dataesr/scientific-paragraphs-categorization huggingface dataset:
sgRNA library target and efficiency evaluation All potential sgRNA binding sites on the S. pneumoniae D39V genome were identified using the R package CRISPRseek  (Zhu et al. , 2014) , taking into account PAM presence and allowing up to eight mismatches between spacers and genome. We set the maximum number of allowed mismatches to eight, because of (1) the exponential growth of computation time with this parameter and (2) any potential effect on a site with >8 mismatches was assumed to be negligible. The S. pneumoniae D39V genome  (Slager et al. , 2018)  was downloaded from NCBI (CP027540. 1) and read into R using the biomartr package  (Drost and Paszkowski, 2017). All identified binding sites can be found in supplementary table  S2. In addition to the standard CRISPRseek output, we assessed for each binding site if it overlapped with any genetic element annotated with a locus_tag key in the GFF file on the non-template (NT) strand.
Lastly, we added to the table the relative distance of the binding site to the start codon of the genetic element it binds to, if any ("dist2SC"). This distance is normalized to the [0,1] interval using feature scaling, where a distance of 0 means binding on the start codon or partially overlap with the 5'-end of the element, and a distance of 1 means binding at or partial overlap with the far 3'-end of the element. Smaller distances are associated with more efficient transcription repression  (Qi et al. , 2013). The custom R script used to produce this table can be found on Github (https://github. com/veeninglab/CRISPRi-seq). The script is written in a generic way, allowing to run the complete pipeline described above for any given NCBI genome, as we did for S. pneumoniae strains TIGR4 (AE005672.
Library preparation, sequencing and data analysis The Illumina libraries were prepared by one-step PCR with oligos listed in supplementary table S8. The isolated gDNAs of S. pneumoniae were used as template for PCR. The index 1, index 2 and adapter sequence were introduced by this one-step PCR. For each 50 µl of PCR reaction, 4 µg of gDNA was used as input template, which enables us to obtain sufficient PCR products with as little as 8 cycles of PCR. In addition, we have tested 10 cycles, 20 cycles and 30 cycles of PCR reaction, and no significant difference was observed (data not shown), indicating no detectable bias introduced by PCR. The amplicons (304 bp) were then purified from a 2% agarose gel. Concentrations of amplicons were then determined by a Qubit assay (Q32854, ThermoFisher Scientific).
Purified amplicons were sequenced on a MiniSeq (Illumina) with a custom sequencing protocol. The first 54 cycles for sequencing of common sequence of amplicons were set as dark cycles, and the following 20 cycles were used for sequencing of the diversified basepairing region of sgRNA. The fastq files generated from sequencing are uploaded to the Sequence Read Archive (SRA) on NCBI with accession number PRJNA611488. The 20 bp base-pairing sequences were trimmed out from read 1 according to their position with Trimmomatic Version 0. 36  (Bolger et al. , 2014). To map the sgRNA sequences, a pseudogenome containing all the sgRNA sequences was prepared, and the sgRNA sequences on the pseudogenome were annotated with sgRNA numbers, 1 to 1499. Then the trimmed reads were mapped to the pseudogenome with Bowtie 2  (Langmead and Salzberg, 2012).
The sgRNAs were counted with featureCounts  (Liao et al. , 2014). The count data of sgRNAs were then analyzed with the DESeq2 package in R for evaluation of fitness cost of each sgRNA. We tested against a log2FC of 1, with an alpha of 0. 05. Whenever log2FC are visualized or reported, these are shrunk with the apeglm method. The R script used for analysis is available at https://github. com/veeninglab/CRISPRi-seq. The size of infection bottlenecks was calculated as reported previously  (Abel et al. , 2015). The doubling time of S. pneumoniae used in the calculation was based on a previous Tn-seq study  (Opijnen and Camilli, 2012)  as 108 minutes.
Acknowledgments We are grateful to  Lance Keller  for critical reading of the manuscript. We appreciate all members of the  Veening lab  for stimulating discussions. This work was supported by the  Swiss National Science Foundation (SNSF)  (project grant  31003A_172861  to J. W. V. ) and JPIAMR grant ( 40AR40_185533  to J. W. V. ) from  SNSF. Work in the Nizet lab is supported by  NIH  grant  AI145325. J. M. K. was supported by the  University of California President's Postdoctoral Fellowship Program (UC PPFP).
IIMN is integrated into various open source feature finding tools and the GNPS environment. Moreover, IIMN-based spectral libraries with a broad coverage of ion species are publicly available. M olecular networking (MN)  1  within the GNPS web platform (http://gnps. ucsd. edu)  2  has been used for the analysis of nontargeted mass spectrometry data in various fields  3, 4. MN relies on the principle that similar structures tend to form similar patterns in fragmentation mass spectra (MS 2 ). MN is built up through the pairwise spectral comparisons of a dataset, creating an MS 2 spectral network. This network is then enriched by annotating the experimental MS 2 spectra against MS 2 spectral libraries  2, 5  or compound databases (Fig. 1 ). In the resulting molecular networks, annotations can be propagated through the network edges to adjacent nodes  6.
This often contributes to the unwanted separation of molecular families (subnetworks) and limits the propagation of library annotations through the networks. The two ion species that are most frequently represented in spectral libraries ([M + H] + and [M + Na] + ) typically stay unconnected. Various tools have been developed for the grouping and annotation of ion species in LC-MS data. The first step, feature grouping, typically involves a retention time filter and the correlation of feature intensities across samples  [10] [11] [12]. Other tools, such as CAMERA and CliqueMS, add a pairwise correlation of feature shapes to the grouping metric  13, 14. RAMClust provides an option to simultaneously process LC-MS data with MS 2 from data-independent acquisition (DIA)  10. While many tools  10, [12] [13] [14] [15]  directly interoperate with the feature-finding software XCMS  16  , MS-FLO was developed to process exported feature lists from MZmine  17  , MS-DIAL  18  , and XCMS.
Generally, after feature grouping, ion species can be identified based on known mass differences. Connecting all ions that originate from the same molecule results in MS  1  -based groups, here referred to as ion identity networks (IIN). In this work, we present Ion Identity Molecular Networking (IIMN) and showcase how to fuse MS  2  -based spectral networks with an additional networking layer based on MS  1  feature shape correlation of identified ion species that originate from the same molecule. IIMN addresses this central bottleneck of unconnected ion adducts in MN and the general problem of feature redundancy in MS-based metabolomics  8, 9. We further show the initial validation of IIMN with a ground truth dataset with induced adduct formation by post-column infusion of salt solutions.
Results Workflow development. The IIMN workflow annotates and connects related ion species in feature-based molecular networks within the GNPS web platform. We integrate IIN into MS  2    studies that make use of product ion scans acquired in datadependent acquisition (DDA) mode. The IIMN workflow comprises feature grouping, feature shape correlation, and identification of ion species using a variety of feature-finding software tools, such as MZmine  17  , XCMS  16  , and MS-DIAL  18  that make use of different algorithms for the identification of ion adducts. A table of extracted MS 1 features, each with a consensus MS 2 spectrum, together with IIN results are then uploaded to GNPS to run the IIMN workflow on the web server. The resulting ion identity molecular networks contain two layers of feature (node) connectivity, linking ion identities of the same compound by MS 1 characteristics and structurally similar compounds by MS 2 spectral similarity (Fig.
1 ). A detailed description of the IIMN workflow as well as a step-by-step tutorial are provided in the method section and can be found online in the GNPS documentation (https://ccms-ucsd. github. io/GNPSDocumentation/ fbmn-iin/). The IIMN workflow is available online (https:// gnps. ucsd. edu/) and the source code is shared on GitHub under an open source license.
Validation of IIMN by post-column infusion of salt solutions. To validate the identification of ion species with IIMN, we created an LC-MS 2 benchmark dataset of a natural product mixture containing 300 compounds, in which we promoted adduct formation by post-column infusion of ammonium acetate or sodium acetate at different concentrations (Fig. 2a-e ). The IIMN networks can be depicted in alternative layouts that illustrate complementary results within the same dataset. GNPS also provides networks with collapsed IIN to reduce the redundancy of different ion species by merging them into a single neutral molecule (M) node (Fig. 2c ). In this dataset, IIMN successfully connects ion identities and reduces the size of a complex network by 56% to four major compounds. The increased connectivity facilitates the propagation of structure annotations to neighboring in-source fragments and an unannotated compound.
Finally, the abundance change of identified adducts ([M + H] + , [M + NH 4 ] + , [M + Na] + ) in our benchmark dataset is in agreement with the different post-chromatography salt infusion conditions (H 2 O, Na-Acetate, or NH 4 -Acetate, Fig. 3 ), which validates ion species identification on a dataset level. For instance, the abundance of [M + Na] + and [M + NH 4 ] + ion identities was significantly (p < 0. 001) higher in the corresponding samples with the post-column infusion of sodium acetate or ammonium acetate, respectively, when compared to the control samples. The exclusive formation of an uncommon [M + ACN + NH 4 ] + in-source cluster after infusion of ammonium ions into an ACN-water gradient further verifies connected ion identities. Application of IIMN to 24 public datasets. To test the workflow with data generated from various sample types and on different experimental platforms, 24 public datasets were processed using the MZmine workflow and its metaCorrelate algorithm for feature grouping and ion identity networking (Fig.
4 , Supplementary Table  1 ). All the specific parameters for processing are provided in the methods section (under Dataset processing). MZmine feature-finding parameters were optimized for each dataset by various coauthors, while the feature grouping and ion identity networking parameters were kept constant for better comparability. IIMN identified biologically relevant metal-binding compounds via post-column-induced ion species. In a native ESIbased metabolomics study, IIMN specifically revealed that the known siderophore yersiniabactin also acts as a zincophore (Supplementary Note 1, Supplementary Fig. 2 )  19  and was validated in animal experiments. Fig. 2  Ion identity molecular networking. Depicted are three visualizations of the same ion identity molecular network from the post-column salt infusion experiments.
This subset of structurally related compounds gives a first statistical proof for high correct annotation rates during IIN in MZmine as adduct formation responds to the corresponding salt infusion, e. g. , higher [M + Na] + abundances in the sodium acetate buffer infusion. [2M+Na] + [M+Na] + [2M+Na] + [M+NH 4 ] + [M-H 2 O+H]+ [M+Na] + [M+K] + [M+ACN+NH 4 ] + [M+H] + [M-H 2 O+H] + [M+ACN+NH 4 ] + [M+Na] + [M+K] + [2M+Na] + [M+H] + [M+ACN+NH 4 ] + [M+ACN+NH 4 ] + [2M+K] + [2M+NH 4 ] + [2M+Na] + [M+K] + [M+Na] + [M-H 2 O+H] + [M+NH 4 ] + [M-H 2 O+H] + [M+H] + [M+H] + A B C'/D' B' A' C D E F For a dataset with 88 extracts from feces and gall bladder of various animals, the comparison between feature-based molecular networking with and without the additional edges from ion identity networking demonstrates how IIMN complements and improves FBMN (Fig.
4 ). From a global view on all 24 datasets, IIMN successfully reduced the number of unconnected LC-MS  2  features and increased the connections to annotated compound structures (Supplementary Fig. 5 , Supplementary Table  2 ). Annotation rates in all 24 datasets of 6% and 12% are in the expected range with contemporary MS 2 library matching  21, 22  and MS 1 ion annotation, respectively, especially with the here chosen restrictive IIN parameters (Fig. 4a ). By propagating spectral library matches to first neighboring IIN nodes, the annotation rates of the test datasets were increased by an average of 35% (Fig. 4a, b ). On the individual dataset level, the highest increase (325%) was observed for dataset 4 with more MS 1 data points per feature and thus better feature shape correlation on the cost of a lower MS 2 acquisition rate.
Most datasets (16 out of 24) experienced an increase greater than 10%, while eight datasets were below this value. After inspecting the LC-MS 2 files, we found various reasons for this difference. Datasets 11 and 12, for example, had a higher focus on MS 2 acquisition with a high topN of MS 2 events in the DDA settings that caused lower MS 1 survey scan frequencies and hence fewer data points per features, resulting in lower IIN correlation and connectivity. For datasets 7 and 19, the MS 2 annotation rate was low to begin with and hence few annotations could be propagated by IIMN. Generation of IIMN-based spectral libraries. Besides the increase in feature annotations in individual datasets, IIMN also enables the generation of propagated spectral libraries, increasing and diversifying the library coverage beyond commonly considered ion species.
In positive ion mode, for example, most mass spectrometrists routinely consider [M + H] + and [M + Na] + adducts, but less frequently [M + NH 4 ] + , [M + Ca] 2+ , [M + K] + , and in-source fragments in their data analysis and hence library contributions. However, while inspecting the relative distribution of ion identities within all 24 datasets, marine samples showed a higher percentage of [M + NH 4 ] + adducts (24 ± 5%) when compared to all other datasets (10 ± 8%). Sodiated adducts that were expected to be elevated in marine samples (due to anticipated higher salt contents in the original sample), in contrast, are evenly distributed between all datasets with an average of 26 ± 6% (Fig. 4c ). On average, protonated species contribute to 23 ± 6% of the overall ion identities in our test    6 ), and suggests that the community should provide MS 2 spectra for other ion species of the same molecules to reference libraries.
Here, IIMN can be used to expand the spectral libraries with additional adducts and in-source fragments in LC-MS experiments, which can significantly increase spectral library coverage and thus MS 2 annotation rates. The potential to use IIMN to propagate spectral library matches to adjacent unannotated features with ion identity is evident from a mean increase of the annotation rate by 35% (Fig. 4a, b ). By propagating high confident spectral matches (in this case, cosine >0. 9 or authentic standards) to connected ion identities from the 24 public datasets and two datasets of natural products from the  Fig. 4  Overview of IIMN results for 24 experimental datasets. a Summarizes the relative number of LC-MS features (with an MS 2 spectrum) that were annotated by ion identities or matches to the GNPS spectral libraries.
The increased annotation rate by propagating library matches to connected unannotated ion identities is highlighted and b displayed as relative gains with a mean increase by 35% compared to all library matches. c Comparison of relative ion formation tendencies measured as the number of ion identities. Boxplots summarize the statistics of overall n = 24 datasets by visualizing the median as a horizontal line, the mean as an x, the first and third quartile as the lower and upper hinges, and the whiskers corresponding to the minimum value below Q1 and the maximum value above Q3 within the 1. 5 × IQR. Source data are provided as a Source Data file. 3  and Fig. 6 ). Increased AnnotaƟon [%] MSV000084118 MSV000084045 MSV000082081 MSV000084134 MSV000082045 MSV000084056 MSV000084007 MSV000084024 MSV000083729 MSV000083772 MSV000084116 MSV000084119 MSV000084158 MSV000083601 MSV000084063 MSV000084107 MSV000084008 MSV000081832 MSV000084112 MSV000084148 [M+Ca] 2+ [M+K] + [M+NH 4 ] + [2M+Na] + [M-H+2Na] + [M+Na] + [2M+H] + [M-nH 2 O+H] + [M+2H] 2+ [M+H] + MSV000084170 MSV000082630 MSV000084099 MSV000084101 Cell Cultures (Bacteria/Fungi) M a r i.
Discussion In conclusion, by establishing relationships between different ion species originating from the same compound and structurally similar compounds, IIMN facilitates molecular network interpretation and compound annotation. The combined networks with two layers of feature connectivity enable strategies to present and analyze mass spectrometry data. Networks with collapsed IIN especially reduce the redundancy of detecting multiple ion species per compound. IIMN successfully connected more related compound annotations in datasets from different analytical platforms and over a variety of small molecule compound classes, hence reducing the number of unconnected nodes and increasing the annotation density in molecular networks. An exciting application of IIMN is the expansion of spectral libraries by (re)-processing public datasets and propagating spectral library annotations to create library entries of connected ion identities.
The identification of ion adducts can reveal novel ionophores, some of which will be biologically relevant and are still underappreciated in the function of small molecules  19, 23. The integration into FBMN and the GNPS environment provided a platform to utilize IIMN in other related bioinformatics tools, e. g. , SIRIUS  24  , CANOPUS  25  , and Qemistree  26  for molecular formula and compound class level annotation, which will also facilitate additional validation of network connectivity. Direct interfaces to the GNPS-Dashboard and MASST  27  support collaborative data visualization and repository scale MS 2 queries, respectively. Furthermore, the open source code and generic connection between feature finding, ion identity molecular networking, and the online GNPS workflow encourage the implementation of interfaces to other feature grouping and ion identification algorithms.
We anticipate that the option to add orthogonal relationships between features to IIMN will stimulate the integration and development of additional tools for spectral alignment and measures of feature-feature relationships  28. To reach a broad user base, we interfaced the IIMN workflow with three widely used open source MS processing tools (MZmine 17 , MS-DIAL  18  , and XCMS  13, 16  ). Detailed documentation and training videos are available online (https://ccms-ucsd. github. io/GNPSDocumentation/fbmn-iin/). The option to directly submit IIMN analysis from MZmine to GNPS in particular provides a simple entry point for new users.
As the ion identity naming was not harmonized, different versions pointing to the same ion identity were added to a total count. A similar ion annotation coverage was found in the GNPS spectral libraries. In contrast, libraries that were generated with the recently described MSMS-Chooser  29  workflow on GNPS or the IIMN-based library extraction workflow, described here, show an overall broader coverage of different adducts, multimers, and in-source fragments. The depicted statistical visualization compares a subset of significant or representative ion identities. The IIMN-based numbers summarize the libraries from both the 24 experimental datasets and the two NIH natural product standards datasets with a total of 2659 library entries. Source data are provided as a Source Data file. solvent B acetonitrile (ACN) + 0.
The general steps to create ion identity molecular networks. The full open source code of the ion string parser and its latest charge lookup table can be found on GitHub (https://github. com/CCMS-UCSD/GNPS_Work flows). IIMN with MZmine. MZmine lacked a functional algorithm to group and annotate different ion species of the same molecules. Therefore, a workflow was implemented and split into separate modules for feature grouping (metaCorrelate), annotation of the most common ions (ion identity networking), an option to add more ion identities to existing IIN iteratively, and modules to validate multimers and in-source fragments based on MS 2 scans. Both the creation and expansion of ion identity networks follow customizable lists of adducts and in-source modifications to cover any type of multimers, in-source fragments, and adducts.
The IIN procedure lists all possible ion identity pairs between two features and ranks them according to the maximum number of features that support a specific annotation, i. e. , the ion identity network size. While a feature might be annotated as two different ion species that point to different metabolites, the current workflow will only create additional IIN edges and ion species metadata for the highestranking ion identity per feature. This filter decreases the number of spurious matches. Finally, the GNPS-FBMN export module was modified to export all needed files to run IIMN. The quant table (. csv) contains grouping and ion identity specific columns, and a new Supplementary Pairs text file lists all additional IIN edges. The user can limit the export to features with MS 2 fragmentation spectra or include those with an ion identity.
Optionally, after the creation of ion identity networks with the main library, further ion identities can be added iteratively to existing networks. This workflow enables the user to divide into commonly and uncommonly detected ion identities and ensures that each network contains at least two or more main ion identities. Finally, an ion identity network refinement provides filters for minimum network size and to only keep the largest (most descriptive) IIN per feature. More on the integration of the new IIMN workflow in MZmine can be found online (http://mzmine. github. io/iin_fbmn). Refer to the documentation and video tutorials on how to apply IIMN within MZmine and GNPS. The Youtube playlist "MZmine: Ion Identity Molecular Networking" contains instructions on data processing for IIMN and FBMN, a minimalistic and full IIMN workflow within MZmine, and theoretical background to feature shape correlation and ion identity molecular networking.
https://ccms-ucsd. github. io/GNPSDocumentation/fbmn-iin-mzmine/ https://www. youtube. com/playlist?list=PL4L2Xw5k8ITyxSyBdrcv70LDKsP8Q NuyN IIMN with XCMS (CAMERA). The XCMS  16  Bioconductor package  32  is the most widely used software for processing untargeted LC-MS-based metabolomics data. Its results can be further processed with the CAMERA  13  package to determine which of the extracted m/z-rt features might be adducts  13  or isotopes  33  of the same original compound. For the integration of XCMS and CAMERA into the IIMN workflow, utility functions were created ('getFeatureAnnotations' and 'getEdgelist') to extract and export MS 1 based feature and edge annotations (i. e. , grouping of features to adduct/isotope groups of the same compound). In addition, the utility function 'formatSpectraForGNPS' is used to export MS 2 spectra.