Bioinformatics

A network regularized linear model to infer spatial expression pattern for single cell

1. Chaohao Gu
2. Hu Chen
3. Zhandong Liu

• Curated by eLife

  eLife Assessment

  The study is useful for advancing spatial transcriptomics through its novel regression-based linear model (glmSMA) that integrates single-cell RNA-seq with spatial reference atlases, and its methodological framework is convincing. The approach demonstrates notable utility by enabling higher-resolution cell mapping across multiple biological systems and spatial platforms compared to existing tools.

Reviewed by eLife

MerQuaCo: a computational tool for quality control in image-based spatial transcriptomics

1. Naomi Martin
2. Paul Olsen
3. Jacob Quon
4. Jazmin Campos
5. Nasmil Valera Cuevas
6. Josh Nagra
7. Marshall VanNess
8. Zoe Maltzer
9. Emily C Gelfand
10. Alana Oyama
11. Amanda Gary
12. Yimin Wang
13. Angela Alaya
14. Augustin Ruiz
15. Cade Reynoldson
16. Cameron Bielstein
17. Christina Alice Pom
18. Cindy Huang
19. Cliff Slaughterbeck
20. Elizabeth Liang
21. Jason Alexander
22. Jeanelle Ariza
23. Jocelin Malone
24. Jose Melchor
25. Kaity Colbert
26. Krissy Brouner
27. Lyudmila Shulga
28. Melissa Reding
29. Patrick Latimer
30. Raymond Sanchez
31. Stuard Barta
32. Tom Egdorf
33. Zachary Madigan
34. Chelsea M Pagan
35. Jennie L Close
36. Brian Long
37. Michael Kunst
38. Ed S Lein
39. Hongkui Zeng
40. Delissa McMillen
41. Jack Waters

• Curated by eLife

  eLife Assessment

  This study provides a valuable contribution to spatial transcriptomics by introducing MerQuaCo, a computational tool for standardizing quality control in image-based spatial transcriptomics datasets. The tool addresses the lack of consensus in the field and provides robust metrics to identify and quantify common imperfections in datasets. The work is supported by an impressive dataset and compelling analyses, and will be of significant interest to researchers focused on data reproducibility and downstream analysis reliability in spatial transcriptomics.

Reviewed by eLife

From Mechanistic Interpretability to Mechanistic Biology: Training, Evaluating, and Interpreting Sparse Autoencoders on Protein Language Models

1. Etowah Adams
2. Liam Bai
3. Minji Lee
4. Yiyang Yu
5. Mohammed AlQuraishi

Reviewed by Arcadia Science

The Implications of Alternative Splicing Regulation for Maximum Lifespan

1. Wei Jiang
2. Sika Zheng
3. Liang Chen

Reviewed by Arcadia Science

Squidly: Enzyme Catalytic Residue Prediction Harnessing a Biology-Informed Contrastive Learning Framework

1. William JF Rieger
2. Mikael Boden
3. Frances Arnold
4. Ariane Mora

• Curated by eLife

  eLife Assessment

  The authors make an important advance in enzyme annotation by fusing biochemical knowledge with language‑model-based learning to predict catalytic residues from sequence alone. Squidly, a new ML method, outperforms existing tools on standard benchmarks and on the CataloDB dataset. The work has solid support, yet clarifications on dataset biases, ablation analyses, and uncertainty filtering would strengthen its efficiency claims.

Reviewed by eLife

Unsupervised Representation Learning of C. elegans Poses and Behavior Sequences From Microscope Video Recordings

1. Maurice Deserno
2. Katarzyna Bozek

• Curated by eLife

  eLife Assessment

  This valuable study introduces a self-supervised machine learning method to classify C. elegans postures and behaviors directly from video data, offering an alternative to the skeleton-based approaches that rely on often error-prone tracking. This novel approach holds promise for advancing ethology research. That said, the strength of evidence is currently incomplete, as key aspects - including measuring head-tail orientation, increased behavioral interpretability, and quantitative comparisons to established methods - are underdeveloped and would benefit from further validation.

Reviewed by Arcadia Science, eLife

Using synthetic RNA to benchmark poly(A) length inference from direct RNA sequencing

1. Jessie J -Y Chang
2. Xuan Yang
3. Haotian Teng
4. Jianshu Zhang
5. Benjamin Reames
6. Shuxin Zhang
7. Vincent Corbin
8. Lachlan J M Coin

Reviewed by GigaScience

Nanopore- and AI-empowered microbial viability inference

1. Harika Ürel
2. Sabrina Benassou
3. Hanna Marti
4. Tim Reska
5. Ela Sauerborn
6. Yuri Pinheiro Alves De Souza
7. Albert Perlas
8. Enrique Rayo
9. Michael Biggel
10. Stefan Kesselheim
11. Nicole Borel
12. Edward J Martin
13. Constanza B Venegas
14. Michael Schloter
15. Kathrin Schröder
16. Jana Mittelstrass
17. Simone Prospero
18. James M Ferguson
19. Lara Urban

Reviewed by GigaScience

WaveSeekerNet: Accurate Prediction of Influenza A Virus Subtypes and Host Source Using Attention-Based Deep Learning

1. Hoang-Hai Nguyen
2. Josip Rudar
3. Nathaniel Lesperance
4. Oksana Vernygora
5. Graham W. Taylor
6. Chad Laing
7. David Lapen
8. Carson K. Leung
9. Oliver Lung

Reviewed by GigaScience

SPEX: A modular end-to-end platform for high-plex tissue spatial omics analysis

1. Xiao Li
2. Ximo Pechuan-Jorge
3. Tyler Risom
4. Conrad Foo
5. Alexander Prilipko
6. Artem Zubkov
7. Caleb Chan
8. Patrick Chang
9. Frank Peale
10. James Ziai
11. Sandra Rost
12. Derrek Hibar
13. Lisa McGinnis
14. Evgeniy Tabatsky
15. Xin Ye
16. Hector Corrada Bravo
17. Zhen Shi
18. Malgorzata Nowicka
19. Jon Scherdin
20. James Cowan
21. Jennifer Giltnane
22. Darya Orlova
23. Rajiv Jesudason

Reviewed by GigaScience