Digital Reading Room 2.0 -- COMP 625: Algorithms for Bioinformatics (Revision 1)

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COMP 625: Algorithms for Bioinformatics focuses on the algorithms that are essential for bioinformatics, with practical projects using Python under the Unix/Linux environment. Bioinformatics is an interdisciplinary field that develops computational methods and software tools to understand biological data, particularly when the data sets are large and complex.

Unit 1: Introduction to Bioinformatics

National Human Genome Research Institute. (n.d.). Human genome project. https://www.genome.gov/about-genomics/educational-resources/fact-sheets/human-genome-project

Unit 2: Introduction to Python Programming and Environment

The Biopython Contributors. (2024). Python tools for computational molecular biology. Biopython. https://biopython.org/

Chang, J., Chapman, B., Friedberg, I., Hamelryck, T., de Hoon, M., Cock, P., Antao, T., Talevich, E., & Wilczyński, B. (2024, January 10). Biopython tutorial and cookbook. Biopython. http://biopython.org/DIST/docs/tutorial/Tutorial.html

Unit 3: Cellular and Molecular Biology Fundamentals

There are no additional readings for this unit.

Unit 4: Basic Processing of Biological Sequences

There are no additional readings for this unit.

Unit 5: Pattern Finding Algorithms

SIB Swiss Institute of Bioinformatics. (n.d.). Database of protein domains, families and functional sites. Prosite. https://prosite.expasy.org/

Roberts, R.J., & Macelis, D. (n.d.). REBASE: The restriction enzyme database. REBASE. http://rebase.neb.com/rebase/rebase.html

Unit 6: Pairwise Sequence Alignment

Biopython. (2020). Bio.pairwise2 module. Biopython. https://biopython.org/docs/1.75/api/Bio.pairwise2.html

Henikoff, S., & Henikoff, J.G. (1992). Amino acid substitution matrices from protein blocks. Proceedings of the National Academy of Sciecnes of the United States of America, 89(22), 10915-10919.

Dayhoff, M.O., Eck, R.V., Chang, M.A., & Sochard, M.R. (1965). Atlas of protein sequence and structure. National Biomedical Research Foundation.

Needleman, S.B., & Wunsch, C.D. (1970). A general method applicable to the search for similarities in the amino acid sequence of two proteins. Journal of Molecular Biology, 48(3), 443-453.

Smith, T.F., & Waterman, M.S. (1981). Identification of common molecular subsequences. Journal of Molecular Biology, 147(1), 195-197.

Levenshtein, V.I. (1966). Binary codes capable of correcting deletions, insertions, and reversals. Soviet Physics Doklady, 163(1), 845-848.

Unit 7: Sequence Searching Algorithms

National Library of Medicine. (2023). Basic local alignment search tool. National Library of Medicine. https://blast.ncbi.nlm.nih.gov/Blast.cgi

National Library of Medicine. (2023). BLAST + executables. National Library of Medicine. https://blast.ncbi.nlm.nih.gov/doc/blast-help/downloadblastdata.html#downloadblastdata

The Biopython Contributors. (2020). Bio.Blast.NCBIWWW module. Biopython. https://biopython.org/docs/1.75/api/Bio.Blast.NCBIWWW.html

Altschul, S.F., Gish, W., Miller, W., Myers, E.W., & Lipman, D.J. (1990). Basic local alignment search tool. Journal of Molecular Biology, 215(3), 403-410.

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 generaltion of protein database search programs. Nucleic Acids Research, 25(17), 3389-3402.

National Library of Medicine. (2023). Standard nucleotide BLAST. National Library of Medicine. https://blast.ncbi.nlm.nih.gov/Blast.cgi?PROGRAM=blastn&PAGE_TYPE=BlastSearch&LINK_LOC=blasthome

Unit 8: Multiple Sequence Alignment

Lipman, D.J., Altschul, S.F., & Kececioglu, J.D. (1989). A tool for multiple sequence alignment. Proceedings of the National Academy of Sciences, 86(12), 4412-4415.

Feng, D., & Doolittle, R.F. (1987). Progressive sequence alignment as a prerequisite to correct phylogenetic trees. Journal of Molecular Evolution, 25(4), 351-360.

Higgins, D.G., & Sharp, P.M. (1988). CLUSTAL: A package for performing multiple sequence alignment on a microcomputer. Gene, 73(1), 237-244.

Thompson, J.D., Higgins, D.G., & Gibson, T.J. (1994). CLUSTAL W: Improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific ap penalties and weight matrix choice. Nucleic Acids Research, 22(22), 4673-4680.

Sievers, F., Wilm, A., Dineen, D., Gibson, T,J., Karplus, K., Li, W., Lopez, R., McWilliam, H., Remmert, M., Söding, J., Thompson, J.D., & Higgins, D.G. (2011). Fast, scalable generation of high-quality protein multiple sequence alignments using Clustal Omega. Molecular Systems Biology, 7(1), 539.

Edgar, R.C. (2004). MUSCLE: Multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Research, 32(5), 1792-1797.

Katoh, K., Misawa, K., Kuma, K., & Miyata, T. (2002). MAFFT: A novel method for rapid multiple sequence alignment based on fast Fourier transform. Nucleic Acids Research, 30(14), 3059-3066.

Unit 9: Phylogenetic Analysis

UPGMA. (2023, September 2). In Wikipedia. https://en.wikipedia.org/w/index.php?title=UPGMA&oldid=1173369648

WPGMA. (2023, September 2). In Wikipedia. https://en.wikipedia.org/w/index.php?title=WPGMA&oldid=1173369909

Sokal, R.R., & Michener, C.D. (1958). A statistical method for evaluating systematic relationships. University of Kansas Science Bulletin, 28, 1409-1438.

Saitou, N., & Nei, M. (1987). The neighbor-joining method: A new method for reconstructing phylogenetic trees. Molecular Biology and Evolution, 4(4), 406-425.

Talevich, E., Invergo, B.M., Cock, P.J., & Chapman, B.A. (2012). Bio.Phylo: A unified toolkit for processing, analyzing and visualizing phylogenetic trees in Biopython. BMC Bioinformatics, 13(1), 209.

MEGA. (n.d.). MEGA Software. https://www.megasoftware.net/

Unit 10: Motif Discovery Algorithms

Hertz, G.Z., Hartzell, G.W., & Stormo, G.D. (1990). Identification of consensus patterns in unaligned DNA sequences knwon to be functionally related. Computer Applications in the Biosciences, 6(2), 81-92.

Hashim, F.A., Mabrouk, M.S., & Al-Atabany, W. (2019). Review of different sequence motif finding algorithms. Avicenna Journal of Medical Biotechnology, 11(2), 130-148.

D'haeseller, P. (2006). What are DNA sequence motifs? Nature Biotechnology, 24, 423-425.

D'haeseller, P. (2006). How does DNA sequence motif discovery work? Nature Biotechnology, 24, 959-961.

Unit 11: Probabilistic Motifs and Stochastic Algorithms

WebLogo. (n.d.). Introduction. WebLogo. https://weblogo.threeplusone.com/

The Biopython Contributors. (2020). Bio.motifs package. Biopython. https://biopython.org/docs/1.76/api/Bio.motifs.html

Unit 12: Hidden Markov Models

Rabiner, L.R. (1989). A tutorial on hidden Markov models and selected applications in speech recognition. Proceedings of the IEEE, 77(2), 257-286.

Eddy, S.R. (1995). Multiple alignment using hidden Markov models. Proceedings. International Conference on Intelligent Systems for Molecular Biology, 3, 114-120.

Drubin, R., Eddy, S., Krogh, A., & Mitchison, G. (1998). Biological sequence analysis: Probabilistic models of proteins and nucleic acids. Cambridge University Press.

HMMER. (n.d.). HMMER: Biosequence analysis using profile hidden Markov models. HMMER. http://hmmer.org/

Unit 13: Graph Algorithms

There are no additional readings for this unit.

Unit 14: Assembling and Matching Reads

There are no additional readings for this unit.