LEADER 01438nam--2200421---450- 001 990000463220203316 005 20100719132104.0 010 $a88-14-06054-1 035 $a0046322 035 $aUSA010046322 035 $a(ALEPH)000046322USA01 035 $a0046322 100 $a20010523d1996----km-y0itay0103----ba 101 $aita 102 $aIT 105 $a||||||||001yy 200 1 $a<> economia delle imprese risiere$fRoberto Candiotto 210 $aMilano$cA. Giuffrè$d1996 215 $aX, 335 p.$d24 cm 225 2 $aUniversità degli studi di Torino$fIstituto di ragioneria ed economia aziendale$hSer.2$v21 410 $12001$aUniversità degli studi di Torino$fIstituto di ragioneria ed economia aziendale$hSer.2$v21 461 1$1001-------$12001 606 0 $aRisicoltura$yItalia$xAspetti economici 606 0 $aRiso$xIndustria$yItalia 676 $a338.17318 700 1$aCANDIOTTO,$bRoberto$0116583 801 0$aIT$bsalbc$gISBD 912 $a990000463220203316 951 $aXXX.B. Coll. 111/ 7 (X 24 XXI 21)$b11307 G$cXXX.B. Coll. 111/ 7 (X 24 XXI)$d00273961 959 $aBK 969 $aECO 979 $aPATTY$b90$c20010523$lUSA01$h1009 979 $aPATTY$b90$c20010523$lUSA01$h1011 979 $c20020403$lUSA01$h1654 979 $aPATRY$b90$c20040406$lUSA01$h1632 979 $aRSIAV4$b90$c20100719$lUSA01$h1321 996 $aEconomia delle imprese risiere$9415607 997 $aUNISA LEADER 04944nam 2200661 450 001 9910812209403321 005 20200520144314.0 010 $a1-119-27217-3 010 $a1-119-27216-5 010 $a1-119-27218-1 035 $a(CKB)4330000000009847 035 $a(EBL)4688974 035 $a(PQKBManifestationID)16379772 035 $a(PQKBWorkID)14944318 035 $a(PQKB)21802650 035 $a(DLC) 2016014704 035 $a(Au-PeEL)EBL4688974 035 $a(CaPaEBR)ebr11266625 035 $a(CaONFJC)MIL956217 035 $a(OCoLC)959149276 035 $a(PPN)199452288 035 $a(MiAaPQ)EBC4688974 035 $a(EXLCZ)994330000000009847 100 $a20160314h20162016 uy| 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 00$aComputational methods for next generation sequencing data analysis /$fedited by Ion Ma?ndoiu, Alexander Zelikovsky 210 1$aHoboken, New Jersey :$cJohn Wiley & Sons,$d[2016] 210 4$d©2016 215 $a1 online resource (461 p.) 225 1 $aWiley series on bioinformatics : computational techniques and engineering 300 $aDescription based upon print version of record. 311 $a1-118-16948-4 320 $aIncludes bibliographical references and index. 327 $aCover; Title Page; Copyright; Contents; Contributors; Preface; About the Companion Website; Part I Computing and Experimental Infrastructure for NGS; Chapter 1 Cloud Computing for Next-Generation Sequencing Data Analysis; 1.1 Introduction; 1.2 Challenges for NGS Data Analysis; 1.3 Background For Cloud Computing and its Programming Models; 1.4 Cloud Computing Services for NGS Data Analysis; 1.5 Conclusions and Future Directions; References; Chapter 2 Introduction to the Analysis of Environmental Sequence Information Using Metapathways; 2.1 Introduction & Overview; 2.2 Background 327 $a2.3 Metapathways Processes2.4 Big Data Processing; 2.5 Downstream Analyses; 2.6 Conclusions; References; Chapter 3 Pooling Strategy for Massive Viral Sequencing; 3.1 Introduction; 3.2 Design of Pools for Big Viral Data; 3.3 Deconvolution of Viral Samples From Pools; 3.4 Performance of Pooling Methods on Simulated Data; 3.5 Experimental Validation of Pooling Strategy; 3.6 Conclusion; References; Chapter 4 Applications of High-Fidelity Sequencing Protocol to RNA Viruses; 4.1 Introduction; 4.2 High-Fidelity Sequencing Protocol; 4.3 Assembly of High-Fidelity Sequencing Data 327 $a4.4 Performance of VGA on Simulated Data4.5 Performance of Existing Viral Assemblers on Simulated Consensus Error-Corrected Reads; 4.6 Performance of VGA on Real Hiv Data; 4.7 Comparison of Alignment on Error-Corrected Reads; 4.8 Evaluating of Error Correction Tools Based on High-Fidelity Sequencing Reads; Acknowledgment; References; Part II Genomics and Epigenomics; Chapter 5 Scaffolding Algorithms; 5.1 Scaffolding; 5.2 State-of-The-Art Scaffolding Tools; 5.3 Recent Scaffolding Tools; 5.4 Scaffolding Software Evaluation; References; Chapter 6 Genomic Variants Detection and Genotyping 327 $a6.1 Introduction6.2 Methods for Detection and Genotyping OF SNPs and Small Indels; 6.3 Methods for Detection and Genotyping of CNVs; 6.4 Putting Everything Together; References; Chapter 7 Discovering and Genotyping Twilight Zone Deletions; 7.1 Introduction; 7.2 Notation; 7.3 Non-Twilight-Zone Deletion Discovery; 7.4 Discovering ""Twilight Zone"" Deletions: New Solutions; 7.5 Genotyping ""Twilight Zone"" Deletions; 7.6 Results; 7.7 Discussion; 7.8 Availability; Acknowledgments; References; Chapter 8 Computational Approaches for Finding Long Insertions and Deletions with NGS Data 327 $a8.1 Background8.2 Methods; 8.3 Applications; 8.4 Conclusions and Future Directions; Acknowledgment; References; Chapter 9 Computational Approaches in Next-Generation Sequencing Data Analysis for Genome-Wide DNA Methylation Studies; 9.1 Introduction; 9.2 Enrichment-Based Approaches; 9.3 Bisulfite Treatment-Based Approaches; 9.4 Conclusion; References; Chapter 10 Bisulfite-Conversion-Based Methods for DNA Methylation Sequencing Data Analysis; 10.1 Introduction; 10.2 The Problem of Mapping BS-Treated Reads; 10.3 Algorithmic Approaches to the Problem Of Mapping BS-Treated Reads 327 $a10.4 Methylation Estimation 410 0$aWiley series on bioinformatics. 606 $aNucleotide sequence$xMethodology 606 $aNucleotide sequence$xData processing 615 0$aNucleotide sequence$xMethodology. 615 0$aNucleotide sequence$xData processing. 676 $a611/.0181663 702 $aMa?ndoiu$b Ion 702 $aZelikovsky$b Alexander 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910812209403321 996 $aComputational methods for next generation sequencing data analysis$91485924 997 $aUNINA