Optimization of de novo Transcriptome Assembly using RNA-seq Data
De novo transcriptome assembly can be generated for non-model organisms from raw RNA-seq reads without the aid of reference genome using wide range of online available tools i.e. Trinity, SOAPdenovo-Trans, Oases etc. This de novo assembly may have acquired some ab- normalities due to sequencing errors or during assembly development i.e. false chimeras, fragmented or incomplete contigs. One of the major challenge is to improve assembly errors to make it biologically correct. Commonly available tools for assembly improvement rely pri- marily on BLAST using closely related species. Here, we are applying an efficient computational approach that does not rely on BLAST searches but uses paired-end information of the reads produced from Illumina sequencing platform and error signatures generated by RNA-seq alignment tools to identify and correct above-mentioned assembly errors and to make assembly more significant as reference transcriptome.
MAPPS: A Web-Based Tool for Metabolic Pathway Prediction and Network Analysis in the Postgenomic Era
Comparative and evolutionary analyses of metabolic networks have a wide range of applications, ranging from research into metabolic evolution through to practical applications in drug development, synthetic biology, and biodegradation. We present MAPPS: Metabolic network Analysis and Pathway Prediction Server https://mapps.lums.edu.pk, a web-based tool to study functions and evolution of metabolic networks using traditional and ‘omics data sets. MAPPS provides diverse functionalities including an interactive interface, graphical visualization of results, pathway prediction and network comparison, identification of potential drug targets, in silico metabolic engineering, host–microbe interactions, and ancestral network building. Importantly, MAPPS also allows users to upload custom data, thus enabling metabolic analyses on draft and custom genomes, and has an ‘omics pipeline to filter pathway results, making it relevant in today’s postgenomic era.
Evolution of Bread Wheat
Modern bread wheat (Triticum aestivum) is a hexaploid, composed of three diploid genomes (A, B and D) of seven chromosomes each, and is thought to be the product of hybridization between emmer (Triticum turgidum, tetraploid AB) and a wild goatgrass (Aegilops tauschii, diploid D) (Heun et al. 1997). Emmer itself is thought to be the product of hybridization between two diploid species: einkorn wheat (Triticum uratu, diploid A) and another goatgrass similar to Aegilops speltoides (diploid B). We are using computational techniques to understand the genetics of bread wheat and the hybridisation events that have lead the evolution of modern day bread wheat from different goat grasses and Eikorn (pasta wheat). This project is in collaboration with Harberd Lab at the Department of Plant Sciences, University of Oxford and uses the latest genomic science including high-throughput sequencing and associated statistical analysis to determine the precise nature and extent of the genetic variation that has led to the modern day bread wheat.