valeo (2004). Downsized target: A tiny protein called ADDL could be the key to Alzheimer's. Scientific American, May, 14-15. -RX-xerox
20) J.Shrager (2003). The fiction of function. Bioinformatics 19(15),1934-1936 -RX-xerox
21) S.Buckingham (2004). Data's future shock. Nature 428,774-777 -RX-xerox
22) S.Buckingham (2003). Programmed for success. Nature 425,209-214 -RX-xerox
23) M.Chicurel (2002). Bioinformatics: bringing it all together. Nature 419, 751-757 -RX-xerox
24) M.Bloom (2001). Biology in silico: the bioinformatics revolution. The Am. Biol. Teacher 63(6),397-403 -RX-xerox
25) P.Baldi and G.Pollastri (2002). A machine learning strategy for protein analysis. IEEE Intelligent Systems Mar/Apr, 28-35 -RX-xerox
26) Yoshida et al. (2001). Chaperonin turned insect toxin. Nature 411,44-44 -RX-xerox
27) E.Pennisi (2003). Gene counters struggle to get the right answer. Science 301,1040-1041 -RX-xerox
28) H.Pearson (2003). Geneticists play the numbers game in vain. Nature 423,576-576 -RX-xerox
29) C.A.Ouzounis and A.Valencia (2003). Early bioinformatics: the birth of a discipline - a personal view. Bioinformatics 19(17),2176-2190 -RX-xerox
30) S.Oliver (2000). Guilt-by-association goes global. Nature 403,601-603 -RX-xerox
31) A.J.Mungall (2003). The DNA sequence and analysis of human chromosome 6. Nature 425,805-812 -RX-xerox
32) E.Mjolsness and D.DeCoste (2001). Machine learning for science: state of the art and future prospects. Science 293,2051-2055 -RX-xerox
33) L.L.Looger et al. (2003). Computational design of receptor and sensor proteins with novel functions. Nature 423,185-190 -RX-xerox
34) S.Karlin et al. (2001). Annotation of the Drosophila genome. Nature 411,259-260 -RX-xerox
35) F.E.Cohen and J.W.Kelly (2003). Therapeutic approaches to protein-misfolding diseases. Nature 426,905-909 -RX-xerox
47) J.D.Watson (1968). The Double Helix. New American Library, New York, 143p. -RX-xerox
48) H.Kolb (2003). How the retina works. American Scientist Jan-Feb -RX-xerox
49) J.-M.Claverie. (2003). Bioinformatics for dummies. Hoboken, Wiley Publishing, 452p. -RX-Z1
50) T.Jiang (2003). Current topics in computational molecular biology. Cambridge, MIT Press, 542p. RX-Z2 

RESEARCH PAPERS
(each student is required to choose one of these for class presentation)

T01: Predicting protein functions with message passing algorithms
M.Leone and A.Pagnani (2004). Bioinformatics (advance access, Sept 17) -RX-xerox

T02: Phylogenomic inference of protein molecular function: advances and challenges
K.Sjolander (2004). Bioinformatics 20(2),170-179 -RX-xerox

T03: Improved prediction of protein secondary structure by use of sequence profiles and neural networks
B.Rost and C.Sander (1993). PNAS USA, 7558-7562. -RX-xerox

T04: a) GoFigure: automated gene ontology annotation; b) AnaGram: protein function assignment
S.Khan et al. (2003). Bioinformatics 19(18),2484-2485 A.J.Perez et al. (2004). Bioinformatics 20(2),291-292 -RX-xerox

T05: Protein structure prediction via combinatorial assembly of sub-structural units
Y.Inbar et al. (2003). Bioinformatics 19(Suppl.1),158-168 -RX-xerox

T06: Protein beta-turn prediction using nearest-neighbor method
S.Kim (2004). Bioinformatics 20(1),40-44 -RX-xerox

T07: MeKE: discovering the functions of gene products from biomedical literature via sentence alignment
J.-H. Chiang and H.-C.Yu (2003). Bioinformatics 19(11),1417-1422 -RX-xerox

T08: The hydrophobic cores of proteins predicted by wavelet analysis
H.Hirakawa et al. (1999). Bioinformatics 15(2),141-148 -RX-xerox

T09: Sensitive pattern discovery with 'fuzzy' alignments of distantly related proteins
A.Heger and L.Holm (2003). Bioinformatics 19(Suppl.1),130-137 -RX-xerox

T10: Prediction of protein subcellular locations using fuzzy k-NN method
Y.Huang and Y.Li (2004). Bioinformatics 20(1),21-28 -RX-xerox

T11: A novel method of protein secondary structure prediction with high segment overlap measure: support vector machine approach
S.Hua and Z.Sun (2001). JMB 308,397-407 -RX-xerox

T12: Prediction of local structure in proteins using a library of sequence-structure motifs
C.Bystroff and D.Baker (1998). JMB 281,565-577 C.Bystroff and D.Baker (1997). Proteins (Suppl.1),167-171 -RX-xerox

T13: Finding nuclear localization signals
M.Cokol et al. (2000). EMBO Reports 1(5),411-415 -RX-xerox

T14: Automatic prediction of protein domains from sequence information using a hybrid learning system
N.Nagarajan and G.Yona (2004). Bioinformatics 20(9),1335-1360 -RX-xerox 

T15: Secondary structure prediction with support vector machines
J.J.Ward (2003). Bioinformatics 19(13),1660-1655 -RX-xerox 

T16: Protein structure prediction using Rosetta
C.A.Rohl et al. (2004). Numerical Computer Methods,66-93 -RX-xerox 

36) RECOMB 2001 -RX-K384.01-
37) RECOMB 2002 -RX-K384.02-
38) RECOMB 2003 -RX-K384.03-
39) RECOMB 2004 -RX-K384.04-
40) K.M.Merz (1994). The protein folding problem and tertiary structure prediction. -RX-A37-
41) R.Nair, B.Rost 2004. Annotating protein function through lexical analysis, AI Mag., Spring 2004, 45-56. -RX-xerox-
42) R.D.King 2004. Applying Inductive logic programming to predicting gene function, AI Mag., Spring 2004, 57-68. -RX-xerox-
43a) LNCS 2149 O.Gascuel, B.M.E.Moret (Eds.) Algorithms in Bioinformatics, 2001  -K397.01-
  N.von Ohsen, R.Zimmer. Improving profile-profile alignments via log average scoring, 11-26. 
  J.Viksna,D.Gilbert. Pattern matching and pattern discovery algorithms for protein topologies. 98-111.
43b) LNCS XXXX R.Guigo,D.Gusfield (Eds.) Algorithms in Bioinformatics, 2002 -K397.02-
43c) LNCS XXXX G.Benson, R.Page (Eds.) Algorithms in Bioinformatics, 2003 -K397.03-
44) LNBI 2666 C.Guerra,S.Istrail (Eds.), Mathematical methods for protein structure analysis and design, 2003. 
  M.Kann, R.A.Goldstein. OPTIMA: A new score function for the detection of remote homologs, 99-108. 
  C.Lundegaard et al. Prediction of protein secondary structure at high accuracy using a combination of many neural networks, 117-122 -RX-xerox-
  F.Seno et al. Learning effective amino-acid interactions, 139-145. 
45) LNCS 2066 O.Gascuel, M.-F. Sagot (Eds.), Computational Biology, 2000
  N.Thierry-Mieg, L.Trilling. InterDB, a prediction-oriented protein interaction database for C.elegans., 135-146. 
46) LNCS 2388 S.-W. Lee, A.Verri (Eds.) Pattern recognition with support vector machines, 2002
  N.Mukherjee, S.Mukherjee. Predicting signal peptides with support vector machines, 1-7.

Bioinformatic links