Biology subjects

Valdes, C.

Publications and source records attributed to Valdes, C..

5 recordsLinked to original sources

bioRxiv2026

An Embeddings Fusion Approach Predicts Disease State from Microbiome Features

BackgroundDeep neural networks are a proven technique for working with high dimensional data because of their ability to draw-out meaningful patterns and create vector representations known as "embeddings", which make it easier to work with learning tasks on large inputs as they capture the semantics and variance of the data. Microbial community abundance profiles are well suited for an embeddings approach due to their high dimensionality, and in this work we introduce a novel approach for generating embeddings from visual representations which encode NCBIs taxonomic tree and microbial compositions as images, enabling the creation of embeddings that capture factors such as disease status, type, and geographical location. ResultsWe profiled 13,534 public human metagenomes spanning 85 studies, 24 disease types, 35 countries, and 31,756 microbial species using a profiling pipeline that indexes NCBIs nucleotide database (nt) across all kingdoms of life. Our model achieves an average classification performance of 84% in distinguishing healthy and disease conditions; 87% for disease types, 99% for body sites, and 88% for geographical locations. It also achieves a 97% accuracy when performing multi-label classification of the four factors combined. ConclusionOur work highlights the use of an embeddings approach that can encode multiple features and create efficient contextualization of profiled metagenomes derived from microbiome samples. The models embeddings can be used to cluster existing samples based on multiple conditions and interpretations, and new embeddings can be quickly created for new samples and fitted to existing clusters to characterize them. This has practical applications for unknown, unlabeled microbiome samples.

bioinformatics↗

bioRxiv2021

Microbiome Maps: Hilbert Curve Visualizations of Metagenomic Profiles

MotivationAbundance profiles from metagenomic sequencing data synthesize information from billions of sequenced reads coming from thousands of microbial genomes. Analyzing and understanding these profiles can be a challenge since the data they represent are complex. Particularly challenging is their visualization, as existing techniques are inadequate when the taxa number is in the thousands. We present a technique, and accompanying software, for the visualization of metagenomic abundance profiles using a space-filling curve that transforms a profile into an interactive 2D image. ResultsWe created JO_SCPLOWASPERC_SCPLOW, an easy to use tool for the visualization and exploration of metagenomic profiles from DNA sequencing data. It orders taxa using a space-filling Hilbert curve, and creates a "Microbiome Map", where each position in the image represents the abundance of a single taxon from a reference collection. JO_SCPLOWASPERC_SCPLOW can order taxa in multiple ways, and the resulting microbiome maps can highlight "hot spots" of microbes that are dominant in taxonomic clades or biological conditions. We use JO_SCPLOWASPERC_SCPLOW to visualize samples from a variety of microbiome studies, and discuss ways in which microbiome maps can be an invaluable tool to visualize spatial, temporal, disease, and differential profiles. Our approach can create detailed microbiome maps involving hundreds of thousands of microbial reference genomes with the potential to unravel latent relationships (taxonomic, spatio-temporal, functional, and other) that could remain hidden using traditional visualization techniques. The maps can also be converted into animated movies that bring to life the dynamicity of microbiomes. AvailabilityJO_SCPLOWASPERC_SCPLOW is freely available at microbiomemaps.org and via biorg.cs.fiu.edu/jasper Contactcvaldes2@unl.edu; giri@fiu.edu Supplementary informationSupplementary materials are available at microbiomemaps.org

bioinformatics↗

bioRxiv2020

Swine viral detection by adapted Next-Generation Sequencing (NGS) for RNA and DNA species reveals first detection of porcine circovirus type 3 (PCV3) in Chile

The outbreak and propagation of COVID-19 have posed a significant challenge to our society, highlighting the relevance of epidemiological monitoring of animal pathogens closely related to humans due to potential zoonosis. To find previously undetected viruses inside Chilean swine farms, we collected non-invasive samples from animals suspected to undergo any viral disease. After screening by Next Generation Sequencing (NGS) adapted to identify viral species with RNA and DNA genomes simultaneously, different viral species were successfully detected. Among viruses with an RNA genome, Porcine Rotavirus A (RVA), Porcine Astrovirus type 5 (PAstV-5) and Porcine Feces-Associated IASV-like virus (PfaIV) were identified; whereas among viruses with DNA genome, Porcine Parvovirus 1 (PPV1), Porcine Circovirus type 1 (PCV1) and Porcine Circovirus type 3 (PCV3) were found. PCV3, a recently described pathogenic virus, was detected for the first time in Chile in different samples from mummified tissue of stillbirths. The whole genome sequence of PCV3 was completed using conventional PCR and Sanger sequencing and compared with previously reported genomes. To the best of our knowledge, this is the first time that an adapted NGS system is used for the screening of DNA and RNA viral species simultaneously inside the Chilean swine industry. This adapted NGS system might be useful for the detection of swine viral emerging diseases worldwide.

genomics↗

bioRxiv2020

Novel Approach for Microbiome Analysis Using Bacterial Replication Rates and Causal Inference with Applications

MotivationMetagenomics sequencing data can be used to compute not just the relative abundance profile, but also the replication rates of every taxon in the microbiome sample. We investigate how the dynamics implied by the replication rates can be used to understand the antibiotic response in microbiomes, given the significant variation in the types of antibiotics and the types of response by different taxa. The analysis is further expanded by factoring in the resistome of the microbiomes, which can be readily profiled from the metagenomic sequence data. The fact that some antibiotics such as {beta}-lactams target replicating cells makes it even more critical to use replication rates to analyze the antibiotic response. ResultsWe introduce a novel approach for metagenomic analysis that integrates microbial community profiling, replication rate calculation, and causal structural learning to analyze the antibiotic response. First, we developed PO_SCPLOWEC_SCPLOWTRO_SCPLOWIC_SCPLOW, which involves efficient cluster computation of bacterial replication rates from metagenomic sequence data. Second, we integrate the abundance profile, replication profile, resistome profile, and environmental variables to perform causality analysis. Finally, we applied the integrated analysis to the data from an infant gut microbiome study. Conclusions from our analysis are as follows: (i) Microbes tend to lower their replication rates in response to {beta}-lactams; (ii) The presence of antibiotic resistance genes combined with the causality analysis strongly suggest that genes fosA5, oqxA, kpnF, arnA, and acrA provides resistance for the taxon K. pneumoniae, allowing it to replicate and dominate the microbiome after the drug ticarcillin-clavulanate was administered; and (iii) Human and donor milk strongly influence the resistome of the infant gut microbiome. AvailabilityThe pipeline for calculation of bacterial replication rates using the high performance computing framework Slurm is available from https://biorg.cs.fiu.edu/petri/.

bioinformatics↗

bioRxiv2018

Modeling of DNA methylation in cis reveals principles of chromatin folding in vivo in the absence of crosslinking and ligation

Mammalian chromosomes are folded into an intricate hierarchy of structural domains, within which topologically associating domains (TADs) and CTCF-associated loops partition the physical interactions between regulatory sequences. Current understanding of chromosome folding largely relies on chromosome conformation capture (3C)-based experiments, where chromosomal interactions are detected as ligation products after crosslinking of chromatin. To measure chromosome structure in vivo, quantitatively and without relying on crosslinking and ligation, we have implemented a new method named damC. DamC combines DNA-methylation based detection of chromosomal interactions with next-generation sequencing and a biophysical model of methylation kinetics. DamC performed in mouse embryonic stem cells provides the first in vivo validation of the existence of TADs and CTCF loops, confirms 3C-based measurements of the scaling of contact probabilities within TADs, and provides evidence that mammalian chromatin in vivo is essentially rigid below 5 kilobases. Combining damC with transposon-mediated genomic engineering shows that new loops can be formed between ectopically introduced and endogenous CTCF sites, which alters the partitioning of physical interactions within TADs. This establishes damC as a crosslinking-and ligation-free framework to measure and modify chromosome interactions combined with a solid theoretical background for rigorous data interpretation. This orthogonal approach to 3C validates the existence of key structural features of mammalian chromosomes and provides novel insights into how chromosome structure within TADs can be manipulated.

genomics↗