Bioinformatics

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

BatchEval Pipeline: batch effect evaluation workflow for multiple datasets joint analysis

1. Chao Zhang
2. Qiang Kang
3. Mei Li
4. Hongqing Xie
5. Shuangsang Fang
6. Xun Xu

• Curated by GigaByte

  Editors Assessment:

  For better data quality assessment of large spatial transcriptomics datasets this new BatchEval software has been developed as a batch effect evaluation tool. This generates a comprehensive report with assessment findings, including basic information of integrated datasets, a batch effect score, and recommended methods for removing batch effects. The report also includes evaluation details for the raw dataset and results from batch effect removal methods. Through peer review and clarification of a number of points it now looks convincing that this tool helps researchers identify and remove batch effects, ensuring reliable and meaningful insights from integrated datasets. Potentially making the tool valuable for researchers who need to analyze large datasets of this type, as it provides an easy and reliable way to assess data quality and ensures that downstream analyses are robust and reliable.

  This evaluation refers to version 1 of the preprint

Reviewed by GigaByte

Benchmarking Reverse Docking through AlphaFold2 Human Proteome

1. Qing Luo
2. Sheng Wang
3. Hoi Yeung Li
4. Liangzhen Zheng
5. Yuguang Mu
6. Jingjing Guo

Reviewed by Arcadia Science

Sensitive remote homology search by local alignment of small positional embeddings from protein language models

1. Sean R Johnson
2. Meghana Peshwa
3. Zhiyi Sun

• Curated by eLife

  eLife assessment

  This important study addresses the problem of detecting weak similarity between protein sequences, a procedure commonly used to infer homology or assign putative functions to uncharacterized proteins. The authors present a convincing approach that combines recently developed protein language models with well-established methods. The benchmarks provided show that the proposed tool is fast and accurate for remote homology detection, making this paper of general interest to all researchers working in the fields of protein evolution and genome annotation.

Reviewed by eLife

OmniNA: A foundation model for nucleotide sequences

1. Xilin Shen
2. Xiangchun Li

Reviewed by Arcadia Science

A Multi-omics Data Analysis Workflow Packaged as a FAIR Digital Object

1. Anna Niehues
2. Casper de Visser
3. Fiona A. Hagenbeek
4. Purva Kulkarni
5. Rene Pool
6. Naama Karu
7. Alida S. D. Kindt
8. Gurnoor Singh
9. Robert R. J. M. Vermeiren
10. Dorret I. Boomsma
11. Jenny van Dongen
12. Peter A. C. 't Hoen
13. Alain J. van Gool

Reviewed by GigaScience