<?xml version="1.0" encoding="UTF-8"?>
<STUDY_SET xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
  <STUDY accession="ERP122168" alias="E-MTAB-9191" broker_name="ArrayExpress" center_name="Laboratory of Molecular Medicine and Genomics">
    <IDENTIFIERS>
      <PRIMARY_ID>ERP122168</PRIMARY_ID>
      <EXTERNAL_ID namespace="BioProject">PRJEB38722</EXTERNAL_ID>
      <SUBMITTER_ID namespace="Laboratory of Molecular Medicine and Genomics">E-MTAB-9191</SUBMITTER_ID>
    </IDENTIFIERS>
    <DESCRIPTOR>
      <STUDY_TITLE>SHOME-BIO (SHOtgun MEtagenomic analysis of BIOlogical entities): from reads to pathogens. A specific and exhaustive pipeline for handling metagenomic shotgun sequencing data.</STUDY_TITLE>
      <STUDY_TYPE existing_study_type="Other"/>
      <STUDY_ABSTRACT>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 SHOME-BIO, a modular and exhaustive pipeline for analysis of biological entity estimation, specific designed for shotgun sequenced clinical samples. SHOME-BIO analysis provides comprehensive taxonomy classification by querying different source database and carry out main steps in metagenomic investigation. SHOME-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.</STUDY_ABSTRACT>
      <STUDY_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 SHOME-BIO, a modular and exhaustive pipeline for analysis of biological entity estimation, specific designed for shotgun sequenced clinical samples. SHOME-BIO analysis provides comprehensive taxonomy classification by querying different source database and carry out main steps in metagenomic investigation. SHOME-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.</STUDY_DESCRIPTION>
    </DESCRIPTOR>
    <STUDY_LINKS>
      <STUDY_LINK>
        <URL_LINK>
          <LABEL>E-MTAB-9191 in ArrayExpress</LABEL>
          <URL>http://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-9191</URL>
        </URL_LINK>
      </STUDY_LINK>
    </STUDY_LINKS>
    <STUDY_ATTRIBUTES>
      <STUDY_ATTRIBUTE>
        <TAG>ENA-FIRST-PUBLIC</TAG>
        <VALUE>2021-07-26</VALUE>
      </STUDY_ATTRIBUTE>
      <STUDY_ATTRIBUTE>
        <TAG>ENA-LAST-UPDATE</TAG>
        <VALUE>2021-07-26</VALUE>
      </STUDY_ATTRIBUTE>
    </STUDY_ATTRIBUTES>
  </STUDY>
</STUDY_SET>
