Bioinformatics

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Bioinformatics: краткое содержание, описание и аннотация

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Praise for the third edition of
“This book is a gem to read and use in practice.”
— "This volume has a distinctive, special value as it offers an unrivalled level of details and unique expert insights from the leading computational biologists, including the very creators of popular bioinformatics tools."
— “A valuable survey of this fascinating field. . . I found it to be the most useful book on bioinformatics that I have seen and recommend it very highly.”
— “This should be on the bookshelf of every molecular biologist.”
— The field of bioinformatics is advancing at a remarkable rate. With the development of new analytical techniques that make use of the latest advances in machine learning and data science, today’s biologists are gaining fantastic new insights into the natural world’s most complex systems. These rapidly progressing innovations can, however, be difficult to keep pace with.
The expanded fourth edition of the best-selling
aims to remedy this by providing students and professionals alike with a comprehensive survey of the current field. Revised to reflect recent advances in computational biology, it offers practical instruction on the gathering, analysis, and interpretation of data, as well as explanations of the most powerful algorithms presently used for biological discovery.
offers the most readable, up-to-date, and thorough introduction to the field for biologists at all levels, covering both key concepts that have stood the test of time and the new and important developments driving this fast-moving discipline forwards.
This new edition features: 
New chapters on metabolomics, population genetics, metagenomics and microbial community analysis, and translational bioinformatics A thorough treatment of statistical methods as applied to biological data Special topic boxes and appendices highlighting experimental strategies and advanced concepts Annotated reference lists, comprehensive lists of relevant web resources, and an extensive glossary of commonly used terms in bioinformatics, genomics, and proteomics
is an indispensable companion for researchers, instructors, and students of all levels in molecular biology and computational biology, as well as investigators involved in genomics, clinical research, proteomics, and related fields.

Bioinformatics — читать онлайн ознакомительный отрывок

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Internet Resources

BLAST
European Bioinformatics Institute (EBI) www.ebi.ac.uk/blastall
National Center for Biotechnology Information (NCBI) blast.ncbi.nlm.nih.gov
BLAST-Like Alignment Tool (BLAT) genome.ucsc.edu/cgi-bin/hgBlat
NCBI Conserved Domain Database (CDD) ncbi.nlm.nih.gov/cdd
Cancer Genome Anatomy Project (CGAP) ocg.cancer.gov/programs/cgap
FASTA
EBI www.ebi.ac.uk/Tools/sss/fasta
University of Virginia fasta.bioch.virginia.edu
RefSeq ncbi.nlm.nih.gov/refseq
Structural Classification of Proteins (SCOP) scop.berkeley.edu
Swiss-Prot www.uniprot.org

Further Reading

1 Altschul, S.F., Boguski, M.S., Gish, W., and Wootton, J.C. (1994). Issues in searching molecular sequence databases. Nat. Genet. 6: 119–129. A review of the issues that are of importance in using sequence similarity search programs, including potential pitfalls.

2 Fitch, W. (2000). Homology: a personal view on some of the problems. Trends Genet. 16: 227–231. A classic treatise on the importance of using precise terminology when describing the relationships between biological sequences.

3 Henikoff, S. and Henikoff, J.G. (2000). Amino acid substitution matrices. Adv. Protein Chem. 54: 73–97. A comprehensive review covering the factors critical to the construction of protein scoring matrices.

4 Koonin, E. (2005. Orthologs, paralogs, and evolutionary genomics). Annu. Rev. Genet. 39: 309–338. An in-depth explanation of orthologs, paralogs, and their subtypes, with a discussion of their evolutionary origin and strategies for their detection.

5 Pearson, W.R. (2016). Finding protein and nucleotide similarities with FASTA. Curr. Protoc. Bioinf. 53: 3.9.1–3.9.23. An in-depth discussion of the FASTA algorithm, including worked examples and additional information regarding run options and use scenarios.

6 Wheeler, D.G. (2003). Selecting the right protein scoring matrix. Curr. Protoc. Bioinf. 1: 3.5.1–3.5.6. A discussion of PAM, BLOSUM, and specialized scoring matrices, with guidance regarding the proper choice of matrices for particular types of protein-based analyses.

References

1 Agarawal, P. and States, D.J. (1998). Comparative accuracy of methods for protein similarity search. Bioinformatics. 14: 40–47.

2 Altschul, S.F. (1991). Amino acid substitution matrices from an information theoretic perspective. J. Mol. Biol. 219: 555–565.

3 Altschul, S.F. and Koonin, E.V. (1998). Iterated profile searches with PSI-BLAST: a tool for discovery in protein databases. Trends Biochem. Sci. 23: 444–447.

4 Altschul, S.F., Gish, W., Miller, W. et al. (1991). Basic local alignment search tool. J. Mol. Biol. 215: 403–410.

5 Altschul, S.F., Madden, T.L., Schäffer, A.A. et al. (1997). Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res. 25: 3389–3402.

6 Brenner, S.E., Chothia, C., and Hubbard, T.J.P. (1998). Assessing sequence comparison methods with reliable structurally identified evolutionary relationships. Proc. Natl. Acad. Sci. USA. 95: 6073–6078.

7 Bücher, P., Karplus, K., Moeri, N., and Hofmann, K. (1996). A flexible motif search technique based on generalized profiles. Comput. Chem. 20: 3–23.

8 Chen, Z. (2003). Assessing sequence comparison methods with the average precision criterion. Bioinformatics. 19: 2456–2460.

9 Dayhoff, M.O., Schwartz, R.M., and Orcutt, B.C. (1978). A model of evolutionary change in proteins. In: Atlas of Protein Sequence and Structure, vol. 5 (ed. M.O. Dayhoff), 345–352. Washington, DC: National Biomedical Research Foundation.

10 Doolittle, R.F. (1981). Similar amino acid sequences: chance or common ancestry. Science 214: 149–159.

11 Doolittle, R.F. (1989). Similar amino acid sequences revisited. Trends Biochem. Sci. 14: 244–245.

12 Gonnet, G.H., Cohen, M.A., and Benner, S.A. (1992). Exhaustive matching of the entire protein sequence database. Proteins. 256: 1443–1445.

13 Gribskov, M., McLachlan, A.D., and Eisenberg, D. (1987). Profile analysis: detection of distantly-related proteins. Proc. Natl. Acad. Sci. USA. 84: 4355–4358.

14 Henikoff, S. and Henikoff, J.G. (1991). Automated assembly of protein blocks for database searching. Nucleic Acids Res. 19: 6565–6572.

15 Henikoff, S. and Henikoff, J.G. (1992). Amino acid substitution matrices from protein blocks. Proc. Natl. Acad. Sci. USA. 89: 10915–10919.

16 Henikoff, S. and Henikoff, J.G. (1993). Performance evaluation of amino acid substitution matrices. Proteins Struct. Funct. Genet. 17: 49–61.

17 Henikoff, S. and Henikoff, J.G. (2000). Amino acid substitution matrices. Adv. Protein Chem. 54: 73–97.

18 Jones, D.T., Taylor, W.R., and Thornton, J.M. (1992). The rapid generation of mutation data matrices from protein sequences. Comput. Appl. Biosci. 8: 275–282.

19 Karlin, S. and 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.

20 Kent, W.J. (2002). BLAT: the BLAST-like alignment tool. Genome Res. 12: 656–664.

21 Lipman, D.J. and Pearson, W.R. (1985). Rapid and sensitive protein similarity searches. Science. 227: 1435–1441.

22 Ma, B., Tromp, J., and Li, M. (2002). PatternHunter: faster and more sensitive homology search. Bioinformatics. 18: 440–445.

23 Pearson, W.R. (1995). Comparison of methods for searching protein sequence databases. Protein Sci. 4: 1145–1160.

24 Pearson, W.R. (2000). Flexible sequence similarity searching with the FASTA3 program package. Methods Mol. Biol. 132: 185–219.

25 Pearson, W.R. (2016). Finding protein and nucleotide similarities with FASTA. Curr. Protoc. Bioinf. 53: 3.9.1–3.9.23.

26 Pearson, W.R. and Lipman, D.J. (1988). Improved tools for biological sequence comparison. Proc. Natl. Acad. Sci. USA. 85: 2444–2448.

27 Rost, B. (1999). Twilight zone of protein sequence alignments. Protein Eng. 12: 85–94.

28 Ryan, J.F., Pang, K., Schnitzler, C.E. et al., and NISC Comparative Sequencing Program. (2013). The genome of the ctenophore Mnemiopsis leidyi and its implications for cell type evolution. Science. 342: 1242592.

29 Schneider, T.D., Stormo, G.D., Gold, L., and Ehrenfeucht, A. (1986). Information content of binding sites on nucleotide sequences. J. Mol. Biol. 188: 415–431.

30 Schnitzler, C.E., Simmons, D.K., Pang, K. et al. (2014). Expression of multiple Sox genes through embryonic development in the ctenophore Mnemiopsis leidyi is spatially restricted to zones of cell proliferation. EvoDevo. 5: 15.

31 Smith, T.F. and Waterman, M.S. (1981). Identification of common molecular subsequences. J. Mol. Biol. 147: 195–197.

32 Staden, R. (1988). Methods to define and locate patterns of motifs in sequences. Comput. Appl. Biosci. 4: 53–60.

33 Tatusov, R.L., Altschul, S.F., and Koonin, E.V. (1994). Detection of conserved segments in proteins: iterative scanning of sequence databases with alignment blocks. Proc. Natl. Acad. Sci. USA. 91: 12091–12095.

34 Tatusova, T.A. and Madden, T.L. (1999). BLAST 2 Sequences, a new tool for comparing protein and nucleotide sequences. FEMS Microbiol. Lett. 174: 247–250.

35 Török, A., Schiffer, P.H., Schintzler, C.E. et al. (2016). The cnidarian Hydractinia echinata employs canonical and highly adapted histones to pack its DNA. Epigenet. Chromatin. 9: 36.

36 Vogt, G., Etzold, T., and Argos, P. (1995). An assessment of amino acid exchange matrices in aligning protein sequences: the twilight zone revisited. J. Mol. Biol. 249: 816–831.

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