GIUV2023-571
The group's objective is the development and application of mathematical methods in the analysis of large volumes of data of biological interest, including data from genomics, metagenomics, transcriptomics, interaction networks, and in general any type that due to its volume and complexity requires the use of high-performance computing techniques. The group has extensive experience in these types of studies, among which we can highlight the analysis of the impact of the human microbiome on the health of the host and its temporal evolution and the discovery and characterisation of viruses in human plasma. The group has developed several tools for taxonomic classification, statistical analysis of temporal variability and modelling of the evolution of the complex system. Relevant contributions have also been made in the field of genomics in health, such as tools for visualising virus insertion sites in the human genome and the implications in gene therapy and the characterisation of metabolic pathways with proteomics data. Microbial single-cell genomics (MSCG) is an innovative methodology for the study of microbial diversity and symbiosis between microbes. Our group is also working...The group's objective is the development and application of mathematical methods in the analysis of large volumes of data of biological interest, including data from genomics, metagenomics, transcriptomics, interaction networks, and in general any type that due to its volume and complexity requires the use of high-performance computing techniques. The group has extensive experience in these types of studies, among which we can highlight the analysis of the impact of the human microbiome on the health of the host and its temporal evolution and the discovery and characterisation of viruses in human plasma. The group has developed several tools for taxonomic classification, statistical analysis of temporal variability and modelling of the evolution of the complex system. Relevant contributions have also been made in the field of genomics in health, such as tools for visualising virus insertion sites in the human genome and the implications in gene therapy and the characterisation of metabolic pathways with proteomics data. Microbial single-cell genomics (MSCG) is an innovative methodology for the study of microbial diversity and symbiosis between microbes. Our group is also working on this line of research, which allows us to obtain the genomes from microbes with characteristics of our interest and detect relationships between microbes that are inside each other or adhered to each other. We can discover new taxonomic lineages and new symbiotic relationships between microbes. The microbial single-cell genomics workflow typically begins with fluorescence-activated cell sorting (FACS) to collect the bacterial cells of interest, based on their cell size, internal granularity or fluorescence acquired by specific probes, which is analysed by the FACS instrument at a rate of several thousand cells per second. Cells are separately placed in 96- or 384-well plates and DNA is extracted by alkaline lysis. A mixture of reagents is then applied to enrich the DNA by whole genome amplification. Femtograms of DNA from a cell are amplified to the amounts required by standard sequencing library preparation kits. The contents of each individual cell are sequenced separately, resulting in single-amplified genomes (SAGs). Our group is proficient in a wide variety of genomic techniques. For example, we often combine the workflow of microbial single-cell genomics with metagenomics, which provides us with information about the composition of the microbial community and the genomic characteristics of the metagenome assembled genomes (MAGs) obtained by contig binning. If the samples contain many host cells, such as animal or human tissues, we assess the microbial composition by sequencing 16S ribosomal DNA amplicons. In addition, to verify our sequence-based observations, we often use traditional cultivation techniques. Our group researches microbes that are important in biomedicine, bioremediation, the pharmaceutical industry etc. For example, we are interested in exploring viral infections in bacterial cells from different ecosystems. Bacteriophages have enormous potential to influence human health indirectly by modifying the bacterial composition of the human microbiome. Our plans are to apply these technologies to predict interactions between bacteriophages and bacteria following a faecal microbiota transplant (FMT), in which bacteriophages from a healthy volunteer are applied to a patient suffering from a gastrointestinal disease.
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- Analisis de datos bioinformaticos.
- Aplicacion de tecnicas de maching learning para crear modelos que caractericen sistemas biologicos.
- Analisis con single-cell viral tagging
- Descubrir nuevos linajes taxonomicos y nuevas relaciones simbioticas entre microbios
- Investigar que microbios pueden tener importancia en biomedicina, biorremedacion e industria farmaceutica.
- Bioinformatics and computation.We work in metagenomic data, genomics, interaction networks and, in general, we apply mathematical methods with high-performance computers for the analysis of large volumes of data. The group has experience in studying the impact of the human microbiome on human health.
| Name | Nature of participation | Entity | Description |
|---|---|---|---|
| VICENTE ARNAU LLOMBART | Director | Universitat de València | |
| Research team | |||
| WLADIMIRO DIAZ VILLANUEVA | Collaborator | Universitat de València | |
| CELESTE ROSA MOYA VALERA | Collaborator | Entitat no especificada | researcher |
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- Bioinformática; aprendizaje automático; computación científica; Microbiología
