Bioinformatics

The snoGloBe interaction predictor reveals a broad spectrum of C/D snoRNA RNA targets

1. Gabrielle Deschamps-Francoeur
2. Sonia Couture
3. Sherif Abou-Elela
4. Michelle S Scott

Reviewed by ScreenIT

Positional SHAP (PoSHAP) for Interpretation of Machine Learning Models Trained from Biological Sequences

1. Quinn Dickinson
2. Jesse G. Meyer

Reviewed by Review Commons

Real-time monitoring and analysis of SARS-CoV-2 nanopore sequencing with minoTour

1. Rory Munro
2. Nadine Holmes
3. Christopher Moore
4. Matt Carlile
5. Alexander Payne
6. Roberto Santos
7. Matt Loose

Reviewed by ScreenIT

Elucidation of the interactions between SARS-CoV-2 Spike protein and wild and mutant types of IFITM proteins by in silico methods

1. Nazli Irmak Giritlioglu
2. Gizem Koprululu Kucuk

Reviewed by ScreenIT

Both simulation and sequencing data reveal coinfections with multiple SARS-CoV-2 variants in the COVID-19 pandemic

1. Yinhu Li
2. Yiqi Jiang
3. Zhengtu Li
4. Yonghan Yu
5. Jiaxing Chen
6. Wenlong Jia
7. Yen Kaow Ng
8. Feng Ye
9. Shuai Cheng Li
10. Bairong Shen

Reviewed by ScreenIT

RBD Double Mutations of SARS-CoV-2 Strains Increase Transmissibility through Enhanced Interaction between RBD and ACE2 Receptor

1. Siddharth Sinha
2. Benjamin Tam
3. San Ming Wang

Reviewed by ScreenIT

RNA splicing programs define tissue compartments and cell types at single-cell resolution

1. Julia Eve Olivieri
2. Roozbeh Dehghannasiri
3. Peter L Wang
4. SoRi Jang
5. Antoine de Morree
6. Serena Y Tan
7. Jingsi Ming
8. Angela Ruohao Wu
9. Tabula Sapiens Consortium
10. Stephen R Quake
11. Mark A Krasnow
12. Julia Salzman

• Curated by eLife

  Evaluation Summary:

  This manuscript describes an analysis of cell type-specific alternative splicing using 10x scRNA-seq data. This work shows that in spite of the challenges associated with the analysis of such datasets, it is possible to identify alternative exons with differential splicing between tissue compartments and to some extent reveal cell types by splicing profiles of single cells. This work is informative regarding what can be done to analyse alternative splicing using 10X data and fills in a gap in the field.

  (This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. The reviewers remained anonymous to the authors.)

Reviewed by eLife

(Machine) Learning the mutation signatures of SARS-CoV-2: a primer for predictive prognosis

1. Sunil Nagpal
2. Nishal Kumar Pinna
3. Divyanshu Srivastava
4. Rohan Singh
5. Sharmila S. Mande

Reviewed by ScreenIT

From Alpha to Zeta: Identifying Variants and Subtypes of SARS-CoV-2 Via Clustering

1. Andrew Melnyk
2. Fatemeh Mohebbi
3. Sergey Knyazev
4. Bikram Sahoo
5. Roya Hosseini
6. Pavel Skums
7. Alex Zelikovsky
8. Murray Patterson

Reviewed by ScreenIT

Epitopedia: identifying molecular mimicry between pathogens and known immune epitopes

1. Christian A Balbin
2. Janelle Nunez-Castilla
3. Vitalii Stebliankin
4. Prabin Baral
5. Masrur Sobhan
6. Trevor Cickovski
7. Ananda Mohan Mondal
8. Giri Narasimhan
9. Prem Chapagain
10. Kalai Mathee
11. Jessica Siltberg-Liberles

Reviewed by ScreenIT