Biology subjects

Mulakken, N. J.

Publications and source records attributed to Mulakken, N. J..

3 recordsLinked to original sources

bioRxiv2025

Beyond Microbial Abundance: Metadata Integration Enhances Disease Prediction in Human Microbiome Studies

Multiple studies have highlighted the human microbiomes potential as a biomarker for diagnosing diseases through its interaction with systems like the gut, immune, liver, and skin via key axes. Advances in sequencing technologies and highperformance computing have enabled the analysis of large-scale metagenomic data, facilitating the use of machine learning to predict disease likelihood from microbiome profiles. However, challenges such as compositionality, high dimensionality, sparsity, and limited sample sizes have hindered the development of actionable models. One strategy to improve these models is by incorporating key metadata from both the host and sample collection/processing protocols. In this paper, we introduce a machine learning-based pipeline for predicting human disease states by integrating host and protocol metadata with microbiome abundance profiles from 68 different studies, processed through a common pipeline. Our findings indicate that metadata can enhance machine learning predictions, particularly at higher taxonomic ranks like Kingdom and Phylum, though this effect diminishes at lower ranks. Our study leverages a large collection of microbiome datasets comprising of 11,208 samples, therefore enhancing the robustness and statistical confidence of our findings. This work is a critical step toward utilizing microbiome and metadata for predicting diseases such as gastrointestinal infections, diabetes, cancer, and neurological disorders.

microbiology↗

bioRxiv2024

In silico analysis reveals differential targeting of enterovirus species by commonly used PCR assays

Quantitative polymerase chain reaction (qPCR) assays are sensitive molecular tools commonly used to quantify pathogens in environmental samples. These assays have become a staple of wastewater-based surveillance and often form the basis of quantitative microbial risk assessments. However, PCR assays may fail to capture all of their intended targets due to signature erosion over time. Here, we performed an in silico analysis of four human enterovirus PCR assays to assess signature erosion against the NCBI virus database. The predicted number of genomes hit by each assay rose alongside total genomes in the database through 2010 but the proportion of predicted hits began to level off with the emergence of the clinically important enterovirus D-68. We found that although all assays captured a majority of enterovirus species, only one recently developed assay adequately captured enterovirus D species. Some assays also appeared more likely to capture non-human enteroviruses than others, an important consideration for data interpretation. We conclude that broad-spectrum virus assays applied to environmental samples should be regularly checked against expanding sequence databases and provide methods to do so.

microbiology↗

bioRxiv2024

Addressing the dynamic nature of reference data: a new nt database for robust metagenomic classification

BackgroundAccurate metagenomic classification relies on comprehensive, up-to-date, and validated reference databases. While the NCBI BLAST Nucleotide (nt) database, encompassing a vast collection of sequences from all domains of life, represents an invaluable resource, its massive size --currently exceeding 1012 nucleotides-- and exponential growth pose significant challenges for researchers seeking to maintain current nt-based indices for metagenomic classification. Recognizing that no current nt-based indices exist for the widely used Centrifuge classifier, and the last public version was released in 2018, we addressed this critical gap by leveraging advanced high-performance computing resources. ResultsWe present new Centrifuge-compatible nt databases, meticulously constructed using a novel pipeline incorporating different quality control measures, including reference decontamination and filtering. These measures demonstrably reduce spurious classifications, and through temporal comparisons, we reveal how this approach minimizes inconsistencies in taxonomic assignments stemming from asynchronous updates between public sequence and taxonomy databases. These discrepancies are particularly evident in taxa such as Listeria monocytogenes and Naegleria fowleri, where classification accuracy varied significantly across database versions. ConclusionsThese new databases, made available as pre-built Centrifuge indexes, respond to the need for an open, robust, nt-based pipeline for taxonomic classification in metagenomics. Applications such as environmental metagenomics, forensics, and clinical metagenomics, which require comprehensive taxonomic coverage, will benefit from this resource. Our new nt-based index highlights the importance of treating reference databases as dynamic entities, subject to ongoing quality control and validation akin to software development best practices. This dynamic update approach is crucial for ensuring the accuracy and reliability of metagenomic analysis, especially as databases continue to expand in size and complexity.

bioinformatics↗