Bioinformatics

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 albeit preliminary findings on transcriptome changes in cardiac lymphatic cells after myocardial infarction in mice. Despite revision, the conclusions of the authors remain uncertain as sample sizes in general are very low, and even sometimes too low to allow for valid statistical comparisons. Accordingly, there are concerns regarding statistical robustness, raised by both the editors and the reviewers. While the single-cell transcriptomic data were analyzed using solid advanced methodology, too few cells were included in the scRNA-seq data set and the spatial transcriptomics analyses. Thus, this study rather represents more a collection of preliminary transcriptomic data than a full scientific report that would definitively advance the field.

Reviewed by eLife

Statistical analysis supports pervasive RNA subcellular localization and alternative 3' UTR regulation

1. Rob Bierman
2. Jui M Dave
3. Daniel M Greif
4. Julia Salzman

• Curated by eLife

  eLife assessment

  This paper describes an important, well-organized study into an under-exploited area of spatial transcriptomics. The limitations of the approach are generally made clear, but there is insufficient orthogonal validation to demonstrate the biological significance of the results, which leads to the evidence for the claims being currently incomplete. Nevertheless, the tools presented will provide a resource to researchers wishing to characterise spatial patterning of mRNAs, and the paper will be of interest to researchers studying cell biology, RNA biology, and method development for spatial transcriptomics/proteomics.

Reviewed by eLife

Discovering genotype-phenotype relationships with machine learning and the Visual Physiology Opsin Database (VPOD)

1. Seth A. Frazer
2. Mahdi Baghbanzadeh
3. Ali Rahnavard
4. Keith A. Crandall
5. Todd H. Oakley

Reviewed by GigaScience

CoCoPyE: feature engineering for learning and prediction of genome quality indices

1. Niklas Birth
2. Nicolina Leppich
3. Julia Schirmacher
4. Nina Andreae
5. Rasmus Steinkamp
6. Matthias Blanke
7. Peter Meinicke

Reviewed by GigaScience

spatiAlign: an unsupervised contrastive learning model for data integration of spatially resolved transcriptomics

1. Chao Zhang
2. Lin Liu
3. Ying Zhang
4. Mei Li
5. Shuangsang Fang
6. Qiang Kang
7. Ao Chen
8. Xun Xu
9. Yong Zhang
10. Yuxiang Li

Reviewed by GigaScience

MISIP: a data standard for the reuse and reproducibility of any stable isotope probing-derived nucleic acid sequence and experiment

1. Abigayle Simpson
2. Elisha M Wood-Charlson
3. Montana Smith
4. Benjamin J Koch
5. Kathleen Beilsmith
6. Jeffrey A Kimbrel
7. Matthew Kellom
8. Christopher I Hunter
9. Ramona L Walls
10. Lynn M Schriml
11. Roland C Wilhelm

Reviewed by GigaScience

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