Biology subjects

Horner, D.

Publications and source records attributed to Horner, D..

6 recordsLinked to original sources

bioRxiv2026

Revealing Subject-Specific Temporal Patterns from Longitudinal Data

MotivationTemporal multivariate data is ubiquitous in many domains, for instance, being collected over time at planned visits (every few months/years) in longitudinal cohorts, or every few minutes/hours in challenge tests. The analysis of such data often focuses on revealing the underlying temporal patterns common across subjects. However, there are subject-specific differences in temporal patterns, which hold the promise to enhance our understanding of underlying mechanisms and facilitate personalized approaches. Nevertheless, extracting subject-specific temporal patterns from longitudinal multivariate data reliably is an open challenge. ResultsWe introduce coupled matrix factorizations (CMF) as effective tools to capture subject-specific temporal patterns focusing on two novel applications: analysis of longitudinal metabolomics data and sensitization data. Our analysis shows that CMF models reliably capture subject-specific (shape) differences in temporal patterns revealing further in-sights compared to the state of the art. In metabolomics, CMF models reveal differences in metabolic responses of individuals (in a postprandial meal challenge) according to anthropometric and insulin sensitivity measures. In sensitization data analysis, CMF-based methods capture differences in temporal trajectories of children according to delivery/birth mode. We demonstrate the reliability of extracted patterns using reproducibility and replicability. AvailabilityThe code is available on https://github.com/cchatzis/Revealing-Subject-specific-Temporal-Patterns-from-Longitudinal-Data. Clinical data is not publicly available due to privacy reasons. Data can be made available under a joint research collaboration by contacting COPSAC (administration@dbac.dk).

bioinformatics↗

bioRxiv2024

Using readmers and hapmers in assessing phase switching after read error correction of Oxford Nanopore Sequences

AO_SCPLOWBSTRACTC_SCPLOWMethods for sequence error correction can improve sequence accuracy; however, there can be unintended errors added during error correction. One such example is phase switching, whereby sequences derived from genomes containing more than one parental copy have contributions from more than one parental haplotype. Such switches are mistakes that can confound downstream analyses especially de novo genome assembly. While DNA sequences do not possess words such as linguistic languages, one can partition a DNA sequence into word-like objects called k-mers. K-mers include pieces of DNA sequence of k length that can describe various properties of DNA sequences. With regard to phase switching, there are k-mers present in one parental haplotype not found in other(s). These so-called hapmers can represent inaccuracies in that parental haplotypes assembly but also correct, unique DNA variation. Here we investigated the effect of DNA sequence error correction on phase switching at the sequence/read level. Using several error-correction methods, we find all methods tested are similar to raw, presumably, phase-switch-free Oxford Nanopore Technologies (ONT) sequences in the percentage of readmers (k-mers from the ONT sequences) matching one parental haplotypes hapmers. This work demonstrates an efficient method to assess if an error-correction method has introduced phase switching implemented in the Julia programming language.

bioinformatics↗

bioRxiv2024

Longitudinal metabolomics data analysis informed bymechanistic models

MotivationMetabolomics measurements are noisy, often characterized by a small sample size and missing entries. While data-driven methods have shown promise in terms of analyzing metabolomics data, e.g., revealing biomarkers of various phenotypes, metabolomics data analysis can significantly benefit from incorporating prior information about metabolic mechanisms. In this paper, we introduce a novel data analysis approach where data-driven methods are guided by prior information through joint analysis of simulated data generated using a human metabolic model and real metabolomics measurements. ResultsWe arrange time-resolved metabolomics measurements of plasma samples collected during a meal challenge test from the COPSAC2000 cohort as a third-order tensor: subjects by metabolites by time samples. Simulated challenge test data generated using a human whole-body metabolic model is also arranged as a third-order tensor: virtual subjects by metabolites by time samples. Real and simulated data sets are coupled in the metabolites mode and jointly analyzed using coupled tensor factorizations to reveal the underlying patterns. Our experiments demonstrate that joint analysis of simulated and real data has a better performance in terms of pattern discovery achieving higher correlations with a BMI (body mass index)-related phenotype compared to the analysis of only real data in males while in females, the performance is comparable. We also demonstrate the advantages of such a joint analysis approach in the presence of incomplete measurements and its limitations in the presence of wrong prior information. AvailabilityThe code for joint analysis of real and simulated metabolomics data sets is released as a GitHub repository. Simulated data can also be accessed using the GitHub repo. Real measurements of plasma samples are not publicly available. Data may be shared by COPSAC through a collaboration agreement. Data access requests should be directed to Morten A. Rasmussen (morten.arendt@dbac.dk).

bioinformatics↗

bioRxiv2023

Characterizing human postprandial metabolic response using multiway data analysis

Analysis of time-resolved postprandial metabolomics data can enhance our knowledge about human metabolism by providing a better understanding of similarities and differences in postprandial responses of individuals, with the potential to advance precision nutrition and medicine. Traditional data analysis methods focus on clustering methods relying on summaries of data across individuals or use univariate methods analyzing one metabolite at a time. However, they fail to provide a compact summary revealing the underlying patterns, i.e., groups of subjects, clusters of metabolites, and their temporal profiles. In this study, we analyze NMR (Nuclear Magnetic Resonance) spectroscopy measurements of plasma samples collected at multiple time points during a meal challenge test from 299 individuals from the COPSAC2000 cohort. We arrange the data as a three-way array: subjects by metabolites by time, and use the CAN-DECOMP/PARAFAC (CP) tensor factorization model to capture the underlying patterns. We analyze the fasting state data to reveal static patterns of subject group differences, and the fasting state-corrected postprandial data to reveal dynamic markers of group differences. Our analysis demonstrates that the CP model reveals replicable and biologically meaningful patterns capturing certain metabolite groups and their temporal profiles, and showing differences among males according to their body mass index (BMI). Furthermore, we observe that certain lipoproteins relate to the group difference differently in the fasting vs. dynamic state in males. While similar dynamic patterns are observed in response to the challenge test in males and females, the BMI-related group difference is only observed in males in the dynamic state.

systems biology↗

bioRxiv2022

Analyzing postprandial metabolomics data using multiway models: A simulation study

BackgroundAnalysis of time-resolved postprandial metabolomics data can improve the understanding of metabolic mechanisms, potentially revealing biomarkers for early diagnosis of metabolic diseases and advancing precision nutrition and medicine. Postprandial metabolomics measurements at several time points from multiple subjects can be arranged as a subjects by metabolites by time points array. Traditional analysis methods are limited in terms of revealing subject groups, related metabolites, and temporal patterns simultaneously from such three-way data. ResultsWe introduce an unsupervised multiway analysis approach based on the CANDECOMP/PARAFAC (CP) model for improved analysis of postpran-dial metabolomics data guided by a simulation study. Because of the lack of ground truth in real data, we generate simulated data using a comprehensive human metabolic model. This allows us to assess the performance of CP models in terms of revealing subject groups and underlying metabolic processes. We study three analysis approaches: analysis of fasting-state data using Principal Component Analysis, T0-corrected data (i.e., data corrected by subtracting fasting-state data) using a CP model and full-dynamic (i.e., full postprandial) data using CP. Through extensive simulations, we demonstrate that CP models capture meaningful and stable patterns from simulated meal challenge data, revealing underlying mechanisms and differences between diseased vs. healthy groups. ConclusionsOur experiments show that it is crucial to analyze both fasting-state and T0-corrected data for understanding metabolic differences among subject groups. Depending on the nature of the subject group structure, the best group separation may be achieved by CP models of T0-corrected or full-dynamic data. This study introduces an improved analysis approach for postprandial metabolomics data while also shedding light on the debate about correcting baseline values in longitudinal data analysis.

systems biology↗

bioRxiv2018

SMRT long-read sequencing and Direct Label and Stain optical maps allow the generation of a high-quality genome assembly for the European barn swallow (Hirundo rustica rustica)

BackgroundThe barn swallow (Hirundo rustica) is a migratory bird that has been the focus of a large number of ecological, behavioural and genetic studies. To facilitate further population genetics and genomic studies, here we present a reference genome assembly for the European subspecies (H. r. rustica).\n\nFindingsAs part of the Genome10K (G10K) effort on generating high quality vertebrate genomes, we have assembled a highly contiguous genome assembly using Single Molecule Real-Time (SMRT) DNA sequencing and several Bionano optical map technologies. We compared and integrated optical maps derived both from the Nick, Label, Repair and Stain and from the Direct Label and Stain (DLS) technologies. As proposed by Bionano, the DLS more than doubled the scaffold N50 with respect to the nickase. The dual enzyme hybrid scaffold led to a further marginal increase in scaffold N50 and an overall increase of confidence in the scaffolds. After removal of haplotigs, the final assembly is approximately 1.21 Gbp in size, with a scaffold N50 value of over 25.95 Mbp.\n\nConclusionsThis high-quality genome assembly represents a valuable resource for further studies of population genetics and genomics in the barn swallow, and for studies concerning the evolution of avian genomes. It also represents one of the very first genomes assembled by combining SMRT long-read sequencing with the new Bionano DLS technology for scaffolding. The quality of this assembly demonstrates the potential of this methodology to substantially increase the contiguity of genome assemblies.

genomics↗