Evidence map›Paper›PMID 42609482›Full record

ArticleNAR genomics and bioinformatics2026

The PhageExpressionAtlas reveals shared and unique transcriptional patterns across phage-host interactions.

Maik Wolfram-Schauerte, Caroline Trust, Nils Waffenschmidt, Kay Nieselt

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Article in NAR genomics and bioinformatics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Maik Wolfram-SchauerteInstitute for Bioinformatics and Medical Informatics, University of Tübingen, Sand 14, D-72076 Baden-Württemberg, Germany.ORCID https://orcid.org/0000-0001-6988-2775
Caroline TrustInstitute for Bioinformatics and Medical Informatics, University of Tübingen, Sand 14, D-72076 Baden-Württemberg, Germany.ORCID https://orcid.org/0009-0007-0840-1537
Nils WaffenschmidtInstitute for Bioinformatics and Medical Informatics, University of Tübingen, Sand 14, D-72076 Baden-Württemberg, Germany.
Kay NieseltInstitute for Bioinformatics and Medical Informatics, University of Tübingen, Sand 14, D-72076 Baden-Württemberg, Germany.ORCID https://orcid.org/0000-0002-1283-7065

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Time-resolved transcriptomic profiling has been used to study phage-host interactions for more than a decade. However, the resulting datasets are not readily accessible for custom re-analysis, and resources are lacking that provide standardized processing, storage, and analysis of transcriptomes from phage infections. Here, we present the PhageExpressionAtlas, the first bioinformatics resource for storing time-resolved dual RNA-sequencing data from phage infections. This data was processed uniformly using a custom analysis pipeline and is presented for interactive exploration through visualization. The PhageExpressionAtlas currently hosts 42 datasets from 23 studies. Using the PhageExpressionAtlas, we replicate key findings from original publications and extend hypothesis testing across multiple phage-host systems. By systematically querying and analyzing the underlying database, we evaluate approaches to phage gene classification and find that uncharacterized phage genes dominate all infection phases, with their distribution depending strongly on the classification strategy. Moreover, we provide a comprehensive view of the expression dynamics of anti-phage defenses as well as host- and phage-encoded anti-defense systems in the infection context, indicating unique and conserved patterns of transcriptional regulation underlying bacterial anti-phage immunity and phage counter-strategies. Together, the PhageExpressionAtlas is a unifying resource that democratizes transcriptomics-driven analyses of phage-host interactions and supports integrative cross-study assessment.

Indexed as

BacteriophagesHost Microbial InteractionsHost-Pathogen InteractionsTranscriptomeComputational BiologyDatabases, GeneticGene Expression Profiling

Identifiers

PMID42609482
PMCPMC13478754

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.