Bioinformatics

Biophysics-based protein language models for protein engineering

1. Sam Gelman
2. Bryce Johnson
3. Chase R. Freschlin
4. Arnav Sharma
5. Sameer D’Costa
6. John Peters
7. Anthony Gitter
8. Philip A. Romero

Reviewed by Arcadia Science

Unsupervised reference-free inference reveals unrecognized regulated transcriptomic complexity in human single cells

1. Roozbeh Dehghannasiri
2. George Henderson
3. Rob Bierman
4. Tavor Baharav
5. Kaitlin Chaung
6. Peter Wang
7. Julia Salzman

• Curated by eLife

  eLife Assessment

  This study presents a valuable advance for the analysis of gene expression variation at the level of individual cells by introducing a novel reference-free framework that can detect splicing, fusion, editing, immune-receptor diversity and repeated elements in sequencing data. The evidence supporting these claims is solid, with rigorous validation on simulated datasets and extensive analysis of full-length single-cell sequencing data demonstrating improved performance over existing methods. This work will be of particular interest to researchers developing methods for high-resolution transcriptome analysis and to those studying cellular heterogeneity in health and disease.

Reviewed by eLife, Arcadia Science

Pathway activation model for personalized prediction of drug synergy

1. Quang Thinh Trac
2. Yue Huang
3. Tom Erkers
4. Päivi Östling
5. Anna Bohlin
6. Albin Osterroos
7. Mattias Vesterlund
8. Rozbeh Jafari
9. Ioannis Siavelis
10. Helena Backvall
11. Santeri Kiviluoto
12. Lukas Orre
13. Mattias Rantalainen
14. Janne Lehtiö
15. Soren Lehmann
16. Olli Kallioniemi
17. Yudi Pawitan
18. Trung Nghia Vu

• Curated by eLife

  eLife Assessment

  This valuable study presents a deep learning framework for predicting synergistic drug combinations for cancer treatment in the AstraZeneca-Sanger (AZS) DREAM Challenge dataset. The level of evidence seems solid, although performance on some datasets seems unconvincing and further validation would be required to demonstrate the generalizability of the model and, in turn, its clinical relevance. The reported tool, DIPx, could be of use for personalized drug synergy prediction and exploring the activated pathways related to the effects of drug combinations.

Reviewed by eLife

Drug combination prediction for cancer treatment using disease-specific drug response profiles and single-cell transcriptional signatures

1. Daniel Osorio
2. Parastoo Shahrouzi
3. Xavier Tekpli
4. Vessela N Kristensen
5. Marieke L Kuijjer

• Curated by eLife

  eLife Assessment

  The study conducted by Hurtado et al. offers important insights and solid evidence regarding the prediction of drug combinations for cancer treatment. By leveraging disease-specific drug response profiles and single-cell transcriptional signatures, this research not only demonstrates a novel and effective approach to identifying potential drug synergies but it also enhances our understanding of the underlying mechanisms of drug response prediction.

Reviewed by eLife

A comprehensive antigen-antibody complex database unlocking insights into interaction interface

1. Yuwei Zhou
2. Wenwen Liu
3. Ziru Huang
4. Yushu Gou
5. Siqi Liu
6. Lixu Jiang
7. Yue Yang
8. Jian Huang

• Curated by eLife

  eLife Assessment

  This useful manuscript provides a newly curated database (termed AACDB) of antibody-antigens structural information, alongside annotations that are either taken and from the PDB, or added de-novo. Sequences, structures, and annotations can be easily downloaded from the AACDB website, speeding up the development of structure-based algorithms and analysis pipelines to characterize antibody-antigen interactions. The methodology presented for this data curation is solid. The curated dataset will be of broad interest and value to researchers interested in antibody-antigen interactions.

Reviewed by eLife

RWRtoolkit: multi-omic network analysis using random walks on multiplex networks in any species

1. David Kainer
2. Matthew Lane
3. Kyle A Sullivan
4. J Izaak Miller
5. Mikaela Cashman
6. Mallory Morgan
7. Ashley Cliff
8. Jonathon Romero
9. Angelica Walker
10. D Dakota Blair
11. Hari Chhetri
12. Yongqin Wang
13. Mirko Pavicic
14. Anna Furches
15. Jaclyn Noshay
16. Meghan Drake
17. A J Ireland
18. Ali Missaoui
19. Yun Kang
20. John C Sedbrook
21. Paramvir Dehal
22. Shane Canon
23. Daniel Jacobson

Reviewed by GigaScience

Defining the limits of plant chemical space: challenges and estimations

1. Chloe Engler Hart
2. Yojana Gadiya
3. Tobias Kind
4. Christoph A. Krettler
5. Matthew Gaetz
6. Biswapriya B. Misra
7. David Healey
8. August Allen
9. Viswa Colluru
10. Daniel Domingo-Fernández

Reviewed by GigaScience

V-pipe 3.0: a sustainable pipeline for within-sample viral genetic diversity estimation

1. Lara Fuhrmann
2. Kim Philipp Jablonski
3. Ivan Topolsky
4. Aashil A Batavia
5. Nico Borgsmüller
6. Pelin Icer Baykal
7. Matteo Carrara
8. Chaoran Chen
9. Arthur Dondi
10. Monica Dragan
11. David Dreifuss
12. Anika John
13. Benjamin Langer
14. Michal Okoniewski
15. Louis du Plessis
16. Uwe Schmitt
17. Franziska Singer
18. Tanja Stadler
19. Niko Beerenwinkel

Reviewed by GigaScience

Network-based anomaly detection algorithm reveals proteins with major roles in human tissues

1. Dima Kagan
2. Juman Jubran
3. Esti Yeger-Lotem
4. Michael Fire

Reviewed by GigaScience

Steamboat: Attention-based multiscale delineation of cellular interactions in tissues

1. Shaoheng Liang
2. Junjie Tang
3. Guanghan Wang
4. Jian Ma

Reviewed by preLights