01991nas a2200373   4500000000100000008004100001653001400042653001400056653002100070653003500091653003300126653002000159653001800179653003400197653002200231653002600253653001900279653001900298653001400317653001200331653001100343653001900354653001400373653001200387653001700399100001900416700001900435245005400454856007100508300001200579490001000591520100200601022001401603       2017                            d10aBinPacker10aCD-HIT-ES10aDe novo assembly10ade novo transcriptome assembly10aDifferential gene expression10aGene expression10aGene Ontology10aGenetic marker identification10aGenome annotation10aPlant gene expression10aPlant Ontology10aPlant Reactome10aRNA QUAST10aRNA-Seq10aSPAdes10aTranscriptiome10aTransRate10aTrinity10aVelvet Oases1 aMatthew Geniza1 aPankaj Jaiswal00aTools for building de novo transcriptome assembly  uhttp://www.sciencedirect.com/science/article/pii/S2214662817301032  a41 - 450 v11-123 aThe availability of RNA-Seq method allows researchers to capture the spatial or temporal profile of transcriptomes from various types of biological samples. <img src="https://ars.els-cdn.com/content/image/1-s2.0-S2214662817301032-gr1.jpg" alt="Expression atlas view of maize genes" style="float:right;width:210px;height:175px;"> The transcriptome data from a species can be analyzed in the context of its sequenced genomes or closely related genome to score biological sample-specific transcript isoforms, novel transcribed regions and to refine gene models including identification of new genes, in addition to the differential gene expression analysis. However, many plant species of importance currently lack a sequenced genome or a closely related reference genome and thus, rely on the de novo methods for generating transcript models and transcriptome assemblies. Here we describe various tools used for de novo transcriptome assembly and discuss the data management practices and standards.  a2214-6628