Bioinformatics

Reviewed by Arcadia Science

A model-based factorization method for scRNA data unveils bifurcating transcriptional modules underlying cell fate determination

1. Jun Ren
2. Ying Zhou
3. Yudi Hu
4. Jing Yang
5. Hongkun Fang
6. Xuejing Lyu
7. Jintao Guo
8. Xiaodong Shi
9. Qiyuan Li

• Curated by eLife

  eLife Assessment

  MGPfactXMBD is a novel computational method for investigating cell evolutionary trajectory for scRNA-seq samples. It is important, with several potential future applications. The authors benchmarked this method using synthetic and real-world samples and showed superior performance for some of the tasks in cell trajectory analysis compared to other methods with compelling evidence.

Reviewed by eLife

A Structural Proteome Screen Identifies Protein Mimicry in Host-Microbe Systems

1. Gabriel Penunuri
2. Pingting Wang
3. Russell Corbett-Detig
4. Shelbi L Russell

Reviewed by Arcadia Science

Unified knowledge-driven network inference from omics data

1. Pablo Rodriguez-Mier
2. Martin Garrido-Rodriguez
3. Attila Gabor
4. Julio Saez-Rodriguez

Reviewed by PREreview

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 within-species diversity, revealing prevalent disease associations

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

Reviewed by Review Commons