dataesr/scientific-paragraphs-categorization huggingface dataset:
In Table  1  we report the running times of both tools on the datasets. We separate the running time required to build the BWT (by using BCR  [42, 43] ) from the running time of EBWT2INDEL for two reasons: first, our tool assumes that the input is represented as a BWT, which is a lossless representation of the input fasta and can therefore replace it; optimizing the BWT-constructions step is a well-studied problem that does not fit the scope of this article. Second, the BWT needs to be built only once for each input dataset. If one wishes to run further analyses on the same dataset, possibly using different parameters, the same BWT can be used. This is not the case for DISCOSNP, whose de Bruijn graph is a lossy representation of the data and depends on the input parameters (k-mer length and minimum coverage).
Comparison with our old tool ebwt2snp We also tested the old version of our tool  [7]  on the 20x real dataset in order to evaluate the improvement in performance of the new version. We recall that the tool  [7]  needs to compute the Generalized Suffix Array (GSA) of the reads set (this can be achieved using BCR). This (uncompressed) data structure is much larger than the ebwt used by eBWT2CLUST, thus a larger pre-processing time and larger disk usage with respect to eBWT2CLUST are expected. We also note that, once the GSA is computed, the tool  [7]  only needs to scan this array once to perform the analysis. The post-processing phase therefore is very fast and uses little RAM. On the 20x real dataset, eBWT2CLUST takes 2h 9' to compute the ebwt (BCR), and 1h 24' to perform the analysis (ebwt2InDel), using 8.
3 GB of RAM and 9. 5 GB of disk space (in addition to the We also show the times required to build the BWT using the tool BCR. All tools were run using one core only input dataset and output). The old tool ebwt2snp requires 14h 13' to compute the Generalized Suffix Array (BCR) and 28' to perform the analysis (ebwt2clust + clust2snp) using 1 GB of RAM (it loads fewer structures in RAM) and 73 GB of disk space (in addition to the input dataset and output). Thus, our new pipeline is, overall, much faster and uses one order of magnitude less disk space due to the use of compressed data structures (faster to calculate and lighter to store).
Enabling Parallelism We run EBWT2INDEL and DISCOSNP++ on the 30x-covered real dataset allowing the two tools to use 24 cores, in order to establish the effectiveness of the parallelization strategies of both tools. The parallel pipeline using HARC followed by BCR and EBWT2INDEL (run in parallel on 24 pieces of the sorted fasta file) terminated in 33 minutes. This is approximately 10 times faster than the complete sequential pipeline (see Table  1 ). By comparing the results with the same ground-truth vcf file used for Table  2 , we obtained a SNP sensitivity of 71. 68%, a SNP precision of 68. 58%, and a F1 score of 70. 09% (setting the minimum-coverage parameter -m to 5, which yielded the best sensitivity/precision trade-off ). As expected (since HARC works only under Hamming distance), INDEL sensitivity and precision dropped to 5.
Real experiment (iii) -whole genome, two individuals In the first whole genome experiment we reported variations contained in the union of the first 320 million 100-bp reads from both datasets github. com/nicolaprezza/ebwt2InDel and www. internationalgenome. org/data-portal/sample/NA12878 (mother and son, respectively). Note that, first, we have filtered the reads by removing those containing the symbol N. In total, the dataset's size amounted to 65. 3 Gbp, that is, a coverage of 10x per individual. We used the same corresponding parameters for the two tools: -m/-c = 3 controls the minimum read/k-mer coverage, -g/-D = 10 controls the maximum INDEL length, and -v/-P = 2 controls the maximum number of differences allowed (in the bubble/left-context of clusters) in addition to the main SNP/INDEL.
In order to count the number of variations output by the tools, we converted their outputs (that is,. fasta files containing DNA fragment pairs testifying the variations) to. vcf files by aligning them against the hg38 Human assembly (using the pipeline described in "VCF creation" subsection). Finally, we removed duplicate entries from the. vcf files (since both tools analyze the dataset and its reverse-complement, some variants could be found twice: on the forward and reverse strands.
Real experiment (iv) -whole genome, one individual We performed one more experiment whose goal was to find heterozygous sites from a 20x-covered dataset of Human individual NA12878. We selected 1. 300. 000. 000 reads from the file ftp://ftp. sra. ebi. ac. uk/vol1/fastq/SRR622/SRR622457/ SRR622457_1. fastq. gz. The whole dataset, together with the reverse-complemented reads, totalled approximately 122 GBp. For the ground truth, we used the NCBI. vcf ftp://ftp-trace. ncbi. nlm. nih. gov/giab/ftp/release/NA12878_HG001/latest/, and validated the calls using hap. py. As in the previous experiment, we used the same corresponding parameters for the two tools: -m/-c = 3, -g/-D = 10, and -v/-P = 2. In order to build a. vcf from the tools' outputs, we aligned the called variations against the hg38 Human assembly, and removed duplicate entries from the resulting.
Real experiment (iii) -whole genome, two individuals The large processing times of our pipeline are paid off by a much larger number of reported variations: using comparable parameters for both tools, the fragments output by EBWT2INDEL (in sequential mode) contained > 5 times more SNPs and > 2 times more INDELs than those output by DISCOSNP++. In parallel mode, EBWT2INDEL found less SNPs, though still > 3 times more than DISCOSNP++. This depends on the sorting strategy of HARC, which in this case separated reads that, in the sequential pipeline, contained areas that clustered together and also yielded SNPs. We note that, while the goal of this experiment was just to compare the raw amount of variations found by the two tools in the merged dataset, in a more realistic application one might be interested in discarding heterozygous sites within the same individual and keep only differences across the two individuals.
This kind of analysis cannot be performed using DISCOSNP++, since this tool does not "color" the paths of bubbles in the de Bruijn graph according to their provenience from the two input datasets (the k-mers of both individuals are pooled together). On the other hand, this analysis can easily be performed with EBWT2INDEL by just comparing the BWTs of the two datasets, or taking as input one single BWT (as we did in our experiment) and a bitvector (the so-called document array) that tells apart eBWT characters of the two individuals.
Resources Despite the complex data structures used, the running times of EBWT2INDEL are not much higher than those of DISCOSNP++: using one core for both tools, EBWT2INDEL is 2. 3 times slower than DISCOSNP++ on the 48x-covered real dataset, and 1. 42 times slower on the 30x-covered real dataset. It is worth to note that, while the running times of our tool scale linearly with the dataset's size, those of DISCOSNP scale linearly with the dataset's complexity (i. e. number of distinct k-mers): this is the reason why the ratio between ours and DISCOSNP++ running times increase with the coverage. If we take into account also BWT construction, this step accounted for 60% of the total processing time in experiments (i) and (ii), and 42% in experiment (iii). By enabling parallelism, our whole pipeline (eBWT construction and SNP analysis) runs in times comparable to those of DISCOSNP++, while also being more sensitive.
As far as the RAM usage of our tool is concerned, we observed that (as expected) it always amounted to 7n bits per base; on the largest dataset (50x synthetic), this was equivalent to 21GB. On the (more realistic) 30x real dataset, our tool used 12. 7 GB of RAM. The RAM usage of our tool was dominated by the variant calling phase; building the BWT required at most 3GB of RAM on the largest dataset.
As a limitation, our parallel pipeline at the moment works for SNPs only, due to the use of an external pre-processing tool that does not take into account the presence of INDELs in the read set. We plan to overcome this limitation in a forthcoming update of our tool. We furthermore report results on a larger (real) whole-genome sequencing experiment whose input consisted of two 10x-covered Human datasets from the 1000 Genomes Project. Also in this case, our tool exhibits a much higher sensitivity than the state-of-the art tool, finding > 5 times more SNPs and > 2 times more INDELs.
Glutaminolysis regulates lesional macrophage efferocytosis in atherosclerosis. To determine the relevance of these observations during disease, we next tested Gls1 requirements in a mouse model of atherosclerosis, in which defective lesional dead cell clearance translates into unstable necrotic core within plaques  (Kojima et al. , 2017; Tabas, 2010). Taking advantage of publicly available gene expression datasets of western diet (WD) fed wild-type (WT) and ApoE-deficient mice  (Grabner et al. , 2009) , and computational analysis of metabolic networks  (Jha et al. , 2015) , we first observed a downregulation of Gls1 expression in atheromatous plaques. This was part of a dysregulated amino acid catabolism module making up a densely connected core with a more global metabolic transcriptome signature (Fig.
We first found that intracellular Gls1 mRNA expression was similarly upregulated by IL-4 stimulation or AC ingestion in a validated gene expression dataset  (Bosurgi et al. , 2017)  (Fig. S3a ). We confirmed this upregulation in a time course experiment (Fig. S3b ), paralleling a drop in cellular glutamine to glutamate ratio (Fig. S3c ). Thus, glutaminolysis is intrinsically regulated in resolving macrophages. To evaluate how IL-4 might couple macrophage glutaminolysis to efferocytosis, we next performed an RNA sequencing (RNA-seq) analysis of PCMs stimulated by IL-4 in vivo. When compared to control PCMs, Gls1-deficient cells had similar transcriptional regulation of genes involved in the sensing of soluble mediators attracting the phagocytes to ACs ('smell' or 'find-me' signals), the direct contact and recognition of ACs by ligand-receptor interactions ('taste' or 'eat me' signals) and the corpse internalization and processing  ('ingestion' and 'digestion' processes)   (Han and Ravichandran, 2011 ) (Fig.
, 2015)  and compared predictions to DreamBio that allows direct computational KEGG pathway mapping. CoMBI-T and DreamBio profiling analyses featured a link between Mac ΔGls1 PCM mitochondrial metabolic reprogramming and lower expression of tricarboxylic acid (TCA) cycle and aspartate-arginino-succinate (AAS) shunt genes. These genes are interconnected to metabolism of glutamine per se, carbohydrates, hexosamine and fatty acids (Fig. 4a  and S4b ). CoMBI-T predictions also revealed that the redox status of reparative Mac ΔGls1 PCMs may connect to the one carbon cycle-centered module known to support the transsulfuration pathway (i. e, GSH synthesis) (Fig. 4a ). KEGG mapping with DreamBio offered an alternative visualization of the perturbed transcriptomic pathways in reparative Gls1-deficient macrophages and highlighted additional reactions that were not yet predicted (Fig.
Glutamine is considered a conditionally essential amino acid because of its role during metabolic stress, including injury  (DeBerardinis and Cheng, 2010; O'Neill and Pearce, 2016). Glutamine utilization is increased in alternative macrophage polarization  (Jha et al. , 2015)  and predicted macrophage activation profiles in aortas of atherosclerotic mouse models  (Tavakoli et al. , 2017). However, the fundamental link between Gls1 and efferocytosis in this setting remains to be elucidated. Strikingly, despite enhanced glutamine utilization in the aortas of atherosclerotic mice, we observed impaired glutamate conversion and reduced Gls1 expression, reflecting perturbed glutaminolysis. In our mouse and human datasets, we also observed that Gls1 expression within plaques was positively associated to canonical alternative polarization markers suggesting that glutaminolysis, rather than glutamine uptake could predict macrophage polarization.
RNA quality and integrity were determined using the Agilent 2100 Bioanalyzer. Biotinylated cRNA was prepared with Illumina TotalPrep RNA Amplification Kit (Ambion, TX, U. S. A) and 750ng cRNA per sample was used for hybridization (Illumina Human Sentrix-8 V2. 0, Beadchip®). Scanning was performed on the Illumina Beadstation 500 (Illumina, CA, U. S. A) and image analysis was done using the Illumina Beadstudio v3 Gene expression software. A total of 22,184 human transcripts were analyzed in R Bioconductor lumi package. 2 Firstly, a variance stabilizing transformation, which is incorporated in the lumi package, was performed. Secondly, Robust Spline Normalization (RSN) algorithm in lumi package was applied to normalize the data. Differential gene expression analysis was done by using the function lmFit() provided in Limma R package on preprocessed transcriptomics data.
10μm cryosections of the aortic sinus were prepared. Oil Red O staining was used to detect neutral lipid content in the plaque combined with a haematoxylin/eosin staining to analyse tissue architecture. Plaque macrophages were visualized using purified anti-CD68 mAb (clone FA-11, AbD Serotec). Anti-rat Alexa Fluor 488-conjugated antibody (A-11006, Life technologies) was used for detection of CD68 staining. For analysis of plaque macrophage proliferation, anti-Ki67 PE conjugated mAb (clone 16A8, BioLegend) was used. Nuclei were revealed with DAPI counterstaining (2μg/ml). TUNEL staining was performed using the DeadEnd™ Fluorometric TUNEL System (Promega). Plaque area quantification were measured with ImageJ software. Echography. Animals were fully anesthetized with 1. 5% Isoflurane before and during ultrasound scanning.
Integrated network analyses. Network-based integration of metabolite and gene expression datasets was conducted using Shiny Gam as previously described  (Jha et al. , 2015). We also developed DreamBio, a complementary topological tool for Integrated Network Analysis mapped into KEGG pathway. The same strategy than Shiny Gam was used by downloading KEGG REACTION, KEGG ENZYME, KEGG COMPOUND, and KEGG GLYCAN databases (August 2013 version) except that GEPHI GEXF (graph exchange format) was converted from KGML to be analyzed through Sigma library js dedicated to graph drawing. Up and down regulated metabolic genes based on p values calculated with Phantasus  (Jha et al. , 2015)  were mapped into models maintaining all essential KEGG pathway attributes. DreamBio will become soon freely available at http://dreamsession.
com/biotest/bioinfo/index. php?w=sigma. Fluxomics. Metabolite extraction of BMDMs was performed on 2. 5 million cells per well using 70°C aqueous 70% ethanol as described previously  (Devos et al. , 2019). At collection, cells were placed immediately on ice, the media was removed, and cells were washed three times with ice-cold PBS to remove residual media. Intracellular metabolites were extracted twice with hot ethanol. For LCMS, samples where dried under nitrogen flow and reconstituted in a milliQ water/acetonitrile (1:1) mixture for injection using a UPLC Acquity (Waters) separation system coupled with a Xevo G2 ToF (Waters). Compounds were ionized using an electrospray ionization source in negative mode. Data processing was performed in MATLAB (Mathworks, Inc. ) using a custom-made in-house protocol.
Compound identification was performed using both retention time of authentic standards and accurate mass with an accepted deviation of 0. 005 Da. Raw data was converted to netCDF format using Chemstation (Agilent), before processing in MATLAB R2014b (Mathworks, Inc. ) using PARADISe software. All MS sample processing and analysis were performed by MS-Omics, Inc. (Copenhagen, Denmark). Metabolomics. Metabolomics analyses were performed at CriBioM as previously described  (Aidoud et al. , 2018). Briefly, control and Gls1-deficient BMDMs were treated with or without 20ng/mL IL-4 as indicated in the figure legends. Metabolites were extracted by exposing cells to cold methanol and analyzed by LC-MS. Typhoon TM Biomolecular Imager. 2µCi of [ 14 C]-labelled glutamine were i. v. injected and mice were sacrificed 15 minutes later.
qPCRs were performed on StepOne device from Applied Biosystem (France). Samples were performed in triplicates. Results are expressed in arbitrary units. Gls1 (GCACATTATTCACCCGGTAACC; CTGCCCACCCACCATCC, Thermofisher) Statistics. Data are expressed as mean+/-SEM. Statistical analysis was performed using a 2-tailed t test or ANOVA (with Tukey's post-test analysis) with GraphPad Prism software. A P value ≤ 0. 05 was considered as statistically significant.
Acknowledgments. We thank  Béatrice Caraveo  for computional development of DreamBio, a novel topological tool for Integrated Network Analysis. We thank  Samantha Fernandez  for the non-invasive study of atheroma plaques by ultrasound echography as part of the  European Center for Research in Imaging (Cerimed) ,  Dr Fréderic Labret  for assistance with flow cytometry,  Dr Véronique Corcelle  for assistance in animal facilities and  Dr. Marie Irondelle  for assistance with confocal microscopy. Financial support and sponsorship. This work was supported by grants from the  Fondation de France (FDF)  and the  European Research Council  ( ERC) consolidator program  ( ERC2016COG724838 ) to L. Y. C. CCMA electron microscopy equipments have been funded by the  Région Sud -Provence-Alpes-Côte d'Azur , the  Conseil Départemental des Alpes Maritimes , and the  GIS-IBiSA.
Microglial transcriptome reveals novel MIA and repopulation modules and their overlap with ASD risk genes Microglia are not only immune surveyors, but also play critical roles in synapse formation  [6]  and neuronal connectivity  [6]. They maintain homeostatic and immunological functions through control of sensome and immune signaling, which are regulated by transcriptional networks  [10]. To determine the transcriptomic changes underlying the MIA microglia phenotype and their reversal by PLX, we performed RNA-sequencing of magnetic CD11B beadsisolated microglia from the whole brain in Saline and MIA male and female offspring at E17, P7, P20, and P60, as well as from MG-REP male and female offspring at P60. In order to verify the purity of RNA-seq across ages and conditions, we assessed expression of cell-type specific markers (derived from Barres dataset of mouse neurons, astrocytes, oligodendrocytes, endothelial cells, and microglia)  [25] , which demonstrate an average of 98% purity, in agreement with the purity calculated from Zhang et al.
(97. 8%)  [25]  and a FACS purified microglia RNA-seq database (98%)  [26]. Since RNA-sequencing samples were from both genders, we evaluated sex as possible confounding effects. Based on this analysis and our balanced experimental design, we concluded that there was no evidence that this variable modified the effect of treatment or any of our downstream conclusions. K-means clustering (k = 5) of 14,225 microglial genes from RNA-seq data revealed distinct transcriptional signatures of immature microglia (IM module, 4681 transcripts, enriched in E17 and P7 Saline), MIA immature microglia (MIA-IM module, 2816 transcripts, enriched in E17 and P7 MIA), Juvenile microglia (JM module, 2318 transcripts, enriched in P20 Saline and MIA), Adult microglia (AM module, 3276 transcripts, enriched in P60 Saline + CTRL, P60 MIA + CTRL and P60 MIA + MG-REP), and repopulated adult microglia (REP-AM module, 1,134 transcripts, enriched in P60 Saline + MG-REP) (clustering and representative genes in Fig.
We crossexamined the 46 genes in the top GO term group, "chemical synaptic transmission" (Fig. 3a , left red box), with RNA-seq data of wild type and Cx3cr1 GFP/+ microglia purified by FACS (GSE53789)  [26]. We confirmed that 45 out of the 46 genes were detected in FACS-purified microglial gene sets. These data show strong evidence that microglia unexpectedly express synaptogenic molecules and that increased gene expression related to synaptic process in MIA microglia is unlikely due to the contamination of neuronal cells, but could reflect aberrant MIA microglial function. We next performed cross-sectional analysis of 14,225 microglial genes in MIA versus Saline microglia using Gene-wise Negative Binomial Generalized Linear Modeling with Quasi-likelihood F-testing (glmQLFit)  [33] , which revealed discrete and overlapping genes significantly altered by MIA at each age [Supplementary Fig.
Assessment of microglia purity To verify purity of RNA-seq across ages and conditions, we assessed expression of cell-type specific markers (derived from Barres dataset of mouse neurons, astrocytes, oligodendrocytes, endothelial cells, and microglia)  [25] , which demonstrate average of 98% purity. The following genes: Dcx, Npas4, Npy, Reln, Tubb3 (neurons), Mag, Mbp, Mog, Ppapdc1a, Sox10 (oligodendrocytes), Aqp4, Gfap, Gdpd2, Plcd4, Slc7a2, (astrocytes), Emcn, Ocln, Pecam1, Slc16a4, Tek (endothelial cells), C1qa, Csf1r, Cx3cr1, Fcrls, Hexb (microglia) were used to determine purity. Transcripts per million (TPM) for each sample was determined from RNAseq data, and percentage of each cell type marker was calculated normalized to the total TPM of purity analysis genes within each sample.
JMP hierarchical clustering and principal components analysis JMP Pro 10 statistical software (SAS) was used for hierarchical clustering, principal component analysis (PCA), and pairwise correlation analyses. For hierarchical clustering, gene expression data were normalized and clustered using k-means centroid-based clustering. PC analysis was performed on the entire gene expression datasets for each group, to show how the PCs explain the variation in the data. The PC points were derived from the eigenvector linear combination of the variables.
Gene ontology (GO) biological functions and pathway analysis To determine the biological meaning of gene lists, we used the Database Annotation, Visualization, and Integrated Discovery (DAVID, version 6. 8)  [71]  to calculate enrichment for gene ontology terms (GO Direct_BP; biological process). Gene lists were analyzed for overrepresentation of biological functions with Bonferroni, Benjamini, and FDR correction for multiple comparisons and in some cases functions with their respective p values of enrichment were graphed in Prism 6. 0 (GraphPad). For Fig. 3a , genes from the AM module were sorted by expression ratio between MIA and Saline. Those genes with an expression ratio <1 in MIA versus Saline were sorted for expression ratio in MIA versus MIA + MG-REP. If direction of change was the same in MIA versus Saline and MIA versus MIA + MG-REP, the gene was included in the final list.
Human gene expression data comparisons Human orthologs of Mus musculus genes were determined using Biomart (http://useast. ensembl. org/biomart). Venn diagrams were created using Venny 2. 1. Microglia gene lists were compared with published lists of ASD, schizophrenia, and intellectual disability-associated genes, synaptic and postsynaptic density molecules and FMRP interacting partners (see Supplementary Table  2  for gene lists and descriptions) using a hypergeometric distribution test (also known as one-tailed Fisher's exact test) in Excel (Microsoft), using a background gene set of 13,757 genes with one-to-one mouse human orthologs, except for the Postsynaptic Density and Synaptic Protein lists, which used a background of 12,622 total protein-coding mouse-human orthologs (from microglial lists) and proteins with mouse orthologous (from Postsynaptic Density and Synaptic Protein lists).