dataesr/scientific-paragraphs-categorization huggingface dataset:
The similarity between expression profiles and amino acid sequences in a given species is reinforced if the same similarity is observed amongst a cousin species. In order to predict gene functional classes, such as the associations between genes and GO terms, Azuaje et al. [10]  combine two types of information: gene expression profile similarity and a GO-based similarity. The average of both similarity indexes is used to cluster genes. With the same objective of predicting gene functional classes, in Li et al. [11] , expression data are combined with biological knowledge by considering subsets of genes associated with one same functional annotation. The subsets of genes are then clustered on the basis of their expression profile similarities. The objective of the paper is to propose a new unsupervised clustering algorithm based on a new distance between genes that actively integrates external biological knowledge into expression data.
To complement the clustering procedure, we propose an automatic validation procedure of gene clusters to facilitate their interpretation. The aim of this procedure is to highlight good candidates for interpretation which are clusters of significantly coexpressed and significantly biologically related genes. It is based on two indicators associated with hypothesis testing. One indicator measures the coexpression of the genes within a cluster, whereas the other quantifies its biological homogeneity. The R code which is used to perform all analyses is available in the form of an R package at http://marie. verbanck. free. fr/packages/.
Results As we propose a new unsupervised clustering algorithm associated with an automatic evaluation of the clusters, we validate the whole methodology on simulated, and real data sets, by comparing it with two of the most classically used gene clustering strategies. On the one hand we compare it with clusters stemming from a Heatmap of the expression data. On the other hand, we choose to generate a coexpression network from the expression data using Weighted Gene Coexpression Network (WGCNA)  [2]. The coexpression network allows to calculate a dissimilarity matrix between genes based on the topological overlap of the nodes of the network. Finally a hierarchical clustering algorithm is computed on the dissimilarity matrix and provides gene clusters.
Simulated data sets In this section, we explain how to simulate expression and GO data sets. To simulate expression data, we use the same procedure as in  [14]. An expression data matrix G sim , constituted of K genes and I samples, is simulated from random drawing in a multivariate Gaussian distribution with a certain correlation structure so that we have underlying clusters of coexpressed genes. Since this way of simulating numerical data is quite classical, we rather insist on the simulation of GO annotation data which is not common in the literature. To simulate GO annotation data we fit the biological principle previously exposed: GO annotations are constituted by information that can be related to the experiment in the study and information that is not. In other words, one part of the simulated GO annotations must have a structure which is similar to the structure of the expression data, and the other must have a random structure.
Results In practice, we apply the three methods to simulated data sets. We consider two sizes of simulated expression data. A first type composed of 10 individuals and 300 genes for which we obtain a partition in 20 clusters for each method. A second type composed of 25 individuals and 1000 genes for which we obtain a partition in 100 clusters for each method. With both types of simulated expression data sets, we associate simulated GO annotations whose intensity of randomness ranges from 1 to 3. For each configuration 100 data sets are generated. Whatever the clustering method, we associate with each cluster, two p-values corresponding each to the coexpression indicator and the biological homogeneity indicator. For a given partition, we measure the proportion of clusters which are: • significantly coexpressed: p-value associated with the CI lower than a chosen threshold • significantly biologically homogeneous: p-value associated with the BHI lower than a chosen threshold • both significantly coexpressed and biologically homogeneous: both p-values associated with the CI and the BHI lower than a chosen threshold Results are gathered in Table  1.
The simulated data sets vary according to the number of samples (I), the number of genes (K) and the intensity of randomness (r). We give the average proportion of clusters (%), among a given partition, which are significantly coexpressed (CI), biologically homogeneous (BHI) or both coexpressed and biologically homogeneous (Both). Let us take the example of simulated expression data sets with 10 individuals and 300 variables, associated with simulated GO annotations with an intensity of randomness of 1. On average the Heatmaps of these data sets provide partitions with 92. 15% of significantly coexpressed clusters. of significantly biologically homogeneous clusters than Heatmap. However, the proportion of biologically homogeneous clusters provided by WGCNA literally drops when the intensity of randomness is very high.
Clusters interpretation Clusters obtained by integrating biological knowledge into expression data, and that present interesting properties, are then good candidates for interpretation. In order to Results for the chicken data set for the three clustering algorithms: Heatmap classification (Heatmap), clustering based on coexpression network (WGCNA) and our clustering algorithm (Integration). We give the percentage of clusters (%), amongst a given partition, which are significantly coexpressed (CI), biologically homogeneous (BHI) or both coexpressed and biologically homogeneous (Both). associate representative GO annotations with clusters, we choose to apply a classical enrichment testing procedure which consists in fisher's exact tests associated with a correction for multiple testing (Benjamini-Hochberg with a 5% cut-off ).
Concretely our methodology shows some limitations as it provides a relatively important proportion of clusters constituted with one single gene. However, it outperforms the other methods: actively integrating biological knowledge into expression data provides partitions with the highest proportion of good candidates. These clusters indeed appears to be good candidates for interpretation as can testify the ones related to Phospholipid and Sphingolipids mechanisms. However an ultimate external biological validation remains to be done, what consists in conducting more advanced biological interpretations.
2012; Le Quesne Stabej et al. 2012). Until recently, CLRN1 (USH3A) was the only gene known responsible for USH3  (Joensuu et al. 2001 ), but the HARS gene was recently proposed as a novel USH3 gene  (Puffenberger et al. 2012). In addition, a twelfth gene, PDZD7, contributes to USH2 as a modifier of the retinal phenotype on a USH2A background or in digenic inheritance with GPR98  (Ebermann et al. 2010 ). Multiple isoforms have been described for most of these genes of which, USH1C, PCDH15, USH2A isoforms have been well characterized  (Bitner-Glindzicz et al. 2000; Verpy et al. 2000; van Wijk et al. 2004; Ahmed et al. 2008). Mutations in MYO7A, USH1C, CDH23, PCDH15, DFNB31, and CIB2 can also cause nonsyndromic hearing loss (NSHL) and mutations in USH2A and CLRN1 give rise to isolated autosomal recessive RP (see retinal and hearing impairment genetic mutation database, which includes USHbases and other NSHL genes: https:// grenada.
Bioinformatics pipeline and prioritization Assembly, coverage, and variant calling Sequence reads were mapped against the human chromosomes reference (hg19) using the GS Reference Mapper software (Roche, version 2. 6 and 2. 7). Average depth of coverage (aDOC) for each region was calculated by dividing the sum of the DOC per base within the specific regions by the total region size (in base pairs). aDOC ¼ P n i¼1 DOC region size The percentage of on target was defined as a ratio between the number of bases aligned in targeted regions and the number of bases mapped in total. On target % ¼ bp aligned on target total mapped bp Â 100 Artifact variants were removed following these criteria: (i) Variants detected in less than 20% of total reads; (ii) Indels with a coverage >20 reads but with a disequilibrium between number of forward and reverse sequences (Fwd or Rev <10%); (iii) Indels distant from exons (more than AE20 intronic flanking nucleotides).
The remaining variants were annotated adding gene name, known polymorphism from dbSNP131, localization in gene, cDNA and protein nomenclature, using either Annovar  (Wang et al. 2010)  or Mutalyzer  (Wildeman et al. 2008). We developed in-house software called "GS data online treatment" (GSdot), available at https://neuro-2. iurc. montp. inserm. fr/454/ to automate the calculations and filters described above. The input files were initially generated by GS Reference mapper. More details on how to use the software and the different steps can be obtained from the website.
Prioritization of variants and determination of pathogenicity After automatic filtering performed by GSdot, all the annotated variant files generated (one per patient) were merged into a single one for manual prioritization of the variants. Prioritization consisted of keeping any known pathogenic mutation and, for any variant of unknown clinical significance (VUCS), retaining if it had been found in fewer than five DNAs from the test sample, and was localized in exon or within 20 bp intron-exon boundary. The pathogenicity of the candidate variants was investigated examining the frequency in patients or controls from our internal database, the locus-specific database LOVD-USHbases, the deafness variation database (http://deafnessvariationdatabase. com/), the exome variant server database (EVS, http://evs.
gs. washington. edu/EVS/) or the genome variant database (1000 genomes, http://www. 1000genomes. org/, and dbSNP, http://www. ncbi. nlm. nih. gov/snp). Specific in silico studies were conducted following our multistep analysis described in  (Roux et al. 2011) , using the Usher Syndrome Missense Analysis software (USMA, https://neuro-2. iurc. montp. inserm. fr/USMA/) and Human Splicing Finder (HSF  [Desmet et al. 2009 ], http://www. umd. be/HSF/), which includes two distinct algorithms, namely HSF and MaxEnt  (Yeo and Burge 2004). In order to assess the impact of missense candidates in non-Usher genes, ortholog and domain alignments were studied from UCSC (http://genome. ucsc. edu/) and Prosite (http://prosite. expasy. org/), respectively. All likely pathogenic variants were confirmed by Sanger sequencing, and familial segregation analyses were performed whenever possible.
Raw data quality Data obtained from the test sample were used to evaluate the quality of raw data. The number of reads per run was estimated on average to be 129,783 of which 98,149 were mapped with a mean length of 431 bp. The average amount of mappable sequence data was 53 Mb. Eighty percent of these sequences overlapped the targeted region and 52% of data were mapped on target (Fig. 1 ). The overall depth of coverage was estimated as 779 across the whole design, ranging from 55. 89 (USH1G) to 106. 99 (TECTA) (Fig. 2 ). Among the 634 targeted regions, only 37 (5. 8%) were covered less than 409 (22 regions between 20 and 409) (Fig. S2 ). Eighteen of these correspond to high GC content (>60%).
Usher diagnosis group Thirteen USH patients (clinically classified as five USH1, seven USH2, and one USH3) without preliminary haplotyping or Sanger sequencing analysis underwent Usher exome screening by NGS. The previously validated filtering and prioritization strategy was applied and selected 80 USH variant candidates, an average of six variants of interest per patient. Among those, some have already been described as nonpathogenic in our local database or in USHbases and were eliminated. The remaining variants are shown in Table  2. We then applied our multistep analysis to classify these 49 variants  (Roux et al. 2011). NGS successfully identified the pathogenic genotype in 10 out of 13 patients (77%). Patients U1157 and U1163 were found to have CDH23 alterations. U1157 carried a newly described variant in position +5 of exon 63, and a minigene analysis was performed to assess the impact of the substitution on the splicing process.
Filtering, prioritization, and classification of variants We divided the filtering, prioritization, and classification of our NGS data into three distinct stages (Fig. 3 ). Analysis of the copious amounts of data generated raises problems at two levels, firstly in checking the validity of the reads (steps 1-3) and, secondly, in determining relevance to disease-causing changes. All the settings were done on the test sample. The first step aims to eliminate the maximum number of false positives without removing the true positives from the dataset. This step has been automated with GSdot software. The generic software GS Reference Mapper generates two lists of possible DNA variations, one containing all signals, and a second supposedly including only the true variants. We first followed the recommendations of the manufacturer and worked with the "cleaned file" (High Confidence Variations file) but realized that the most frequent mutation in USH2A c.
2299delG, responsible for 10-45% of USH2A pathogenic alleles in Europe  (Dreyer et al. 2008; Aller et al. 2010; Le Quesne Stabej et al. 2012) , was systematically excluded from the second list. This pinpoints a drawback of the software, which is poor in detecting changes within or near homopolymeric stretches. We therefore chose to use as input for our custom software GSdot the complete list of aligned DNA alterations, which explains the relatively high number of candidate DNA variants at the very beginning of the workflow (mean 4674), the majority of which, 92. 3% (i. e. , 4316/4674) are removed/excluded by the first filter. The second stage consisted in the selection of candidate pathogenic variants. This step remains to be automated. We chose not to depend on external software such as pathogenic predictors or external databases.
This strategy has proven to be effective as only eight variants per patient remained after this screening. At this point, every variant was confirmed by Sanger sequencing, which revealed that a few false positives were still present, and this suggests that more stringent filters need to be applied. External software and databases were fully integrated in the third step, which focused on the classification of the Usher candidate variants. Our multistep strategy of classification was applied to any putative splicing alteration as well as to variants expected to impact on the protein structure, and proved to be very efficient  (Roux et al. 2011; Baux et al. 2013). This was possible thanks to our in-house experience of molecular alterations of the Usher genes in USH patients, our creation and maintenance of USMA and of an internal database, and curation of USHbases which contains and correlates all the published information.
Mutations previously identified by Sanger sequencing and the additional mutations detected by (NGS) in 12 patients from the test sample. p. (Gly23*) c. 67G>Tp. (Gly23*) All missense variations were classified as likely pathogenic (UV3), based on familial segregation analysis, low frequencies in public databases and in silico predictions. NA, gene not analyzed by Sanger sequencing; Undef, data not accurate enough to clearly discriminate a clinical subtype. 1 Previously described variant. References for published DNA variations as well as dbSNP identifiers are all included in USHbases. ª 2013 The Authors. Molecular Genetics & Genomic Medicine published by Wiley Periodicals, Inc.
: a, protein translation predicts a PTC; b, allele frequency (public databases or control samples analyzed by our laboratory); c, allele frequency (patients); d, in silico predictions (missense variants); e, in silico predictions (splicing); f, minigene analysis; g, segregation analysis; h, patient genotype. 1 Variant that could alter splicing. 2 Previously described variant. References for published DNA variations as well as dbSNP identifiers are all included in USHbases. ª 2013 The Authors. Molecular Genetics & Genomic Medicine published by Wiley Periodicals, Inc.
806C>T -p. (Pro269Leu) UV1 d PDZD7 c. 1452G>T -p. (=) UV2 e S660 No OTOF c. 1392+1G>T -p. (?) Pathogenic a CDH23 c. 3995T>G -p. (Ile1332Ser) UV3 d Contributors to classification: a, protein translation predicts a PTC; b, allele frequency (public databases or control samples analyzed by our laboratory); c, allele frequency (patients); d, in silico predictions (missense variants); e, in silico predictions (splicing); f, minigene analysis; g, segregation analysis; h, patient genotype. No candidate pathogenic alterations have been identified for patient S11. 1 Previously described variant. References for published DNA variations as well as dbSNP identifiers are all included in USHbases. Gene and protein reference sequences are listed in TableS2of the online Supporting Information.
Strain selection and DNA isolation A complete list of the selected L. paracasei strains and their origin can be found in Table  1. These strains were selected based on our previous MLST analysis of multiple strains and AFLP genotyping analysis (data not shown) to represent the most genetically diverse set of known L. paracasei strains  [13]. Strains with a CNCM code have been deposited in the CNCM public library (Institute Pasteur, Paris, France). For DNA preparation, 2 ml of overnight culture was pelleted, washed and resuspended in TES buffer (N-[tris(hydroxymethyl)methyl]-2-aminoethanesulfonic acid). Cell lysis was performed with lysozyme (360 mg/ml) and mutanolysin (140 U /ml) during 2 h at 37uC, then 300 ml water was added and 80 ml of 20% SDS solution. The DNA extraction was done using phenol/chloroform  (36).
casei ATCC 334  [31] , BL23  [32]  and Zhang  [33]. The 10 draft L. paracasei genomes with our detailed annotation have been deposited at DDBJ/EMBL/GenBank as Whole Genome Shotgun Bioprojects PRJNA178446-PRJNA178455. The other 24 draft L. paracasei genomes were only deposited as contig nucleotide sequences in Whole Genome Shotgun Bioprojects PRJNA178422-PRJNA178445. All sequences can be found under umbrella BioProject PRJNA183193.
Comparative genomics/orthology prediction All protein sequences of the 37 L. paracasei genomes were subjected to an orthology prediction using OrthoMCL  [57] , with default settings. The protein sequences of 183 OGs with exactly one member in each L. paracasei genome were aligned using MUSCLE  [58]. These alignments were concatenated after which a maximum-likelihood tree was constructed using PHYML  [59]. The output from OrthoMCL was parsed, using ad hoc Python scripts, into a single gene presence/absence matrix. This presence/absence matrix was fed into Genesis  [60]  to perform a hierarchical clustering on the data. Annotation was manually improved, using different sources (public strains, previously annotated genomes and RAST) and using tools such as NCBI BLASTP (http://blast. ncbi. nlm.
Gene trait matching (GTM) and visualization of the GTM data GTM was performed with Phenolink, a random forest (RF)based phenotype/genotype matching algorithm  [49]  http:// bamics2. cmbi. ru. nl/websoftware/phenolink/). Phenolink works on the basis of Random Forest classification but with some minor adjustments to make it more suitable for GTM (see Methods). The gene presence/absence matrix and the experimental growth data were used as an input for Phenolink, together with a consensus annotation file of the ortholog groups. Typically, sugar growth data was divided into two classes (growth or no growth). Phenolink automatically performs GTM analyses for all the phenotypes and summarizes these data in a single HTML file that can easily be converted into a MSExcel spreadsheet. These output files use a colour scheme to indicate for each measured phenotype which OGs were important in the GTM (importance.
Surface-associated and secreted proteins All extracellular proteins encoded in the reference genomes of L. casei ATCC 334 and BL23 have previously been predicted and classified in the LAB Secretome Database  [74]. The major surface-associated and secreted proteins identified in the 37 L. paracasei genomes are described below and summarized in Table  2. All L. paracasei strains (except strain Lpp125) have a 7-gene cluster with the pilin-specific spaBCA genes, and all have the spaDEF genes; this suggests that all L. paracasei strains have the potential to synthesize pili which may play a role in mucus binding, adhesion or biofilm formation. A very recent study suggests that upstream insertion of an IS element, as in L. rhamnosus, may be required to activate these genes  [3]. Similarly, both cell-wall hydrolases Msp2/p40 and Msp1/p75 appear to be encoded in all L.
This gene cassette with a Ser/Ala-rich surface-anchored protein is also found in L. rhamnosus strains. A 4-gene cassette encoding 3 extracellular proteins with signal peptides is present in L. casei Zhang (LCAZH_0540-0543) and only in the very closely related L. paracasei strain Lpp46. These extracellular proteins of unknown function do not have LPxTG-type peptidoglycan anchors, and are not homologous to any proteins in the NCBI database. As this locus has a GC content of 39. 4% this cassette was probably acquired through HGT by the direct ancestor of these 2 related strains. Two strains (Lpp17 and Lpp230) are found to contain a 6-gene WXG100 secretion system (Wss), flanked by a transposase, which has previously been called the Type VII secretion system  [77, 78, 79, 80, 81, 82, 83]. This gene cluster has highest similarity to clusters in E.
This work was partially supported by a  KIT grant  from the  Kluyver Centre for Genomics of Industrial Fermentation , which is part of the  Netherlands Genomics Initiative (NGI)  and  Danone Research  financed part of this study, including subcontracting to NIZO food research (MW, JB) and  Microbial Bioinformatics (RS). TS, JvHV and CC obtained financial support from the  ERA-Net PathoGenoMics  ( ANR-10-PATH-004  project). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. The authors declare that TS, JvHV and CC work for Danone Research, part of the  Danone Group. Danone is selling products that contain Lactobacilli. Danone Research financed part of this study, including subcontracting to NIZO food research (MW, JB) and  Microbial Bioinformatics (RS).
Follow-up The study subjects were living in 24 villages, to which access was particularly difficult due to a lack of suitable roads. For treatment and data collection, we followed-up each patient at a pre-defined interval between June 2009 and June 2011. The baseline prevalence survey was conducted in March 2009 subsequent to which the treatment was started. The follow-up (three-monthly) time-points were June 2009, September 2009, December 2009, and June 2011. Treatment was delivered cost-free (including no user fee) at all times and was provided in two manners. First, at the door-steps (i. e. home-delivery of the treatment supply) of the patients (March 2009 to March 2010, primary-treatment period) in which all patients were given medicines at their home that were sufficient to last for next three months.