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Evaluation and annotation of Bemisia tabaci transcriptome data using paired-end RNA sequencing

Evaluation and annotation of Bemisia tabaci transcriptome data using paired-end RNA sequencing

Naik, Nikunj Satishchandra (2017) Evaluation and annotation of Bemisia tabaci transcriptome data using paired-end RNA sequencing. MPhil thesis, University of Greenwich.

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Abstract

Bemisia tabaci (B. tabaci), a major crop pest of important food security crops, transmits more than 100 plant viruses. Despite its global importance as one of the world’s top 100 invasive species, limited genomic resources are available in public domains. In this study, transcriptomic datasets from cassava and non-cassava colonizing B. tabaci populations were used to compare sequence divergence between populations.

We sequenced transcriptomes of three cassava colonizing populations and three populations which do not colonize cassava as a host plant to generate a large catalogue of transcripts for comparative analysis. Twenty four de novo assemblies using multi k-mer values were produced using four transcriptome assemblers, Trinity, Velvet/Oases, SOAPdenovo-Trans and CLC Bio to maximize the diversity and completeness of assembled transcripts. Trinity assemblies were chosen based on evaluation statistics using Transrate, DETONATE, BUSCO and CRB-BLAST. The resulting clustered assembly consisted of 185,895-287,559 contigs, ~38% (for non-cassava) and ~34% (for cassava populations) of which could be functionally annotated when compared against NCBI non redundant database using BLASTX.

The assembled transcriptome data will serve as a genomic resource for future B. tabaci studies. Furthermore, our results also showed the performance of publicly available transcriptome assemblers as well as important factors affecting de novo assembly.

Item Type: Thesis (MPhil)
Uncontrolled Keywords: Bemisia tabaci (B. tabaci); pest management; cassava; food security;
Subjects: S Agriculture > SB Plant culture
Faculty / Department / Research Group: Faculty of Engineering & Science > Natural Resources Institute
Faculty of Engineering & Science > Natural Resources Institute > Agriculture, Health & Environment Department
Last Modified: 09 Apr 2019 11:51
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
Selected for GREAT 2019: None
URI: http://gala.gre.ac.uk/id/eprint/23499

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