Bioinformatics

NucBalancer: streamlining barcode sequence selection for optimal sample pooling for sequencing

1. Saurabh Gupta
2. Ankur Sharma

• Curated by GigaByte

  Editors Assessment:

  This paper presents NucBalancer, a R-pipeline and Shiny app designed for the optimal selection of barcode sequences for sample multiplexing in sequencing. Providing a user-friendly interface aiming to make this process accessible to both bioinformaticians and experimental researchers, enhancing its utility in adapting libraries prepared for one sequencing platform to be compatible with others. Important now with the introduction of additional sequencing platforms by Element Biosciences (AVITI System) and Ultima Genomics (UG100) increasing the diversity and capability of genomic research tools available. NucBalancer’s incorporation of dynamic parameters, including customizable red flag thresholds, allows for precise and practical barcode sequencing strategies. This adaptability is key in ensuring uniform nucleotide distribution, particularly in MGI sequencing and single-cell genomic studies, leading to more reliable and cost-effective sequencing outcomes across various experimental conditions. All the code is available under an open source license, and upon review the authors have also shared the code for the Shiny app.

  This evaluation refers to version 1 of the preprint

Reviewed by GigaByte

Faster model-based estimation of ancestry proportions

1. Cindy G. Santander
2. Alba Refoyo Martinez
3. Jonas Meisner

Reviewed by Peer Community in Evolutionary Biology

Exploring Evolution to Uncover Insights Into Protein Mutational Stability

1. Pauline Hermans
2. Matsvei Tsishyn
3. Martin Schwersensky
4. Marianne Rooman
5. Fabrizio Pucci

Reviewed by Arcadia Science

Deep-learning-based gene perturbation effect prediction does not yet outperform simple linear baselines

1. Constantin Ahlmann-Eltze
2. Wolfgang Huber
3. Simon Anders

Reviewed by preLights, PREreview

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 developed a robust machine learning approach to predict radio sensitivity in patients with NPC based on a defined gene signature. Some key aspects of this signature have been validated in vitro using relevant cell lines which strengthens the conclusions of this important and convincing study. The publication will be of interest to clinicians working on this indication as well as a more broader readership made up of scientists working on radiation biology and those with a bioinformatics/machine learning background.

Reviewed by eLife

Deep generative design of neutralizing nanobodies against SARS-CoV-2 variants

1. Liyun Huo
2. Tian Tian
3. Yanqin Xu
4. Qin Qin
5. Xinyi Jiang
6. Qiang Huang

Reviewed by Arcadia Science

Integer programming framework for pangenome-based genome inference

1. Ghanshyam Chandra
2. Md Helal Hossen
3. Stephan Scholz
4. Alexander T Dilthey
5. Daniel Gibney
6. Chirag Jain

Reviewed by Arcadia Science

BioFunctional: A Comprehensive App for Interpreting and Visualizing Functional Analysis of KEGG Pathways and Gene Ontologies

1. Alejandro Rodriguez
2. Antonio Monleon Getino

Reviewed by Arcadia Science

Generative Models Validation via Manifold Recapitulation Analysis

1. Nicolo’ Lazzaro
2. Gianluca Leonardi
3. Raffaele Marchesi
4. Massimiliano Datres
5. Anna Saiani
6. Jacopo Tessadori
7. Alejandro Granados
8. Johan Henriksson
9. Marco Chierici
10. Giuseppe Jurman
11. Toma Tebaldi
12. Gabriele Sales

Reviewed by Arcadia Science

InteracTor: A new integrative feature extraction toolkit for improved characterization of protein structural properties

1. Jose Cleydson F. Silva
2. Layla Schuster
3. Nick Sexson
4. Matias Kirst
5. Marcio F. R. Resende
6. Raquel Dias

Reviewed by Arcadia Science