Bioinformatics

TOGGLE Unveils Functional Heterogeneity and Epigenetic Memory in Single Cells

1. Junpeng Chen
2. Tianda Sun
3. Tian Song
4. Zhouweiyu Chen
5. Haowei Xu
6. Zixuan Guo
7. Eric Jiang
8. Yibing Nong
9. Tao Yuan
10. Charles C. Dai
11. Yexing Yan
12. Jinwen Ge
13. Haihui Wu
14. Tong Yang
15. Shanshan Wang
16. Zixiang Su
17. Park Tian
18. Xiao Yang
19. Ahmed Abdelbsset-Ismail
20. You Li
21. Changping Li
22. Richa A. Singhal
23. Kailin Yang
24. Lu Cai
25. Zixin Wu
26. Alex P. Carll

Reviewed by Review Commons

ProteomeLM: A proteome-scale language model enables accurate and rapid prediction of protein–protein interactions and gene essentiality across taxa

1. Cyril Malbranke
2. Gionata Paolo Zalaffi
3. Anne-Florence Bitbol

Reviewed by Arcadia Science

RAG-ESM: Improving Pretrained Protein Language Models via Sequence Retrieval

1. Damiano Sgarbossa
2. Anne-Florence Bitbol

Reviewed by Arcadia Science

In silico repurposed drugs against monkeypox virus

1. Hilbert Yuen In Lam
2. Jia Sheng Guan
3. Yuguang Mu

Reviewed by Rapid Reviews Infectious Diseases

Investigating the native functions of [NiFe]-CODH through genomic context analysis

1. Maximilian Böhm
2. Henrik Land

• Curated by eLife

  eLife Assessment

  This valuable work analyzes a large dataset of [NiFe]-CODHs, integrating genomic context, operon organization, and clade-specific gene neighborhoods to discern patterns of diversification and adaptation. A consistent examination of CODH genomic contexts, including CODH-HCP co-occurrence, informs interpretations of enzymatic activity, biotechnological potential, and differential functional roles, in line with current standards in genomic enzymology. With solid support, this work provides a broadly informative contribution to the field.

Reviewed by eLife

Enhanced semantic classification of microbiome sample origins using large language models (LLMs)

1. Daniela Gaio
2. Janko Tackmann
3. Eugenio Perez-Molphe-Montoya
4. Nicolas Näpflin
5. David Patsch
6. Lukas Malfertheiner
7. Matteo Eustachio Peluso
8. Christian von Mering

Reviewed by GigaScience

An interpretable Graph-Regularized Optimal Transport Framework for Diagonal Single-Cell Integrative Analysis

1. Zexuan Wang
2. Qipeng Zhan
3. Shu Yang
4. Zhuoping Zhou
5. Mengyuan Kan
6. Tianhuan Zhai
7. Li Shen

Reviewed by GigaScience

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