Biology subjects

Mahlich, Y.

Publications and source records attributed to Mahlich, Y..

4 recordsLinked to original sources

bioRxiv2026

VaLPAS: Leveraging variation in experimental multi-omics data to elucidate protein function

MotivationDespite continuing advances in sequencing and computational function determination, large parts of the studied gene, protein, and metabolite space remain functionally undetermined. Most function assignment is driven by homology searches and annotation transfer from known and extensively studied proteins but often fails to leverage available experimental omics data generated via technologies like mass-spectrometry. ResultsThe VaLPAS (Variation-Leveraged Phenomic Association Screen) framework is available as a Python package and provides a user-friendly platform for calculation of associations between expression patterns of genes or proteins in multi-omic datasets based on various statistical and learning methods. The goal of this approach is to shed light on the functional dark matter of protein space by elucidating previously unknown functions of molecules using guilt by association with molecules of known function. We present results demonstrating the utility of VaLPAS to identify high-confidence predictions for a subset of genes/proteins of unknown function in a previously published multi-omics dataset from the oleaginous yeast, Rhodotorula toruloides. AvailabilityVaLPAS is written in Python. The code is hosted on github (https://github.com/PNNL-Predictive-Phenomics/valpas/).

bioinformatics↗

bioRxiv2025

spammR: an R package designed for analysis and integration of spatial multi-omic measurements

SummarySpatial omics is a young and evolving field and as such shows rapid development of novel technologies and analysis methods to measure transcripts, proteins, metabolites, and post-translational modifications at high spatial resolution. These advances in technology have enabled the simultaneous generation of abundance profiles for multiple different omics types and associated microscopy imaging data, as well as their analysis in a spatial context. However, most analytical tools are designed for spatial transcriptomics platforms and are challenging to use in other contexts such as mass spectrometry-based measurements or metagenomics. To this end we present spammR (spatial analysis of multi-omics measurements in R), an R package that enables end-to-end analysis with a specific focus on mass-spectrometry derived spatial omics datasets with the goal of integration across multiple data types (e.g. sequencing, metabolites, proteins) within the same tissue sample. Availability and implementationspammR is implemented in R. The package is currently installable from GitHub (https://github.com/PNNL-CompBio/spammR).

bioinformatics↗

bioRxiv2022

Rethinking bacterial relationships in light of their molecular abilities

Determining the repertoire of a microbes molecular functions is a central question in microbial biology. Modern techniques achieve this goal by comparing microbial genetic material against reference databases of functionally annotated genes/proteins or known taxonomic markers such as 16S rRNA. Here we describe a novel approach to exploring bacterial functional repertoires without reference databases. Our Fusion scheme establishes functional relationships between bacteria and assigns organisms to Fusion-taxa that differ from otherwise defined taxonomic clades. Three key findings of our work stand out. First, bacterial functional comparisons outperform marker genes in assigning taxonomic clades. Fusion profiles are also better for this task than other functional annotation schemes. Second, Fusion-taxa are robust to addition of novel organisms and are, arguably, able to capture the environment-driven bacterial diversity. Finally, our alignment-free nucleic acid-based Siamese Neural Network model, created using Fusion functions, enables finding shared functionality of very distant, possibly structurally different, microbial homologs. Our work can thus help annotate functional repertoires of bacterial organisms and further guide our understanding of microbial communities.

bioinformatics↗

bioRxiv2019

An exhaustive analysis of single amino acid variants in helical transmembrane proteins

Single nucleotide variants (SNVs) have been widely studied in the past due to being the main source of human genetic variation. Less is known about the effect of single amino acid variants (SAVs) due to the immense resources required for comprehensive experimental studies. In contrast, in silico methods predicting the effects of sequence variants upon molecular function and upon the organism are readily available and have contributed unexpected suggestions, e.g. that SAVs common to a human population (shared by >5% of the population) have, on average, more significant impact on the molecular function of proteins than do rare SAVs (shared by <1% of the population). Here, we investigated the impact of variants in a human population upon helical transmembrane proteins (TMPs). Three main results stood out. Firstly, common SAVs, on average, have stronger effects than rare SAVs for TMPs, and are enriched, in particular, in the membrane helices. Secondly, proteins with seven transmembrane helices (7TM, including GPCRs, i.e. G protein-coupled receptors) are depleted of SAVs in comparison to other proteins, possibly due to increased evolutionary constraints in these important proteins. Thirdly, rare SAVs with strong effect are significantly absent (over common SAVs) in signal peptide regions.

bioinformatics↗