Bioinformatics

Unveiling the influence of tumor and immune signatures on immune checkpoint therapy in advanced lung cancer

1. Nayoung Kim
2. Sehhoon Park
3. Areum Jo
4. Hye Hyeon Eum
5. Hong Kwan Kim
6. Kyungjong Lee
7. Jong Ho Cho
8. Bo Mi Ku
9. Hyun Ae Jung
10. Jong-Mu Sun
11. Se-Hoon Lee
12. Jin Seok Ahn
13. Jung-Il Lee
14. Jung Won Choi
15. Dasom Jeong
16. Minsu Na
17. Huiram Kang
18. Jeong Yeon Kim
19. Jung Kyoon Choi
20. Hae-Ock Lee
21. Myung-Ju Ahn

• Curated by eLife

  eLife Assessment

  The authors utilized single-cell RNA-seq profiling of non-small cell lung cancer (NSCLC) patient tumor samples to generate useful insights into the determinants of immune checkpoint inhibitor (ICI) responsiveness in NSCLC patients. While some of the findings add weight to the current literature, the analysis is incomplete due to the small cohort size and heterogeneous population which has limited their ability to draw statistically supported conclusion after adjusting for multiple hypothesis testing, as well as the lack of functional characterization of the findings. This study would benefit from external cohorts to both validate the findings and justify the statistical analysis undertaken.

Reviewed by eLife

Coenzyme-Protein Interactions since Early Life

1. Alma Carolina Sanchez-Rocha
2. Mikhail Makarov
3. Lukáš Pravda
4. Marian Novotný
5. Klára Hlouchová

• Curated by eLife

  eLife Assessment

  This study presents a valuable examination of the prevalence of interactions between amino acids from different periods of Earth's history and coenzymes. While the premise of this work is well founded and the analysis is solid, with more data, the interpretation could change. This manuscript would be of interest to evolutionary biologists and biophysicists.

Reviewed by eLife

BFVD - a large repository of predicted viral protein structures

1. Rachel Seongeun Kim
2. Eli Levy Karin
3. Martin Steinegger

Reviewed by Arcadia Science

Benchmarking tRNA-Seq quantification approaches by realistic tRNA-Seq data simulation identifies two novel approaches with higher accuracy

1. Tom Smith
2. Mie Monti
3. Anne E Willis
4. Lajos Kalmár

• Curated by eLife

  eLife Assessment

  This study provides an important resource by thoroughly benchmarking multiple sequencing-based tRNA quantification methods. The suggested best practice is supported by convincing evidence from in silico experiments in multiple scenarios.

Reviewed by eLife

A novel channel invariant architecture for the segmentation of cells and nuclei in multiplexed images using InstanSeg

1. Thibaut Goldsborough
2. Alan O’Callaghan
3. Fiona Inglis
4. Léo Leplat
5. Andrew Filby
6. Hakan Bilen
7. Peter Bankhead

Reviewed by Arcadia Science

An antimicrobial drug recommender system using MALDI-TOF MS and dual-branch neural networks

1. Gaetan De Waele
2. Gerben Menschaert
3. Willem Waegeman

• Curated by eLife

  eLife Assessment

  This valuable study presents a machine learning model to recommend effective antimicrobial drugs from patients' samples analysed with mass spectrometry. The evidence supporting the claims of the authors is convincing. This work will be of interest to computational biologists, microbiologists, and clinicians.

Reviewed by eLife

Single-Cell and Spatial Transcriptomic Analyses Reveals the Dynamic Transcript Profiles of Myocardial Lymphangiogenesis post Myocardial Infarction

1. Jiaqi He
2. Dali Zhang
3. Haixu Song
4. Ziqi Liu
5. Dan Liu
6. Xiaolin Zhang
7. Xiaojie Zhao
8. Yan Zhang
9. Jing Liu
10. Jiaxin Xu
11. Chenghui Yan
12. Yaling Han

• Curated by eLife

  eLife Assessment

  This study presents useful, yet preliminary, findings on the transcriptomic changes in cardiac lymphatic cells after myocardial infarction in mice. The conclusions of the authors remain uncertain as sample sizes for lymphatic endothelial cells are very low. The single-cell transcriptomic data were analyzed using solid advanced methodology and may be used as a starting point for future studies of the impact of lymphatic cells on heart disease.

Reviewed by eLife

Modeling transcriptional profiles of gene perturbation with deep neural network

1. Wenke Liu
2. Xuya Wang
3. D R Mani
4. David Fenyö

Reviewed by preLights

TopOMetry systematically learns and evaluates the latent dimensions of single-cell atlases

1. Davi Sidarta-Oliveira
2. Ana Domingos
3. Licio A Velloso

• Curated by eLife

  eLife Assessment

  This study presents TopOMetry, an important novel dimensionality reduction method that addresses a signficant challenge in the analysis of single-cell RNA sequencing data. The authors provide convincing evidence of the method's utility across various tasks, including estimating intrinsic dimensionalities and identifying cell types. The work would benefit from more rigorous validation and a reorganization of the text.

Reviewed by eLife

Establishing comprehensive quaternary structural proteomes from genome sequence

1. Edward Alexander Catoiu
2. Nathan Mih
3. Maxwell Lu
4. Bernhard Palsson

• Curated by eLife

  eLife assessment

  This study presents an important platform for mapping mutation effects onto higher-level protein structural information, addressing a significant gap in current research. While the work is ambitious and incorporates often-overlooked aspects of higher-order structure, the strength of the evidence supporting some results seems incomplete. The quaternary structure modeling appears to underestimate oligomeric proteins compared to previous studies, and the mutation analysis lacks crucial baseline information. Despite these limitations, the method has potential for broader applications and generalization to additional organisms, warranting further development and refinement.

Reviewed by eLife