Bioinformatics

Deep learning for rapid analysis of cell divisions in vivo during epithelial morphogenesis and repair

1. Jake Turley
2. Isaac V Chenchiah
3. Paul Martin
4. Tanniemola B Liverpool
5. Helen Weavers

• Curated by eLife

  eLife assessment

  In this valuable study, the authors use deep learning models to provide solid evidence that epithelial wounding triggers bursts of cell division at a characteristic distance away from the wound. The documentation provided by the authors should allow other scientists to readily apply these methods, which are particularly appropriate where unsupervised machine-learning algorithms have difficulties.

Reviewed by eLife

Protein language model-embedded geometric graphs power inter-protein contact prediction

1. Yunda Si
2. Chengfei Yan

• Curated by eLife

  eLife assessment

  This study presents a useful deep learning-based inter-protein contact prediction method named PLMGraph-Inter which combines protein language models and geometric graphs. The evidence supporting the claims of the authors is solid. The authors show that their approach may be used in cases where AlphaFold-Multimer performs poorly. This work will be of interest to researchers working on protein complex structure prediction, particularly when accurate experimental structures are available for one or both of the monomers in isolation.

Reviewed by eLife

Chlomito: a novel tool for precise elimination of organelle genome contamination from nuclear genome assembly

1. Wei Song
2. Chong Li
3. Yanming Lu
4. Dawei Shen
5. Yunxiao Jia
6. Yixin Huo
7. Weilan Piao
8. Hua Jin

Reviewed by Arcadia Science

The probability of edge existence due to node degree: a baseline for network-based predictions

1. Michael Zietz
2. Daniel S Himmelstein
3. Kyle Kloster
4. Christopher Williams
5. Michael W Nagle
6. Casey S Greene

Reviewed by GigaScience

Digital Microbe: A Genome-Informed Data Integration Framework for Collaborative Research on Emerging Model Organisms

1. Iva Veseli
2. Michelle A. DeMers
3. Zachary S. Cooper
4. Matthew S. Schechter
5. Samuel Miller
6. Laura Weber
7. Christa B. Smith
8. Lidimarie T. Rodriguez
9. William F. Schroer
10. Matthew R. McIlvin
11. Paloma Z. Lopez
12. Makoto Saito
13. Sonya Dyhrman
14. A. Murat Eren
15. Mary Ann Moran
16. Rogier Braakman

Reviewed by preLights

A novel machine learning algorithm selects proteome signature to specifically identify cancer exosomes

1. Bingrui Li
2. Fernanda G Kugeratski
3. Raghu Kalluri

• Curated by eLife

  eLife assessment

  This important study introduces a novel AI method for the analysis of published data, with practical implications for early cancer diagnosis. The results are supported by compelling evidence.

Reviewed by eLife

Evaluating the Utilities of Foundation Models in Single-cell Data Analysis

1. Tianyu Liu
2. Kexing Li
3. Yuge Wang
4. Hongyu Li
5. Hongyu Zhao

Reviewed by Arcadia Science

voyAGEr, a free web interface for the analysis of age-related gene expression alterations in human tissues

1. Arthur L Schneider
2. Rita Martins-Silva
3. Alexandre Kaizeler
4. Nuno Saraiva-Agostinho
5. Nuno L Barbosa-Morais

• Curated by eLife

  eLife assessment

  This work presents an important online platform designed to facilitate the exploration of genes and genetic pathways implicated in human aging. Leveraging a new inference methodology, the tool enables the identification and visualization of key genes and tissues impacted by aging, facilitating scientific discovery. The methods and analyses are convincing and will be broadly used by scientists aiming to mine human aging RNA-seq data.

Reviewed by eLife

SAW: an efficient and accurate data analysis workflow for Stereo-seq spatial transcriptomics

1. Chun Gong
2. Shengkang Li
3. Leying Wang
4. Fuxiang Zhao
5. Shuangsang Fang
6. Dong Yuan
7. Zijian Zhao
8. Qiqi He
9. Mei Li
10. Weiqing Liu
11. Zhaoxun Li
12. Hongqing Xie
13. Sha Liao
14. Ao Chen
15. Yong Zhang
16. Yuxiang Li
17. Xun Xu

• Curated by GigaByte

  Editors Assessment:

  One limiting factor in the adoption of spatial omics research are workflow systems for data preprocessing, and to address these authors developed the SAW tool to process Stereo-seq data. The analysis steps of spatial transcriptomics involve obtaining gene expression information from space and cells. Existing tools face issues with large data sets, such as intensive spatial localization, RNA alignment, and excessive memory usage. These issues affect the process's applicability and efficiency. To address this, this paper presents a high-performance open-source workflow called SAW for Stereo-Seq. This includes mRNA position reconstruction, genome alignment, matrix generation, clustering, and result file generation for personalized analysis. During review the authors have added examples of MID correction in the article to make the process easier to understand. And In the future, more accurate algorithms or deep learning models may further improve the accuracy of this pipeline.

  *This evaluation refers to version 1 of the preprint

Reviewed by GigaByte

Generating single-cell gene expression profiles for high-resolution spatial transcriptomics based on cell boundary images

1. Bohan Zhang
2. Mei Li
3. Qiang Kang
4. Zhonghan Deng
5. Hua Qin
6. Kui Su
7. Xiuwen Feng
8. Lichuan Chen
9. Huanlin Liu
10. Shuangsang Fang
11. Yong Zhang
12. Yuxiang Li
13. Susanne Brix
14. Xun Xu

• Curated by GigaByte

  Editors Assessment:

  This paper describes a new spatial transcriptomics method that that utilizes cell nuclei staining images and statistical methods to generate high-confidence single-cell spatial gene expression profiles for Stereo-seq data. STCellbin is an update of StereoCell, now using a more advanced cell segmentation technique, so more accurate cell boundaries can be obtained, allowing more reliable single-cell spatial gene expression profiles to be obtained. After peer review more comparisons were added and more description given on what was upgraded in this version to convince the reviewers. Demonstrating it is a more reliable method, particularly for analyzing high-resolution and large-field-of-view spatial transcriptomic data. And extending the capability to automatically process Stereo-seq cell membrane/wall staining images for identifying cell boundaries.

  This evaluation refers to version 2 of the preprint

Reviewed by GigaByte