Bioinformatics

Miniaturizing, Modifying, and Magnifying Nature’s Proteins with Raygun

1. Kapil Devkota
2. Daichi Shonai
3. Joey Mao
4. Young Su Ko
5. Wei Wang
6. Scott Soderling
7. Rohit Singh

Reviewed by Arcadia Science

HyperMPNN – A general strategy to design thermostable proteins learned from hyperthermophiles

1. Moritz Ertelt
2. Phillip Schlegel
3. Max Beining
4. Leonard Kaysser
5. Jens Meiler
6. Clara T. Schoeder

Reviewed by Arcadia Science

demuxSNP: supervised demultiplexing scRNAseq using cell hashing and SNPs

1. Michael P. Lynch
2. Yufei Wang
3. Laurent Gatto
4. Aedin C. Culhane

Reviewed by GigaScience

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 STRAIN 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