Author

Raja Appuswamy

EURECOMORCID 0000-0001-5887-4091Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Research Focus:benchmarking· 2 Approach:experimental assay· 2 Approach:statistical model· 2 Assay:DNA methylation· 1 Approach:machine learning (classical)· 1 Research Focus:software / tool· 1 Research Focus:variant effect prediction· 1 Assay:whole-exome sequencing· 1

Frequent Co-Authors

Nimisha ChaturvediiD· 3 shared Yiqing YaniD· 3 shared Conor CrosbieiD· 1 shared Jacques FellayiD· 1 shared Lorenzo TattiniiD· 1 shared Nimesh PinnamaneniiD· 1 shared Sachin ChalapatiiD· 1 shared

Activity by Year

2018-2025 · peak 1/yr

Co-Author Collaboration Network

7 top collaborators · 10 mutual co-author links

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3 shared papersBioinformatics

Nimisha ChaturvediORCID

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Author profile →

4 papers

Recent·Most cited

Accurate Variant Classification in Tumour-Only Genomic Data Using Interpretable Tabular Models

Lorenzo Tattini, Yiqing Yan, Nimisha Chaturvedi, Raja Appuswamy

This paper presents a workflow using XGBoost, LightGBM, and TabNet to classify variants in tumour-only data, showing improved accuracy across ancestries and tumour types.

bioRxiv · Dec 12, 2025 · PreprintBioinformatics

Scaling Logical Density of DNA storage with Enzymatically-Ligated Composite Motifs

Yiqing Yan, Nimesh Pinnamaneni, Sachin Chalapati, Conor Crosbie, Raja Appuswamy

The paper introduces composite motifs and enzymatic techniques to improve DNA storage density and presents an end-to-end experiment achieving 84 bits/cycle logical density.

Scientific Reports · Sep 25, 2023Peer-reviewedSynthetic Biology

Accel-Align: A Fast Sequence Mapper and Aligner based on the Seed-Embed-Extend Method

Yiqing Yan, Nimisha Chaturvedi, Raja Appuswamy

Accel-Align introduces a new sequence aligner using seed-embed-extend methodology, achieving 3-12x speed improvements over existing aligners without sacrificing accuracy.

BMC Bioinformatics · May 20, 2021Peer-reviewedBioinformatics

Sequence Alignment Through the Looking Glass

Raja Appuswamy, Jacques Fellay, Nimisha Chaturvedi

This study analyzes how sequence aligners use modern processors and finds they underutilize hardware, identifying bottlenecks for future aligner design.

bioRxiv · Apr 11, 2018 · PreprintBioinformatics