Author

N. Auslander

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

Biology Disciplines

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Microbiology· 4 Cancer Biology· 1

Signature Assays, Organisms & Methods

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Approach:statistical model· 3 Organism:bacteria (other)· 2 Approach:deep learning· 2 Organism:human· 2 Assay:metagenomics· 2 Research Focus:benchmarking· 1 Assay:bulk RNA-seq· 1 Research Focus:database / resource· 1

Frequent Co-Authors

A. B. Gussow· 3 shared E. V. Koonin· 3 shared Y. I. Wolf· 3 shared A. Lischka· 1 shared A. Lucas· 1 shared B. M. Ghadimi· 1 shared C. Montagna· 1 shared D. Bronder· 1 shared

Activity by Year

2019-2023 · peak 3/yr

Co-Author Collaboration Network

10 top collaborators · 14 mutual co-author links

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3 shared papersMicrobiology

A. B. Gussow

Mutual co-authors in cluster

Author profile →

5 papers

Recent·Most cited

kMermaid: Ultrafast functional classification of microbial reads

A. Lucas, D. E. Schaffer, J. Wickramasinghe, N. Auslander

kMermaid provides an ultrafast and accurate method for classifying metagenomic reads into functional protein clusters, improving upon existing methods with lower computational demands.

bioRxiv · Aug 28, 2023 · PreprintMicrobiology

Prediction of the virus incubation period for COVID-19 and future outbreaks

A. B. Gussow, N. Auslander, Y. I. Wolf, E. V. Koonin

The paper predicts the upper limit of the incubation period for viruses like SARS-CoV-2 using genomic features to aid in outbreak control.

BMC Biology · Nov 30, 2020Peer-reviewedMicrobiology

Seeker: Alignment-free identification of bacteriophage genomes by deep learning

N. Auslander, A. B. Gussow, S. Benler, Y. I. Wolf, E. V. Koonin

Seeker identifies unknown bacteriophages in metagenomic data using deep learning without relying on sequence similarity to known phages.

Nucleic Acids Research · Oct 12, 2020Peer-reviewedMicrobiology

Genomic determinants of pathogenicity in SARS-CoV-2 and other human coronaviruses

N. Auslander, A. B. Gussow, G. Faure, Y. I. Wolf, F. Zhang, E. V. Koonin

This study identifies genomic features distinguishing highly pathogenic SARS-CoV-2, SARS-CoV, and MERS-CoV from less pathogenic coronaviruses using comparative genomics and machine learning.

bioRxiv · Apr 5, 2020Microbiology

Cancer-type specific aneuploidies hard-wire chromosome-wide gene expression patterns of their tissue of origin

N. Auslander, K. Heselmeyer-Haddad, S. Patkar, D. Hirsch, J. Camps, M. Brown, D. Bronder, W.-D. Chen, R. Lokanga, D. Wangsa, D. Wangsa, Y. Hu, A. Lischka, R. Braun, G. Emons, , t. ried

The paper shows cancer-type specific aneuploidies correlate with normal tissue gene expression patterns, suggesting they hard-wire chromosome-wide expression levels to support tumorigenesis.

bioRxiv · Mar 1, 2019Cancer Biology