Author

Rahila Sardar

Jamia Hamdard

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Bioinformatics· 3

Signature Assays, Organisms & Methods

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Approach:statistical model· 3 Assay:whole-genome sequencing· 2 Research Focus:database / resource· 1 Research Focus:differential expression· 1 Research Focus:gene regulatory network inference· 1 Organism:multiple species· 1 Approach:simulation· 1 Research Focus:variant calling· 1

Frequent Co-Authors

Dinesh Kumar GuptaiD· 3 shared Abhinav KaushikiD· 1 shared Asif MohmmediD· 1 shared Deepshikha SatishiD· 1 shared Neetu Tyagi· 1 shared Rajan PandeyiD· 1 shared Shakir AliiD· 1 shared Shweta BirlaiD· 1 shared

Activity by Year

2019-2021 · peak 1/yr

Co-Author Collaboration Network

8 top collaborators · 14 mutual co-author links

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3 shared papersBioinformatics

Dinesh Kumar GuptaORCID

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3 papers

Recent·Most cited

Comparative analysis of codon usage patterns in SARS-CoV-2, its mutants and other respiratory viruses

Neetu Tyagi, Rahila Sardar, Dinesh Kumar Gupta

The paper analyzes codon usage bias in SARS-CoV-2 genomes and compares it with other respiratory viruses to identify evolutionary determinants and adaptation features.

bioRxiv · Mar 3, 2021 · PreprintBioinformatics

Comparative analyses of SAR-CoV2 genomes from different geographical locations and other coronavirus family genomes reveals unique features potentially consequential to host-virus interaction and pathogenesis

Rahila Sardar, Deepshikha Satish, Shweta Birla, Dinesh Kumar Gupta

The paper compares SARS-CoV2 genomes from different regions with other coronaviruses to identify unique mutations and host miRNA interactions that may influence viral pathogenesis and host response.

Heliyon · Aug 20, 2020Peer-reviewedBioinformatics

ApicoTFdb: The comprehensive web repository of apicomplexan transcription factors and regulators.

Rahila Sardar, Abhinav Kaushik, Rajan Pandey, Asif Mohmmed, Shakir Ali, Dinesh Kumar Gupta

ApicoTFdb is a database of transcription factors and regulators in apicomplexan species, including experimentally validated and computationally predicted TFs and TAFs across 14 species.

Database · Jan 1, 2019Peer-reviewedBioinformatics