Author

Amay Ajaykumar Agrawal

Helmholtz Centre for Infection ResearchORCID 0009-0002-6385-2983Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioinformatics· 2 Microbiology· 1

Signature Assays, Organisms & Methods

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Approach:experimental assay· 2 Approach:statistical model· 2 Assay:whole-genome sequencing· 2 Organism:bacteria (other)· 1 Research Focus:cell type annotation· 1 Research Focus:database / resource· 1 Research Focus:drug response / drug discovery· 1 Research Focus:gene regulatory network inference· 1

Frequent Co-Authors

Olga V. KalininaiD· 3 shared Chantal D. BaderiD· 2 shared Rolf MülleriD· 2 shared Ronald GarciaiD· 2 shared Alexander PopoffiD· 1 shared Alexey A. GurevichiD· 1 shared Alper YurtseveniD· 1 shared Andreas KelleriD· 1 shared

Activity by Year

2023-2026 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 37 mutual co-author links

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3 shared papersBioinformatics

Olga V. KalininaORCID

Mutual co-authors in cluster

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3 papers

Recent·Most cited

MicrobiologyAdvanced SciencePeer-reviewedAug 6, 2026

Unveiling the taxonomic diversity and unprecedented biosynthetic treasure of the phylum Myxococcota

Amay Ajaykumar Agrawal, Ronald Garcia, Guangyi Chen…·Rolf Müller

The paper presents 154 new Myxococcota genomes, expands taxonomy, and identifies novel natural products, highlighting their biosynthetic potential for drug discovery.

4 citationstop 1%

bacteria (other)whole-genome sequencing

𝕏 PostBluesky

BioinformaticsbioRxivPreprintFeb 9, 2026

LoReMINE: Long Read-based Microbial genome mining pipeline

Amay Ajaykumar Agrawal, Chantal D. Bader, Ronald Garcia…·Olga V. Kalinina

LoReMINE is an automated pipeline for microbial genome mining that integrates assembly, classification, and biosynthetic gene cluster prediction from long-read data.

DOI 10.64898/2026.02.02.703231

long-read sequencingexperimental assay

𝕏 PostBluesky

BioinformaticsbioRxivPreprintSep 6, 2023

Machine learning and phylogenetic analysis allow for predicting antibiotic resistance in M. tuberculosis

Alper Yurtseven, Sofia Buyanova, Amay Ajaykumar Agrawal…·Olga V. Kalinina

The paper introduces a phylogeny-related parallelism score to improve machine learning prediction of antibiotic resistance in M. tuberculosis by incorporating evolutionary relationships.

DOI 10.1101/2023.09.06.556328

humanwhole-genome sequencing

𝕏 PostBluesky