dataesr/scientific-paragraphs-categorization huggingface dataset:
Behavioural analysis using FastData Monitor software One of the challenges of high-throughput behavioural analysis is to sort and extract data in a simple way. We have developed software to extract behavioural information from raw data in collaboration with ViewPoint Life Sciences (France). The FastData Monitor software is able to filter and sort the result files and can be used for routine calculations such as sums, means and standard deviations without the need to learn a programming language. Finally, the user is able to choose between different types of charts including column, pie, bar or stacked column charts in order to visualise results. These settings can be saved in a layout file and reused thus standardising the processing and reporting of data. The behavioural parameters presented in this study have been extracted from films of zebrafish locomotion using FastData Monitor.
In this experiment only one area of interest corresponding to an individual aquarium was defined. However, multiple areas of interest can be defined within ZebraLab allowing the preference of a fish for different areas within an aquarium to be measured  [18]. The resting time corresponds to the amount of time spent inactive during data acquisition and distance, speed and resting time can be calculated for both individual animals and groups of fish. To develop the FastData Monitor software Microsoft Visual Studio (Microsoft, USA) was used in conjunction with the. NET framework (Microsoft, USA) on Windows 7. It is able to process data contained in an Excel sheet. The user interface also allows novel customised formulae to be defined based on columns and rows of data. The software then produces layouts that contain the sorting parameters and formulae to be applied to the input data as well as the chart representations that will be generated at the end of the experiment.
Layouts can be reused to reproduce the same operations on different data. FastData Monitor can standardise data manipulation and chart production across laboratories thereby improving the reliability and repeatability of behavioural experiments. A basic version of the FastData Monitor software is available for use via the following link: http://fastdatamonitor. vplsi. com/ download/BasicFastDataMonitorSetup. msi. The software requires that data are uploaded as an Excel file in a specific format. More details regarding this issue will be provided upon request.
2C ). casper juveniles swam for a similar amount of time as AB, whereas ABstrg, EK, TU and WIK all swam significantly more (Fig. 2C ). When looking at global activity levels (calculated by plotting the total distance swum every 5 minutes during a one hour experiment), AB, EK and TU juveniles swam consistently further than ABstrg, casper and WIK (Fig. 2D ; for statistics, refer to table 1). We also compared the surface area of these animals (which is an indirect measure of their overall size; 1 pixel corresponds to 2. 23 mm 2 ) to the average swimming speed. Lines of best fit applied to these data showed no correlation between the size of a fish and speed at which it swam (Fig. 2E ). However, this analysis did reveal that casper juveniles had a much smaller surface area than fish of other strains at this age (Fig.
5A, B, C ). Since data from individual fish were hard to analyse statistically, we counted the mean number of peaks of acceleration shown by single individuals of each strain during a 5-minute experiment. Fast swim events appeared to be highly variable at the three stages analysed. At 6 dpf, AB and ABstrg both showed 28 peaks of acceleration. In contrast to this, casper, EK, TU and WIK all showed significantly fewer peaks of acceleration (Fig. 5D ). At 1 month of age, AB, casper, TU and WIK showed a similar number of peaks of acceleration. However, ABstrg and EK showed more peaks and thus an increased number of fast swim events (Fig. 5E ). At 3 months, AB, casper, EK and WIK showed a similar number of peaks, whereas ABstrg and casper showed significantly fewer fast swim events (Fig. 5F ). We also analysed the amount of acceleration within these peaks of locomotion.
Discussion In this study we have compared the locomotor behaviour of six zebrafish strains as they mature from larval to adult stages. We chose to use AB as a reference to compare behaviour across strains. AB has been maintained in different laboratories for many years, its life-history is well characterised and it is freely available from Zebrafish International Resource Centre (ZIRC). In order to improve handling of the large datasets generated in this experiment we developed novel software, ''FastData Monitor'', that allows semi-automated analysis of several components of zebrafish locomotion. As zebrafish behavioural research becomes more popular the establishment of standardised protocols becomes crucial. Behavioural experiments are notoriously susceptible to large variation  [22]  and the use of software to automate behavioural analysis will help yield stable results across labs.
The size of a fish's body can also directly alter the biomechanics of locomotion. We were unable to measure the surface area of 6 dpf larvae due to their small size and the detection limit of our setup. However, at both 1 month and 3 months we were able measure fish using the ZebraLab software regardless of their pigmentation levels (for example, even though casper mutants are more transparent than AB fish their detectability was not affected; Fig. S2A-D ). There was no correlation between surface area (an indirect measure of body size) and swimming speed at 1 month whereas at three months there was a weak negative correlation for AB, ABstrg and EK. These findings suggest that it is only at adult stages that a fish's body size impacts its locomotion. The striking changes to body morphology and locomotion during larval and juvenile development raise questions about their behavioural significance.
Work in the L. B-C. laboratory was funded by the  EU projects NeuroXsys  ( FP7/2007-2013  grant agreement no  223262 ) and  ZF-Health  ( FP7/2010-2015  grant agreement no  242048 ), the  ANR  (grants  ANR-08-CEX-08-000-01  and  ANR-2012-BSV4-0004-01 ), the  ENP , and the  FRM  ( FRP «Equipe» DEQ20120323692 ). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Description of Framework Our proposed approach with MDA is supported by a framework which is going to operate transformation models and checks of the constraints. This framework is realised relying on Eclipse Modelling Framework (EMF)  [16]  [17]  [18]  based metamodeling facilities. EMF is an implementation of the MOF particularly adapted to the applications based on a model of structured data, which is the case for PLM systems. In the Eclipse environment, EMF enables the implementation of DSL (Domain Specific Language). Furthermore, the genericity of Eclipse plugins allows leaning on EMF to propose inherited corresponding mechanisms in the models presented in section 2. So, we propose designer wizards to help him in his choices. We propose three wizards' levels. -The first level correspond to the definition of rules of the business concepts.
Material and software For the computations that follow, we used a machine at the University Paris Diderot Paris 7, with the following configuration: 2 Quad-Core Xeon X5450 3. 0GHz/2x6MB, 8GB 667MHz FBD RAM. The program implementing the new quality assessment method has been developed in Java Web with the NetBeans 6. 1 Integrated Development Environment, the Tomcat 6 application server and the database server MySQL 5. The web survey was developed in PHP5 and MySQL 5. The tiles of each magnification level of the WSI need to be accessible to perform the analysis. Many opensource programs  [6, 7]  as well as proprietary ones  [8]  can be used to extract WSI files from different formats (3dHistech, Aperio, Hamamatsu, Olympus) into series of tiles at different magnification levels.
As further improvements of our method, we will contextualize the assessment by refining the thresholds depending on staining and lesion. In terms of computing speed, Zerbe et al. [5]  showed a distributed computing model to assess the focus sharpness of a WSI, generating a focus assessment map of the WSI at a given magnification level in around 6 minutes per gigapixel per computer. We analyzed on our machine (see Material and software sub-section) 8 complete 1. 73 gigapixel digital slides in 400 seconds as eight distinct threads, equivalent to 34 Megapixels per second or 2 gigapixels per minute, per computer. Already 12 times faster than the previous method, we are currently optimizing the program into a multi-thread, multi-node parallel processing system using C++ with OpenMP and OpenMPI libraries to scale it up to match demanding industry requirements.
5x, 86% non-blank, 34% sharp), (493 tiles, 5. 0x, 83% non-blank, 33% sharp), (1751 tiles, 10. 0x, 77% non-blank, 31% sharp), (6589 tiles, 20. 0x, 76% non-blank, 25% sharp). The WSI is thus considered as of insufficient quality in terms of blurriness, for all its magnification levels being under their respective blur assessment thresholds. scanned. It can also be used downstream to reacquire the complete slides that are below the quality threshold for surgical pathology analysis. We are currently optimizing the program to improve its speed and refining its threshold, according to the magnification levels, the staining of the slides, and the type of acquisition devices used. Such quality assessment scores could be integrated as metadata in WSI shared in clinical, research or teaching contexts, for a more efficient medical informatics workflow.
Acknowledgements The authors are grateful for financial support from the  French Agence Nationale de la Recherche (ANR). We also like to thank our colleagues in the  Pathology Department of the Saint-Louis Hospital  and the  Hematology University Institute  who made all these experiments easier,  Tzuchien Tho  for his careful reading of the manuscript and helpful suggestions and  Julie Auger-Kantor  for her immense help and support throughout this work.
Results: We have implemented MeDiChISeq, a regression-based approach, which -by following a learning processdefines a representative binding pattern from the investigated ChIP-seq dataset. Using this model MeDiChISeq identifies significant genome-wide patterns of chromatin-bound factors or chromatin modification. MeDiChISeq has been validated for various publicly available ChIP-seq datasets and extensively compared with other peak callers. Conclusions: MeDiChI-Seq has a high resolution when identifying binding events, a high degree of peakassessment reproducibility in biological replicates, a low level of false calls and a high true discovery rate when evaluated in the context of gold-standard benchmark datasets. Importantly, this approach can be applied not only to 'sharp' binding patterns -like those retrieved for transcription factors (TFs) -but also to the broad binding patterns seen for several histone modifications.
Irrespective of whether ChIP-chip or ChIP-seq is used, the aim of the corresponding data analysis is to identify patterns in the reconstructed signal profiles that reflect the bona fide enrichment of the factor/modification of interest across the entire genome. Several pattern reconstruction methodologies have been described to date using approaches based on different concepts to define what constitutes an enrichment event or peak. The simplest concept defines an enrichment region based on a user-chosen read count intensity threshold  [2, 3]. Other methodologies evaluate background levels from control (non-enriched) datasets to assess enrichment confidence p-values in the chromatin immuno-precipitated (ChIP) profile from a binomial distribution model  [4, 5]. In the same manner, when no control samples are available, the background is usually estimated from a Poisson distribution model  [4, 6, 7].
Implementation MeDiChISeq is based on MeDiChI, a model-based deconvolution method designed for the analysis of ChIP-chip datasets  [15]  to which an important number of novel implementations have been added to enable the analysis of datasets generated by massive-parallel sequencing. Specifically, while the regression-based calculator embedded on MeDiChI is essentially the same in MeDiChISeq (see Additional file 1), the major novel implementations incorporated for transforming MeDi-ChI into a Peak caller dedicated to the analysis of ChIP-Seq datasets comprise (1) the preprocessing of mapped sequence files to generate read-count intensity files compatible with MeDiChI readout; (2) the enhancement of the peaks' confidence assessment by including local and global background comparisons as well as the use of input control datasets when available and (3) the implementation of a multicore processing structure to accommodate computation requirements observed when MeDiChI was applied to larger genomes than those that have been used for its release.
ChIP-seq datasets MeDiChISeq processes mapped sequence files in different formats (e. g. BED, BAM). Read-count intensity profiles are reconstructed from mapped read files by elongating each read to a user-defined length (default read elongation: 150nt) and counting the elongated read overlaps within a defined window (default wiggle-format files resolution: 10nt). While the read elongation parameter is generally provided by the user, we have incorporated in MeDiChISeq a function that predicts a suitable read elongation from the information retrieved in the ChIP-seq profile itself (see Additional file 2 and below). In ChIP-chip the reconstructed signal intensity is generated by comparing the immunoprecipitated information (IP) with the control dataset, while in ChIP-seq the IP and control datasets are processed separately.
Establishing a representative binding pattern by applying an iterative learning process One of the main advantages of MeDiChISeq is its capacity of inferring a representative binding pattern (referred to as "kernel") from the provided ChIP-Seq dataset. As illustrated in the Additional file 3, this is performed by fitting a binding pattern model to a reduced number of genomic regions, which are selected by applying a read-count intensity cutoff criterion. This cutoff can be defined as a given read-count intensity or by a quantile intensity parameter. Model fitting is performed in an iterative manner by evaluating in each round the number of peaks that fit the best to the current model and adjusting its parameters (shape and scale of Gamma distribution) by minimizing the regression residuals.
Thus, the representative binding pattern or kernel modeled by the iterative regression approach is used to deconvolve binding events over the entire ChIP-seq profile. For this, the dataset is subdivided into overlapping windows (default parameter 20,000 nt window length; a contiguous window overlap is defined to cover at least one peak length) and the precomputed kernel is used to identify those enrichment patterns that fit best. Like in the case of MeDiChI, the likelihood of an enrichment event to match the trained kernel is related to the ChIP-seq background and is estimated by applying a non-parametric bootstrap approach  [15]. MeDiChISeq compares for this purpose the putative binding sites identified by kernel fitting with the "kernel-fitting residuals" (i. e. , those not complying with the model, and corresponding to the background).
When available, a control dataset (e. g. , non-enriched sample or IP with non-specific IgG) is included during the binding site assessment. Indeed, whenever an enrichment event matches with the trained kernel, the kernelfitting process is also performed in the control dataset for the corresponding genomic region. If in a given chromatin region both the enrichment and the control dataset comply with the trained kernel, the confidence of the identified binding site in the immunoprecipitated dataset is corrected as follows: Confidence Threshold CT ð Þ ¼ -log p-value IP À Á ÃIntensity IP --log p-value Control À Á Ã Intensity Control À Á This approach enhances the confidence of the predicted binding event by evaluating its uncertainty from different perspectives, namely relative to a local background, relative to the identified patterns across the genome (global background) and relative to the enrichment seen in the control sample.
Note that the described control sample-based confidence correction is based on the assumption that the compared datasets (IP and control) present comparable sequencing depth levels. It is important to mention that some methodologies apply in case of divergent sequencing depths linear scaling corrections; however we have shown in a previous study that important differences in sequencing depths may give rise to non-linear differences between compared datasets  [16]. In contrast to other methods, we do not suggest a default p-value threshold but provide a comprehensive list of all identified binding sites (complying with the kernel fitting) and their associated confidence descriptors such that the user can chose the optimal confidence threshold. In fact, defining a default p-value threshold may be misleading for inexperienced users, who may consider such reference as a gold standard rather than evaluating by other approaches the degree of false calls for a given p-value.
Results and discussion In this study the performance of MeDiChISeq has been evaluated with a large number of publicly available ChIP-seq datasets. These include the TFs SRF, MAX, NRSF  [17]  and the sequence-specific insulator protein CTCF  [18] , all of which present sharp peaks in their ChIP-seq profiles. Moreover, also broad patterns characteristic of some histone modifications, such as histone H3 lysine 4 trimethylation (H3K4me3), and lysine 9 (H3K9Ac) or lysine 27 (H3K27Ac) acetylation, were also included using published data sets  [18]. Importantly, MeDiChISeq performance was compared to three other peak callers, which are representatives of the different methodologies implemented over the years: MACS models the background according to a Poisson distribution  [7] , BayesPeak takes advantage of a fully Bayesian hidden Markov model to identify binding events  [10] , and PeakRanger applies in addition to background modelling, in a second round a "summit-valley-alternator" algorithm to scan for significant summits  [12].
Even more importantly, differences in the number of sites identified by each peak caller are observed also with biological replicates, which likely reflect inherent differences in the characteristics of each of such datasets. Note that in the present study we considered only ChIP-seq profiles that were published as biological replicates. To compare commonly identified sites we used the predicted peak summits ±50nt flanking sequences; as BayesPeak does not specify summits, the centre of the predicted peak base was used. This comparison revealed that MeDiChISeq identified the majority of sites predicted by the other methods (Figure  1B , Additional file 8). Notably, when comparing the fraction of peaks shared among peak callers MeDiChISeq performs best for both sharp and broad binding patterns (CTCF and H3K4me3), while most of the other peak callers present significantly lower fractions of shared peaks, as seen for H3K4me3 (Figure  1B ).
MeDiChISeq's sensitivity evaluated by their performance in reproducibility assays Figure  1  illustrates that a comparison of peak caller performances under default parameters is unsatisfactory. In fact, default confidence thresholds that are too relaxed will increase the amount of false positive calls, while too stringent conditions will produce false negatives. To circumvent this problem, peak caller performance can be evaluated in the context of reproducibility assays by comparing binding site predictions for biological replicates and ranking them according to confidence descriptors. The underlying assumption is that true binding sites will be retrieved in both replicate datasets within a similar confidence ranking, while low confidence peaks, which are expected to contain also false positives, will show lower consistency in the reproducibility assay.
The consistency between ranked peak confidence descriptors was previously formalized based on a copula mixture model, which estimates the probability that each pair of peaks is reproducible. This probability was described as "Irreproducibility Discovery Rate (IDR)  [20]  and has been used by the ENCODE consortium to identify a transition from signal to noise when peak caller binding site predictions were evaluated  [21]. Here we have compared peak caller performance in the context of reproducibility across replicate ChIP-seq datasets. Importantly, MeDiChISeq showed the highest number of reproducible peaks in CTCF and NRSF ChIPseq datasets (Figure  2A ). Also for broader patterns like H3K27ac and H3K4me3 MeDiChISeq identified the highest number of reproducible peaks at acceptable IDR thresholds (e.
g. , 0. 1 or 90% reproducible discovery). Note that the IDR progression curve for the histone modification mark H3K4me3 continues to increase rather slowly above this threshold, suggesting retrieval of an important number of irreproducible events among the significant top-ranked peaks in the replicate dataset. That the other peak callers identify less than 5,000 H3K4me3 peaks with IDR levels below 10% supports the view that for broader binding patterns the assessment of IDRs by applying standard approaches becomes suboptimal  [20]. An important limitation of the above analysis is the potential variability between compared biological replicate datasets, as technical differences between the compared profiles may exist (e. g. , sequencing depth differences; Peak caller deconvolution performance for broad patterns).
To circumvent this limitation, we treated the predictions of two peak callers as "virtual replicates" for IDR analyses for a number of individual ChIP-seq datasets (Figure  2B ). We thus ask if two peak callers identify binding events/marks in the same ChIPseq dataset with similar confidence (i. e. , if a top ranked peak of peak caller A is also top ranked by peak caller B). This novel type of comparison demonstrated that MeDiChISeq identifies higher numbers of reproducible peaks when compared with other methods. In fact, in the case of CTCF datasets, MeDiChISeq-MACS (A) MeDiChISeq and three other peak callers (MACS, BayesPeak and PeakRanger) were used to identify binding events in ChIP-seq datasets for three TFs (SRF, MAX, NRSF), the sequence-specific insulator protein CTCF and two histone modification marks (H3K9Ac, H3K27Ac, H3K4me3).
The default confidence threshold parameters described for each peak caller were applied to assess the number of peaks per dataset. Note that for each ChIP-seq two biological replicates were processed. (B) Peaks commonly identified by two of the indicated peak callers for two replicates of CTCF (top panel) and H3K4me3 (bottom panel) are displayed as percentages of the total sites found by a given method (indicated at the right). (C) Representative genome browser screenshots illustrating the ability to deconvolve binding/modification patterns of peak callers. Note that most of the peak callers identify a similar number of "sharp" binding events for CTCF, while MeDiChISeq has the highest potential of deconvolution for the H3K4me3 pattern. performed best for the first replicate, while MeDiChI-Seq-PeakRanger won in the case of the second replicate (Figure  2B ).