Bioinformatics

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

Lack of evidence for increased transcriptional noise in aged tissues

1. Olga Ibañez-Solé
2. Alex M Ascensión
3. Marcos J Araúzo-Bravo
4. Ander Izeta

• Curated by eLife

  Evaluation Summary:

  The authors aim to tackle a fundamental question with their study: whether there is a direct age-associated increase of transcriptional noise. To investigate this question, they develop tools to analyze single-cell sequencing data from mouse and human aging datasets. Ultimately, application of their novel tool (Scallop) suggests that transcriptional noise does not change with age, changes in transcriptional noise can be attributed to other sources such as subtle shifts in cell identity. This study is in principle of broad interest, but it currently lacks a definitive demonstration of the robustness of Scallop. Systematic testing of this new package would ultimately strengthen the key conclusion of the work and give additional users more confidence when using the tool to estimate expression noise.

  (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

Comprehensive phylogenetic analysis of the ribonucleotide reductase family reveals an ancestral clade

1. Andrew A Burnim
2. Matthew A Spence
3. Da Xu
4. Colin J Jackson
5. Nozomi Ando

• Curated by eLife

  Evaluation summary

  Ribonucleotide reductases (RNRs) have fascinated biologists and chemists, as these enzymes catalyze the conversion of ribonucleotides (NDPs or NTPs) to deoxynucleotides (dNDP or dNTPs), which are essential for DNA biosynthesis in all organisms. Given this role, they have been postulated to be the link in the transition from an RNA/protein to a DNA world. In addition, RNRs use an array of protein, metal-based, and nucleotide radicals for the reaction they catalyze. This paper creatively combines two methods of analysis to propose a new evolutionary model for the diversification observed for the RNR family into the three classes: I, II and III. The work is of interest to students of molecular evolution, RNRs and colleagues interested in the origin of life.

  (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, Reviewer #2 and Reviewer #3 agreed to share their name with the authors.)

Reviewed by eLife

Standardized genome-wide function prediction enables comparative functional genomics: a new application area for Gene Ontologies in plants

1. Leila Fattel
2. Dennis Psaroudakis
3. Colleen F. Yanarella
4. Kevin O. Chiteri
5. Haley A. Dostalik
6. Parnal Joshi
7. Dollye C. Starr
8. Ha Vu
9. Kokulapalan Wimalanathan
10. Carolyn J. Lawrence-Dill

Reviewed by GigaScience

Evaluation of in silico predictors on short nucleotide variants in HBA1, HBA2, and HBB associated with haemoglobinopathies

1. Stella Tamana
2. Maria Xenophontos
3. Anna Minaidou
4. Coralea Stephanou
5. Cornelis L Harteveld
6. Celeste Bento
7. Joanne Traeger-Synodinos
8. Irene Fylaktou
9. Norafiza Mohd Yasin
10. Faidatul Syazlin Abdul Hamid
11. Ezalia Esa
12. Hashim Halim-Fikri
13. Bin Alwi Zilfalil
14. Andrea C Kakouri
15. ClinGen Hemoglobinopathy Variant Curation Expert Panel
16. Marina Kleanthous
17. Petros Kountouris

• Curated by eLife

  Evaluation Summary:

  The increased use of gene and exome sequencing of individuals for diagnostic purposes has led to the identification of numerous single nucleotide variants (SNVs). However, annotating the probable clinical significance of every newly identified variant relies on multiple criteria, and in silico predictions can be used by curation experts to classify variants in databases. Since the reliability of such predictions is of paramount importance, this study compares the performance of 31 computational tools in classifying the pathogenicity of SNVs in the human adult globin genes and proposes an improved approach to achieve balanced predictions. The paper will be of interest to scientists and clinicians in the field of hemoglobinopathies.

  (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

Comprehensive machine-learning survival framework develops a consensus model in large-scale multicenter cohorts for pancreatic cancer

1. Libo Wang
2. Zaoqu Liu
3. Ruopeng Liang
4. Weijie Wang
5. Rongtao Zhu
6. Jian Li
7. Zhe Xing
8. Siyuan Weng
9. Xinwei Han
10. Yu-ling Sun

• Curated by eLife

  Evaluation Summary:

  This work sets out to develop a better machine learning-based predictor of survival/prognosis for patients diagnosed with pancreatic cancer, by developing a large combinatorial family of machine learning methods based on a high-dimensional set of -omics and other patient data features; using ten publicly available data sets. A reduced set of features (giving rise to a signature called AIDPS that involves 9 genes) was identified. Unfortunately, the authors used all ten data sets both in the discover stage and in the validation stage of their study. There was also a large mismatch between the initial number of covariates (15,288 genes) and the number of samples (n=1280). The combinatorial ensemble of ML models makes for an unwieldy methodology that is difficult to interpret or duplicate.

  (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

CriSNPr, a single interface for the curated and de novo design of gRNAs for CRISPR diagnostics using diverse Cas systems

1. Asgar H Ansari
2. Manoj Kumar
3. Sajal Sarkar
4. Souvik Maiti
5. Debojyoti Chakraborty

• Curated by eLife

  Evaluation Summary:

  The web-based software developed in this study will be of interest to researchers who develop CRISPR-based diagnostic methods. The use of CRISPR-Cas to rapidly identify specific mutations in both cancer and infection is an evolving field with good potential to play a role in future research and diagnostics. This software will facilitate the implementation of such technologies and is therefore useful.

  (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 agreed to share their name with the authors.)

Reviewed by eLife