dataesr/scientific-paragraphs-categorization huggingface dataset:
The UCYN-A732 probe designed against UCYN-A by targeting the 16S rRNA  15  has only one mismatch with the UCYN-A2 sequence and a competitor was designed to distinguish specifically UCYN-A1 and UCYN-A2 clades with high specificity (Supplementary Table  1 ). The specificity of the new probes was checked with the online tool ProbeCheck (http://www. cme. msu. edu/ RDP/) and by searching in the GenBank database (http://www. ncbi. nlm. nih. gov/ index. html) to detect potential matching sequences in non-target groups. CARD-FISH assay and epifluorescence microscopy. A preliminary doublehybridization assay using the universal haptophyte PRYM02 probe  40  and UCYN-A732 was first applied to check whether the partner of UCYN-A in our sample belong to class Prymnesiophyceae. To specifically target the different UCYN-A lineages and their prymnesiophyte hosts, a double-CARD-FISH assay was performed for each partnership (according to the multi-colour CARD-FISH protocol  41  ).
Libraries were sequenced on Illumina HiSeq2000 instrument (Illumina, San Diego,CA) using 100 base-length read chemistry in a paired-end mode. Sequencing depth for each sample is detailed in Table  1. Nucleotide data deposition. Nucleotides data used in this study have been deposited in the European Nucleotide Archive (ENA; www. ebi. ac. uk/ena) under the following accession numbers: ERR1001626-27, ERR1007415-18, ERR1013384-85, ERR599006, ERR599010, ERR599022, ERR599126, ERR599237, ERR599240, ERR599250, ERR599253, ERR599275 and ERR599311. Fragment recruitment analysis from omics data sets. BLAST þ v2. 2. 25 was used to recruit metagenomic and metatranscriptomic reads similar to the two UCYN-A genomes sequenced up to date  11, 12  using default parameter values, except for the following: -perc_identity 50, -evalue 0.
Acknowledgements We are indebted to the scientists and crew on board of Tara Oceans expedition. We thank  V. Balague  ´and  I. Forn  for technical support,  P. Sa  ´nchez and  R. Logares  for providing access to bioinformatic resources and for bioinformatic advice, and  I. Ferrera  for critical reading. This work was supported by the  Spanish Ministry of Science and Innovation  grant  CGL2011-26848/BOS MicroOcean PANGENOMICS , and  TANIT  ( CONES 2010-0036 ) from the  Agencia de Gestiod'Ajuts Universitaris i Recerca  to S. G. A. This research was further supported by  European Molecular Biology Laboratory  (EMBL),  Investissements d'avenir grants Oceanomics  ( ANR-11-BTBR-OOO8 ) and  France Genomique  ( ANR-10-INBS-09-08 ). F. M. C. -C. and A. M. C. held FPI (MICINN) fellowships and G. S. held PhD JAE-Predoc (CSIC) fellowship.
Development of an Operational Version of the NutriCog Battery In order to obtain our final Web-based instrument, the NutriCog battery, the following working steps were undertaken: (1) adaptation of the task instructions to a self-administered computerized framework, (2) prototype development, (3) pilot testing, and (4) revision of the prototype. Pilot testing consisted of assessing comprehensibility of the instructions, timing, and potential technical glitches. These pilot tests have been conducted in our research institute, within a sample of individuals with varying age, education level, and sex. Lessons that we have learned during the development of the operational version of our battery include the importance of constructive exchanges within an interdisciplinary team of neuropsychologists, epidemiologists, and computer scientists in order to find solutions that are relevant in terms of neuropsychological paradigms, the planned epidemiological investigations, and feasibility in terms of software development.
Conclusions The concordance of the self-administered version and the supervised version of the Web-based NutriCog cognitive test battery was roughly similar to that observed for test-retest investigations of other test batteries. This indicates that these two different modes of administration provide similar information. In large epidemiological studies like the NutriNet-Santé cohort, the objective of cognitive evaluations is not to provide data with high validity in a clinical context, but to measure cognitive performances in a rapid and simple manner, with sufficient quality to permit valid conclusions on the population level. Given the drastic reduction of the logistical and financial burden that can be obtained by using fully self-administered tools, Web-based cognitive test batteries such as NutriCog provide interesting alternatives to supervised tools in the context of large cohort studies.
Acknowledgments The  NutriNet-Santé study  is supported by the  French Ministry of Health , the  Institut de Veille Sanitaire , the  Institut National de Prévention et d'Education pour la Santé , the  Fondation pour la Recherche Médicale , the  Institut National de la Santé et de la Recherche Médicale , the  Institut National de la Recherche Agronomique , the  Conservatoire National des Arts et Métiers , and the  Paris 13 University. None of the funding bodies had any involvement in study design, in the collection, analysis and interpretation of the data, in writing the manuscript, or the decision to submit. KEA was supported by a  doctoral fellowship  from the  University of Paris 13. We thank all the scientists, dietitians, technicians, and assistants who helped carry out the  NutriNet-Santé study.
Eukaryotic genomes contain a variety of structured patterns: repetitive elements, binding sites of DNA and RNA associated proteins, splice sites, and so on. Often, these structured patterns can be formalized as motifs and described using a proper mathematical model such as position weight matrix and IUPAC consensus. Two key tasks are typically carried out for motifs in the context of the analysis of genomic sequences. These are: identification in a set of DNA regions of over-represented motifs from a particular motif database, and de novo discovery of over-represented motifs. Here we describe existing methodology to perform these two tasks for motifs characterizing transcription factor binding. When applied to the output of ChIP-seq and ChIP-exo experiments, or to promoter regions of co-modulated genes, motif analysis techniques allow for the prediction of transcription factor binding events and enable identification of transcriptional regulators and co-regulators.
For instance, the α-satellite family of repeats (∼171 bp tandem repeats) are bound by the fundamental component of the centromere CENP-C, and are essential for centromere function by ensuring proper chromosome segregation in mitosis and meiosis  (Politi et al. , 2002). The TandemSWAN software (http://favorov. bioinfolab. net/swan/tool. html) allows the annotation of exact and fuzzy tandem repeats in genomic sequences  (Boeva et al. , 2006). It is usual to mask such repeats in order to avoid artifact discovery, for example, during analysis of next-generation sequencing data.
, 2014). The approach proposed by  Mathelier and Wasserman (2013)  has been included in the JASPAR database. Slim probabilistic graphical models, implemented by  Keilwagen and Grau (2015) , can be used via a Galaxy wrapper (http://galaxy. informatik. uni-halle. de); the authors also provide an intuitive model visualization. In addition, motifs can be modeled and searched for using kmer frequencies via support vector machine (SVM) approaches  (Holloway et al. , 2005; Jiang et al. , 2007; Gorkin et al. , 2012; Fletez-Brant et al. , 2013). This class of approaches can be successfully applied to PBM data  (Agius et al. , 2010; Mordelet et al. , 2013). One of the important advantages of these graphical model and SVM-based approaches is that they can account for variable spacing between half-sites of two-box TFs (examples of such motifs are shown in Figure  6A ).
Detection of TFBSs with Known PWMs Detection of TF binding motif instances for known motifs has its application in promoter analysis or the analysis of more distant regulatory regions (enhancers), where the goal is to find TFs possibly regulating corresponding genes. Scanning a set of sequences with PWMs of known motifs can also be used to detect co-factor binding in ChIP-seq-derived binding site regions of a TF of interest. Alternatively, one can use known-motif discovery to assess the effect of SNPs and mutations on TF binding. With the increase in the number of sequenced genomes, the second question has recently gained in importance, and novel tools permitting annotation of variants within TF motif instances have begun to be developed  (Boyle et al. , 2012; Ward and Kellis, 2016). There exist several public and commercial databases storing PWMs for known TF binding motifs.
A Markov chain employed as a background model allows us to capture dependencies between nucleotides. This can take into account low or high frequencies of CpG nucleotides in the set of enhancer or promoter sequences. An automatic scan of a set of DNA sequences using motifs from the databases listed above, with tool-specific cutoffs, is available through the following websites and programs: • AME or FIMO of the MEME suite  (McLeay and Bailey, 2010)  http://meme-suite. org/ • SeqPos of Galaxy Cistrome  (Liu et al. , 2011)  http:// cistrome. org/ap/ • PWMScan of PWMTools  (Iseli et al. , 2007)  http://ccg. vital-it. ch/pwmtools/pwmscan. php • oPOSSUM-3  (Kwon et al. , 2012)  http://opossum. cisreg. ca/oPOSSUM3/ • HOMER  (Heinz et al. , 2010)  http://homer. salk. edu/homer/.
The ChIP-exo technique provides an even better resolution of binding sites than ChIP-seq, at the expense of a more elaborate library preparation protocol, including an exonuclease step. In this section, we focus on de novo motif discovery in ChIP-seq datasets. ChIP-seq yields a set of genomic regions (also called peaks) that are thought to contain TFBSs. The output of a ChIP-seq experiment can include tens of thousands of peaks, some longer than 1000 bp. Each peak position has a weight reflecting how often a given DNA fragment was cross-linked with the protein of interest during the ChIP stage (coverage profiles). There exist a large number of methods for the de novo detection of over-represented motifs. The classical tool, MEME  (Bailey et al. , 2009) , was developed for motif discovery in a small number of short DNA sequences, and scales poorly to large ChIP-seq datasets.
, 2012) , cERMIT  (Georgiev et al. , 2010) , and RSAT-peak-motifs FIGURE 5 | Two modes of multiple motif detection: "Mask sequences" mode to discover binding motifs of the same TF, and "Mask motifs" mode to discover binding motifs of co-factors. After the first motif is identified, either all sequences containing this motif instance are removed from further analysis (sequences in gray, "Mask sequences" mode), or motif instances are masked (motif instances in gray, "Mask motifs" mode). The second motif is defined as the motif with the highest KDIC in the remaining nucleotide sequences. (Medina-Rivera et al. , 2015)  show very competitive runtimes on large ChIP-seq datasets. However, probabilistic approaches (e. g. , ChIPMunk, Dimont) may provide higher accuracy results  (Grau et al. , 2013).
Thus, binding motifs of co-factors can be detected as overrepresented motifs after the motif instances of the main TF have been masked. When a binding motif is identified de novo, it is possible to compare its PWM or IUPAC consensus with the known motif PWMs stored in the TF motif databases via: • JASPAR  (Mathelier et al. , 2016) -http://jaspar. genereg. net/, • Motif Comparison Tool of the MEME Suite  (Gupta et al. , 2007) -http://meme-suite. org/tools/tomtom • MACRO-APE  (Vorontsov et al. , 2013) -http://autosome. ru/macroape/ • STAMP  (Mahony and Benos, 2007) -http://www. benoslab. pitt. edu/stamp. In this section, we have focused on the prediction of TFSB sites in a set of rather short regulatory regions provided by the user (regulatory regions obtained from ChIP-seq experiments). However, in some situations, one may be interested in analyzing much larger genomic regions (up to the whole genomes).
The presence of clusters of the same motif in one sequence is not taken into account by this model. The ZOOPS model is also applied by motif discovery tools designed to analyze ChIP-seq data (described above). There are several major caveats to this approach. First, not every motif incidence corresponds to a true binding event. Thus, the definition of promoter length affects the results of the analysis. Larger promoter regions are likely to include a certain number of false predictions of binding sites, and at the same time are likely to capture more true binding sites. The use of large regions upstream of TSS in promoter analysis is especially unjustified when looking for short or highly degenerate motifs. The second caveat is that genes can be regulated by TF binding to distant regulatory elements: enhancers.
cs. tau. ac. il/ amadeus/-requires program download; can search for pairs of co-occurring motifs; accepts gene lists as input • i-cisTarget  (Herrmann et al. , 2012; Imrichová et al. , 2015)  https://gbiomed. kuleuven. be/apps/lcb/i-cisTarget/accepts. BED files or gene names; when gene names are provides, motif search is performed in 20 Kb window around gene TSSs overlapping with predefined candidate regularity regions • Pscan  (Zambelli et al. , 2009)  http://www. beaconlab. it/ pscan-requires a gene list and provides a choice of 5 lengths for promoter intervals • OTFBS  (Zheng et al. , 2003)  http://genome. ucsf. edu/∼ jiashun/OTFBS/-online version accepts no more than 200 sequences in FASTA format • Asap  (Marstrand et al. , 2008)  http://servers. binf. ku. dk/ asap/-accepts sequences in FASTA format; PWM threshold should be selected by the user • oPOSSUM-3  (Kwon et al.
, 2015) , or using the Z-transform of scores  (Linhart et al. , 2008; Zambelli et al. , 2009). Correction for multiple tests is optionally performed by some methods  (Marstrand et al. , 2008). As mentioned earlier, complementary information about sequence conservation, regions of open chromatin, and presence of specific histone marks, helps to increase TFBS prediction accuracy  (Cuellar-Partida et al. , 2012; Grant et al. , 2015; Imrichová et al. , 2015). Promoter analysis usually predicts binding sites independently for several TFs. However, some recent approaches propose a different strategy, where the goal is to detect combinations of binding sites of several TFs forming cisregulatory modules (CRMs). These approaches can be based on both de novo discovery of motifs, or using available motifs from databases.
They can be applied to a set of promoter sequences, but also on predefined sets of enhancers, which can be obtained, for example, using profiles of histone marks. Some methods such as Allegro  (Halperin et al. , 2009)  can take into account a range of changes in gene expression to better predict CRMs. • Online tools: • MatrixCatch  (Deyneko et al. , 2013)  http://www. generegulation. com/cgi-bin/mcatch/MatrixCatch. pl-works with TFBS PWMs from the TRANSFAC R database; accepts a set of sequences in FASTA format • ModuleMiner  (Loo et al. , 2008)  http://tomcatbackup. esat. kuleuven. be/moduleminer/-accepts Ensembl gene IDs to look for conserved CRMs upstream gene TSSs; • PC-TraFF  (Meckbach et al. , 2015)  http://pctraff. bioinf. med. uni-goettingen. de/-uses TRANSFAC R PMWs on gene IDs or sequences in FASTA format • DistanceScan  (Shelest et al.
, 2010)  https://www. omnifung. hki-jena. de/Rpad/Distance_Scan/index. htm-requires an output from FIMO or Match • oPOSSUM-3  (Kwon et al. , 2012)  http://opossum. cisreg. ca/ oPOSSUM3/-requires the name of the anchoring TF • MCAST  (Grant et al. , 2015)  http://meme-suite. org/tools/ mcast-a tool from the extensive MEME suite; searches for clusters of provided motifs in sequences in FASTA format • Cluster-Buster  (Frith et al. , 2003)  http://zlab. bu. edu/ cluster-buster/-searches for motif clusters; accepts PMWs in JASPAR or TRANSFAC R formats • Offline tools: • ModuleDigger, CPModule, CORECLUST: stand-alone programs that require a set of known PWMs as input  (Sun et al. , 2009 (Sun et al. , , 2012;; Nikulova et al. , 2012). Validation of TFBSs can be carried out using a combination of chromatin immunoprecipitation with an antibody specific to the TF of interest, and real time PCR with primers specific to the predicted target region.
, 2010; Guo et al. , 2012; Starick et al. , 2015). Below, we briefly describe the main elements of ChIP-x data analysis. In the first step of ChIP-x data analysis, by extending each read to the length of the initial immunoprecipitated DNA fragment, it is possible to identify areas of fragment overlap and locate candidate regions of TF-DNA binding. These regions with high fragment density are called candidate peaks  (Fejes et al. , 2008). Not every peak contains a true binding site. Low peaks (with moderate read density) can appear by chance. Thus, to characterize the read enrichment and discriminate true binding from background noise, a statistical model needs to be applied. There are more than 20 different tools that perform this task for ChIP-x TF data  (Wilbanks and Facciotti, 2010; Kim et al.
, 2011). The background model may be based on the uniform distribution of sequenced reads along the genome. Under such a background model, a Poisson test can be applied to evaluate the significance of read over-representation in a given region  (Zhang et al. , 2008). Often, in the ChIP-seq protocol, a negative control experiment is performed to assess the distribution of sequenced reads in the background. Recent studies have shown that an appropriate control data set is critical for analysis of any ChIP-seq experiment, because of biases in DNA breakage during sonication  (Landt et al. , 2012). The ChIP-exo datasets are usually generated with negative controls. In  (Boeva et al. , 2010) , we presented a peak and motif calling algorithm, MICSA, based on the idea that functional binding sites of TFs should contain a consensus motif (or a set of consensus motifs).
The MISCA workflow consists of four phases: (i) identification of all candidate peaks using read extension, (ii) identification of binding motif PWMs from a subset of peaks, (iii) detection of motif instances in all candidate peaks, and (iv) optimization of the peak calling output by calculating statistics taking into account information about both motif instance and depth of coverage. Importantly, MICSA identifies several binding motifs. The statistics calculated by MICSA allow us to retain strong binding sites (i. e. , regions with high numbers of overlapping fragments) as well as weak binding sites with strong motif instances in the peak center (Figure  7 ). Weak binding sites without strong motif instances are removed from the final dataset. When applied to a ChIPseq dataset for oncogenic TF EWS-FLI1, MICSA identified two consensus motifs (Figure  6B ): a (GGAA) ≥6 microsatellite, and a motif corresponding to the consensus RCAGGAARY, further referred to as the ETS motif.
In fact, in 15 cases out of 215, GEM outperformed both MEME and ChIPmunk. Using the output of GEM on ENCODE ChIP-seq data in five different cell lines,  Guo et al. (2012)  studied pairwise binding relationships between different TFs. As a result, 390 pairs of TFs were shown to have significant binding distance constraints within a 100 bp distance, including known interaction pairs MYC-MAX, FOS-JUN, and CTCF-YY1. The concept of combining ChIP-exo read density with motif information has been employed in the ExoProfiler computational pipeline  (Starick et al. , 2015). ExoProfiler searches for both de novo motifs and known motifs from the JASPAR database. It then extracts regions in ChIP-seq peaks centered on motifs, and analyzes strand specific read density. By applying ExoProfiler to glucocorticoid receptor (GR) ChIP-exo data,  Starick et al.
1 physically modulates the expression of its target genes  (Ridinger-Saison et al. , 2012). Analysis of the Spi-1/PU. 1 ChIP-seq dataset resulted in a total of 17,781 binding site regions, which were assigned to genes using the Nebula peak-to-gene annotation module  (Boeva et al. , 2012). Of the 21 Spi-1/PU. 1 binding sites tested, 20 were validated using real time PCR. As we detected instances of the binding motif in 88% of the Spi-1/PU. 1-bound regions, we concluded that in erythroleukemia, Spi-1/PU. 1 binds to DNA directly. Interestingly, bound to a gene or even to a gene promoter, Spi-1/PU. 1 rarely causes transcriptional modulation. Half of all mouse genes contained Spi-1/PU. 1 binding sites, i. e. , within a -30 kb region upstream of the TSS to +5 kb downstream of the transcription end, but only 8.
CONCLUSION Sequence analysis methods are extremely useful for decrypting the complex structure of patterns and motifs present in eukaryotic genomes. In particular, motif discovery methods applied to promoter/enhancer or ChIP-seq peak sequences enable detection of TFBSs in genomic DNA. In this review, we have presented de novo motif discovery techniques, and methods to find over-represented binding motifs of TFs with known motifs (PWMs). We have demonstrated that the application of these techniques improves accuracy of peak calling during ChIPseq data analysis, and may provide novel biological insights into mechanisms of transcriptional regulation when sequence analysis is coupled with the analysis of gene expression changes. We expect that with time, motif discovery methods will become even more user-friendly, and will allow rapid processing of large datasets, while TRANSFAC R , JASPAR, and other databases will include an increasing number of TF motifs extracted from ChIPseq experiments.
Modern enterprise information systems are characterized by large amounts of data issued from various internal and external business applications, often stored and archived in different supports (databases, documents, etc. The nature of this data (voluminous, unstructured, heterogeneous, inconsistent, etc. ) makes them difficult to use for analysis. In fact, it is typically an issue of big data analytics. The main objective of our research project is to design a solution to detect opportunities (projects, new markets, skills, tenders, etc. ) in the continually growing data, while adapting to its constraints. The extracted information should help users to take proactive actions to improve their business (e. g. , identify a consultant skill that can be aligned with a given tender). In this project we are interested in text data.
INTRODUCTION Big Data mining is a recent and actual research trend  (Diebold, 2012). Several approaches were experimented in several domains like: mobile communications  (Laurila, et al. , 2012) , biology  (Howe, et al. 2008) , economics  (World Economic Forum, 2012) ,  (Letouzé, 2012) , marketing  (Fan, et al. , 2015) , decision making  (Probst, et al. , 2013) , etc. In Big Data mining, it is usual to deal with 3V problems: Volume, Variety, and Velocity. Recently, two more Vs were proposed: Variability and Value  (Fan and Bifet, 2012). This last V is very important in a business-oriented mining, which has for objective to valuate internal and/or external data through mining. In our case, the values we want to highlight are: • Fast answer to customer's requests • Understand customer's problems and determine new proposals to help solving it • Find new business opportunities through new projects, new markets, skills, tenders, etc.
Some papers propose to use graph-oriented databases  (Lin, 2014) , which allow much faster responses than classical relational databases, due to local dependencies of data. A particular model of graph based on RDF (Resource Description Framework) retained our attention. RDF based model consists of "triples" [subject] -> [predicate] ->  [object] , which define conceptually a labelled graph  (Bönström, 2003). That allows representing data dependencies in a clear, simple and efficient way, and allows fast access to data in graph-oriented databases. One of the main problems is: how to build this graph? I. e. what are the data sources and how is extracted pertinent information. Some documents are relatively structured, like competencies records, but the most are non-structured and thus need to be threated specifically.