Bioinformatics

Modeling causal signal propagation in multi-omic factor space with COSMOS

1. Aurelien Dugourd
2. Pascal Lafrenz
3. Diego Mañanes
4. Victor Paton
5. Robin Fallegger
6. Anne-Claire Kroger
7. Denes Turei
8. Yunfan Bai
9. Yuxin Li
10. Michael Trogdon
11. Drew Nager
12. Shibing Deng
13. Chen Shen
14. John D. Lapek
15. Blerta Shtylla
16. Julio Saez-Rodriguez

Reviewed by Review Commons

LadybirdBase: A comprehensive biology, ecology, and omics resource for ladybird beetles (Coccinellidae)

1. Yi‐Fei Sun
2. Kun‐Yu Yang
3. Hao Li
4. Yuan‐Sen Liang
5. Li‐Qun Cai
6. Jia‐Yi Xie
7. You‐Wen Zhang
8. Jia‐Yong Liang
9. Qian Mou
10. Ying‐Min Wang
11. Dan Chen
12. Meng‐Xue Qi
13. Luis Carlos Ramos Aguila
14. Muhammad Asghar Hassan
15. Hao‐Sen Li
16. Hong Pang

Reviewed by PREreview

Extending Protein Language Models to a Viral Genomic Scale Using Biologically Induced Sparse Attention

1. Thibaut Dejean
2. Barbra D. Ferrell
3. William Harrigan
4. Zachary D. Schreiber
5. Rajan Sawhney
6. K. Eric Wommack
7. Shawn W. Polson
8. Mahdi Belcaid

Reviewed by GigaScience

KG-Microbe - Building Modular and Scalable Knowledge Graphs for Microbiome and Microbial Sciences

1. Brook E. Santangelo
2. Harshad Hegde
3. J. Harry Caufield
4. Justin Reese
5. Tomas Kliegr
6. Lawrence E. Hunter
7. Catherine A. Lozupone
8. Christopher J. Mungall
9. Marcin P. Joachimiak

Reviewed by GigaScience

NanoporeDB: A Structural Resource Of Multimeric Protein Nanopores In Single-Molecule Sensing

1. Yuqian Liu
2. Zidong Su
3. Wenzhen Yang
4. Denghui Li
5. Jiawen Zhang
6. Yuning Zhang
7. Tao Zeng
8. Yong Zhang
9. Yuxiang Li
10. Guangyi Fan
11. Kailong Ma
12. Shanshan Liu
13. Xun Xu
14. Yuliang Dong
15. Zongan Wang

Reviewed by GigaScience

ScopeViewer: A Browser-Based Solution for Visualizing Spatial Transcriptomics Data

1. Danni Luo
2. Sophie Robertson
3. Yuanchun Zhan
4. Ruichen Rong
5. Shidan Wang
6. Xi Jiang
7. Sen Yang
8. Suzette Palmer
9. Liwei Jia
10. Qiwei Li
11. Guanghua Xiao
12. Xiaowei Zhan

Reviewed by GigaScience

ChatMDV: Democratising Bioinformatics Analysis Using Large Language Models

1. Maria Kiourlappou
2. Peter Todd
3. Yaxuan Kong
4. Jayesh Hire
5. Sibgathullah Furquan Nawab Mohammed
6. Martin Sergeant
7. Stefan Zohren
8. Jim Hughes
9. Stephen Taylor

Reviewed by GigaScience

CPSM: R-package of an Automated Machine Learning Pipeline for Predicting the Survival Probability of Single Cancer Patient

1. Harpreet Kaur
2. Pijush Das
3. Kevin Camphausen
4. Uma Shankavaram

Reviewed by GigaScience

Product-stabilized filamentation by human glutamine synthetase allosterically tunes metabolic activity

1. Eric Greene
2. Richard Muniz
3. Hiroki Yamamura
4. Samuel E Hoff
5. Priyanka Bajaj
6. Markus Tecson CB Tecson
7. Cyrina Geluz
8. D John Lee
9. Erin M Thompson
10. Angelika Arada
11. Gyun Min Lee
12. Massimiliano Bonomi
13. Justin M Kollman
14. James S Fraser

• Curated by eLife

  eLife Assessment

  This manuscript employs cryo-EM, mutational analysis, and biochemical assays to explore the molecular basis by which glutamine promotes filamentation and regulates the activity of human glutamine synthetase (hGS) by stabilizing interactions between hGS decamers. The combined structural and biochemical data supporting the proposed mechanism are solid, although the evidence for a higher-order filamentous architecture and the precise assignment of the interface density would benefit from additional support. This work will be of particular interest and useful to groups interested in understanding the molecular basis of nutrient sensing, cellular metabolism, and structural regulation of enzyme activity.

Reviewed by eLife

TSvelo: Comprehensive RNA velocity by modeling the cascade of gene regulation, transcription and splicing

1. Jiachen Li
2. Zhe Wang
3. Hong-Bin Shen
4. Ye Yuan

• Curated by eLife

  eLife Assessment

  This study presents a valuable RNA velocity solution which integrates cell differentiation and gene regulation, with a balance between neuralODE and raw gene space. The evidence supporting the claims of the authors is solid, although inclusion of discussion on the challenges in capturing cell cycle transitions would have strengthened the study. The work will be of interest to scientists working in the field of computational biology and gene regulation.

Reviewed by eLife