dataesr/scientific-paragraphs-categorization huggingface dataset:
FUN-LDA model with nine classes. Here we use data for four activating histone modifications, namely H3K4me1, H3K4me3, H3K9ac, H3K27ac, and DNase for 127 different cell types and tissues represented in the Roadmap datasets (see Supplemental Tables  S1  and S2 ). Not all of the histone marks were profiled for each of the 127 different cell types and tissues. However, using the relationships between different marks within and across tissues, signal tracks have been predicted for each of these marks across all tissues  [8, 15]. We make use of these predicted signal tracks to compute integrated functional scores for every possible position in the human genome for 127 cell types and tissues. Specifically, using the perplexity based criterion (see Methods section) and prior knowledge on the relationship of histone modifications and chromatin states, we investigated models with varying number of classes, and have chosen as our final model a model with nine classes (as shown in Supplemental Figure  S1  the perplexity measure begins to plateau starting with models with 9 classes).
The median Jaccard index for variants falling in promoters is 0. 33, and 0. 16 for variants falling in enhancers, concordant with existing literature showing that there tends to be more sharing across tissues for predicted functional variants in promoters vs. those in enhancers  [26]. 2. 2. Enrichment analyses using eQTLs from the Genotype-Tissue Expression project, Geuvadis and TwinsUK data. eQTLs from the Genotype-Tissue Expression project. The Genotype-Tissue Expression (GTEx) project is designed to establish a comprehensive data resource on genetic variation, gene expression and other molecular phenotypes across multiple human tissues  [27]. We focus here on the cis-eQTL results from the GTEx V6 release comprising RNA-seq data on 7, 051 samples in 44 tissues, each with at least 70 samples (Supplemental Table  S4 ).
2. 4. 2. Confirmed regulatory variants (emVars) from a multiplexed reporter assay. In  [64] , the authors have applied a new version of the massively parallel reporter assay (MPRA) to identify variants with effects on gene expression. In particular, they apply it to 32, 373 variants from 3, 642 cis-expression quantitative trait loci and control regions in lymphoblastoid cell lines (LCLs), and identify 842 variants showing differential expression between alleles, or emVars, expressionmodulating variants. We use this set of 842 emVars as positive control variants. Our negative control variants are those variants tested using the MPRA where neither allele showed differential expression relative to the control, applying a threshold of 0. 1 for the Bonferroni corrected p value. After removing from the list of positive and negative control variants those variants that we could not map to a genomic location using the Ensembl database (http://grch37.
For all methods, we calculate the scores for these motifs by averaging across all bases in the motifs. As shown in Table  3 , FUN-LDA has better accuracy compared with GenoSkyline, ChromHMM, IDEAS and Segway, and for HepG2 the improvement is substantial. We have attempted to form the functional group in an objective manner, based on prior knowledge on what functional classes from the different segmentation approaches (ChromHMM, Segway and IDEAS) should be considered active functional elements. We have performed an additional analysis where we have computed the AUC for all combinations of states (with individual AUC ≥ 0. 5) for each segmentation method and selected the state combination with highest AUC for the three datasets above. The results from these analyses are shown in Supplemental Table  S10.
We note also that the experimental datasets we use here (from the MPRA experiments, and validated enhancers in ultra conserved regions) do not have gold standard labels (for example, the sensitivity for the MPRA assay in  [64]  is estimated to be between 9% -24%), and therefore there is an upper limit to the AUC we can achieve on these datasets even with an optimal method. These cell type and tissue specific functional scores have numerous applications. We have shown here for the first time in the literature that eQTLs from several large studies such as GTEx, Geuvadis and TwinUK cohort, are most enriched in the functional components from relevant Roadmap tissues. As shown before in  [29] , and as illustrated here as well, they can be used to infer the most relevant cell types and tissues for a trait of interest, and can help focus the search for causal variants in complex traits by restricting the set of candidate variants to only those that are predicted to be functional in tissues relevant for the trait under consideration.
Heuristic methods exist based on computing the perplexity of a model with a given number of clusters on held out datasets. Perplexity is used in information theory to describe how well a statistical model fits the data. The lower the perplexity, the better the model, and its generalization performance. In our case, if we let L(Z t i ) = log(p(Z t i |α)) be the log-likelihood for a held out set of variants for each tissue group t i , the perplexity is defined as perplexity(Z test ) = exp{- T i=1 L(Z t i ) M i=1 m i }, where T is the total number of tissues and m i is the number of variants for tissue t i. Evaluating the perplexity measure directly is computationally intractable (the computation of the likelihood for each tissue involves a summation over K m i terms with K being the number of classes), and therefore we use the lower bound on the log-likelihood, i.
e. L(a, w|α) (see Supplemental Material), to derive an upper bound on the perplexity. This upper bound on the perplexity is referred to as the variational Bayesian bound on the perplexity. In the large data limit, the bound on the log perplexity evaluated on the training data converges to the Bayesian information criterion (BIC) for the model  [71]. If the training and testing datasets are assumed to come from the same distributions, then the variational Bayesian bound on the log perplexity converges to the BIC.
ChromHMM. ChromHMM [14]  is a method for chromatin state discovery and characterization by integrating multiple chromatin datasets. The underling algorithm is a multivariate Hidden Mixture Model that produces a segmentation of the genome; each segment is assigned a putative function based on enrichment analyses of different biological states in these segments. The ChromHMM 25-state model [15]  is based on 12 marks, and, like ours, uses imputed data: H3K4me1, H3K4me2, H3K4me3, H3K9ac, H3K27ac, H4K20me1, H3K79me2, H3K36me3, H3K9me3, H3K27me3, H2A. Z and DNase. ChromHMM is based on complete pooling of data from multiple tissues and fitting a single model to this superdataset. Segway. Segway [16]  is a genome segmentation approach, like ChromHMM, based on a dynamic Bayesian network (DBN) model.
Acknowledgments We gratefully acknowledge support by  National Institutes of Health  grants  MH106910  (DB, ZH, II-L), AR065963 (LP, AC),  DK105124  and the  Herbert Irving Scholars Award (KK) ,  MH100233  and the  Seaver Foundation (JDB) , the  ATIP-Avenir program  (VB). We thank  Bin Xu  and  Badri Vardarajan  for helpful discussions. We thank  Andrew Brown  for making the data on lead eQTLs in the Geuvadis and TwinsUK cohort available to us.
4. 5. 3. 4. 5. 4. 53454 ChromHMM. ChromHMM [14]  is a method for chromatin state discovery and characterization by integrating multiple chromatin datasets. The underling algorithm is a multivariate Hidden Mixture Model that produces a segmentation of the genome; each segment is assigned a putative function based on enrichment analyses of different biological states in these segments. The ChromHMM 25-state model [15]  is based on 12 marks, and, like ours, uses imputed data: H3K4me1, H3K4me2, H3K4me3, H3K9ac, H3K27ac, H4K20me1, H3K79me2, H3K36me3, H3K9me3, H3K27me3, H2A. Z and DNase. ChromHMM is based on complete pooling of data from multiple tissues and fitting a single model to this superdataset. Segway. Segway [16]  is a genome segmentation approach, like ChromHMM, based on a dynamic Bayesian network (DBN) model.
estimates for top tissues for 21 complex traits Figure 2. Enrichment estimates (the proportion of SNP heritability in the functional component divided by the proportion of SNPs in that component) for different methods across top tissues for 21 complex traits. Enrichment estimates for DNase are omitted since they do not make sense for continuous annotations, such as DNase. Enrichment Dataset Ultraconserved FUN-LDA Method Elements GenoSkyline ChromHMM Segway IDEAS DNase DNase-narrow hESC Derived CD184+ Endoderm Cultured Cells 0. 568 Top Tissue AUC hESC Derived CD184+ Endoderm Cultured Cells 0. 658 Primary hematopoietic stem cells 0. 697 hESC Derived CD56+ Ectoderm Cultured Cells 0. 604 HUES6 Cells 0. 588 hESC Derived CD184+ Endoderm Cultured Cells 0. 646 hESC Derived CD184+ Endoderm Cultured Cells 0.
Highlighting tumoral mutations is a key step in oncology for personalizing care. Considering the genetic heterogeneity in a tumor, software used for detecting mutations should clearly distinguish real tumor events of interest that could be predictive markers for personalized medicine from false positives. OutLyzer is a new variant-caller designed for the specific and sensitive detection of mutations for research and diagnostic purposes. It is based on statistic and local evaluation of sequencing background noise to highlight potential true positive variants. 130 previously genotyped patients were sequenced after enrichment by capturing the exons of 22 genes. Sequencing data were analyzed by HaplotypeCaller, LofreqStar, Varscan2 and OutLyzer. OutLyzer had the best sensitivity and specificity with a fixed limit of detection for all tools of 1% for SNVs and 2% for Indels.
Research Paper healthy tissue, thereby reducing the number of mutated alleles by dilution. In view of these constraints, a highly sensitive process is required to avoid false negative results. The analysis of sequencing can itself be misleading owing to a PCR reaction bias during sample preparation  [7]  or to sequencer reading errors  [8]. Low level mutations may also be difficult to distinguish from a noise background generated by such technical limitations. Consequently, ensuring a high specificity is critical in diagnostic testing to avoid false positives. Dedicated bioinformatics tools can help to ensure good sensitivity and specificity. Detection of mutations is a key step in bioinformatics analysis and is performed by variant-calling software. An example of the numerous variant-callers currently available is HaplotypeCaller in the GATK suite  [9].
The 16 SNVs detected only by HaplotypeCaller also had a low Phred Score with mapping issues, just like the 60 SNVs, corresponding to 10 unique variants detected by Lofreq alone. Other SNVs found by OutLyzer only, Lofreq and Varscan, OutLyzer and Varscan, or Lofreq and Varscan and OutLyzer together were low allele-ratio events with good quality metrics. SNV found by Varscan only showed lower quality metrics and some were highly recurrent events between samples (190 variants for 5 unique variants). To enhance the clarity of indel analysis, comparative data were firstly cleaned manually to remove two recurrent false-positives with a low allele ratio (detected by all variant-callers) that were induced by mapping issues and were present in most patients. All the expected events in this dataset were detected by all variant-callers, so their levels of performance were similar (Figure  1B ).
DISCUSSION Highlighting mutational events with a very low allele frequency is challenging but essential in oncology in the search for somatic mutations characterizing tumor    S1 ) with KRAS c. 38G > A mutation (green arrow). Data are represented in transcript orientation. heterogeneity  [6]. Next Generation Sequencing (NGS) technologies have become powerful tools for helping to diagnose pathologies and establishing therapeutic strategies. However, it may be difficult to interpret their results and to differentiate false and true positives. Many tools are available to detect somatic mutations, each with its advantages and drawbacks. Tools such as HaplotypeCaller  [9]  were initially designed for discovering germline mutations so they are very specific but lack sensitivity, especially for detecting low-allele-frequency mutations.
This detection threshold is produced per patient, either for each region specified in an associated BED (Browser Extensible Data) File or for a specific genomic position, such as KRAS mutational hotspots. In a second operating mode, it is also able to provide immediately the major sequencing features for a specified genomic position, including sequencing depth, reference and alternative allele (if present), the number of forward and reverse reads that carry this alternative allele, the average sequencing quality, and an estimation of local sequencing background noise. OutLyzer estimates the sum of several background noise sources, including sequencing mistakes, errors generated by sample preparation and bioinformatics analysis, based on an outlier detection algorithm. Here, detection of outliers is used to highlight true mutations but outlying data have to be used carefully depending on the experimentation type so as to fit the analysis as well as possible  [14].
This limitation may be not overpassed by bioinformatics improvements. Therefore, alternative sample preparation before sequencing could limit bias to enhance the detection of low allele frequency mutations. For example, sample preparation protocols based on the use of a random index can simulate the double-stranded sequencing of a unique DNA fragment with a suitable bioinformatics analysis. Such protocols could eliminate PCR and sequencing errors and distinguish true somatic mutations occurring on both strands from errors generated during the analytical process  [16]. Today sequencing technologies make it possible to explore numerous diseases and characterize many genetic abnormalities. Software such as OutLyzer could prove useful by highlighting mainly true positive mutations in the sequencing background and focusing only on the most relevant information.
OutLyzer implementation OutLyzer is a tool written in Python  [17]  programming language that runs on Linux. It requires the SAMTOOLS  [18]  suite and additional python libraries: scpiy, numpy, subprocess, and multiprocessing. It was tested on Fedora 21 and CentOS 6. 7 Linux distribution with SAMTOOLS 1. 2 and Python 2. 7. 8 versions. OutLyzer has two main operating modes: (i) as a classical variant-caller; (ii) as a tool to evaluate local quality metrics. By giving it a chromosomal for a given sample, it quickly evaluates whether a mutation is present at this position, specifies all raw sequencing information and evaluates local background noise. OutLyzer uses BAM (Binary Alignment Map) files as input for analysis. It is preferable to use the BWA  [19]  / GATK  [9]  bioinformatic pipeline to produce BAM files, according to the Broad Institute recommendations.
Tumor samples Paraffin-embedded tumor samples from 130 patients with colorectal (70 samples), ovarian (50 samples), lung (4 samples), breast (4 samples), skin (1 sample) and stomach (1 sample) cancer, were selected for highthroughput sequencing analysis (Supplementary Table  S1 ). All patients had been previously sequenced by the target-specific techniques in the time of the initial diagnosis (Sanger sequencing, Cold-PCR followed by Pyrosequencing, SNaPSHOT). Additionally, the Horizon DX Quantitative Multiplex Reference Standard (Horizon Discovery Group, Cambridge Research Park, Waterbeach, Cambridge, UK) was added to the dataset containing miscellaneous SNVs (Single Nucleotide Variations), insertion and deletion events at various allele frequency.
ACKNOWLEDGEMENTS We thank the  SéSAME (Séquençage pour la santé, l'Agronomie, la Mer et l'Environnement ) sequencing platform,  Aude Lamy  ( CHRU Rouen, France ) , Christian www. impactjournals. com /oncotargetBastard  ( Centre Henri Becquerel, Rouen ) and  Alexandra Lespagnol  ( CHRU Rennes, France ) for providing some tumor samples.
The first step in the infection of humans by microbial pathogens is their adherence to host tissue cells, which is frequently based on the binding of carbohydrate-binding proteins (lectin-like adhesins) to human cell receptors that expose glycans. In only a few cases have the human receptors of pathogenic adhesins been described. A novel strategy-based on the construction of a lectin-glycan interaction (LGI) network-to identify the potential human binding receptors for pathogenic adhesins with lectin activity was developed. The new approach is based on linking glycan array screening results of these adhesins to a human glycoprotein database via the construction of an LGI network. This strategy was used to detect human receptors for virulent Escherichia coli (FimH adhesin), and the fungal pathogens Candida albicans (Als1p and Als3p adhesins) and C.
In this report, we present a novel glycan array-based network strategy aimed at identifying the potential biological binding receptors for adhesin lectins. First, the glycan determinants of the lectins are determined from the experimentally evaluated glycanbinding specificities of the lectins by glycan array analysis. Next, the GlycoSuiteDB glycoproteomic database of the UniCarbKB platform  (46, 47)  is searched for these determinants to obtain a set of human glycoproteins expressing the glycan determinants that are of interest and are considered potential targets for lectin recognition. By performing additional queries of the GlycoSuiteDB database, these potential target glycoproteins can be further linked to the cell types on which they are present, the tissues and body systems, and the disease state (if applicable).
Finally, the network is analyzed in order to profile the potential adhesin binding receptors in the host with prioritization, based on experimental binding data, of the most relevant interactions. New potential targets for the selected adhesins were predicted and experimentally confirmed. The constructed networks are referred to as lectin-glycan interaction (LGI) networks. We explored this strategy and constructed LGI networks for two classes of pathogenic microbial adhesins that are characterized by lectin-like properties, i. e. , the bacterial UPEC adhesin FimH-LD, as well as the yeast C. albicans and C. glabrata adhesins (i. e. , Als1p and Als3p and Epa1p, Epa6p, and Epa7p, respectively). The LGI networks constructed were corroborated by comparison with interaction data available in the literature, and some links in the networks were experimentally confirmed by quantitative lectin-glycan interaction analysis (by surface plasmon resonance [SPR] and atomic force microscopysingle-molecule force spectroscopy [AFM-SMFS]).
Modeling and visualization of LGI networks. The proposed LGI network has been modeled as a weighted, undirected graph composed of a set of nodes (i. e. , lectin, glycan, glycan determinant, glycoprotein, disease, tissue, body system) and a set of edges connecting the pair of nodes (see Materials and Methods and the supplemental material). An edge connecting the two nodes can represent biochemical interactions, as well as biological and/or hierarchical associations between the nodes. With each node, we associate a node relevance, and with each edge, we associate an edge relevance, which indicates the relevance of the interaction between the connected nodes. The proposed representation allows visualization of the network in multiple ways in order to (i) highlight a set of glycoproteins as promising receptor candidates that were obscured in the huge amount of data in the LGI network and (ii) predict the potential binding receptors for several lectins.
In our specific case, the network involved a combination of both experimental data and the data from the publicly available Glyco-SuiteDB database. The binding specificity and strength of microbial lectins (FimH-LD, N-Als, and N-Epa) were experimentally determined by glycan array screening. The measured binding strengths were exported into a spreadsheet, whereas those glycan structures and determinants that are recognized by the adhesins were queried in the GlycoSuiteDB database. The query provided a list of hundreds of potential target proteins (cases of glycan deter-minants not attached to any protein were also taken into account), and they were further linked to cell types, tissues, body systems, and diseases (if available) through additional queries in the same database. The resultant nodes and edges/links (associations) provided the LGI network definition in its initial state, where all of the nodes and edges were considered equally relevant; a relevance quantification stage followed.
e. , for Gal␤-1,4-GlcNAc, the largest node size in the network but unlabeled because of the low binding intensity (the label was filtered out in the program for better visualization of the more relevant nodes [see the supplemental material]). In the Als network (Fig. 4B ), mucins are mainly linked to the GlcNAc␤-1,3-Gal determinant, which is also the most relevant (high binding   S1  in the supplemental material. (B) Circular view of the network (generated with the LGI network algorithm). The glycan determinants queried in the UniCarbKB database, the human glycoproteins bearing the glycan determinants, and the related diseases are depicted as blue, green, and red nodes, respectively. Connections between nodes are depicted with blue arrows (to indicate the determinants expressed on each glycoprotein) or green arrows (to indicate the diseases associated with the altered glycosylation of each glycoprotein).
This effect was abrogated by mannose or in the absence of the mannose-specific adhesin on the bacterial fimbriae. The same synergistic effect could not be correlated with the expression of P fimbriae, which would be carrying the PapG adhesin. These data and the presence of C3 in the FimH LGI network suggest that the glycosylated complement protein, scavenged by and directly associated with the FimH LDs, could facilitate bacterial entry into uroepithelial host cells. A genetically engineered Pseudomonas aeruginosa strain that expresses type 1 fimbriae can specifically adhere to breast cancer cells overexpressing the EGFR and block the EGFR signaling pathway  (55). The effect of fimbriae on receptor signaling has been described as mannose sensitive. Indeed, the LGI network suggests the recognition by FimH-LD of the EGFR from carcinoma cells.
albicans by host cells. It has also been demonstrated previously that Als3p binds to N-and E-cadherins, which are present on endothelial and epithelial cells, respectively  (77). As shown in Table  S1  in the supplemental material, additional reported experimental results confirmed some Als LGI network nodes, such as for chondroitin sulfate proteoglycan 4, -casein, and CG-a/b. FN, laminin, and mucin recognition by Candida adhesins. Using SPR, we characterized the interaction of N-Epa1p with mucin (Fig. 6B ). A comparable K D value was found for the N-Epa1p-FN interaction  (29). The specific binding inhibition by lactose corroborated the specificity of glycan recognition by N-Epa1p. These data confirmed, for the first time, the ability of N-Epa1p to bind mucins. As shown in the LGI network, adhesion of Epa adhesins to mucins is extremely relevant in the context of host adherence and is mediated by multiple O-glycan determinants.