dataesr/scientific-paragraphs-categorization huggingface dataset:
Results are qualitatively in agreement with experimental data, but this study dissected the influence of different protocols used for simulations, highlighting the need for more advanced techniques in order to allow a quantitative interpretation of the results. Pio et al. [8]  describe a database and a web tool for the consensus analysis of miRNA-mRNA interactions, a resource that is very useful since such interaction data is noisy and difficult to explore. The tool integrates the predictions that are provided by some known tools. Then it runs an ensemble classifier on the data with the aim of optimizing results. The work also compares ComiRNet with similar tools, showing its superiority in terms of better false positives and false negatives rates. Caravagna et al. [9]  describe in their "Software" article a Python-based package, intended for public release, useful to analyze time series signals, and specifically designed for Systems Biology data.
The work of Sanavia et al. [11]  represents one of the few methods available. The FunPat approach combines functional annotations, clustering and gene selection and allows one to identify differentially expressed (DE) genes from time-course RNA-seq experiments. In particular, the method provides clusters of DE genes associated to temporal patterns and specific biological terms. The application to two real datasets confirms the ability of FunPat to produce results with high reproducibility on different time series expression data. In the near future, thanks to the decreasing cost of sequencing, it is very likely that time course experiments with RNA-seq will become routinely used to study gene expression dynamics. D'Antonio et al. [12]  propose a novel pipeline for the cloud-based analysis of RNA-seq data, called RAP (RNAseq Analysis Pipeline).
RAP has been implemented as a combination of analysis modules, which cover the most common applications of RNA-seq data. Since it is offered as a cloud-based web application, it is especially attractive for novice users or for researchers that might not have access to the necessary computational resources. However, the provided flexibility in terms of configuration options also makes it useful for advanced users. Martignetti et al. [13]  describe a new intuitive computational approach to infer cancer-related regulatory activity of miRNAs on transcriptome, by combining miRNA and mRNA expression data with sequence-based target predictions. The Authors apply the method to breast cancer samples and identify potentially interesting miRNA-target correlations, in particular in the triple negative tumor subtype.
[19]  presents a bioinformatics resource for the analysis of grapevine genomics data, BioWine. Its main added value resides in its ability to integrate RNA-seq experiments and in its connection to many external databases, which also allows users to exploit gene set enrichment. The web interface is very friendly and effective, so that the platform can be a useful tool for data analysis by end-users and it can become a standard interface for the analysis of genomes of agricultural interest. Politi et al. [20]  present a work on the analysis of the effect of cell-to-cell variability in the parameter estimation from experimental data, in the context of synthetic biology. This work provides an analysis of how various kinds of noise affect input-output function of gene regulatory circuits, with special care to potential modularity of the circuits, i.
All ingredients are present to integrate gene order and sequence evolution models, yet this leap has not been taken, mostly because of computational issues. Reconstructing gene order histories is often hard  [18]. A computational solution to reconstruct gene orders and scale up with the size of datasets is to see a genome as a set of independently evolving adjacencies, i. e. the links between consecutive genes  [19]. One can reconstruct ancestral gene orders following three main steps: • Group potentially homologous adjacencies (they connect homologous pairs of genes) • For each group, reconstruct the common history of adjacencies, by recovering ancestral ones • Assemble the ancestral adjacencies in each ancestral species to obtain ancestral chromosomes The assumption that adjacencies evolve independently allows quick computations at the second step: the size of the data can be an order of magnitude larger than without the assumption.
We base on the parsimony algorithm of DeCo  [10]  that we adapt to Felsenstein's maximum likelihood algorithm  [1]  with a birth/death process that models the evolution of adjacencies. We compute the most likely adjacencies in ancestral genomes and the quantity of gains and losses of adjacencies in all the branches of a species trees, thus providing an insight into the dynamics of rearrangements in these lineages. The model is implemented in Bio++  [23] , the present form allowing at most one duplication node between two speciation nodes in gene trees. We compute the likelihood of gene orders in a set of 12 drosophila whose genomes are annotated in the Ensembl Metazoa  [24]  database. We optimize branch lengths in a species phylogeny and construct ancestral genomes. We compare the results with a parsimony approach, showing that while most adjacencies inferred by parsimony have a good probability, a non negligible proportion (> 11%) are not supported (posterior probability < 0.
• Compute the likelihood of the adjacency given the observed adjacencies. • Compute a posteriori probabilities of presence of ancestral adjacencies. The likelihood computation for one adjacency tree allows to obtain a likelihood for the whole dataset by multiplying all likelihoods, considered as independent, and to optimize parameters. These can concern branch lengths on the species tree, or a law of differential fragility for different genome sites, modeling different susceptibility to rearrangements among chromosomal regions  [25, 26].
Likelihood computation Likelihood is computed in the rooted tree of possible adjacencies in a bottom-up way. From here, we describe adjacency nodes with single letters for better clarity. We denote as D i the data that is below node i. Take a speciation node i in the tree, which descendants are nodes j and k (with branch lengths respectively t 1 and t 2 ). Let x, y, z ∈ [0, 1] be the respective states of i, j, k. We compute the partial conditional likelihoods of D i in the classical way: L(D i |x) = ( 1 y=0 P xy (t 1 )L(D j |y)) • ( 1 z=0 P xz (t 2 )P(D k |z)) (7) Now, let i be a duplication node with two children j and k. Since it concerns only one branch in the species tree, there is a unique branch length t involved. We defined the model of evolution such that the contribution of one child is included using the basic transition matrix P(t) and the contribution of the other child (the child on the duplicated branch) is included using the transition matrix N 1 (t).
Implementation and availability We implemented the model of evolution and the likelihood calculation algorithm in the Bio++ library (http:// biopp. univ-montp2. fr/). The algorithm that builds the trees of possible adjacencies was implemented in a separate program which also uses Bio++. Reconciliation was performed with DeCo  [10]. All the analytical formulas in our model were computed using Maxima (see Additional file 1). These programs are available upon request to the authors.
Dataset We selected 12 drosophila species from the Ensembl Metazoa  [24]  database. We used the species tree from  [28] , the gene trees and the chromosomal locations from Ensembl Metazoa. We pruned the gene trees to keep only the drosophilae clade, and reconciled them with the species tree using  [10]. We reduced the dataset to the 9223 gene trees with at most one duplication between two speciation nodes in reconciled gene trees. We built a set of extant adjacencies by connecting consecutive genes in the reduced dataset, provided they were on the same chromosome or scaffold. We built 13059 trees of possible adjacencies from this set of reconciled gene trees and extant adjacencies. By maximum likelihood, we optimized the branch lengths of the species tree using our model of evolution, from the 3608 trees of possible adjacencies without any duplication (Figure  4 ).
Ancestral adjacencies We computed posterior probabilities of presence and absence for all possible ancestral adjacencies, given the optimized branch lengths. We report in Table  1  the number of genes and adjacencies in extant and ancestral species. Note that the difference between the number of genes and adjacencies in extant species gives the number of chromosomes or scaffolds. This goes from the well assembled melanogaster genomes in 8 scaffolds to simulans with 445 scaffolds, with all intermediaries. Despite the fact that assembly is incomplete, we have enough adjacencies in the dataset to make a signal for the reconstruction of ancestral adjacencies. And indeed, 54222 adjacencies with posterior probability > 0. 9 are proposed. The signal is weaker for ancient species, as in ANC10, with only 2360 adjacencies for 8026 genes, depicting a very fragmented ancestral genome.
Comparison with parsimony We compare the results with those obtained by  [10]  (DeCo software) on the same data (Figure  5 ). DeCo reconstructs ancestral adjacencies according to a parsimony principle, whereas we reconstruct all possible ancestral adjacencies along with a posterior probability of presence for each one. Most of the adjacencies reconstructed by Column "genes" is the number of genes in the dataset. Column "coverage" is the proportion of the genes in the dataset to the total number of genes annotated in Ensembl. Column "adjacencies" is the number of adjacencies in an extant genome or adjacencies in ancestral genomes with a posterior probability > 0. 9. Column "genes with more than 2 adjacencies" is the number of genes involved in more than 2 adjacencies of posterior probability > 0.
Discussion Probabilistic models of evolution have at least four advantages over parsimony approaches: they provide more accurate results in presence of many mutations; they provide a natural support scheme of the results in the form of a probability of ancestral states; the likelihood is computed by an integration over all scenarios rather than choosing only one, even if optimal; and several models at different scales of the genome can be integrated. But most probabilistic models of gene order evolution are computationally intractable on large datasets, working with too large state spaces. Coding gene order by binary characters is a solution, like for many characters characterized by their presence or absence. Then it is possible, like in  [30] , to use a standard model of binary sequence evolution to achieve a probabilistic reconstruction of phylogenies and ancestral gene orders based on the presence/absence of adjacencies in extant species.
We found that a significant number of adjacencies inferred by parsimony on a drosophila dataset are not supported by a probabilistic model. It corroborates the usual findings in evolutionary models each time reasonably distant species are compared, whether it is sequence evolution  [1] , gene content evolution  [32] , or gene order evolution  [12]. There are still several limitations to this work. For the moment the computation time is one of them, the efficiency of optimization algorithms coupled with our model allowed us to work only on a small fixed phylogeny. Theoretically we could even infer phylogenies, coupling a model of sequence evolution and such a model of genome organization evolution, but it will necessitate algorithmic progresses. Another limit is that our current implementation only handles independent duplication events, although we are also developing a model for joint duplications.
Acknowledgements Publication of this work is funded by the  Agence Nationale pour la Recherche ,  Ancestrome  project  ANR-10-BINF01-01. This work was performed using the computing facilities of the  Computing Center LBBE/PRABI. This article has been published as part of  BMC Bioinformatics Volume 16 Supplement 14 , 2015: Proceedings of the 13th Annual Research in  Computational Molecular Biology (RECOMB) Satellite Workshop on Comparative Genomics: Bioinformatics. The full contents of the supplement are available online at http://www. biomedcentral. com/bmcbioinformatics/supplements/ 16/S14.
Because Heligeom is bundled with the freely available Python/C++ library PTools  [31, 49] , our approach can therefore benefit from existing PTools functionalities such as coarse graining, energy calculations, and diverse sampling protocols, which can be combined to arrive at novel strategies for investigating helical assemblies. Although more complex systems may also be treated using these tools, our specific targets in this article are recombinase protein assemblies, principally RecA, in which multiple association states have been documented. As will be seen, the RecA recombinase may be decomposed into two regions: a rigid core domain and more mobile regions. This decomposition allows an indirect treatment of the latter's role. We show that this approach is well adapted for modelling a variety of fiber morphologies that correspond remarkably well to both known and proposed forms of recombinase protein assemblies.
Overall approach The approach we present combines the PyATTRACT and Heligeom modules of the PTools Python/C++ library  [31] , along with other functions of the library, in order to investigate the geometry of open oligomeric filaments (a schematic view of the overall approach is presented in Fig. 1 ). The process starts with the structure of a unique monomer of the system under study, which is reduced to coarse grain representation and then docked against itself using AT-TRACT  [49, 50]. The docking results are then processed by Heligeom to provide the helical parameters corresponding to regular assemblies based on each binding mode (see below). Postprocessing allows retaining only the most favorable binding modes for regular self-assembly of the monomers. The generated structural families can then be investigated in terms of their Binding geometries with near-cyclic organizations but suffering from steric clashes are submitted to an optimization process in which they are adjusted towards the two closest cyclic geometries (S1 Protocol, supporting information).
Screw transformations with Heligeom Heligeom is a Python module that interfaces with the Python/C++ library PTools  [31, 49]. It is packaged with the latest version of PTools and includes a variety of scripts of varying complexity, including those developed for the present study (Boyer et al. , manuscript in preparation). The fundamental operation of Heligeom centers on the definition of the screw transformation. In general, the coordinates of a given monomeric unit can be derived from those of another through such a transformation  [52] , defined by the position O and direction O of a screw axis, a rotation of angle θ around this axis and a translation value trans parallel to the axis (shown schematically in S2 Fig,  supporting information ). Regular repetition of the screw transformation generically leads to a helical shape.
In addition to extracting screw parameters, the same Heligeom command can generate fiber structures of arbitrary length. Heligeom can also be used to simulate the assembly of monomers along a non-linear path. Once the screw transformation has been defined from the structures of two interacting monomers, oligomeric assemblies can be reconstructed along any given curved axis. Details can be found in S1 Protocol of the supporting information. In the present study, scripts using Heligeom have been developed, either as post-processing tools to extract screw parameters and build fiber models from ATTRACT docking simulation output, or to directly incorporate screw analysis into sampling procedures such as Monte Carlo exploration (see below).
PDB files Coordinate data was obtained from the Protein Data Bank  [53]. Two crystal structures of RecA were used, with PDB codes 2REB  [21]  and 3CMW  [23]. These structures are considered to be representative of the two known forms of the RecA filament: inactive or compressed (observed in the presence of ADP or without any cofactor) and active or extended (observed in the presence of non-hydrolyzable ATP analogs and with bound DNA)  [14]. RecA in these two crystal structures presents large geometry differences at three locations: the N-terminal domain (residues 1-37) and the loops L1 (residues 156-165) and L2 (residues 194-210), which are disordered in the 2REB structure. These regions, together with terminal residues 1-5 and 329-333 which are disordered in 2REB, were pruned before rigid body docking was performed (next section).
The intrinsically disordered C-terminal residues 324-352 of the RecA protein are not present in either the 2REB or the 3CMW crystal structures. The remaining monomer core structures, respectively referred to as 2REB core and 3CMW core differ by less than 1 Å Cα-RMSD. Structure 2REB was solved at a resolution of 2. 3 Å in space group P6 1 with no bound DNA or cofactor. It is very similar to the structure with PDB code 1REA, which was obtained with ADP as a cofactor but which contains only Cα atoms; the Cα-RMSD (root mean squared deviation) between the two entries is 0. 3 Å. For this reason, the 2REB structure has been assimilated to the inactive form of the filament. The 3CMW structure consists of 5 monomers expressed as a single-chain fusion protein (space group P2 1 2 1 2) and was crystallized with ADP-AlF 4 -Mg, a non-hydrolyzable analog of ATP, in the presence of DNA with a resolution of 2.
Discussion In this study, we have taken an integrated approach to investigating RecA supramolecular assemblies, by sampling possible modes of association via docking and Monte Carlo simulations coupled with helical analysis using a new software tool, Heligeom, in the PTools modelling package. The first result is that interactions between rigid core regions of successive RecA monomers suffice to account for all known oligomeric forms of RecA assembly, including those that have been postulated but never solved at atomic resolution such as lefthanded helices. An important role for the RecA rigid core in filament morphology was expected because of its large contribution to the interface in the 2REB and 3CMW forms (Table  1 ). However, because flexible regions contribute by approximately the same amount, the central role of the rigid core needed to be established.
The building-block role for the RecA rigid core region gives latitude to flexible and mobile regions of the protein  [63] , single side-chain mutations  [64]  as well as external factors (ATP, ions, DNA  [65]  or associated proteins  [58] ), to modulate energetic preferences among binding modes and possibly to actively control the passage between binding modes, as postulated by Chen and collaborators  [19]. Our results suggest for example that the ATP cofactor stabilizes the X Ã form of RecA association (S2 Dataset and S7 Fig, supporting information). Karlin and Broccieri  [66]  proposed that the stabilization of the filament active form may involve the specific formation of inter-monomer salt bridges. Indeed salt bridges involving highly conserved residues Glu96-Lys250, Glu154-Arg176 (S1 Dataset and S3 Fig, supporting information) can be found deeply buried in the monomer-monomer interface in structure 3CMW, but not precisely in the way that was anticipated.
S1 Protocol. Filament construction with PTools/Heligeom. (PDF) S2 Protocol. Exploring RecA interfaces with PTools/Heligeom. (PDF) S3 Protocol. Measuring protein filament groove width. (PDF) S1 Dataset. Interface residues in the X and X Ã modes of association. (PDF) S2 Dataset. Influence of RecA flexible/mobile components on its binding modes. (PDF) S1 Fig. Scheme of geometric filtering.
(EPS) S2 Fig. Scheme of the Heligeom screw motion model between two monomers A and B. The screw axis is defined by point O and direction Δ. The transformation from monomer A to monomer B is the combination of a rotation θ around the axis and a translation trans along the axis. (EPS) S3 Fig. Conserved surface residues. (Top) residues on the 2REB core surface have been colored according to the sequence conservation index (CI) of 63 RecA proteins (Karlin and Brocchieri, J Bacteriology 1996, 178, 1881-1894); selected values are reported in Table S1-1 of S1 Dataset, supporting information; (bottom) the residues have been colored according to the docking results; reproduced from Fig. 2D, main manuscript. (EPS) S4 Fig. Helical parameters from Heligeom for regular assemblies obtained from ATTRACT docking simulations in RecA.
The authors acknowledge support from  Université Paris 6  (to B. B. ), the  French CNRS  ( Centre National de la Recherche Scientifique , to J. E. ),  IFCPAR (Indo-French Center for the Promotion of Advanced Research , to C. H. R. ), the  German DFG (Deutsche Forschungsgemeinschaft , within  SFB749 , project  C5 , to M. Z. ), and the " Initiative d'Excellence" program  from the  French State  (Grant " DYNAMO ",  ANR-11-LABX-0011-01 , to C. P. and C. H. R. ). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
[21]. Primers Vaiomer 1F and 1R include specificity for the 16S rDNA gene of 95% of the bacteria in the Ribosomal Database Project and part of the P5/P7 adapter targeted by the second PCR step (CTTTCCCTACACGAC and GGAGTTCAGACGTGT). Our primer design and 2 step PCR strategy allow shorter primers which are more suitable for the amplification of bacterial DNA extracted from tissue samples carrying large amounts of PCR inhibitors and eukaryotic DNA. This PCR was performed using 2 U of a DNA-free Taq DNA Polymerase and 1x Taq DNA polymerase buffer (MTP Taq DNA Polymerase, Sigma). The buffer was complemented with 10 nmol of dNTP mixture (Euromedex, Souffelweyersheim, France), 15 nmol of each primer (Sigma) and Nuclease-free water (Ambion, Life Technologies) in a final volume of 50 μl. The PCR reaction was carried out on a Veriti Thermal Cycler (Life Technologies) as follows: an initial denaturation step (94°C for 10 min), 35 cycles of amplification (94°C for 1 min, 68°C for 1 min and 72°C for 1 min) and a final elongation step at 72°C for 10 min.
Statistics, data analysis and figures preparation Custom R and Perl scripts were used to perform the data acquisition from Mothur output files, the statistical data analysis and generate the figures. The 16S metagenomics profiles are displayed using heatmaps and barplots. Heatmaps were generated using the gplots CRAN library (v2. 11. 0) to plot the relative abundance of sequences for each sample assigned to the taxa at the different taxonomic levels. For better visualization of low abundant taxa, the data were log10-transformed. Barplots represent the proportion of sequences classified to taxa at the different taxonomic levels. The proportions presented are either those calculated on single samples or averages calculated over different sequencing triplicates. For clarity, the low abundant taxa (mean proportion <0.
To determine the suitable sequencing depth for each tissue, a rarefaction curve was plotted for each sample. The rarefaction curve plots the number of OTU as a function of the number of sequences sampled in the clean sequence working set. We used a step of 100 sequences for the X axis and sampled each step 1000 times to calculate the average number of OTU plotted on the Y axis. For better readability, only the median curve for each tissue is shown. Sequencing data are available on the European Nucleotide Archive (ENA) under the study ID "PRJEB10949" at http://www. ebi. ac. uk/ena/data/view/PRJEB10949. Custom R and Perl scripts are available on sourceforge at http://sourceforge. net/projects/tissue-microbiota/files/.