Bioinformatics

BatchEval Pipeline: batch effect evaluation workflow for multiple datasets joint analysis

1. Chao Zhang
2. Qiang Kang
3. Mei Li
4. Hongqing Xie
5. Shuangsang Fang
6. Xun Xu

• Curated by GigaByte

  Editors Assessment:

  For better data quality assessment of large spatial transcriptomics datasets this new BatchEval software has been developed as a batch effect evaluation tool. This generates a comprehensive report with assessment findings, including basic information of integrated datasets, a batch effect score, and recommended methods for removing batch effects. The report also includes evaluation details for the raw dataset and results from batch effect removal methods. Through peer review and clarification of a number of points it now looks convincing that this tool helps researchers identify and remove batch effects, ensuring reliable and meaningful insights from integrated datasets. Potentially making the tool valuable for researchers who need to analyze large datasets of this type, as it provides an easy and reliable way to assess data quality and ensures that downstream analyses are robust and reliable.

  This evaluation refers to version 1 of the preprint

Reviewed by GigaByte

Benchmarking reverse docking through AlphaFold2 human proteome

1. Qing Luo
2. Sheng Wang
3. Hoi Yeung Li
4. Liangzhen Zheng
5. Yuguang Mu
6. Jingjing Guo

Reviewed by Arcadia Science

Sensitive remote homology search by local alignment of small positional embeddings from protein language models

1. Sean R Johnson
2. Meghana Peshwa
3. Zhiyi Sun

• Curated by eLife

  eLife assessment

  This important study addresses the problem of detecting weak similarity between protein sequences, a procedure commonly used to infer homology or assign putative functions to uncharacterized proteins. The authors present a convincing approach that combines recently developed protein language models with well-established methods. The benchmarks provided show that the proposed tool is fast and accurate for remote homology detection, making this paper of general interest to all researchers working in the fields of protein evolution and genome annotation.

Reviewed by eLife

OmniNA: A foundation model for nucleotide sequences

1. Xilin Shen
2. Xiangchun Li

Reviewed by Arcadia Science

A Multi-omics Data Analysis Workflow Packaged as a FAIR Digital Object

1. Anna Niehues
2. Casper de Visser
3. Fiona A. Hagenbeek
4. Purva Kulkarni
5. Rene Pool
6. Naama Karu
7. Alida S. D. Kindt
8. Gurnoor Singh
9. Robert R. J. M. Vermeiren
10. Dorret I. Boomsma
11. Jenny van Dongen
12. Peter A. C. 't Hoen
13. Alain J. van Gool

Reviewed by GigaScience

Machine Learning Made Easy (MLme): a comprehensive toolkit for machine learning–driven data analysis

1. Akshay Akshay
2. Mitali Katoch
3. Navid Shekarchizadeh
4. Masoud Abedi
5. Ankush Sharma
6. Fiona C Burkhard
7. Rosalyn M Adam
8. Katia Monastyrskaya
9. Ali Hashemi Gheinani

Reviewed by GigaScience

Vulture: cloud-enabled scalable mining of microbial reads in public scRNA-seq data

1. Junyi Chen
2. Danqing Yin
3. Harris Y H Wong
4. Xin Duan
5. Ken H O Yu
6. Joshua W K Ho

Reviewed by GigaScience

Predicting metabolic modules in incomplete bacterial genomes with MetaPathPredict

1. David Geller-McGrath
2. Kishori M Konwar
3. Virginia P Edgcomb
4. Maria Pachiadaki
5. Jack W Roddy
6. Travis J Wheeler
7. Jason E McDermott

• Curated by eLife

  eLife assessment

  This landmark study presents MetaPathPredict, a method that uses a stacked ensemble of neural networks to predict the presence or absence of KEGG modules based on annotated features in the genome. The evidence supporting the conclusions is compelling, with a tool that allows for prediction of KEGG modules in sparse gene sequence datasets.

Reviewed by eLife

AutoPeptideML: a study on how to build more trustworthy peptide bioactivity predictors

1. Raúl Fernández-Díaz
2. Rodrigo Cossio-Pérez
3. Clement Agoni
4. Hoang Thanh Lam
5. Vanessa Lopez
6. Denis C Shields

Reviewed by Arcadia Science

LazyAF, a pipeline for accessible medium-scale in silico prediction of protein-protein interactions

1. Thomas C. McLean

Reviewed by Arcadia Science