dataesr/scientific-paragraphs-categorization huggingface dataset:
The association observed for small GTPases was also accompanied by changes affecting GO terms related to the cytoskeleton. Overall, our findings, therefore, point toward altered synaptic plasticity in the lateral amygdala in relation to ELA and depression and reveals part of underlying epigenetic mechanisms at DNA methylation and histone levels. While few molecular studies in humans previously documented this hypothesis  66  , it strongly resonates with the wealth of human imaging and animal data that shows structural and functional plasticity in this brain region as a function of stressful experiences  4. Finally, we wondered whether mCAC may also contribute to molecular responses to ELA in the brain. We found that similar numbers of differential methylation events could be detected across CAC and CG contexts in ELA subjects, suggesting that both contexts might be sensitive to behavioral regulation.
These 2 studies suggest that DNMT3a and MeCP2 may be implicated in human in the particular cross-talk that emerges during brain maturation between mCAC and specific histone modifications (H3K27ac, H3K4me1, H3K36me3, and H3K9me3). Therefore, future investigations should focus on these marks, and related histone-modifying enzymes, to better decipher the lifelong impact of ELA on molecular epigenetic interactions. Of note, this study has limitations. First, due to technical constraints at the beginning of the project, pools of amygdala tissue from several subjects were analyzed for ChIP-Seq, while RNA-Seq and WGBS were conducted separately for each sample. While this may have affected our results (for example, by obscuring subtle subject-specific histone changes not detected at pool level), the convergence of functional annotations observed across multiple types of data suggests a modest detrimental impact of the pooling approach.
plot  82. Inter-sample correlations and hierarchical clustering were achieved using deepTools. Identification of differential enrichment sites for each histone mark was done using dif-fReps with window size 1000 bp, sliding step 100 bp, and fragment size 200 bp  34. A FDR < 10% and P < 0. 0001 for the negative binomial tests were used as significance cutoffs. ChromHMM was used to partition the genome into 200-bp bins and annotate them to chromatin states  15. A 10-state model was chosen and applied to all datasets. A consensus map was first created for general characterization of the amygdala chromatin states, using genomic regions with at least 6/11 samples in accordance with a state. For comparisons of the two clinical groups, group-specific maps were defined using regions showing at least a 70% agreement between samples (at least 3/4C pools and 5/7 ELA pools).
State transitions (ST) were then defined as regions with differing states between the C and ELA maps. For characterization of the distribution of DS and ST, we used the region_analysis package (Python v3) to annotate them to genomic features  34. WGBS library preparation. Whole-genome sequencing libraries were generated from 700 to 1000 ng of genomic DNA spiked with 0. 1% (w/w) unmethylated λ DNA (Promega) previously fragmented to 300-400 base pairs (bp) peak sizes using the Covaris focused-ultrasonicator E210. Fragment size was controlled on a Bioanalyzer DNA 1000 Chip (Agilent) and the KAPA High-Throughput Library Preparation Kit (KAPA Biosystems) was applied. End repair of the generated dsDNA with 3′-or 5′-overhangs, adenylation of 3′-ends, adaptor ligation, and clean-up steps were carried out as per KAPA Biosystems' recommendations.
Cytosine overlapping SNPs from dbSNPs (137) and CpGs located within ENCODE DAC blacklisted regions or Duke excluded regions  84  were discarded. DNA methylation data characterization. All analyses conducted to characterize the genome-wide abundance and distribution of CG and CAC methylation were done by focusing on cytosines showing a coverage > = 5 (Fig. 2  and Supplementary Figs. 6 7 8 9 ). We used the region_analysis package  34  to assign each cytosine to a genomic feature (Supplementary Fig. 7 ), using the Ensembl v75 annotation for consistency with RNA-Sequencing data analysis (see below). MethySeekR was used to call CpG-rich, unmethylated regions (UMR), as well as CpG-poor low-methylated regions (LMR), as described by Burger et al. 85. Differential methylation analysis. Differential methylation analysis was conducted using BSmooth, as described previously  86.
The context of each C was determined, which allowed us to classify each C of the genome as CG or CAC. Methylation levels for each site were estimated by counting the number of reported C ("methylated" reads) divided by the total number of reported C and T ("methylated" plus "unmethylated" reads) at the same position of the reference genome. To identify differentially methylated regions in the CG context, we performed a strand-independent analysis of CG methylation where counts from the two Cs in a CG and its reverse complement (position i on the plus strand and position i + 1 on the minus strand) were combined and assigned to the position of the C in the plus strand. The summarized methylation estimates of strand-merged CG sites from the 21 ELA and 17 control samples were used to identify differences in methylation, using the R package BSmooth/BSseq 37 at default parameters.
15b ). RNA-Sequencing alignment, counting, and differential expression analysis. As described previously  87  , we used: FASTX-Toolkit (hannonlab. cshl. edu/fastx_toolkit/ links. html) and Trimmomatic 78 for adapter trimming; Bowtie2 for alignment; TopHat  88  for transcript alignment; HTSeq-count  89  or Kallisto  41  for counting; and DESeq2  90  for differential expression analysis. Following high-throughput sequencing, 100 bp paired-end reads were aligned to the hg19 human genome using TopHat v2. 1. 0 (tophat. cbcb. umd. edu/) with a mate insert distance of 75 bp (-r) and library type fr-unstranded. Reads passing a mapping quality of at least 50 were used for gene and transcript quantification. Gene annotations from the Ensembl release 75 were used for gene-level quantification. We used HTSeq-count version 0.
6. 1p1 (www-huber. embl. de/users/anders/HTSeq/doc/overview. html), using the intersection-nonempty mode, and results were combined to form a count matrix of 20,893 transcribed RNAs across 50 samples. As an alternative strategy to HTSeq-count, we also processed reads through the pseudo-aligner Kallisto. Here, expression counts were obtained for isoforms using Kallisto (v. 0. 43. 0). Then, the tximport (v. 1. 0. 3) R package was used to reconstruct gene-level counts using the isoform-level counts generated by Kallisto. For differential expression analysis, genes with no mapped fragments were removed. Furthermore, genes with low counts were removed by keeping only those with at least 20 counts per subject in average. Using HTSeq-count, differential expression analysis was performed using the DESeq2 general linear model (GLM) using the following covariates: gender  91  , age  92  , pH, PMI, and RIN  93  , based on previous literature documenting their impact on human brain RNA-Seq datasets.
Gene set enrichment analysis (GSEA). GSEA was performed as previously described  40, 42. Log 2 fold changes were obtained for each gene from the differential gene expression analysis. Genes were ranked based on their fold changes where genes with the highest positive fold changes were at the top of the list and those with the lowest negative fold changes were at the bottom of the list. The ranked gene list was then used as an input for the GSEAPreranked tool, with the "classic" enrichment score calculation option selected. The C2 curated gene sets molecular signatures database was used to identify enriched gene sets (family-wise error rate, FWER < 0. 1). Rank-Rank Hypergeometric Overlay (RRHO2). RRHO2 was performed as described by Cahill et al. 47  , using the corresponding R package: https://github.
com/ Caleb-Huo/RRHO2. Briefly, two lists of genes present in both the lateral amygdala and ACC RNA-Seq datasets were identified, and the following metric computed for each brain region: -log 10 (P value) × sign(log 2 fold change). Then, the RRHO2 function was applied to the two gene lists at default parameters (with step size at 140). The significance of hypergeometric overlaps is reported as log 10 P values corrected using the Benjamini-Yekutieli procedure. Lists of genes generated by RRHO2, and corresponding to most significant hypergeometric overlaps, were then used for GO enrichment analysis, similar to RNA-Seq data. Deconvolution of cellular composition. To assess the abundance of various cell types in our amygdala samples, we used BSEQ-sc  43  and the CIBERSORT  44  algorithm and applied these to both our RNA-Sequencing and WGBS data.
For deconvolution of these data, we used as reference "signatures": (i) for gene expression: a matrix built from single-nuclear RNA-Sequencing data recently generated by our group using prefrontal cortical tissue  45  (archived on GEO Datasets under the reference series GSE144136), and analyzed using unsupervised graph-based clustering  94  , and (ii) for DNA methylation: the non-CG methylation matrix generated by Luo et al. 46  using single-nucleus methyl-cytosine sequencing. Relative fractions of cells were computed and are displayed in Supplementary Fig. 17. Gene ontology. We used GREAT v3. 0. 0  36  to identify the enrichment of gene categories in differential sites (DS) or state transition sites (ST) obtained from ChIP-Seq experiments, and for DMRs from WGBS experiments. DS, ST, and DMRs were associated with genes using the default proximal (5 kb upstream, 1 kb downstream of TSS) and distal (+/-1 Mb of TSS) definition of regulatory regions.
Acknowledgements P. E. L. was supported by fellowships from the " Fondation Fyssen ", the  Canadian Institutes of Health Research  ( 201311MFE-320636-218885 ), the  American Foundation for Suicide Prevention  ( PDF-0-081-13 ), the  "Fondation pour la Recherche Médicale " ( ARF20160936006 ), and by fundings from the  Bettencourt-Schueller Foundation , the  Brain Canada Foundation , the  "Fondation Deniker", the "Fondation de France " (N°E ngt: 00081244 ), and the  "Union Nationale de Familles et Amis de Personnes Malades et/ ou Handicapées Psychiques ".
Data availability Raw and processed data reported in this study using brain tissue from the lateral amygdala and anterior cingulate cortex are publicly available via the Gene Expression Omnibus with accession "GSE151827". All other relevant data supporting the key findings of this study are available within the article and its Supplementary Information files or from the corresponding author upon reasonable request. A reporting summary for this article is available as a Supplementary Information file. Source data are provided with this paper.
Novel Approach with Direct Resistome Analysis. Genomic screening is an alternative approach for studying resistance and providing a better understanding of the behavior of bacterial isolates  [185]. e development of nextgeneration sequencing has led to lower costs, reduced screening delay, and increased sequencing speeds combined with updated databases providing access to a large amount of information. e mcr-1 gene was initially discovered by whole     MCR3-R: 5′-TTAACGAAATTGGCTGGAACA-3′   [9, 10, 20, 23, 24, 72, 74, 157, 158, 174, 177, 180, [186] [187] [188] [189] [190] [191] [192] [193] [194] [195] [196] [197] [198] [199] [200] [201] [202] [203] [204]  (Figure  2 ). e technologies used to completely sequence the bacterial genome are Illumina (Illumina Inc. , San Diego, CA, USA), which produces short sequences (300 bp) and requires several days, and PacBio RS II (Pacific Biosciences, Menlo Park, CA, USA), which produces a single real-time molecule producing long sequences (60kb) in a few hours  [205].
e use of Illumina sequencers is not suitable for covering bacterial genomes with multiple repetitive elements because too many sequence pieces are obtained after assembly, whereas PacBio RS II delivers a single sequence without missing regions  [174]. Sekizuka et al. performed a hybrid analysis using the two technologies to analyze three Inc2 plasmids and found that they were highly conserved with the exception of the shufflon region, meaning that the combination of the two methods enables to analyze rearragements in highly recombinant regions  [174]. e sequences obtained are assembled, the genome is annotated, and then a mapping is carried out against a reference plasmid, in general pHNSHP45 for mcr-1  [6]. e aligned sequences are then compared to one of the resistance gene databases: Antibiotic Resistance Gene-Annotation  [206] , ResFinder  [207] , Comprehensive Antibiotic Resistance Database  [208] , and Antibiotic Resistance Genes Database  [209].
ey could also be compared to the plasmid genome, with GenEpid-J  [210]  or PlasmidFinder, which enabled the discovery of the mcr-4 gene  [11]  and presents the advantage of screening multiple genes and detecting the coexistence of several genes including carbapenemases. Lindsey et al. proposed a whole protocol for plasmid sequencing  [211]. More specifically, PointFinder was developed to detect chromosomal point mutations associated with antimicrobial resistance  [212]. Whole genome sequencing combined with new bioinformatic tools improves our ability to detect several resistance genes at the same time  [186, 205]  but presents the same limitations than PCR: new genes are not recognized by these techniques, which require the continuous updating of databases  [175, 213]  that should be merged into a single reference database  [213].
DATA PROCESSING In this section, a data-processing structure for the extraction of the information from corpus is presented. The datasets used in this paper are taken from the Miller Center(https://millercenter. org/), one of the representative databases of the U. S. history and civil discourse. During the data processing, each piece of information in the database is extracted into several descriptive attributes including the personal information of each President, the public speeches of the President, and pictures of the President. Figure1 summarizes the architecture of the proposed data processing, which will be subsequently described in detail.
Word Validation In this section, the filtered word candidates are verified using several English dictionaries and User Dictionary. The output of the word-candidate filtration is required for the verification. Also for the verification, an algorithmic working base was developed with several English dictionaries, as shown in figure  2. The proposed algorithm automatically compares the results with the several English dictionaries, and if the dictionary shows a result, it returns the word candidate as the meaningful result. Otherwise, It is discarded. However, Sometimes there are words have no definition in the dictionary but have a meaning. For this case, we made user dictionary collection in our database. If we store the words and meaning of words in the User dictionary, the algorithm can recognize more words.
Verification for a Comparison with the Real Corpus Previous research has shown that the visual-analysis result for corpus data is required to verify and prove findings in a comparison with real documents. In the example of figure  5 , this visual tool is synchronized with real corpus, and so that it is simultaneously verified using the corpus data. PreechVis uses a Word Cloud (b) to provide a visually distinguishable overview of the President's speech. This visual method is useful for learning about the number and kinds of words that are present in the corpus data. In the side view (c), the user can easily search the color legend about the used Word Cloud and the sorted word results. Additionally, PreechVis can also display the view of the real speech for the verification (a) that is on the left side of figure  5  for the verification.
Several interviews were conducted with domain experts who study computational-linguistic and natural-language processing, and these researchers agreed that the exploration of the multiword for each gram is a major strength of PreechVis. Further, the proposed visual tool can facilitate a quick exploration of the corpus information and more accurate results can be obtained, as shown in the presented case studies. The case studies three important implications and usage scenarios confirm the usability and effectiveness of the system. This study presents two important implications. First, the study reveals a data-processing method that can obtain more accurate results than when we do analysis without multiword. This study uses a linguistic approach to obtain more accurate wordcombination words, and this was explained in the data-processing without manual work.
Furthermore, the initial costs are relatively high in comparison to other targeted sequencing methods  [11]. To overcome this problematic, Qiagen (Hilden, Germany) developed a novel sequencing system, the GeneReader, specifically designed for the needs of routine molecular diagnostics. In contrast to the existing solutions, the GeneReader system provides, in theory, a complete workflow from sample preparation to data analysis. This should decrease hands-on time during sample preparation, as well as increase traceability and reproducibility. Furthermore, bioinformatic analysis is integrated into the workflow and automated to further improve the quality and efficacy. All related information, like lot numbers, qualitative and quantitative information, as well as results are stored in a central software platform making the use of spreadsheets or other complicated solutions expendable.
Discussion The introduction of targeted therapies into the clinical treatment of patients with advanced lung adenocarcinoma has made routine testing of tumor mutations a daily task in molecular pathology laboratories. The Qiagen GeneReader system is the first sequencing system commercially available that spans the whole workflow from nucleic acid isolation to data analysis, with the potential to replace currently used "homebrew" workflows for the genetic analysis, as previously demonstrated in comparison to Illumina MiSeq sequencing, Sanger sequencing and pyrosequencing; systems which are commonly used for genetic assessment in routine molecular pathology  [15, 16]. This is the first study to report on the comparison of the GeneReader system (Qiagen) with a widely used NGS system, the Ion PGM (Thermo Fisher Scientific, Waltham, MA, USA) in a large cohort of clinical samples from advanced lung adenocarcinoma patients.
We demonstrate the general usability of the GeneReader system for routine practice in a pathology laboratory setting. The system can easily be installed and integrated into a routine pathology and only limited training of the technical personnel is necessary. The high traceability is an advantage for daily routine sequencing  [17, 18]. The Ion PGM is not provided with a fully integrated tracking system and thus, we had to develop our own workflow and sample tracking system in the laboratory. This timely and quite complicated process was not necessary for the GeneReader workflow as all systems and the necessary software were directly delivered and installed and worked nearly out of the box in our hands. However, the high degree of automation did not result in reduced hands-on times and faster sequencing processing.
Compared to our commonly used NGS workflow using the Ion PGM system, the Qiagen system showed higher hands-on times as well as delayed sample-to-diagnosis times. Only the time needed for data analysis was considerably reduced using the GeneReader system. However, both workflows fit into a normal Monday-Friday working week and, therefore, the different sequencing times should have only a limited impact on the following clinical treatment decision. The higher sample throughput might favor the GeneReader workflow for laboratories with more samples and will consequently shorten the time from sample preparation to diagnosis. Nevertheless, a low throughput is obtained substantially faster using our accredited Ion PGM workflow. Besides the processing time, both systems clearly demonstrated that the workflow can be successfully implemented in a routine laboratory.
Data analysis was performed using Torrent Server™ (v 5. 0) and Ion Reporter™ Server hosting informatic tools (Ion Reporter™ Software v5. 0) for variant analysis, filtering and annotations. The sequencing run was considered successful and the quality adequate when the following quality metrics were met: (1) mapped reads ≥300,000; (2) average base coverage depth ≥300; (3) amplicons with at least 300 reads: ≥99%; (4) no strand bias: ≥95%; (5) amplicons read end-to-end: ≥99%. Integrative Genome Viewer (IGV) was utilized for visualization. The cut-off was set at 300× coverage and minimum 5% allelic frequency. Mutation detection sensitivity of each Ion PGM™ run was determined by AcroMetrix™ Oncology Hotspot Control for heterozygous mutations in seven different genes: EGFR (ENSG00000146648), KRAS (ENSG00000133703), BRAF (ENSG00000157764), NRAS (ENSG00000213281), ALK (ENSG00000171094), PIK3CA (ENSG00000121879) and ERBB2 (ENSG00000141736).
Acknowledgments: The authors would like to thank  Cancéropôle PACA ;  Conseil Départemental des Alpes-Maritimes , Appel à projet santé; Ligue contre le cancer- Comité départemental des Alpes-Maritimes  and the  French Government (National Research Agency, ANR)  through the  "Investments for the Future" LABEX SIGNALIFE: program  reference #  ANR-11-LABX-0028-01.
This allows the reuse of efficient algorithms designed for short-read data and permits the definition of novel modular hybrid assembly pipelines. Using comprehensive standard datasets and benchmarks, we show how Fast-SG outperforms the state-of-the-art short-read aligners when building the scaffolding graph and can be used to extract linking information from either raw or error-corrected long reads. We also show how a hybrid assembly approach using Fast-SG with shallow long-read coverage (5X) and moderate computational resources can produce long-range and accurate reconstructions of the genomes of Arabidopsis thaliana (Ler-0) and human (NA12878). Conclusions: Fast-SG opens a door to achieve accurate hybrid long-range reconstructions of large genomes with low effort, high portability, and low cost.
The method that we propose, Fast-SG, uses an alignment-free algorithm  [25]  strategy as well as information from varied sequence sources (Illumina, Pacific Biosciences, and Oxford Nanopore) and was conceived to maximize scalability, speed, and modularity. The latter characteristic, in particular, allows one to define novel hybrid assembly pipelines, which permits the efficient assembly of large genomes. Fast-SG was extensively tested using a comprehensive set of standard datasets  [3, 26]  and benchmarks. We show that Fast-SG enables the hybrid assembly of large genomes and is especially effective with shallow long-read coverage data (5X-10X). Our hybrid strategy consists of the construction of several synthetic mate-pair libraries that could have an insert size up to Bacterial Artificial Chromosome (BACs,180 kb) and can be combined with a short-read scaffolder to generate long-range scaffolds.
Datasets and software We collected a comprehensive collection of standard datasets (Table  1 ) that are frequently used to benchmark the new sequencing technologies, scaffolding tools, or genome assembly pipelines. Long-read datasets were used to investigate the capacity of Fast-SG to extract linking information from long reads and then the performance of short-read scaffolders fed with Fast-SG when compared to a dedicated long-read scaffolder. In the first case, the genome of Escherichia coli K12 was adopted as it has been sequenced by multiple long-read technologies and is commonly used to validate the long-read algorithms  [4]. In the second case, both E. coli K12 and Saccharomices cerevisiae W303 (Table  1 ) were used to prove that short-read scaffolders can use synthetic mate-pair libraries extracted from long reads.