dataesr/scientific-paragraphs-categorization huggingface dataset:
Except CORTEX, none of the other de novo tools could be run on more than two datasets. As shown in Figure  5b , DIS- COSNP runs faster than CORTEX, while needing much less memory: applied on 30 individuals, CORTEX requires 3 h 38 s and 9658MB of memory while DISCOSNP runs in 37 min and requires 9MB. For all methods, running time grows linearly with respect to the number of read sets, but with a smaller coefficient for DISCOSNP (see Supplementary Figure  S3  of Supplementary Additional File 1). Furthermore, in Figure  5a  and in b, we present the results obtained by the hybrid approach when assembling one of the 30 read sets for creating a reference sequence (with SOAPdenovo2 (  23 )) and then mapping the read sets to this  reference (with Bowtie 2 (  24 )). Finally, SNPs were called with GATK  (25).
Detection of isolated SNPs from diploids, simulated from human chromosome 1 In this section, we present various results obtained by DIS- COSNP on a more complex genome, namely diploid read datasets simulated from two human chromosome 1 individuals, to discover both heterozygous and homozygous SNPs. As expected, when the complexity and the repeat content of the input genome increase, DISCOSNP makes more erroneous calls and misses more real SNPs. Whereas precision remains reasonable, with <3% of false positive calls, recall is more impacted with 72% of the isolated SNPs that are recovered. Among the 72 518 SNPs that are missed, 86% fall in repeated regions of human chromosome 1 (as masked by RepeatMasker from UCSC Genome Browser). In order to increase the recall and detect some of the SNPs located in repeated regions, we can change the filtering parameter that controls the branching features of the detected bubbles.
e. for which at least one individual is homozygous, the recall stays high (above 77. 8 % over such SNPs). Comparative results of DISCOSNP, CORTEX, BUBBLEPARSE and the hybrid SOAPdenovo2 + Bowtie2 + GATK approaches on the two diploid human chromosome 1 dataset. Precision versus recall curves are obtained by ranking the predicted SNPs. Each data point is obtained at a given rank threshold, where precision and recall values are computed for all SNPs with better ranks than this threshold. In this framework COR- TEX does not rank the predicted SNPs, its results are thus represented by a single point. Plain lines for DISCOSNP and BUBBLEPARSE were obtained while discarding all branching bubbles (options -b 0 and depth = 0 respectively), whereas dotted lines were obtained when allowing for some branchings (options -b 1 and depth = 1 respectively).
Qualitatively, the precision and recall values remain comparable between all tested methods on this dataset. Table  2  shows that, with stringent parameters (d = 0 for BUBBLEPARSE and b = 0 for DIS-COSNP), all tools have a precision of 96. 50 ± 0. 72% and a recall of 71. 20 ± 1. 50%. With more sensitive parameters (d = 1 for BUBBLEPARSE and b = 1 for DISCOSNP), DISCOSNP achieves the best precision/recall compromise, notably with the highest recall value (79. 22%). Figure  7  shows a precision-recall curve for each software, according to its own ranking method. Unlike DISCOSNP and Bubbleparse, the hybrid approach gives high ranks to many false positive predictions. We therefore conclude that ranks are not a viable indicator to filter out the false positives for this method. The DISCOSNP ranking approach enables to reach a precision of ∼100% for a high (≈60%) recall range, slightly outperforming Bubbleparse.
Results on real data: detection of SNPs from two mouse strains To analyze the behavior of DISCOSNP on real data, we detected SNPs between two publicly available mouse datasets produced by an Illumina GA2 sequencer. Taken together, these datasets contain 2. 88 billion of 100-bp reads. The first one was generated from the FVB/NJ mouse inbred strain, while the second one was generated from the C57BL/6NJ reference line. A previous study by  Wong et al. (18)  mapped the reads from the FVB/NJ strain to the C57BL/6J reference sequence, and detected ≈5 million homozygous SNPs, among which 2 472 456 are isolated SNPs. In the following, we refer to their set of isolated SNPs as IS. DISCOSNP was run with k = 31 and c = 5 on the FVB/NJ and C57BL/6NJ read sets, and discovered 2 065 833 isolated SNPs. As presented in Figure  9  (left), 84.
filter DISCOSNP predictions. By removing SNPs for which the Phi coefficient is ≤0. 2, the number of predicted SNPs drops to 1 794 515. As depicted in Figure  9  (right), 96. 3% of the filtered SNPs are also in IS, while roughly the same proportion of IS SNPs are found, compared to the unfiltered results (70. 0%). Therefore, by keeping only SNPs with a Phi coefficient greater than 0. 2, we discard almost exclusively SNPs that are found only by DISCOSNP, and which might be false positives. Note that in this experiment, we can not use the terms 'precision' and 'recall' for describing results quality. Indeed, the IS dataset was obtained via a mapping approach, and thus it can neither be considered as an exhaustive, nor a perfect list of isolated SNPs. It is worth stressing that the results of Wong et al.
were obtained by running a complex pipeline, involving a highquality reference genome, 6 distinct tools, followed by a filtering step composed of 14 non-automated filters (coverage, quality, genotype, etc. ). On the other hand, DISCOSNP did not require any third-party tool, or any manual tuning. Finally, this experiment showed that DISCOSNP scales remarkably well to large amounts of data. The KISSNP2 module required 34 h and 4. 5GB of memory. The KISSREADS module, which assesses the average quality and coverage of the results, took 78. 5 h and 5. 7GB memory. In comparison, NIKS, KISSNP, CORTEX and BUBBLEPARSE exceeded the memory limit on a server with 512GB RAM.
Evaluating DISCOSNP recall on a validated Saccharomyces cerevisiae SNP set Using a set of biologically validated SNPs predicted from an artificial evolution study on S. cerevisiae  (19) , DISCOSNP recall could be evaluated on real read datasets. Among the 30 reference validated isolated SNPs, 28 were predicted by DISCOSNP, thus giving an estimated recall of 93. 3%. The two SNPs were correctly predicted by using the -b 2 DISCOSNP option that reports all bubbles including branching ones. This suggests that these SNPs may be located in complex regions of the yeast genome. Overall, this experiment demonstrates the good performances of DISCOSNP at discovering SNPs from pooled samples, even those with low allelic frequencies: most of the reference SNPs were reported in the initial study with a MAF <20%.
DISCUSSION DISCOSNP is robust with respect to input parameters, and easy to use. The input of the software is an ordered list of any number of raw read dataset(s) (fasta or fastq, gzip compressed or not), and two parameters (k-mer size and minimal coverage threshold). These two parameters have limited impact on results quality (see Supplementary Additional File 1, Figures  S1  and S2 ), as long as they are coherent with respect to the input data (read length and approximate coverage). The output is a fasta file composed of sequences containing ranked SNPs, together with their coverage and average PHRED quality. As there exists no exhaustive and perfect list of the isolated SNPs present in a real dataset, a large part of the results discussed in this paper were obtained on simulated data. However, we paid special attention to the simulations being realistic.
The use of simulated data allowed us to evaluate the correctness of our method in a controlled environment, and to analyze false positive and false negative calls. This highlighted that the main source of wrong calls is the presence of repeated sequences in the genomes. Two distinct strategies may be adopted for distinguishing SNPs from approximate repeats. On complex genomes, which are repeat-prone, the choice of the strategy influences the results. Removing all branching bubbles (-b 0 option) is a good way to obtain high confident coverage (precision of ≈97% on human datasets) at the expense of missing some SNPs located into repeats. On the other hand, accepting simply branching bubbles, i. e. non-symmetrical (-b 1 option), enables to detect more SNPs that are located in repeated regions, but leads to an increase of the number of false positives and to the detection of non-isolated SNPs.
CONCLUSION DISCOSNP is an integrated reference-free software designed to find SNPs that can be subsequently used as high-quality genetic markers. DISCOSNP combines several advantages: (i) robust detection and ranking of isolated SNPs, (ii) support for any number of read datasets, (iii) scaling to big data studies thanks to a highly memory efficient data structure, (iv) lower running times than other reference-free tools and (v) an easy to use software, capable of processing billions of reads from a mammalian genome with a single command. The experiments on a dataset composed of two simulated diploids show that DISCOSNP provides similar results, both in terms of precision and recall, to those of other stateof-the-art reference-free SNP detection methods, while being faster and needing at least two orders of magnitude less memory.
Experiments on simulated haploids show that, when analyzing together more than two individuals, DIS- COSNP outperforms the other tools, both in terms of computational resources and results quality. Applied on real datasets, results confirm the capacity of DISCOSNP to scaleup to large volumes of data (less than 6GB memory on 3 billion Illumina reads), as well as its high precision, i. e. an experimental validation conducted on an arthropod species (the tick I. ricinus) on which de novo sequencing was performed, confirmed 96% of the predicted SNPs that were tested. The DISCOSNP source code, available under CeCILL license, can be downloaded from http://colibread. inria. fr/ discoSnp/. Moreover, this web page shows how to integrate DISCOSNP in any Galaxy instance using the GenOuest Tool Shed.
ACKNOWLEDGEMENTS The authors warmly thank  Zamin Iqbal  and  Richard Leggett  for interesting discussions and valuable help on using CORTEX and BUBBLEPARSE tools. We also thank the GenOuest (genouest. org)cluster team, as well as the  Bordeaux Bioinformatics Center  (http://www. cbib. u-bordeaux2. fr/en)team, who allowed us to perform all the tests.
e11 2 PAGE 8 OF 11 Figure 7. 28117 Figure 7. Comparative results of DISCOSNP, CORTEX, BUBBLEPARSE and the hybrid SOAPdenovo2 + Bowtie2 + GATK approaches on the two diploid human chromosome 1 dataset. Precision versus recall curves are obtained by ranking the predicted SNPs. Each data point is obtained at a given rank threshold, where precision and recall values are computed for all SNPs with better ranks than this threshold. In this framework COR- TEX does not rank the predicted SNPs, its results are thus represented by a single point. Plain lines for DISCOSNP and BUBBLEPARSE were obtained while discarding all branching bubbles (options -b 0 and depth = 0 respectively), whereas dotted lines were obtained when allowing for some branchings (options -b 1 and depth = 1 respectively).
In the simulation, cell viability was evaluated 48 h after the start of exposure. The response of 500 cells was simulated for each Nutlin concentration. doi:10. 1371/journal. pcbi. 1003991. g002 differences in the p53 transcriptional activity or in the abundance of downstream molecules can contribute to set the threshold value, but for sake of simplicity and since there are no clear experimental evidences, they are not included in the present model. Note that the dose-response data reported in  [6]  are given as a function of the total Nutlin concentration in the medium. Some degree of Nutlin binding to the culture medium proteins, however, has been demonstrated in  [58] , and from such measurements we could estimate the equilibrium association constant and the concentration of medium binding sites (see Fig.
In the present study, we aimed to further explore this hypothesis, using prey distributions as proximal predictors of habitat quality. In the absence of synoptic datasets of prey, i. e. mainly micronekton, at such large spatial scales, we turned to numerical models. One original modelling approach has been developed  [13]  as a component of the Spatial Ecosystem And Population Dynamics Model (SEAPODYM). This model describes six functional groups of prey defined by their daily vertical migration patterns in three biological layers from c. 0 to. 1,000 m. It has been successfully used to analyse tuna catch data  [14] , simulate habitats and movements of turtles  [15]  and predict the large-scale population dynamics of several tuna species based on hindcast simulations from coupled physical-biogeochemical models  [13, [16] [17] [18].
Data were collected following the same standardised aerial protocol in both regions. This was based on a line-transect methodology  [26] , in which the angle to the track line was recorded for each observation. This angle, together with the flight altitude, informed about the perpendicular distance from the track line, required for the estimation of effort per grid cell (estimation of the effective strip width by distance sampling, ESW see  [11]  and  [12] ). Survey platforms were high-wing double engine aircrafts equipped with bubble windows, allowing a vertical observation of the sea surface. Sightings and group size were recorded at the lowest possible taxonomic level. Beaufort sea-state, turbidity, glare severity, cloud coverage and an index of subjective conditions were also collected on board as detection covariates.
The total survey effort was 83,726 km in the SWIO  [11]  and 98,476 km in FP  [12]. Observed encounter rates collected on effort amounted 0. 0282 Delphininae per km 2 in SWIO and 0. 0018 in FP, 0. 176 Globicephalinae per km 2 in SWIO and 0. 022 in FP and 0. 007 individuals per km 2 in SWIO against 0. 004 in FP for sperm and beaked whales (see Figure  S1  and Table  S1 , and Mannocci and colleagues  [11, 12]  for details on the sightings). Data can be found online on the PELAGIS Observatory website (http://www. observatoire-pelagis. cnrs. fr).
Data processing Effective strip widths (ESWs) were estimated for each cetacean guild by fitting detection functions to perpendicular distances using multiple covariate distance sampling  [44]  to model the effect of both distance and detection conditions on detection probability. Results are presented in  [11]  for the SWIO and  [12]  for FP. To model cetacean habitats, we used micronekton outputs from SEAPODYM as predictors. Euphotic depth (as used in SEAPO-DYM and computed from the VGPM model) was included, to consider variations in vertical accessibility of micronekton to cetaceans between the different geographic sectors. Since SEAPODYM outputs were provided at a 0. 25u60. 25u spatial resolution, cetacean sightings and sampled surface areas were aggregated at the same temporal resolution on the same horizontal grid using  ArcGIS 10 [45].
Micronekton biomass and production were log-transformed prior to model selection to limit outliers. To avoid over-fitting of the data while allowing the curve to be non-linear, we constrained the maximum number of degrees of freedom for each spline to three. A selection procedure was implemented based on models with between one and four covariates, excluding all combinations of covariates with a pair-wise correlation higher than 0. 7 in the SWIO, and 0. 8 in FP (Table  S2 ). These correlations were calculated with the Spearman correlation test implemented in R  [50] , using the Hmisc package  [51]. The selection criterion was the Generalised Cross-Validation score (GCV, the lower the better). The GCV score is a prediction error criterion: it estimates the mean error, by removing each datum in turn and re-predicting it from the model fitted to the remaining data  [52].
Even if the ocean reanalysis was produced with data assimilation techniques, the physical forcing (especially the currents) is not always fully consistent with in situ measurements, particularly in the deeper layer and the equatorial region (e. g. ,  [76] ). The VGPM model used to compute vertically integrated primary production provides an estimate inferred from empirical relationships based on surface chlorophyll content  [77]. The parameterisation of coefficients of energy transfer from primary production to functional groups is still preliminary and under revision using parameter optimisation approach with large datasets of acoustic data. Nevertheless, the similar predicted distributions obtained when using SEAPODYM outputs and static and oceanographic variables  [11, 12] , suggest that SEAPODYM micronekton outputs may be accurate enough to provide robust predictions at the regional scale.
Acknowledgments We are grateful to the  PELAGIS Observatory , especially to  Olivier Van Canneyt , who organised and supervised the aerial surveys. We thank all observers and aircraft crew members for their professionalism and friendship in the air and on land. We thank  Beatriz Calmettes  for her patience in preparing and transferring SEAPODYM outputs and  Inna Senina  for fruitful exchanges regarding the analyses. Lastly, we thank warmly reviewers for their wise advises.
Gram-negative carbapenem-resistant bacteria, in particular those producing New Delhi Metallo-betalactamase-1 (NDM-1), are a major global health problem. To inform the scientific and medical community in real time about worldwide dissemination of isolates of NDM-1-producing bacteria, we used the PubMed database to review all available publications from the first description in 2009 up to 31 December 2012, and created a regularly updated worldwide dissemination map using a web-based mapping application. We retrieved 33 reviews, and 136 case reports describing 950 isolates of NDM-1-producing bacteria. Klebsiella pneumoniae (n= 359) and Escherichia coli (n=268) were the most commonly reported bacteria producing NDM-1 enzyme. Several case reports of infections due to imported NDM-1 producing bacteria have been reported in a number of countries, including the United Kingdom, Italy, and Oman.
There has been an increase in the number of articles about the 'New Delhi Metallo-betalactamase-1' enzyme added to the PubMed database since 2010 (Figure  1 ), but the current spread of NDM-1-prodcucing bacteria is likely broader than the published reports suggest. To conduct an eReview of all published isolates worldwide as of end 2012, we used in this article the Google Maps application to simplify and accelerate access to documentation, organise information about published isolates of NDM-1-producing bacteria and provide realtime information to the scientific and medical community about published isolates of NDM-1-producing bacteria around the world. Few studies have used this type of automated system to investigate, in real time, web-based electronic reports for the purpose of monitoring the spread of infectious diseases caused by influenza A (H1N1) and Dengue viruses  [20, 21].
Google Maps is a widely available, free of charge, and extremely powerful tool for visualisation with a simple, intuitive interface that requires little training or experience to use it. It can be run on any conventional desktop computer or laptop, and there is also a Google Maps application available for mobile phones  [22]. Because a visual representation of scientific data is more informative than a written description, this article describes the development of an internet-based mapping and geo-referencing application for tracking the worldwide dissemination of NDM-1-producing bacteria as an example of this application. We analysed in this article the medical literature from the first case report in December 2009 until 31 December 2012.
Literature search in the PubMed database We started by retrieving all published articles from the PubMed database using 'NDM-1' and 'New Delhi Metallo-beta-lactamase-1' as keywords, from the first case report in 2009 until 31 December 2012. We included in our analysis only the first publications that reported on isolates of NDM-1-producing bacteria. We excluded all consecutive publications about the same isolates with descriptions of genomic or protein analysis or others types of analysis. After reading and analysing the full article, we specifically extracted the year of detection of the isolates, their geographic location (city and country), the NDM-1-producing bacterial species, the number of published isolates, the type of case reports, the title and the full reference for the published article, the link to the isolates description in PubMed database.
Construction of Google Maps We used Google Maps to create an electronic map depicting the geographic locations of case reports of NDM-1-producing bacteria listed in our database. Google provides full documentation for Google Maps, tutorials and other materials to help users take full advantage of the application (https://maps. google. com). The locations on the map were tagged using different symbols for each type of report of NDM-1producing bacteria. Clicking the tags will provide a display of the important information about the selected article (the same information stored in the columns in the database). If there are several tags within close proximity to one another, the tags expand outward to facilitate selection of a single tag. Google Maps navigation tabs in the upper left of the screen can be used to zoom in on an area of interest.
The eReview display To visualise the case reports of NDM-1-producing bacteria detected that have appeared around the world since the first description, we developed a Google Maps application as described in the methods section that is regularly updated and freely available on line at the following website: http://www. mediterraneeinfection. com/article. php?laref=318&titre=new-delhimetallo-lactamase-around-the-world. As soon as an article with the keyword 'NDM-1' or 'New Delhi Metallobeta-lactamase-1' is added to the PubMed database, we automatically receive an alert by email. In less than 10 minutes, we are able to analyse the article, extract the relevant information about the published isolates, add it to our own database and update the map so that the information is freely accessible. Other NDM enzymes are not included in the manuscript but have been added in the Google map website.
The advantages of Google Maps also include the immediate access to the PubMed publications from the link in the case report description and the real-time update of the map as soon as an article is added on the PubMed database. Google Maps represents a new generation of interactive review capability; it is easy to use, and it is accessible everywhere by everyone, facilitating the diffusion and the circulation of knowledge. Simple mapping in public health is not new. The cholera map by John Snow marked a critical turn in the use of maps to understand geographic patterns of disease  [185]. Moreover, the geographic distribution of scientific data is a growing area of interest in many fields, including infectious diseases  [20, 186] , paleontology  [187] , natural products research  [22] , microbial marine biology  [188] , ecology  [189] , and archaeology  [190].
NDM-1: New Delhi Metallo-beta-lactamase-1  [191]. Another online, real-time disease outbreak monitoring system, 'HealthMap', developed by the John Brownstein and his team in 2008, has demonstrated the effectiveness of collecting new media sources for improved situational awareness of infectious worldwide  [192]. Given the popularity of Google Maps, it can be expected that Google will continue to add new features, such as higher resolution, more options for the maps, threedimensional views, and a Smartphone application. Smartphone applications are a growing field that offers novel approaches, with software that allows data entry and retrieval of data from the maps using a mobile phone  [193, 194]. The possibilities are vast and for all those interested to better convey information we propose to keep an open mind and test different visual representations.