Main.Schedule History

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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.

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Jeremy Buhler and Martin Tompa. [[http://www.liebertonline.com/doi/abs/10.1089/10665270252935430 | Finding motifs using random projections ]]. Journal of Computational Biology. April 2002, 9(2): 225-242.

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Jeremy Buhler and Martin Tompa. Finding motifs using random projections. Journal of Computational Biology. April 2002, 9(2): 225-242.

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Reading: Alkes Price, Sriram Ramabhadran and Pavel A. Pevzner. Finding subtle motifs by branching from sample strings.

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Reading: Alkes Price, Sriram Ramabhadran and Pavel A. Pevzner. Finding subtle motifs by branching from sample strings.
Jeremy Buhler and Martin Tompa. [[http://www.liebertonline.com/doi/abs/10.1089/10665270252935430 | Finding motifs using random projections ]]. Journal of Computational Biology. April 2002, 9(2): 225-242.

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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.

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Lecture: Motif finding algorithms [ slides ].

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Lecture: Motif finding algorithms [ slides ].

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Lecture: Motif finding algorithms.

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Lecture: Motif finding algorithms [ slides ].

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Week 14:

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

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

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Lecture: Multiple sequence alignment (cont)

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Lecture: Multiple sequence alignment (cont) and whole genome alignment [ slides ].

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Week 12:

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Week 13:

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Week 12:

Tuesday, 11/15
Lecture: Multiple sequence alignment (cont)

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Reading: Chapter 5 in Durbin and Eddy.

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Reading: Chapter 5 in Durbin and Eddy.

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RC Edgar and S Batzoglou. http://ai.stanford.edu/~serafim/Publications/2006_MSA_COSB.pdf?. Current Opinion in Structural Biology

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RC Edgar and S Batzoglou. Multiple sequence alignment. Current Opinion in Structural Biology

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RC Edgar and S Batzoglou. ai.stanford.edu/~serafim/Publications/2006_MSA_COSB.pdf?. Current Opinion in Structural Biology

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RC Edgar and S Batzoglou. http://ai.stanford.edu/~serafim/Publications/2006_MSA_COSB.pdf?. Current Opinion in Structural Biology

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Reading: Chapter 5 in Durbin and Eddy.

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Reading: Chapter 5 in Durbin and Eddy.

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Reading: Chapter 6 in Durbin and Eddy.

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Reading: Chapter 6 in Durbin and Eddy. Two more up-to-date resources:
RC Edgar and S Batzoglou. ai.stanford.edu/~serafim/Publications/2006_MSA_COSB.pdf?. 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.

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Thursday, 11/10
Lecture: Multiple sequence alignment [ slides ].
Reading: Chapter 6 in Durbin and Eddy.

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Week 12:

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

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Lecture: Applications of HMMs [ slides ].

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Lecture: Applications of HMMs - gene finding [ slides ].

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Lecture: Applications of HMMs (cont).

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

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Tuesday, 11/1'

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Tuesday, 11/1

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Tuesday, 11/3'

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Tuesday, 11/3

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Week 11:

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

Tuesday, 11/3'
Lecture: Applications of HMMs (cont).

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Lecture: Learning hidden Markov models - continuing with slides that discuss [ HMMs ].

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Lecture: Learning hidden Markov models - continuing with slides that discuss HMMs.

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Lecture: Learning hidden Markov models - continuing with the HMM [ slides ].

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Lecture: Learning hidden Markov models - continuing with slides that discuss [ HMMs ].

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Lecture: Discussion of the projects. Learning hidden Markov models - continuing with the HMM [ slides ].

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Lecture: Discussion of the projects.

Thursday, 10/27
Lecture: Learning hidden Markov models - continuing with the HMM [ slides ].

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Tuesday, 10/15

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Tuesday, 10/25

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'Lecture:'' Discussion of the projects. Learning hidden Markov models - continuing with the HMM [ slides ].

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Lecture: Discussion of the projects. Learning hidden Markov models - continuing with the HMM [ slides ].

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Week 10:

Tuesday, 10/15
'Lecture: Discussion of the projects. Learning hidden Markov models - continuing with the HMM [ slides ].
Reading:'' Chapter 3 in Durbin and Eddy.

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Lecture: Nathan will present BWA.

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Nathan will present BWA.

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Lecture: Zhisheng will present Gsnap.

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Zhisheng will present Gsnap.

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Thursday, 10/13'

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Thursday, 10/13

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Week 9:

Tuesday, 10/18
Lecture: 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'
Lecture: 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.

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Tuesday, 10/11

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Thursday, 10/13

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Reading:: Trapnell C, Pachter L, and Salzberg SL. TopHat: discovering splice junctions with RNA-Seq. Bioinformatics (2009) 25 (9): 1105-1111.

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Reading: Trapnell C, Pachter L, and Salzberg SL. TopHat: discovering splice junctions with RNA-Seq. Bioinformatics (2009) 25 (9): 1105-1111.

Tuesday, 10/11
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).

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Reading:: Trapnell C, Pachter L, and Salzberg SL. TopHat: discovering splice junctions with RNA-Seq. Bioinformatics (2009) 25 (9): 1105-1111.

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Week 8:

Tuesday, 10/11
Lecture: Jeremy will present the TopHat spliced alignment program.

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Lecture: Hidden Markov models (cont).

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

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Reading: Chapter 3 in Durbin and Eddy; another classical resource is Rabiner's tutorial. The notation in the slides follows the textbook.

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Reading: Section 3.2 in Durbin and Eddy; another classical resource is Rabiner's tutorial. The notation in the slides follows the textbook.

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Reading: Chapter 3 in Durbin and Eddy; another classical resource is Rabiner's tutorial. The notation in the slides follows the textbook.

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Reading: Section 3.1 in Durbin and Eddy; another classical resource is Rabiner's tutorial. The notation in the slides follows the textbook.

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Thursday, 10/6
Lecture: Hidden Markov models (cont).

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Week 7:

Tuesday, 10/4
Lecture: Hidden Markov models [ slides ]
Reading: Chapter 3 in Durbin and Eddy; another classical resource is Rabiner's tutorial. The notation in the slides follows the textbook.

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Lecture: Hidden Markov models - an introduction.

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Lecture: Hidden Markov models - an introduction [ slides ].
Reading: Chapter 3 in Durbin and Eddy; another classical resource is Rabiner's tutorial. The notation in the slides follows the textbook.

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Lecture: Multiple sequence alignment [ slides ]
Reading: Chapter 6 in Durbin, Eddy, Krogh, Mitchison (6.1 - 6.4). A recent review of MSA methods is found e.g. in:
W. Pirovano and J. Heringa. Multiple sequence alignment. In: Methods Mol Biol. 2008;452:143-61.

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Lecture: Hidden Markov models - an introduction.

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W. Pirovano and J. Heringa. [[http://www.ncbi.nlm.nih.gov/pubmed/18566763

 | Multiple sequence alignment]]. In:  Methods Mol Biol. 2008;452:143-61.
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W. Pirovano and J. Heringa. Multiple sequence alignment. In: Methods Mol Biol. 2008;452:143-61.

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Thursday, 9/29
Lecture: Multiple sequence alignment [ slides ]
Reading: Chapter 6 in Durbin, Eddy, Krogh, Mitchison (6.1 - 6.4). A recent review of MSA methods is found e.g. in:
W. Pirovano and J. Heringa. [[http://www.ncbi.nlm.nih.gov/pubmed/18566763

 | Multiple sequence alignment]]. In:  Methods Mol Biol. 2008;452:143-61.
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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.

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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.

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

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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 ].

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Lecture: Local alignment - the Smith-Waterman algorithm; substitution matrices (slide set continued)

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Lecture: Local alignment - the Smith-Waterman algorithm; substitution matrices ) [ slides ]

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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 ].

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Lecture: More substitution matrices - Markov models and PAM matrices (slide set continued); heuristic alignment algorithms: Fasta and BLAST [ slides ]

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Lecture: More substitution matrices - Markov models and PAM matrices (slide set continued).
heuristic alignment algorithms: Fasta and BLAST [ slides ]

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Lecture: More substitution matrices - Markov models and PAM matrices (slide set continued)

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Lecture: More substitution matrices - Markov models and PAM matrices (slide set continued); heuristic alignment algorithms: Fasta and BLAST [ slides ]

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Week 4:

Tuesday, 9/13
Lecture: More substitution matrices - Markov models and PAM matrices (slide set continued)
Reading: Chapter 6 of Bioinformatics algorithms.

Week 3:

Tuesday, 9/6
Lecture: Local alignment - the Smith-Waterman algorithm; substitution matrices (slide set continued)
Reading: Chapter 6 of Bioinformatics algorithms.

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Lecture: Pairwise sequence alignment - introduction to dynamic programming algorithms [ slides ]

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Lecture: Pairwise sequence alignment - introduction to dynamic programming algorithms [ slides ]

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Global pairwise alignment - longest common subsequence and global alignment using the Needleman Wunsch algorithm [ Path:../../pdfs/alignment2.pdf ]

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Global pairwise alignment - longest common subsequence and global alignment using the Needleman Wunsch algorithm [ slides ]

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Lecture: Pairwise sequence alignment [ slides ]

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Lecture: Pairwise sequence alignment - introduction to dynamic programming algorithms [ slides ]

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Thursday, 9/1
Lecture: Global pairwise alignment - longest common subsequence and global alignment using the Needleman Wunsch algorithm [ Path:../../pdfs/alignment2.pdf ]

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Lecture: Course introduction (cont). Gene finding in prokaryotes [ slides ]. Assignment 1 is posted.

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Lecture: Course introduction (cont). Gene finding in prokaryotes [ slides ].

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Week 2:

Tuesday, 8/30
Lecture: Pairwise sequence alignment [ slides ]
Reading: Chapter 6 of Bioinformatics algorithms [ pdf ].

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Week 2:

Tuesday, 8/30
Lecture: Pairwise sequence alignment [ slides ]
Reading: Chapter 6 of Bioinformatics algorithms [ pdf ].

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Lecture: Course introduction (cont). Gene finding in prokaryotes [ slides. Assignment 1 is posted.

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Lecture: Course introduction (cont). Gene finding in prokaryotes [ slides ]. Assignment 1 is posted.

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Lecture: Course introduction (cont). Assignment 1 is posted.

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Lecture: Course introduction (cont). Gene finding in prokaryotes [ slides. Assignment 1 is posted.

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Week 2:

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Thursday, 8/25
Lecture: Course introduction (cont). Assignment 1 is posted.
Tuesday, 8/30
Lecture: Pairwise sequence alignment [ slides ]
Reading: Chapter 6 of Bioinformatics algorithms [ pdf ].

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Done.

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Week 1:

Tuesday, 8/23
Lecture: Course introduction [ slides ]
Reading: Alex Zien's A primer on molecular biology [ pdf ].
Martin Tompa's notes [ pdf ].

May 03, 2011, at 10:38 AM MST by 129.82.44.191 -
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Week 1: 1/21 - 1/23

Lectures: Course overview, biological preliminaries
Reading: Here are some "biology for computer scientists" pointers:

  • The first chapter of the book Kernel methods in computational biology pdf
  • Book chapter from Larry Hunter pdf
  • Notes from Martin Tompa pdf
  • And a paper from Sean Eddy about the nature of research in bioinformatics link

Week 2: 1/26 - 1/30

Lectures: Gene finding in prokaryotes, intro to sequence alignment
Reading: ch 6 in Jones and Pevzner.

Week 3: 2/2 - 2/6

Lectures: Dynamic programming and methods for pairwise sequence alignment: global and local. Reading: ch 6 in Jones and Pevzner

Week 4: 2/9 - 2/13

Lectures: Sequence alignment: Amino acid scoring matrices, handling affine gap penalties, alignment with linear memory

Week 5: 2/16 - 2/20

Lectures: Heuristic alignment methods - FASTA and BLAST. Spliced alignment

Week 6: 2/23 - 2/27

Lectures: Multiple sequence alignment

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Done.

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Spliced alignment

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Spliced alignment

Week 6: 2/23 - 2/27

Lectures: Multiple sequence alignment

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Lectures: Course overview, biological preliminaries, gene finding in prokaryotes

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Lectures: Course overview, biological preliminaries

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  • And a paper from Sean Eddy about the nature of research in bioinformatics link
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  • And a paper from Sean Eddy about the nature of research in bioinformatics link

Week 2: 1/26 - 1/30

Lectures: Gene finding in prokaryotes, intro to sequence alignment
Reading: ch 6 in Jones and Pevzner.

Week 3: 2/2 - 2/6

Lectures: Dynamic programming and methods for pairwise sequence alignment: global and local. Reading: ch 6 in Jones and Pevzner

Week 4: 2/9 - 2/13

Lectures: Sequence alignment: Amino acid scoring matrices, handling affine gap penalties, alignment with linear memory

Week 5: 2/16 - 2/20

Lectures: Heuristic alignment methods - FASTA and BLAST. Spliced alignment

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  • The first chapter of the book Kernel methods in computational biology ]
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  • The first chapter of the book Kernel methods in computational biology pdf
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  • The first chapter of the book Kernel methods in computational biology [ pdf ]
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  • The first chapter of the book Kernel methods in computational biology ]
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  • The first chapter of the book Kernel methods in computational biology pdf
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  • The first chapter of the book Kernel methods in computational biology [ pdf ]
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The first chapter of the book Kernel methods in computational biology pdf
Book chapter from Larry Hunter pdf
Notes from Martin Tompa pdf
A paper from Sean Eddy about the nature of research in bioinformatics link
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  • The first chapter of the book Kernel methods in computational biology pdf
  • Book chapter from Larry Hunter pdf
  • Notes from Martin Tompa pdf
  • And a paper from Sean Eddy about the nature of research in bioinformatics link
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Lectures: Course overview, biological preliminaries, gene finding in prokaryotes

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Lectures: Course overview, biological preliminaries, gene finding in prokaryotes

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A paper from Sean Eddy about the nature of research in bioinformatics [[link]]
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A paper from Sean Eddy about the nature of research in bioinformatics link
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A paper from Sean Eddy about the nature of research in bioinformatics link
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A paper from Sean Eddy about the nature of research in bioinformatics [[link]]
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Lectures: Course overview, biological preliminaries, gene finding in prokaryotes
Reading: Here are some "biology for computer scientists" pointers:
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Lectures: Course overview, biological preliminaries, gene finding in prokaryotes Reading: Here are some "biology for computer scientists" pointers:

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A paper from Sean Eddy about the nature of research in bioinformatics

link

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A paper from Sean Eddy about the nature of research in bioinformatics link
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The first chapter of the book Kernel methods in computational biology

pdf

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The first chapter of the book Kernel methods in computational biology pdf
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link

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link

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Coming soon!

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Week 1: 1/21 - 1/23

Lectures: Course overview, biological preliminaries, gene finding in prokaryotes
Reading: Here are some "biology for computer scientists" pointers:
The first chapter of the book Kernel methods in computational biology

pdf

Book chapter from Larry Hunter pdf
Notes from Martin Tompa pdf
A paper from Sean Eddy about the nature of research in bioinformatics

link

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Coming soon!