Representation counts: the impact of embedding models on disease detection tasks from microbiome sequencing data

The human microbiome, the ensemble of microorganisms found in and on the human body, plays a key role in human health and disease. However, the current state of microbiome analysis represents a significant challenge for machine learning algorithms. Datasets of microbiome sequences are often characterized by a regime of large dimensionality and...

Benchmarking of viral quasispecies assembly algorithms

Viral quasispecies refers to viral populations that comprises of numerous viral strains closely related to each other due to within-host evolution or co-infection. The reconstruction of viral strain-specific genomes using sequencing reads is referred to as viral quasispecies assembly, and it is also crucial to determine the relative abundances...

The impact of sequencing errors and contaminating viruses on SARS-CoV-2 variant detection by sequencing wastewater-sourced viral RNA

Since the start of the SARS-CoV-2 pandemic, the monitoring of SARS-CoV-2 by way of viral RNA sequencing of wastewater has proven to be an efficient and effective way of estimating COVID-19 cases in population groups. A recently developed pipeline also enables us to estimate SARS-CoV-2 variant abundance using viral samples from wastewater. This...

SARS-CoV-2 lineage abundance quantification in wastewater: a benchmark study for the identification of optimal reference set design

Lineage abundance estimation of SARS-CoV-2 in wastewater is a technique that aims to monitor the lineage prevalence in communities and help contain the COVID-19 pandemic. Lineages are collections of closely related mutants of a virus. It is suggested that the genome sequences of lineages differ across the globe due to random mutations or...

Effect of Output Granularity on SARS-CoV-2 Variant Abundance Estimates using Domestic Wastewater Sequencing

Monitoring of SARS-CoV-2 variants is crucial to efforts in combating the COVID-19 pandemic. Lineage level abundance estimates for SARS-CoV-2 can be obtained from viral material present in domestic wastewater. The abundance predictions can be made at different levels of granularity-individual lineage level(high granularity) or variant level(low...

Kallisto Repurposed: Using sequencing reads from the spike, nucleocapsid, and a middle region of nsp3 in the kallisto pipeline to better predict SARS-CoV-2 variants in wastewater

During a viral infection, we expel remnants of the virus. This makes it possible to conduct wastewater analysis which aid in the efforts to track the evolution of the current Covid-19 pandemic. It has been shown that by repurposing the kallisto algorithm, the abundance of SARS-CoV-2 variants in wastewater samples can be estimated. Since this is...