Bioinformatics

Robust estimation of cancer and immune cell-type proportions from bulk tumor ATAC-Seq data

1. Aurélie Anne-Gaëlle Gabriel
2. Julien Racle
3. Maryline Falquet
4. Camilla Jandus
5. David Gfeller

• Curated by eLife

  eLife assessment

  This study presents an important computational tool for the quantification of the cellular composition of human tissues profiled with ATAC-seq. The methodology and its application results on breast cancer tumor tissues are convincing. It advances existing methods by utilizing a comprehensive reference profile for major cancer-relevant cell types, compatible with a widely-used cell type deconvolution tool.

Reviewed by eLife

Assessing the ability of ChatGPT to extract natural product bioactivity and biosynthesis data from publications

1. Thomas L. Kalmer
2. Christine Mae F. Ancajas
3. Zihao Cheng
4. Abiodun S. Oyedele
5. Hunter L. Davis
6. Allison S. Walker

Reviewed by Arcadia Science

Benchmarking reveals superiority of deep learning variant callers on bacterial nanopore sequence data

1. Michael B Hall
2. Ryan R Wick
3. Louise M Judd
4. An N Nguyen
5. Eike J Steinig
6. Ouli Xie
7. Mark Davies
8. Torsten Seemann
9. Timothy P Stinear
10. Lachlan Coin

• Curated by eLife

  eLife assessment

  This important study shows how a combination of the latest generation of Oxford Nanopore Technology long reads with state-of-the art variant callers enables bacterial variant discovery at an accuracy that matches or exceeds the current "gold standard" with short reads. The work thus heralds a new era, in which Illumina short-read sequencing no longer rules supreme. While the inclusion of a larger number of reference genomes would have enabled an even more fine-grained analysis, the evidence as it is supports the claims of the authors convincingly. The work will be of interest to anyone performing sequencing for outbreak investigations, bacterial epidemiology, or similar studies.

Reviewed by eLife

RiboSnake – a user-friendly, robust, reproducible, multipurpose and documentation-extensive pipeline for 16S rRNA gene microbiome analysis

1. Ann-Kathrin Dörr
2. Josefa Welling
3. Adrian Dörr
4. Jule Gosch
5. Hannah Möhlen
6. Ricarda Schmithausen
7. Jan Kehrmann
8. Folker Meyer
9. Ivana Kraiselburd

• Curated by GigaByte

  Editors Assessment:

  This new software paper presents RiboSnake, a validated, automated, reproducible analysis pipeline implemented in the popular Snakemake workflow management system for microbiome analysis. Analysing16S rRNA gene amplicon sequencing data, this uses the widely used oQIIME2 [ tool as the basis of the workflow as it offers a wide range of functionality. Users of QIIME2 can be overwhelmed by the number of options at their disposal, and this workflow provides a fully automated and fully reproducible pipeline that can be easily installed and maintained. Providing an easy-to-navigate output accessible to non bioinformatics experts, alongside sets of already validated parameters for different types of samples. Reviewers requested some clarification for testing, worked examples and documentation, and this was improved to produce a convincingly easy-to-use workflow. Hopefully opening up an already very established technique to a new group of users and assisting them with reproducible science.

  This evaluation refers to version 1 of the preprint

Reviewed by GigaByte

TooManyCellsInteractive: a visualization tool for dynamic exploration of single-cell data

1. Conor Klamann
2. Christie Lau
3. Gregory W. Schwartz

Reviewed by GigaScience

A multi-gene predictive model for the radiation sensitivity of nasopharyngeal carcinoma based on machine learning

1. Kailai Li
2. Junyi Liang
3. Nan Li
4. Jianbo Fang
5. Xinyi Zhou
6. Jian Zhang
7. Anqi Lin
8. Peng Luo
9. Hui Meng

• Curated by eLife

  eLife assessment

  The authors have conducted a convincing study utilizing machine learning algorithms to construct a validated radiotherapy sensitivity score (NPC-RSS) for predicting radiosensitivity in nasopharyngeal carcinoma patients that will be useful in a translational/clinical setting for predicting the best radiotherapy route for patients. They have also explored the biological mechanisms underlying the relationship between NPC-RSS and radiotherapy response, thus implicating certain pathways that could be targeted to enhance radiotherapy response or prevent radio-resistance.

Reviewed by eLife

Phyloformer: Fast, accurate and versatile phylogenetic reconstruction with deep neural networks

1. Luca Nesterenko
2. Luc Blassel
3. Philippe Veber
4. Bastien Boussau
5. Laurent Jacob

Reviewed by Arcadia Science

Identifying images in the biology literature that are problematic for people with a color-vision deficiency

1. Harlan P Stevens
2. Carly V Winegar
3. Arwen F Oakley
4. Stephen R Piccolo

• Curated by eLife

  eLife assessment

  In this important study, the authors manually assessed randomly selected images published in eLife between 2012 and 2022 to determine whether they were accessible for readers with deuteranopia, the most common form of color vision deficiency. They then developed an automated tool designed to classify figures and images as either "friendly" or "unfriendly" for people with deuteranopia. Such a tool could be used by journals or researchers to monitor the accessibility of figures and images, and the evidence for its utility was solid: it performed well for eLife articles, but performance was weaker for a broader dataset of PubMed articles, which were not included in the training data. The authors also provide code that readers can download and run to test their own images, and this may be of most use for testing the tool, as there are already several free, user-friendly recoloring programs that allow users to see how images would look to a person with different forms of color vision deficiency. Automated classifications are of most use for assessing many images, when the user does not have the time or resources to assess each image individually.

Reviewed by eLife

MGPfactXMBD: A Model-Based Factorization Method for scRNA Data Unveils Bifurcating Transcriptional Modules Underlying Cell Fate Determination

1. Jun Ren
2. Ying Zhou
3. Yudi Hu
4. Jing Yang
5. Hongkun Fang
6. Xuejing Lyu
7. Jintao Guo
8. Xiaodong Shi
9. Qiyuan Li

• Curated by eLife

  eLife assessment

  This manuscript describes a novel computational method to investigate cell evolutionary trajectory for scRNA-seq samples. This is an important tool for estimating pseudotime in the evolutionary path through modelling the bifurcations in a Gaussian process. While the evaluation of the method is extensive and compelling, the reviewers suggested further analyses to ensure that the method is indeed robust. When these issues are addressed, this will be of substantive value to biologists interested in scRNA-seq bioinformatic methods.

Reviewed by eLife

Pseudo-grading of tumor subpopulations from single-cell transcriptomic data using Phenotype Algebra

1. Namrata Bhattacharya
2. Anja Rockstroh
3. Sanket Suhas Deshpande
4. Sam Koshy Thomas
5. Anunay Yadav
6. Chitrita Goswami
7. Smriti Chawla
8. Pierre Solomon
9. Cynthia Fourgeux
10. Gaurav Ahuja
11. Brett G Hollier
12. Himanshu Kumar
13. Antoine Roquilly
14. Jeremie Poschmann
15. Melanie Lehman
16. Colleen C Nelson
17. Debarka Sengupta

• Curated by eLife

  eLife assessment

  This valuable study introduces SCellBOW, a novel tool leveraging natural language processing techniques to enhance cell clustering and infer survival risks from single-cell RNA sequencing data. The methodology and results are convincing, demonstrating superior clustering performance and the ability to assign risk scores to cancer cell clusters across multiple datasets. SCellBOW's unique approach promises significant advancements in understanding cancer cell heterogeneity and identifying aggressive cancer cell subgroups.

Reviewed by eLife