Bioinformatics

MorphoNet 2.0: An innovative approach for qualitative assessment and segmentation curation of large-scale 3D time-lapse imaging datasets

1. Benjamin Gallean
2. Tao Laurent
3. Kilian Biasuz
4. Ange Clement
5. Noura Faraj
6. Patrick Lemaire
7. Emmanuel Faure

• Curated by eLife

  eLife Assessment

  This important work presents technical and conceptual advances with the release of MorphoNet 2.0, a versatile and accessible platform for 3D+T segmentation and analysis. The authors provide compelling evidence across diverse datasets, and the clarity of the manuscript together with the software's usability broadens its impact. Although the strength of some improvements is hard to fully gauge given sample complexity, the tool is a significant step forward that will likely impact many biological imaging fields.

Reviewed by eLife

EssSubgraph improves performance and generalizability of mammalian essential gene prediction with large networks

1. Haimei Wen
2. Susan Carpenter
3. Karen McGinnis
4. Andrew Nelson
5. Keriayn Smith
6. Tian Hong

Reviewed by GigaScience

GFFx: A Rust-based suite of utilities for ultra-fast genomic feature extraction

1. Baohua Chen
2. Dongya Wu
3. Guojie Zhang

Reviewed by GigaScience

Improving the Reliability and Quality of Nextflow Pipelines with nf-test

1. Lukas Forer
2. Sebastian Schoenherr

Reviewed by GigaScience

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