CicArVarDB: SNP and InDel database for advancing genetics research and breeding applications in chickpea

Molecular markers are valuable tools for breeders to help accelerate crop improvement. High throughput sequencing technologies facilitate the discovery of large-scale variations such as single nucleotide polymorphisms (SNPs) and simple sequence repeats (SSRs). Sequencing of chickpea genome along with re-sequencing of several chickpea lines has enabled the discovery of 4.4 million variations including SNPs and InDels. Here we report a repository of 1.9 million variations (SNPs and InDels) anchored on eight pseudomolecules in a custom database, referred as CicArVarDB that can be accessed at http://cicarvardb.icrisat.org/. It includes an easy interface for users to select variations around specific regions associated with quantitative trait loci, with embedded webBLAST search and JBrowse visualisation. We hope that this database will be immensely useful for the chickpea research community for both advancing genetics research as well as breeding applications for crop improvement

Genome-Wide Identification, Characterization, and Expression Analysis of Small RNA Biogenesis Purveyors Reveal Their Role in Regulation of Biotic Stress Responses in Three Legume Crops

Biotic stress in legume crops is one of the major threats to crop yield and productivity. Being sessile organisms, plants have evolved a myriad of mechanisms to combat different stresses imposed on them. One such mechanism, deciphered in the last decade, is small RNA (sRNA) mediated defense in plants. Small RNAs (sRNAs) have emerged as one of the major players in gene expression regulation in plants during developmental stages and under stress conditions. They are known to act both at transcriptional and post-transcriptional levels. Dicer-like (DCL), Argonaute (AGO), and RNA dependent RNA polymerase (RDR) constitute the major components of sRNA biogenesis machinery and are known to play a significant role in combating biotic and abiotic stresses. This study is, therefore, focused on identification and characterization of sRNA biogenesis proteins in three important legume crops, namely chickpea, pigeonpea, and groundnut. Phylogenetic analysis of these proteins between legume species classified them into distinct clades and suggests the evolutionary conservation of these genes across the members of Papillionidoids subfamily. Variable expression of sRNA biogenesis genes in response to the biotic stresses among the three legumes indicate the possible existence of specialized regulatory mechanisms in different legumes. This is the first ever study to understand the role of sRNA biogenesis genes in response to pathogen attacks in the studied legumes

3D chromatin remodelling in the germ line modulates genome evolutionary plasticity

Chromosome folding has profound impacts on gene regulation, whose evolutionary consequences are far from being understood. Here we explore the relationship between 3D chromatin remodelling in mouse germ cells and evolutionary changes in genome structure. Using a comprehensive integrative computational analysis, we (i) reconstruct seven ancestral rodent genomes analysing whole-genome sequences of 14 species representatives of the major phylogroups, (ii) detect lineage-specific chromosome rearrangements and (iii) identify the dynamics of the structural and epigenetic properties of evolutionary breakpoint regions(EBRs) throughout mouse spermatogenesis. Our results show that EBRs are devoid of programmed meiotic DNA double-strand breaks (DSBs) and meiotic cohesins in primary spermatocytes, but are associated in post-meiotic cells with sites of DNA damage and functional long-range interaction regions that recapitulate ancestral chromosomal configurations. Overall, we propose a model that integrates evolutionary genome reshuffling with DNA damage response mechanisms and the dynamic spatial genome organisation of germ cell

Pearl millet genome sequence provides a resource to improve agronomic traits in arid environments

Pearl millet [Pennisetum glaucum (L.) R. Br., syn. Cenchrus americanus (L.) Morrone], is a staple food for over 90 million poor farmers in arid and semi-arid regions of sub-Saharan Africa and South Asia. We report the ~1.79 Gb genome sequence of reference genotype Tift 23D2B1-P1-P5, which contains an estimated 38,579 genes. Resequencing analysis of 994 (963 inbreds of the highly cross-pollinated cultigen, and 31 wild accessions) provides insights into population structure, genetic diversity, evolution and domestication history. In addition we demonstrated the use of re-sequence data for establishing marker trait associations, genomic selection and prediction of hybrid performance and defining heterotic pools. The genome wide variations and abiotic stress proteome data are useful resources for pearl millet improvement through deploying modern breeding tools for accelerating genetic gains in pearl millet.publishersversionPeer reviewe

Transcriptome analysis of Primordial Germ Cells of birds

Primordial germ cells (PGCs) are germline competent cells which form the functional gametes of the animal. The potential usage of avian PGCs in producing genetically modified birds has driven research in the derivation, culturing, and genetic manipulation of PGCs. In chicken blastoderm, approximately 50 PGCs are present which proliferate in both male and female embryos until stage HH34 (day 8) and subsequently follow different differentiation pathways in male and female gonads. I investigated the hypothesis that chicken migratory stage PGCs are not initially determined to an oocyte or spermatogonial fate. To understand the differences in genetic mechanisms between male and female chicken PGCs, I studied the RNA transcriptome of PGCs from chicken. Analysis of RNA-Seq data of chicken PGCs reveals transcriptome divergence between the male and female cells and identified 150 differentially expressed genes (DEGs). The cultured female PGCs showed higher expression of cell adhesion genes like NCAM2 and PCDH9, and SMAD7B than male PGCs and also showed that dosage compensation is not maintained throughout the Z sex chromosome. To identify novel germ cell and stem cell factors in avian PGCs, I compared the transcriptome of chicken PGCs with immortalized chicken cell lines. As a result, a set of genes were identified which are specific to germ cells including DAZL, DDX4, DDX43, PNLDC1, DMRT1, DMRTB1, and FKBP6. This analysis also helped to identify a suite of pluripotency genes expressed in PGCs: NANOG, OCT4, LIN28, SOX3, GNOT1, TGIF2, PRDM14 and many others. Furthermore, a cross-species transcriptome comparison between in vitro cultured chicken and goose PGC transcriptomes revealed that the expression of these sets of germ cell-specific genes and pluripotent genes expression is conserved in PGCs from these two avian species. This study also revealed the contrasting gene regulatory networks involved in the selfrenewal are active in chicken and goose PGCs. Chicken PGCs exhibit expression of both Activin and BMP signalling pathway genes whereas BMP signalling pathway genes are active in goose PGCs. PRDM14 belongs to the family of the transcription factors containing a conserved N-terminal SET regulatory domain. In mouse, Prdm14 gene expression is limited to the pluripotent cells and essential for the development of the germ cell lineage. In chicken, the PRDM14 knockout embryos do not form a primitive streak. I characterized germ cell development in PRDM14 null chicken embryos and found that PRDM14 has a crucial role in the survival and maintenance of germ cells. Extending my transcriptome analysis to wild-type and PRDM14 null embryos identified DEGs and regulatory pathways possibly responsible for the gastrulation phenotype in the null embryos

Flowchart of NGS-QCbox pipeline illustrating the two modes of usage namely <i>quick</i> and <i>complete</i>.

<p>NGS-QCbox comprises of two workflow modes namely <i>quick</i> and <i>complete</i>. In <i>quick</i> mode, read/base level metrics are computed in parallel using Raspberry, an in-house tool, both before and after quality trimming. On the other hand, <i>complete</i> mode is full-fledged quality control and variant calling pipeline that integrates quick mode and additionally generates genome coverage information in parallel. Quality of the data generated could be assessed using this information.</p