Author

Andreas Scherer

ORCID 0000-0002-5590-6980Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Approach:statistical model· 3 Research Focus:variant calling· 3 Organism:human· 2 Research Focus:variant effect prediction· 2 Assay:whole-genome sequencing· 2 Approach:benchmark study· 1 Research Focus:benchmarking· 1 Research Focus:database / resource· 1

Frequent Co-Authors

Gabe RudyiD· 4 shared Nathan FortieriD· 3 shared Alessandro DavassiiD· 1 shared Christiane SchereriD· 1 shared Darby KammeraadiD· 1 shared James Grover· 1 shared Tina HaniD· 1 shared

Activity by Year

2020-2026 · peak 1/yr

Co-Author Collaboration Network

7 top collaborators · 12 mutual co-author links

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4 shared papersBioinformatics

Gabe RudyORCID

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4 papers

Recent·Most cited

Analyzing the Performance of Deep Learning Splice Prediction Algorithms

Nathan Fortier, Gabe Rudy, Andreas Scherer

This study compares deep learning splice prediction algorithms, finding open-source reimplementations match the original SpliceAI in performance while outperforming traditional methods.

PLoS ONE · May 13, 2026Peer-reviewedBioinformatics

VSPGx: A High-Accuracy Pharmacogenomics Interpretation Software Solution with Automated CPIC Guideline Integration

Nathan Fortier, Gabe Rudy, Andreas Scherer

VSPGx is a pharmacogenomics software that accurately identifies diplotypes and annotates them with CPIC guidelines, outperforming existing tools in synthetic and clinical benchmarks.

bioRxiv · Nov 24, 2025 · PreprintBioinformatics

Analyzing Performance of Twist Bioscience Exome Enrichment with Spike-in CNV Backbone Panels at Various Probe Densities Leveraging Golden Helix VS-CNV Analysis Software

Nathan Fortier, Gabe Rudy, Tina Han, Alessandro Davassi, Andreas Scherer

This study evaluates the performance of Twist Bioscience's enhanced exome capture kit with VS-CNV 2.6 in detecting CNVs, achieving 100% sensitivity across probe densities.

bioRxiv · May 19, 2024 · PreprintBioinformatics

Analysis of 46,046 SARS-CoV-2 whole-genomes leveraging principal component analysis (PCA)

Christiane Scherer, James Grover, Darby Kammeraad, Gabe Rudy, Andreas Scherer

This study applies PCA to 46,046 SARS-CoV-2 genomes to uncover previously unnoticed virus lineages and mutation patterns.

bioRxiv · Dec 20, 2020 · PreprintBioinformatics