Bioinformatics

Reproducible processing of TCGA regulatory networks

1. Viola Fanfani
2. Katherine H. Shutta
3. Panagiotis Mandros
4. Jonas Fischer
5. Enakshi Saha
6. Soel Micheletti
7. Chen Chen
8. Marouen Ben Guebila
9. Camila M. Lopes-Ramos
10. John Quackenbush

Reviewed by GigaScience

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
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. Natarajan Arul 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

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