Bioinformatics

Annotation of Hox cluster and Hox cofactor genes in the Asian citrus psyllid, Diaphorina citri, reveals novel features

1. Teresa D. Shippy
2. Prashant S. Hosmani
3. Mirella Flores-Gonzalez
4. Lukas A. Mueller
5. Wayne B. Hunter
6. Susan J. Brown
7. Tom D’Elia
8. Surya Saha

Reviewed by GigaByte

Annotation of putative circadian rhythm-associated genes in Diaphorina citri (Hemiptera: Liviidae)

1. Max Reynolds
2. Lucas de Oliveira
3. Chad Vosburg
4. Thomson Paris
5. Crissy Massimino
6. Jordan Norus
7. Yasmin Ortiz
8. Michelle Espino
9. Nina Davis
10. Ron Masse
11. Alan Neiman
12. Rachel Holcomb
13. Kylie Gervais
14. Melissa Kemp
15. Maria Hoang
16. Teresa D. Shippy
17. Prashant S. Hosmani
18. Mirella Flores-Gonzalez
19. Kirsten Pelz-Stelinski
20. Jawwad A. Qureshi
21. Lukas A. Mueller
22. Wayne B. Hunter
23. Joshua B. Benoit
24. Susan J. Brown
25. Tom D’Elia
26. Surya Saha

Reviewed by GigaByte

Manual curation and phylogenetic analysis of chitinase family genes in the Asian citrus psyllid, Diaphorina citri

1. Teresa D. Shippy
2. Sherry Miller
3. Blessy Tamayo
4. Prashant S. Hosmani
5. Mirella Flores-Gonzalez
6. Lukas A. Mueller
7. Wayne B. Hunter
8. Susan J. Brown
9. Tom D’Elia
10. Surya Saha

Reviewed by GigaByte

Nomograms of human hippocampal volume shifted by polygenic scores

1. Mohammed Janahi
2. Leon Aksman
3. Jonathan M Schott
4. Younes Mokrab
5. Andre Altmann
6. On behalf of for the Alzheimer’s Disease Neuroimaging Initiative

• Curated by eLife

  Evaluation Summary:

  This manuscript considers whether genetic information can improve the clinical utility of population norms derived from brain imaging data. The authors propose to incorporate polygenic scores into normative models of hippocampal volume to improve predictions of neurodegenerative disease. This approach is elegantly demonstrated in this manuscript and may be useful for clinical translation of population neuroimaging.

  (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. Reviewer #1 and Reviewer #2 agreed to share their name with the authors.)

Reviewed by eLife

Bayesian Inference of Dependent Population Dynamics in Coalescent Models

1. Lorenzo Cappello
2. Jaehee Kim
3. Julia Palacios

Reviewed by ScreenIT

Predictions of immunogenicity reveal potent SARS-CoV-2 CD8+ T-cell epitopes

1. David Gfeller
2. Julien Schmidt
3. Giancarlo Croce
4. Philippe Guillaume
5. Sara Bobisse
6. Raphael Genolet
7. Lise Queiroz
8. Julien Cesbron
9. Julien Racle
10. Alexandre Harari

Reviewed by ScreenIT

Using unsupervised learning algorithms to identify essential genes associated with SARS-CoV-2 as potential therapeutic targets for COVID-19

1. Golnaz Taheri
2. Mahnaz Habibi

Reviewed by ScreenIT

Unsupervised outlier detection applied to SARS-CoV-2 nucleotide sequences can identify sequences of common variants and other variants of interest

1. Georg Hahn
2. Sanghun Lee
3. Dmitry Prokopenko
4. Jonathan Abraham
5. Tanya Novak
6. Julian Hecker
7. Michael Cho
8. Surender Khurana
9. Lindsey R. Baden
10. Adrienne G. Randolph
11. Scott T. Weiss
12. Christoph Lange

Reviewed by ScreenIT

AI-based search for convergently expanding, advantageous mutations in SARS-CoV-2 by focusing on oligonucleotide frequencies

1. Toshimichi Ikemura
2. Yuki Iwasaki
3. Kennosuke Wada
4. Yoshiko Wada
5. Takashi Abe

Reviewed by ScreenIT

Systematic lncRNA mapping to genome-wide co-essential modules uncovers cancer dependency on uncharacterized lncRNAs

1. Ramkrishna Mitra
2. Clare M Adams
3. Christine M Eischen

• Curated by eLife

  Evaluation Summary:

  Mitra et al. developed an analysis framework using large-scale pan-cancer omics datasets to discover the roles of 30 long non-coding RNAs (lncRNAs) in cancer proliferation and growth. Direct function-testing experiments were also performed to validate the biological mechanisms of two lncRNAs. The analysis framework developed here can serve as a resource to study the functions of lncRNA in cancer, and the computational framework can also be further extended to study cancer-relevant transcriptional and post-transcriptional regulation.

  (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. Reviewer #1 and Reviewer #2 agreed to share their name with the authors.)

Reviewed by eLife