Bioinformatics

Challenges and Progress in RNA Velocity: Comparative Analysis Across Multiple Biological Contexts

1. Sarah Ancheta
2. Leah Dorman
3. Guillaume Le Treut
4. Abel Gurung
5. Loïc A. Royer
6. Alejandro Granados
7. Merlin Lange

Reviewed by Review Commons

TSTA: thread and SIMD-based trapezoidal pairwise/multiple sequence-alignment method

1. Peiyu Zong
2. Wenpeng Deng
3. Jian Liu
4. Jue Ruan

• Curated by GigaByte

  Editors Assessment:

  The article presents strategies for accelerating sequence alignment using multithreading and SIMD (Single Instruction, Multiple Data) techniques, and introduces a new algorithm called TSTA (Thread and SIMD-Based Trapezoidal Pairwise/Multiple Sequence-Alignment). The Technical Release write-up presenting a detailed description of TSTA's performance in pairwise sequence alignment (PSA) and multiple sequence alignment (MSA), and compares it with various existing alignment algorithms. Demonstrating the performance gains achieved by vectorized SIMD technology and the application of threading. Testing and debugging a few errors, and adding some more background detail, demonstrating it can achieve faster comparison speed. Demonstrating TSTA's efficacy in pairwise sequence alignment and multiple sequence alignment, particularly with long reads, and showcasing considerable speed enhancements compared to existing tools.

  This evaluation refers to version 1 of the preprint

Reviewed by GigaByte

Chromosome-level genome assembly and annotation of the crested gecko, Correlophus ciliatus, a lizard incapable of tail regeneration

1. Marc A. Gumangan
2. Zheyu Pan
3. Thomas P. Lozito

• Curated by GigaByte

  Editors Assessment:

  The crested gecko (Correlophus ciliatus), is a lizard species endemic to New Caledonia, and a potentially interesting model organism due to its unusual (for a gecko) inability to regenerate amputated tails. With that in mind here is presented a new reference genome for the species, assembled using PacBio Sequel II platform and Dovetail Omni-C libraries. Producing a genome with a total size of 1.65 Gb, 152 scaffolds, a L50 of 6, and N50 of 109 Mb. Peer review making sure more detail was added on data acquisition and processing to enhance reproducibility. In the end producing potentially useful data for studying the genetic mechanisms involved in loss of tail regeneration.

  This evaluation refers to version 1 of the preprint

Reviewed by GigaByte

SMARTER-database: a tool to integrate SNP array datasets for sheep and goat breeds

1. Paolo Cozzi
2. Arianna Manunza
3. Johanna Ramirez-Diaz
4. Valentina Tsartsianidou
5. Konstantinos Gkagkavouzis
6. Pablo Peraza
7. Anna Maria Johansson
8. Juan José Arranz
9. Fernando Freire
10. Szilvia Kusza
11. Filippo Biscarini
12. Lucy Peters
13. Gwenola Tosser-Klopp
14. Gabriel Ciappesoni
15. Alexandros Triantafyllidis
16. Rachel Rupp
17. Bertrand Servin
18. Alessandra Stella

• Curated by GigaByte

  Editors Assessment:

  This paper presents the SMARTER database, a collection of tools and scripts to gather, standardize, and share with the scientific community a comprehensive dataset of genomic data and metadata information on worldwide small ruminant populations. Which has come out of the EU multi-actor (12 country) H2020 project called SMARTER: SMAll RuminanTs breeding for Efficiency and Resilience. This bringing together genotypes for about 12,000 sheep and 6,000 goats, alongside phenotypic and geographic information. The paper providing insight into how the database was put together, presenting the code for the SMARTER—frontend, backend and API, alongside instructions for users. Peer review tested the platform and provided suggestions on improving the metadata. Demonstrating the project provides valuable information on sheep and goat populations around the world, that can be an essential tool for ruminant researchers. Enabling them to generate new insights and offer the possibility to store new genotypes and drive progress in the field.

  This evaluation refers to version 1 of the preprint

Reviewed by GigaByte

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 predictions of gene perturbation effects do not yet outperform simple linear baselines

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

Reviewed by preLights, PREreview

V-pipe 3.0: a sustainable pipeline for within-sample viral genetic diversity estimation

1. Lara Fuhrmann
2. Kim Philipp Jablonski
3. Ivan Topolsky
4. Aashil A Batavia
5. Nico Borgsmüller
6. Pelin Icer Baykal
7. Matteo Carrara
8. Chaoran Chen
9. Arthur Dondi
10. Monica Dragan
11. David Dreifuss
12. Anika John
13. Benjamin Langer
14. Michal Okoniewski
15. Louis du Plessis
16. Uwe Schmitt
17. Franziska Singer
18. Tanja Stadler
19. Niko Beerenwinkel

Reviewed by GigaScience, GigaByte

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