dataesr/scientific-paragraphs-categorization huggingface dataset:
To our knowledge, a chemical entity that is related to stenothricin with a mass shift of -41 Da has not been described in any database or in the literature. The most abundant analog, stenothricin-GNPS 2 (m/z 1105) was purified and the MS/MS spectra manually compared to MS/MS spectra produced from stenothricin D. This confirmed structural similarity (Fig. 5b ,c Supplementary Fig. 9 ). Differential 2D NMR (Supplementary Fig. 10 -14, Supplementary Table  13 , and Supplementary Note 9), Marfey's analysis  38  (Supplementary Fig. 15 ), and genome mining (Supplementary Fig. 16 ,17, Supplementary Table  14 , and Supplementary Note 10) all support that the -41 Da mass shift is due to a lysine to serine substitution. The structural comparison between stenothricin D and stenothricin-GNPS has identified a potential role for the lysine residue of stenothricin D in biological function.
Conclusion GNPS provides a community-led knowledge space in which NP data can be shared, analyzed and annotated by researchers worldwide. It enables a cycle of annotation, in which users curate data, continuous dereplication for product identification, and houses a knowledge base of reference spectral libraries and public datasets. Selected views from community members were sought by nature Biotechnology and are presented, together with author responses, in BOX 1. The transformation of deposited spectra into living data that is enabled by the GNPS platform could mediate connections between researchers and has the potential to transform data networks into social networks. Of 1,272 compound identifications obtained by continuous identification with the GNPS-Community library, 1,063 (83. 6%) were made using reference spectra that were not uploaded by the submitter.
In other words, the vast majority of identifications were enabled by other community members. This reuse of knowledge and data is analogous to other community-wide curation efforts including Wikipedia and crowd-sourced dictionaries. Since their initial deposition, 59% of datasets have an increased number of identifications, with the average dataset more than doubling the number of identifications since submission (Supplementary Fig. 19 ). GNPS enables facile sharing of individual analyses (Supplementary Fig. 20 ) and uses molecular networks to reveal connections between datasets from different laboratories and biological sources that would otherwise remain disconnected. To date, 3,145 analysis jobs have included files shared between GNPS users, encompassing 548 unique pairs of individuals' collaborations.
GNPS recasts public datasets as "conversation starters" in a data-mediated social network. Although we have described only one simple application of GNPS in this Perspective ( identification of a stenothricin analog), the community has already begun to utilize GNPS to expedite natural product analysis  28, 41, 43, 45, 46, 50, 52. Furthermore, we expect the user base of GNPS to expand to include other communities that use MS/MS data, including those studying metabolomics, exposomes, the chemistry of the human habitat, drug discovery, microbiomes, immunology, food industry, agricultural industry, stratification of patients in clinical trials, clinical adsorption/metabolism and ocean science to name a few, resulting in different GNPS workflows  42, 44, 47, 51, 53. As previously shown in genomics  9  and protein structure analysis  54  , the models of global collaboration and social cooperation that are present in GNPS could empower scientific communities to collectively translate big data into shared, reusable knowledge and profoundly influence the way we explore molecules using mass spectrometry.
1% formic acid. Compounds from NIH Clinical Collections and FDA Library part 2 Library employed a step gradient for chromatographic separation [5% solvent B (2:98 water:acetonitrile) containing 0. 1% formic acid for 1. 5 min, a step gradient of 5% B-50% B in 0. 5 min, held at 50% B for 2 min, a second step of 50% B-100% B in 6 min, held at 100% B for 0. 5 min, 100%-5 % B in 0. 5 min and kept at 5% B for 0. 5 min]. The flow rate was 0. 5 mL/min. The mass spectrometer was operated in data dependent positive ion mode; automatically switching between full scan MS and MS/MS acquisitions. Full scan MS spectra (m/z 50 -1500) were acquired in the TOF and the top ten most intense ions in a particular scan were fragmented using collision induced dissociation (CID) utilizing stepping.
GNPS Collections -Spectral Library Creation All raw data were centroided and converted to 32-bit uncompressed mzXML file using Bruker Data Analysis. A script was developed to select all possible MS/MS spectra in each LC-MS/MS run that could correspond to a compound present in the sample. For each compound, we calculated the theoretical mass M from its chemical composition and searched for the M+H, M+2H, M+K, and M+Na adducts. Putative identifications included all MS/MS spectra whose precursor m/z had a ppm error <50 compared to the theoretical mass of each possible precursor m/z; all tandem MS/MS spectra with an MS1 precursor intensity of <1E4 were ignored. All candidate identifications were manually inspected and the most abundant representative spectrum for each compound was added to the corresponding library at the gold or bronze level based upon an expert evaluation of the spectrum quality.
GNPS-Community Contributed Spectral Library Processing and Control User contributed library spectra are not filtered or altered in any way from the user submission. MS/MS spectra are extracted from the submitted data and are made available in the GNPS libraries. The list and description of metadata fields can be found in GNPS online documentation. To preserve provenance information, the full input file is also retained and made available for download for each library spectrum (e. g. link). Different levels of reference spectra submissions are enforced with access restrictions on a per user basis. The description of each of the quality levels: Gold, Silver and Bronze and be found in Supplementary Table  3. While any MS/MS spectrum can be Bronze quality level in the GNPS libraries, Silver contributions require peer-reviewed publication of the MS/MS spectra, and Gold contributions require MS/MS spectra to be of synthetics or purified compounds with complete structural characterization.
1 % TFA and spotted on a Bruker MSP 96 anchor plate. The sample was dried and analyzed by either a Microflex or Autoflex MALDI-TOF MS (Bruker Daltonics). Mass spectra were obtained using the FlexControl software and a single spot acquisition of 80 shots. MALDI-TOF MS data was analyzed by FlexAnalysis software. Fractions containing m/z values putatively assigned to stenothricin-GNPS were combined and further purified by a two-step reversed-phase HPLC procedure (Solvent A: water with 0. 1% TFA; Solvent B: ACN with 0. 1% TFA). Initial HPLC analysis (SUPELCO C18, 5 μm, 100 Å, 250 × 10. 0 mm) utilized a linear gradient from 50% to 75% solvent B in 35 min at flow rate 2 mL/min. Fractions containing target peptide m/z values as detected by MALDI-TOF MS were collected, combined, and evaporated. Subsequent HPLC analysis (Thermo, Syncronis Phenyl HPLC, 5 μm, 150 × 4.
Stenothricin-GNPS NMR 50 μg stenothricin-GNPS 2 was dissolved in 30 μL of CD 3 OD for NMR acquisition. 1  H-NMR spectra were recorded on Bruker Avance III 600 MHz NMR with 1. 7 mm Micro-CryoProbe at 298 K, with standard pulse sequences provided by Bruker. The NMR spectrum was overlayed with the NMR spectrum from stenothricin D and analyzed using the MestReNova software  37.
Genome sequencing and de novo assembly Streptomyces sp. DSM5940 Streptomyces sp. DSM5940 genome was subjected to partial genome sequencing by Ion Torrent and Illumina MiSeq with paired end sequencing. The resulting contigs were assembled by Geneious 5. 1. 1 using the S. roseosporus 15998 genome sequence as template. Sequences have been deposited in NCBI with accession number assignment pending.
Sequence definition of the gene cluster in Streptomyces sp. DSM5940 To identify the Strenothricin-GNPS gene cluster, the Streptomyces sp. DSM5940 genome was annotated using Artemis  62, 63. Non-ribosomal peptide synthesis (NRPS) biosynthetic gene clusters were manually assigned using the Artemis Comparison Tool (an "all-againstall" BLAST (NCBI) comparison of proteins within the database)  64. The adenylation domains of each NRPS gene cluster were further assessed using NRPSpredictor2  65, 66. The predicted 10 amino acid codes for each A-domain within the NRPS gene clusters was manually compared to those predicted for the putative stenothricin gene cluster from S. roseosporus  37. The gene cluster with highest A-domain similarity was putatively identified as the stenothricin-GNPS gene cluster.
Phylogenetic Analysis of C-domains To determine whether the stenothricin and stenothricin-GNPS gene clusters code for similar amino acid stereochemistry, the condensation domain (C-domain) sequences in the putative stenothricin-GNPS and stenothricin gene clusters were aligned with a subset of C-domain sequences representing the six C-domain families (heterocyclization, epimerization, dual condensation/epimerization (dual), condensation of L amino acids to L amino acids (L to L), and condensation of D amino acids to L amino acids (D to L), and starter) using ClustalW2  67.
Is this interface different to others available? Respondent 1 (anonymous). The interface is different from others that are available. We laud the efforts of trying to combine as many mass spectra databases as possible and to provide analytical tools to help you hone in on significant aspects for your spectra. Our main concern about the User Interface is the complexity -it's a bit difficult to navigate/use but it can likely be learned once you become familiar with the layout of the site and the intent of each page. Respondent 2 (Bo Li and Ashley Kretsch). This is the first interface of its kind that I have worked with, but I have limited experience in metabolomics and molecular networking prior to GNPS.
Would you consider using this interface to deposit all spectra/experiments straight into the database as you work? Respondent 1. Probably not. The main issue we would be concerns about the privacy of the data. If this was a community-agreed upon repository for post-publication deposition, we would consider it. (editors note: data can be made private if wished). Respondent 2. My work deals mostly with comparative metabolomics and structure identification, so my data might not be useful for the metabolite community as a whole compared to some of these broader databases. I like the feature where you can pick what data is accessible to the whole group and what is private to the user.
Do you think there are problems with existing databases for MS? Respondent 1. For our case they work fine, but as I mentioned we usually have 1) genetic information and 2) purified compounds. For this case the existing databases are fine. Respondent 2. We find that with our smaller molecules, our library hits can be unrelated to our compounds despite a high cosine score. This might be due to the low number of fragments (ie for compounds less that 200).
Comment by GNPS Having MS/MS of pure compounds in GNPS is incredibly valuable for the community. provide the ability of non-natural product scientists to search their data. For example, a microbiome person may be aiming to understand the biology of a soil community or a gut community and find matches to purified standards. This is akin to someone who studied an enzymatic reaction in detail and deposited this information in GenBank as this information has much wider utility. Regarding matches to small molecules, this is unfortunately a known inherent limitation of the chemical properties of small molecules in the gas phase -there will indeed be fewer fragment ions with small molecules and these are indeed more challenging to match than larger molecules that generate more fragment ions. We recommend a minimum of 6 MS/MS fragment ions to match in addition to the parent mass and we show in supplementary materials how these settings result in very low estimated false discovery rates.
Comment by GNPS We emphasize that in the GNPS vision of "by the community for the community", all GNPS public data, spectral libraries, data analysis workflows and continuous identification results are publically, immediately and freely available to all users, not just GNPS administrators. In difference from Google searches, a more related analogy could be to compare GNPS to the community process of determining gene annotations and making genomic information accesible -if no one had shared gene annotations that were carefully curated in a community-wide platform, BLAST would not be very informative. We further note that in the same way that the openness of NCBI repositories (e. g. , GenBank) and algorithms (e. g. , BLAST) have not resulted in 'unfair advantages' for NIH intra-mural researchers, we also expect that the openness of GNPS data and algorithms will enable researchers equally across the community, regardless of their affiliation or geographical location (a trend that is already supported by the GNPS community including users from 100+ countries, several of which have already published independently of GNPS administrators).
Comment by GNPS Sharing data is becoming a requirement by funding agencies because it extensively benefits the whole research community. For example, Genbank is an open repository that has become all but indispensable in genomics research. GNPS aims to meet this pressing need in the natural product field with respect to mass spectrometry. Further supporting the substantial community-wide benefits of data sharing, we are already observing that 83. 6% of identifications made in public data were derived by matching to reference spectra uploaded to GNPS by another member of the community. In addition to these benefits, the open availability of GNPS reference spectra and public data have also already driven the development of new bioinformatics tools. Last but not least, we emphasize the example provided by Respondent 2 of one way in which the availability of open data combined with open algorithms can be used to support new discoveries -a usage pattern that we have found to be popular at GNPS as many users now import data from other datasets to aid or extend the scope of their own research.
Are there creative aspects of GNPS that you feel deserve highlighting? Respondent 1. The clustering idea is a great idea. Or rather, the idea of applying computational techniques to large scale datasets to direct experimental research is a firstrate idea. As the data accrues, it may be true that the predictive power of these spectra get better and better. So we would say that the creativity is actually in keeping the idea simple (clustering spectra with similarly occurring peaks). This will hopefully allow the scaling of data to the point that old data begins to provide useful clues that would have been difficult to achieve at smaller scales. Respondent 2. The web interface, especially the networks, where clicking between two compounds shows a side by side comparison, points can be labeled by library hits or m/z values, size can be proportional to intensity, and color coded by what groups this compound is included in.
It allows easy transition to view all of the available features of each compound. (a) Representation of interactions between the natural product community, GNPS spectral libraries, and GNPS datasets. At present 221,083 MS/MS spectra from 18,163 unique compounds are used for the search in the GNPS. These include both 3 rd party libraries such as MassBank, ReSpect, and NIST, as well as spectral libraries created for GNPS (GNPS-Collections) and spectra from the natural product community (GNPS-Community). GNPS spectral libraries grow through user contributions of new identifications of MS/MS spectra. To date, 55 community members have contributed 8,853 MS/MS spectra from 5,568 unique compounds (30. 5% of the unique compounds available). In addition, on-going curation efforts have already yielded 563 annotation updates for library spectra.
The utility of these libraries is to dereplicate compounds (recognition previously characterized and studied known compounds), in both public and private data. This dereplication process is performed on all public datasets and results are automatically reported, thus enabling users to query all datasets/organisms/conditions. Automatic reanalysis of all public data creates a virtuous cycle in which contributions to libraries can be matched to all public data. Combined with molecular networking (Fig. 3 ), this automatic reanalysis empowers community members to identify analogs that can then be added to GNPS spectral libraries. (b) The GNPS platform has grown to serve a global user base of 9,200+ users from 100 countries. (a) The computational resources of the metabolomics and natural products community fall into two main categories: i) Reference collections (red dots) of MS/MS spectral libraries and ii) Data Repositories (blue dots)designed to publicly share raw mass spectrometry data associated with research projects.
(c) Searching all public GNPS datasets revealed that Massbank/ ReSpect/NIST libraries matched to 1,217 unique compounds, with GNPS libraries increasing unique compound matches by 41% (corresponding to 29% of total unique matches) with an accompanying 4% increase in spectral library size. Overall, GNPS libraries increase the total number of spectra matched in public datasets by 144% (59% of total public MS/MS matches) and spectra matches across all GNPS public and private data by 767% (88% of all MS/MS matches). (d) The distribution of precursor masses in all GNPS public datasets is shown in gray and compared to the precursor mass distributions of Massbank, ReSpect, NIST, and GNPS libraries. Though GNPS libraries have a combined size that is smaller than MassBank/ReSpect/NIST, GNPS libraries have a higher proportion of molecules in the higher m/z range and therefore complement the proportionately lower precursor mass molecules in other libraries.
(e) The quality of spectrum matches obtained by searching against the available spectral libraries is assessed by user ratings (1 to 4 stars see Supplementary Table  6 ) of continuous identification results. User ratings of 2. 5+ stars for 98%+ of GNPS library matches compares favorably with the 90% mark for NIST matches, whose high marks demonstrate how important these 3 rd party libraries still are to the GNPS platform. We note that the lower mark for NIST matches does not suggest lower quality spectra. It is more likely explained by its higher emphasis on lower precursor mass molecules with spectra that have fewer peaks and are generally harder to match. (a) A global snapshot of the state of MS/MS matching of public natural product datasets available in GNPS using molecular networking and library search tools.
Thus, 55. 3% of the MS/MS spectra at least have one related MS/MS spectrum in spectral networks, with 44. 7% having none. In this 44. 7% of the data, each MS/MS spectrum has been observed in two separate instances and should not constitute noise. Altogether, this analysis indicates that most of the chemical space captured by mass spectrometry remains unexplored. (b) In the past year, there has been significant growth in the GNPS spectral libraries, driving growth in the match rates of all public data. The number of unique compounds matched in the public data has increased 10x; the number of total spectra matched has increased 22x; and the average match rate has increased 3x. It is expected that identification rates will continue to grow with further contributions from the community to the GNPS-Community spectral library.
Acknowledgements This work was partially supported by  National Institution of Health (NIH)  Grants  5P41GM103484-07 ,  GM094802 ,  AI095125 ,  GM097509 ,  S10RR029121 ,  UL1RR031980 ,  GM085770 ,  U01TW0007401 ,  U01AI12316-01 ; NB was also partially supported as an Alfred P Sloan Fellow. In addition, this work was supported by the  National Institute of Allergy and Infectious Diseases (NIAID) ,  National Institutes of Health, Department of Health and Human Services , under Contract Number  HHSN272200800060C. VVP is supported by the  NIH  Grant  K01 GM103809. LMS is supported by  National Institutes of Health IRACDA   K12 GM068524  award. TLK is supported by the  United States -Israel Binational Agricultural Research and Development Fund Vaadia-BARD  No. FI-494-13. CP is supported by  Science  without  Borders Program  from  CNPq.
AMCR is supported by  São Paulo Research Foundation (FAPESP)  grant# 2014/01651-8, 2012/18031-7. KK was supported by a fellowship within the  Postdoc-Programme  of the  German Academic Exchange Service (DAAD). MC was supported by a  Deutsche Forschungsgemeinschaft (DFG)  postdoctoral fellowship. EB is supported by a  Marie Curie IOF  Fellowship within the  7th European Community Framework Program  ( FP7-PEOPLE-2011-IOF , grant number  301244-CYANOMIC ). CCL was supported by a grant from  Ministry of Science and Technology of Taiwan  ( MOST103-2628-B-110-001-MY3 ). PC and BOP were supported by the  Novo Nordisk Foundation. Lixin Zhang  and  Xueting Liu  are supported by  National Program on Key Basic Research Project  ( 2013BC734000 ) and the  National Natural Science Foundation of China  ( 81102369  and  31125002 ).
The plasma membrane intrinsic proteins (PIPs) (with 13 isoforms further subdivided in the PIP1 and PIP2 subgroups), and the tonoplast intrinsic proteins (TIPs) (with 10 homologs) are the most abundant aquaporins in the plasma membrane and the tonoplast, respectively  (5, 6). Two other subclasses include nodulin-26-like proteins (NIPs) and small basic intrinsic proteins (SIPs), with nine and three homologs, respectively  (5) (6) (7). The response of plant roots to environmental and hormonal stimuli is mediated through long-term transcriptional control of aquaporin functions, together with multiple post-translational mechanisms, such as phosphorylation, that affect the activity of aquaporins, their targeting to their destination compartment, or their stability. PIP aquaporins show a conserved phosphorylation site in their first cytosolic loop (loop B) and, in the case of PIP2 isoforms, multiple phosphorylations in adjacent sites of their C-terminal tail  (1) , PhosPhAt database (http://phosphat.
A quantitative IP-MS strategy, together with data from available databases, allowed to build an interconnected PIP network of about 900 proteins. Next, we focused on those protein interaction partners (next called interactants) that show a physical interaction with PIPs. We hypothesized that these interactants may provide novel insights into the molecular regulation of PIP aquaporins. Here, we explore novel functional roles of phospholipases D and RLKs. The latter can have opposite effects on aquaporin activity through specific molecular mechanisms.
Samples were concentrated with a pre-column (Thermo Scientific, C18 Pep-Map100, 300 m ϫ 5 mm, 5 m, 100 A) at a flow rate of 20 l/min using 0. 1% formic acid. After preconcentration, peptides were separated with a reversed-phase capillary column (Thermo Scientific, C18 PepMap100, 75 m ϫ 250 mm, 3 m, 100 A) at a flow rate of 0. 3 l/min using a two steps gradient (8% to 28% acetonitrile in 40 min, then 28% to 42% in 10 min), and eluted directly into the mass spectrometer. Proteins were identified by MS/MS by information-dependent acquisition of fragmentation spectra of multiple charged peptides. Up to twenty data-dependent MS/MS spectra were acquired in positive ion mode. MS/MS raw data were analyzed using Bruker Compass Data Analysis software (Automatic Engine Version 4. 1 (Build 359)) to generate the peak lists.