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020 _a9781447166931
_9978-1-4471-6693-1
024 7 _a10.1007/978-1-4471-6693-1
_2doi
050 4 _aQH324.2-324.25
072 7 _aPSA
_2bicssc
072 7 _aUB
_2bicssc
072 7 _aCOM014000
_2bisacsh
082 0 4 _a570.285
_223
100 1 _aAxelson-Fisk, Marina.
_eauthor.
245 1 0 _aComparative Gene Finding
_h[electronic resource] :
_bModels, Algorithms and Implementation /
_cby Marina Axelson-Fisk.
250 _a2nd ed. 2015.
264 1 _aLondon :
_bSpringer London :
_bImprint: Springer,
_c2015.
300 _aXX, 382 p. 81 illus.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aComputational Biology,
_x1568-2684 ;
_v20
505 0 _aIntroduction -- Single Species Gene Finding -- Sequence Alignment -- Comparative Gene Finding -- Gene Structure Submodels -- Parameter Training -- Implementation of a Comparative Gene Finder -- Annotation Pipelines for Next Generation Sequencing Projects.
520 _aThis unique text/reference presents a concise guide to building computational gene finders, and describes the state of the art in computational gene finding methods, with a particular focus on comparative approaches. Fully updated and expanded, this new edition examines next-generation sequencing (NGS) technology, including annotation pipelines for NGS data. The book also discusses conditional random fields, enhancing the broad coverage of topics spanning probability theory, statistics, information theory, optimization theory, and numerical analysis. Topics and features: Introduces the fundamental terms and concepts in the field, and provides an historical overview of algorithm development Discusses algorithms for single-species gene finding, and approaches to pairwise and multiple sequence alignments, then describes how the strengths in both areas can be combined to improve the accuracy of gene finding Explores the gene features most commonly captured by a computational gene model, and explains the basics of parameter training Illustrates how to implement a comparative gene finder, reviewing the different steps and accuracy assessment measures used to debug and benchmark the software Examines NGS techniques, and how to build a genome annotation pipeline, discussing sequence assembly, de novo repeat masking, and gene prediction (NEW) Postgraduate students, and researchers wishing to enter the field quickly, will find this accessible text a valuable source of insights and examples. A suggested course outline for instructors is provided in the preface. Dr. Marina Axelson-Fisk is an Associate Professor at the Department of Mathematical Sciences of Chalmers University of Technology, Gothenburg, Sweden.
650 0 _aComputer science.
650 0 _aBioinformatics.
650 1 4 _aComputer Science.
650 2 4 _aComputational Biology/Bioinformatics.
650 2 4 _aBioinformatics.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9781447166924
830 0 _aComputational Biology,
_x1568-2684 ;
_v20
856 4 0 _uhttp://dx.doi.org/10.1007/978-1-4471-6693-1
912 _aZDB-2-SCS
942 _cEBK
999 _c52741
_d52741