dataesr/scientific-paragraphs-categorization huggingface dataset:
CONCLUDNG REMARKS PepExplorer is recommended for large-scale shotgun proteomic experiments, that is, those in which a considerable number of spectra are generated, as in the datasets presented. Its use is not recommended for analyzing small collections of spectra such as those obtained when analyzing a two-dimensional gel spot. In such cases MS-BLAST (28) should be used instead. A key realization brought about by modern biotechnology has been that underneath the myriad unknown organisms lies great potential  (46). Current strategies inspired by this realization include exploring extreme biomes for so-called extremophiles, a peculiar class of organisms that are generally responsible for the biosynthesis of molecular components useful for pharmaceutical or industrial applications. Perhaps one of the best examples has been the discovery of Thermus aquaticus and its heat-resistant polymerases, elected by Science in 1989 as the "molecules of the year"  (47)  and which have since aided in the development of biotechnology tools and ultimately facilitated the engineering of more effective drugs.
However, instrument time, expertise in data analysis, and financial resources are current bottlenecks for many groups. Here we described a new methodology for dealing with de novo sequencing approaches, taking into account rigorous statistical criteria. We clearly demonstrated its efficiency in a controlled but real experiment with the PFU modified database and then presented the most comprehensive proteomic profile of B. jararaca plasma. Efforts such as the present work are necessary, as they expand the possibilities of what can be achieved in proteomics and in the study of organism biology. In the near future we plan to automate the integration of data between different strategies like PSM and de novo, aiming at a wider perspective for mass-spectral analyses. Availability of PepExplorer, the Raw Data, and Results-PepExplorer and supplementary files, including the B.
As phosphorylation has many biochemical and cellular changes both as input and as outcome, it induces a network that intersects with a plethora of biological processes. Continuous efforts and methodological developments have greatly augmented our knowledge of how human kinases interact with other proteins or small molecules. More than 11,000 protein-protein physical interactions (PPIs) involving at least one kinase can be retrieved from public databases that compile the individual efforts of a whole community. Different technologies were used to unravel these data with a strong contribution of affinity purification-mass spectrometry (AP-MS)  [6] [7] [8]  and the yeast 2-hybrid (Y2H) system  [9, 10]. Known kinase interactions were obtained thanks to a multitude of publications, which did not necessarily focus on kinases but nonetheless included some of those enzymes.
Human KSIs have been studied more sparsely than PPIs so far, although data exist from several databases and new large-scale efforts were undertaken. CEASAR, a protein microarray-based strategy unraveled 3,656 KSIs for 289 protein kinases  [13] , bringing the number of known KSIs to more than 8500. Furthermore, computer-inferred KSIs such as the 75,000+ KSIs predicted by NetworKIN  [14] , exploiting experimental phospho-proteomic data, known kinase substrate specificity, and kinase physical and genetic interactions, might provide a useful complement after stringent filtering before more experimental KSI are measured. The regulatory function of kinases has been observed to be deregulated in many diseases, in particular cancer. Accordingly, a large number of kinase inhibitors have been developed by drug discovery laboratories and pharmaceutical companies, which are mostly small molecules or antibodies.
To relate kinases with their PPI partners as well as their substrates calls for further annotating such a network with the perturbation entry points available for disease therapy or chemical biology. Several large in vitro kinase screens have provided comprehensive and quantitative drug-protein interaction data (DPIs) for a lot of cancer kinase inhibitors. In this category, 72 inhibitors were screened against 442 kinases by Ambit Biosciences  [15]  and another set of 178 inhibitors against 300 kinases by the Peterson's group  [16]. Furthermore, specialized databases collect DPIs from a broad range of scientific reports, e. g. DrugBank  [17]  (78 DPIs). Finally, chemical proteomics has emerged as a very interesting, more physiologically correct alternative to in vitro screens, where immobilized compounds serve as bait in affinity purifications to identify kinase inhibitor protein targets in an unbiased and cell type-dependent manner  [18, 19].
This methodology has been applied successfully to small molecules inhibiting protein kinase activity  [20] [21] [22] [23] [24] [25] [26] [27] [28]. Altogether, the availability of kinase PPIs, DPIs, as well as KSIs in unprecedented, large numbers created a unique opportunity for assembling a kinase-centered network combining these three kinds of interactions and to perform a global study of kinases in their environment. We hence collected and integrated data from the various sources mentioned above and computed such a network. We started our analysis by examining how protein interaction information was correlated with classical kinase research. Investigating the global topology of PPI and KSI networks we could obtain new insights in how they differ as well as refine previous hypotheses regarding the existence of global kinase communication ways.
Construction of the network The list of human kinases with kinase group assignment was downloaded from UniProt web site and was comprised of 508 kinases. Assignment to kinases families (sub-groups) was obtained from kinase. com web site  [29]. Binary PPIs were collected from several public repositories: IntAct  [30] , MINT  [31] , InnateDB  [32] , DIP  [33] , HPRD  [34] , MatrixDB  [35]. Binary PPIs were generated following the spoke model in the case of AP-MS data. PPIs were also obtained from two repositories describing protein complexes: CORUM  [36]  and NCI PID  [37]. We used the matrix model to obtain binary interactions in this case. Experimentally determined KSIs were obtained from IntAct, PhosphoSitePlus  [38] , and Phospho. ELM  [39]  databases along with Newman et al. [13]  supplementary material.
Computational inferences of KSI were retrieved from NetworKIN  [14]  website. DPIs were compiled from DrugBank  [17]  (inhibitors of human protein kinases only), in vitro kinase screens  [15, 16] , and several chemical proteomics publications  [20, [23] [24] [25] 27]. All interaction datasets downloaded from public databases were obtained from the versions available on the 10 th of January 2014. Only human-human interactions derived from physical association methods were retained. UniProtKB/Swiss-Prot  [40]  human protein sequences were downloaded on the same day and all protein accession codes mapped to Swiss-Prot and updated to the current primary accession code to avoid duplications that can typically account for 2-5% of the proteins when public databases are merged directly. Management of the PPI downloads was operated by a database system implemented in-house using Postgres and Perl.
DPIs and KSIs were downloaded manually and further processed by dedicated Perl and R scripts. Computational KSIs were filtered by comparing with experimental data, see Results and discussion. DPIs were also submitted to filtering. In vitro screen data were imposed a maximum cut-off of 1000 nM (Refs  [15, 27] ) and 50% of remaining activity (Ref. [16] ). Chemical proteomics data, except Ref. [27] , were taken as filtered in the original publications (Refs  [20, [23] [24] [25] ). We further extracted a list of strong interactors by first requiring more stringent thresholds (100 nM for Ref. [15] ; 200 nM for Ref. [27] ; 25% remaining activity for Ref. [16] ; no addition for Refs  [11, [14] [15] [16] ). In addition, for each compound in each dataset separately, targets had to be either outliers with the strongest affinity or hits with an affinity not worse than 5 times the most potent target (References  [15, 16, 27] ) or 10 times the most potent target (References  [20, [23] [24] [25] ).
Accumulation of knowledge in kinase interaction research Collecting PPI data from multiple sources, we kept track for each of the PubMed ID (PMID) and the year of publication. In combination with a resource provided by the NCBI that links genes to publications (gene2pubmed, ftp://ftp. ncbi. nlm. nih. gov/gene/DATA/), we could investigate how the community focused its attention to kinases comparing dates of general publications with publications disclosing kinase PPIs from 1975 to 2013. By summing the number of publications of each type for every kinase, we observed a strong correlation between general knowledge and knowledge about interaction partners (Fig. 2A ). This means that popular and well-studied kinases were predominantly selected for performing PPI experiments, network biologists aiming at further characterizing "well-known" kinases.
The level of attention to cancer versus non-cancer kinases in general research as opposed to PPI mapping was measured by the total number of publications available for the respective kinase classes. In Fig. 2C  (first two columns), one can read that PPI kinase research was much less biased towards cancer than general kinase research. In the case of PPIs, kinase interactions could be measured without choosing a kinase as bait. That is, information was acquired without a kinase being in the original research focus. Consequently, we repeated our analysis only considering publications disclosing kinase interactions where the kinase was the bait and we found the same trend (Fig. 2C , third column). We finally wanted to assess whether the trend by which kinase PPI data were highly correlated with general information about kinases was not biased by protein abundance.
For the purpose of this study we preferred to capture trends reflecting the interest of the drug development community and, to a certain extent, the difficulty in targeting certain kinases. Our reference network contained compiled data from large in vitro screens and chemical proteomics that were filtered according to criteria indicating clear inhibition of a kinase by a compound. Here, we needed to define reasonable, more stringent criteria to relate each compound to "strong" targets, i. e. targets for which potency compared to other targets of the same molecule was significantly stronger thus making the compound a likely selective inhibitor for these selected targets (Materials and methods). We obtained a strong target list comprised of 218 kinases interacting with 170 compounds and forming 1261 DPIs.
Discussion and conclusions We compiled a large corpus of interaction data around human protein kinases and performed an initial characterization of this broad kinase network. Our results are based on available data and clearly may be affected by experimental bias of the underlying technologies. Yet, we feel confident that the general trends observed here are properties of the network too robust to be changed dramatically by acquisition of additional knowledge in the future. The analysis of how information was acquired in kinase research comparing network biology efforts identifying protein-protein interactions with classical methodology showed that a clear correlation exists between the two (Fig. 2A ). Network-orientated work has been so far directed as a complement and this trend was stable over the time period for which we had data, i.
Existence of a compound in our dataset for kinases with at least four substrates among APC partners are indicated by a green node. We indicated a few compounds that have a name or provided the PubChem accession code (CID). scope of studies realized by the network biology community where authors frequently aim at characterizing an entire pathway or a set of related protein complexes, which causes the inclusion of additional baits, kinases in our case, that are not immediately associated with cancer if cancer was the object of the study. Furthermore, a second, confounding effect is introduced by our partial knowledge of which kinases are cancer-related and to which extent. The boundary between cancer and non-cancer genes remains incompletely characterized and might be more diffuse than previously thought.
Acknowledgments We thank the public databases and repositories which collected all the interaction data we used in this work:  IntAct, MINT, InnateDB, DIP, HPRD, MatrixDB, CORUM, NCI PID, PhosphoSitePlus, Phospho. ELM, NetworKIN , and  DrugBank. JC  was supported by an  Austrian Science Fund (FWF)  grant no. P 24321-B21.
Background: Scientific workflows management systems are increasingly used to specify and manage bioinformatics experiments. Their programming model appeals to bioinformaticians, who can use them to easily specify complex data processing pipelines. Such a model is underpinned by a graph structure, where nodes represent bioinformatics tasks and links represent the dataflow. The complexity of such graph structures is increasing over time, with possible impacts on scientific workflows reuse. In this work, we propose effective methods for workflow design, with a focus on the Taverna model. We argue that one of the contributing factors for the difficulties in reuse is the presence of "antipatterns", a term broadly used in program design, to indicate the use of idiomatic forms that lead to over-complicated design.
Workflows in Taverna As mentioned earlier, this work is specific to the Taverna workflow model  [1] , which we briefly summarize here. Examples of Taverna workflows are given throughout the paper. Taverna combines a dataflow model of computation with a functional model that accounts for list data processing. A workflow consists of a set of processors, which represent software components such as Web Services and may be connected to one another through data dependencies links. This can be viewed as a directed acyclic graph in which the nodes are processors, and the links specify the data flow. Processors have named input and output ports, and each link connects one output port of a processor to one input port of another processor. A workflow has itself a set of input and output ports, and thus it can be viewed as a processor within another workflow, leading to structural recursion.
Note that it is possible that the same workflow contains the two kinds of anti-pattern. Interestingly, 25. 7% of the workflows of the myExperiment set contains at least one anti-pattern. Although anti-pattern A appears in only 5. 5% of the total, it is particularly costly because it involves multiple executions of the same processor with the exact same input, therefore being able to remove it would be particularly beneficial. The prevalence of pattern B suggests that workflow designers may not know the list processing properties of Taverna (or functional languages). As for the BioVel private workflows, 40. 8% include at least one anti-pattern, all of kind B and thus none contains any kind A. Additionally, we have observed that a workflow from BioVel contains, on average, fewer anti-patterns than, on average, a workflow from myExperiment.
In the BioVel data set, DistillFlow is able to remove all the anti-patterns in 82. 7% of the cases and at least one anti-pattern in all the workflows (100%). Only five (particularly big) workflows have remaining anti-patterns. All of them have actually one remaining anti-pattern, as indicated in Figure  8. Additional experiments allowed us to state that on this corpus, DistillFlow removes one node per workflow on average, compared to three in myExperiment. In very large workflows of BioVel (these are as large as the largest workflows in myExperiment), up to 15 nodes are removed, compared to 31 in myExperiment. In conclusion, the additional curation steps that occur in the BioVel community clearly make the produced workflows being of better quality; however some of these workflows could still benefit from our distilling approach.
However, it may also be the case that anti-patterns cannot be removed because removal would imply merging nodes which would create a new reduction node, making the structure of the transformed workflows more intricate. The number of reduction nodes is actually a commonly used metric to measure how far from an SP structure a structure may be  [17]. In that sense, merging such nodes would make the rewritten workflow being further from an SP structure compared to the original workflow structure. 65 workflows from the myExperiment corpus and five from the BioVel data set are involved in such a situation. The illustrative example for DistillFlow of Figure  6  is one such example: merging nodes #9 and #11 would introduce a new reduction node. In the original graph, node #9 appears in an autonomous subgraph while node #11 does not belong to this autonomous subgraph.
We call such intermediate processors trace nodes and their outgoing edges linked to the workflow outputs are called trace links. Several workflows depicted in this paper have trace links. For example, in Figure  10  on the top, the link that goes from the processor g 6 directly to the workflow output O a is a trace link: when the workflow will be executed, the same data (produced by g 6 ) will be sent both directly to the workflow output O a and to the downstream part of the workflow. By doing this, the workflow designer may want to keep track of the data produced by g 6. However, as the processor get_gi will consume O a to produce to its turn some data, these produced data will have O a in their provenance information. O a will thus be automatically tracked by the provenance module of Taverna.
Very interestingly, the number of anti-patterns per workflow and the number of duplicated nodes involved in each anti-pattern is also much lower in the BioVel workflow set than in the myExperiment workflow set. The additional curation and quality control effort that is placed on the BioVel collection, compared to the more heterogeneous workflows in myExperiment, is then confirmed by our study. We have shown that both data sets may still benefit from our approach.
Figure7. More generally, Figure7shows that the number of remaining anti-patterns is low compared to the number of anti-patterns in original versions of workflows. Interestingly, additional experiments showed that on average three copies of processors are removed per workflow and this number is even particularly high for some workflows (up to 31). In the BioVel data set, DistillFlow is able to remove all the anti-patterns in 82. 7% of the cases and at least one anti-pattern in all the workflows (100%). Only five (particularly big) workflows have remaining anti-patterns. All of them have actually one remaining anti-pattern, as indicated in Figure8. Additional experiments allowed us to state that on this corpus, DistillFlow removes one node per workflow on average, compared to three in myExperiment.
Acknowledgements Authors would like to thank the participants and chairs of the NETTAB 2012 workshop for the very interesting feedback they gave on a preliminary version of this work. We deeply thank the reviewers of the NETTAB 2012 Supplement for their comments and suggestions. Authors would also like to thank all the scientists collaborators and members of the group of  Carole Goble  ( University of Manchester ) and in particular members of the  BioVel  project who allowed us to use their set of workflows. J. Chen  has been supported by the  China Scholarship Council. This work has been funded in part by the  EPS Research Council: EPSRC  grant  EP/G026238/1  "myGrid: A  Platform for e-Biology Renewal ".
Results Genotyping data from 48,119 SNPs in 144 backcross pigs (25% Iberian × 75% Landrace) was employed for GWAS of fatty acid related traits in the Longissimus dorsi muscle. For all 15 phenotypes, estimated SNP additive effects were standardized (z-scores) by subtracting the mean and dividing by the phenotype-specific standard deviation. After applying a series of selection criteria (see Methods), a total of 1,096 SNPs were retained to build the AWM matrix. Correlations between phenotypes were calculated using AWM columns (standardized SNP effects across traits) and were visualized as a hierarchical tree cluster, in which strong positive and negative correlations are displayed as proximity and distance, respectively (Figure  1 ). The observed cluster distribution is in concordance with the physiological similarities and relationships among FA.
For every node we computed the total number of connections based on significant interactions. Table  1  lists the ten most connected nodes and Additional file 1: Table  S1  their positional concordance with fat-related QTL deposited in the Pig QTL Database. Gene ontology (GO) and pathway enrichment analyses were performed to gain insight into the predicted gene network. Overrepresented GO terms in the network included: "Cellular component organization" (P = 4. 02 × 10 -6 , FDR = 3. 95 × 10 -2 ), "Cellular component organization or biogenesis" (P = 7. 34 × 10 -6 , FDR = 3. 6 × 10 -2 ), "Cell projection morphogenesis" (P = 9. 59 × 10 -5 , FDR = 9. 42 × 10 -2 ), "Fatty acid metabolic process" (P = 5. 89 × 10 -4 , FDR = 1. 03 × 10 -2 ), "Glycerolipid metabolic process" (P = 1. 2371 × 10 -3 , FDR = 1.
Experimental validation: From co-association to co-expression analysis in liver and adipose tissues The expression of the three TF across Longissimus dorsi muscle (LD), adipose and liver tissues was explored. In concordance with previous results suggesting that highly connected TF are in general broadly expressed across tissues  [15] , the three TF were expressed across all the studied tissues. Further, a comparison between Iberian and Landrace pig breeds revealed significant increase fold changes (FC) in the liver of Iberian pigs for the expression of NCOA2 (FC = 1. 56, P < 0. 01) and EP300 (FC = 1. 23, P < 0. 05) (Figure  3 ). The expression patterns of 43 genes in liver and 40 genes in adipose tissue were successfully measured across 55 backcross animals. In liver, the expression data of twelve additional genes were also included in the coexpression analysis (see Methods).
We hypothesize that these TF mediate a highly inter-connected regulatory cascade including pathways such as HIF-1, AR, ER and Wnt/β-catenin that seem pivotal for lipid metabolism. The role of these pathways in the transcriptional regulation of lipid metabolism is a subject of intense studies  [17, 38, 39, [51] [52] [53] [54]. A functional cooperation between the three TF in the modulation of these pathways is evident from our results and supported by literature evidence. For example, according to String database  [55, 56]  (http://string-db. org/), experimental data confirmed that protein-protein interaction exists among, EP300, NCOA2, FHL2, AR and ESR1 (Additional file 10: Figure  S3 ). In addition, EP300 and NCOA2 take part on the AR and ER pathways and both, NCOA2 and FHL2 are AR co-regulators  [43, 57, 58].
Phenotypic traits, animals and genotypes Data from 144 pigs (25% Iberian × 75% Landrace), representing 26 full-sib families, from backcrossing five F1 males with 26 Landrace sows was utilized. Details about the management conditions and the phenotype information have been previously reported  [65] [66] [67]. For this study and based on an previous principal components analysis  [66]  we selected 15 of the total 48 traits representing the most informative phenotypes within the dataset. Nine of the 15 traits were related to IMF fatty acid (FA) composition in LD muscle, seven correspond to indices of FA metabolism and the last one is the IMF percentage (Additional file 11: Table  S8 ). The Porcine SNP60K BeadChip (Illumina)  [68]  was used to genotype a total 197 pigs, including the 144 phenotyped animals and the founder population.