Biology subjects

Sowpati, D. T.

Publications and source records attributed to Sowpati, D. T..

4 recordsLinked to original sources

bioRxiv2018

Long-read genome sequence and assembly of Leptopilina boulardi: a specialist Drosophila parasitoid

BackgroundLeptopilina boulardi is a specialist parasitoid belonging to the order Hymenoptera, which attacks the larval stages of Drosophila. The Leptopilina genus has enormous value in the biological control of pests as well as in understanding several aspects of host-parasitoid biology. However, none of the members of Figitidae family has their genomes sequenced. In order to improve the understanding of the parasitoid wasps by generating genomic resources, we sequenced the whole genome of L. boulardi.\n\nFindingsHere, we report a high-quality genome of L. boulardi, assembled from 70Gb of Illumina reads and 10.5Gb of PacBio reads, forming a total coverage of 230X. The 375Mb draft genome has an N50 of 275Kb with 6315 scaffolds >500bp, and encompasses >95% complete BUSCOs. The GC% of the genome is 28.26%, and RepeatMasker identified 868105 repeat elements covering 43.9% of the assembly. A total of 25259 protein-coding genes were predicted using a combination of ab-initio and RNA-Seq based methods, with an average gene size of 3.9Kb. 78.11% of the predicted genes could be annotated with at least one function.\n\nConclusionOur study provides a highly reliable assembly of this parasitoid wasp, which will be a valuable resource to researchers studying parasitoids. In particular, it can help delineate the host-parasitoid mechanisms that are part of the Drosophila - Leptopilina model system.

genomics↗

bioRxiv2018

Patterns of microsatellite distribution reflect the evolution of biological complexity

Microsatellites, also known as Simple Sequence Repeats (SSRs), are evolutionarily conserved repeat elements distributed non-randomly in all genomes. Many studies have investigated their pattern of occurrence in order to understand their role, but their identification has largely been non-exhaustive and limited to a few related species or model organisms. Here, we identify ~685 million microsatellites from 719 eukaryotes and analyze their evolutionary trends from protists to mammals. We document novel patterns uniquely demarcating closely related species, including in pathogens like Leishmania as well as in higher organisms such as Drosophila, birds, primates, and cereal crops. The distribution of SSRs in coding and non-coding regions reveals taxon-specific variations in their exonic, intronic and intergenic densities. We also show that specific SSRs accumulate at longer lengths in higher organisms indicating an evolutionary selection pressure. In general, we observe greater constraints in the SSR composition of multicellular organisms with complex cell types, while simpler organisms show more diversity. The conserved microsatellite trends and species-specific signatures identified in this study closely mirror phylogenetic relationships and we hypothesize that SSRs are integral components in speciation and the evolution of organismal complexity. The microsatellite dataset generated in this work provides a large number of candidates for functional analysis and unparalleled scope for understanding their roles across the evolutionary landscape.

genomics↗

bioRxiv2017

Epigenomic and genomic landscape of Drosophila melanogaster heterochromatic genes

Heterochromatin is associated with transcriptional repression. In contrast, several genes in the pericentromeric regions of Drosophila melanogaster are dependent on this heterochromatic environment for their expression. Heterochromatic genes encode proteins involved in various developmental processes. Several studies have shown that a variety of epigenetic modifications is associated with these genes. Here we present a comprehensive analysis of the epigenetic landscape of heterochromatic genes across all the developmental stages of Drosophila using the available histone modification and expression data from modENCODE. We find that heterochromatic genes exhibit combinations of active and inactive histone marks that correspond to their level of expression during development. Thus, we classified these genes into three groups based on the combinations of histone modifications present. We also looked for potential regulatory DNA sequence elements in the genomic neighborhood of these genes. Our results show that Nuclear Matrix Associated Regions (MARs) are prominently present in the intergenic regions of heterochromatic genes during embryonic stages suggesting their plausible role in pericentromeric genome organization. We also find that the intergenic sequences in the heterochromatic regions have binding sites for transcription factors known to modulate epigenetic status. Taken together, our meta-analysis of the various genomic datasets suggest that the epigenomic and genomic landscape of the heterochromatic genes are distinct from that of euchromatic genes. These features could be contributing to the unusual regulatory status of the heterochromatic genes as opposed to the surrounding heterochromatin, which is repressive in nature.

genomics↗

bioRxiv2017

C-State: An interactive web app for simultaneous multi-gene visualization and comparative epigenetic pattern search

BackgroundComparative epigenomic analysis across multiple genes presents a bottleneck for bench biologists working with NGS data. Despite the development of standardized peak analysis algorithms, the identification of novel epigenetic patterns and their visualization across gene subsets remains a challenge.\n\nResultsWe developed a fast and interactive web app, C-State (Chromatin-State), to query and plot chromatin landscapes across multiple loci and cell types. C-State has an interactive, JavaScript- based graphical user interface and runs locally in modern web browsers that are pre-installed on all computers, thus eliminating the need for cumbersome data transfer, pre-processing and prior programming knowledge.\n\nConclusionsC-State is unique in its ability to extract and analyze multi-gene epigenetic information. It allows for powerful GUI-based pattern searching and visualization. We include a case study to demonstrate its potential for identifying user-defined epigenetic trends in context of gene expression profiles.

bioinformatics↗