Bioinformatics

Improved integration of single cell transcriptome data demonstrated on heart failure in mice and men

1. Mariano Ruz Jurado
2. Lukas Stefan Tombor
3. Mani Arsalan
4. Thomas Holubec
5. Fabian Emrich
6. Thomas Walther
7. Andreas M. Zeiher
8. Marcel H. Schulz
9. Stefanie Dimmeler
10. David John

Reviewed by GigaScience

Protein language models are biased by unequal sequence sampling across the tree of life

1. Frances Ding
2. Jacob Steinhardt

Reviewed by Arcadia Science

Single-cell transcriptome analysis of cavernous tissues reveals the key roles of pericytes in diabetic erectile dysfunction

1. Seo-Gyeong Bae
2. Guo Nan Yin
3. Jiyeon Ock
4. Jun-Kyu Suh
5. Ji-Kan Ryu
6. Jihwan Park

• Curated by eLife

  eLife assessment

  The authors provide important insights into the pathogenesis of erectile dysfunction (ED) in patients with diabetes. The authors present compelling evidence, using single-cell transcriptomic analysis in both mouse and human cavernous tissues, to support their claims regarding the key roles of pericytes in diabetic ED. The identification of LBH as a potential pericyte-specific marker in both mouse and human tissues further strengthens their findings. This well-written manuscript offers novel and significant contributions to the field, identifying potential therapeutic targets for further investigation.

Reviewed by eLife

INAEME: Integral Neoantigen Analysis with Entirety of Mutational Events

1. Vladimir Kovacevic
2. Ognjen S. Milicevic
3. Nevena M. Ilic Raicevic
4. Milica Kojicic
5. Ana Mijalkovic Lazic
6. Nikola Skundric
7. Jack DiGiovanna

Reviewed by PREreview

Context-Aware Amino Acid Embedding Advances Analysis of TCR-Epitope Interactions

1. Pengfei Zhang
2. Seojin Bang
3. Michael Cai
4. Heewook Lee

• Curated by eLife

  eLife assessment

  This study provides an important tool for predicting binding between immune cells receptors and antigens based on protein sequence data. The analysis convincingly showed the tool's effectiveness in both supervised TCR binding prediction and unsupervised clustering, surpassing existing methods in accuracy and reducing annotation costs. This study will be of interest to immunologists and computational biologists.

Reviewed by eLife

Phantasus, a web application for visual and interactive gene expression analysis

1. Maksim Kleverov
2. Daria Zenkova
3. Vladislav Kamenev
4. Margarita Sablina
5. Maxim N Artyomov
6. Alexey A Sergushichev

• Curated by eLife

  eLife assessment

  This study presents a useful tool called Phantasus, a web application to analyze gene expression data generated by microarray or RNA-seq technologies. The web application will help biologists end users, and non-bioinformatics experts to analyze new data or replicate transcriptomic studies. Local use of the Phantasus through its Bioconductor package reveals an incomplete functionality concerning the current best practices in analyzing bulk RNA-seq data.

Reviewed by eLife

Evaluating generalizability of artificial intelligence models for molecular datasets

1. Yasha Ektefaie
2. Andrew Shen
3. Daria Bykova
4. Maximillian G. Marin
5. Marinka Zitnik
6. Maha Farhat

Reviewed by Arcadia Science

Genes in Humans and Mice: Insights from Deep learning of 777K Bulk Transcriptomes

1. Zheng Su
2. Mingyan Fang
3. Andrei Smolnikov
4. Fatemeh Vafaee
5. Marcel E. Dinger
6. Emily C. Oates

Reviewed by Arcadia Science

CELEBRIMBOR: Pangenomes from metagenomes

1. Joel Hellewell
2. Samuel T. Horsfield
3. Johanna von Wachsmann
4. Tatiana Gurbich
5. Robert D. Finn
6. Zamin Iqbal
7. Leah W. Roberts
8. John A. Lees

Reviewed by Arcadia Science

Assessment of Gene Set Enrichment Analysis using curated RNA-seq-based benchmarks

1. Julián Candia
2. Luigi Ferrucci

Reviewed by Arcadia Science