dataesr/scientific-paragraphs-categorization huggingface dataset:
Genome Sequencing Information Genome project history Strain FF2 T was selected for sequencing on the basis of its phylogenetic position and 16S rRNA similarity to other members of the family Microbacteriaceae. It was the first sequenced genome of a Diaminobutyricimonas species and the first genome of Diaminobutyricimonas massiliensis sp. nov. The GenBank accession number is CCSB00000000 and consists of 39 large contigs. Table  3  shows the project information and its association with MIGS version 2. 0 compliance  [20]. Growth conditions and DNA isolation Diaminobutyricimonas massiliensis sp. nov. , strain FF2 T (= CSUR P3023 = DSM 27836), was grown on Columbia agar enriched with 5% sheep's blood (bioMérieux) at 37°C in an aerobic atmosphere. Bacteria grown on five plates were collected and resuspended in 500 μL of Tris-EDTA buffer 10×.
Within this run, the index TABLE 2. Differential phenotypic characteristics of Diaminobutyricimonas massiliensis strain FF2 T (data from this study), Labedella gwakjiensis strain KSW2-17  [37] , Diaminobutyricimonas aerilata strain 6408J-67  [7] , Lysinimonas soli strain SGM3-12  [38]  and Cryobacterium psychrophilum strain DSM 4854  [39]    The mate-pair library was prepared with 1 μg of genomic DNA using the Nextera mate-pair Illumina guide. The genomic DNA sample was simultaneously fragmented and tagged with a mate-pair junction adapter. The profile of the fragmentation was validated on an Agilent 2100 BioAnalyzer (Agilent Technologies, Santa Clara, CA, USA) with a DNA 7500 labchip. The DNA fragments ranged in size from 1 kb up to 6. 6 kb, with an optimal size at 4. 5 kb. No size selection was performed, and 368 ng of tagmented fragments was circularized.
Genome annotation Open reading frame (ORF) prediction was performed using Prodigal  [21]  with default parameters. We removed the predicted ORFs if they spanned a sequencing gap region. Functional assessment of protein sequences was performed by comparing them with sequences in the GenBank  [22] , and Clusters of Orthologous Groups (COGs) databases using BLASTP. tRNAs, rRNAs, signal peptides and transmembrane helices were identified using tRNAscan-SE 1. 21  [23] , RNAmmer  [24] , SignalP  [25]  and TMHMM  [26] , respectively. Artemis  [27]  was used for data management, while DNA Plotter  [28]  was used for visualization of genomic features. In-house perl and bash scripts were used to automate these routine tasks. ORFans were sequences which have no homology in a given database, i. e. in a nonredundant (nr) or identified if their BLASTP E value was lower than 1e-03 for alignment lengths greater than 80 amino acids.
If alignment lengths were smaller than 80 amino acids, we used an E value of 1e-05. PHAST was used to identify, annotate and graphically display prophage sequences within bacterial genomes or plasmids  [29]. To estimate the nucleotide sequence similarity at the genome level between Diaminobutyricimonas massiliensis and another six members of the family of Microbacteriaceae, we determined the average genomic identity of orthologous gene sequences (AGIOS) parameter as follows: orthologous proteins were detected using the Proteinortho software (with the following parameters: E value 1e-5 , 30% identity, 50% coverage and algebraic connectivity of 50%)  [30]  and genomes compared two by two (Fig. 6 ). After fetching the corresponding nucleotide sequences of orthologous proteins for each pair of genomes, we determined the mean percentage of nucleotide sequence identity using the Needleman-Wunsch global alignment algorithm.
Extended insights We compared the genome from Diaminobutyricimonas massiliensis strain FF2 T to those of other members of the Microbacteriaceae family, including Agromyces italicus strain DSM 16388 (GenBank accession number ATXF00000000), Clavibacter michiganensis strain NCPPB 382 (AM711867), Leifsonia xyli strain CTCB07 (AE016822), Microbacterium paraoxydans strain 77MFTsu3. 2 (AQYI00000000), Microbacterium testaceum strain StLB037 (AP012052) and Rathayibacter toxicus strain DSM 7488 (AUDF00000000). The draft genome of Diaminobutyricimonas massiliensis  (3. 22 Mb).
One of the main criteria for the drug selection was that these three subgroups of drugs, linked to three different organs may share basic, well-known pathways, reliable biomarkers and various biochemical processes although they cause organ-specific toxicity. Thus, the selection of chemicals was based on the well documented adverse drug reactions (ADR) due to the pharmacological effect that triggers toxicity in different organs. Based on the publicly available databases and literature search, 12 central nervous system (CNS) relevant drugs were selected to represent four different categories: (I) neuroactive and neurotoxic: CNS-drugs with strong neurotoxicity (amiodarone, buflomedil, chlorpromazine), (II) neuroactive and non-neurotoxic: CNS-drugs with no or weak neurotoxic effects (carbamazepine, diazepam, propofol); (III) non-neuroactive but neurotoxic: non-CNS drugs with significant neurotoxic effects (cisplatinum, ciprofloxacin, cyclosporine A); (IV) non-neuroactive and non-neurotoxic: non-CNS drugs with no or weak neurotoxic effects (loperamide, nadolol, ondansetron).
1% for long-term treatments for metabolomics and proteomics). At each time point of medium change (6 well plates, 1 Â 10 6 cells/well) 350 ll of the supernatant were collected, transferred to a 1 ml Eppendorf tube to determine the LDH content. This assay was applied because it allowed further cultivation and taking of  were added and incubated at room temperature for 30 min. The enzymatic reaction was stopped by adding 50 ll of 1 N HCl and the samples' absorption was read at 490 nm in a microplate reader (iMarkTM, Bio Rad). Data were exported with MPM6 software and statistically processed. Means were calculated for all technical triplicates and the biological replicates. Data were normalized to positive controls (100% lysis with TritonX100) and effects of DMSO were eliminated by solvent-only treated controls.
The mean free brain/plasma ratios were calculated from three individual experiments for each drug. One hour ratios were calculated by dividing the free drug concentration in the receiver compartment by the free drug concentration in the donor compartment after 1 h. The experimental data at one hour (i. e. concentration in both compartments at the beginning of the experiment and after 1 h) were computed using the blue-norna Ò steady-state calculator (www. blue-norna. com) to predict the.
(2009)  for amiodarone,  Sadeque et al. (2000)  for loperamide and  Regenthal et al. (1999)  for all other studied compounds were used. The data on drug binding to plasma proteins were retrieved from the literature or from public resources such as DrugBank (http://drugbank. ca/), and DailyMed (http://dailymed. nlm. nih. gov/dailymed/about. cfm). To estimate the concentration unbound in human plasma (Cu,pl), the total plasma concentrations of drugs found in human plasma (Ct,pl) were multiplied by the unbound fraction in human plasma (fu,pl) (Eq. (  1 ): Cu,pl = Ct,pl ⁄ fu,pl). The Cu,br values were then estimated by multiplying the Cu,pl by the predicted steady-state Cu,br/Cu,pl ratios obtained from the in vitro BBB model (Eq. (  2 ): Cu,br = Cu,pl ⁄ steady-state Cu,br/Cu,pl).
Metabolomics: processing of samples and data analysis For the analysis of the cellular metabolome, a non-targeted analysis were carried out by recording high resolution 1 H NMR spectra of hydrophilic cell extracts at 600 MHz without preseparation on a Bruker DMX 600 spectrometer equipped with a 5 mm DCH cryoprobe (both by Bruker Biospin GmbH, Rheinstetten, Germany). Water suppression was achieved by use of the NOESYGPPR1D-pulse program from the Bruker library. For Fourier transformation, 256 scans and 16 dummy scans with an acquisition time of 4. 95 s were recorded in the non-spin-mode. Spectral width is 13227. 5 Hz. Instrument control, data recording and baseline correction were carried out on Bruker WIN-NMR Suite. 1 H NMR spectra were inspected visually to exclude any outliers due to inadequate water suppression or baseline distortions.
Basic multivariate data analysis and a database comparison of the peaks was performed and all metabolites were identified using the Profiler™-function and the compound library of Chenomx NMR Suite (Chenomx Inc. ) as described before  (Sieber et al. , 2009). Values are presented as fold changes per sample pair (treated vs. medium control containing carrier only and cultured under identical conditions for the same time). Significance was tested by 2 way ANOVA with Bonferronis posttest; values with p > 0. 05 where excluded.
Proteomics: processing of samples and data analysis For the proteomics analysis treated cells were lysed in ice cold 100% methanol to stop enzymatic activities. The protein measurements and identification were performed by HPLC-MS following the procedure described by  Wang et al. (2005)  with modifications as described by  Wilmes et al. (2013). Briefly, in preparation for the measurement with HPLC-MS the peptides were labeled with iTRAQ. The basis for the iTRAQ quantification were the reporter ions extracted from HCD spectra. To identify the peptides separate searches for CID (collision-induced dissociation) and HCD (higher energy collisional dissociation) spectra were made against an interfaced version of SwissProt Database on multiple search engines including Mascot, OMSSA and XTandem. To ensure that only unique peptide hits were shown, an ensuing false discovery rate (FDR, filtering at 5%) approach using a decoy database was performed.
Each sample was measured in triplicate using exclusion lists, i. e. after the first run the Raw file was sent to Proteome Discoverer to generate a list of all precursor m/z values which were already identified. For the second run the exclusion list, which was created after the first run, was added to the instrument method. With the exclusion list it was possible to avoid the fragmentation of all precursor ions, which were already identified in the run before. For the third run both Raw files from the previous runs were used to create the exclusion list. This procedure facilitated the identification of more proteins compared to using a single measurement. Identification and quantification of proteins were performed using an integrated pipeline of OpenMS  (Sturm et al. , 2008)  and the isobar package  (Breitwieser et al.
, 2011). For identification, a consensus approach was used, i. e. , the data was analyzed by multiple identification engines: X! Tandem 2009. 04. 01. 1  (Craig and Beavis, 2004) , OMSSA 2. 1. 9 (Geer et al. , 2004) , Mascot v2. 3  (Perkins et al. , 1999). The results were combined using the Consensus ID approach  (Nahnsen et al. , 2011). A 5% false discovery rate (FDR) for peptides was enforced using a target-decoy approach. For peptide quantification the OpenMS tool iTRAQAnalyzer was used to extract the quantitative information from HCD spectra and correct for isotopic impurities applying a non-negative least squares procedure (NNLS). The resulting quantitative information was subsequently mapped back to the previously acquired identification data for subsequent protein quantification using the isobar software package  (Breitwieser et al.
ingenuity. com). The program uses a knowledgebase derived from the scientific literature to relate genes or proteins based on their interactions and functions. IPA generates information on biological networks, canonical pathways and functions relevant to the dataset. Highly regulated biological networks and functions were identified using association rules among focus genes/proteins. Raw data were processed with Excel and subsequently analyzed with IPA. The raw data were converted from log10 to log2 and a p-value of 0. 05 (log ratio 1. 5) was applied as threshold.
The dimension of the chip is 5 Â 5 cm 2 with a 2. 1 mm 2 recording matrix in the center with 32 passive electrodes and indium tin oxide conductors. The hydrophobic insulation material was activated by a short flaming (butane) through a stainless steel mask and subsequent treatment with poly-D-lysine and laminin. Thus, cell adhesion was ensured within a defined, adhesive area only. For recording, the MEAs were placed into a sterilized recording chamber and maintained at 37 °C. The recordings were made in serum-free medium and the pH was kept constant at 7. 4 with a continuous stream of filtered, humidified air with 5% CO 2. Recording was performed with a computer-controlled 64-channel MEA workstation data acquisition system (Plexon, Inc. , Dallas, TX, USA) providing amplification, filtering (3 Hz-7 kHz), and digital signal processing of MEA signals.
Multi-parametric data processing To obtain quantitative data from the one-hour recordings for each of 8 or more concentrations over a wide range, stable activity phases of the last 30 min were selected for evaluation. The multichannel signal acquisition system delivered single neuron action potential (spike) data including their waveforms. Spike identification and separation were accomplished using a templatematching algorithm in real time. This permitted the simultaneous extracellular recording of action potentials from up to 128 neurons. The spikes were recorded as spike trains which include clusters, so-called bursts which were quantitatively described via direct spike train analysis using NPWaveX (NeuroProof GmbH, Rostock, Germany). This yields a description of the pattern characteristics consisting of 200 parameters belonging to five general categories:, burst structure, oscillation, synchronization, connectivity and general activity.
For direct comparability all parameters were normalized for each experiment and each experimental treatment with regard to the corresponding values of the native reference activity. This multiparametric description of the network activity patterns provides a high content analysis characterizing the drug and concentration dependent changes  (Johnstone et al. , 2010; Weiss, 2011). The data are expressed as series means ± SEM. The distributions of the absolute parameters were tested for normality. Using the SPSS (Social Sciences Statistical Software Package), significances of changes induced by the drug exposure were tested by paired t-tests followed by Dunnett's multiple comparison post hoc test with the native activity as the common control and p < 0. 05 was considered statistically significant.
Pattern recognition and classification analysis of functional compound response patterns To determine the effects on the electrical activity which were specific for each of the four groups of the drugs in Table  1  these data were further analyzed using methods of pattern recognition. The purpose of pattern recognition is to obtain a reproducible quantitative description of objects or situations which allowed their classification in two or more classes. The recorded time series contain the complex changes in the activity pattern which the administration of a drug causes and leaves behind as a fingerprint. From this a data record was extracted, defined as the numerical description of the changes of the activity pattern of one single experimental recording caused by a drug described by the 200 activity features for one concentration and one measurement, which is represented as one data point defined by 200 parameters, which can be envisioned as a data point in a 200-dimensional space.
The data records of all treatments of a given drug represent the drug signature. This is like the drug's fingerprint as it represents the numerical representation of all changes of the activity patterns caused by the drug. The drug signature comprises the information from all 200 concentration-response curves. The activity-describing parameters that were used for the drug classification comprise the 30 parameters provided by the software package NeuroExplorer (Nex Technologies, Madison USA) (see e. g. Gramowski et al. , 2004) , while the remaining 170 are generated by the software package NPWaveX (NeuroProof GmbH, Rostock Germany). These parameters describe different categories of spike train phenomena such as changes in the general activity, the burst structure, network synchronicity and the oscillatory behavior of the activity.
For each concentration, stable activity phases after drug application were selected and the 200 spike train parameters were calculated and normalized by the native activity preceding drug application using NPWaveX Ò (NeuroProof GmbH, Rostock, Germany)  (Parenti et al. , 2013; Weiss, 2011). These data records were computed for all 12 compounds and their concentrations yielding a total of 743 data records. Classifications were performed with artificial neural networks which were trained as multilayer perceptrons with a resilient back-propagation algorithm  (Riedmiller and Braun, 1993). All classification and validation steps were performed in a blinded fashion. We used an artificial neural network architecture without hidden layers which is justified because of the biological activity noise.
Analysis of electrical activity patterns The changes of neurotransmitters and their synaptic transmission-related protein machinery observed in proteomics and metabolomics analyses should also be reflected by changes in the electrical activity of neuronal ensembles. Therefore, the effects of the 12 selected drugs on the action potential patterns in the neuronal network cultures (2D model) were analyzed for its predictivity for neurotoxicity. Already with only 12 drugs divided into four groups of neuroactive and neurotoxic compounds for which a dataset of 200 acute concentration-response curves (electrical activity fingerprint) was established the drugs were almost all classified correctly which means that specific fingerprints of activity patterns for each class can be identified. The incomplete separation of drugs of class II and IV may be explained by the heterogeneity of the compounds grouped together in a preliminary way for training the data classification algorithm in this study.
Acknowledgements This work was performed within the  Large-Scale Integrating  Project  PREDICT IV , '' Profiling the toxicity of new drugs: A non animal-based approach integrating toxico-dynamics and biokinetics '' funded by the  EU   7th Framework Programme  (Contract No. 202222 ). http://www. predict-iv. toxi. uni-wuerzburg. de. We thank  Hannelore Popa-Henning  for the administrative work and organization. The excellent technical support of  Kristine Gürtler ,  Rachid Aïjjou ,  Johan Hachani ,  Denise Tavel  and  Brigitte Delacuisine  is gratefully acknowledged. Appendix A. Supplementary material.
Supported files As input rCGH supports Agilent Human CGH data, from 44K to 400K, and Affymetrix, SNP6 and cytoScanHD. All are provided in text format by platform-specific softwares: standard Agilent text files are exported from Agilent Feature Extraction software (FE), while Affymetrix cychp. txt, cnchp. txt or probeset. txt files are obtained by processing Affymetrix CEL files through ChAS or Affymetrix Power Tools (APT) softwares: both are freely available at http://www. affymetrix. com. Custom arrays can also be supported, provided the data format complies with the requirements (see Supplementary Methods for details).
Web server version Independently of rCGH, we have developed aCGH-viewer: an interactive visualization available as a web application. Its implementation is freely available for installation on a server. As inputs, the application requires segmentation tables built through either rCGH, or any other workflow, provided the data are of the same form as the standard CBS outputs. This application is designed for use by clinicians and biologists and does not require bioinformatic expertise. It allows individual profiles to be shared, discussed and annotated in tumor board committees and finally saved for traceability (see Supplementary Methods for details).
Conclusion In this work, we present the R package rCGH: a comprehensive aCGH analysis workflow, with features and functionalities particularly well adapted to precision medicine. rCGH ensures the traceability of the entire process of individual samples and provides interactive visualization tools allowing to better interpret-and potentially reprocess-genomic profiles, individually. The web-server application can assist oncologists in reviewing copy-number alterations in genomic profiles and identifying matched therapeutic orientations.
Viruses of microbes impact all ecosystems where microbes drive key energy and substrate transformations including the oceans, humans and industrial fermenters. However, despite this recognized importance, our understanding of viral diversity and impacts remains limited by too few model systems and reference genomes. One way to fill these gaps in our knowledge of viral diversity is through the detection of viral signal in microbial genomic data. While multiple approaches have been developed and applied for the detection of prophages (viral genomes integrated in a microbial genome), new types of microbial genomic data are emerging that are more fragmented and larger scale, such as Single-cell Amplified Genomes (SAGs) of uncultivated organisms or genomic fragments assembled from metagenomic sequencing.
Here, we present VirSorter, a tool designed to detect viral signal in these different types of microbial sequence data in both a reference-dependent and reference-independent manner, leveraging probabilistic models and extensive virome data to maximize detection of novel viruses. Performance testing shows that VirSorter's prophage prediction capability compares to that of available prophage predictors for complete genomes, but is superior in predicting viral sequences outside of a host genome (i. e. , from extrachromosomal prophages, lytic infections, or partially assembled prophages). Furthermore, VirSorter outperforms existing tools for fragmented genomic and metagenomic datasets, and can identify viral signal in assembled sequence (contigs) as short as 3kb, while providing near-perfect identification (>95% Recall and 100% Precision) on contigs of at least 10kb.
Because VirSorter scales to large datasets, it can also be used in "reverse" to more confidently identify viral sequence in viral metagenomes by sorting away cellular DNA whether derived from gene transfer agents, generalized transduction or contamination. Finally, VirSorter is made available through the iPlant Cyberinfrastructure that provides a web-based user interface interconnected with the required computing resources. VirSorter thus complements existing prophage prediction softwares to better leverage fragmented, SAG and metagenomic datasets in a way that will scale to modern sequencing. Given these features, VirSorter should enable the discovery of new viruses in microbial datasets, and further our understanding of uncultivated viral communities across diverse ecosystems.
, 2015). This is not surprising, given that 86% of the 1,531 genomes of viruses that infect bacteria and archaea available at RefSeq are associated with only 3 of 61 known host phyla (based on the viral genomes available in NCBI RefseqVirus v69, January 2015). One way forward is to better detect and catalog viral sequence data from rapidly expanding microbial genomic datasets. First, prophages, which result from the integration of a temperate virus genome into a microbial host genome, are present in ∼60% of sequenced bacteria  (Casjens, 2003; Canchaya, Fournous & Brüssow, 2004). Second, Single-cell Amplified Genome (SAG) datasets are now routinely generated to provide genome sequence data and inferences about metabolic capacity for novel microbes  (Swan et al. , 2011; Kamke, Sczyrba & Ivanova, 2013; Rinke et al.