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for annotation, visualization and analysis in functional genomics research. Bioinformatics.
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68. Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, et al. The sequence alignment/map
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11 Design and Analysis of RNA Sequencing Data
173
eukaryotic transcriptomes. Front Genet. 2015;6:361.
51. Gopinath GR, Cinar HN, Murphy HR, Durigan M, Almeria M, Tall BD, et al. A hybrid
reference-guided de novo assembly approach for generating Cyclospora mitochondrion
genomes. Gut Pathog. 2018;10:15.
52. Lischer HEL, Shimizu KK. Reference-guided de novo assembly approach improves genome
reconstruction for related species. BMC Bioinform. 2017;18(1):474.
53. Martin J, Bruno VM, Fang Z, Meng X, Blow M, Zhang T, et al. Rnnotator: an automated de
novo transcriptome assembly pipeline from stranded RNA-Seq reads. BMC Genomics.
2010;11:663.
54. Robertson G, Schein J, Chiu R, Corbett R, Field M, Jackman SD, et al. De novo assembly and
analysis of RNA-seq data. Nat Methods. 2010;7(11):909–12.
55. Grabherr MG, Haas BJ, Yassour M, Levin JZ, Thompson DA, Amit I, et al. Full-length
transcriptome assembly from RNA-Seq data without a reference genome. Nat Biotechnol.
2011;29(7):644–52.
56. Compeau PE, Pevzner PA, Tesler G. How to apply de Bruijn graphs to genome assembly. Nat
Biotechnol. 2011;29(11):987–91.
57. Zerbino DR, Birney E. Velvet: algorithms for de novo short read assembly using de Bruijn
graphs. Genome Res. 2008;18(5):821–9.
58. Schulz MH, Zerbino DR, Vingron M, Birney E. Oases: robust de novo RNA-seq assembly
across the dynamic range of expression levels. Bioinformatics. 2012;28(8):1086–92.
59. O’Neil ST, Emrich SJ. Assessing De Novo transcriptome assembly metrics for consistency and
utility. BMC Genomics. 2013;14:465.
60. Moreno-Santillan DD, Machain-Williams C, Hernandez-Montes G, Ortega J. De Novo
transcriptome assembly and functional annotation in five species of bats. Sci Rep. 2019;9
(1):6222.
61. Evangelistella C, Valentini A, Ludovisi R, Firrincieli A, Fabbrini F, Scalabrin S, et al. De novo
assembly, functional annotation, and analysis of the giant reed (Arundo donax L.) leaf
transcriptome provide tools for the development of a biofuel feedstock. Biotechnol Biofuels.
2017;10:138.
62. UniProt C. The universal protein resource (UniProt). Nucleic Acids Res. 2008;36(Database
issue):D190–5.
63. Finn RD, Bateman A, Clements J, Coggill P, Eberhardt RY, Eddy SR, et al. Pfam: the protein
families database. Nucleic Acids Res. 2014;42(Database issue):D222–30.
64. Lagesen K, Hallin P, Rodland EA, Staerfeldt HH, Rognes T, Ussery DW. RNAmmer: consistent
and rapid annotation of ribosomal RNA genes. Nucleic Acids Res. 2007;35(9):3100–8.
65. Almagro Armenteros JJ, Tsirigos KD, Sonderby CK, Petersen TN, Winther O, Brunak S, et al.
SignalP 5.0 improves signal peptide predictions using deep neural networks. Nat Biotechnol.
2019;37(4):420–3.
66. Conesa A, Gotz S, Garcia-Gomez JM, Terol J, Talon M, Robles M. Blast2GO: a universal tool
for annotation, visualization and analysis in functional genomics research. Bioinformatics.
2005;21(18):3674–6.
67. Kanehisa M, Goto S. KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res.
2000;28(1):27–30.
68. Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, et al. The sequence alignment/map
format and SAMtools. Bioinformatics. 2009;25(16):2078–9.
69. Barnett DW, Garrison EK, Quinlan AR, Stromberg MP, Marth GT. BamTools: a C++ API and
toolkit for analyzing and managing BAM files. Bioinformatics. 2011;27(12):1691–2.
11 Design and Analysis of RNA Sequencing Data
173