Transcriptome-based Gene Networks for Systems-level Analysis of Plant Gene Functions

• Main Author: Gupta, Chirag
• Language: EN
• Published: University of Arkansas 2017
• Subjects: Cellular biology|Agriculture|Bioinformatics
• Online Access: http://pqdtopen.proquest.com/#viewpdf?dispub=10636543

<p> Present day genomic technologies are evolving at an unprecedented rate, allowing interrogation of cellular activities with increasing breadth and depth. However, we know very little about how the genome functions and what the identified genes do. The lack of functional annotations of genes greatly limits the post-analytical interpretation of new high throughput genomic datasets. For plant biologists, the problem is much severe. Less than 50% of all the identified genes in the model plant <i>Arabidopsis thaliana,</i> and only about 20% of all genes in the crop model <i>Oryza sativa</i> have some aspects of their functions assigned. Therefore, there is an urgent need to develop innovative methods to predict and expand on the currently available functional annotations of plant genes. With open-access catching the &lsquo;pulse&rsquo; of modern day molecular research, an integration of the copious amount of transcriptome datasets allows rapid prediction of gene functions in specific biological contexts, which provide added evidence over traditional homology-based functional inference. The main goal of this dissertation was to develop data analysis strategies and tools broadly applicable in systems biology research. </p><p> Two user friendly interactive web applications are presented: The Rice Regulatory Network (RRN) captures an abiotic-stress conditioned gene regulatory network designed to facilitate the identification of transcription factor targets during induction of various environmental stresses. The <i>Arabidopsis </i> Seed Active Network (SANe) is a transcriptional regulatory network that encapsulates various aspects of seed formation, including embryogenesis, endosperm development and seed-coat formation. Further, an edge-set enrichment analysis algorithm is proposed that uses network density as a parameter to estimate the gain or loss in correlation of pathways between two conditionally independent coexpression networks.</p><p>