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Rajinder Gupta

Rajinder Gupta

Bioinformatician — transcriptomics, scRNA-seq, spatial transcriptomics

rajinder4489
Dresden, Germany
Joined March 2026
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Summary

Expert in transcriptomics and RNA-Seq analyses: Rajinder Gupta's work centers on transcript-level and transcriptomics methods (bulk and single-cell), including development of FuSe and multiple RNA-Seq comparison and biomarker studies. oup+2
Active academic author with open-source contributions: He has authored peer-reviewed papers in Bioinformatics, BMC Cancer and Archives of Toxicology and makes code available publicly (e.g., FuSe and ML_biomarkers on GitHub). github+2
International research and training trajectory: Educated in India (B.Tech, M.Tech), completed PhD research at Maastricht University (toxicogenomics/bioinformatics), and has held research and bioinformatics positions across Europe including TUM, LMU and TU Dresden. vannut+2
Collaborative, multidisciplinary contributor: Publications and ORCID record show collaborations across toxicogenomics, pharmacology/toxicology, stem-cell and neurobiology research groups, indicating work across multiple biological domains and large author teams. google+2

Work

Education

Projects

Writing

FuSe: a tool to move RNA-Seq analyses from chromosomal/gene loci to functional grouping of mRNA transcripts

January 1, 2021

Describes FuSe, a method and software to predict functional similarity between protein-coding transcripts and group them to improve interpretation of RNA-Seq data; includes implementation details and demonstration on paracetamol exposure data.

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Identifying novel transcript biomarkers for hepatocellular carcinoma (HCC) using RNA-Seq datasets and machine learning

January 1, 2021

Used multiple machine learning algorithms on RNA-Seq data to identify transcript-level biomarkers for HCC and provided a pipeline and code (ML_biomarkers) for reproducible biomarker discovery.

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Comparing in vitro human liver models to in vivo human liver using RNA-Seq

January 1, 2020

RNA-Seq based comparison of several human in vitro liver cell models with human liver tissue using CellNet, non-DEG analyses and differential transcript usage to assess model similarity to in vivo liver.

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An Integrative Approach for Mapping Differentially Expressed Genes and Network Components Using Novel Parameters to Elucidate Key Regulatory Genes in Colorectal Cancer

January 1, 2015

Systems-biology approach integrating microarray analyses and network motif detection to identify key regulatory genes involved in colorectal cancer progression.

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