Biology subjects

Kiani, G.

Publications and source records attributed to Kiani, G..

2 recordsLinked to original sources

bioRxiv2019

Revisiting the landscape of evolutionary breakpoints across human genome using multi-way comparison

Genome rearrangement is one of the major forces driving the processes of the evolution and disease development. The chromosomal position affected by these rearrangements are called breakpoints. The breakpoints occurring during the evolution of species are known to be non randomly distributed. Detecting their landscape and mapping them to genomic features constitute an important features in both comparative and functional genomics. Several studies have attempted to provide such mapping based on pairwise comparison of genes as conservation anchors. With the availability of more accurate multi-way alignments, we design an approach to identify synteny blocks and evolutionary breakpoints based on UCSC 45-way conservation sequence alignments with 12 selected species. The multi-way designed approach with the mild flexibility of presence of selected species, helped to have a better determination of human lineage-specific evolutionary breakpoints. We identified 261,391 human lineage-specific evolutionary breakpoints across the genome and 2,564 dense regions enriched with biological processes involved in adaptive traits such as response to DNA damage stimulus, cellular response to stress and metabolic process. Moreover, we found 230 regions refractory to evolutionary breakpoints that carry genes associated with crucial developmental process such as organ morphogenesis, skeletal system development, chordate embryonic development, nerve development and regulation of biological process. This initial map of the human genome will help to gain better insight into several studies including developmental studies and cancer rearrangement processes.

genomics↗

bioRxiv2016

CASTOR: A machine learning platform for reproducible viral genome classification

MotivationAdvances in cloning and sequencing technology yielded a massive number of genome of virus strains. The classification and annotation of these genomes constitute important assets in the discovery of genomic variability, taxonomic characteristics and disease mechanisms. Existing classification methods are often designed for a well-studied virus. Thus, the viral comparative genomic studies could benefit from more generic, fast and accurate tools for classifying and typing newly sequenced strains of diverse virus families.\n\nResultsHere, we introduce a fast, accurate and generic virus classification platform, CASTOR, based on a machine learning approach. CASTOR is inspired by a well-known technique in molecular biology: Restriction Fragment Length Polymorphism (RFLP). It simulates the restriction digestion of genomic material by different enzymes into fragments in-silico. It uses two metrics to construct feature vectors for machine learning algorithms in the classification step. We benchmark CASTOR for the classification of distinct datasets of Human Papillomaviruses (HPV), Hepatitis B Viruses (HBV) and Human Immunodeficiency viruses (HIV). Results reveal true positive rates of 99%, 99% and 98% for HPV Alpha species, HBV genotyping and HIV M group subtyping respectively. Furthermore, CASTOR shows a competitive performance compare to well-known HIV-specific classifier REGA and COMET on whole genome and pol fragments. With such prediction rates, genericity and robustness, as well as rapidity, such approach could constitute a reference in large-scale virus studies. Finally, we developed the CASTOR web platform for open access and reproducible viral machine learning classifiers.\n\nAvailabilityhttp://castor.bioinfo.uqam.ca\n\nContactdiallo.abdoulaye@uqam.ca

bioinformatics↗