Project description:Despite the overwhelming information about sRNAs, one of the biggest challenges in the sRNA field is characterizing sRNA targetomes. Thus, we develop a novel method to identify RNAs that interact with a specific sRNA, regardless of the type of regulation (positive or negative) or targets (mRNA, tRNA, sRNA). This method is called MAPS: MS2 affinity purification coupled with RNA sequencing. As proof of principle, we identified RNAs bound to RybB, a well-characterized E. coli sRNA. Identification of RNAs co-purified with MS2-RybB in a rne131 ΔrybB strain. RybB (without MS2) was used as control
Project description:Despite the overwhelming information about sRNAs, one of the biggest challenges in the sRNA field is characterizing sRNA targetomes. Thus, we develop a novel method to identify RNAs that interact with a specific sRNA, regardless of the type of regulation (positive or negative) or targets (mRNA, tRNA, sRNA). This method is called MAPS: MS2 affinity purification coupled with RNA sequencing. As proof of principle, we identified RNAs bound to RyhB, a well-characterized E. coli sRNA. Identification of RNAs co-purified with MS2-RyhB in a rne131 ?ryhB strain. RyhB (without MS2) was used as control
Project description:This is a dataset used for the orchestration of molecular networking which led the discovery of polyacetylated 18-norspirostanol saponins from Trillium tschonoskii.
Project description:These included influents and effluents collected in January 2019 at 16 WWTPs located in 16 Chinese provinces, influents and effluents collected from August 2019 to October 2019, at 15 WWTPs in 15 Chinese provinces, effluents collected at 2 WWTPs in the Wujing river in August 2020. The full process samples at a certain WWTP in Nanjing, Jiangsu were collected twice, in March and June 2023, respectively. The names of positive and negative files are one-to-one correspondence. Unless otherwise noted, those containing nxx or n-xx are in negative ion mode. Both pxx and p-xx are in positive ion mode. In represents water inflow and out represents water outflow. If there are only in and digits, it is positive ion mode.
Project description:<p>Large-scale metabolite annotation is a challenge in liquid chromatogram-mass spectrometry (LC-MS)-based untargeted metabolomics. Here, we develop a metabolic reaction network (MRN)-based recursive algorithm (MetDNA) that expands metabolite annotations without the need for a comprehensive standard spectral library. MetDNA is based on the rationale that seed metabolites and their reaction-paired neighbors tend to share structural similarities resulting in similar MS2 spectra. MetDNA characterizes initial seed metabolites using a small library of MS2 spectra, and utilizes their experimental MS2 spectra as surrogate spectra to annotate their reaction-paired neighbor metabolites, which subsequently serve as the basis for recursive analysis. Using different LC-MS platforms, data acquisition methods, and biological samples, we showcase the utility and versatility of MetDNA and demonstrate that about 2000 metabolites can cumulatively be annotated from one experiment. Our results demonstrate that MetDNA substantially expands metabolite annotation, enabling quantitative assessment of metabolic pathways and facilitating integrative multi-omics analysis.</p><p><br></p><p><strong>Aging mouse liver positive mode</strong> is reported in <a href='https://www.ebi.ac.uk/metabolights/MTBLS601' rel='noopener noreferrer' target='_blank'><strong>MTBLS601</strong></a>.</p><p><strong>Aging mouse liver negative mode</strong> is reported in <a href='https://www.ebi.ac.uk/metabolights/MTBLS606' rel='noopener noreferrer' target='_blank'><strong>MTBLS606</strong></a>.</p><p><strong>Aging fruit fly positive mode</strong> is reported in <a href='https://www.ebi.ac.uk/metabolights/MTBLS612' rel='noopener noreferrer' target='_blank'><strong>MTBLS612</strong></a>.</p><p><strong>Aging fruit fly negative mode</strong> is reported in <a href='https://www.ebi.ac.uk/metabolights/MTBLS615' rel='noopener noreferrer' target='_blank'><strong>MTBLS615</strong></a>.</p>
Project description:<p>Large-scale metabolite annotation is a challenge in liquid chromatogram-mass spectrometry (LC-MS)-based untargeted metabolomics. Here, we develop a metabolic reaction network (MRN)-based recursive algorithm (MetDNA) that expands metabolite annotations without the need for a comprehensive standard spectral library. MetDNA is based on the rationale that seed metabolites and their reaction-paired neighbors tend to share structural similarities resulting in similar MS2 spectra. MetDNA characterizes initial seed metabolites using a small library of MS2 spectra, and utilizes their experimental MS2 spectra as surrogate spectra to annotate their reaction-paired neighbor metabolites, which subsequently serve as the basis for recursive analysis. Using different LC-MS platforms, data acquisition methods, and biological samples, we showcase the utility and versatility of MetDNA and demonstrate that about 2000 metabolites can cumulatively be annotated from one experiment. Our results demonstrate that MetDNA substantially expands metabolite annotation, enabling quantitative assessment of metabolic pathways and facilitating integrative multi-omics analysis.</p><p><br></p><p><strong>Aging mouse liver positive mode</strong> is reported in <a href='https://www.ebi.ac.uk/metabolights/MTBLS601' rel='noopener noreferrer' target='_blank'><strong>MTBLS601</strong></a>.</p><p><strong>Aging mouse liver negative mode</strong> is reported in <a href='https://www.ebi.ac.uk/metabolights/MTBLS606' rel='noopener noreferrer' target='_blank'><strong>MTBLS606</strong></a>.</p><p><strong>Aging fruit fly positive mode</strong> is reported in <a href='https://www.ebi.ac.uk/metabolights/MTBLS612' rel='noopener noreferrer' target='_blank'><strong>MTBLS612</strong></a>.</p><p><strong>Aging fruit fly negative mode</strong> is reported in <a href='https://www.ebi.ac.uk/metabolights/MTBLS615' rel='noopener noreferrer' target='_blank'><strong>MTBLS615</strong></a>.</p>
Project description:<p>Large-scale metabolite annotation is a challenge in liquid chromatogram-mass spectrometry (LC-MS)-based untargeted metabolomics. Here, we develop a metabolic reaction network (MRN)-based recursive algorithm (MetDNA) that expands metabolite annotations without the need for a comprehensive standard spectral library. MetDNA is based on the rationale that seed metabolites and their reaction-paired neighbors tend to share structural similarities resulting in similar MS2 spectra. MetDNA characterizes initial seed metabolites using a small library of MS2 spectra, and utilizes their experimental MS2 spectra as surrogate spectra to annotate their reaction-paired neighbor metabolites, which subsequently serve as the basis for recursive analysis. Using different LC-MS platforms, data acquisition methods, and biological samples, we showcase the utility and versatility of MetDNA and demonstrate that about 2000 metabolites can cumulatively be annotated from one experiment. Our results demonstrate that MetDNA substantially expands metabolite annotation, enabling quantitative assessment of metabolic pathways and facilitating integrative multi-omics analysis.</p><p><br></p><p><strong>Aging mouse liver positive mode</strong> is reported in <a href='https://www.ebi.ac.uk/metabolights/MTBLS601' rel='noopener noreferrer' target='_blank'><strong>MTBLS601</strong></a>.</p><p><strong>Aging mouse liver negative mode</strong> is reported in <a href='https://www.ebi.ac.uk/metabolights/MTBLS606' rel='noopener noreferrer' target='_blank'><strong>MTBLS606</strong></a>.</p><p><strong>Aging fruit fly positive mode</strong> is reported in <a href='https://www.ebi.ac.uk/metabolights/MTBLS612' rel='noopener noreferrer' target='_blank'><strong>MTBLS612</strong></a>.</p><p><strong>Aging fruit fly negative mode</strong> is reported in <a href='https://www.ebi.ac.uk/metabolights/MTBLS615' rel='noopener noreferrer' target='_blank'><strong>MTBLS615</strong></a>.</p>
Project description:<p>Large-scale metabolite annotation is a challenge in liquid chromatogram-mass spectrometry (LC-MS)-based untargeted metabolomics. Here, we develop a metabolic reaction network (MRN)-based recursive algorithm (MetDNA) that expands metabolite annotations without the need for a comprehensive standard spectral library. MetDNA is based on the rationale that seed metabolites and their reaction-paired neighbors tend to share structural similarities resulting in similar MS2 spectra. MetDNA characterizes initial seed metabolites using a small library of MS2 spectra, and utilizes their experimental MS2 spectra as surrogate spectra to annotate their reaction-paired neighbor metabolites, which subsequently serve as the basis for recursive analysis. Using different LC-MS platforms, data acquisition methods, and biological samples, we showcase the utility and versatility of MetDNA and demonstrate that about 2000 metabolites can cumulatively be annotated from one experiment. Our results demonstrate that MetDNA substantially expands metabolite annotation, enabling quantitative assessment of metabolic pathways and facilitating integrative multi-omics analysis.</p><p><br></p><p><strong>Aging mouse liver positive mode</strong> is reported in <a href='https://www.ebi.ac.uk/metabolights/MTBLS601' rel='noopener noreferrer' target='_blank'><strong>MTBLS601</strong></a>.</p><p><strong>Aging mouse liver negative mode</strong> is reported in <a href='https://www.ebi.ac.uk/metabolights/MTBLS606' rel='noopener noreferrer' target='_blank'><strong>MTBLS606</strong></a>.</p><p><strong>Aging fruit fly positive mode</strong> is reported in <a href='https://www.ebi.ac.uk/metabolights/MTBLS612' rel='noopener noreferrer' target='_blank'><strong>MTBLS612</strong></a>.</p><p><strong>Aging fruit fly negative mode</strong> is reported in <a href='https://www.ebi.ac.uk/metabolights/MTBLS615' rel='noopener noreferrer' target='_blank'><strong>MTBLS615</strong></a>.</p>