Biology subjects

Veldsman, W. P.

Publications and source records attributed to Veldsman, W. P..

6 recordsLinked to original sources

bioRxiv2024

The antimicrobial peptides pipeline: a bacteria-centric AMP predictor

Antimicrobial peptides (AMPs), unlike antibiotics, are encoded in genomes. AMPs are exported from the cell after expression and translation. In the case of bacteria, the exported peptides target other microbes to give the producing bacterium a competitive edge. While AMPs are sought after for their similar antimicrobial activity to traditional antibiotics, it is difficult to predict which combinations of amino acids will confer antimicrobial activity. Many computer algorithms have been designed to predict whether a sequence of amino acids will exhibit antimicrobial activity, but the vast majority of validated AMPs in databases are still of eukaryotic origin. This defies common sense since the vast majority of life on earth is prokaryotic. The antimicrobial peptides pipeline, presented here, is a bacteria-centric AMP predictor that predicts AMPs by taking design inspiration from the sequence properties of bacterial genomes with the intention to improve detection of naturally occurring bacterial AMPs. The pipeline integrates multiple concepts of comparative biology to search for candidate AMPs at the primary, secondary and tertiary peptide structure level. Results showed that the antimicrobial peptides pipeline identifies known AMPs that are missed by state-of-the-art AMP predictors, and that the pipeline yields more AMP candidates from real bacterial genomes than from fake genomes, with the rate of AMP detection being significantly higher in the genomes of seven nosocomial pathogens than in the fake genomes.

bioinformatics↗

bioRxiv2023

Detection of short identity by descent segments using low-frequency variants

Rare diseases affect millions of individuals worldwide, yet diagnostic yields for them still remain low. Among variant identification approaches, identity by descent (IBD) mapping is used to identify disease susceptible variants originating from a recent common ancestor among affected individuals, but existing IBD detection models struggle to identify these variants in short IBD segments. Here, we introduce SILO, a novel model to detect disease susceptible variants in both short and long IBD segments. SILO employs a two-stage procedure to detect IBD segments. In the first stage, SILO identifies long IBD segments based on common variants. In the second stage, SILO utilizes rare variants to detect short IBD segments using a seed-and-extend algorithm. We evaluated SILO in simulated data and real data from the 1000 Genomes Project. Our results demonstrate that SILO outperforms existing models in detecting disease susceptible variants within short IBD segments, and show comparable performance in longer IBD segments. These findings highlight the potential of SILO to increase diagnostic yields for rare diseases by enhancing the identification of previously overlooked disease susceptible variants in short IBD segments.

genetics↗

bioRxiv2023

Structural and Functional Disparities within the Human Gut Virome in terms of Genome Topology and Representative Genome Selection

Circularity confers protection to viral genomes where linearity falls short, thereby fulfilling the form follows function aphorism. Yet, a shift away from morphology-based classification towards molecular and ecological classification of viruses is currently underway within the field of virology. Recent years have seen drastic changes in the ICTV operational definitions of viruses, particularly for those tailed phages that inhabit the human gut. After the abolition of the order Caudovirales, these tailed phages are best defined as members of the class Caudoviricetes, with taxonomic uncertainty prevailing at more granular taxa. In order to determine the epistemological value of genome topology in the context of the human gut virome, we designed a set of seven experiments to assay the impact of genome topology and representative viral selection on biological interpretation. Using Oxford Nanopore long reads for viral genome assembly coupled with Illumina short read polishing, we show that circular and linear virus genomes differ remarkably in terms of genome quality, GC-skew, tRNA gene frequency, structural variant frequency, cross-reference functional annotation (COG, KEGG, Pfam, and TIGRfam), state-of-the-art marker-based classification, and phage-host interaction. The disparity profile furthermore changes during dereplication. In particular, our phage-host interaction results demonstrate that proportional abundances are incomparable without due regard for genome topology and dereplication threshold, which necessitates the need for standardized reporting. As a best practice guideline, we recommend that comparative studies of the human gut virome should report the ratio of circular to linear viral genomes ({Delta}CL) along with the dereplication threshold so that structural and functional metrics can be placed into context when assessing biologically relevant metagenomic properties such as proportional abundance.

genomics↗

bioRxiv2023

Accurate and interpretable gene expression imputation on scRNA-seq data using IGSimpute

Single-cell RNA-sequencing (scRNA-seq) enables the quantification of gene expression at the transcriptomic level with single-cell resolution, enhancing our understanding of cellular heterogeneity. However, the excessive missing values present in scRNA-seq data (termed dropout events) hinder downstream analysis. While numerous imputation methods have been proposed to recover scRNA-seq data, high imputation performance often comes with low or no interpretability. Here, we present IGSimpute, an accurate and interpretable imputation method for recovering missing values in scRNA-seq data with an interpretable instance-wise gene selection layer. IGSimpute outperforms ten other state-of-the-art imputation methods on nine tissues of the Tabula Muris atlas with the lowest mean squared error as the chosen benchmark metric. We demonstrate that IGSimpute can give unbiased estimates of the missing values compared to other methods, regardless of whether the average gene expression values are small or large. Clustering results of imputed profiles show that IGSimpute offers statistically significant improvement over other imputation methods. By taking the heart-and-aorta and the limb muscle tissues as examples, we show that IGSimpute can also denoise gene expression profiles by removing outlier entries with unexpected high expression values via the instance-wise gene selection layer. We also show that genes selected by the instance-wise gene selection layer could indicate the age of B cells from bladder fat tissue of the Tabula Muris Senis atlas. IGSimpute has linear time-complexity with respect to cell number, and thus applicable to large datasets.

bioinformatics↗

bioRxiv2022

Taxonbridge: an R package to create custom taxonomies based on the NCBI and GBIF taxonomies

SummaryBiological taxonomies establish conventions by which researchers can catalogue and systematically compare their work using nomenclature such as species binomial names and reference identifiers. The ideal taxonomy is unambiguous and exhaustive; however, no such single taxonomy exists, partly due to continuous changes and contributions made to existing taxonomies. The degree to which a taxonomy is useful furthermore depends on context provided by such variables as the taxonomic neighbourhood of a species (e.g., selecting arthropod or vertebrate species) or the geological time frame of the study (e.g., selecting extinct versus extant species). Collating the most relevant taxonomic information from multiple taxonomies is hampered by arbitrarily defined identifiers, ambiguity in scientific names, as well as duplicated and erroneous entries. The goal of taxonbridge is to provide tools for merging the Global Biodiversity Information Facility (GBIF) Backbone Taxonomy and the United States National Center for Biotechnology Information (NCBI) Taxonomy in order to create consistent, deduplicated and disambiguated custom taxonomies that reference both extant and extinct species. AvailabilityTaxonbridge is available as a package in the Comprehensive R Archive Network (CRAN) repository: https://CRAN.R-project.org/package=taxonbridge. Contactwernerpieter.veldsman@unil.ch

bioinformatics↗

bioRxiv2018

GRID-seq assisted computational prediction of transcription factor binding motifs using multivariate mahalanobis distance analysis reveals that RNA-chromosomal interaction may act as a proxy indicator of true positive transcriptional activity.

Experimental validation of computationally predicted transcription factor binding motifs is desirable. Increased RNA levels in the vicinity of predicted protein-chromosomal binding motifs intuitively suggest regulatory activity. With this intuition in mind, the approach presented here juxtaposes publicly available experimentally derived GRID-seq data with binding motif predictions computationally determined by deep learning models. The aim is to demonstrate the feasibility of using RNA-sequencing data to improve binding motif prediction accuracy. Publicly available GRID-seq scores and computed DeepBind scores could be aggregated by chromosomal region and anomalies within the aggregated data could be detected using mahalanobis distance analysis. A mantels test of matrices containing pairwise hamming distances showed significant differences between 1) randomly ranked sequences, 2) sequences ranked by non-GRID-seq assisted scores, and 3) sequences ranked by GRID-seq assisted scores. Plots of mahalanobis ranked binding motifs revealed an inversely proportional relationship between GRID-seq scores and DeepBind scores. Data points with high DeepBind scores but low GRID-seq scores had no DNAse hypersensitivity clusters annotated to their respective sequences. However, DNase hypersensitivity was observed for high scoring DeepBind motifs with moderate GRID-seq scores. Binding motifs of interest were recognized by their deviance from the inversely proportional tendency, and the underlying context sequences of these predicted motifs were on occasion associated with DNAse hypersensitivity unlike the most highly ranked motif scores when DeepBind was used in isolation. This article presents a novel combinatory approach to predict functional protein-chromosomal binding motifs. The two underlying methods are based on recent developments in the fields of RNA sequencing and deep learning, respectively. They are shown to be suited for synergistic use, which broadens the scope of their respective applications.

bioinformatics↗