Bioinformatics

The enduring advantages of the SLOW5 file format for raw nanopore sequencing data

1. Hasindu Gamaarachchi
2. Sasha Jenner
3. Hiruna Samarakoon
4. James M. Ferguson
5. Ira W. Deveson

Reviewed by GigaScience

GTestimate: Improving relative gene expression estimation in scRNA-seq using the Good-Turing estimator

1. Martin Fahrenberger
2. Christopher Esk
3. Arndt von Haeseler

Reviewed by GigaScience

Deep learning reveals endogenous sterols as allosteric modulators of the GPCR–Gα interface

1. Sanjay Kumar Mohanty
2. Aayushi Mittal
3. Namra Farooqi
4. Aakash Gaur
5. Subhadeep Duari
6. Saveena Solanki
7. Anmol Kumar Sharma
8. Sakshi Arora
9. Suvendu Kumar
10. Vishakha Gautam
11. Nilesh Kumar Dixit
12. Karthika Subramanian
13. Tarini Shankar Ghosh
14. Debarka Sengupta
15. Shashi Kumar Gupta
16. Arul Natarajan Murugan
17. Deepak Sharma
18. Gaurav Ahuja

• Curated by eLife

  eLife Assessment

  The authors present a set of wrappers around previously developed software and machine-learning toolkits, and demonstrate their use in identifying endogenous sterols binding to a GPCR. The resulting pipeline is potentially useful for molecular pharmacology researchers due to its accessibility and ease of use. However, the evidence supporting the GPCR-related findings remains incomplete, as the machine-learning model shows indications of overfitting, and no direct ligand-binding assays are provided for validation.

Reviewed by eLife

Convolutional neural networks and outline analyses for archaeobotanical studies of domestication and subspecific identification

1. Vincent Bonhomme
2. Laurent Bouby
3. Julien Claude
4. Camille Dham
5. Muriel Gros-Balthazard
6. Sarah Ivorra
7. Angèle Jeanty
8. Clémence Pagnoux
9. Thierry Pastor
10. Jean-Frédéric Terral
11. Allowen Evin

Reviewed by Peer Community in Archaeology

Subtle methodological variations substantially impact correlation test results in ecological time series

1. Caroline Cannistra
2. Linh Hoang
3. Alex E Yuan
4. Wenying Shou

• Curated by eLife

  eLife Assessment

  This study presents a valuable in-depth comparison of statistical methods for the analysis of ecological time series data, and shows that different analyses can generate different conclusions, emphasizing the importance of carefully choosing methods and of reporting methodological details. The evidence supporting the claims, based on simulated data for a two-species ecosystem, is solid, although testing on more complex datasets could be of further benefit. This paper should be of broad interest to researchers in ecology.

Reviewed by eLife

Decoding molecular mechanisms for loss-of-function variants in the human proteome

1. Matteo Cagiada
2. Nicolas Jonsson
3. Kresten Lindorff-Larsen

• Curated by eLife

  eLife Assessment

  This work introduces FunC-ESMs, a proteome-scale framework to classify loss-of-function missense variants into distinct mechanistic groups by combining two complementary state-of-the-art machine learning models. The strength of evidence is convincing, supported by solid benchmarking, integration with experimental datasets, and careful methodological design. The significance of the findings is valuable, providing a resource of clear interest to researchers and diagnostic laboratories working on variant interpretation.

Reviewed by eLife

Cellpose-SAM: superhuman generalization for cellular segmentation

1. Marius Pachitariu
2. Michael Rariden
3. Carsen Stringer

Reviewed by Arcadia Science

Translational buffering tunes gene expression in mouse and human

1. Shilpa Rao
2. Aden Y Le
3. Logan Persyn
4. Can Cenik

Reviewed by Review Commons

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