dataesr/scientific-paragraphs-categorization huggingface dataset:
The BioMart Community Portal (www. biomart. org) is a community-driven effort to provide a unified interface to biomedical databases that are distributed worldwide. The portal provides access to numerous database projects supported by 30 scientific organizations. It includes over 800 different biological datasets spanning genomics, proteomics, model organisms, cancer data, ontology information and more. All resources available through the portal are independently administered and funded by their host organizations. The BioMart data federation technology provides a unified interface to all the available data. The latest version of the portal comes with many new databases that have been created by our ever-growing community. It also comes with better support and extensibility for data analysis and visualization tools.
SOFTWARE BioMart is an open source data management system, which is based on a data federation model  (1). Under this model, each data source is managed, updated and released independently by their host organization while the BioMart software provides a unified view of these sources that are distributed worldwide. The data sources are presented to the user through a unified set of graphical and programmatic interfaces so that they appear to be a single integrated database. To navigate this database and compile a query the user does not have to learn the underlying structure of each data source but instead use a set of simple abstractions: datasets, filters and attributes. Once a user's input is provided, the software distributes parts of the query to individual data sources, collects the data and presents the user with the unified result set.
The BioMart software is data agnostic and its applications are not limited to biological data. It is cross-platform and supports many popular relational database managements systems, including MySQL, Oracle, PostgreSQL. It also supports many third party packages such as Taverna (2), Galaxy (3), Cytoscape (4) and biomaRt  (5) , which part of the Bioconductor (6) library. The BioMart project currently maintains two independent code bases: one written in Java and one written in Perl. For more information about the architecture and capabilities of each of the packages please refer to previous publications  (1, 7). The latest version of the Java based BioMart software has been significantly enhanced with new additions to the existing collection of graphical user interfaces (GUIs). It has also been re-engineered to provide better support and extensibility for data analysis and visualization tools.
DATA The BioMart community consists of a wide spectrum of different research groups that use the BioMart technology to provide access to their databases. It currently comprises 30 scientific organizations supporting 38 database projects that contain over 800 different biological datasets spanning ge-W592 Nucleic Acids Research, 2015, Vol. 43, Web Server issue nomics, proteomics, model organisms, cancer data, ontology information and more. The BioMart community is constantly growing and since the last publication (9), 11 new database projects have become available. As new BioMart databases become available locally they also become gradually integrated into the BioMart Community Portal. The main function of the portal is to provide a convenient single point of access to all available data that is distributed worldwide (Figure  1 ).
PORTAL The current version of the BioMart Community Portal operates two different instances of the web server: one implemented in Perl and the other in Java. Both servers support complex database searches and although they use different types of GUIs, they share the same navigation and query compilation logic based on selection of datasets, filters and attributes  (9, 10). The Java version of the portal also includes a section for specialized tools, which consists of the following: Sequence retrieval, ID Converter and Enrichment Analysis. Sequence retrieval allows easy querying of sequences while the ID Converter tool allows users to enter or upload a list of identifiers in any format (currently supported by Ensembl), and retrieve the same list converted to any other supported format. The enrichment tool supports enrichment analysis of genes in all species included in the current Ensembl release.
For each of those species a broad range of gene identifiers is available. Furthermore, the tool supports cross species analysis using Ensembl homology data. For instance, it is possible to perform a one step enrichment analysis against a human disease dataset using experimental data from any of the species for which human homology data is available. Finally, the enrichment tool facilitates analysis of BED files containing genomic features such as Copy Number Variations or Differentially Methylated Regions. The output is provided in tabular and network graphic format (Figure  2 ).
The programmatic access complies with a standard BioMart interface: dataset, filter and attribute. curl -data-urlencode query@query. xml http://central. biomart. org/martservice/results or its Perl-based counter-part as below curl -data-urlencode query@query. xml http: //www. biomart. org/biomart/martservice By default, query sets the attribute processor to 'TSV' requesting tab-delimited results (Figure  3B ). Alternatively, by setting processor to 'JSON', would return JSON formatted results (Figure  3C ), which are readily consumable by thirdparty web-based clients saving overhead of parsing and format translations. Please note that JSON format is only available in the java version. A simple way to compile a web service query for later programmatic use is to use one of the web GUIs and generate the query XML using REST/SOAP button.
After following the steps outlined by the GUI and clicking the 'results' button, the user needs to click the REST/SOAP button, save the query and run it as described above. Alternatively a user can take advantage of the programmatic access to all the metadata defining marts, datasets, filters and attributes. The access to the metadata served by the Java and Perl BioMart servers is provided using the following webservice requests: Java (central. biomart. org) r.
VEGA Manual annotation of vertebrate genome sequences Wellcome Trust Sanger Institute (WTSI), UK WormBase C. elegans and related nematode genomic information Cold Spring Harbor Laboratory (CSHL), US a Denotes new databases that have become available since last publication (9 Please note that the granularity between mart and dataset has been improved in the Java version through the introduction of multiple dataset configs. This facilitates the endusers to browse various views of the same dataset, which are presented through the portal either using a different GUI or subsets of data.
QUERY EXAMPLES Given the coverage of the current BioMart datatsets, many relevant biological questions can be answered. For example, a researcher who has detected potentially pathogenic variants in FGFR2 (ENSG00000066468) from exome sequencing patients may be interested if the same variants have been previously described and if they were associated with the same or similar diseases. To answer this, integrated data from Ensembl can be queried as shown in Table  2  to display all known variants annotated within FGFR2 that are predicted as pathogenic by SIFT  (11)  and Polyphen  (12). The genomic position outputs can be compared to the researcher's variants and the phenotype data used to assess candidacy for their cases. For example, the first batch of results shows a C->G variant at position 121520160 on chromosome 10 that is associated with Apert syndrome (OMIM:176943).
CONCLUSIONS Since its conception as a data-mining interface for the Human Genome Project (13) BioMart has rapidly grown to become an international collaboration involving a large number of different groups and organizations both in academia and in industry  (14). It has been successfully applied to many different types of data including genomics, proteomics, model organisms, cancer data, etc. , proving that its generic data model is widely applicable. BioMart has also provided a first successful solution for the unprecedented data management needs of the International Cancer Genome Consortium proving that the federated model scales well with the amounts of data generated by Next Generation Sequencing  (48). There are a number of important factors that contributed to the BioMart's success and its adoption by many different types of projects around the world as their data management platform.
BioMart's ability to quickly deploy a website hosting any type of data, user-friendly GUI, several programmatic interfaces and support for third party tools has proved to be an attractive solution for data managers who were in need of a rapid and reliable solution for their user community. BioMart has also proven to be a platform of choice for many smaller organizations that lack the necessary resources to embark on the development of their own data management solution. As a result, more and more database projects have become accessible through the BioMart interface. The arrival of these new resources coupled with the data federation technology provided by the BioMart software has galvanized the creation of the BioMart Community Portal. The federated model has proven to be very cost-effective since all development and maintenance of individual databases is left to the individual data providers.
It also has proven to be very scalable as the internet and database traffic is handled by the local BioMart servers. As a result the BioMart Community Portal service has grown impressively not only in terms of available data but also the level of service. The BioMart community portal now averages over million requests per our services per day. Building on this level of service and the wealth of information that has become accessible through the BioMart interface, the BioMart Community Portal has effectively introduced a new, more scalable and much more cost-effective alternative to the large data stores maintained by specialized organizations.
ACKNOWLEDGEMENT We are grateful to the following organizations for providing support for the  BioMart  project:  European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, UK ;  Ontario Institute for Cancer Research, Toronto ,  Canada ;  San Raffaele Scientific Institute, Milan, Italy and King Abdulaziz University, Jeddah ,  Saudi Arabia. FUNDING The BioMart Community Portal is a collaborative, community effort and as such it is the product of the efforts of dozens of different groups and organizations. The individual data sources that the portal comprises are funded separately and independently. In particular:  Wellcome Trust  [ 077012/Z/05/Z  to COSMIC mart];  Spanish Government  [ BIO2011-23920  and  CSD2009-00080  to BioMart database of the  Regulatory Genomics group at Pompeu Fabra University ];  Sandra Ibarra Foundation for Cancer  [FSI2013];  Breast Cancer Campaign Tissue Bank  [ 09TB-BAR  to BCCTB bioinformatics portal];  Office of Science of the U.
S. Department of Energy  [ DE-AC02-05CH11231  to Phytozome]; Global Frontier Project (to i-Pharm research) funded by the  Ministry of Science, ICT and Future Planning  through the  National Research Foundation of Korea  ( NRF-2013M3A6A4043695 );  Agence National de la Recherche  [ ANR-10-BLAN-1122 ,  ANR-12-BSV6-0017-03 ,  ANR-14-CE10-0005-03  to ParameciumDB and cilDB];  Centre National de la Recherche Scientifique; Center for Genome Regulation  [SalmonDB; Fondap-1509007 to A. M. and A. D. G. ];  Center for Mathematical Modelling [Basal-PFB 03  to A. M. and A. D. G. ];  Wellcome Trust  ( WT095908  and  WT098051  to R. K. , T. M. and A. Z. );  European Molecular Biology Laboratory ;  Japanese Ministry of Education, Culture, Sports, Science and Technology [FAN-TOM5 BioMart ; for  RIKEN OSC  and  RIKEN PMI  to  Yoshihide Hayashizaki , and for  RIKEN CLST].
Acknowledgments We thank  J. -C. Manuguerra  for sharing preliminary results on the effectiveness of the REVAMP RDT and  L. Simonsen  for early discussions. We furthermore acknowledge research funding from the  Medical Research Council , the  Bill and Melinda Gates Foundation , the  MIDAS network of the National Institute of General Medical Sciences (National Institutes of Health) , the  Health Protection Research Units of the National Institute for Health Research , the  European Union Seventh Framework Programme  [FP7/2007-2013] under Grant Agreement no  278433-PREDEMICS , and the  Wellcome Trust.
Such a strategy would obviate the need to incorporate fluorophores directly into the Oligopaint probes and thereby reduce the number and, hence, cost of fluorophore-labelled oligos needed to utilize heavily multiplexed libraries. Figure  1b -d illustrates our strategy for testing the potential of secondary oligos. We first used a database of orthogonal sequences  8  to design six 32-base DNA oligos with thermodynamic properties predicted to be optimal for hybridization in the conditions of our FISH protocols (Supplementary Table  1 ). Then, using touch-up PCR  9  , we placed a binding site for one or more of the secondary oligos 5 0 of the primer sequences in MainStreet (Fig. 1b ); this strategy allows binding sites to be added to any existing Oligopaint library and is compatible with both our published probe synthesis protocols  7, 10  (Supplementary Figs  1  and 2 , Methods) as well as alternative methods for generating Oligopaints, such as our 1-day method using lambda exonuclease  11, 12  (Supplementary Fig.
We have addressed this challenge by developing homologue-specific OligoPaints, or 'HOPs. ' HOPs take advantage of the abundant and well-characterized single-nucleotide polymorphism (SNP) data, such as those provided by the Wellcome Trust Sanger Mouse Genomes Project  50  and the Drosophila Genetic Reference Panel (DGRP)  51. In our approach, we first generate short blocks of reference genomic sequence centred on each SNP in the region we wish the HOPs to target (Supplementary Fig. 14 ). We then input these blocks into our Oligopaint probe discovery pipeline  7  to identify probe sequences that overlap the location of at least one SNP, are genomically unique, and have suitable thermodynamic properties. Finally, we run a custom Python script to insert the SNP variant(s) into the probe sequences.
Again, we observed co-localization of 100% of Xist signals with those of the 129-specific HOPs (n ¼ 37 nuclei, 52 signals and n ¼ 38 nuclei, 50 signals, respectively; Supplementary Fig. 18 ). In addition, quantification of the frequency of 'crosstalk' between the partner HOPs, wherein weak staining in the channel for a particular HOP occasionally accompanies a much stronger signal in the channel of its partner HOP, revealed that the smaller sets of HOPs displayed less crosstalk (18. 1% for 2. 6 Mb, n ¼ 138 signals; 1. 4% for 998 kb, n ¼ 144 signals; 0% for 490 kb, n ¼ 132 signals; Supplementary Fig. 18 ). In sum, our data provide strong evidence that the HOP system can efficiently and reliably distinguish the maternal and paternal homologous chromosomes in the MEF cell culture. We have also had success with HOPs in Drosophila.
The PCR reaction was then collected, concentrated using spin columns (Zymo D4031) and digested with lambda exonulcease (New England Biolabs M0262). Five units of lambda exonulcease were added per every 100 ml of unconcentrated PCR reaction (for example, use 50 units if the labelling PCR had a volume of 1 ml before concentration by the spin column) and the reaction was incubated at 37 °C for 30 min in a programmable thermocycler and then stopped by incubation at 75 °C for 10 min. Finally, the digestion products were concentrated using ethanol precipitation and quantified using spectrophotometry. For a detailed protocol, please see the Oligopaints website (http://genetics. med. havard. edu/oligopaints). Probe design. The 19q13. 11-q13. 12, 27E7-28D3, 19q13. 2-q13. 31 and 19q13. 32-q13. 33 libraries were constructed from our public database of 32mer probe sequences  7  (also see http://genetics.
42 lens and an Olympus XM-10 camera or a Zeiss LSM-780 laser scanning confocal microscope using a 63x oil NA 1. 40 lens. Olympus images were captured and analysed using Olympus CellSens software, and Zeiss images were captured and analysed using Zeiss Zen software. Images were processed using the respective microscope-specific software and Adobe Photoshop. Quantification of FISH signals. FISH signals were counted manually using Z-stacks (that is, not using maximum Z projections). Two signals separated by an edge-to-edge distance of o1 mm were considered a single focus. The staining efficiency for a given channel (% labelling) indicates the number of nuclei with at least one focus in a given experiment. In two-colour experiments, % Co-localization indicates the percentage of signals produced by the secondary oligo that also had a co-localizing signal from the primary probe.
Also see ref. 66. Samples were selected in an experimenter-blind manner and imaged at 60 Hz for 32,000-65,000 frames (based on molecule localization rate). Photoactivation of dyes was tuned with a 405 laser for which the intensity was increased slowly throughout the image acquisition from 0 mW towards a maximum intensity of 1 mW to maintain an approximately uniform molecule localization rate for the first half of the acquisition. The same rate of 405 amplification was used for all cells imaged within a sample. STORM image construction. Molecule localization movies were fit using the 3D-DAOSTORM algorithm  67. Localizations were plotted as single points or as Gaussian spots with widths normalized to the number of photons measured per localization using custom software written in MATLAB (see https://github.
com/ZhuangLab/ matlab-storm). The average photons per localization was 44,000. STORM images were constructed from the registration of Cy5 single-molecule fluorescence events, and no appreciable foci were detected in the absence of primary Oligopaint probe (data not shown). Single-molecule fluorescence events were localized with an average precision of B9 nm (s. d. ) and a resolution (FWHM) of B20 nm. DNA-PAINT microscopy. Fluorescence imaging was carried out on an inverted Nikon Eclipse Ti microscope (Nikon Instruments) with the Perfect Focus System, applying an objective-type TIRF configuration using a Nikon TIRF illuminator with an oil-immersion objective (CFI Apo TIRF Â 100, NA 1. 49, Oil) yielding a pixel size of 160 nm. Two lasers were used for excitation-488 nm (200 mW nominal, Coherent Sapphire) and 647 nm (300 mW nominal, MBP Communications).
DNA-PAINT image construction. Super-resolution DNA-PAINT images were reconstructed using spot-finding and two-dimensional Gaussian fitting algorithms implemented in LabVIEW  37, 38. Localizations are represented Gaussian spots with widths normalized to the localization accuracy. All DNA-PAINT images were constructed from ATTO 655 localizations and co-localized with a diffraction-limited ATTO 488 focus. A simplified version of the DNA-PAINT software is available for download at http://www. dna-paint. net/ or http://molecularsystems. net/software/. Single-molecule fluorescence events were localized with an average precision of 6. 5 nm (s. d. ) and a resolution (FWHM) of B15. 3 nm.
Acknowledgements We thank members of the  Wu lab ,  K. Ahmad ,  N. Apostolopoulos ,  L. Cai ,  M. Cowley ,  S. Elledge ,  R. Kingston ,  D. Moazed ,  R. Oakey ,  G. Orsi ,  T. Schmidt  and  D. Zhang  for helpful discussions;  G. Church ,  B. Harada ,  P. Huang ,  S. Kennedy ,  S. McCarroll ,  D. Reich ,  C. Seidman ,  J. Seidman  and  F. Winston  for discussion, equipment and technical assistance;  S. Clewley  and  J. H. McDonald  for computational assistance. Finally, we express belated appreciation to  Rachel O'Neill  and  Judy Brown , who tested our original Oligopaints technology when it was first developed. B. J. B. , R. B. M. , E. F. J. , C. K. -K. , F. B. , C. Y. F. , J. E. , M. A. H. , H. G. H. and C. -t. W. were supported by awards from  NIH/NIGMS  ( RO1GM61936 ,  5DP1GM106412 ) and  Harvard Medical School (HMS)  to C.
The X-ray properties of the central part of Ophiuchus were also analysed in detail by  Million et al. (2010) , based on Chandra data. These authors showed the existence of a small displacement between the X-ray peak and the cD galaxy (∼ 2 kpc), and of several strong features, such as sharp fronts and a very steep temperature gradient between the cool core (kT=0. 7 keV) and a region located only 30 kpc away (kT=10 keV). All these properties suggest that the central regions of Ophiuchus (within 150 kpc of the cluster centre) show evidence for merging, whereas at radii larger than 150 kpc the cluster appears relatively isothermal with a constant metallicity. At even higher energies, Ophiuchus is also part of the sample of nearby clusters where evidence for indirect detection of dark matter has been searched for in Fermi gamma-ray data  (Hektor et al.
The spectroscopic catalogue Redshifts of bright galaxies were obtained covering a very large area on the sky with the AAO 6dF, CTIO 1. 5m and Lick 3m telescopes  (Wakamatsu et al. 2005) , and are listed in Table  A. 3. We also obtained spectroscopic data with the ESO VLT/UT1 telescope using FORS2 in two adjacent fields covering the very central region of the cluster, for a total exposure time of 1500s. Spectra were obtained for 55 objects, out of which 54 gave reliable redshifts. Out of these, 17 proved to be galaxy spectra (about 14 belonging to the Ophiuchus cluster) and 37 were stars. We retrieved redshifts in the NED data base in a region shown in Fig. 3. We also retrieved a spectrum of the cD galaxy taken by John Huchra at CTIO and measured its velocity to be cz=8844 ± 60 km/s. This is within the error of the systemic velocity of the cluster 8878 ± 76 km/s (as inferred from the dynamical analysis, see Sect.
In reality, this is an underestimate, because several systematic uncertainties are not taken into account (see  Munari et al. 2013  for more details). Considering that the formal uncertainties on the σ v -determined r 200 values are smaller than the real ones, these values can be considered to be in fair agreement with the MAMPOSSt results. The MAMPOSSt results are also in agreement with the r 200 values obtained by applying the Caustic technique of  Diaferio & Geller (1997;  see also  Diaferio 1999)  to our spectroscopic dataset (red line and orange shaded region in Fig. 9 ). This technique does not require the assumption of dynamical equilibrium, but suffers from an uncertain calibration which is generally determined using numerical simulations. We adopt here the caustic mass calibration factor of  Gifford et al.
The presence of substructures We made a dynamical analysis with the Serna-Gerbal technique (hereafter SG, 1996 release, Serna & Gerbal 1996), a hierarchical code (based on spectroscopic redshifts and optical magnitudes) designed to detect substructures in the optical. The SG method has proved to be quite powerful to show evidence for substructures in nearby clusters  (   (Guennou et al. 2014 ). We applied it here to the catalogue of 89 galaxies with both redshifts and r ′ band magnitudes within the 1 × 1 deg 2 Megacam area. Assuming a value of the mass to luminosity ratio (here taken to be 100), the SG method allows to estimate the masses of the structures that it detects. Although the absolute masses are not accurate (the typical uncertainty is clearly larger than 10 14 M ⊙ ), the mass ratios of the various structures are well determined  (Guennou et al.
, 2010) , was estimated to be 3. 9 ± 0. 7 (mean ± standard deviation). This marginal area for rubber tree cultivation is subjected to the Southeast Asian monsoon, with heavy rainfall between April and October. Local microclimate was monitored automatically with a Minimet weather station (Skye Instruments Ltd, UK) attached to a data logger recording air temperature, relative humidity, incident short wave radiation and rainfall at 30-min intervals. Reference evapotranspiration (ET0) was calculated according to  Allen et al. (1998)  using the data collected from the weather station.