Week 15:

Tuesday, 12/6
Lecture: Everything we haven't covered about bioinformatics yet [ slides ].
Tuesday, 12/8
Lecture: No lecture - we will meet instead during finals week for project presentations.

Week 14:

Tuesday, 11/29
Lecture: Motif finding algorithms [ slides ].
Reading: Modan K Das and Ho-Kwok Dai. A survey of DNA motif finding algorithms.

Thursday, 12/1
Lecture: Motif finding algorithms (continued).
Reading: Alkes Price, Sriram Ramabhadran and Pavel A. Pevzner. Finding subtle motifs by branching from sample strings.
Jeremy Buhler and Martin Tompa. Finding motifs using random projections. Journal of Computational Biology. April 2002, 9(2): 225-242.

Week 13:

Tuesday, 11/15
Lecture: Multiple sequence alignment (cont) and whole genome alignment [ slides ].

Thursday, 11/17
Lecture: Motif finding [ slides ].

Week 12:

Tuesday, 11/8
Lecture: Applications of HMMs - describing gene families with profile HMMs.
Reading: Chapter 5 in Durbin and Eddy.

Thursday, 11/10
Lecture: Multiple sequence alignment [ slides ].
Reading: Chapter 6 in Durbin and Eddy. Two more up-to-date resources:
RC Edgar and S Batzoglou. Multiple sequence alignment. Current Opinion in Structural Biology Volume 16, Issue 3, June 2006, pages 368-373.
CB Do and K Katoh. Protein multiple sequence alignment. Methods in Molecular Biology, 2008, Volume 484, IV, pages 379-413.

Week 11:

Tuesday, 11/1
Lecture: Applications of HMMs - gene finding [ slides ].
Reading: Chapter 4 in Durbin and Eddy.

Tuesday, 11/3
Lecture: Applications of HMMs - pairwise alignment (slides continued). Reading: Chapter 4 in Durbin and Eddy.

Week 10:

Tuesday, 10/25
Lecture: Discussion of the projects.

Thursday, 10/27
Lecture: Learning hidden Markov models - continuing with slides that discuss HMMs.
Reading: Chapter 3 in Durbin and Eddy.

Week 9:

Tuesday, 10/18
Nathan will present BWA. Li H, Durbin R. Fast and accurate long-read alignment with Burrows-Wheeler transform. Bioinformatics 2010;26(5):589-95.
Arpita will present SOAPsplice. Huang S, Zhang J, Li R, Zhang W, He Z, Lam T-W, Peng Z and Yiu S-M. SOAPsplice: genome-wide ab initio detection of splice junctions from RNA-Seq data. Frontiers in Genomic Assay Technology (2010) 2:46.

Thursday, 10/13
Zhisheng will present Gsnap. T.D. Wu and S. Nacu. Fast and SNP-tolerant detection of complex variants and splicing in short reads. Bioinformatics (2010) 26: 873-881.
Indika will present RUM. Gregory R. Grant, Michael H. Farkas, Angel D. Pizarro, Nicholas F. Lahens, Jonathan Schug, Brian P. Brunk, Christian J. Stoeckert, John B. Hogenesch, and Eric A. Pierce. Comparative analysis of RNA-Seq alignment algorithms and the RNA-Seq unified mapper (RUM). Bioinformatics (2011) 27(18): 2518-2528.

Week 8:

Tuesday, 10/11
Lecture: Jeremy will present the TopHat spliced alignment program. Reading: Trapnell C, Pachter L, and Salzberg SL. TopHat: discovering splice junctions with RNA-Seq. Bioinformatics (2009) 25 (9): 1105-1111.

Thursday, 10/13
Lecture: Fayyaz will present PalMapper; Mo will present SpliceMap. Reading: De Bona, F. et al., Optimal spliced alignments of short sequence reads. ECCB08/Bioinformatics, 24 (16):i174, 2008.
and
Kin Fai Au, Hui Jiang, Lan Lin, Yi Xing, and Wing Hung Wong. Detection of splice junctions from paired-end RNA-seq data by SpliceMap. Nucleic Acids Research (2010).

Week 7:

Tuesday, 10/4
Lecture: Hidden Markov models [ slides ]
Reading: Section 3.2 in Durbin and Eddy; another classical resource is Rabiner's tutorial. The notation in the slides follows the textbook.

Thursday, 10/6
Lecture: Hidden Markov models - the Viterbi, forward and backward algorithms. Reading: Section 3.2 in Durbin and Eddy.

Week 6:

Tuesday, 9/27
Lecture: Guest lecture on using RNA-seq data for prediction of alternative splicing [ slides ]
Reading: SpliceGrapher manuscript will be provided by email.

Thursday, 9/29
Lecture: Hidden Markov models - an introduction [ slides ].
Reading: Section 3.1 in Durbin and Eddy; another classical resource is Rabiner's tutorial. The notation in the slides follows the textbook.

Week 5:

Tuesday, 9/20
Lecture: linear-space alignment [ slides ]
Reading: Linear space alignment is presented in Chapter 7 of Bioinformatics algorithms. The original paper is:
E.W. Myers and W. Miller. Optimal alignments in linear space. Computer Applications in Biosciences, 4:11-17, 1988 [ pdf ].

Next generation sequencing technology [ slides ]
Reading: Elaine R. Mardis. A decade’s perspective on DNA sequencing technology. Nature 470:198–203,2011.

Thursday, 9/22
Lecture: Using next generation sequencing technology for studying the transcriptome. The spliced alignment problem [ slides ].
Reading: Davide Campagna, Alessandro Albiero, Alessandra Bilardi, Elisa Caniato, Claudio Forcato, Svetlin Manavski, Nicola Vitulo, and Giorgio Valle. PASS: a program to align short sequences. Bioinformatics (2009) 25(7): 967-968.
Section 6.14 in Jones and Pevzner.

Week 4:

Tuesday, 9/13
Lecture: More substitution matrices - Markov models and PAM matrices (slide set continued).
heuristic alignment algorithms: Fasta and BLAST [ slides ]
Reading: Chapter 6 of Bioinformatics algorithms.
Thursday, 9/15
Lecture: BLAST (continued) and linear-space alignment [ slides ]
Reading: Linear space alignment is presented in Chapter 7 of Bioinformatics algorithms. The original paper is:
E.W. Myers and W. Miller. Optimal alignments in linear space. Computer Applications in Biosciences, 4:11-17, 1988 [ pdf ].

Week 3:

Tuesday, 9/6
Lecture: Local alignment - the Smith-Waterman algorithm; substitution matrices ) [ slides ]
Reading: Chapter 6 of Bioinformatics algorithms.

Week 2:

Tuesday, 8/30
Lecture: Pairwise sequence alignment - introduction to dynamic programming algorithms [ slides ]
Reading: Chapter 6 of Bioinformatics algorithms [ pdf ].
Thursday, 9/1
Lecture: Global pairwise alignment - longest common subsequence and global alignment using the Needleman Wunsch algorithm [ slides ]

Week 1:

Tuesday, 8/23
Lecture: Course introduction [ slides ]
Reading: Alex Zien's A primer on molecular biology [ pdf ].
Martin Tompa's notes [ pdf ].
Thursday, 8/25
Lecture: Course introduction (cont). Gene finding in prokaryotes [ slides ].