dataesr/scientific-paragraphs-categorization huggingface dataset:
Individual protein data and analysis The PolyprOnline web server provides access to different assignment methods and allows visualization of both regular secondary structure and PPII helix (Figure  3 ). We have recently underlined the discrepancies between the three different secondary structure methods able to assign PPIIs, and proposed a novel PPII assignment using the de facto standard DSSP assignment method  (10, 14). To better visualize the secondary structure and PPII assignments given by PROSS  (21) , SEGNO  (22) , XTLSSTR  (23)  and our DSSP-PPII  (10, 14) , they are all displayed at the bottom of sequence One letter code is used to represent specific conformation. Letters are coloured accordingly to more general class of secondary structure (e. g. helix residue in red, strand in green, PPII helix in blue non-regular secondary structure in grey, coil being in dark grey colour) for a fast visualization of overall local structures.
Protein structures dataset A subset of the experimental protein structures extracted from the PDB was selected based on the resolution methods (RX), quality of structures (resolution lower than 3. 0 A ˚and R-factor lower than 1. 0) limited redundancy (proteins share no more than 90% of identity between each others) using webserver PISCES  (20). The full list of selected structures comprised 24 761 protein chains and is available on database. The list is regularly updated. Data flow in PolyprOnline system. Access to the system can be done in two ways: through 'Simple query' for the analysis of one or more protein structures from their PDB code and through 'Advanced query' for performing more complex queries using different criteria such as resolution (A ˚), protein length, minimal and maximal number or percentage of residues in PPII conformation assigned by a particular tool.
The last type of advanced query allows local structure search on specific positions using secondary structure patterns. It is also possible to dynamically upload and process a PDB file if it is absent of the database. The query is then processed to be interpreted by our Database Management System. In the case where a PDB structure is not found in the database, a PDB file can be downloaded from the Protein Databank website and dynamically processed by the system. PolyprOnline webserver offers the following outputs to display results: Summary of all protein identified by PDB code, title, size, resolution and PPII content, printed in a sortable table according to the values in different columns (Figure  2 ). From this table, individual protein data analysis can be accessed individually (Figure  3 ).
Assignment of PPII and other secondary structures there is a limited number of tools for assigning PPII number. The tools available today are XTLSSTR, PROSS (version September 2004) and SEGNO (version 3. 1). We have added our PPII DSSP-based program DSSP (CMBI version 2000) developed in our laboratory to this list  (10). As we have previously explained, the use of multiple tools is necessary because it has been shown that PPII assignments using several methods yielded different results  (10). Secondary structures assigned by PROSS (  21   DSSP-PPII is a new method for PPII recently developed in our laboratory  (10). It is based on the most popular secondary assignment tools: DSSP  (14). DSSP assignment is based on the identification of precise hydrogen bond patterns corresponding to regular secondary structures.
Assignment strategy of PPII is based on simple set of basic rules to have the highest agreement with PROSS, SEGNO and XTLSSTR methods. PPII are assigned solely in the coil region for at least two consecutive amino acids in coil with U ¼ À75 6 e and W ¼ þ145 6 e with e ¼ 29. Basic assignment of secondary structure in DSSP defines eight types of secondary structures: a helix (H), extended b strand in parallel and or anti-parallel b-sheet conformation (E), isolated b-strand (B), 310 helix (G), Pi helix (I), bend (S) and coil (O, coded as '-' in this database). This is the basic assignment to which helix PPII (P) has been added.
With more than 72% of residues in PPII conformation, this anti-freeze protein contains the highest percentage of PPII of our database. It also be noted, in these examples, that the organization of these PPII present characteristics of this regular conformation: rather isolated and exposed prolines for cyclin-dependant kinase regulation subunit (A), and the characteristics of other regular secondary structures: (i) similarities with a helix motifs such as PPII-beta-beta motif in Thrombospondin (B) and Atratoxin of cobra venom (D), (ii) and analogy with both alpha and beta motif such in GTP-binding protein obg (C) and snow flea anti-freeze protein (E) where PPII arrangements appear as a six anti-parallel PPII helices bundle. All theses PPII have in common a broad exposure to the solvent as it has already been highlighted in previous studies  (11).
Figure1. Data flow in PolyprOnline system. Access to the system can be done in two ways: through 'Simple query' for the analysis of one or more protein structures from their PDB code and through 'Advanced query' for performing more complex queries using different criteria such as resolution (A ˚), protein length, minimal and maximal number or percentage of residues in PPII conformation assigned by a particular tool. The last type of advanced query allows local structure search on specific positions using secondary structure patterns. It is also possible to dynamically upload and process a PDB file if it is absent of the database. The query is then processed to be interpreted by our Database Management System. In the case where a PDB structure is not found in the database, a PDB file can be downloaded from the Protein Databank website and dynamically processed by the system.
) are as follow: a helix (H), b turn (T), b strand (E), PPII (P), and coil (C). Assignments are based exclusively on U and W dihedral angles. The algorithm XTLSSTR (23) uses two angles and three distances to assign secondary structure from coordinates of PDB files. It assign secondary structures: a helix (H and h), 3 10 helix (G and g), hydrogen bonded b turn (T), nonhydrogen-bonded b turn (N), Extended b strand (E and e) and PPII (P and p) SEGNO (22) uses also the U and W dihedral angles coupled with other angles to assign the secondary structures. It assign a helix (H), b-strand (E and e), isolated b-strand (B and b) 3 10 helix (G and g), p-helix (I), coil (O, coded as '-' in this database) and PPII (P and p).
Acknowledgements The authors would like to thank  Stephane Te  ´letchea for corrections and comments on the manuscript. Funding: This work was supported by grants from the  Ministry of Research (France) ;  University Paris Diderot ,  Sorbonne Paris Cite' (France) ; the  National Institute for Blood Transfusion (INTS, France) ; the  Institute for Health and Medical Research (INSERM, France) ; and  'Investissements d'avenir', Laboratory of Excellence GR-Ex (France)  to R. C. , S. L. , A. G. B. and J. -C. G; Funding for open access charge:  Institute for Health and Medical Research (INSERM, France).
The causal genes of a specific phenotype are generally expected to be functionally close to each other  [11] [12] [13]. Candidate genes are therefore picked from high-throughput data on the basis of their biological proximity to core genesgenes already known to be responsible for the phenotype. We recently developed a novel approach, the "human gene connectome" (HGC). The HGC consists of a method and database describing the set of in silico-predicted biologically plausible routes and distances between all pairs of human genes. We used this method to generate a "gene-specific connectome" for each human gene, making it possible to rank all human genes in terms of their biological proximity to a core gene of interest. We have demonstrated that the HGC is an effective approach for identifying Mendelian disease-causing genes in high-throughput genetic data, by the ranking of genes according to their biological proximity to core genes known to be associated with the phenotype of interest, as demonstrated by a case study of herpes simplex encephalitis (HSE) and TLR3 pathway genes  [10].
We present here the human gene connectome server (HGCS): a novel, effective and easy-to-use interactive online interface through which users can submit any gene list generated by high-throughput techniques (or specific candidate genes of interest) for automatic ranking in terms of biological distance and connectivity p-value to the known core genes of the phenotype of interest, and the predicted route between the genes of interest. The HGCS is based on the HGC-derived concept of biological distance between gene pairs (that are either directly or indirectly connected), and provides, for the first time, an opportunity for investigators of all backgrounds to prioritize independently lists of genes of any size, according to their biological distance to core genes. We also provide a new database of 14,129 human gene-specific connectomes.
Generation of the HGC and of all human gene-specific connectomes We extracted data for all direct human protein-protein physical interactions from the updated String version 9. 05 (328,391 direct protein-protein binding interactions in the current version, versus 146,566 in the previous version, and a higher level of accuracy)  [9]  and inverted the interaction confidence scores to obtain direct biological distances, which we used to create a weighted graph of all available human genes. We applied a shortest distance algorithm to find the biological distance and route between all pairs of human genes, to generate the full HGC, with the Python NetworkX package for complex network analysis  [17]. We then generated a gene-specific connectome, by ranking all human genes according to their HGC-predicted biological distance to a core gene.
Database and online server implementation The full HGC and gene annotation data are stored on a server, as indexed tables in a MySQL database. All human gene-specific connectomes were converted into a MySQL table. The gene aliases and annotations were compiled from Ensembl BioMart  [18, 19]. The main code for ranking and annotations was written in PHP, so that it could be run directly from the server. The program uses mysqli_query() commands to access the database and generate queries. The program is designed to maximize gene discovery, by automatically detecting gene aliases if the input is not the conventional gene name, and adding the full gene name (e. g. Toll-like receptor 3 for TLR3) and alternative aliases.
Computing resources and programming languages We generated the new HGC and all derived human gene-specific connectomes with a Mac Pro computer with a 12-core Intel CPU and 96 GB RAM. The initial data filtering and text mining of the String database were performed with the Perl programming language. The HGC, gene-specific connectomes and simulations were generated with the Python programming language. The HGCS is hosted on The Rockefeller University LAMP shared server, with a VMware instance of 4 GB.
Assessment of the performance of the HGCS We assessed the power of the HGCS to detect Mendelian disease-causing mutations from the WES data of 16 patients with severe Mendelian diseases: 7 patients with HSE, 7 patients with MSMD and 2 patients with IPD. The genes with disease-causing mutations in the HSE patients were shown experimentally to be TICAM1 (TRIF, in two patients), TRAF3, TBK1 (in two patients), and UNC93B1 (in two patients)  [20] [21] [22] [23]. The genes with disease-causing mutations in the MSMD patients were shown experimentally to be IFNGR2 (in two patients), ISG15, STAT1, IL12RB1 (in two patients each), and IL12B  [24] [25] [26] [27] [28]. RBCK1 was identified as the gene with (5) Extraction, from Ensembl BioMart, of all human protein IDs, gene IDs, and their corresponding conventional and full names.
disease-causing mutations in the IPD patients  [16]. We performed standard filtering for the variants: (i) excluding synonymous variations, (ii) keeping rare variations, with a frequency <1% in the 1000 Genomes  [29]  and NHLBI Exome Variant Server (http://evs. gs. washington. edu/EVS/) databases, and (iii) accounting for sequencing batch effects and highly mutated genes (which are less likely to be morbid) by in-house filtering of variants appearing in more than 0. 6% of the patients in all disease cohorts other than for the specific disease tested (0. 6% being the most stringent criterion that does not filter out the true disease-causing gene in all patients, for which filtering allows the removal of false-positive genes abundant in WES data because they are naturally highly mutated or due to sequencing errors causing the same false mutations to appear in various WES samples).
The human gene-specific connectome database We generated and made available 14,129 human genespecific connectomes, each containing the set of all human genes ranked by their biological proximity to the specific core gene of interest. Each gene-specific connectome contains the following data categories regarding the nature of the connection between the core gene and the target genes: HGC-predicted biological distance, rank among all human genes according to distance to the core gene, p-value for connectivity, BRP, the ratio between the core gene-target gene distance and the median distance between the core gene and all human genes, the ratio between the core gene-target gene distance and mean distance between the core gene and all human genes, the sphere around the core gene (simplified percentile metrics), the predicted route (i.
Additional files 17: Table  S17  shows comparisons of the performances of the HGCS, FunCoup and HumanNet interfaces for the detection of disease-causing genes from WES data. In summary, for the 16 Mendelian disease-causing genes for the patients whose WES data were studied here, the HGCS outperformed FunCoup in 15 of the 16 tests, and outperformed HumanNet in 14 of the 16 tests. One of the major aims in studies of Mendelian diseases is to identify, at the single-patient level, a single gene associated with the disease. In this respect, the HGCS is more effective than FunCoup and HumanNet, because it is the only interface that ranks all candidate genes on the basis of their relationship to the given core gene. The other interfaces involve a binary yes/no indication of relatedness to core genes, making it difficult to differentiate between the genes related to the core gene and to identify the specific disease-causing gene.
Discussion and conclusions We present here the HGCSthe first online platform for prioritizing any number of genes on the basis of their biological distance to any number of core genes and the relationships between them. We are making available an updated database of all human gene-specific connectomes. We demonstrate the high performance of the HGCS for high-throughput Mendelian and monogenic studies. We propose an effective method for the use of the HGCS to detect new disease-related genes, based on the collation of central core genes known to be associated with the disease and their use to rank the candidate genes by distance, P-value, or BRP (a less stringent scoring, better reflecting the mutual connection when the target gene is less central, but probably associated with a higher false-positive rate).
Acknowledgments We thank  Aric Hagberg  for advice about graph theory;  George K. Lee ,  Gale Kremer  and  Joseph Alexander  for helping with the webserver;  Yelena Nemirovskaya ,  Eric Anderson , and  Tiffany Nivare  for administrative support;  Jeanne Garbarino  for student mentoring coordination;  Michael Ciancanelli ,  Ruben Martinez Barricarte ,  Janet Markle  and  Fabien Lafaille  for testing and discussion; and  Yael Pinchevsky Itan  for design advice and cover image illustration. YI was funded by an  AXA Research Fund postdoctoral fellowship. This work was funded in part by the  National Center for Research Resources  and the  National Center for Advancing Sciences (NCATS) ,  National Institutes of Health (NIH)  Grant  8 UL1 TR000043.
As part of fMRI data analysis, the pyhrf package provides a set of tools for addressing the two main issues involved in intra-subject fMRI data analysis: (1) the localization of cerebral regions that elicit evoked activity and (2) the estimation of activation dynamics also known as Hemodynamic Response Function (HRF) recovery. To tackle these two problems, pyhrf implements the Joint Detection-Estimation framework (JDE) which recovers parcel-level HRFs and embeds an adaptive spatio-temporal regularization scheme of activation maps. With respect to the sole detection issue (1), the classical voxelwise GLM procedure is also available through nipy, whereas Finite Impulse Response (FIR) and temporally regularized FIR models are concerned with HRF estimation (2) and are specifically implemented in pyhrf.
Several parcellation tools are also integrated such as spatial and functional clustering. Parcellations may be used for spatial averaging prior to FIR/RFIR analysis or to specify the spatial support of the HRF estimates in the JDE approach. These analysis procedures can be applied either to volume-based data sets or to data projected onto the cortical surface. For validation purpose, this package is shipped with artificial and real fMRI data sets, which are used in this paper to compare the outcome of the different available approaches. The artificial fMRI data generator is also described to illustrate how to simulate different activation configurations, HRF shapes or nuisance components. To cope with the high computational needs for inference, pyhrf handles distributing computing by exploiting cluster units as well as multi-core machines.
In this context, pyhrf aims to extract dynamic features from fMRI data, especially the Blood Oxygenated Level Dependent (BOLD) modality  (Ogawa et al. , 1990). The observed BOLD signal is an indirect measure of the neural activity via the oxygen variation induced by the neuro-vascular coupling. Therefore, analysis methods have to formalize a hemodynamic model in order to make inference on neural processes. However, even if BOLD variations are known to correlate with neural activity, it is difficult to disentangle the dynamics of neural and the vascular components. As the employed methodology mainly uses linear systems, dynamic processes are summarized within the so-called Hemodynamic Response Function (HRF), which is the impulse response that links neuronal activity to the fMRI signal. In fact, the package offers various tools to analyze evoked fMRI data ranging from spatial mappings such as those provided by the General Linear Model (GLM) framework  (Friston et al.
Spatial regularization, which is necessary due to the low SNR in fMRI, is not enforced in the same way between methods. In the GLM, FIR, and RFIR cases, there is no embedded spatial regularization within models. Indeed, the data are usually spatially smoothed with a fixed Gaussian kernel in preprocessing. In contrast, JDE incorporates spatial correlation through hidden Markov models. The amount of spatial correlation is automatically tuned and also adaptive across brain regions, therefore avoiding any prior invariant smoothing. pyhrf is mainly written in python with some C code to cope with computationally demanding parts of algorithms. This python choice has been made possible thanks to the nipy 1 project and especially nibabel 2 to handle data reading/writing in the NIFTI format. In terms of package maturity, pyhrf is a research tool which has the ambition to target cognitive neuroscientists and clinicians.
Efforts are made in terms of user-friendliness and the design is a trade-off between mutability which is required by methodological research where specifications change frequently and usability where user interfaces should be as stable as possible to ease external non-developer use cases. The rest of the paper is organized as follows. First, methods available in the package are presented, comprising parcellation and detection/estimation analyses. Then, the workflow and design of the pyhrf package are detailed. These cover the user interface and code snippets for the main analysis treatments, simulation framework, distributed computations and data viewer. Results illustrate the outcome of geometric and functional parcellations and their impact on detection/estimation treatments. Finally, conclusions are drawn and perspectives for future developments are indicated.
METHODS The are two main kinds of fMRI data analysis methods available in pyhrf: (1) parcellation tools that segment the brain into disjoint sets of positions and (2) activation detection/HRF estimation tools that highlight correlations between the input experimental paradigm and variations in the measured fMRI signal. The first kind comprises two spatial parcellation tools: Voronoibased random parcellation, as reviewed by  Aurenhammer and Klein (2000)  and balanced partitioning, developed in  Elor and Bruckstein (2009). The second kind comprises the GLM introduced in  Friston (1998) , the FIR model described in  Henson et al. (2000) , the RFIR model developed in  Ciuciu et al. (2003)  and the JDE approach presented in  Vincent et al. (2010)  and  Risser et al. (2011). The GLM and FIR GLM procedures are provided by nipy while RFIR and JDE are originally implemented in pyhrf.
For all these methods, we refer to their respective bibliographical references for an extensive presentation of their methodology. Nonetheless, the main aspects of these methods are summarized in what follows to allow comparison between them, especially in terms of model structure and assumptions. After detailing notations, we introduce detection/estimation methods, namely GLM, FIR and RFIR, which require the measured fMRI signal and the timing of the experimental paradigm as input. After setting the generative model common to all detection/estimation methods and a brief comparative overview, each approach is presented in more details. Subsequently, parcellation methods are presented. Spatial parcellation approaches can be applied directly to the input fMRI data and only depend on its geometry.
Functional data We consider the usual case of evoked fMRI data analysis where the experimental paradigm comprising M conditions is known. The signal measured at each time of repetition (TR) is denoted y j = {y j,n } n = 1:N where N is the number of scans. Stimulus timing onsets for a given experimental condition m = 1 : M are encoded by variable x m so that x m t = 1 if a stimulus occurs at time t up to a time step t, else x m t = 0. The time step is such that t TR and depends on the actual temporal resolution sought by the analysis method.
Balanced partitioning. The goal of balanced partitioning is to build parcels of equal sizes. In the case of a non-regular topology such as the brain, there is no morphological tool to deterministically solve such partitioning problem which is known to be NP-complete as mentioned in  Andreev and Räcke (2004). Hence, the algorithm implemented in pyhrf employs a heuristic and relies on a multi-agent system that mimics the inflation of balloons in a fixed volume  (Elor and Bruckstein, 2009) , as illustrated in Figure  2  (right). Balanced partitioning is useful to test the effect of parcel size. In pyhrf, balanced partitioning is implemented in pure python with a position-wise main loop and is hence rather slow: ∼1 min to split 6000 voxels into 20 parcels. However, this performance is sufficient since we only employ balanced partitioning in the case of small scale testing data sets or when parcels obtained on real data are too big.