Human pathogens
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA314149 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR3214090.fastq.gz | Fastqsanger.gz | |||
| SRR3214091.fastq.gz | Fastqsanger.gz | |||
| SRR3214092.fastq.gz | Fastqsanger.gz | |||
| SRR3214093.fastq.gz | Fastqsanger.gz | |||
| SRR3214094.fastq.gz | Fastqsanger.gz |
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Project description:Next-Generation-Sequencing (NGS) technologies have led to important improvement in the detection of new or unrecognized infective agents, related to infectious diseases. In this context, NGS high-throughput technology can be used to achieve a comprehensive and unbiased sequencing of the nucleic acids present in a clinical sample (i.e. tissues). Metagenomic shotgun sequencing has emerged as powerful high-throughput approaches to analyze and survey microbial composition in the field of infectious diseases. By directly sequencing millions of nucleic acid molecules in a sample and matching the sequences to those available in databases, pathogens of an infectious disease can be inferred. Despite the large amount of metagenomic shotgun data produced, there is a lack of a comprehensive and easy-use pipeline for data analysis that avoid annoying and complicated bioinformatics steps. Here we present HOME-BIO, a modular and exhaustive pipeline for analysis of biological entity estimation, specific designed for shotgun sequenced clinical samples. HOME-BIO analysis provides comprehensive taxonomy classification by querying different source database and carry out main steps in metagenomic investigation. HOME-BIO is a powerful tool in the hand of biologist without computational experience, which are focused on metagenomic analysis. Its easy-to-use intrinsic characteristic allows users to simply import raw sequenced reads file and obtain taxonomy profile of their samples.
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Project description:Lyme neuroborreliosis (LNB) is a nervous system infection caused by tick-borne spirochetes of the Borrelia burgdorferi sensu lato complex and is one of the most frequent bacterial infections of the nervous system in Europe. Early diagnosis remains challenging due to limited sensitivity and specificity of current tests and the need for invasive lumbar punctures, highlighting the need for improved and potentially less invasive diagnostic tools. In this study, we used bottom-up proteomics to analyze 308 cerebrospinal fluid (CSF) samples and 207 plasma samples from well-characterized cohorts, including patients with LNB, viral meningitis, other manifestations of Lyme borreliosis without central nervous system involvement, and controls with normal CSF findings. We identified diagnostic panels of regulated proteins and evaluated their performance using machine learning–based classifiers. The resulting models distinguished LNB from viral meningitis and controls in CSF with areas under the ROC curve (AUC) of 0.92 and 0.90, respectively. In plasma, LNB was distinguished from controls with an AUC of 0.80. These findings suggest a potential diagnostic role for proteomics-based biomarker panels in LNB and provide a large-scale, publicly available proteomic resource for further biomarker discovery and validation studies. NOTE the CSF search and raw files can be found in PXD067474.
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