dataesr/scientific-paragraphs-categorization huggingface dataset:
SNiPlay is a web-based tool for detection, management and analysis of genetic variants including both single nucleotide polymorphisms (SNPs) and InDels. Version 3 now extends functionalities in order to easily manage and exploit SNPs derived from next generation sequencing technologies, such as GBS (genotyping by sequencing), WGRS (whole gresequencing) and RNA-Seq technologies. Based on the standard VCF (variant call format) format, the application offers an intuitive interface for filtering and comparing polymorphisms using user-defined sets of individuals and then establishing a reliable genotyping data matrix for further analyses. Namely, in addition to the various scaled-up analyses allowed by the application (genomic annotation of SNP, diversity analysis, haplotype reconstruction and network, linkage disequilibrium), SNiPlay3 proposes new modules for GWAS (genome-wide association studies), population stratification, distance tree analysis and visualization of SNP density.
INTRODUCTION Single nucleotide polymorphisms (SNPs) are genetic variants commonly used to identify candidate genes and genotype-phenotype association studies. With next generation sequencing (NGS), genome sequencing is becoming inexpensive and routine, and the discovery of large numbers of SNPs is facilitated. Indeed, with the availability of reference genome along with sequencing data derived from WGRS (whole-genome re-sequencing), GBS (genotyping by sequencing), RAD-Seq and RNA-Seq technologies, millions of variants including SNPs are easily released. To make exploration and large scale analyses of genomic variations simple and accessible, there is a need for applications based on efficient databases and convivial interfaces. Most of the existing tools are command-line  (1)  or dedicated to one type of analysis like GWAS (genome-wide association studies)  (2, 3)  or phylogeny  (4).
Here we report the version 3 of the SNiPlay application (5) that shows significant improvements for managing next generation data in terms of data filtering, analysis and visualization. Indeed, we improved the performance of SNiPlay for filtering large NGS datasets in a few seconds and for providing genome-wide analyses and visualizations. In addition to the previous analyses allowed by the application (genomic annotation of SNP, diversity analysis, haplotype reconstruction and network, linkage disequilibrium), SNi-Play3 proposes new modules for GWAS, population stratification, distance tree analysis and visualization of SNP density. To the best of our knowledge, no other web application allows the integration of a so large set of analyses from massive genotyping data at the whole-genome level.
PROCESS OVERVIEW The SNiPlay pipeline components have been updated to be able (i) to manage variants data derived from NGS technology and (ii) to process data at the whole-genome scale. One significant improvement is the ability to handle the standard VCF format (variant call format) as input files. Indeed, with the recent emergence of powerful software packages dedicated to analysis of NGS data such as VCFtools  (6)  or W296 Nucleic Acids Research, 2015, Vol. 43, Web Server issue Snpeff  (7)  it has become possible to offer biologists an efficient complete analysis of a massive dataset at once in a few minutes. An overview of the process is presented Figure  1A. The application offers numerous functionalities with attractive display layouts including GWAS, population structure, haplotype and linkage disequilibrium (LD) analyses, diversity analysis, SNP comparison between groups and general statistics about polymorphisms.
Starting from a VCF file as entry point, the process first annotates the variants using an annotated reference genome to produce a new VCF file from which variants and genotyping data can be then mined and sent into a series of modules in charge of various processes. User has then the possibility to analyze variants either at the genome level or at the gene level. Most of the modules process genome-wide studies except for haplotype analyses--successively powered by the Gevalt software  (8)  for haplotype reconstruction and Haplophyle (9) for haplotype network--for which the analysis is done gene by gene or for user-defined genomic regions if do not exceed 200 variants (up to 200 regions). In this latter case, genes can be selected or directly provided as a list, while genomic regions can be defined by entering the limits, the application will loop and process these regions.
The different analyses are proposed to be computed either for a dataset directly uploaded by a user or for a genotyping dataset already available in the server as a VCF flat file associated to an annotated genome. All tools and workflows are incorporated into the Galaxy framework, one of the most popular analysis platforms for NGS data, that offers easy access to numerous bioinformatic applications and strongly supports reproducibility of analysis steps  (10) (11) (12). In addition to the pipelines offered through the website, our Galaxy workflows also include a pre-processing pipeline for SNP calling based on the GATK package (13) (Figure  1B ).
Variant annotation SnpEff  (7)  was implemented for variant annotation. When a VCF file is submitted, SnpEff is computed to rapidly categorize the effects of variants in the reference genome sequence selected by the user. SnpEff annotates variants based on their genomic locations (annotated genomic locations can be intronic, exonic or intergenic) and predicts coding effects (mainly synonymous or non-synonymous amino-acid replacement). At the time of writing, about 20 plant genomes (for which genome sequence and Generic File Format (GFF3 annotation file) have been indexed and built into a SnpEff database) are available in the website. The process can be also applied to any other plant, animal or bacteria species, by providing the genome file and its structural annotation. Users can request for a new genome to be added as an available genome database.
Following the SnpEff analysis, the process also calculates for each intragenic variant its position within the gene, so that it can be subsequently reintegrated in a FASTA sequence file for diversity analyses on genes for instance. An option anticipates the situations where the input VCF contains variant positions on the genes instead of chromosomes. This can be the case for RNA-Seq experiments for which reads may have been mapped on coding sequences (CDS or full length mRNAs). This option activates a module that uses the GFF annotation file to locate variants on the chromosomes and recode the VCF file prior to launch SnpEff.
SNP filtering By establishing 'on the fly' a data matrix combining a subset of individuals and a subset of chromosomes or genes, variants can be filtered out using numerous criteria such as MAF (Minor Allele Frequency), type of variants (SNP or InDel) and missing data ratio. While former version of SNiPlay was built on MySQL database to store and query variants, the version 3 is now based on new technologies or approaches more efficient for the mining of millions of variants at the whole-genome level. For this purpose two methods are thus proposed: (i) The first one is based on VCFtools program  (6)  providing easily accessible methods for working with complex genetic variation data in the form of VCF files. Based on flat files, its binary executable makes it possible to quickly filter out specific variants for a subset of individuals (combining, amongst others, -indv, -chr, -maf parameters).
Population structure analysis Population stratification represents a major challenge in genome-wide association studies. Admixture (  15 ) is a software tool for maximum likelihood estimation of individual ancestries from multi-locus SNP genotype datasets. It uses the same statistical model as STRUCTURE (  16 ) but calculates estimates much more rapidly using a fast numerical optimization algorithm and is thus more appropriate for integration in a web server. Setting different values of K to be tested, K being the number of subpopulations in the panel, the server provides the percentage of admixtures (Q matrix) for each K-value and plots these Q estimates into comprehensive ready-to-print images. In addition, it also plots the standard error of the cross-validation error estimates so that user can assess the best/correct value for K (Figure  2 ).
As alternatives, other methods can also be used to evaluate population structure such as the multidimensionalscaling (MDS) method. Similarly to the principalcomponent analysis and in contrast to dendrogram inferring methods that generate hierarchical structures, this technique produces two-dimensional or three-dimensional plots in which the entries are spread according to their relatedness. The MDS analysis in SNiPlay makes use of the plink software  (17)  and outputs a graphical plot of the studied individuals in two or three dimensions, so that populations can be easily identified. In addition, to illustrate the panel organization, a distance tree based on SNP data can also be generated from a whole allelic dataset, combining dnadist from the PHYLIP package  (18)  for distance matrix calculation and FastME  (19)  for tree reconstruction.
Genome-wide association studies (GWAS) GWAS have proven a useful technique for identifying genetic loci involved in the control of agronomic traits. Using an efficient implementation of softwares devoted to GWAS, including TASSEL (20) (General Linear Model (GLM) or Mixed Linear Model (MLM) methods) or MLMM (Multi-Locus Mixed Model) R package  (21) , phenotypic values of traits can be uploaded and mapped with an accurate model in order to estimate causal variants. This module offers a user-friendly interface that includes interactive Manhattan plots of association P-values along the chromosomes, as well as the QQ-plots commonly used for GWAS to show the importance of correcting for population structure and kinship information (Figure  3 ). Identifying causal SNPs remains a key challenge in GWAS era, as many variants fall into non-coding regions.
Other facilities General statistics. Using VCFtools, this module provides and displays various statistics about the dataset such as annotation statistics, type of variations or heterosygosity and missing data per sample. SNP density across genome. SNP density can be investigated and compared between different species, subsets of individuals or categories of SNPs (intronic versus exonic for instance). In addition, density of SNPs is also calculated across several samples taken individually and displayed as a heatmap, reflecting thus the difference with the reference genome. This functionality can be of great interest for whole genome structure analysis.
Diversity analyses. With the same philosophy, various diversity indices (nucleotide diversity (), Tajima's D-test of neutral evolution  (23) , fixation index Fst and Ts/Tv ratio) can be calculated for a predefined size of sliding window using VCFtools and displayed along chromosomes. Moreover, a gene by gene analysis performed by the Egglib libray (24) allows scanning for candidate genes evolving under selection. Haplotype analysis from phased VCF. The VCF format anticipates the coding of allele phasing information (allele pairs specified by 0|1 instead of 0/1 if phased with the previous variant) in order to define haplotype blocks. Thus, if provided in the VCF, a dedicated module of SNiPlay takes advantage of the phasing information to reconstitute and output haplotype blocks of high SNP density regions for which there is a continuum of phasing at the reads level.
IMPLEMENTATION SNiPlay is written in Perl; the SNiPlay suite is available as a one-click installation from any local Galaxy instance via the Galaxy Tool Shed (http://toolshed. g2. bx. psu. edu/) or can be downloaded from the SourceForge code source repository (http://sourceforge. net/projects/sniplay/), and installed on a web server. More information on the different installation procedures can be found in the package. The web application is connected to an High Performance Computing (HPC) cluster that manages the jobs sent by the web server (using Opal Perl services and SGE). The install procedure of the application includes a configuration file which has to be adjusted in order to indicate if jobs must be computed locally (by default) or in a remote cluster. Except for tree rendering and haplotype network, all graphical layouts are based on the Highcharts API (http://api.
The Different Views of Protein Structures The primary sequence of the protein-the succession of amino acids-is assumed to encompass all the information necessary for its function. The protein structures resolved from X-ray crystallography or Nuclear Magnetic Resonance (NMR) (see Figures  1A, B ) can be obtained in the Protein DataBank format (PDB,  Bernstein et al. , 1977; Berman et al. , 2000). From the very beginning, theoreticians or experimentalists have described local protein structures by using three states (see Figure  1C ,  Corey and Pauling, 1953; Kabsch and Sander, 1983; Eisenberg, 2003). Two of them are repetitive structures stabilized by hydrogen bond patterns, namely the α-helices and the β-sheets (composed of β-strands). These structures are connected with more variable structures, i.
, 1977; Berman et al. , 2000) , containing the 3D atomic coordinates. (B) The atoms are bound to build the protein backbone and side-chain residues. (C) From this information, secondary structures are performed  (Kabsch and Sander, 1983). (D) From crystallographic data, B-factors are analyzed underlining (E) rigid to flexible residues. (F) More precise analyses shows missing residues revealing. (G) disorder regions  (Uversky et al. , 2000; Dunker et al. , 2001). (H) Interestingly same or similar proteins can be found in the PDB  (Berman et al. , 2000) , and (I) in numerous times with the missing regions resolved, leading to (J) a more complex definition and an ambiguity between flexibility and disorder. Protein visualization was created by the program PyMOL (http://www. pymol. org, Delano, 2013).
The proteins used are two proteases (PDB codes 1dbi chain A, and 1wmd chain A, for this last only residues 1-306 are shown for more clarity). Berman et al. , 2000). These ambiguous regions, termed Dual Personality Fragments (DPFs, see  Dunker, 2007; Zhang et al. , 2007) , complicate the distinction and per se the definition of disorder versus flexibility (see Figure  1J ).
RMSDA formula where V 1 is the vector of 8 dihedral angles extracted from the 5 residues long window, and V 2 is the 8 vector of dihedral corresponding to the individual PB type. The PB with the lowest RMSDA, is assigned to the corresponding position for that window. This PB captures the overall local conformation and approximates the transition along the main-chain smoothly. PB assignments can be done using the Python PBxplore tool (https://github. com/pierrepo/PBxplore, in preparation). The result is a translation of a 3D structure into a 1D sequence of PBs. Interestingly, the subtle differences between protein conformations can be captured by the assignment of the PB sequences. By analyzing the variation of PBs assigned at a given position for multiple conformers, the local conformational properties and corresponding changes can be easily identified.
This PB derived-entropy index is an interesting feature of PBs, which can be used to analyze PB prediction (de  Brevern et al. , 2000)  or an ensemble of structures, corresponding to the same protein solved in different experiments, or to several structures extracted from MD simulation  (Jallu et al. , 2012). Note that PBxplore can be used to calculate N eq , and to visualize in various ways the PB variation for each position from a collection of models or through a MD trajectory (de  Brevern et al. , 2005). Other interesting SAs used in the flexibility context. We have proposed an extension of our SA through a novel library consisting of 120 overlapping structural classes of 11-residues fragments, firstly defined as PBs series  (Benros et al. , 2006). This library was constructed with an original unsupervised structural clustering method called the Hybrid Protein Model (de  Brevern and Hazout, 2003).
, 2004; Tung et al. , 2007 ). An interesting point was the analysis of the correlation between local flexibility and variability in the assignment. Thereafter, they have developed GSATools, (http://mathbio. nimr. mrc. ac. uk/wiki/GSATools,  Pandini et al. , 2013) , composed of a set of programs, that encode ensembles of protein conformations into alignments of structural strings using their Structural Alphabet. This software package is particularly well suited for the investigation of the conformational dynamics of local structures, the analysis of functional correlations between local and global motions, and the mechanisms of allosteric communication. It performs a wide range of statistical analyses using a various set of external tools, mainly from R  (Ihaka and Gentleman, 1996)  and Python  (Python Software Foundation, 2015).
The software has been integrated into the GROMACS environment  (Lindahl et al. , 2001; Van Der Spoel et al. , 2005). The user must compile it specifically. GSATools was used to finely analyse the NtrC receiver domain and its homologs CheY and FixJ. For this purpose, different conformations of the protein extracted from a MDs simulation were encoded. The distributions of SA strings were used to compute different mutual information matrices using information theory. Remarkably, they were able to detect allosteric signal transmission from protein dynamics  (Pandini et al. , 2012). They also applied this methodology to a larger set of related proteins to show how evolutionary conservation and binding promiscuity have opposite effects on intrinsic protein dynamics  (Fornili et al. , 2013). Other examples are provided in Section 4.
Interestingly five years later, an attempt to generate better models with the best available methods was not crowned with success (de  Brevern et al. , 2009; Smolarek et al. , 2010). It took us one year to build such models (models are available at Model Archive website (http://modelarchive. org/,  Schwede et al. , 2009). The N terminus is particularly important in the infection by Plasmodium vivax  (Batchelor et al. , 2014). It is nearly 55 residues long and different disorder prediction methods (i. e. , DisEMBL,  Linding et al. , 2003 or PrDOS, Ishida and Kinoshita, 2007) , predicted as partially disordered, with the beginning of the sequence as fully disordered. To evaluate the different conformational states of the Duffy protein, we carried out numerous MDs simulated annealing simulations with the GROMACS software  (Lindahl et al.
Analyses of static structures (e. g. , B-factor, electrostatics), give only a limited view of the protein complex behavior, contrary to MDs simulations which are able to some extent, to reproduce the inner dynamics of protein structures. α and β subunits of integrins are associated to rigid, flexible and even disorder properties (such as Duffy protein presented in the section above). We ran independent MDs simulations on different systems, i. e. , the wild type but also variants and mutants, using GROMACS MDs package  (Van Der Spoel et al. , 2005)  to examine specific regions of αIIbβ3. We observed different opposite behaviors depending on the region and mutants studied. Hence, we studied the Cab3 a+ alloantigen resulting from a Leu841Met substitution in the αIIb chain. This polymorphism might result in severe life-threatening thrombocytopenias.
The building of the protein dataset was quite strict leaving only 76 high quality complexes representing very different configurations with free and bound forms  (Swapna et al. , 2012). Accordingly, structural changes occurring between the free and bound forms of the protein were analyzed using three different measures: the Cα root mean square deviation, the percentage of PB change and a specific PB substitution score. This last score relies on a PB structural substitution matrix that quantifies the cost to replace a given PB by another PB. The more similar the PBs, the more favorable the substitution score. Consequently, this score permits to quantify the conformational change by distinguishing similar PBs from to the most distinct ones. Comparison between unbound and bound forms shows that significant structural rearrangement occurs at the interface but also in regions away from the interface upon the formation of a highly specific, stable and functional complex.
, 2011). Protein families with more diverse interactions exhibit less average disorder over all members of the family  (Fong and Panchenko, 2010). Inter-domain linkers are evolutionarily well conserved and are constrained by the domain-domain interface interactions  (Bhaskara et al. , 2013). An interesting resource is the ComSin database which provides a collection of structures of proteins solved in unbound and bound form, targeted toward disorder-order transitions  (Lobanov et al. , 2010).
, 2012; Xin and Radivojac, 2012)  by subtle but common mechanisms of allostery. Some prediction approaches and are included into dedicated databases  (Matlock et al. , 2015) , but few analyzed precisely the whole PTMome. This led us to conduct a deep analysis of structures of the same protein with or without PTMs. As an example, we selected 157 PDB chains of the human Cyclin-dependent kinase 2 (UniProt AC: P24941) in complex form, and 222 PDB chains of unbound monomer. Based on data from PTM-SD  (Craveur et al. , 2014) , a database of structurally solved and annotated post-translational modifications, 112 chains among the 157   Huntington et al. , 2000)  showing the strand inserted in the β-sheet after cleavage, (B) the uncleaved form (PDB code 1HP7,  Kim et al. , 2001)  showing the wild whole loop.
Indeed, in a recent paper,  Scott and Strauss (Scott and Straus, 2015)  underlines the bias related to the use of RMSD, which needs beforehand an optimally superimposed approach often remains as rigid bodies. They proposed an elegant method, fleximatch, of protein structure comparison that tries to take flexibility into account. As it was done for protein superimposition methods  (Yang and Tung, 2006; Tung et al. , 2007; Tung and Yang, 2007; Le et al. , 2009; Budowski-Tal et al. , 2010; Gelly et al. , 2011; Leonard et al. , 2014) , SA is an efficient approach, not considering proteins as rigid bodies. We underline the interest of our approach based on Protein Blocks with the PBxplore tools (https://github. com/pierrepo/PBxplore, in preparation) or GSAtools (http://mathbio. nimr. mrc. ac. uk/wiki/ GSATools,  Pandini et al.
, 2013)  in other cases. The use of SAs and the development of associated metrics such as N eq is required to study the details and begin to understand the complexity of protein flexibility. It allows discriminating flexibility from mobility and deformability, which is not currently considered by other available methods. Nonetheless, it also had drawbacks as no simple threshold will guide the researcher to point out that certain segment is THE highly flexible part and not the other, same as for RMSF. In the same way, use of information theory with GSATools also requires expertise. Moreover, as SA represents a simplification of the 3D description, its results can be compared to the Normal Mode Analysis based on Elastic Network Model  (Suhre and Sanejouand, 2004; Tiwari et al. , 2014; Eyal et al.