LEADER 04022nam 22006015 450 001 9910299247603321 005 20200701210059.0 010 $a1-4471-6693-0 024 7 $a10.1007/978-1-4471-6693-1 035 $a(CKB)3710000000399775 035 $a(EBL)2096777 035 $a(SSID)ssj0001500977 035 $a(PQKBManifestationID)11852303 035 $a(PQKBTitleCode)TC0001500977 035 $a(PQKBWorkID)11523029 035 $a(PQKB)10538805 035 $a(DE-He213)978-1-4471-6693-1 035 $a(MiAaPQ)EBC2096777 035 $z(PPN)258850434 035 $a(PPN)185489796 035 $a(EXLCZ)993710000000399775 100 $a20150413d2015 u| 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aComparative Gene Finding $eModels, Algorithms and Implementation /$fby Marina Axelson-Fisk 205 $a2nd ed. 2015. 210 1$aLondon :$cSpringer London :$cImprint: Springer,$d2015. 215 $a1 online resource (396 p.) 225 1 $aComputational Biology,$x1568-2684 ;$v20 300 $aDescription based upon print version of record. 311 $a1-4471-6692-2 320 $aIncludes bibliographical references at the end of each chapters and index. 327 $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. 330 $aThis unique text/reference presents a concise guide to building computational gene finders, and describes the state of the art in computational gene finding methods, with a particular focus on comparative approaches. Fully updated and expanded, this new edition examines next-generation sequencing (NGS) technology, including annotation pipelines for NGS data. The book also discusses conditional random fields, enhancing the broad coverage of topics spanning probability theory, statistics, information theory, optimization theory, and numerical analysis. Topics and features: Introduces the fundamental terms and concepts in the field, and provides an historical overview of algorithm development Discusses algorithms for single-species gene finding, and approaches to pairwise and multiple sequence alignments, then describes how the strengths in both areas can be combined to improve the accuracy of gene finding Explores the gene features most commonly captured by a computational gene model, and explains the basics of parameter training Illustrates how to implement a comparative gene finder, reviewing the different steps and accuracy assessment measures used to debug and benchmark the software Examines NGS techniques, and how to build a genome annotation pipeline, discussing sequence assembly, de novo repeat masking, and gene prediction (NEW) Postgraduate students, and researchers wishing to enter the field quickly, will find this accessible text a valuable source of insights and examples. A suggested course outline for instructors is provided in the preface. Dr. Marina Axelson-Fisk is an Associate Professor at the Department of Mathematical Sciences of Chalmers University of Technology, Gothenburg, Sweden. 410 0$aComputational Biology,$x1568-2684 ;$v20 606 $aBioinformatics 606 $aComputational Biology/Bioinformatics$3https://scigraph.springernature.com/ontologies/product-market-codes/I23050 606 $aBioinformatics$3https://scigraph.springernature.com/ontologies/product-market-codes/L15001 615 0$aBioinformatics. 615 14$aComputational Biology/Bioinformatics. 615 24$aBioinformatics. 676 $a572.860285 700 $aAxelson-Fisk$b Marina$4aut$4http://id.loc.gov/vocabulary/relators/aut$01059827 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910299247603321 996 $aComparative Gene Finding$92508549 997 $aUNINA