dataesr/scientific-paragraphs-categorization huggingface dataset:
Ltd, China) were used. The emission spectra were recorded every 30 s by a portable EPP2000 (StellarNet Inc. , United States) spectrometer equipped with a CCD-based detector, connected to an optical fiber, which was submersed in the reactor content. Here, a high solution concentration is necessary to guarantee a better signal-to-noise ratio of the in situ XRD experiments. All DESY data have been analysed with aid of the Fit2D,  42  DESY data helper and in situ luminescence data helper computer programs. An aluminium window in the beam output opening of the reactor holder functioned as an external standard for normalizing the intensities of the XRD patterns to account for intensity fluctuation of the X-ray beam. 43 he same batch reactor geometry was used for the measurements taken at SOLEIL.
However, there a white X-ray beam in the energy range of 20-80 keV was used for energy dispersive XRD (EDXRD) with a Ge solid state detector (Canberra Industries, France) at a 2θ angle of 4°. Experiment number I II III IV V VI VII VIII IX X XI XII Setup A A B B B B B D D D C B Beamline P09 P09 - - - - - - - - PSICHÉ P06 X- Setup B. The Setup B is composed by a stationary EasyMax® 102 (Mettler Toledo, Gießen, Germany) synthesis workstation, with integrated automatic dosing system, stirring unit and temperature control. In addition, the EasyMax® work station allow the simultaneous measurements of in situ pH value and ion conductivity, in combination with a total reflectance Fourier transform infrared (ATR-FT-IR) spectroscopy (Mettler Toledo, Gießen, Germany). The collected IR data was analysed using the IR spectrum of the solvent as baseline for enhancing the IR signal assigned to the starting materials and product.
Simultaneously, fluorescence spectra of the sample on the tape were detected by an optical fiber connected to the same EPP2000 spectrometer and 365 nm UV LEDs used in the in situ reactor-based XRD experiments described in section 2. 2. The streaming reaction was carried out for 1 hour. The collected X-ray diffraction images were processed by the Offline Data Analysis program,  44  which performs background subtraction and looks for sharp Bragg spots in the frame. Those that contained a diffraction pattern from a crystal (a "hit") were saved for later processing. A total of 413 hit images were found in the collected data and were later analysed.
To obtain the merged powder pattern, each hit image was analysed using the indexamajig program of the CrystFEL analysis package  56, 57  to find all of the diffraction spots and integrate them. A histogram was then made from this list of spot positions and intensities and normalized by the number of pixels contributing to each bin. Such a merging and normalization results in a pattern that resembles a typical powder diffraction pattern. 58 The difference being that the merged serial powder pattern does not have contributions from the background or peak broadening due to crystallite size and strain, as they are removed during spot integration. A pattern related to the average structure factor (F-pattern) can also be obtained by normalizing the serial data by the number of spots contributing to bin in the histogram.
Acknowledgements The authors thank the  German Research Foundation (DFG)  within the  Priority Program 1415  and project  TE 1147/1-1 , the  German Electron Synchrotron (DESY) , the  MATsynCell consortium  and the  Daimler Benz Foundation  ( 32-11/15 ) for the financial support. Thanks to  Dr. Milan Köppen  for the development of the in situ data helper software used for analysing the DESY, luminescence and IR data. Dr. A. K. Inge  for the help analysing the SOLEIL data. To  Marvin Radke  for the pictures of the In situ Crystallization Cell at Kiel University and the setup used at DESY. Also thanks to the  ILACS Group  for the enriching discussions and  Prof. Wolfgang Bensch  for providing the equipment needed to perform the experiments. Parts of this research were carried out at PETRA III at  DESY , a member of the  Helmholtz Association (HGF).
Almost 20 years after the publication of a first draft of the reference human genome sequence  5, 6  , a reference protein interactome map is yet to be reported. Proteins are biochemically more complex than DNA, the interactome is much more dynamic than the genome, and the search space for interactions requires testing all-by-all pairwise combinations, making interactome mapping extremely challenging. Approaches to human proteome-wide protein-protein interaction (PPI) mapping either aim to identify protein complex assemblies using mass spectrometry  [7] [8] [9]  or direct PPIs using binary screening methods such as yeast two-hybrid (Y2H) followed by empirical validation using orthogonal assays  [10] [11] [12]. In contrast to protein complex mapping, binary interactome mapping is based on interrogating pairs of proteins for interaction independently from any particular endogenous cellular context, thereby generating relatively unbiased systematic PPI datasets.
Screen PPIs were recovered at rates that were similar or superior to Lit-BM over a large range of score thresholds (Fig. 1e , Extended Data Fig. 1e -g, Supplementary Table  5 ), confirming the high quality of the interactome dataset. Each additional screen identified novel PPIs and proteins, with the largest gains obtained by switching assay versions (Fig. 1f , g, Extended Data Fig. 1d ), highlighting the importance of performing multiple screens and using several assay versions. The dataset, versioned HI-III-19 (Human Interactome obtained from screening Space III, published in 2019), contains 52,569 verified PPIs involving 8,275 proteins (Supplementary Table  6 ). Given its systematic nature, completeness and scale, we consider HI-III-19 to be the first draft of the Human Reference Interactome (HuRI).
To assess whether assay complementarity can partially stem from different steric constraints in the protein fusions, we integrated HuRI with protein structure data  20  and observed that the dataset is depleted for PPIs where the interaction interface is a short spatial distance (< 20Å) from the protein terminus fused to the AD domain (Extended Data Fig. 2a , b, Supplementary Table  7 ). These results provide the first systematic investigation into the impact of protein tags on PPI detection. Molecular mechanisms can be more readily inferred from direct PPIs, yet, the fraction of direct versus indirect PPIs reported in various human protein interactome maps is unknown. Using three-dimensional structural information from protein complexes with at least three subunits  20, 21  , we show that the vast majority of PPIs in HuRI (90%) correspond to direct biophysical contacts, significantly higher than in Lit-BM (81%, P = 0.
019, two-sided Fisher's exact test, n = 121 (HuRI), 410 (Lit-BM)) or in protein complex interactome maps (less than 50%, P < 0. 001 for all, two-sided Fisher's exact test) (Fig. 1h , Supplementary Table  8 ), demonstrating that HuRI represents a unique dataset of direct PPIs. Combining HuRI with all previously published systematic screening efforts at CCSB yields 64,006 binary PPIs involving 9,094 proteins (HI-union) (Supplementary Table  9 ), which is approximately five-fold more PPIs than the entirety of high-quality binary PPIs curated from the literature (Fig. 1i ). The union of Lit-BM and HI-union represent the most complete collection of high quality direct PPI data available to date (http://interactome. dfci. harvard. edu/huri/). PPIs in HuRI vary by the number of screens and assay versions in which they were detected (Extended Data Fig.
Identification of subcellular compartment-specific roles of PPIs Proteins are localized to specific subcellular compartments to exert functions that can depend both on the subcellular environment and the local PPI network. Despite available proteome-wide datasets on the localization of individual proteins  38  , experimental determination of cellular localization-specific PPI networks remains challenging  39. We find that proteins localized to a diverse range of subcellular compartments are evenly represented in HuRI (Fig. 4a ) suggesting that cellular localization-specific PPI networks can be inferred for many different cellular compartments via integration of HuRI with available protein localization data. One such compartment, extracellular vesicles (EVs), has been intensively studied using proteomics approaches  40  , however, our understanding of the molecular mechanisms that lead to protein recruitment into EVs and subsequent secretion, remains limited.
Principles of tissue-specific cellular function Despite recent advances in systematic genome-wide identification of tissue-preferentially expressed genes (TiP genes) 2,44 , we lack a concrete understanding of how the surprisingly small set of TiP genes operate together and coordinate their activity with the core housekeeping cellular machinery to mediate tissue-specific functions. Insights can be obtained from investigating the tissue-specific network context of TiP proteins, inferred from integrating protein interactome data with tissue transcriptomes. However, we find that protein complex  [7] [8] [9]  and literature-curated interactome maps  45  as well as our previously published binary PPI datasets  10, 11  are strongly depleted for TiP proteins, whereas they are well-represented in HuRI, making it the most suitable interactome map available to study the network context of TiP proteins (Fig.
Definition of the human protein-coding genome GENCODE  97  annotation (ftp://ftp. sanger. ac. uk/pub/gencode/Gencode_human/release_27) and DNA and protein sequences were filtered for information on transcript entries. After removing PseudoAutosomal Regions on the Y chromosome (PAR) genes, all genes of gene_type "protein_coding" with annotated transcripts yielded the 19,818 protein-coding genes used in this study. External datasets were mapped via gene or protein IDentifiers (IDs) to the Ensembl gene ID space, and genes, proteins, or transcripts that did not map were removed.
Treatment of pairs scored as spontaneous auto-activators Because validation data has suggested that the CHX control is over-sensitive for 2µ vectors and removes many actual PPIs, we performed one more pairwise test for pairs that scored as spontaneous auto-activators in the pairwise test in assay versions 2 and 3. For these retests, the DB-ORF was separately mated with an "AD-nul" plasmid without any ORF in the cloning site. If a yeast colony grew more strongly when mated to the corresponding AD-ORF than the AD-null strain, we "rescued" this PPI and added it to the dataset after sequence confirmation.
Testing of PPIs in MAPPIT that were found in multiple screens and analysis of data Randomly sampled PPIs were selected for validation assessment from individual screens and from additional randomly-sampled pairs from subsets of PPIs that were defined by the number of screens in which the PPI was detected. Every pair in HuRI that was also in any of the literature curated datasets [Lit-BM / Lit-BS (literature binary singleton pairs) / Lit-NB (literature not binary)] was also selected for validation assessment.
Entrez gene IDs were mapped to Ensembl gene IDs and the dataset was restricted to proteincoding genes. For each gene from FANTOM 3 , we computed the mean of cage peak TPM values of all samples associated to any FANTOM primary cell category. Samples were mapped to primary cells using FANTOM SSTAR (http://fantom. gsc. riken. jp/5/sstar/). Primary cell categories that were considered to be biologically too similar and of close TPM expression were merged. TPM values were log2 transformed and a gene was considered expressed if its value > 0. RNA-seq data from 64 cell lines processed at the gene level with TPM values downloaded on December 1st, 2017 from the Human Protein Atlas 13 (https://proteinatlas. org). Genes with TPM ≥ 1 were considered expressed.
Analysis of the fraction of PPIs in dataset that are direct We queried Interactome3D 21 (version 2018_04) for complexes involving three or more proteins with experimental structure available. For all combinations of protein pairs within a complex, Interactome3D calculated the number of residue-residue contacts by accounting for hydrogen bonds, van der Waals interactions, and salt and disulfide bridges. We defined protein pairs with five or more contacts as direct, and remaining pairs as indirect. Separately for each given PPI dataset, the fraction of direct PPIs was calculated as the number of direct PPIs divided by the number of direct and indirect PPIs.
Construction of Lit-BM Literature curated datasets were derived from the Mentha resource  45  , which aggregates literature-curated PPIs from five source databases: MINT  105  , IntAct 106 , DIP  107  , MatrixDB  108  and BioGRID  109. The data downloaded from Mentha was dated August 28th 2017. Data was filtered to have valid IDs for the UniProt accession numbers, Pubmed IDs and PSI-MI terms. Each piece of evidence for a protein pair must consist of a Pubmed ID and an interaction detection method code in the PSI-MI controlled vocabulary  110. Duplicated evidence arises in cases where different source databases curate the same paper. We merged duplicated entries for each pair, as detected by multiple pieces of evidence with the same Pubmed ID and experimental interaction detection codes which are either the same or have an ancestor-descendent relationship in the PSI-MI ontology.
An example term in the first category is "colocalization". All PPI evidences annotated with "invalid" terms are removed and not considered. The other two categories are used to divide the protein pairs in the literaturecurated dataset into three categories, as follows. If a pair has no evidence that correspond to a binary method it is classified as Lit-NB, if a pair has only one piece of evidence with a binary method it is classified as Lit-BS and if a pair has multiple pieces of evidence, with at least one corresponding to a binary method then it is annotated as Lit-BM. The resulting number of PPIs in each category is shown in (Extended Data Fig. 9a ). The interactions that make up the literature come from experiments that span a broad range of different sizes. It is possible that there could be a difference in average quality between experiments that report just a single or small number of PPIs and those that use highthroughput techniques to identify large numbers of PPIs.
In order to investigate this we divided the Lit-BM and Lit-BS into High-Throughput (HT) and Low-Throughput (LT) subsets, based on the size of the smallest experiment, that provided a piece of evidence for that pair using a binary experimental method. If 50 or more PPIs were reported in that experiment, it was classified as HT and otherwise was classified as LT. Random samples of these subsets were pairwise tested using Y2H (assay version 1) and MAPPIT (Extended Data Fig. 9b, c , Supplementary Table  24,   25). We observed no significant difference between the validation rates of the HT and LT subsets and so we made the decision not to implement any cutoff in experiment size when defining the literature dataset. The main differences from the previous literature curated dataset Lit-BM-13  10  are the databases used.
Processing of previously generated binary interactome maps at CCSB To update the previous CCSB (the Center for Cancer Systems Biology) binary interactome maps and convert them into Ensembl gene ID pairs, for each map, we retrieved the original ORF based IDs stored in our database and mapped the ORF IDs to Ensembl gene IDs as described above. PPIs involving ORFs that remained unmapped or were discarded or mapped to non-protein-coding genes were removed.
Processing of gene properties for network degree correlation analysis We calculated the correlation and the partial correlation between network degree of each network and protein properties within the set of proteins with at least one interaction in the corresponding network and with the value for the corresponding property, using Matlab (version 2016a). To calculate the partial correlation based on expression, we used HEK293T proteomic data from the BioPlex 123 v1. 0 study, proteomics data from the HeLa cell line from the QUBIC study, GTEx tissue expression data and the number of publications from NCBI (method above) for the networks BioPlex, QUBIC, CoFrac and Lit-BM, respectively.
Calculation of protein coverage of networks by subcellular compartments The subcellular localization dataset was retrieved from Cell Atlas 38 and processed as described above. Any compartments with less than 100 proteins were not considered in this analysis, and additional extracellular region and extracellular vesicle annotations from the GO database were included. Fisher's exact test was used to test for an enrichment or depletion of the proteins of each network restricted to those that have at least one localization annotation. Odds ratios were provided in log scale and colored with gray for non-significant enrichment (P > 0. 05).
Acquisition and processing of extracellular vesicle (EV) proteomic data from the EVpedia database We used EVpedia database  40, 124, 125  (version: April 30th 2018) to define vesicular proteins. In EVpedia, there are 487 studies for EV proteomics from human samples and an "Identification Count" for each protein, which is the number of EV proteomic studies with the corresponding protein detected. If a protein was found in more than 45 different studies (~10% of the studies), we defined it as a vesicular protein, resulting in 2,548 proteins in total (about top 10% among all the identified EV proteins). The Largest Connected Component (LCC) of this EV network, containing 525 proteins, was visualized with Cytoscape 115 (v3. 4. 0). To test the significance of this EV protein network, we compared the number of PPIs between EV proteins in this network to the number of PPIs between EV proteins obtained in 1,000 degree-controlled randomized networks, deriving an empirical P < 0.
Design and Transfection of gRNA and selection for KO cells We used gRNA shared in TKO database  126. U373vIII cells were prepared at 70% confluency prior transfection. For each gene to be KO, a pool of plasmids carrying the gRNAs were transfected using JetPrime according to the manufacturer's guidelines (Polyplus Transfection). The cells were submitted to Puromycin selection 36h after 48h of transfection. KO of SDCBP was confirmed by Western Blot 127 with the rabbit anti-SDCBP antibody (ab133267 with lot number GR282684-7) purchased from Abcam (Cambridge).
Separation was achieved using a Dionex Ultimate 3000 uHPLC at 220 nL/min with a gradient of 2-35% organic solvents (0. 1% formic acid in acetonitrile) over 3 h. Peptides were analyzed using an Orbitrap Fusion Tribrid mass spectrometer (Thermo Fisher Scientific) operating at 120,000 resolution (FWHM in MS1, 15,000 for MS/MS). All experiments were carried out in three biological replicates. The quantification of proteins was done by Maxquant program (v1. 5. 6. 5) with default parameters of label-free quantification with UniProt database of human proteins (SwissProt release September 2016). To define the decreased proteins after knockout, P-value was calculated by fitting a Gaussian kernel density estimation (KDE) to the distribution of t-statistics obtained by randomly permuting the WT/KO labels for all proteins, as previously described  128.
To generate the benchmark distribution for each gene, we perform 10,000 degree-preserved randomizations over the human interactome and score each gene in each of the random networks. Functional annotations of genes with GO Biological Process terms were obtained as described above and further restricted to annotations with the evidence codes EXP, IMP, TAS, HMP, HEP, IDA, IGI, NAS, HGI, IEP, IC, HDA to avoid circularity introduced from using inferred GO annotations based on PPI data to infer more GO annotations using PPI data again.
Generation of apoptosis candidate networks Networks around OTUD6A and C6ORF222 were drawn with Cytoscape 115 (v3. 7. 0). All interaction partners of OTUD6A and C6ORF222 in HuRI were selected as well as indirect interaction partners, if they had a known apoptosis GO annotation (restricted to those used in the gene function prediction, see above), to visualize the network neighborhood used to obtain the apoptosis prediction for the two candidate genes. This reference network was filtered using transcript expression data from GTEx for colon_transverse (expression cutoff > 5) and transcript expression data from the BLUEPRINT project  61, 62  for major eosinophils (log2(TPM+1) > 0. 3). The same expression data was used to adjust the size of the nodes in the network to reflect transcript expression levels.
Processing of pairwise test data Each spot was scored with a growth score ranging from 0 to 4, 0 being no growth, 1 being one or two colonies, 2 being some colonies, 3 being lot's of colonies, 4 being a big fat spot where no individual colonies can be distinguished. Pairs for which the SC-Leu-Trp spot was scored as 3 or 4 and for which the CHX and the 3AT spot were valid (yeasts were spotted and no contamination or other experimental failure) were considered as successfully tested. Successfully tested pairs were further classified into auto-activators, if growth on CHX was >2 or growth on CHX was = 1 and less than 3 on 3AT, else negatives, if there was no growth (growth score = 0) on 3AT or positives if there was growth > 0 on 3AT and no growth on CHX or growth > 2 and growth = 1 on CHX. Pairs were scored blindly with respect to their identity.
Analysis of PPI perturbation data Only pairs that were successfully tested, classified as positive or negative, for which the wildtype allele was classified as positive with a growth score ≥ 2, and that were sequence-confirmed were considered for all further analysis. An interaction was considered perturbed by an allele, if the growth score of the wild-type pair was at least 2 growth scores above the growth score for that interaction with the respective allele. If an interaction involving an allele was tested in multiple assay versions (because the wild-type PPI has been found in those originally), then a final decision on that interaction for being perturbed or not was based on the results from the assay version where the wild-type interaction reached the highest growth score (in case of ties, order of priority was given to assay version 1, 2, 6).
Finding tissue-regulated splicing exon For tissue-regulated alternative splicing information, we used a dataset  78  previously defined in 49 tissues, which was kindly shared by members of the Blencowe lab, and is now publicly available via vastDB  135. For each of the events, we mapped Ensembl exon accession with respect to their genome coordinate information. A tissue regulated exon was defined, if there is more than 25% difference between maximum Percent Spliced-In (PSI) and minimum PSI (ΔPSI > 25) as previously described  78.
Acknowledgements We thank  Pablo Porras Millan  and the  IntAct team  for their help in disseminating our PPI data via IntAct, pre-and post-publication. We thank  Ulrich Braunschweig ,  Jonathan Ellis , and  Benjamin J. Blencowe  for sharing isoform-resolved expression data and discussions on NCK2. We also thank  Qian Zhu  and  Olga G. Troyanskaya  as well as  Joshua Pan  and  Cigall Kadoch  for sharing co-expression and co-fitness data, respectively. We thank  Katharine S. Tuttle  for help with graphics. This work was primarily supported by the  National Institute of Health (NIH) National Human Genome Research Institute (NHGRI)  grant  U41HG001715  (M. V. , F. P. R. , D. E. H. , M. A. C. , G. D. B, J. T. ) with additional support from  NIH  grants  P50HG004233  (M. V. and F. P. R. ),  U01HL098166  (M.