Biology subjects

Rath, S. N.

Publications and source records attributed to Rath, S. N..

6 recordsLinked to original sources

bioRxiv2025

Genome assemblies of Indian desi cattle reveals hotspots of rearrangements and immune-related genetic diversity

India, home to the worlds largest cattle population, hosts native dairy breeds essential to its agricultural economy because of their adaptability and resilience. This study characterizes the genomes of five prominent breeds Gir, Kankrej, Red Sindhi, Sahiwal and Tharparkar, highlighting their unique genomic characteristics. The de novo assemblies ranged from 2.70-2.78 Gb in size, with 90% of the genomes assembled in just 56 to 1,663 scaffolds. The use of reference-guided scaffolding further enhanced these genomes, resulting in 93.3-96.7% pseudomolecule coverage with strong BUSCO scores (94.1-95.5%). Comparative analyses revealed 87-95% synteny with the Brahman genome and identified 19.84-153.16 Mb of structural rearrangements per genome, including inversions, translocations, and duplications. Synteny diversity analysis uncovered 10,643 perfectly collinear regions spanning 87.3 Mb and 6,622 hotspots of rearrangement (HOT regions) covering 55.18 Mb. These HOT regions, characterized by high synteny diversity, were significantly enriched with immune-related genes. Moreover, immune-related gene clusters, including MHC, NKC, and LRC, were identified within HOT regions in the desi reference genome. Our findings provide valuable insights into the genetic diversity of desi cattle breeds. The high-quality genome assemblies generated in this study will serve as valuable resources for future research in genetic improvement, disease resistance, and environmental adaptation.

genomics↗

bioRxiv2024

Constructing a Draft Indian Cattle Pangenome Using Short-Read Sequencing

BackgroundIndian cattle known as desi cattle, renowned for their adaptability to harsh environments and diverse phenotypic traits, represent a valuable genetic resource. While reference genome assemblies have been instrumental in advancing cattle genomics, they often fail to capture the full spectrum of genetic variation present within diverse populations. To address this limitation, we aimed to construct a pangenome for desi cattle by identifying and characterizing Non-Reference Novel Sequences (NRNS). FindingsWe sequenced 68 desi cattle genomes representing seven distinct breeds, generating 48.35 billion short reads. A PanGenome Analysis (PanGA) pipeline was developed in Bash scripts to process these data to identify NRNS missing in the reference genome. A total of 13,065 NRNS with a cumulative length of [~]41 Mbp were identified that exhibited substantial variation across the population. These NRNS were found to be exclusive to Indian desi cattle, matching only 4.1% with the Chinese indicine pangenome. However, a significant proportion ([~]40%) of NRNS displayed ancestral origins within the Bos genus. These sequences were enriched in genic regions, suggesting functional roles, and were associated with quantitative trait loci (QTLs), particularly for milk production. Compared to a single reference genome, the pangenome approach significantly enhanced read mapping accuracy, reduced spurious SNP calls, and facilitated the discovery of novel genetic variants. ConclusionsThis study has successfully established a within-species cattle pangenome specifically focused on desi cattle breeds from India. Our findings highlight the importance of pangenome based analyses for understanding the complex genetic architecture of desi cattle.

genomics↗

bioRxiv2024

Advancing the Indian Cattle Pangenome: Characterizing Non-Reference Sequences in Bos indicus

BackgroundIndia, with the worlds largest cattle population and more than 50 registered breeds of Bos indicus, stands as a vital reservoir of genetic diversity. However, the abundant diversity among Indian cattle breeds highlights the inadequacy of a single reference sequence to represent the entire genomic content of desi cattle. We recognize the need to capture the genomic differences within the Bos indicus population as a whole, and specifically within the dairy cattle subset by identifying non-reference sequences and constructing a pangenome. FindingFive representative genomes of prominent dairy breeds, including Gir, Kankrej, Tharparkar, Sahiwal, and Red Sindhi, were sequenced using 10X Genomics Linked-Read technology. Assemblies generated from these linked-reads ranged from 2.70 Gb to 2.77 Gb,comparable to the Bos indicus Brahman reference genome. A pangenome of Bos indicus cattle was constructed by comparing the newly assembled genomes with the reference using alignment and graph-based methods, revealing 8 Mb and 17.7 Mb of novel sequence respectively. A confident set of 6,844 Non-Reference Unique Insertions (NUIs) spanning 7.57 Mbs was identified through both methods, representing the pangenome of Indian Bos indicus breeds. Comparative analysis with previously published pangenomes unveiled 2.8 Mb (37%) commonality with the Chinese indicine pangenome and only 1% commonality with the Bos taurus pangenome. Among these, 2,312 NUIs - encompassing [~]2 Mb, were commonly found in 98 samples of the 5 breeds and designated as Bos indicus Common Insertions (BICIs) in the population. Furthermore, 926 BICIs were identified within 682 protein-coding genes, 54 long non-coding RNAs (LncRNA), and 18 pseudogenes. These protein-coding genes were enriched for functions such as chemical synaptic transmission, cell junction organization, cell-cell adhesion, and cell morphogenesis. The protein-coding genes were found in various prominent Quantitative Trait Loci (QTL) regions, suggesting potential roles of BICIs in traits related to milk production, reproduction, exterior, health, meat, and carcass. Notably, 63.21% of the bases within the BICIs call set contained interspersed repeats, predominantly LINEs. Additionally,70.28% of BICIs are shared with other domesticated and wild species, highlighting their evolutionary significance. ConclusionThis is the first report unveiling a robust set of NUIs defining the pangenome of Bos indicus breeds of India. The analyses contribute valuable insights into the genomic landscape of desi cattle breeds.

genomics↗

bioRxiv2023

Thin Microfluidic Chips with Active Valves

We report the fabrication of very thin microfluidic active and passive devices on rigid and flexible substrates for sample-space-restricted applications. Thin glass coverslips are commonly used substrates, but these being fragile often crack during experiments, leading to device failure. Here, we used PET as a flexible substrate to fabricate robust thin devices. We proposed a simpler process for PET-PDMS bonding without any silane, adhesive, and/or plasma treatment. We presented the compatibility of the thin devices with a digital in-line holographic microscope (DIHM) as a use case. The substitution of the conventional microscope with DIHM in microfluidic large-scale integrated systems renders simplicity, cost-effectiveness, portability, and miniaturization of the overall system. It also enables a customized and parallel multisite optical observation for a complex microfluidic circuit chip. These chips comprise various microfluidic components made of active microvalves, particularly Quake valves. We also successfully demonstrated the function of microvalves fabricated with our method to regulate the fluidic flow. Thus, are suited to making sophisticated microfluidic circuit chips to fit a variety of applications like organ-on-chip, cell culture, wearable biosensors, pressure sensors, etc.

bioengineering↗

bioRxiv2020

Elucidation of Genome Polymorphisms in Emerging SARS-CoV-2

The COVID-19 pandemic is having a devastating effect on the healthcare system and the economy of the world. The unavailability of a specific treatment regime and a candidate vaccine yet opens up scope for new approaches and discoveries of drugs for mitigation of the sufferings of humankind due to the disease. The present isolated whole-genome sequences of SARS-CoV-2 from 11 different nations subjected to evolutionary study and genome-wide association study through in silico approaches including multiple sequence alignment, phylogenetic study through MEGA7 and have been analyzed through DNAsp respectively. These investigations recognized the nucleotide varieties and single nucleotide mutations/polymorphisms on the genomic regions as well as protein-coding regions. The resulted mutations have diversified the genomic contents of SARS-CoV-2 according to the altered nucleotides found in 11 genome sequences. India and Nepal have found to have progressively more distinct species of SARS-CoV-2 with variations in Spike protein and Nucleocapsid protein-coding sites. These genomic variations might be the explanation behind the less case fatality rate of India and Nepal dependent on the populaces. The anticipated idea of this investigation upgrades the information about genomic medication and might be useful in the planning of antibodies against SARS-CoV-2.

bioinformatics↗

bioRxiv2020

Identification of peptide candidate against COVID-19 through reverse vaccinology: An immunoinformatics approach

Novel corona virus disease 2019 (COVID-19) is emerging as a pandemic situation and declared as a global health emergency by WHO. Due to lack of specific medicine and vaccine, viral infection has gained a frightening rate and created a devastating state across the globe. Here the authors have attempted to design epitope based potential peptide as a vaccine candidate using immunoinformatics approach. As of evidence from literatures, SARS-CoV-2 Spike protein is a key protein to initiate the viral infection within a host cell thus used here as a reasonable vaccine target. We have predicted a 9-mer peptide as representative of both B-cell and T-cell epitopic region along with suitable properties such as antigenic and non-allergenic. To its support, strong molecular interaction of the predicted peptide was also observed with MHC molecules and Toll Like receptors. The present study may helpful to step forward in the development of vaccine candidates against COVID-19. O_FIG_DISPLAY_L [Figure 1] M_FIG_DISPLAY C_FIG_DISPLAY

bioinformatics↗