Bioinformatics

Spotless, a reproducible pipeline for benchmarking cell type deconvolution in spatial transcriptomics

1. Chananchida Sang-aram
2. Robin Browaeys
3. Ruth Seurinck
4. Yvan Saeys

• Curated by eLife

  eLife assessment

  This study makes a valuable contribution to spatial transcriptomics by rigorously benchmarking cell-type deconvolution methods, assessing their performance across diverse datasets with a focus on biologically relevant, previously unconsidered aspects. The authors demonstrate the strengths of RCTD, cell2location, and SpatialDWLS for their performance, while also revealing the limitations of many methods when compared to simpler baselines. By implementing a full Nextflow pipeline, Docker containers, and a rigorous assessment of the simulator, this work offers robust insights that elevate the standards for future evaluations and provides a resource for those seeking to improve or develop new deconvolution methods. The thorough comparison and analysis of methods, coupled with a strong emphasis on reproducibility, provide solid support for the findings.

Reviewed by eLife

The Diverse Evolutionary Histories of Domesticated Metaviral Capsid Genes in Mammals

1. William S Henriques
2. Janet M Young
3. Artem Nemudryi
4. Anna Nemudraia
5. Blake Wiedenheft
6. Harmit S Malik

Reviewed by Arcadia Science

Pseudo-spectral angle mapping for automated pixel-level analysis of highly multiplexed tissue image data

1. Madeleine S. Durkee
2. Junting Ai
3. Gabriel Casella
4. Thao Cao
5. Anthony Chang
6. Ariel Halper-Stromberg
7. Bana Jabri
8. Marcus R. Clark
9. Maryellen L. Giger

Reviewed by Arcadia Science

AlphaFind: Discover structure similarity across the entire known proteome

1. David Prochazka
2. Terezia Slaninakova
3. Jaroslav Olha
4. Adrian Rosinec
5. Katarina Gresova
6. Miriama Janosova
7. Jakub Cillik
8. Jana Porubska
9. Radka Svobodova
10. Vlastislav Dohnal
11. Matej Antol

Reviewed by Arcadia Science

Genome-scale annotation of protein binding sites via language model and geometric deep learning

1. Qianmu Yuan
2. Chong Tian
3. Yuedong Yang

• Curated by eLife

  eLife assessment

  The authors introduce a valuable machine-learning model for predicting binding sites of diverse ligands, including DNA, RNA, peptides, proteins, ATP, HEM, and metal ions, on proteins. The method is freely accessible and user-friendly. The authors have conducted thorough benchmarking and ablation studies, providing convincing evidence of the model's overall performance, despite some imperfections of the comparisons to other methods that arise from intrinsic differences between training methods and data.

Reviewed by eLife

Branching topology of the human embryo transcriptome revealed by Entropy Sort Feature Weighting

1. Arthur Radley
2. Stefan Boeing
3. Austin Smith

Reviewed by Review Commons

Molecular Property Diagnostic Suite for COVID-19 (MPDS ^COVID-19 ): An open access disease specific drug discovery portal

1. Lipsa Priyadarsinee
2. Esther Jamir
3. Selvaraman Nagamani
4. Hridoy Jyoti Mahanta
5. Nandan Kumar
6. Lijo John
7. Himakshi Sarma
8. Asheesh Kumar
9. Anamika Singh Gaur
10. Rosaleen Sahoo
11. S. Vaikundamani
12. N. Arul Murugan
13. U. Deva Priyakumar
14. G.P.S. Raghava
15. Prasad V. Bharatam
16. Ramakrishnan Parthasarathi
17. V. Subramanian
18. G. Madhavi Sastry
19. G. Narahari Sastry

• Curated by GigaByte

  Editors Assessment:

  MPDSCOVID-19 has been developed as a one-stop solution for drug discovery research for COVID-19, running on the Molecular Property Diagnostic Suite (MPDS) platform. This is built upon the open-source Galaxy workflow system, integrating many modules and data specific to COVID-19. Data integrated includes SARS-CoV-2 targets, genes and their pathway information; information on repurposed drugs against various targets of SARS-CoV-2, mutational variants, polypharmacology for COVID-19, drug-drug interaction information, Protein-Protein Interaction (PPI), host protein information, epidemiology, and inhibitors databases. After improvements to the technical description of the platform, testing helped demonstrate the potential to drive open-source computational drug discovery with the platform.

  This evaluation refers to version 1 of the preprint

Reviewed by GigaByte

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