Download e-book for kindle: Comparative Gene Finding: Models, Algorithms and by Marina Axelson-Fisk

By Marina Axelson-Fisk

Comparative genomics is an rising box, that is being fed through an explosion within the variety of attainable organic sequences. This has resulted in a major call for for swifter, extra effective and extra strong machine algorithms to research this massive volume of data.

This detailed text/reference describes the cutting-edge in computational gene discovering, with a specific specialise in comparative methods. offering either an outline of a few of the equipment which are utilized within the box, and a concise advisor on how computational gene finders are outfitted, the ebook covers a large diversity of themes from likelihood concept, facts, details thought, optimization conception and numerical research. The textual content assumes the reader has a few heritage in bioinformatics, specifically in arithmetic and mathematical records. A uncomplicated wisdom of study, likelihood idea and random tactics may additionally relief the reader.

Topics and features:

  • Describes how algorithms and series alignments will be mixed to enhance the accuracy of gene finding
  • Introduces the fundamental organic phrases and ideas in genetics, and gives an historic evaluation of set of rules development
  • Explores the gene gains most ordinarily captured by means of a computational gene version, and describes crucial sub-models used
  • Discusses the algorithms most typically used for single-species gene finding
  • Investigates ways to pairwise and a number of series alignments
  • Explains the fundamentals of parameter education, masking a few of the diverse parameter estimation and optimization ideas ordinary in gene finding
  • Illustrates the best way to enforce a comparative gene finder, explaining the several steps and numerous accuracy evaluate measures used to debug and benchmark the software

A beneficial textual content for postgraduate scholars, this publication offers necessary insights and examples for researchers wishing to go into the sphere fast. as well as the explicit concentrate on the algorithmic information surrounding computational gene discovering, readers receive an creation to the basics of computational biology and organic series research, in addition to an summary of the real mathematical and statistical functions in bioinformatics.

Dr. Marina Axelson-Fisk is an affiliate Professor on the division of Mathematical Sciences of Chalmers collage of expertise, Gothenburg, Sweden.

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Extra resources for Comparative Gene Finding: Models, Algorithms and Implementation

Sample text

Proc. Int. Conf. Intell. Syst. Mol. Biol. 5, 56–64 (1997) 12. : GeneWise and GenomeWise. Genome Res. 14, 988–995 (2004) 13. : Genomic exploration of the hemiascomycetous yeasts: 4. The genome of Saccharomyces cerevisiae revisited. FEBS Lett. 487, 31–36 (2000) 14. : The SWISSPROT protein knowledgebase and its supplement TrEMBL in 2003. Nucleic Acids Res. 31, 365–370 (2003) 15. : GENMARK: parallel gene recognition for both DNA strands. Comput. Chem. 17, 123–133 (1993) 16. : ExonHunter: a comprehensive approach to gene finding.

Recognition of protein coding genes in the yeast genome at better than 95% accuracy based on the Z curve. Nucleic Acids Res. 28, 2804–2814 (2000) 113. : Identification of protein coding regions in the human genome by quadratic discriminant analysis. Proc. Natl. Acad. Sci. USA 94, 565–568 (1997) Chapter 2 Single Species Gene Finding A gene finding model usually consists of a main algorithm that serves as a kind of “umbrella” algorithm for a large number of rather complex submodels. The submodels represent various features of a gene, such as exons, introns, and splice site models.

Symp. Biocomput. 8, 375–387 (2003) 112. : Recognition of protein coding genes in the yeast genome at better than 95% accuracy based on the Z curve. Nucleic Acids Res. 28, 2804–2814 (2000) 113. : Identification of protein coding regions in the human genome by quadratic discriminant analysis. Proc. Natl. Acad. Sci. USA 94, 565–568 (1997) Chapter 2 Single Species Gene Finding A gene finding model usually consists of a main algorithm that serves as a kind of “umbrella” algorithm for a large number of rather complex submodels.

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