Bioinformatics

Identification of type 2 diabetes- and obesity-associated human β-cells using deep transfer learning

1. Gitanjali Roy
2. Rameesha Syed
3. Olivia Lazaro
4. Sylvia Robertson
5. Sean D McCabe
6. Daniela Rodriguez
7. Alex M Mawla
8. Travis S Johnson
9. Michael A Kalwat

• Curated by eLife

  eLife assessment

  This is a useful study that applies deep transfer learning to assign patient-level disease attributes to single cells of T2D and non-diabetic patients, including obese patients. This analysis identified a single cluster of T2D-associated β-cells; and two subpopulations of obese- β-cells derived from either non-diabetic or T2D donors. The findings were validated at the protein level using immunohistochemistry on islets derived from non-diabetic and T2D organ donors, contributing solid experimental evidence for the computational analyses.

Reviewed by eLife

A Transparent and Generalizable Deep Learning Framework for Genomic Ancestry Prediction

1. Camille Rochefort-Boulanger
2. Matthew Scicluna
3. Raphaël Poujol
4. Jean-Christophe Grenier
5. Pierre Luc Carrier
6. Sébastien Lemieux
7. Julie G Hussin

Reviewed by Arcadia Science

ASPEN: Robust detection of allelic dynamics in single cell RNA-seq

1. Veronika Petrova
2. Muqing Niu
3. Thomas S. Vierbuchen
4. Emily S. Wong

Reviewed by Review Commons

Evolutionary Reasoning Does Not Arise in Standard Usage of Protein Language Models

1. Yasha Ektefaie
2. Andrew Shen
3. Lavik Jain
4. Maha Farhat
5. Marinka Zitnik

Reviewed by Arcadia Science

SeuratExtend: Streamlining Single-Cell RNA-Seq Analysis Through an Integrated and Intuitive Framework

1. Yichao Hua
2. Linqian Weng
3. Fang Zhao
4. Florian Rambow

Reviewed by GigaScience

Micro𝕊plit: Semantic Unmixing of Fluorescent Microscopy Data

1. Ashesh Ashesh
2. Federico Carrara
3. Igor Zubarev
4. Vera Galinova
5. Melisande Croft
6. Melissa Pezzotti
7. Daozheng Gong
8. Francesca Casagrande
9. Elisa Colombo
10. Stefania Giussani
11. Elena Restelli
12. Eugenia Cammarota
13. Juan Manuel Battagliotti
14. Nikolai Klena
15. Moises Di Sante
16. Raghabendra Adhikari
17. Daniel Feliciano
18. Gaia Pigino
19. Elena Taverna
20. Oliver Harschnitz
21. Nicola Maghelli
22. Norbert Scherer
23. Damian Edward Dalle Nogare
24. Joran Deschamps
25. Francesco Pasqualini
26. Florian Jug

Reviewed by Arcadia Science

Integrated flexible DNA methylation-chromatin segmentation modeling enhances epigenomic state annotation

1. Nihit Aggarwal
2. Johanna Elena Schmitz
3. Lukas Laufer
4. Sven Rahmann
5. Jörn Walter
6. Abdulrahman Salhab

Reviewed by Arcadia Science

A knowledge-based distance metric highlights underperformance of variant effect predictors on gain-of-function missense variants

1. Lukas Gerasimavicius
2. Joseph A. Marsh

Reviewed by Arcadia Science

Chevreul: an R bioconductor package for exploratory analysis of full-length single cell sequencing

1. Kevin Stachelek
2. Bhavana Bhat
3. David Cobrinik

• Curated by GigaByte

  Editors Assessment:

  This paper presents Chevreul, a new open-source R Bioconductor (meta-)package for processing and integration of scRNA-seq data from cDNA end-counting, full-length short-read or long-read protocols. Alongside a R Shiny app for easy visualization, formatting, and analysis for exploratory analyses of scRNA-seq data processed in the SingleCellExperiment Bioconductor or Seurat formats. The name of the tool is inspired by the colour theorist Michel-Eugène Chevreul and the optical illusion of the same name. To demonstrate the use of Chevreul, the authors provide a sample analysis, which helps to demonstrate how users can visualize a wide range of parameters, enabling transparent and reproducible scRNA-seq analyses. Peer review also pushing the author to provide extensive guidance materials to assist with use. Being implemented in R, the R package and integrated Shiny application are freely available under an open-source MIT license in Bioconductor and their GitHub page here: https://github.com/cobriniklab/chevreul

  This evaluation refers to version 1 of the preprint

Reviewed by GigaByte

Machine Learning and Network Analysis to Predict Hypothetical Protein Functions of Aeromonas hydrophila

1. Harun Pirim
2. Zaidur Rahman
3. Saviz Saei
4. Sangam Gyawali
5. Mohammad Marufuzzaman
6. Nazanin Tajik
7. Hasan Tekedar

Reviewed by Arcadia Science