An RNA-Seq bioinformatics pipeline for data processing of Arabidopsis thaliana datasets

Deshpande, S, James, A ORCID: https://orcid.org/0000-0001-9274-7803, Franklin, CH, Leach, L and Yang, J, 2017. An RNA-Seq bioinformatics pipeline for data processing of Arabidopsis thaliana datasets. In: Proceedings of ICBRA 2017: International Conference on Bioinformatics Research and Applications 2017, Barcelona, Spain, 8-10 December 2017. New York: Association for Computing Machinery, pp. 1-8. ISBN 9781450353823

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Abstract

Floral transition is a crucial event in the reproductive cycle of a flowering plant during which many genes are expressed that govern the transition phase and regulate the expression and functions of several other genes involved in the process. Identification of additional genes connected to flowering genes is vital since they may regulate flowering genes and vice versa. Through our study, expression values of these additional genes has been found similar to flowering genes FLC and LFY in the transition phase. The presented approach plays a crucial role in this discovery. An RNA-Seq computational pipeline was developed for identification of novel genes involved in floral transition from A. thaliana apical shoot meristem time-series data. By intersecting differentially expressed genes from Cuffdiff, DESeq and edgeR methods, 690 genes were identified. Using FDR cutoff of 0.05, we identified 30 genes involved in glucosinolate and glycosinolate biosynthetic processes as principle regulators in the transition phase which provide protection to plants from herbivores and pathogens during flowering. Additionally, expression profiles of highly connected genes in protein-protein interaction network analysis revealed 76 genes with non-functional association and high correlation to flowering genes FLC and LFY which suggests their potential and principal role in floral regulation not identified previously in any studies.

Item Type:	Chapter in book
Alternative Title:	Identification of novel flowering genes using RNA-Seq pipeline employing combinatorial approach in Arabidopsis thaliana time -series apical shoot meristem data
Publication Title:	International Journal of Bioinformatics Research and Applications
Creators:	Deshpande, S., James, A., Franklin, C.H., Leach, L. and Yang, J.
Publisher:	Association for Computing Machinery
Place of Publication:	New York
Date:	December 2017
ISBN:	9781450353823
ISSN:	1744-5485
Identifiers:	Number Type 10.1145/3175587.3175592 DOI
Divisions:	Schools > School of Science and Technology
Record created by:	Linda Sullivan
Date Added:	21 Feb 2018 11:02
Last Modified:	09 Feb 2024 11:34
URI:	https://irep.ntu.ac.uk/id/eprint/32775

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