Bioinformatics

Recommendations for Uniform Variant Calling of SARS-CoV-2 Genome Sequence across Bioinformatic Workflows

1. Ryan Connor
2. Migun Shakya
3. David A. Yarmosh
4. Wolfgang Maier
5. Ross Martin
6. Rebecca Bradford
7. J. Rodney Brister
8. Patrick S. G. Chain
9. Courtney A. Copeland
10. Julia di Iulio
11. Bin Hu
12. Philip Ebert
13. Jonathan Gunti
14. Yumi Jin
15. Kenneth S. Katz
16. Andrey Kochergin
17. Tré LaRosa
18. Jiani Li
19. Po-E Li
20. Chien-Chi Lo
21. Sujatha Rashid
22. Evguenia S. Maiorova
23. Chunlin Xiao
24. Vadim Zalunin
25. Lisa Purcell
26. Kim D. Pruitt

Reviewed by Arcadia Science

Neuroendocrinology of the lung revealed by single-cell RNA sequencing

1. Christin S Kuo
2. Spyros Darmanis
3. Alex Diaz de Arce
4. Yin Liu
5. Nicole Almanzar
6. Timothy Ting-Hsuan Wu
7. Stephen R Quake
8. Mark A Krasnow

• Curated by eLife

  eLife assessment

  This study delineates the transcriptomics of lung neuroendocrine cells and provides important new information on the nature of these cells in normal mouse lungs and in a sample of a human lung carcinoid. It will inform future studies investing the roles of PNECs in health and disease.

Reviewed by eLife

Taxonium, a web-based tool for exploring large phylogenetic trees

1. Theo Sanderson

• Curated by eLife

  eLife assessment

  Sanderson developed novel interactive software for visualizing phylogenetic trees representing millions of sequences. This is a fundamental advance over previous software that is typically limited to trees with a few thousand tips. Taxonium has been used intensively by the virus evolution community over the past months and has thus already proven its utility and performance.

Reviewed by eLife

A highly contiguous, scaffold-level nuclear genome assembly for the fever tree (Cinchona pubescens Vahl) as a novel resource for Rubiaceae research

1. Nataly Allasi Canales
2. Oscar A. Pérez-Escobar
3. Robyn F. Powell
4. Mats Töpel
5. Catherine Kidner
6. Mark Nesbitt
7. Carla Maldonado
8. Christopher J. Barnes
9. Nina Rønsted
10. Natalia A. S. Przelomska
11. Ilia J. Leitch
12. Alexandre Antonelli

Reviewed by GigaByte

svaRetro and svaNUMT: modular packages for annotating retrotransposed transcripts and nuclear integration of mitochondrial DNA in genome sequencing data

1. Ruining Dong
2. Daniel Cameron
3. Justin Bedo
4. Anthony T. Papenfuss

Reviewed by GigaByte

T cell receptor convergence is an indicator of antigen-specific T cell response in cancer immunotherapies

1. Mingyao Pan
2. Bo Li

• Curated by eLife

  eLife assessment

  This manuscript reports an association between TCR convergence and involvement in an antigen-specific response. TCR convergence is assessed as a potential biomarker of response to immune checkpoint blockade (ICB). From jointly analyzing TCR-seq data, single-cell RNA-seq data, and antigen-specific TCR information, the authors provided evidence that convergence is a potential indicator for ongoing T cell antigen-specific response. Overall, the analyses are sound the manuscript is well-written, and the study provides the first evidence that TCRseq alone could be used to predict clinical outcomes.

Reviewed by eLife

SIMMER employs similarity algorithms to accurately identify human gut microbiome species and enzymes capable of known chemical transformations

1. Annamarie E Bustion
2. Renuka R Nayak
3. Ayushi Agrawal
4. Peter J Turnbaugh
5. Katherine S Pollard

• Curated by eLife

  eLife Assessment:

  The authors aim to predict bacterial enzymes responsible for drug biotransformation, and the work showcases the potential of this approach as a hypothesis generator for characterizing and validating novel bacterial enzymes in vitro. The authors describe the relevance of an accurate input (in terms of reaction completeness, including cofactors and reaction products) as paramount for the quality of the prediction. The conclusions, however, require additional experimental and non-experimental validations.

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

Reviewed by eLife

Generative power of a protein language model trained on multiple sequence alignments

1. Damiano Sgarbossa
2. Umberto Lupo
3. Anne-Florence Bitbol

• Curated by eLife

  Evaluation Summary:

  This valuable paper proposes an innovative iterative masking approach that enables models such as the MSA Transformer to generate new protein sequence designs, which are validated using a wide-ranging set of computational experiments. A key strength of the MSA Transformer is the ability to learn and generalize across protein families, enabling impressive performance across a range of downstream tasks. However, to date, these models have not been used to generate new protein sequence designs. The approach proposed in this paper is quite novel, and a number of metrics are used to examine the resulting performance of the MSA Transformer at generating new protein sequences from specific families.

  (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

Tourmaline: A containerized workflow for rapid and iterable amplicon sequence analysis using QIIME 2 and Snakemake

1. Luke R Thompson
2. Sean R Anderson
3. Paul A Den Uyl
4. Nastassia V Patin
5. Shen Jean Lim
6. Grant Sanderson
7. Kelly D Goodwin

Reviewed by GigaScience

ProteInfer, deep neural networks for protein functional inference

1. Theo Sanderson
2. Maxwell L Bileschi
3. David Belanger
4. Lucy J Colwell

• Curated by eLife

  Evaluation Summary:

  The authors describe a newly developed software, ProteInfer, that analyses protein sequences to predict their functions. It is based on a single convolutional neural network scan for all known domains in parallel. This software provides a convincing approach for all computational scientists as well as experimentalists working near the interface of machine learning and molecular biology.

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

Reviewed by eLife