Lindez, P. P. | bioRxiv Science

bioRxiv2024

BinBencher: Fast, flexible and meaningful benchmarking suite for metagenomic binning

New methods for metagenomic binning are typically evaluated using benchmarking software, and become tuned to maximize whatever criterion is measured by the benchmark. Subtleties in benchmarking procedures can cause misleading evaluations, derailing method development. Differences between procedures used to evaluate binning tools make them hard to compare, which slows progress in the field. We introduce BinBencher, a free software suite for benchmarking, and show how BinBencher produces evaluations that are more biologically meaningful than alternative benchmarking approaches.

bioinformatics↗

bioRxiv2022

FAVA: High-quality functional association networks inferred from scRNA-seq and proteomics data

Protein networks are commonly used for understanding how proteins interact. However, they are typically biased by data availability, favoring well-studied proteins with more interactions. To uncover functions of understudied proteins, we must use data that are not affected by this literature bias, such as single-cell RNA-seq and proteomics. Due to data sparseness and redundancy, co-expression analysis becomes complex. To address this, we have developed FAVA (Functional Associations using Variational Autoencoders), which compresses high-dimensional data into a low-dimensional space. FAVA infers networks from high-dimensional omics data with much higher accuracy than existing methods, across a diverse collection of real as well as simulated datasets. FAVA can process large datasets with over 0.5 million conditions and has predicted 4,210 interactions between 1,039 understudied proteins. Our findings showcase FAVAs capability to offer novel perspectives on protein interactions. FAVA functions within the scverse ecosystem, employing AnnData as its input source.

bioinformatics↗