Author

Komal Sanjeev

SIB Swiss Institute of Bioinformatics

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

Biology Disciplines

Click to filter author papers

Bioinformatics· 1 Genetics· 1

Signature Assays, Organisms & Methods

Click to filter · or open tag

Approach:statistical model· 2 Assay:bulk RNA-seq· 1 Research Focus:database / resource· 1 Research Focus:genome assembly· 1 Organism:human· 1 Organism:multiple species· 1 Research Focus:variant calling· 1 Research Focus:variant effect prediction· 1

Frequent Co-Authors

Amina EchchikiiD· 1 shared Angélique Escoriza· 1 shared Anne NiknejadiD· 1 shared Aurélie ComteiD· 1 shared Balazs LaurenczyiD· 1 shared Emilie Person· 1 shared Frederic BastianiD· 1 shared Gilles Parmentier· 1 shared

Activity by Year

2015-2020 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 45 mutual co-author links

Click any collaborator node to inspect or filter shared papers

Focal author (center)Selected co-authorORCID co-authorUnlinked name

Curved chords = mutual co-authorship

1 shared paperBioinformatics

Amina EchchikiORCID

Mutual co-authors in cluster

Author profile →

2 papers

Recent·Most cited

The Bgee suite: integrated curated expression atlas and comparative transcriptomics in animals

Frederic Bastian, Julien Roux, Anne Niknejad, Aurélie Comte, Sara S Fonseca Costa, Tarcisio Mendes de Farias, Sébastien Moretti, Gilles Parmentier, Valentine Rech de Laval, Marta Rosikiewicz, Julien Wollbrett, Amina Echchiki, Angélique Escoriza, Walid H. Gharib, Mar Gonzàlez-Porta, , Marc Robinson‐Rechavi

Bgee provides an integrated expression atlas and comparative transcriptomics resource for multiple animal species using curated healthy wild-type expression data.

Nucleic Acids Research · Sep 15, 2020Peer-reviewedBioinformatics

Maximum Likelihood Estimation of Biological Relatedness from Low Coverage Sequencing Data

Mikhail Lipatov, Komal Sanjeev, Rob Patro, Krishna R Veeramah

This paper develops a method to accurately estimate biological relatedness from low coverage sequencing data, outperforming existing methods like PLINK in simulated and real datasets.

bioRxiv · Jul 29, 2015 · PreprintGenetics