Bioinformatics

stMMR: accurate and robust spatial domain identification from spatially resolved transcriptomics with multi-modal feature representation

1. Daoliang Zhang
2. Na Yu
3. Wenrui Li
4. Xue Sun
5. Qi Zou
6. Xiangyu Li
7. Zhiping Liu
8. Zhiyuan Yuan
9. Wei Zhang
10. Rui Gao

Reviewed by GigaScience

CAT Bridge: An Efficient Toolkit for Gene-Metabolite Association Mining from Multi-Omics Data

1. Bowen Yang
2. Tan Meng
3. Xinrui Wang
4. Jun Li
5. Shuang Zhao
6. Yingheng Wang
7. Shu Yi
8. Yi Zhou
9. Yi Zhang
10. Liang Li
11. Li Guo

Reviewed by GigaScience

Metagenome-assembled genomes from a population-based cohort uncover novel gut species and within-species diversity, revealing prevalent disease associations

1. Kateryna Pantiukh
2. Kertu Liis Krigul
3. Oliver Aasmets
4. Elin Org

Reviewed by Review Commons

ConvCGP: A Convolutional Neural Network to Predict Genotypic Values of Rice Traits from Compressed Genome-Wide Polymorphisms

1. Tanzila Islam
2. Chyon Hae Kim
3. Hiroyuki Shimono
4. Akio Kimura
5. Hiroyoshi Iwata

Reviewed by Arcadia Science

A Large-Scale Foundation Model for RNA Function and Structure Prediction

1. Shuxian Zou
2. Tianhua Tao
3. Sazan Mahbub
4. Caleb N. Ellington
5. Robin Algayres
6. Dian Li
7. Yonghao Zhuang
8. Hongyi Wang
9. Le Song
10. Eric P. Xing

Reviewed by Arcadia Science

Miniature: Unsupervised glimpses into multiplexed tissue imaging datasets as thumbnails for data portals

1. Adam J Taylor

Reviewed by Arcadia Science

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