Project description:The yeast Saccharomyces cerevisiae is an important component of the wine fermentation process and determines various attributes of the final product. However, lactic acid bacteria (LAB) are also an integral part of the microflora of any fermenting must. Various wine microorganism engineering projects have been endeavoured in the past in order to change certain wine characteristics, namely aroma compound composition, ethanol concentration, levels of toxic/ allergenic compounds etc. Most of these projects focus on a specific gene or pathway, whereas our approach aims to understand the genetically complex traits responsible for these phenotypes in a systematic manner by implementing a transcriptomic analysis of yeast in mixed fermentations with the LAB O. oeni. Our aim is to investigate interactions between yeast and LAB on a gene expression level to identify targets for modification of yeast and O. oeni in a directed manner. Our goal was to identify the impact that the common wine microorganism O. oeni (malolactic bacteria) has on fermenting yeast cells on a gene expression level. To this end we co-inoculated the yeast and bacteria at the start of fermentation in a synthetic wine must, using yeast-only fermentations witout O. oeni as a control.
Project description:We performed shallow whole genome sequencing (WGS) on circulating free (cf)DNA extracted from plasma or cerebrospinal fluid (CSF), and shallow WGS on the tissue DNA extracted from the biopsy in order to evaluate the correlation between the two biomaterials. After library construction and sequencing (Hiseq3000 or Ion Proton), copy number variations were called with WisecondorX.
Project description:Chromosomal copy number variations (CNV) have been associated with various neurological and developmental disorders and chromosomal microarray (CMA) is a method of choice to diagnose Copy Number Gain/Loss syndromes. Recently, next-generation sequencing (NGS)-based low-coverage whole genome sequencing (LC-WGS) has been applied to detect Copy Number Gain/Loss syndromes. This dataset is intended to be used as a “Golden standard data set” for development of LC-WGS analysis method. It consists of patients (n=63) who have a mental delay and/or physical disability phenotype and normal (n=20) phenotype.
Project description:This is a use case to show that, given any automatic metagenomic classification model for the documents, we can convert those to ONNX (Open Neural Network Exchange) format; it also consists of the Dockerfile that can be used to prepare a docker image. This conversion ensures interoperability and open access. The ONNX format utility can perform the following essential tasks: model conversion, inference, inspection, and optimization. Reference: 1) https://github.com/elixir-europe/biohackathon-projects-2022/tree/main/9 2) https://www.ebi.ac.uk/biomodels/search?query=Maaly+Nassar&domain=biomodels 3) https://gitlab.com/maaly7/emerald_metagenomics_annotations 4) This model is built upon the model of the following publication: Maaly Nassar, Alexander B Rogers, Francesco Talo', Santiago Sanchez, Zunaira Shafique, Robert D Finn, Johanna McEntyre, A machine learning framework for discovery and enrichment of metagenomics metadata from open access publications, GigaScience, Volume 11, 2022, giac077, https://doi.org/10.1093/gigascience/giac077
2023-05-25 | BIOMD0000001068 | BioModels
Project description:Various LAB on native grass silage
Project description:Whole genome sequencing (WGS) of tongue cancer samples and cell line was performed to identify the fusion gene translocation breakpoint. WGS raw data was aligned to human reference genome (GRCh38.p12) using BWA-MEM (v0.7.17). The BAM files generated were further analysed using SvABA (v1.1.3) tool to identify translocation breakpoints. The translocation breakpoints were annotated using custom scripts, using the reference GENCODE GTF (v30). The fusion breakpoints identified in the SvABA analysis were additionally confirmed using MANTA tool (v1.6.0).
