dataesr/scientific-paragraphs-categorization huggingface dataset:
Results and conclusion: The interaction network we unraveled incorporates 1018 proteins connected by 1492 direct binary interactions and includes 1420 novel protein-protein interactions. Computational, experimental and literature-based analyses were performed to assess the overall quality of this network. Interestingly, LGMD proteins were shown to be highly interconnected, in particular indirectly through sarcomeric proteins. In-depth mining of the LGMD-centered interactome identified new candidate genes for orphan LGMDs and other neuromuscular disorders. The data also suggest the existence of functional links between LGMD2B/dysferlin and gene regulation, between LGMD2C/γ-sarcoglycan and energy control and between LGMD2G/telethonin and maintenance of genome integrity. This dataset represents a valuable resource for future functional investigations.
Gene Ontology (GO) term analysis showed that the LGMD-centered interactome and especially its HC sub-network, is much more specifically enriched in proteins associated with the muscular tissue and the cytoskeleton than the literature-based dataset. Among the interesting outcomes of our study are the strong inter-connectivity of the LGMD proteins which, in addition to several direct links, present a number of indirect associations thanks to sarcomeric proteins, the identification of candidate genes for orphan neuromuscular disorders (NMDs) and the discovery of new possible functions for LGMD proteins. In particular, we suggest the existence of functional links between LGMD2B/dysferlin and gene regulation, between LGMD2C/γ-sarcoglycan and energy control and between LGMD2G/telethonin and maintenance of genome integrity.
Antibodies The antibodies used for immunoprecipitation of the baits are BD Biosciences anti-TCAP (T26820-050), Novocastra Laboratories Ltd anti-DYSF (NCL-Hamlet) and Santa Cruz Biotechnology anti-ABI1 (sc-30038), anti-ACTN2 (sc-15335), anti-DES (sc-14026), anti-MYOM1 (sc-30390) and anti-TCAP (sc-8725). The antibodies used for prey detection by western blot are Abcam anti-SNAPIN (ab37496), Abnova anti-ADPGK (H00083440-M01), anti-APPL1 (H00026060-A01) and anti-ENO1 (H00002023-M04), Aviva anti-KBTBD10 (ARP38732_T100) and Santa Cruz Biotechnology anti-KIF1B (sc28540) and anti-KTN1 (sc33562). The antibodies used for immunochemistry and Duolink assays and their corresponding dilutions are: Abcam anti-CMYA5 (ab75351, 1:50) and anti-OPTN (ab23666, 1:100), Abgent anti-DGKD (AP8126b, 1:50), Abnova anti-DNAJB6 (H00010049-M01, 1:100) and anti-EEF1G (H00001937-M01, 1:50), Novocastra Laboratories Ltd anti-DYSF (NCL-Hamlet, 1:20), Proteintech Group anti-SNAPIN (10055-1 AP, 1:50), Santa Cruz Biotechnology anti-ACTN2 (sc-15335, 1:100), anti-ALMS1 (sc-54507, 1:50), anti-APPL1 (sc-67402, 1:50), anti-DES (sc-14026, 1:100), anti-FLNC (sc-48495, 1:100), anti-KIF1B (sc-28540, 1:50), anti-MYOM1 (sc-30390, 1:100), anti-MYOM2 (sc-50435, 1:200) and anti-NEB (sc-28286, 1:100) and Sigma anti-NPHP3 (HPA009150, 1:75).
nih. gov/ij/). A series of controls were performed for each analysis (bait antibody only, prey antibody only and negative control for which the primary antibody is omitted). For quantification analysis: three images were acquired under the same conditions (laser power, PMT gain and pinhole) for each experiment. For each image, five fibers were randomly selected and used to count all positive spots within each compartment (total of 15 cells). The regions of interest (ROI) for membrane and cytoplasm compartments were separately delimitated manually and signal quantification was performed on all identified spots using the ImageJ software. For each compartment, we considered that the PPI was validated by the assay when the mean signal ratio between the PLA images of the PPI, "PPI signal", and the control images of the prey, "PREY signal", was superior to 0.
For the LGMD-centered dataset, a list of official gene symbols was used to identify the proteins and within each identified GO cluster, GO terms were analyzed in terms of hierarchy to identify the most specialized children terms common to all proteins within the cluster and these terms were reported as "shared GO" annotations (Additional file 1: Table  S1 ). To analyze whether our datasets were statistically different from a random dataset, GO clustering was also performed with a list of Uniprot accessions for the 19220 human protein-coding genes (HGNC, www. genenames. org/). The number (Shared-i) of human proteins with which a given bait protein (Bait-I) shared a GO cluster was calculated for all three GO classes (BP, MF and CC) and all baits and the number of protein pairs not sharing a GO cluster was deduced (NonShared-i = 19220 -Pi).
The overall frequency of expected shared and non shared protein pairs was calculated as the ratio between the sum of Shared-i and the sum of all pairs (76 × 19220), and the sum of NonShared-i and the sum of all pairs, respectively. A Chi-2 test (P<0. 05) was used to compare expected values with observed values from the LGMD-centred dataset or the subset consisting of all PBS-A to -C categories. GO enrichment analyses were performed using the DAVID Functional Annotation tool with Uniprot accession numbers as identifiers, the Homo sapiens background and the GOTERM_FAT annotation categories. Enrichment at 1% significance level was defined with a modified Fisher exact P value (the "EASE" score) as recommended by the DAVID interface. Statistical analysis of obtained proportions for the other analyses was done using the Fisher test function in R.
For each bait domain, we first assessed its toxicity and auto-activation capacity by a small-scale Y2H screen and then performed a large-scale Y2H assay by screening a high-complexity cDNA prey library obtained by random priming of poly(A)+ RNA from adult and fetal human skeletal muscles that we constructed for this purpose. The bait interaction was tested against an average of 103 million prey clones to insure a ten-fold coverage of the prey library. Positive prey clones were sequenced and compared to the NCBI human RefSeq database for prey identification. Contig assembly of positive clones was performed to isolate the minimum interacting domain(s) on each prey sequence [Selected Interacting Domains (SID)]. We used clone coverage and local topology information to compute a confidence score [Predicted Biological Score (PBS)] and classify each PPI into five categories: PBS-A, -B or -C for the most reliable interactions, PBS-D for putative interactions involving a single bait clone and PBS-E for interactions involving highly connected proteins.
We then examined the interaction networks resulting from the first screenings according to the PBS categories and literature data and conducted selection of secondary and tertiary baits. First, we isolated 54 prey proteins of interest to design 57 new bait domains for a second round of screenings and then, we used the resulting Y2H network to select 10 additional proteins corresponding to 11 baits for a third and last round of screening. Two of the chosen baits showed autoactivation capacities (CMYA5 Q3501-K4069 and RCOR3 M1-L296 fragments) and were therefore discarded. Overall, we successfully carried out 87 large-scale Y2H screenings using 76 different bait proteins. The comprehensive set of baits is listed in Additional file 2: Table  S2.
The most frequent InterPro domains found within SIDs (in more than 4% of the SIDs; Table  3 ) were Immunoglobulin-like domains (IPR003599; IPR003598 and IPR007110), Zinc Finger C2H2 (IPR007087), Fibronectin type III domain (IPR003961), Ankyrin repeat (IPR002110) and Nebulin motif (IPR000900). Finally, we compared the occurrence of InterPro domains fully included within the set of analyzed SIDs with their occurrence in the full human proteome according to  [25]. Interestingly, three among the five top-ranking domains in Human were also present with a high frequency in the analyzed dataset although with different ranks and percentages (Table  3 ). The other two most frequent domains in our subset (Ankyrin repeat and Nebulin motif) were largely over-represented (p-value = 7. 62e-16 and <2. 81e-20, respectively) compared to the global human proteome whereas the three and four top-ranking domains in Human (EGF/laminin and P-loop nucleotide triphosphate hydrolase) were scarce in the analyzed SIDs.
Comparison with known interactions data We first compared our LGMD-centered Y2H dataset with literature-curated interactions between the corresponding mammalian proteins that are referenced in the molecular interaction database iRefWeb (http:// wodaklab. org/iRefWeb/ [22] ). This web interface reports data on PPIs consolidated from major public databases such as IntAct, BIND or HPRD. Among our PPIs, 72 were already reported, representing 4. 8% of our map (Additional file 1: Table  S1 ), a figure slightly above others found in large-scale Y2H studies aimed at exploring the human interactome (3. 4% [2] and 3. 8%  [6] ). We observed a strong enrichment for literature-based interactions within the PBS-A category for which 18% of PPI  (43)  correspond to previously known interactions (p-value = 1.
Examination of GO annotations We performed computational analyses using the Database for Annotation, Visualization, and Integrated Discovery (DAVID) web-resource (http://david. abcc. ncifcrf. gov/) to detect pairs of interacting proteins known to participate in similar biological processes (BP), to be part of the same cell components (CC) or to perform similar molecular functions (MF). In the LGMD-centered network, 84, 72 and 82% of proteins were annotated for the BP, CC and MF GO categories, respectively. Among the annotated proteins, we identified 449 pairs of proteins (30%) that share a GO annotation, including 168 pairs within the PBS-A, -B, or -C categories (34%; Additional file 1: Table  S1 ). More precisely, among PPIs from the PBS-A, -B and -C categories, a significantly higher number of PPIs with shared BP or CC GO annotations compared to the Human proteome were detected (Chi-2 test, P<0.
The NMD proteins in the LGMD-centered network We examined our LGMD-centered interactome map to pinpoint interactions involving proteins identified as the genetic cause of one or more hereditary NMDs by searching the OMIM database. We identified 199 proteins of the network that are associated with human monogenic diseases including 77 proteins whose defects have been described as the genetic cause of one or more hereditary NMD (Additional file 4: Table  S4 ). Among this last group, 43 proteins correspond to myopathies including the recently described DNAJB6  [30] , 20 to Figure  4  Proximity ligation assays of a subset of interactions. A/ Graphs showing the ratio of the signal for the PPI divided by the signal for the prey protein alone with respect to the ROI corresponding to membrane (light grey) or cytoplasm (dark grey) for the bait proteins DYSF, APPL1, MYOM2, OPTN and SGCG.
Properties of the LGMD-centered network and biological functions of the proteins To investigate which biological functions are associated with the LGMD-centered network, we performed a GO term enrichment analysis. In this analysis, we compared the LGMD dataset to a high-confidence (HC) network encompassing the most meaningful PPIs as defined with combined results from the experimental and computational analyses described above and a literature-based network. The HC dataset was built by extracting from the LGMD-centered map the 491 PPIs classified in the PBS-A, -B, or -C categories plus PPIs from the PBS-D and -E categories for which we obtained additional evidence of the interaction. First, we added the 22 of our Y2H interactions from the PBS-D or -E categories that have been described in other studies (Additional file 1: Table  S1 ).
Second, PPIs from the PBS-D or -E categories were added to the HC dataset if experimental results from coimmunoprecipitation, immunofluorescence or PLA as described above confirmed the Y2H interaction. In total, 20 experimentally cross-validated interactions from the PBS-D category were included in the HC dataset. Third, were also included in the HC network, 107 pairs of interacting proteins from the PBS-D or -E categories that either share a BP GO annotation or share a CC and a MF GO annotation. Finally, since our dataset unraveled a high average level of relationship between NMD proteins, 65 PPIs in the PBS-D and -E categories and involving a NMD prey protein were added to the HC network. The resulting HC dataset consists of 497 proteins and 705 PPIs (Additional file 1: Table  S1 ) and includes 174 and 40 PPIs from the PBS-D and -E categories, respectively.
The literature-based dataset was constructed by combining all direct protein-protein interactions reported in the literature for proteins of the HC dataset or LGMD-causing proteins that were not included in our initial set of baits. It is interesting to note that only two interactions for the five LGMD glycosyltransferases have been identified in previous reports [between Fukutin and protein O-linked-mannose beta-1,2-N-acetylglucosaminyltransferase 1 (POMGnT1) and between protein O-mannosyl-transferase 1 (POMT1) and 2 (POMT2)]. Since the glycosyltransferases are all described or predicted to be single or multipass membrane proteins, the rarity of PPI and their absence in our LGMDcentered network are possibly explained, since identification of interactions in the vicinity of membranes has proven to be difficult with the Y2H method.
We searched for previously published experimental binary and direct interactions using the iRefWeb interface and identified 2675 direct binary interactions (Additional file 6: Table  S5 ). This literaturebased network consists of 2239 proteins and 3304 PPIs. Characteristics of the three networks were analyzed (Table  4 )  [31] ) and showed a connectivity of 0. 022, 0. 024 and 0. 041 for the original and HC LGMD-centered networks and the literature-based network, with the proteins being associated with 19. 7, 9. 7 and 25. 4 PPIs on average, respectively. Network heterogeneity, a parameter that reflects the tendency of a network to contain hub nodes was 2. 895, 1. 964 and 3. 833 for the original and HC LGMD-centered networks and the literaturebased network, respectively. Using the DAVID web-resource and an appropriate level of abstraction of the annotations, we performed a GO term enrichment analysis of the three datasets in order to check if they presented any specificity in terms of biological process, cellular localization or molecular function of their proteins as compared to the human proteome (Figure  7 ).
Not unexpectedly, they show an over-representation of proteins involved in muscle biology, even quite exclusively for both the original and HC LGMD-centered networks that share very similar profiles (Figure  7 ). The biological processes with most striking enrichments in all datasets are muscle contraction, cytoskeleton organization and muscle organ development. The cellular components with the highest representation include contractile fibers and cytoskeleton. The enriched molecular functions are cytoskeletal protein binding and structural constituents of muscle protein. In addition to these terms, the literature-based network shows additional terms with a significant enrichment of proteins involved in cell cycle and death, localized in the cytosol and the nucleus and with broader molecular functions (binding of enzyme, transcription factor or ATP and protein kinase).
The PBS score was previously demonstrated to successfully predict the reliability of a PPI on a subset of Y2H interactions where the authors experimentally Figure  7  GO enrichment analysis of the three datasets compared to the human proteome. A GO term enrichment analysis of the three datasets (LGMD-centred, HC and literature-enriched datasets) was performed using the DAVID web-resource and an appropriate level of abstraction of the annotations, The bar charts depict enriched GO terms of the three datasets compared to the human proteome in the three branches of the GO structure as a function of the EASE score. confirmed 79% of all PPIs from the PBS-A, -B or -C categories by pull-down or co-immunoprecipitation assays  [14]. The PBS takes into account both local parameters, such as the number of identical or independent fragments found for each partner and global information derived from the entire network such as reciprocal interactions, highly connected domains etc.
Acknowledgements This work was supported by the  Association Française contre les Myopathies  and the  Jain Foundation. We thank  Dr. Anne Friedrich  for technical assistance on gene coordinates and cytogenetic bands studies and  Roseline Yao  and  Julie Dumonceaux  from the  Inserm   U790  for the R9 cells. We are grateful to the sequencing and histological departments of Genethon and to  Jean Pascal Lepetit-Stoffaes ,  Drs Alain Meil ,  Vincent Collura  and  Etienne Formstecher from Hybrigenics  for excellent assistance.
INTRODUCTION Traditional databases aim at data management, i. e. , they help organize, structure and query data. They are transaction processing-oriented and are often qualified as production databases. In opposition, data warehouses have a very different vocation: analyzing data  (Kimball & Ross, 2002; Inmon, 2005)  by exploiting specific models (star, snowflake and constellation schemas). They are termed as On-Line Analytical Processing (OLAP) databases. Data are then organized around indicators called measures, and analysis axes called dimensions. Dimension attributes either form a hierarchy or are just descriptive. Dimension hierarchies allow for obtaining views of data at different granularities, i. e. , summarized or detailed through roll-up and drill-down operations, respectively. Research in data warehousing and OLAP has produced important technologies for the design, management and use of information systems for decision support.
3. Our third contribution studies the problem of specifying changes in multidimensional databases. These changes may be motivated by evolutions of user requirements, as well as changes in Web sources  (Hurtado et al. , 1999b; Espil & Vaisman, 2001). The rule-based multidimensional model we provide supports both data and structure changes. The approach consists of creating new hierarchy levels in OLAP dimensions for data warehouse schema evolution according to relevant personalized analysis needs  (Bentayeb et al. , 2008; Favre et al. , 2007). Throughout this chapter, we use a running example to illustrate our contributions. It is extracted from a project we jointly carried out with linguist colleagues. This project (named CLAPI) dealt with the on-line integration, storage, management and analysis of spoken language interaction corpora  (Aouiche et al.
, 2003). Funded by the French Ministry of Higher Education and Research, it provides Web access1 to the CLAPI corpus database through user-friendly query and quantitative analysis tools (a new feature in spoken language research) that allow researchers in linguistics to elaborate hypotheses and validate results on a significant volume of data. A conceptual schema of our excerpt from CLAPI is provided in Figure  1. A corpus is made of audio and/or video recordings of real-life interactions (e. g. , classrooms, divorce conciliations, conflicts in queues…). Each speaker in a recording is identified (with a pseudo; the database is anonymous) and may appear in several interactions. To be exploited by linguists, recordings are textually transcribed. These transcriptions are actually structured in XML and feature both tokens (i.
In the mediator-based approach  (Rousset, 2002) , data remain located at their original sources. User queries are executed through a mediator-wrapper system  (Goasdoué et al. , 2000). A mediator reformulates queries according to the content of the various accessible data sources, while the wrapper extracts the selected data from the target source. The major advantage of this approach especially in a Web context is its flexibility, since mediators are able to reformulate and/or approximate queries to better satisfy the user. However, when data sources are updated, modified data are lost, which is not pertinent in a decision-support context where data historicity is important. In opposition, in the data warehouse approach, selected data from various sources are centralized in a new multidimensional database, the data warehouse  (Inmon, 2005; Kimball, 2002).
The integration of complex data in a database is an issue that has been little studied. Jensen et al. (2001)  propose a general system architecture for integrating XML and relational data sources at the conceptual level in a Web-based OLAP database. A "UML snowflake diagram" is built by choosing the desired UML classes from any source. The process is deployed through a graphical interface that deals only with UML classes and makes data sources transparent to the designer. Other approaches for an easier integration of complex data semantics have also been published. A framework for video content understanding, consisting of an expert system that uses a rule-based engine, domain knowledge, visual detectors and metadata to enhance video detection results and presenting the semi-automatic construction of multimedia ontologies, is presented in  (Jaimes et al.
, 2003). Semantic indexing techniques for complex data also exist  (Stoffel et al. , 1997). These techniques are based on domain knowledge made available in the form of ontologies. This research overcomes the difficulty of efficiently integrating semantic knowledge stored as ontologies in ways that support the efficient indexing of large databases. Finally, Baumgartner et al. (2005)  describe how public information can be extracted automatically from Web sites, transformed into structured data formats, and used for data analysis in Business Intelligence systems. Authors design an architecture, called Lixto, for processing Web data in an efficient manner. They also exploit the XML formalism to manage extracted data and make them exploitable into an SAP business information warehouse. To process complex data, it is also important to consider their metadata.
To take this information into account, it is necessary to use appropriate management tools. An integrative and uniform model for metadata management in data warehousing environments uses a uniform representation approach based on UML to integrate technical and semantic metadata and their interdependencies  (Stohr et al. , 2002). Standards for describing resources that we consider as complex data already exist. The Resource Description Framework  (Lassila & Swick, 1999 ) is a W3C-approved standard that uses metadata to describe Web contents. It is possible to describe all kind of resources using RDF. Another important standard is MPEG-7  (Manjunath et al. , 2002). Its most important goal is to provide a set of methods and tools for the different aspects of multimedia content description. MPEG-7 focuses on the standardization of a common interface for describing multimedia materials (representing information about contents and metadata).
The introduced concepts are: hierarchies, various types of star, snowflake and constellation schemas  (Kimball 2002) , parameters, and weak attributes  (Golfarelli et al. , 1998). However, dimensional modeling must be adapted to take into account the specificities of complex data. Some proposals regard multidimensional modeling by using XML as a base language for describing data warehouses. Krill affirms that vendors such as Microsoft, IBM, and Oracle largely employ XML in their database systems for interoperability between data warehouses and tool repositories  (Krill, 1998). Nevertheless, we distinguish two separate approaches in this field. The first approach focuses on the physical storage of XML documents in data warehouses. XML populates warehouses since it is considered an efficient technology to support data within well suited structures for interoperability and information exchange.