dataesr/scientific-paragraphs-categorization huggingface dataset:
UMD-Predictor Ecosystem The UMD-Predictor was designed as a stand-alone platform that contains all predictions for single-nucleotide substitutions (280,315,899) of human transcripts from the human reference genome GRCh37. It is freely available for noncommercial use at http://umd-predictor. eu. It can be queried either directly on-line through a user-friendly Web interface or through Webservices to facilitate its integration in any bioinformatics pipeline. The Web interface allows: (i) single analysis of all mutations from a gene selected by gene symbol, Ensembl gene ID, Ensembl transcript ID, RefSeq peptide ID, or Uniprot ID; (ii) multiple analysis of mutations from different loci or chromosomal positions; or (iii) a VCF file analysis. A predictions table containing advanced filtration and sorting options allow rapid access to single predictions.
Pathogenicity Predictions Evaluation To assess the pathogenicity predictions of the UMD-Predictor system, we collected data from 141,246 annotated variations, either pathogenic or nonpathogenic, using four reference datasets: Varibench [Sasidharan  Nair and Vihinen, 2013]  with dbSNP  [Sherry et al. , 2001]  (19,335 pathogenic, 7,897 nonpathogenic); Uniprot [UniProt Consortium, 2014] (20,821 pathogenic, 36,825 nonpathogenic); Clinvar  [Landrum et al. , 2014]  (10,669 pathogenic, 1,817 nonpathogenic), and PredictSNP  [Bendl et al. , 2014]  (24,082 pathogenic, 19,800 nonpathogenic). These datasets were then used for prediction assessment using the seven most used and reliable prediction tools: SIFT 5. 1. 1  [Sim et al. , 2012] , Polyphen 2. 2. 2  [Adzhubei et al. , 2010] , Provean 1. 1. 3 [Choi et al.
Data from the Varibench/dbSNP dataset are presented in Table  1 , whereas data from other datasets are available in the Supporting Information (Supp. Figs. S2-S15; Supp. Tables  S1-S14 ). As shown for this dataset, UMD-Predictor provided with more accurate results  for PPV, NPV, sensitivity, and MCC, whereas MutationTaster 2 was more sensitive. The DOR and log(DOR) data also demonstrated a better efficiency for UMD-Predictor with a DOR of 86. 6 versus 12. 6 for the second most efficient predictor system. The log(DOR) measures consistently demonstrated an almost 2-logs improvement compared with other predictors as exemplified for this dataset (4. 46 vs. 2. 53). As shown in Figure  1 , this high difference is driven by a strong improvement in specificity with a very good sensitivity. It is important to note that MutationTaster2 automatically annotates variations as disease causing when marked as pathogenic in ClinVar.
Even in the situation of the ClinVar dataset, MutationTaster2 did not perform better than UMD-Predictor (Supp. Figs. S8-S11; Supp. Tables  S7-S10 ). In parallel with global statistical parameters, we evaluated the sensitivity of the various methods to distinguish pathogenic and nonpathogenic mutations using the receiver operating characteristic (ROC) curve that represents the sensitivity as a function of falsepositive rate. As shown in Figure  2  and Table  2 , the UMD-Predictor ROC-AUC (area under curve) shows a significant improvement compared with other systems (0. 954 vs. 0. 834 for the second most efficient tool).
Impact of Mutations Frequency in the General Population As indicated in Material and Methods section, the new UMD-Predictor algorithm integrates a penalty score when a mutation has been described at a high frequency in the human population. Such information has already been used by other systems such as MutationTaster, which automatically annotates mutations as polymorphisms if they have been reported more than four times at a homozygous state in the 1000 genomes or HapMap projects. To assess the benefit of this new parameter, we removed its value from the  Min ROC area, lower bound for the confidence interval of a vector of length two; Max ROC area, upper bound for the confidence interval of a vector of length two. All data were generated using the "ci. cvAUC" function of the "cvAUC" package (https://github.
com/ledell/cvAUC) for the ROCR R-package  [Sing et al. , 2005]. Min ROC area, lower bound for the confidence interval of a vector of length two; Max ROC area, upper bound for the confidence interval of a vector of length two. All data were generated using the "ci. cvAUC" function of the "cvAUC" package (https://github. com/ledell/cvAUC) for the ROCR R-package  [Sing et al. , 2005]. UMD-Predictor values were obtained without the mutations' frequency information. algorithm and compared the sensitivity of methods in distinguishing pathogenic and nonpathogenic variants. Even without adjustment of the other parameters, the UMD-Predictor still gives the most accurate predictions (0. 79 vs. 0. 77 for PPH2 and CADD), whereas it is outscored by CADD for AUC of 0. 828 vs. 0. 834 (Fig. 3 ; Table  3 ).
WES Annotation One of the main applications of global pathogenicity prediction systems is the annotation of NGS data, either WES or WGS. To assess the ability of the various systems in this context, we used data from three clinical diagnostic exomes. Three criteria were analyzed: the annotation processing-time (PT), the correct annotation of disease-causing mutations, and the number of annotated potential pathogenic variations (NV). As shown in Table  4 , the UMD-Predictor system was able to process a VCF file of about 56,700 variations in 180 sec (from 93 to 240 sec), whereas other systems did it on average in 2,991 sec (from 240 to 11,160sec). SIFT 5. 1. 1, Polyphen 2. 2. 2, Provean 1. 1. 3, CONDEL 1. 5, and CADD required a preprocessing of the VCF file that was not taken into account in this evaluation.
Discussion The now widely used WES and WGS technologies produce huge amount of variations from which only a handful are useful for genetic counseling or gene discovery. Therefore, it is critical to access variant annotations to select candidate pathogenic mutations. Unfortunately, this cannot be achieved through variants annotations from central-or locus-specific databases as their content is still limited and of heterogeneous quality. In addition, disease-causing mutations are often private and therefore not reported. In this situation, it is necessary to rely on bioinformatics prediction systems to annotate all variants and facilitate the critical filtration process that should ideally result in a limited set of candidate pathogenic mutations, as they need to be individually validated through familial segregation and in vitro or animal models.
It can also be used for any variant evaluation through a user-friendly interface at http://umd-predictor. eu. To improve the predictions accuracy, we developed three new features. The first one is able to quantify the impact of a given amino acid substitution based on conservation between 100 species. To do so, it clusters species into five groups and the conservation of a specific residue is assessed using a window of ±3 residues. The second one corresponds to an automatic key residue annotation system able to extract relevant information from the Uniprot/SwissProt dataset. Finally, the third one takes into account the frequency information from the dbSNP data (build 138)  (NCBI Resource Coordinators, 2015). To evaluate the efficiency of this new pathogenicity prediction system, we collected data from 141,246 annotated variations, either pathogenic or nonpathogenic, using four reference datasets.
These datasets were then used for prediction assessment using the seven most used and reliable prediction tools: SIFT 5. 1. 1  [Sim et al. , 2012] , Polyphen 2. 2. 2  [Adzhubei et al. , 2010] , Provean 1. 1. 3 [Choi et al. , 2012] , Mutation Assessor 2  [Reva et al. , 2011] ,  CON-DEL 1. 5 [González-Pérez and L ópez-Bigas, 2011] , MutationTaster 2  [Schwarz et al. , 2014] , and CADD  [Kircher et al. , 2014]. All these predictors are considered as standards in silico predictive algorithms for missense predictions by the American College of Medical Genetics and Genomics  [Richards et al. , 2015]. All evaluations demonstrate better accuracy (0. 85), specificity (0. 95), Matthews' correlation coefficient (0. 69), and DOR (86. 6) for the UMD-Predictor system. If it is well accepted that conservation between species is an important element to highlight key residues, we believe that allele frequency among humans could be considered as a complementary "conservation score" in a specific species.
To evaluate the ability of the various predictors to match these criteria, we used VCF datasets resulting from three WES performed in a clinical diagnostic context and for which pathogenic mutations have been confirmed. We demonstrated that all systems were able to accurately annotate the identified pathogenic mutations as candidate pathogenic mutations. Nevertheless, these pathogenic mutations were always part of a list of candidate pathogenic mutations that ranged from 540 to 3,401. In each situation, UMD-Predictor provided the shortest list of candidate mutations, therefore being the most accurate. These results impacted the downstream filtration and validation processes that were reduced by approximately 64%. Overall, we demonstrated that a single system, using a combinatorial prediction algorithm, could perform better than other prediction systems.
This pinpoints that it is not required to develop expert systems aggregating predictions  [González-Pérez and L ópez-Bigas, 2011; Olatubosun et al. , 2012]  from various primary tools to improve efficiency. In addition, machine-learning systems also prove to be limited, most probably because of the current training datasets quality/completeness. Moreover, we believe that such combinatorial approach could be applied for the prediction of mutations impact on other signals such as transcription factors binding sites, enhancers, miRNA, or ultraconserved regions. As underlined by  Pabinger et al. (2014] , legal issues might arise when annotating lists of variants through on-line systems as they do not guarantee data confidentiality. To solve this issue, once batch analyses have been performed, corresponding files are automatically deleted from the UMD-Predictor system and no data is stored.
As described by the American College of Medical Genetics and Genomics  [Richards et al. , 2015] , bioinformatics pipelines play a pivotal role in NGS analysis. They include a multistep processing that could result from many different programs and databases combinations and involves handling large amounts of data. They typically include mapping and assembly, sequence alignments, and variant calling (including SNV, structural variations, and copy-number variations). Once SNVs have been identified, a second layer of multistep processing is initiated to annotate each variant and use several filtration processes depending on the mode of inheritance, the disease frequency, the phenotype, and the related tissue expression pattern. A key element of this layer is the pathogenicity prediction step that could be obtained from different systems.
Contract grant sponsors: The  European Union Seventh Framework Program  (grant no. 305444 ); "An integrated platform connecting databases, registries, biobanks and clinical bioinformatics for rare disease research" (RD-CONNECT; www. rdconnect. eu;grant no. 200754 ); " Genotype-To-Phenotype Databases: A Holistic Solution " (GEN2PHEN; www. gen2phen. org);The  FHU A * MIDEX  project  MARCHE  n. ANR-11-IDEX-001-02  funded by the " Investissement d'avenir" French government program , managed by the  French National Research Agency (ANR). ; bioinformatics; NGS; substitution; synonymous; nonsynonymous; nonsense.
Reannotation of strain PAK genome using transcriptomic data revealed numerous RNA-based regulatory elements To study bacterial transcriptional response to PAK_P3 infection, it was first crucial to exhaustively characterize the genome of its host, P. aeruginosa strain PAK. Initially, a draft genome was produced and assembled (6. 28 Mbp, 66. 3% GC content and 6,267 predicted ORFs). Next, a detailed genome reannotation was performed based on RNA-Seq data generated from exponentially growing and uninfected PAK cells (S1 Table ). Using COV2HTML  [7]  to visualize transcripts, we manually reannotated 32 open reading frames (ORFs) (by detection of an alternative start codon) and defined 63 new putative coding sequences. Among them, 39 have been previously annotated in other P. aeruginosa genomes while the other 24 are new hypothetical coding sequences that display no homology to sequences in databases and may be considered as strain-specific (S1 Table ).
Adsorption assay and one-step growth experiment Bacteriophage growth parameters were assessed as described previously  [43]. Briefly, a culture of strain PAK was infected at low MOI (0. 1) and incubated 5 min at 37°C with agitation allowing bacteriophage particles to adsorb. Following a 1000-fold dilution, two 100 μL samples were collected every 2 min and either kept on ice until titration, or mixed with CHCl 3. For each time point we thus determined the free bacteriophage count (samples with CHCl 3 ) as well as the number of free bacteriophages and infective centres (samples without CHCl 3 ) to calculate eclipse and latency periods respectively. Experimental data were fitted with a logistical function: f ðxÞ ¼ a 1 þ e ÀkðxÀx c Þ ð1Þ a: ordinate corresponding to the asymptote when x!+1, represents the maximal pfu count.
P. aeruginosa strain PAK genome sequencing Genomic DNA was isolated from P. aeruginosa strain PAK and pyrosequencing was performed on a Roche 454 FLX system with Titanium chemistry at the University of Texas Genomic Sequencing and Analysis Facility. The draft assembly of ~6. 3 Mbp consists of 9 scaffolds, 490 large contigs, and 616 total contigs and was annotated at the University of Maryland Institute for Genomic Sciences using the IGS Prokaryotic Annotation Pipeline  [44]. Scaffolds deposited in GenBank can be accessed via Bioproject accession no. PRJNA232360. More details are available in S1 Text.
5. 1. These alignments were then summarized into count tables of Unique Gene Reads that map to phage or host gene features respectively. RNA-Seq data have been deposited in NCBI-GEO with accession no. GSE76513. RNA-Seq coverage visualization is available through the COV2HTML software at [https:// mmonot. eu/COV2HTML/visualisation. php?str_id=-32] for a comparison of the host (0 min / 13 min) and [https://mmonot. eu/COV2HTML/visualisation. php?str_id=-34] for a comparison of the phage (3. 5 min/ 13 min) Strain PAK reannotation and prediction of ncRNAs with RNA-Seq data RNA-Seq data of uninfected strain PAK were visualized using COV2HTML  [7]. Reads mapping forward and reverse strands were manually scanned over the whole genome. Both coding regions and intergenic regions displaying an unexpected transcription profile were examined using CLC Genomics Workbench 7.
Statistical analysis Each statistical comparison presented was performed using the DESeq2  [46]  R/Bioconductor package to normalize samples to each other and then test for differential expression. Notably, we have chosen to normalize the population of reads that map to each genome independently of the other. In the context of a phage infected cell rapidly replacing host transcripts with phage transcripts, this has artificially enriched host reads and depleted phage reads progressively over the course of infection by normalizing away the biologically relevant shift in each organism's proportion of the total reads in the cell. However, this has also allowed us to both show and test for differential expression of both phage and host gene features independently of the more global swing towards phage transcription.
Metabolite annotation and statistical analysis was performed using Matlab R2013b (Mathworks, Natick, MA, United States) according to the ion annotation protocol described by Fuhrer et al. [17]. With this method, 6006 ions were detected and 918 of them could be assigned to known P. aeruginosa metabolites. After removal of ion adducts, 377 ions were retained that were annotated as 518 metabolites (including mass isomers). Differential analysis was performed for each time point versus time point zero using a t-test for two samples with unequal variances (Welch test). For metabolic pathway enrichment, lists of significantly changing metabolites for each time point were created based on the thresholds of │Log 2 (fold change)│ ! 0. 5 and adjusted p-value < 0. 1. In each list, metabolites were sorted by the adjusted p-value, and the pathway enrichment procedure was performed for each subset of size 1 to the size of the significant list using Fischer test as described in  [48]  and the smallest p-value for each pathway was reported.
Acknowledgments We are grateful to  Zhou Xu  ( CNRS UMR8226 , Paris, France) and  Mart Krupovic  ( Molecular Biology of the Gene in Extremophiles, Institut Pasteur, Paris, France ) for critical comments and helpful suggestions on the manuscript. We thank  Henrik Almblad  ( Department of Immunology and Microbiology, Faculty of Health and Medical Sciences, Costerton Biofilm Center, University of Copenhagen, Copenhagen, Denmark ) for providing pDONRPEX18Gm plasmid.
The XPS data were evaluated by the CasaXPS software. The binding energies of all spectra were calibrated with respect to the binding energies of C 1s at 284. 6 eV. The bright-field TEM images where taken using JEOL 2100F operating at 200 kV. For these experiments the hybrid materials were supported on a copper grid covered with a thin amorphous carbon film. SEM and EDX analyses were performed with a Nova Nano-SEM 450 microscope with EDX detector from EDAX. Infrared spectra of samples deposited on ITO surface were recorded in reflectance mode using the Nicolet iN10-MX-FTIR microscope (Thermo Scientific) with the liquid nitrogen cooled MCT detector. An area of 100 mm 2 was sampled for a single spectrum. The spectral resolution was 4 cm À1 and typically 128 scans were averaged for a single spectrum.
For the background collection, a bare ITO surface was used. Zeta potential and dynamic light scattering (DLS) measurements were performed using a Malvern ZetaSizer NanoZS instrument. Cyclic voltammetry and chronoamperometry experiments were performed using a BioLogic SP-300 (Bio-Logic Science Instruments) electrochemical system with dedicated EC-Lab software v. 10. 19 in a conventional three electrode cell. Differential pulse voltammetry was performed with an Autolab, NoVa software 1. 10 version. Glassy carbon disc electrode (0. 007 cm 2 , Minerals) was used as working electrode. For electrochemical measurements, platinum wire (d = 0. 5 mm or d = 1 mm) and Ag|AgCl|KCl sat. were used as the counter and reference electrodes, respectively. All electrochemical experiments were carried out at 22 AE 2 1C.
Although this shift is almost similar for all modified electrodes, MoS 2 /CNPÀ film shows superior electrocatalytic properties in terms of current density. On the basis of the amount of carbon nanoparticles estimated from XPS data (see below) one would expect largest current density for (MoS 2 /CNP+) film. Perhaps other factors as their aggregation, the electric charge of their functionalities and the electrostatic interaction with the scaffold and with the substrate may play important role. The electrocatalytic activity of the nanohybrids modified electrodes was also investigated for dopamine. Dopamine (DA) is a neurotransmitter that regulates our movement and emotional responses. 55 A major problem with measurement of dopamine stems from the low resolution between DA and interferences such as ascorbic acid and uric acid.
Acknowledgements This work was partially sponsored by the  National Science Centre  (Project No. 2011/03/B/ST4/02620 ). Purchase of  Scanning Electron Microscope from resources of NanOtechnology, Biomaterials and aLternative Energy Source  for  ERA integration grant  ( FP7-REGPOT-CT-2011-285949-NOBLESSE ) is gratefully acknowledged. Supply of carbon nanoparticles from  Cabot Corporation  is gratefully acknowledged. A. C. and J. D. gratefully thank  Dr Zahra Taleat  for helpful discussions. J. D. also thank  Dr Ievgen Obraztsov  for helpful discussions.
For example, we included genomic regions with high and low CpG density, GC content and repetitive DNA overlap. As an additional challenge, we included a single-nucleotide polymorphism (SNP) that replaces a potentially methylated CpG by an always unmethylated TpG dinucleotide in some of the reference samples. To each contributing laboratory we sent aliquots of ~1 µg DNA for each of the 32 reference samples. In addition, we provided a npg A n A ly s i s standardized information package comprising general instructions, documentation templates and the list of the 48 target genomic regions (Supplementary Data 2). Each region had one designated target CpG for which the DNA methylation level was to be measured, and we asked the contributing research groups to return DNA methylation measurements for each of the reference samples.
We gave no further instructions on how to design the assays or how to derive the DNA methylation measurements for the target CpG from the raw data. Moreover, we asked research groups not to exchange any information among each other, and they did not have access to the Infinium 450k data used for region selection. We designated 16 of the 48 target regions as mandatory ('core regions') and let scientists in each contributing research group for themselves decide how many of the remaining 32 regions they would cover in addition to the core regions. On average, assay design was attempted for 30 genomic regions and was successful in 95% of cases (Supplementary Table  1 ). The known SNP at one of the target CpGs was detected and reported by 9 of the 17 research groups who contributed locus-specific assays.
Documentation of DNA methylation assays, results and analysis workflows. The details for all contributed DNA methylation assays are available in Supplementary Data 1. These reports include a short assay summary, quality control data for the received reference DNA samples, and detailed descriptions of the design and execution of each contributed assay. They follow the standardized reporting template from the information package that was sent to all contributing laboratories (Supplementary Data 2). DNA methylation measurements for each assay, genomic region and reference sample are available in Supplementary Data 3. Illumina 450k microarray data are available at the NCBI Gene Expression Omnibus under the accession number GSE77965. Finally, the source code (written in R) underlying the bioinformatic analysis is available in a public repository (http://biomarker-benchmark.
For each method and each analysis, we recorded the root mean square error (RMSE) between the prediction and the target value. As no single global assay gave fully consistent results, we chose as global target the mean of the two best-performing assays (HPLC-MS and Pyroseq NBL2), and we replaced the four mean values that were inconsistent with the known change in concentration in the titration series by imputed values that were calculated as the mean of the two neighboring values in the titration series. The e1071 R package was used for support vector regression, randomForest for random forest regression and DMwR for cross-validation. Analysis of discriminatory power. We trained linear support vector machines using patient-stratified cross-validation, such that each prediction used four tumor/patient pairs for training and left two pairs out for test-set validation.