Bioinformatics

Fourier spectral density of the coronavirus genome

1. H.S. Tan

Reviewed by ScreenIT

SARS-CoV-2 transcriptome analysis and molecular cataloguing of immunodominant epitopes for multi-epitope based vaccine design

1. Sandeep Kumar Kushwaha
2. Veerbhan Kesarwani
3. Samraggi Choudhury
4. Sonu Gandhi
5. Shailesh Sharma

Reviewed by ScreenIT

ESC - a comprehensive resource for SARS-CoV-2 immune escape variants

1. Mercy Rophina
2. Kavita Pandhare
3. Afra Shamnath
4. Mohamed Imran
5. Bani Jolly
6. Vinod Scaria

Reviewed by ScreenIT

Modeling the Impact of Lock-down on COVID-19 Spread in Malaysia

1. Altahir A. Altahir
2. Nirbhay Mathur
3. Loshini Thiruchelvam
4. Ghulam E. Mustafa Abro
5. Syaimaa’ S. M. Radzi
6. Sarat C. Dass
7. Balvinder Singh Gill
8. P. Sebastian
9. Saiful A.M. Zulkifli
10. Vijanth S. Asirvadam

Reviewed by ScreenIT

Characterization of the NiRAN domain from RNA-dependent RNA polymerase provides insights into a potential therapeutic target against SARS-CoV-2

1. Abhisek Dwivedy
2. Richard Mariadasse
3. Mohammed Ahmad
4. Sayan Chakraborty
5. Deepsikha Kar
6. Satish Tiwari
7. Sankar Bhattacharyya
8. Sudipta Sonar
9. Shailendra Mani
10. Prafullakumar Tailor
11. Tanmay Majumdar
12. Jeyaraman Jeyakanthan
13. Bichitra Kumar Biswal

Reviewed by ScreenIT

Single-cell RNA expression profiling of ACE2, the receptor of SARS-CoV-2

1. Yu Zhao
2. Zixian Zhao
3. Yujia Wang
4. Yueqing Zhou
5. Yu Ma
6. Wei Zuo

Reviewed by ScreenIT

AI-guided discovery of the invariant host response to viral pandemics

1. Debashis Sahoo
2. Gajanan D. Katkar
3. Soni Khandelwal
4. Mahdi Behroozikhah
5. Amanraj Claire
6. Vanessa Castillo
7. Courtney Tindle
8. MacKenzie Fuller
9. Sahar Taheri
10. Thomas F. Rogers
11. Nathan Beutler
12. Sydney I. Ramirez
13. Stephen A. Rawlings
14. Victor Pretorius
15. David M. Smith
16. Dennis R. Burton
17. Laura E. Crotty Alexander
18. Jason Duran
19. Shane Crotty
20. Jennifer M. Dan
21. Soumita Das
22. Pradipta Ghosh

Reviewed by ScreenIT

Mapping single-cell atlases throughout Metazoa unravels cell type evolution

1. Alexander J Tarashansky
2. Jacob M Musser
3. Margarita Khariton
4. Pengyang Li
5. Detlev Arendt
6. Stephen R Quake
7. Bo Wang

• Curated by eLife

  Evaluation Summary:

  The development of single-cell genomic methods has transformed our understanding of cell types and their attributes across organisms. Here, Tarashansky et al develop SAMap (Self-Assembling Manifold mapping), a graph-based data integration method which builds upon their previously described SAM algorithm to facilitate assignment of homologous genes and cell types across diverse species. As the authors show, this empowers comparative analyses across phyla to facilitate cellular annotation and examine the evolutionary origins of cellular diversity. Overall, the manuscript is well-written and the algorithm has the potential to be foundation for comparative cellular atlasing.

  (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

Expanding the MECP2 network using comparative genomics reveals potential therapeutic targets for Rett syndrome

1. Irene Unterman
2. Idit Bloch
3. Simona Cazacu
4. Gila Kazimirsky
5. Bruria Ben-Zeev
6. Benjamin P Berman
7. Chaya Brodie
8. Yuval Tabach

• Curated by eLife

  Evaluation Summary:

  The manuscript has the potential to be of broad interest to neuroscientists who are aiming to leverage concepts and tools of evolutionary biology to identify novel gene targets and much-needed therapeutic interventions. The follow up experiments are detailed, well thought out, and do a good job of proving the potential of the identified drugs in alleviating molecular signatures in in vitro disease models. However, the link between comparative genomic analysis and identification of specific drugs is not yet sufficiently established and doesn't convincingly demonstrate the usability of the evolutionary pipeline in identifying novel therapeutics.

  (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

Single cell transcriptome analysis defines heterogeneity of the murine pancreatic ductal tree

1. Audrey M. Hendley
2. Arjun A. Rao
3. Laura Leonhardt
4. Sudipta Ashe
5. Jennifer A. Smith
6. Simone Giacometti
7. Xianlu L Peng
8. Honglin Jiang
9. David I. Berrios
10. Mathias Pawlak
11. Lucia Y. Li
12. Jonghyun Lee
13. Eric A. Collisson
14. Mark Anderson
15. Gabriela K. Fragiadakis
16. Jen Jen Yeh
17. Jimmie Ye Chun
18. Grace E. Kim
19. Valerie M. Weaver
20. Matthias Hebrok

• Curated by eLife

  Evaluation Summary:

  In this study, the authors present a high-resolution single-cell transcriptomic atlas of the pancreatic ductal tree. Their analysis unveiled important heterogeneity within the pancreatic ductal tree and identified unique cellular states. Overall, the results presented here suggest distinct functional roles for subpopulations of duct cells in maintenance of duct cell identity and implication in chronic pancreatic inflammation. Finally, such detailed analysis of the pancreatic duct tree is relevant also in the context of cancer biology and might help elucidating the transition from pancreatitis to pancreatic cancer and/or different predisposition to cancer.

  (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