Exploring flowering gene networks in soybean and Arabidopsis through transcriptome analysis

Flowering determines plant's survival and reproductive success. Flowering transition is triggered by coincidence of various external and internal cues. The overall purpose of this thesis was to explore the flowering gene networks in soybean through an RNASeq based transcriptome analysis.;Chapter One discusses the effect of three photoperiod treatments, short day (SD), long day (LD) and shift from 3 weeks LD to 1 week SD on six domesticated varieties of Glycine max; a reference variety, Williams 82, Clark and it's four NILs that are polymorphic for E loci (E1, E2, E3, and E5) and a soybean ancestor, Glycine soja. Samplings were performed at three time points in a day: early morning (6:30), afternoon (14:30), and evening (22:30), with three biological replicas. By performing different pairwise comparisons of NILs, the probable roles of different E loci had also been demonstrated.;Chapter Two discusses the exploration and reconstruction of flowering gene networks in soybean using two approaches like gene gene co-expression (using Pearson correlations) and Graphical Gaussian Models (GGM) (using partial correlations). To argument the obtained networks, we performed a comparative analysis with the known transcription factor -- target information available from the Arabidopsis Gene Regulatory Information Server (AGRIS).;Chapter Three describes an effort to understand the complex networks of interactions among Arabidopsis genes by identifying network motifs. Transcriptional regulatory networks are classified into developmental transcriptional networks (DTNs), which work on longer time scales with sensory transcriptional networks (STNs) that work on smaller timescales. We used flowering gene network as a representative of DTNs and compared with four other gene networks (STNs), using the gene interactions from AGRIS. Since AGRIS networks were sparse we augmented them using CASPIAN which helped identify some interesting motifs (significantly detected) in flowering genes dataset.