dataesr/scientific-paragraphs-categorization huggingface dataset:
The enrichment set test was computed with the genes associated with one of the origin classes (suppressed, new, etc. ), and the p value was computed using 10,000 permutations (origin-gene associations). De novo motif discovery. The RSAT peak-motifs program  58  was used to detect de novo motifs around the IS summits from -1 kb to +1 kb. Among the results, the motif found by positions-analysis for 6-7 nt and with the lowest e-value and the highest significance was selected. Episomal DNA replication assay. The HEK-293 cell line that stably expressing EBNA1 (HEK293 EBNA1+) was cultured in DMEM with 10% fetal calf serum and 220 µg/ml neomycin. The HEK293 cells was originally received from DSMZ (DSMZ No: ACC 305). CMV-EBNA1 was stably integrated into the chromosome after linearization and selected with 220 µg/ml Neomycin.
Acknowledgements We would like to thank  Jacques van Helden  ( Aix-Marseille University ) and our lab members for helpful discussions. We thank  Marie-Paule Teulade-Fichou  ( Institut Curie, Orsay, France ) for providing us PhenDC3. We thank  J. Walter  for the anti-CDC45 antibodies,  I. Mattaj  and  J. Blow  for the anti-ELYS antibody. We are grateful to the  Genotoul Bioinformatics Platform Toulouse Midi-Pyrenees  for computing and storage resources. We also thank  E. Andermarcher  for critical reading of the manuscript. The research leading to these results has received funding from the  European Research Council  (FP7/2007-2013 Grant Agreement no. 233339 ). This work was also supported by the  ARC foundation  and  ANR14-CE10-0019 , and by the  MSDAVENIR Fund GENE-IGH. PP was supported by a post-doctoral fellowship from the  ARC Foundation (Fondation ARC pour la Recherche sur le Cancer ).
Data availability The SNS-seq and RNA-seq data are deposited at the NCBI GEO (GSE126477) [https:// www. ncbi. nlm. nih. gov/geo/query/acc. cgi?acc=GSE126477] and [http://rsat-tagc. univmrs. fr/g4/g4_data. html]. R scripts used for figure creation are deposited under [https:// github. com/LacroixLaurent/G4Hunter_mm10_Ori] and http://rsat-tagc. univ-mrs. fr/g4/ g4_data. html]. Data supporting the findings of this study are available within the paper and its supplementary information files, including uncropped scans of the most important blots. All the data are available from the authors upon reasonable request.
Code availability For MACS2 see https://github. com/taoliu/MACS. For SICER see https://home. gwu. edu/ ~wpeng/Software. htm. For GenomicRanges: https://bioconductor. org/packages/release/ bioc/html/GenomicRanges. html. For DESeq2 see http://bioconductor. org/packages/ release/bioc/html/DESeq2. html For G4-Hunter see https://github. com/LacroixLaurent/ G4HunterPaperGit. For Fgsea https://bioconductor. org/packages/release/bioc/html/ fgsea. html. For RSAT http://rsat-tagc. univ-mrs. fr/rsat/RSAT_home. A reporting summary for this Article is available as a Supplementary Information file.
Ethical approval and preregistration Both experiments received approval by the ethical committee at the FSU Jena before they were conducted. All our predictions and sample sizes were preregistered on the Open Science Framework in advance. For Experiment 1, this happened before data collection was underway; for Experiment 2, due to a technical malfunction, the registration occurred after 3 of the 48 pairs had been tested, but no changes were made to the preregistration document in the meantime. The registrations can be accessed at https://osf. io/rb hk2/ (Experiment 1) and https://osf. io/tn6e8/ (Experiment 2).
First, the two predictor variables were centered to remove collinearity between the main effects and the interaction, and thus to make parameters interpretable as the total main effects  (Schielzeth, 2010). Then the model was estimated using the R package lme4  (Bates, M€ achler, Bolker, & Walker, 2015). More precisely, accuracy was predicted by shared visual context (dummy-coded with 1 being shared), trial number, and their multiplicative interaction, while the maximal random effects structure was included in the model (cf. Barr, Levy, Scheepers, & Tily, 2013). This maximal structure consisted of random intercepts for pairs and random slopes for trial number. There were significantly positive estimates for the effects of shared visual context and trial number, but not for their interaction (see Table  1 ).
Does the shared visual context increase the number of conventions? This time, the more systematic questionnaires allow us to separate the reported colors one by one instead of relying on categories chosen by the senders. This highlights the diversity of conventions in different pairs even more (cf. Fig. 11 ). Again, symbols were mapped to specific color hues, but also to other features such as perceived brightness levels. We computed the normalized Levenshtein distance within dyads to measure their alignment after the experiment. This was done using the R package stringdist (Van der Loo, 2014) for each of the 32 colors assessed in the post-experiment questionnaire, with missing values for each color in a given pair if either of the participants had not chosen any symbol for the color. This conservative approach produced a large amount of missing values (274 cases or 17.
Analysis The data and the code to do the statistical analysis and create the figures is available at https:// github. com/danilinares/2018LinaresAguilarLopezmoliner  (Linares, 2019 ; copy archived at https:// github. com/elifesciences-publications/2018LinaresAguilarLopezmoliner). The model fitting, goodness of fit and model selection (likelihood ratio test and Akaike information criterion) was performed using the R package quickspy  (Linares and Lopez-Moliner, 2016) , which under the development version allows fitting several psychometric functions conjointly.
Experiment 1 (asymmetric task) For one of the participants that could perform the symmetric task, the responses were not modulated by the orientation of the grating for the asymmetric task and the data was not analyzed. To estimate sensory and decisional biases and assess which bias model was better, we used the same procedure described for the symmetric task. R c2 c1 e ÀðsÀcw Þ 2 2 ds ¼ Fðc 2 À cw Þ À Fðc 1 À cw Þ ¼ F ÀiÀðdSÀdD 2 Þ s À F ÀiÀðdSÀdD 1 Þ s ( 13 ) where d S ¼ b a d D1 ¼ c 1 a d D2 ¼ c 2 a s ¼ 1 a (14) The model assumes that for one of the references (right) the criteria are symmetric d D2 ¼ Àd D1 , but not for the other reference (left). The statistical model that incorporates the two locations of the reference is thus gðk 1 ;. ; k 2M ; d S ; d D ; sÞ ¼ Y M i¼1 n k i nk i É ki R 1 À É R ð Þ nÀki Y 2M i¼Mþ1 n k i É ki L 1 À É L ð Þ nÀki (15) where k i is the number of times that the participant responds aligned and É R ¼ F ÀiÀðdSþdD 1 Þ s À F ÀiÀðdSÀdD 1 Þ s É L ¼ F ÀiÀðdSÀd 0 D 2 Þ s À F ÀiÀðdSÀd 0 D 1 Þ s ( 16 ) This was the best model for 5 or the 32 groups (likelihood ratio tests, see Materials and methods).
Acknowledgments The research was funded by the  Departament de Salut of the Generalitat de Catalunya  ( PERIS-ICT Ref :  SLT002/16/00338 ;  PERIS Ref :  SLT006/17/00362 ), the  Catalan government  (Ref:  2017SGR-48 ), the  Fundacion Alicia Koplowitz , project Ref:  PSI2017-83493 R ,  AEI/Feder ,  UE  and  CERCA Programme / Generalitat de Catalunya. Part of this work was developed at the building  Centro Esther Koplowitz, Barcelona. We thank  Joa ˜o Barbosa ,  Albert Compte  and  Genı ´s Prat  for comments on the manuscript.
The isolation of microorganisms from microbial community samples often yields a large number of conspecific isolates. Increasing the diversity covered by an isolate collection entails the implementation of methods and protocols to minimize the number of redundant isolates. Matrix-assisted laser desorption-ionization time-of-flight (MALDI-TOF) mass spectrometry methods are ideally suited to this dereplication problem because of their low cost and high throughput. However, the available software tools are cumbersome and rely either on the prior development of reference databases or on global similarity analyses, which are inconvenient and offer low taxonomic resolution. We introduce SPeDE, a user-friendly spectral data analysis tool for the dereplication of MALDI-TOF mass spectra. Rather than relying on global similarity approaches to classify spectra, SPeDE determines the number of unique spectral features by a mix of global and local peak comparisons.
This approach allows the identification of a set of nonredundant spectra linked to operational isolation units. We evaluated SPeDE on a data set of 5,228 spectra representing 167 bacterial strains belonging to 132 genera across six phyla and on a data set of 312 spectra of 78 strains measured before and after lyophilization and subculturing. SPeDE was able to dereplicate with high efficiency by identifying redundant spectra while retrieving reference spectra for all strains in a sample. SPeDE can identify distinguishing features between spectra, and its performance exceeds that of established methods in speed and precision. SPeDE is open source under the MIT license and is available from https://github. com/LM-UGent/SPeDE. IMPORTANCE Estimation of the operational isolation units present in a MALDI-TOF mass spectral data set involves an essential dereplication step to identify redundant spectra in a rapid manner and without sacrificing biological resolution.
In a landmark study, Lagier and colleagues cultured more than 900,000 isolates from the human gut  (13). These included over 77% of all prokaryotes previously identified in the human gut, in addition to organisms corresponding to previously unassigned OTUs  (13). However, the application of high-throughput cultivation techniques leads to the isolation of multiple conspecific strains or genetically identical clones, inflating the downstream cost of analyses. To control cost, various dereplication methods can be implemented. These typically involve typing and filtering out conspecifics based on 16S rRNA gene sequence or matrix-assisted laser desorption-ionization time-of-flight (MALDI-TOF) mass spectrometry (MS) profiles  (14) (15) (16). MALDI-TOF MS has proven to be a fast and cost-effective method for dereplication and identification when coupled to a profile database with extensive taxonomic coverage of a sample, with as little as 0.
1% of isolates from the gut microbiota further requiring identification via 16S rRNA gene sequencing  (17). Furthermore, recent technical and technological advances have enabled MALDI-TOF MS profiling of bacteria at the strain level  (18) , indicating a taxonomic resolution potentially exceeding that of 16S rRNA sequencing for some taxa. However, commercial MALDI-TOF MS databases are mostly populated with spectra for isolates of clinical or food safety relevance, which makes identification coupled to dereplication impractical for environmental samples containing a high diversity of rarely sampled bacteria  (19). Retroactively updating the database with spectra from newly cultured isolates is possible but time-consuming, compromising the throughput of the culturomics approach. In addition, extending commercial databases may involve lengthy clearance procedures by regulatory agencies, such as the U.
RESULTS Rationale and purpose of SPeDE. We designed SPeDE to identify isolates from recurrent taxa in culture collections in a time-and cost-efficient manner. MALDI-TOF MS spectra are compared in a pairwise manner using local and global measures. SPeDE does not attempt to classify the samples according to a reference database but instead computes the number of exclusive spectral features between pairs of spectra. Pairs of spectra for which all features of one element are shared with the other are considered redundant and grouped in single operational isolation units (OIUs) represented by a reference spectrum. Program parameters affect the sensitivity at which discriminating features are detected, allowing various degrees of taxonomic resolution. Optimization of similarity threshold parameters for the identification of nonredundant spectra.
To determine optimal values for the PPMC threshold and peak accuracy window, we applied SPeDE to a benchmark data set of 167 strains covering 132 genera and 143 species. These included several species of Burkholderia and Lactobacillus, which are notoriously difficult to discriminate by mass spectrometry  (23) (24) (25). To account for closely related strains in the estimation of true-and false-positive spectral matches, we divided the data set into 149 OTUs, which we defined as groups of strains with an intragroup pairwise genome-wide average nucleotide identity (ANI) of Ͼ98%. This ANI threshold was empirically determined to give the widest precision range in response to SPeDE parameter changes. We obtained 19 to 32 spectra for each strain, for a total of 5,228 spectra. A quality assessment routine built in the SPeDE algorithm rejected 28 spectra as poor quality, as they contained less than 5 peaks with a signal-to-noise ratio (S/N) of Ͼ30.
Rapid analysis and dereplication are crucial when performing large-scale culturomics experiments when long-term storage of cultures is limited or not feasible. We argue that the high-throughput capacity, speed, and low cost of a dereplication pipeline built around MALDI-TOF MS and SPeDE vastly outweigh the benefits of 16S rRNA gene sequence-based dereplication techniques. In conclusion, SPeDE is a fully automated, scalable algorithm which can be run on a single workstation. SPeDE has been designed to deal with large quantities of data like those generated in culturomics experiments and can process more than 5,000 MALDI-TOF mass spectra in less than an hour on a Linux or Windows laptop computer. SPeDE can be run from a graphical user interface or from the command line and can be fully integrated in a culturomics pipeline designed to automatically retrieve the cultivable diversity from complex samples.
MALDI-TOF MS data sets. The strains used in the benchmark data set were derived from the BCCM/LMG Bacteria Collection and the research collection of LM-UGent. In total 167 different strains were used. The bacterial set consisted of 6 major bacterial phyla (Actinobacteria, Bacteroidetes, Deinococcus, Firmicutes, Proteobacteria, and Epsilonbacteraeota) representing 16 classes and 132 genera. The strains used in the lyophilization data set to estimate the effect of biological variability were derived from the BCCM/LMG Bacteria Collection. In total, 79 strains covering four major bacterial phyla were used. The 25 strains used in the Lactobacillus brevis study were obtained from the BCCM/LMG Bacteria Collection and the research collection of LM-UGent and were isolated from 22 different isolation sources.
(ii) MALDI-TOF MS data acquisition. Bacterial cell extracts (1 l) of the lyophilization study samples were spotted on a target plate (Bruker Daltonik, Bremen, Germany) in duplicate, and samples of the benchmark study were spotted seven to eight times and dried in air at room temperature. The sample spot was overlaid with 1 l of matrix solution (10 mg/ml ␣-cyano-4-hydroxycinnamic acid in acetonitrilewater-trifluoroacetic acid [TFA] [50:47. 5:2. 5]). Each target plate comprised one spot of pure matrix solution, used as a negative control, and one spot of Bacterial Test Standard (Bruker Daltonik, Bremen, Germany), used for calibration. The target plate was measured automatically on a Bruker Microflex LT/SH (lyophilization study) or Bruker Microflex LT/SH s-Smart (benchmark study) platform (Bruker Daltonik, Bremen, Germany).
The target plates of the benchmark study were measured 4 times, thus obtaining a total of 28 to 32 replicate spectra for each strain. The spectra were obtained in linear, positive ion mode using FlexControl (v3. 4) software according to the manufacturer's recommended settings (Bruker Daltonik, Bremen, Germany). Each final spectrum resulted from the sum of the spectra generated at random positions to a maximum of 240 shots per spectrum. For Lactobacillus brevis cultures, bacterial cell suspensions were spotted (1 l) on a target plate (Applied Biosystems, USA) in 4 replicates and dried in air at room temperature. The sample spot was overlaid with 1 l of matrix solution (5 mg/ml ␣-cyano-4-hydroxycinnamic acid in acetonitrile-water-TFA [50:47. 5:2. 5]). Each target plate comprised one spot of pure matrix solution, used as a negative control, and one spot of Bacterial Test Standard (Bruker Daltonik, Bremen, Germany), used for calibration.
The target plate was measured automatically on a 4800 Plus MALDI TOF/TOF analyzer (Applied Biosystems, USA). The spectra were obtained in the linear, positive ion mode using and covered a mass range of 2 to 20 kDa. Each final spectrum resulted from the sum of the spectra generated at random positions to a maximum of 2,000 shots per spectrum. The laser intensity was set to between 4,200 and 5,700 procedure defined units (pdu). The mass spectra were retrieved as t2dfiles from the 4800 Plus MALDI TOF/TOF analyzer via the 4000 series Explorer software. Data Explorer (v4. 0) software (Applied Biosystems, USA) was used to convert the t2dfiles into text files. (iii) Bruker Biotyper identification. The spectra were compared to those in the Bruker MBT 7712 MSP library using MBT Compass Explorer software according to the manufacturer's settings (Bruker Daltonics, Bremen, Germany) to verify the authenticity of the strains.
The scores obtained were reported to be of high-confidence identification (score, Ͼ2. 0), low-confidence identification (score, 1. 70 to 1. 99), and no identification possible (score, Ͻ1. 70). (iv) Mass spectrum preprocessing. Mass spectra were converted to text format using the Flex-Analysis batch processing tool (Bruker Daltonics, Germany). Peak lists were generated using the continuous wavelet transformation (CWT) peak detection algorithm described by Du and colleagues with the following parameter settings: a signal-to-noise ratio of 3 and a relative amplitude threshold of 0. 0001  (33). The R script used to generate the peak list is available at https://github. com/LM-UGent/SPeDE/tree/ master/data_preprocessing/peak_calling. To control for small variations in m/z axis values between runs, raw spectra were normalized to a fixed m/z axis, referred to as "regridding.
" The weighted average of the raw spectrum data was calculated for each m/z value of the fixed grid. The script used to regrid the spectra is available at https://github. com/LM-UGent/SPeDE/tree/master/data_preprocessing/regridding. Genome sequencing, assembly, and analysis. (i) DNA extraction. Genomic DNA for genome sequencing was extracted using the procedure of Gevers et al. (34) ,  Wilson (35) , or Pitcher et al. (36)  or using a Maxwell 16 tissue DNA purification kit (catalog number AS1030; Promega, Madison, WI, USA) and a Maxwell 16 instrument (catalog number AS2000; Promega). Gram-positive bacterial cultures were incubated with 5 mg of lysozyme (Serva) and 40 l mutanolysin (5,000 U/ml; Sigma) dissolved in 110 l of TE buffer (10 mM Tris Cl, 1 mM EDTA). DNA integrity and purity were evaluated on a 1.
0% (wt/vol) agarose gel and by spectrophotometric measurements at 234, 260, and 280 nm. A Quantus fluorimeter and a QuantiFluor One double-stranded DNA system (Promega) were used to measure the DNA concentration. (ii) Genome sequencing, assembly, and ANI calculations. Paired-end 2 ϫ 150-bp libraries were prepared at the Wellcome Trust Human Genome Center (Oxford, UK) using a NEBNext DNA library kit for Illumina (New England Biolabs, Ipswich, MA, USA) and sequenced on an Illumina HiSeq 4000 instrument. Sequencing reads were prepared for assembly by adapter trimming and read filtering using the Trimmomatic tool  (37). Reads with phred scores below 30 were removed, and nonpaired reads were discarded. Reads were assembled using the SPAdes (v3. 10. 1) program  (38)  and kmer lengths of 21, 33, 55, 77, and 99.
Short contigs (Ͻ500 bp) or contigs with an average genome coverage of Ͻ50% were removed. To rule out possible contamination or mislabeling of samples, 16S rRNA gene sequences were extracted from the assembled genomes using the Barrnap (v0. 6) program (https://github. com/tseemann/ barrnap) and compared to available sequences for the strain. Pairwise average nucleotide identity (ANI) values were calculated in two steps. First, genome distance values (corresponding approximately to 1 Ϫ ANI) were calculated between all possible genome pairs using Mash (v2. 0) software  (39). For genome pairs with Mash distance values of Ͻ0. 1, genome distances were refined by calculating ANI values using OrthoANI (v0. 90) software  (40). Final genome distance values are given as percent ANI, with scores below 90% corresponding to [1 Ϫ (mash distance)] ϫ 100.
A matrix of ANI values is available in Data Set S1 in the supplemental material. OTUs were determined using the genome distance between pairs of strains in the benchmarking set as 1 Ϫ ANI and performing hierarchical clustering in R using the hclust function and the ward. D2 method. The dendrogram was cut using the R function cutree with a height parameter of 2, yielding groups of strains where all intragroup pairwise ANI values were Ͼ98%. As a reference, the commonly accepted threshold for species delineation is ANI values of 95 to 96%  (26). (iii) Phylogenetic analyses. Forty single-copy, conserved marker protein sequences were extracted from assembled genomes using FetchMG software  (41). FetchMG automatically extracts the sequences of 40 universal gene markers which were found in a single copy in bacterial genomes and which have been used to reconstruct robust phylogenies across the tree of life  (42).
Core protein sequences were aligned with the Muscle program  (43). Each alignment was trimmed to remove poorly aligned regions using the Trimal program  (44)  and concatenated into a superalignment. This superalignment was used to create an approximate maximum likelihood phylogenetic tree using FastTree (v2) software with the JTTϩCAT model  (45). The resulting phylogenetic tree was annotated and visualized using the iTol web service  (46). Description of the SPeDE algorithm. (i) Overview. The objective of culturomics experiments is to isolate a maximal number of distinct or new taxa. To this purpose, SPeDE is based on unicity measures of MALDI-TOF mass spectra instead of global similarity measures. The algorithm relies on peak matching coupled to spectrum similarity in an area around the peaks to determine unique spectral features (USFs).
Peaks are matched if they fall within the peak accuracy window (700 ppm), and spectra are matched to all references resulting in a Dice coefficient of Ͼ70%. (iv) Output. The default output format is a CSV table matching all samples to the references. Optionally, a USF matrix can be exported. Code to generate a dendrogram based on sample distance is available in a Jupyter notebook at https://github. com/LM-UGent/SPeDE/tree/master/output _dendrogram. Input files are a USF matrix and a table containing the percentage of samples matched to the references. A bar plot shows the abundance of each OIU in the set of samples. The dendrogram can be exported in PDF and Newick formats. (v) Implementation and availability. SPeDE is implemented in Python (v3) software. A graphical user interface was developed for Microsoft Windows, but the software can be run as a command line tool under Windows, Linux, or MacOS.