dataesr/scientific-paragraphs-categorization huggingface dataset:
VELs Two sets of VELs were analysed in this study, from CRC (colorectal cancer) and MCF-7 (breast cancer) cell lines. First, the list of CRC gained and lost VELs described in Akhtar-Zaidi et al. (26)  were downloaded from GEO (GSE36204). For downstream analyses hg18 VELs were converted into hg19 coordinates using the liftover tool of UCSC (http://genome. ucsc. edu/cgi-bin/hgLiftOver) with default parameters returning 2604 gained and 3448 lost CRC VELs. On the other hand, to create the MCF-7 VELs we applied the same procedures as described in the materials and methods of Akhtar-Zaidi et al. on H3K4me1 of MCF-7/MCF-10A data sets produced by Choe et al. (27). Sequencing data were downloaded from the Sequence Read Archive (SRA045635) of the National Center for Biotechnology Information. Read alignments, peaks calls and VEL detection were all performed using the exact same procedure as described in Akhtar-Zaidi et al.
We obtained a set of 3163 gained and 2791 lost MCF-7 VELs using a normal distribution with a standard deviation of 1 to model the uniformly distribution of reads. To assess the MCF-7 VELs detection procedure we also generated VELs with relaxed thresholds (SD = 0. 75, 5295 for Gained, 5087 for Lost) and report those result in Supplementary Figure  S9. IntervalStats tool was used to compute and identify significant overlapping TFBS with VELs. TF enrichments in VELs were calculated via a hypergeometric test. We have extracted RNA-seq data for MCF-7 and MCF-10A cell lines, respectively, from GEO (GSE48213). We compared gene expression (FPKM, fragments per kilobase per million mapped reads) of genes associated with gained, lost and control VEL. Genes were associated with VEL regions using the GREAT tool (great.
RESULTS Our overall approach is to analyse ChIP-seq data sets for TFs from public repositories in order to provide a comprehensive map of regulatory elements. To do this we first extracted and processed all available public data sets, which were merged with the ENCODE TF catalogue to produce an extensive repertoire of regulatory regions in the human genome (Flowchart Supplementary Figure  S1 ). We then used this regulatory catalogue to identify genome-wide colocalized TFs and TFs enriched in VEL present in colon cancer and breast cancer cell lines. We further present an online tool providing a full web access to this catalogue and allow users to annotate their genomic regions with regulatory elements.
Integration of various ChIP-seq data sources We initially selected 696 data sets from public repositories, such as the GEO  (28)  and AE  (29). To ensure consistency and comparability, each of these ChIP-seq data sets was reanalysed from the raw reads, through our ChIP-seq analysis pipeline which included read mapping, peak calling and quality assessment based on ENCODE quality criterions. Those quality criterions determined whether a data set would be included or not for further downstream analyses or excluded. Indeed, ChIP-seq data sets are not all equal in terms of quality  (10) , these differences come from the rapid development of next-generation sequencers and sequencing techniques, but also from experimental improvements in library preparations and quality. To address this variability of ChIP-seq quality we used two metrics independent of peak calling based on EN-CODE ChIP-seq guidelines and practices.
By using these four scores we were able to evaluate ChIP-seq data and filter out low quality data sets (Supplementary Figure  S2 ). We initially collected, analysed and assessed 696 raw TF ChIP-seq data files in various conditions from Illumina sequencers and available in public data warehouses (GEO, AE). After applying our quality filters based on these four ChIP-seq metrics we retained 395 data sets (54. 3%) from 135 different GEO series (GSEs) involving 132 TFs (Supplementary Table  S1 ). More precisely, we define here a 'data set' as a ChIP-seq experiment in a given GEO series (e. g. GSE41561), for a given TF (e. g. ESR1), in a particular biological condition (i. e. cell line, tissue type, disease state or experimental conditions; e. g. MCF-7). Data sets were labelled with the concatenation of these three pieces of information (e.
g. GSE41561. ESR1. MCF-7). A data set may correspond to several replicates when multiple samples were done in the same GEO series, for the same TF under identical biological conditions. TFBS were computed following our peak-calling pipeline using MACS tool as described in detail in the method section. We identified 8. 9 million ChIP-seq peaks bound by TFs in the human genome across all data sets. The complete data sets (mapped reads and called peaks) are available from our online companion (http://tagc. univ-mrs. fr/remap/). Binding sites for similar TFs but produced from different data sets mapped onto the genome revealing tight and dense co-localization of sites in many regions of the genome (Figure  1A ). This clustering of binding sites from different data sets is illustrated as an example within the first intron of SMAD4 and ELAC1 genes by a FOXA1 site recapitulated by 14 data sets.
Those data sets correspond to 7 different GEO series (GSE), 4 different cell lines (MCF-7, ZR-75-1, LNCaP, C4-2B), 4 different institutes (the Cancer Research UK Cambridge Institute, the Genomic Institute of Singapore, the University of Helsinki, the Washington University School of Medicine) and finally 3 different antibodies (Abcam ab5089, ab23738, ab4124). Interestingly, it can be noted that the summits (vertical bars) of the peaks aggregate closely from each other. Those aggregations of FOXA1 summits are an illustration of what is globally observed across millions of peaks on the genome. This FOXA1 example is a simple demonstration that global integrative analyses of ChIP-seq data sets can improve the detection of TFBS. Indeed, the presence of a binding site in different data sets at this position for FOXA1 reinforces the possibility that this specific TF is binding the DNA in vivo at this genomic location.
The ESR1 cluster regroups 8 out 10 GEO series and consists mainly of the MCF-7 cell type with different experimental conditions. It can be noted that the CTCF insulator forms a tight cluster containing all the CTCF data sets in this analysis (Figure  1B    CTCF bindings are shared across all different ChIP-seq experiments regardless of the cell types. This result complements current findings about CTCF  (30, 31)  where there is little difference of CTCF binding between cell types (3). Taken together these clusters indicate that a consistent analytic approach of multiple data sets can recapitulate binding sites and reveal that some TFs bind similar locations in the genome. Although the integration of independent ChIP-seq studies is challenging, the overlap observed between peaks for similar TF validate this approach in order to improve the annotation of regulatory regions in the human genome.
To test whether this catalogue of TFBS could consolidate DNA motifs found in current databases or provide alternative/variable motifs we performed de novo motif discovery (Figure  2F ). When available we examined Jaspar  (36)  and FactorBook/ENCODE (19) DNA motifs for a given TF and compared those with our top motifs discovered by RSAT peak-motifs tool  (18). An example for the CTCF DNA logo is shown in Figure  2F  where subtle bases variations can be observed as well as consolidation of unchanged bases in the motif. It is well established that ChIP-seq provide an excellent way to perform de novo motif analyses as DNA motifs are enriched around peaks summits helping the detection of the DNA motifs for those TFs  (14, 37). Our catalogue of TFBS can contribute to refine or consolidate TF DNA matrices currently present in motif databases.
This loss of DNA binding in MCF-7 enhancers could indeed deregulate normal cellular processes. Regarding colorectal VELs, we find TP63 to be highly enriched within lost VELs (Figure  5D ), it is a member of p53 family of TFs and has been shown to suppress tumorigenesis and metastasis  (45). Interestingly, SUZ12 and EZH2 are enriched in lost VELs. SUZ12 is part of the polycomb group (PcG) family of proteins forming a complex with EZH2. The PcG is thought to play a role in the epigenetic repression systems. Those proteins are involved in maintaining the transcriptional repressive state of genes, leading to transcriptional repression of the affected target gene. The loss of DNA bindings for TP63, SUZ12 and EZH2 in those enhancers could activate genes involved in tumorigenesis and metastasis. Interestingly, DCP1A also know as TF SMIF is the most enriched TF in this group, its protein is expressed the most in colon RKO cancer cells according to the MaxQuant database  (46).
(D) Distribution of CRMs at TSS (±2. 5 kb) for increasing levels of combinatorial binding complexity from 2 to 50+ TFs. (E) Proportion of our regulatory catalogue covering different types of genomic features. Percentages of elements recovered are shown for CRMs only (green) and both CRMs and singletons (blue). (F) The WebLogo position weight matrix diagrams for CTCF identified across the diverse databases, showing subtle position-specific differences. (G) DNA sequence constraint around the peak summits of FOXA1, CTCF, CEBPA, NFYB were plotted by observed-expected GERP scores (22).
, orange). This indicates that most 1 kb chr18: 48,494,000 48,494,500 48,495,000 48,495,500 48,496,000 48,496,500 48,497,000 UCSC Genes ELAC1 SMAD4 REMAP Public Txn Factor datasets GSE40129. FOXA1. mcf7_veh GSE28264. FOXA1. lncap_etoh GSE28264. FOXA1. lncap_etoh GSE23852. FOXA1. mcf7_e2 GSE28264. FOXA1. lncap_dht GSE25710. FOXA1. zr751 GSE26831. FOXA1. t47d_dmso GSE23852. FOXA1. mcf7_etoh GSE25710. FOXA1. t47d GSE40129. FOXA1. mcf7_gatakd_veh 7 GEO Series 4 Cell lines GSE26831. FOXA1. mcf7_e2 GSE30623. FOXA1. lncap1f GSE30623. FOXA1. lncap1f5_sifoxa1 4 Institutes 3 Antibodies GSE40050. FOXA1. c42b_dht GSE40050. FOXA1. c42b_etoh Summits 100 Vert. 4. 88 _ Conservat. 0 - Scores -4. 5 _ ENCODE Txn Factor B 395 datasets.
1  we illustrate a scenario for collaborative trip planning to attend a conference. For accomplishing this common goal, two users need to gather general information about a conference such as the conference dates and location, buy flights, book hotel, etc. Each user can perform the required tasks individually. However, if users want to travel together, some coordination and communication will be required for booking the same flights, hotels etc. Moreover, users might decide to share the work, for example one user can book for both participants. In the scenario presented by Fig. 1  it is possible to notice the many Web sites that will be visited by users. As we shall see, despite the fact that there is dependency between the information provided, Web sites are not integrated. While building service-based software such as mashups can be a solution for combining data and information from different providers, many times this approach might have limitations as they can hardly integrate all possible opportunistic tasks that users might have in mind; for example, checking the traffic situation on the way to the airport, booking the preference users' restaurant at the destination, etc.
Web augmentation and data integration tools After having found useful information over the Web, it is very likely that information will be used to accomplish other tasks. Indeed, quite often the contents users need to accomplish their goals are scattered around different Web sites. In more recent years, several tools have been developed to support the integration of Web contents into a seamlessly user interface. By doing so, the interface produced is "augmented" with respect to what users can do at the original Web sites. We can identify two coarsegrained approaches for developing such Web augmentation tools: (i) mashing up contents or services in a new application and (ii) augmenting the original application, generally by running adaptation scripts on the client side. Mashups  (Yu et al. , 2008; Wong and Hong, 2007)  are an interesting alternative for final users to combine existing resources and services in a new specialized application.
Most of the approaches aim at allowing users without programming skills to produce these new applications. Visual and intuitive tools such as  (Daniel et al. , 2009; Yahoo Pipes!, 2012)  simplify the development of these applications. Some of the approaches are focused on UI integration, but otherssuch as Yahoo Pipes! (Yahoo Pipes!, 2012) -are focused on data integration. One of the main problems about mashups is that they, usually, are based on Web services. The fact is that most Web applications do not provide Web services to access their functionality or information. In order to solve this problem,  Han and Tokuda (2008)  propose an approach to integrate contents of third party applications by describing and extracting these contents at the client-side and then, using these contents later by generating virtual Web services that allow accessing them.
As we said before, the goal of mashups tools is to integrate UI or data in order to generate a new application. Clearly, this means that Web applications are taken off their original versions, which could not be desirable for many users; moreover Web applications are really dynamic about functionalities or contents, for example, news, special offers, etc. In contraposition, several tools for Web augmentation exist which aim to modify the preferred Web sites of users, while maintaining intrinsic features of these. Usually, these tools are implemented as browser's plug-ins that allow users to either install or generate scripts for modifying the Web pages they visit; for example  GreaseMonkey (Diaz et al. , 2010; GreaseMonkey, 2012; Pilgrim, 2005)  and Scriptish  (Scriptish, 2012). Nowadays, the most popular tool is GreaseMonkey, a browser plug-in for client-side scripting.
Basically GreaseMonkey executes scripts (JavaScript files installed and created by users) when a target Web page is loaded. Then the Web page DOM is modified before it is shown to the user. Most of the GreaseMonkey scripts adapt Web pages in a static way, i. e. that the adaptation goals do not contemplate which is the user task or concern. Although passing information among Web applications with GreaseMonkey scripts is not so easy to achieve, this is not the common goal of GreaseMonkey scripts. However, there are other approaches for supporting users' task in several ways. For example, MozillaUbiquity (MozillaUbiquity, 2012) -another Firefox plug-intries to improve user experience by empowering his browser with commands for dispatching operations related with his task. With MozillaUbiquity users execute commands (developed by the community) for specific operations; while some of them are more task-related (for instance, to publish some text from the Web site he is navigating in a specific social network) others can be addressed for adapting the current Web page in some way (for example, highlighting some text).
While CoScripter is more of a tool for supporting repetitive business processes, we think that users' tasks cannot be only supported by filling forms but that there may exist several kinds of DOMs interventions for improving users' experience. CoScripter is not the only "high level API" for supporting processes on the Web. For example, ChickenFoot  (Bolin et al. , 2005)  is a tool for programming users scripts and it extends JavaScript by defining new commands like "click()", "enter()", "find()", etc. , which make easier the development of Web automation scripts. Although ChickenFoot has a powerful and expressive language, it hardly contemplates fine changes in the adaptation process and it prevents reuse of scripts as these are really DOM-dependent. The tool Koala  (Little et al. , 2007)  proposes a more natural scripting language; for example, the expression "click('search button')" in ChickenFoot would be equivalent to "Click 'search button'" in Koala.
In Fig. 3a , we show how the Highlight augmenter is triggered by right-click the target Pocket element, which opens the contextual menu with all the augmenters available. Fig. 3b  shows the Web page already augmented, after executing the augmenter. Note that the PointOfInterest was collected from Wikipedia and is available on other Web sites. Since the augmenter was executed with the concept PointOfInterest, all its instances were contemplated for being highlighted. The augmenter can be executed only with one instance, for example "La Rambla". Currently, a large set of augmenters are deployed with the CSN framework. These augmenters are grouped in Table  1  according to the following categories: data collectors (which at aimed to help users to collect information from Web pages), filling forms (which are focusing on users tasks involving Web forms), select data (in the Web page by editing CSS properties), navigation (which adds elements to Web pages for helping users to improve navigation) and accessibility (which concerns edits on the Web page that are aimed to improve the accessibility of the Web page).
Creating and describing a simple augmenter in the CSN framework This section shows how new augmenters can be created and instantiated into the CSN Framework. Overall, there is no need to create new augmenters to follow our approach for building distribute user interface. However, the example below illustrates the potential of the framework to create new behavior and user interfaces that can be useful for building DUIs for the Web. Generally speaking the CSN framework provides a template that can integrate a file containing a JavaScript functions in which a new object has to be defined inheriting from AbstractAugmenter, as illustrated by Fig. 6. In order to create an augmenter the following elements should be described: (i) Metadata information used to identify the augmenters; (ii) a method getAugmenterInstance which is used to get augmenter instances; (iii) a class definition; in the example the class HighlightingAugmenter is the new augmenter object.
It is also necessary to provide a value to the label instance variable; this value is used to create the menu showing to users that the augmenter is available for use; and (iv) an extension point of heritance: the last line from the example shows how the new augmenter is a subclass of AbstractAdapter (the extension point for augmenters). By following the simple template above, developers can create a huge set of augmenters to feature adaptations on the DOM of the Web pages where the augmenter is triggered. The use of the template ensures that the JavaScript functions are compatible with other augmenters in the framework. Thus, a community of developers can create augmenters and share them with all other developers and end-users of the CSN plugin.
3. Peter decided to create a procedure as it is shown at the leftside of the browser window in Fig. 18. The procedure is built incrementally while Peter is navigating the different Web sites required to accomplish the task. As he collects data during his navigation, it becomes recorded as part of the procedure itself. For example, the first tasks in the list is "Provide a URL" where we can read the parameter "URL ¼http://www. interact2013. org" indicating the Web site visited by the user. We can also read that "Provide a URL" is a primitive task, which means that users should inform themselves which Web site they want to make part of the procedure. Because users might have many browser windows opened at the same time to perform different tasks, it is important to let users inform whether or not a specific Web site been visualized should be part of the procedure he has in mind.
A chronometer was used to record user performance but sessions were not video-taped. At first participants were asked to fill in a pre-questionnaire in order to collect demographic data and their experience with Web applications. Then, we have explained to the participants the goals of the study and they were asked to think aloud during the testing session. Participants were also introduced to our tools and get a short explanation about how it works. In order to make sure that all participants would receive the same information the instructions were provided by a 6 min video demonstrating the use of the procedure player. Then we have asked participants to envisage a professional travel to the conference INTERACT with a colleague, named John, who is keen to share his trip plan with him. The travelers had to book a hotel in the city where the conference takes place.
, 1999; Heath, 2010). Web augmenters can automate some of these tasks and perform adaptations on Web pages to be visualized in the Web browser. These new components let users perform additional tasks that go beyond of traditional Web users. Nonetheless, these are extensions that should be coded by developers. The impact of the developer community in developing scripts is known in the GreaseMonkey community  (GreaseMonkey, 2012)  and motivates this kind of research. Some of the aspects of procedure modeling and execution discussed in this paper might resemble some techniques for enduser programming  (Lieberman et al. , 2006; Lin et al. , 2009). In the past, experiments with mashups tools for end-user programming  (Lin et al. , 2009)  have demonstrated that some visual tools such as CoScript  (Bogart et al.
We present ProbMetab, an R package which promotes substantial improvement in automatic probabilistic LC-MS based metabolome annotation. The inference engine core is based on a Bayesian model implemented to: (i) allow diverse source of experimental data and metadata to be systematically incorporated into the model with alternative ways to calculate the likelihood function and; (ii) allow sensitive selection of biologically meaningful biochemical reactions databases as Dirichlet-categorical prior distribution. Additionally, to ensure result interpretation by system biologists, we display the annotation in a network where observed mass peaks are connected if their candidate metabolites are substrate/product of known biochemical reactions. This graph can be overlaid with other graph-based analysis, such as partial correlation networks, in a visualization scheme exported to Cytoscape, with web and stand alone versions.
Although computational strategies have been used to filter and annotate mass peaks in LC-MS experiments  (Dunn et al. , 2012) , these methods do not include the addition of external information into a mathematical model in a principled way. Recently,  (Rogers et al. , 2009)  put forward a proof-of-concept in which information incorporated to a probabilistic model provides better annotation  (Breitling et al. , 2013). Their Bayesian model introduces the elegant idea of using a set of known chemical reactions among candidate compounds to improve annotation since certain combinations, detected together, would make more biochemical sense than others. The state-of-the-art in probabilistic annotation established by  (Rogers et al. , 2009)  did not include: an integrative computational implementation; a practical connection to public databases biological such as KEGG or MetaCyc  (Altman et al.
Methods Aiming clarity, we refer the interest reader to the original work  (Rogers et al. , 2009)  and its subsequent extention  (Rogers et al. , 2010)  for mathematical details. Here we briefly recall the essential ingredients of their model. The surjective mapping between mass peaks onto compounds m � → c; m ∈ {1,. , M }, c ∈ {1,. , C} and M ≤ C; is represented by a column vector � z m ∈ {1, 0} C such that |� z m | = 1. The joint distribution of all mappings given the observed mass spectra is: f (� z 1 ,. , � z M |x 1 ,. , x M , I 1 (t),. , I M (t), θ M+1 ,. , θ N ), abreviated by: f (Z|� x, � θ). In this notation, Z concatenates all column vectors in a matrix; x m is the m-th mass peak's actual observed mass and I m (t) its measured intensity (possible a time-series and/or repeated experiments); and � θ encodes all other N necessary parameters such as � I(t), spectrometer precision, known compounds in the biological database chosen, adjacency matrix of bioreaction graphs, hyperparameters, and so on.