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Sandeep Saini

Researcher at Panjab University, Chandigarh

Publications -  6
Citations -  34

Sandeep Saini is an academic researcher from Panjab University, Chandigarh. The author has contributed to research in topics: Genome & Gene. The author has an hindex of 3, co-authored 6 publications receiving 21 citations.

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Genome-wide computational prediction of miRNAs in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) revealed target genes involved in pulmonary vasculature and antiviral innate immunity.

TL;DR: Gen enrichment tool analysis and substantial literature evidences suggests role of genes like BMPR2 and p53 in pulmonary vasculature and antiviral innate immunity respectively and predicted 26 mature miRNAs from genome of SARS-CoV-2 that targets human genes involved in pathways like EGF receptors signaling, apoptosis signaling, VEGF signaling, FGF receptor signaling.
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Computational prediction of miRNAs in Nipah virus genome reveals possible interaction with human genes involved in encephalitis.

TL;DR: Predicting significant genes in human that can be inhibited by miRNAs of NiV and results in etiology of encephalitis are reported, which shows their involvement in host defense, blood brain barrier, neurogenesis, mental retardation and encephalopathy.
Posted ContentDOI

Genome Wide Computational Prediction of miRNAs in Kyasanur Forest Disease Virus and their Targeted Genes in Human

TL;DR: The virus genome was scanned for prediction of miRNAs that can inhibit host target genes and all four pre-miRNAs were classified as real.
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Deciphering the functional role of hypothetical proteins from Chloroflexus aurantiacs J-10-f1 using bioinformatics approach.

TL;DR: Most of the high confidence-hypothetical proteins of Chloroflexus aurantiacus J-10-f1 were involved in photosynthesis, carbohydrate metabolism, biofuel production and cellulose synthesis processes and few could provide resistance to bacteria at high temperature due to their thermophilic nature.
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Comparative Analysis of Gene Prediction Tools: RAST, Genmark hmm and AMIgene

TL;DR: Whether the prediction tools and pipeline are providing same or different result for the same genome or not is compared, to find the reason behind the variation in prediction result and try to relate the analysis with nowadays high throughput data analysis.