dataesr/scientific-paragraphs-categorization huggingface dataset:
Code and data availability. The SymPKF package is free and open-source. It is distributed under the CeCILL-B free software license. The source code is provided through a GitHub repository at https://github. com/opannekoucke/sympkf (last access: 22 March 2021). A snapshot of SymPKF is available at https: //doi. org/10. 5281/zenodo. 4608514 (Pannekoucke, 2021c). The data used for the simulations presented here are generated at runtime when using the Jupyter notebooks.
In addition, we mapped the genes encoding the 'actinobacterial signature' previously identified  34  as those coding sequences (CDSs) that are nearly universal among actinobacteria. In contrast, we searched the S. ambofaciens ATCC 23877 genome for variable regions, potentially acquired by horizontal gene transfer. For this purpose, we identified unique genes as well as genomic islands (GIs, Supplementary Data 1, Supplementary Fig. 1 ). These are defined as DNA sequences that are inserted in regions of synteny (i. e. same orthologous gene order in closely related genomes, see the "Methods" section). Thus genes from the core, the actinobacterial signature, and/or presenting a high level of persistence are enriched in the central region, whereas GIs and unique CDSs are enriched in chromosome extremities.
Identification of NAPSFs. In this study, we considered NAPs and chromosome structural factors sensu lato by including orthologues of classical NAPs and structural factors (HU, sIHF, Lsr2, Lrp/AsnC, SMC, Dps, CbpA, DnaA, or IciA family proteins)  69  and/or proteins associated with S. coelicolor chromatin  70. This list is available in Supplementary Data 3. Definition of indices for genome conservation analyses. We selected 125 Streptomyces genomes from the NCBI database representative of the Streptomyces genus by keeping only complete genomes of distinct species. When genomes share an average nucleotide identity (ANIb) value greater than or equal to 96%, they are considered as members of the same species  30  (Supplementary Table  1 ). We made one exception by keeping two strains of S. ambofaciens (ATCC 23877 and DSM 40697).
Directional RNA-seq libraries were constructed using the Illumina ScriptSeq kit V2 (discontinued) for samples corresponding to C5 conditions and Illumina Stranded library preparation kit for all other samples, according to the manufacturer's recommendations. Libraries were pooled in equimolar proportions and sequenced (Paired-end 2 × 43 bp) with an Illumina NextSeq500 instrument, using a NextSeq 500 High Output 75 cycles kit. Demultiplexing was done (bcl2fastq2 V2. 2. 18. 12) and adapters (adapter_3p_R1: AGATCGGAAGAGCACACGTCTGAACT; adapter_ 3p_R2: AGATCGGAAGAGCGTCGTGTAGGGA) were trimmed with Cutadapt1. 15, only reads longer than 10 bp were kept. STAR software  72 (v2. 5. 4)  was used for mapping RNA-seq to the reference genome (genome-build-accession NCBI_Assembly: GCF_001267885. 1) containing only one terminal inverted repeat (TIR).
This avoids any biases with multiple mapping within the duplicated extremities of the genome (since the two TIR sequences are indistinguishable). We used the featureCounts program  73  (v2. 0. 1) to quantify reads (in sense-and antisense-orientation) generated from RNAsequencing technology in terms of "Gene" feature characteristics of S. ambofaciens ATCC 23877 annotation (GCF_001267885. 1_ASM126788v1_genomic. gffreleased on 06/15/2020). Bioinformatic analysis of RNA-seq count data. SARTools (Statistical Analysis of RNA-Seq data Tools, v1. 6. 3) DESeq2-based R pipeline 74 was used for systematic quality controls and the detection of differentially expressed genes. PCA and sample clustering used homoscedastic data transformed with Variance Stabilizing Transformation (VST). For the differential analysis, Benjamini and Hochberg's method was used with a threshold of statistical significance adjusted to 0.
05. The reference condition was C1 (medium "MP5_24h"). All parameters of SARTools that have default values were kept unchanged. Genes with null read counts in all samples were not taken into account for the analysis with DESeq2. Genes corresponding to the second TIR (not used for the mapping) or the rRNAs (RiboZero treatment) were excluded from further analysis. The statistical report of this RNAseq analysis is presented in Supplementary Data 4. Supplementary Data 3 presents the DESeq2 normalized counts per kb for each gene in each growth condition. To ensure that antisense-transcription did not affect the normalization of sensetranscription data, the latter were normalized as follows: the percentage of transcription in antisense orientation of each gene was determined with the raw data, then the normalized antisense-transcription was evaluated by applying this percentage to the normalized sense-transcript counts.
To determine the category of gene expression the normalized counts obtained by the SARTools DESeq2-based pipeline were again normalized with respect to gene size (number of DESeq2 normalized reads/gene size × 1000 bp, normRPK). This allowed us to compare directly the relative levels of gene expression of individual genes. Data were analyzed with R software  75  and the Integrative Genomics Viewer (IGV, v2. 8. 0) tool was used to simultaneously visualize RNAseq data and genomic annotations  76. Multidimensional analyses of the data. We used the FactoShiny R package  77  (v2. 4) to perform clustering, principal component analyses, and correspondence analyses. This overlay factor map presented in Supplementary Fig. 4c  results from the correspondence analysis performed using a contingency table indicating the number of genes in each category of expression level depending on the genome features of interest.
Custom-made adapters  23  were ligated overnight at 4 °C. Ligase was inactivated by incubating the tubes at 65 °C for 20 min. To purify DNA fragments ranging in size from 400 to 900 pb, a PippinPrep apparatus (SAGE Science) was used. For each library, one PCR reaction of 12 cycles was performed using 2-3 µl of 3C library, 0. 2 µM Illumina primers PE1. 0 and PE2. 0, and 1 unit of Taq Phusion (Finnzymes). The PCR product was purified on Qiagen MinElute columns and dimers of primers were removed from the 3C library by using AMPure XP beads following the manufacturer's protocol (Beckman Coulter). Finally, libraries were subjected to 75 bp paired-end sequencing in an Illumina sequencer (NextSeq500). Processing of sequencing data. PCR duplicates from each 3C library sequence dataset were discarded using the 6 Ns of custom-made adapters  23.
Here, we compute it for the frequency of contacts. More precisely, it is defined as: I(s) = var(s)/P(s), where P(s) and var(s) are, respectively, the mean value and variance of the frequency of contacts between bins separated by the genomic distance. These quantities are then computed separately in each compartment of the genome. The larger the dispersion index is, the more variable are the contact frequencies within a compartment. Statistical procedure. Data were analyzed with R software  75. For the RNA-seq analyses, three independent experiments (performed on different days) were carried out for each studied condition, except for C3, C9, and C10 that were performed in duplicate and C5 in quadruple. The statistical significance of RNA-seq analysis was assessed using the SARTools DESeq2-based pipeline 74.
Code availability The scripts used for data analyses are available on the following Github links: RNA-seq analyses (https://github. com/PF2-pasteur-fr/SARTools) 74 , 3C-seq contact-map analyses (https://github. com/koszullab/E_coli_analysis), 3C-seq frontier index analyses (https:// github. com/VickyTche/Frontier_Index_Streptomyces. git; https://osf. io/a23de/; https:// doi. org/10. 17605/OSF. IO/A23DE 84 ), persistence and core genome analyses (https:// github. com/jnlorenzi/pipeline-core-and-persistence; https://doi. org/10. 5281/ zenodo. 5067212  85 ), Synteruptor software associated code (https://github. com/jnlorenzi/ synteruptor; https://doi. org/10. 5281/zenodo. 5080081  86 ).
However, they differ in their tolerance to the inclusion of low-scoring nodes and the topology of the solution. Lastly, other methods also consider the topology of the network, favoring groups of nodes that are not only high-scoring but also densely interconnected; such is the case of HotNet2  [16] , SConES  [17] , and SigMod  [18]. In this work, we studied the relevance of network-based approaches to genetics by applying these six network methods to GWAS data. They use different interpretations of the omnigenic model and provide a representative view of the field. We worked on the GENESIS dataset  [19] , a study on familial breast cancer conducted in the French population. After a classical GWAS approach, we used these network methods to identify additional breast cancer susceptibility genes.
GENESIS dataset, preprocessing, and quality control The GENE Sisters (GENESIS) study investigated risk factors for familial breast cancer in the French population  [19]. Index cases were patients with infiltrating mammary or ductal adenocarcinoma, who had a sister with breast cancer, and tested negative for BRCA1 and BRCA2 pathogenic variants. Controls were unaffected colleagues or friends of the cases born around the year of birth of their corresponding case (± 3 years). We focused on the 2 577 samples of European ancestry, of which 1 279 were controls, and 1 298 were cases. The genotyping platform was the iCOGS array, a custom Illumina array designed to study the genetic susceptibility to hormone-related cancers  [20]. It contained 211 155 SNPs, including SNPs putatively associated with breast, ovarian, and prostate cancers, SNPs associated with survival after diagnosis, and SNPs associated to other cancer-related traits, as well as candidate functional variants in selected genes and pathways.
Networks. Gene networks: The mathematical formulations of the different network methods are compatible with any type of biological network (e. g. , from protein interactions or gene co-expression). Here, we used protein-protein interaction networks (PPIN) for all genecentric network methods, as PPINs are interpretable, well-characterized, and the methods were designed to run efficiently on them. We built our PPIN from both binary and co-complex interactions stored in the HINT database (release April 2019)  [24]. Unless otherwise specified, we used only interactions coming from high-throughput experiments, leaving out targeted studies that might bias the topology of the PPIN. Out of the 146 722 interactions from highthroughput experiments that HINT stores, we could map 142 541 to a pair of gene symbols, involving 13 619 genes.
For any two nodes i and j, E ij models the amount of heat that diffuses from node j to node i. Hence, E ij can be interpreted as a (non-symmetric) similarity between those two nodes. To obtain densely connected solutions, HotNet2 prunes E, only preserving edges such that w(E) > δ. Lastly, HotNet2 evaluates the statistical significance of the solutions by comparing their size to the size of PPINs obtained by permuting the node scores. We assigned the initial node scores as in Nakka et al. [29] , giving a 0 to the genes unlikely to be truly associated with the disease, and -log 10 (P-value) to those likely to be. In the GENESIS dataset, the threshold separating both was a P-value of 0. 125, which we obtained using a local FDR approach  [30]. HotNet2 has two parameters: the restart probability β, and the threshold heat δ.
We adjust these P-values for multiple testing through a Benjamini-Hochberg correction. We used the implementation of LEAN from the LEANR R package  [32]. SConES SConES searches the minimal, modular, and maximally associated subnetwork in a SNP graph  [17]. Specifically, it solves the problem arg max where λ and η are parameters that control the sparsity and the connectivity of the model. The connectivity term penalizes disconnected solutions, with many edges between selected and unselected nodes. Given a λ and an η, Eq 5 has a unique solution that SConES finds using a graph min-cut procedure. As in Azencott et al. [17] , we selected λ and η by crossvalidation, choosing the values that produce the most stable solution across folds. In this case, the selected parameters were η = 3. 51, λ = 210.
29 for SConES GS; η = 3. 51, λ = 97. 61 for SConES GM; and η = 3. 51, λ = 45. 31 for SConES GI. We used the version on SConES implemented in the R package martini  [33, 34]. SigMod SigMod searches the highest-scoring, most densely connected subnetwork  [18]. It addresses an optimization problem similar to that of SConES (Eq 5), but with a different connectivity term that favors solutions containing many edges: arg max S2G X v2V S wðvÞ |ffl ffl ffl ffl ffl {zffl ffl ffl ffl ffl } association þ l X v2V X u2V S W vu |ffl ffl ffl ffl ffl ffl ffl ffl fflffl {zffl ffl ffl ffl ffl ffl ffl ffl fflffl } connectivity À ZjV S j |ffl {zffl } sparsity : ð6Þ As for SConES, this optimization problem can also be solved by a graph min-cut approach. SigMod presents three important differences with SConES. First, it was designed for PPINs.
Pathway enrichment analysis We searched for pathways enriched in the solution of each network method. We conducted a hypergeometric test on pathways from Reactome  [36]  using the function enrichPathway from the ReactomePA R package  [37]. The universe of genes included any gene that we could map to a SNP in the iCOGS array (Section 2. 2). We adjusted the P-values for multiple testing as in Benjamini and Hochberg  [38]  (BH): pathways with a BH adjusted P-value < 0. 05 were deemed significant.
Benchmark of methods We evaluated multiple properties of the different methods (described in Sections 2. 5. 1 and 2. 5. 2) through a 5-fold subsampling setting. We applied each method to 5 random subsets of the original GENESIS dataset containing 80% of the samples (train set). When pertinent, we evaluated the solution on the remaining 20% (test set). We used the 5 repetitions to estimate the average and the standard deviation of the different measures. Every method and repetition was run in the same computational settings (Section 2. 8).
Comparison to state-of-the-art An alternative way to evaluate the methods is by comparing their solutions to an external dataset. For that purpose, we used the 153 genes associated to familial breast cancer on DisGeNET  [41]. Across this article, we refer to these genes as breast cancer susceptibility genes. Additionally, we used the summary statistics from the Breast Cancer Association Consortium (BCAC), a meta-analysis of case-control studies conducted in multiple countries. BCAC included 13 250 641 SNPs genotyped or imputed on 228 951 women of European ancestry, mostly from the general population  [42]. Through imputations, BCAC includes more SNPs than the iCOGS array used for GENESIS (Section 2. 1). However, in all the comparisons in this paper we focused on the SNPs that passed quality control in GENESIS.
Code and data availability We developed computational pipelines for several steps of GWAS analyses, such as physically mapping SNPs to genes, computing gene scores, and running six different network methods. We created a pipeline with a clear interface that should work on any GWAS dataset for each of those processes. They are compiled in https://github. com/hclimente/gwas-tools. The code that applies them to GENESIS, as well as the code that reproduces all the analyses in this article are available at https://github. com/hclimente/genewa. We deposited all the produced gene solutions on NDEx (http://www. ndexbio. org), under the UUID e9b0e22a-e9b0-11e9-bb65-0ac135e8bacf. Summary statistics for SNPs and genes are available at S1 and S2 Tables, respectively. We cannot share genotype data publicly for confidentiality reasons, but are available from GENESIS.
Conventional SNP-and gene-based analyses retrieve the FGFR2 locus in the GENESIS dataset We conducted association analyses in the GENESIS dataset (Section 2. 1) at both SNP and gene levels (Section 2. 2). At the SNP level, two genomic regions had a P-value lower than the Bonferroni threshold on chromosomes 10 and 16 (S2A Fig). The former overlaps with the gene FGFR2, the latter with CASC16 and the protein-coding gene TOX3. Variants in both FGFR2 and TOX3 have been repeatedly associated with breast cancer susceptibility in other case-control studies  [42] , in studies on BRCA1 and BRCA2 carriers  [44] , and in hereditary breast and ovarian cancer families negative for mutations in BRCA1 and BRCA2  [45]. At the gene level, only FGFR2 was significantly associated with breast cancer (S2B Fig).
Network methods successfully identify genes associated with breast cancer We applied six network methods to the GENESIS dataset (Section 2. 3. 3). As none of the networks examined by LEAN was significant (Benjamini-Hochberg [BH] correction adjusted Pvalue < 0. 05), we obtained five solutions (Fig 1 ): one for each of the remaining four genebased methods, and one for SConES GI (which works at the SNP level). These solutions differed in many aspects, making it hard to draw joint conclusions. For starters, the overlap between the genes featured in each solution was relatively small (Fig  1A ). However, the methods tended to agree on the genes with the strongest signal: genes selected by more methods tended to have lower P-value of association (Fig  1B ). Another major difference was the solution size: the largest solution, produced by HotNet2, contained 440 genes, while heinz's contained only 4 genes.
(D) True positive rate and false positive rate of the methods, obtained using different parameter combinations (Section 3. 7). We used as true positives BCAC-significant SNPs (for SConES and χ 2 + Bonferroni) and genes (for the remaining methods, Section 2. 6). We used the whole dataset in this panel. https://doi. org/10. 1371/journal. pcbi. 1008819. g003 In the GENESIS dataset, heinz's solution was very conservative, providing a small solution with the lowest median P-value (Table  2 ). By repeatedly selecting this compact solution, heinz was the most stable method (Fig  3B ). Its conservativeness stemmed from its preprocessing step, which modeled the gene P-values as a mixture model of a beta distribution and a uniform distribution, controlled by an FDR parameter. Due to the limited signal at the gene level in this dataset (S2B Fig), only 36 genes retained a positive score after that transformation.
However, this small solution did not provide much insight into the susceptibility mechanisms to cancer. Importantly, it ignored genes that were associated with cancer in this dataset, like FGFR2. On the other end of the spectrum, dmGWAS, HotNet2, and SigMod produced large solutions. DmGWAS' solution was the lowest scoring solution on average because of its greedy framework, which is biased towards larger solutions  [54]. It considered all nodes at distance 2 of the examined subnetwork and accepted a weakly associated gene if it was linked to another, high scoring one. Aggregating the results of successive greedy searches exacerbates this bias, leading to a large, tightly connected cluster of unassociated genes (Fig  4A ). This relatively low signal-to-noise ratio combined with the large solution requires additional analyses to draw conclusions, such as enrichment analyses.
In the same line, HotNet2's solution was even harder to interpret, being composed of 440 genes divided into 135 subnetworks. Lastly, Sig-Mod missed some of the highest scoring breast cancer susceptibility genes in the dataset, like FGFR2 and TOX3. Another peculiarity of network methods was their relationship to betweenness centrality. We studied random rewirings of the PPIN that preserved node centrality (Section 2. 7). In this setting, network methods favored central genes (Fig  4B ) even though highly central genes often had no association to breast cancer susceptibility (Fig  4C ). We found this bias especially in SigMod (S5 Fig) , which selected three highly central, unassociated genes in both the PPIN and in many of the random rewirings: COPS5, CUL3, and FN1. However, as we showed in Section 3.
3. 3) by applying them to the GENESIS GWAS dataset on familial breast cancer (Section 2. 1). DmGWAS, Heinz, HotNet2, SConES, and SigMod all yielded compelling solutions, which include (but are not limited to) known breast cancer susceptibility genes (Section 3. 2). In general, the selected genes and SNPs were more central than most other genes and SNPs, agreeing with the observation that disease genes are more central  [49]. However, very central nodes are also more likely to be connecting any given random pair of nodes, making them more likely to be selected by network methods (Section 3. 6). However, we found support in the literature for the involvement of the selected highly central genes (COPS5, FN1, and CUL3) in breast cancer susceptibility (Sections 3. 3, 3. 6, and 3. 8). Despite these similarities, the methods' solutions were notably different.
5), which suggests that cases and controls are difficult to separate in the GENESIS dataset. This may be due to limited statistical power, which reduces the ability to identify relevant SNPs. However, in any event, we do not expect to separate people who have or will develop cancer from others on the sole basis of their genomes, ignoring all environmental factors and chance events. Hence, network methods were preferable to the logistic regression classifier since they did "no worse" at classification while providing an interpretable solution. Using an algorithm other than L1-penalized logistic regression might improve classification accuracy. Specifically, PRS-based strategies seem to perform slightly better on GENESIS  [62]. Second, all methods were remarkably unstable, yielding different solutions for slightly different inputs.
In this work, we have compared our results to significant genes and SNPs in the BCAC study  [42]. Network methods showed modest precision but much higher recall at recovering BCAC hits (Section 3. 4). While precision might be desirable when a subset of useful markers is required (for instance, for diagnosis), higher recall is desirable in exploratory settings. Nonetheless, BCAC was not an ideal ground truth. First, the studied populations are not entirely overlapping: BCAC focused on a pan-European cohort, while GENESIS targeted the French population. Second, the study designs differed: a high proportion of breast cancer cases investigated in BCAC were sporadic (not selected according to family history), while GENESIS was a homogeneous dataset not included in BCAC focused on the French high-risk population attending the family cancer clinics.
Finally, and this is indeed the motivation for this study, GWAS are unlikely to identify all genes relevant for the disease: some might only show up in rare-variant studies; others might have too small effect sizes. Network methods account for this by including genes with low association scores but with relevant topological properties. Hence, network methods and GWAS, even when well-powered, are unlikely to capture exactly the same sets of genes. This might partly excuse the low precisions displayed in Section 3. 4 and the low AUC displayed in Section 3. 5. As not all PPIN databases compiled the same interactions, the choice of the PPIN determines the final output. In this work, we used only interactions from HINT from high-throughput experiments (Section 2. 3. 2). This responded to concerns about adding interactions identified in targeted studies and falling into "rich getting richer" problems: since popular genes have a higher proportion of their interactions described  [10, 24] , they might bias discovery towards themselves by reducing the average shortest path length between two random nodes.