Bioinformatics

A modelling framework for the prediction of the herd-level probability of infection from longitudinal data

1. Aurélien Madouasse
2. Mathilde Mercat
3. Annika van Roon
4. David Graham
5. Maria Guelbenzu
6. Inge Santman Berends
7. Gerdien van Schaik
8. Mirjam Nielen
9. Jenny Frössling
10. Estelle Ågren
11. Roger Humphry
12. Jude Eze
13. George Gunn
14. Madeleine K. Henry
15. Jörn Gethmann
16. Simon J. More
17. Nils Toft
18. Christine Fourichon

Reviewed by Peer Community in Animal Science

A translational transcriptomic signature of vaccine reactogenicity for the evaluation of novel formulations

1. Jérémie Becker
2. Maroussia Roelens
3. Kendra Reynaud
4. Laurent Beloeil

• Curated by eLife

  eLife Assessment

  This study proposes a cross-species transcriptomic framework to predict vaccine reactogenicity, with implications for preclinical vaccine safety assessment. The findings show that mouse muscle transcriptomic signatures capture conserved inflammatory programs and can identify highly reactogenic formulations, with supportive but limited evidence for finer discrimination among licensed human vaccines. Overall, the work establishes a valuable foundation for translational biomarkers of reactogenicity, although the strength of evidence for broad cross-species predictive performance remains incomplete and would benefit from further validation.

Reviewed by eLife

End-to-end proteogenomics for discovery of cryptic and non-canonical cancer proteoforms using long-read transcriptomics and multi-dimensional proteomics

1. Katarzyna Kulej
2. Asher Preska Steinberg
3. Jinxin Zhang
4. Gabriella Casalena
5. Eli Havasov
6. Sohrab P Shah
7. Andrew McPherson
8. Alex Kentsis

• Curated by eLife

  eLife Assessment

  This manuscript describes a useful integrated proteogenomics pipeline to enable the discovery of novel peptides in cancer cell lines. The method combines long-read RNA sequencing with a multi-protease digestion and proteomics approach. The method is a further development of the authors' previous approaches to identify cancer-specific peptides; however, the current study focuses on a single cell line, and the characterization remains incomplete and lacks validation for candidate alterations. The manuscript will be of interest to scientists focusing on identifying unique alterations in cancer cells.

Reviewed by eLife

Leveraging Disease Association Degree for High-Accuracy MicroRNA Target Prediction

1. Baiming Chen

• Curated by eLife

  eLife Assessment

  This valuable study introduces miRTarDS, a novel computational framework that predicts microRNA-target interactions based on a publicly available pretrained Sentence-BERT language model and downstream classification analysis. The strength of the evidence is incomplete, as the evaluation framework relies on unreliable ground-truth and false sets. Furthermore, the analysis fails to compare miRTarDS against existing state-of-the-art biomedical language models.

Reviewed by eLife

Federated Knowledge Retrieval Elevates Large Language Model Performance on Biomedical Benchmarks

1. Janet Joy
2. Andrew I. Su

Reviewed by GigaScience

SpaceBF: Spatial coexpression analysis using Bayesian Fused approaches in spatial omics datasets

1. Souvik Seal
2. Brian Neelon

Reviewed by GigaScience

nf-core/proteinfamilies: A scalable pipeline for the generation of protein families

1. Evangelos Karatzas
2. Martin Beracochea
3. Fotis A. Baltoumas
4. Eleni Aplakidou
5. Lorna Richardson
6. James A. Fellows Yates
7. Daniel Lundin
8. nf-core community
9. Aydin Buluç
10. Nikos C. Kyrpides
11. Ilias Georgakopoulos-Soares
12. Georgios A. Pavlopoulos
13. Robert D. Finn

Reviewed by GigaScience

The Hok bacterial toxin: diversity, toxicity, distribution and genomic localization

1. Andrés Escalera-Maurer
2. Adriana Messineo
3. Thibaud T. Renault
4. Elena Nicollin
5. Erika Castaneda-Sastre
6. Matthieu Brunot
7. Cléo Berrehail
8. Anaïs Le Rhun

Reviewed by Review Commons

Interpreting the Effects of DNA Polymerase Variants at the Structural Level Using MAVISp and Molecular Dynamics Simulations

1. Matteo Arnaudi
2. Karolina Krzesińska
3. Ludovica Beltrame
4. Pablo Sánchez-Izquierdo Besora
5. Matteo Tiberti
6. Mef Nilbert
7. Anna Rohlin
8. Elena Papaleo

Reviewed by Review Commons

A genotype-phenotype transformer to assess and explain polygenic risk

1. Ingoo Lee
2. Zachary S. Wallace
3. Yuqi Wang
4. Sungjoon Park
5. Hojung Nam
6. Amit R. Majithia
7. Trey Ideker

Reviewed by Review Commons