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Biology subjects

Sehgal, P.

Publications and source records attributed to Sehgal, P..

4 recordsLinked to original sources
bioRxiv2021

A genome-wide circular RNA transcriptome in Rat

BackgroundCircular RNAs are a novel class of non-coding RNAs that backsplice from 5 donor site and 3 acceptor site to form a circular structure. A number of circRNAs have been discovered in model organisms including human, mouse, Drosophila, among other organisms. There are a few candidate-based studies on circular RNAs in rat, a well studied model organism. The availability of a recent dataset of transcriptomes encompassing 11 tissues, 4 developmental stages and 2 genders motivated us to explore the landscape of circular RNAs in the organism. MethodologyIn order to understand the difference among different pipelines, we have used the same bodymap RNA sequencing dataset. A number of pipelines have been published to identify the backsplice junctions for the discovery of circRNAs but studies comparing these tools have suggested that a combination of tools would be a better approach to identify high-confidence circular RNAs. We employed 5 different combinations of tools including tophat_CIRCexplorer2, segemehl_CIRCexplorer2, star_CIRCexplorer, Bowtie2_findcirc and Bowtie2_findcirc (noHisat2) to identify circular RNAs from the dataset. ResultsOur analysis identified a number of tissue-specific, developmental stage specific and gender specific circular RNAs. We further independently validated 16 circRNA junctions out of 24 selected candidates in 5 tissue samples. We additionally estimated the quantitative expression of 5 circRNA candidates using real-time PCR and our analysis suggests 3 candidates as tissue-enriched ConclusionThis study is one of the most comprehensive studies that provides a circular RNA transcriptome as well as to understand the difference among different computational pipelines in Rat.

molecular biology↗
bioRxiv2021

Insights from Genomes and Genetic Epidemiology of SARS-CoV-2 isolates from the state of Andhra Pradesh

Coronavirus disease (COVID-19) emerged from a city in China and has now spread as a global pandemic affecting millions of individuals. The causative agent, SARS-CoV-2 is being extensively studied in terms of its genetic epidemiology using genomic approaches. Andhra Pradesh is one of the major states of India with the third-largest number of COVID-19 cases with limited understanding of its genetic epidemiology. In this study, we have sequenced 293 SARS-CoV-2 genome isolates from Andhra Pradesh with a mean coverage of 13,324X. We identified 564 high-quality SARS-CoV-2 variants, out of which 15 are novel. A total of 18 variants mapped to RT-PCR primer/probe sites, and 4 variants are known to be associated with an increase in infectivity. Phylogenetic analysis of the genomes revealed the circulating SARS-CoV-2 in Andhra Pradesh majorly clustered under the clade A2a (94%), while 6% fall under the I/A3i clade, a clade previously defined to be present in large numbers in India. To the best of our knowledge, this is the most comprehensive genetic epidemiological analysis performed for the state of Andhra Pradesh.

genomics↗
bioRxiv2020

High throughput detection and genetic epidemiology of SARS-CoV-2 using COVIDSeq next generation sequencing

The rapid emergence of coronavirus disease 2019 (COVID-19) as a global pandemic affecting millions of individuals globally has necessitated sensitive and high-throughput approaches for the diagnosis, surveillance and for determining the genetic epidemiology of SARS-CoV-2. In the present study, we used the COVIDSeq protocol, which involves multiplex-PCR, barcoding and sequencing of samples for high-throughput detection and deciphering the genetic epidemiology of SARS-CoV-2. We used the approach on 752 clinical samples in duplicates, amounting to a total of 1536 samples which could be sequenced on a single S4 sequencing flow cell on NovaSeq 6000. Our analysis suggests a high concordance between technical duplicates and a high concordance of detection of SARS-CoV-2 between the COVIDSeq as well as RT-PCR approaches. An in-depth analysis revealed a total of six samples in which COVIDSeq detected SARS-CoV-2 in high confidence which were negative in RT-PCR. Additionally, the assay could detect SARS-CoV-2 in 21 samples and 16 samples which were classified inconclusive and pan-sarbeco positive respectively suggesting that COVIDSeq could be used as a confirmatory test. The sequencing approach also enabled insights into the evolution and genetic epidemiology of the SARS-CoV-2 samples. The samples were classified into a total of 3 clades. This study reports two lineages B.1.112 and B.1.99 for the first time in India. This study also revealed 1,143 unique single nucleotide variants and added a total of 73 novel variants identified for the first time. To the best of our knowledge, this is the first report of the COVIDSeq approach for detection and genetic epidemiology of SARS-CoV-2. Our analysis suggests that COVIDSeq could be a potential high sensitivity assay for detection of SARS-CoV-2, with an additional advantage of enabling genetic epidemiology of SARS-CoV-2.

genomics↗