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:<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>
Project description:Full-scan, data-dependent acquisition (DDA), and data-independent acquisition (DIA) are the three common data acquisition modes in high resolution mass spectrometry-based untargeted metabolomics. It is an important yet underrated research topic on which acquisition mode is more suitable for a given untargeted metabolomics application. In this work, we compared the three data acquisition techniques using a standard mixture of 134 endogenous metabolites and a human urine sample. Both hydrophilic interaction and reversed-phase liquid chromatographic separation along with positive and negative ionization modes were tested. Both the standard mixture and urine samples generated consistent results. Full-scan mode is able to capture the largest number of metabolic features, followed by DIA and DDA (53.7% and 64.8% respective features fewer on average in urine than full-scan). Comparing the MS2 spectra in DIA and DDA, spectra quality is higher in DDA with average dot product score 83.1% higher than DIA in Urine(H), and the number of MS2 spectra (spectra quantity) is larger in DIA (on average 97.8% more than DDA in urine). Moreover, a comparison of relative standard deviation distribution between modes shows consistency in the quantitative precision, with the exception of DDA showing a minor disadvantage (on average 19.8% and 26.8% fewer features in urine with RSD < 5% than full-scan and DIA). In terms of data preprocessing convenience, full-scan and DDA data can be processed by well-established software. In contrast, several bioinformatic issues remain to be addressed in processing DIA data and the development of more effective computational programs is highly demanded.