dataesr/scientific-paragraphs-categorization huggingface dataset:
Global models detect un-annotated breakpoints better than default models Leave-one-out cross-validation was used to estimate the breakpoint detection of each model. Figure  6  shows the error rates of each model, across both annotation data sets. It is clear that the training procedure makes no difference for models pelt. default, glad. default, dnacopy. default, cghseg. mBIC, gada. default and cghFLasso, which are default models with no smoothness parameters. Each of these models is inferior to its respective global model in terms of breakpoint detection. The large error of these models suggest that the assumptions of their default parameter values do not hold in the neuroblastoma data set. More generally, these error rates suggest that smoothness parameter tuning is critically important to obtain an accurate smoothing of real copy number profiles.
This is a relatively small investment compared to the time required to write the code for data analysis, which is typically on the order of days or weeks. In addition, in the neuroblastoma data, we observed that annotating only about 10 of 575 profiles was sufficient to learn a smoothness parameter that achieves the model-specific optimal breakpoint detection. More generally, our results suggest that after obtaining a moderately sized database of annotations, data analysis time is better spent designing and testing better models. Additionally, the learned models generalized very well between annotators. So breakpoint annotations are a feasible approach for finding an accurate model and smoothing parameter for real copy number profiles. We compared local models for single profiles with global models selected using annotations from several profiles.
Our results indicate that even the best models have non-zero training and testing breakpoint detection error, which could be improved. To make a model that perfectly fits the training annotations, a dynamic programming algorithm called SegAnnot was proposed to recover the most likely breakpoints that are consistent with the annotation data  [24]. The test error of the cghseg model can be lowered by choosing chromosome-specific λ parameter values as a function of features such as variance and the number of points sampled  [25]. Developing a model that further lowers the test error remains an interesting direction of future research. We have solved the problem of smoothness parameter selection using breakpoint annotations, but the question of detecting CNAs remains. By constructing a database of annotated regions of CNAs, we could use a similar approach to train models that detect CNAs.
GUIs for annotating copy number profiles Assume that we have n DNA copy number profiles, and we would like to accurately detect their breakpoints. The first step of annotation-based modeling is to plot the data, visually identify breakpoints, and save these regions to an annotation database. We created 2 annotation graphical user interfaces (GUIs) for this purpose: a Python program for low-density profiles called annotate_breakpoints. py, and a web site for larger profiles called SegAnnDB. We used Tkinter in Python's standard library to write annotate_breakpoints. py, a cross-platform GUI for annotating low-density DNA copy number profiles. The annotator loads several profiles from a CSV file, plots the data, and allows annotated regions to be drawn on the plot and saved to a CSV file for later analysis.
The annotator does not support zooming so is not suitable for annotating high-density profiles. It is available in the annotate_regions package on the Python Package Index: http://pypi. python. org/pypi/annotate_regions SegAnnDB is a web site that can be used to annotate low to high-density copy number profiles. After copy number data in bedGraph format is uploaded, the site uses D3 to show plots which can be annotated  [26]. The annotations can then be downloaded for later analysis. As shown in Figure  10 , the plots can be zoomed for detailed annotation of high-density copy number profiles. The free/open-source software that runs the web site can be downloaded from the breakpoints project on INRIA GForge: https://gforge. inria. fr/scm/viewvc. php/webapp/?root= breakpoints.
Data: neuroblastoma copy number profiles We analyzed a new data set of n = 575 copy number profiles from aCGH microarray experiments on neuroblastoma tumors taken from patients at diagnosis. The microarrays were produced using various technologies, so do not all have the same probes. The number of probes per microarray varies from 1719 to 71340, and the median spacing between probes varies from 40 Kb to 1. 2 Mb. In this article we analyzed the normalized logratio measurements of these microarrays, which we have made available as neuroblastoma$profiles in R package neuroblastoma on CRAN. Two different expert annotations were used to construct 2 annotation databases based on these profiles (Table  1 ). The 2 annotation data sets are mostly consistent, but the detailed annotations provide more precise breakpoint locations (Figure  3 ).
The "original" annotations were created using the "Systematic" protocol. First, a set of 6 genomic regions was chosen. Then, each of these regions was inspected on scatterplots of each profile. Breakpoint annotations were recorded by typing 0 or 1 in spreadsheet with one row for each of the 575 profiles and one column for each of the 6 regions. Entries with 0 were 0breakpoints annotations a = [0, 0] and entries with 1 were >0breakpoints annotations a = [1, ∞). These annotations are available as neuroblastoma$annotations in R package neuroblastoma on CRAN. The "detailed" annotations were constructed using the "Any" protocol. The data were shown as scatterplots in a graphical user interface (GUI) that allows zooming and direct annotation on the plotted profiles. Annotators were asked to label any regions for which they were sure of the annotation.
Algorithms: copy number profile smoothing models We considered smoothing models from the bioinformatics literature with free software implementations available as R packages on CRAN, R-Forge, or Bioconductor  [27] [28] [29]. For each algorithm, we considered three types of training for the smoothness parameter λ: • Default models can be used when functions give default parameter values, or do not have smoothness parameters that vary the number of breakpoints. • Local models choose a smoothness parameter that maximizes agreement with the annotations from a single profile. • Global models choose a smoothness parameter that maximizes agreement with the entire database of training annotations. In the following paragraphs, we discuss the precise meaning of the smoothness parameter λ in each of the algorithms.
Acknowledgements Thanks to  Edouard Pauwels  for many helpful discussions and comments to simplify the mathematics on an early draft of the paper. This work was supported by  Digiteo  [ DIGITEO-BIOVIZ-2009-25D  to T. D. H. ]; the  European Research Council  [ SIERRA-ERC-239993  to F. B;  SMAC-ERC-280032  to J-P. V. ]; the  French National Research Agency  [ ANR-09-BLAN-0051-04  to J-P. V. ]; the  Annenberg Foundation  [to G. S. ]; the  French Programme Hospitalier de Recherche Clinique  [ PHRC IC2007-09  to G. S. ]; the  French National Cancer Institute  [INCA-2007-1-RT-4-IC to G. S. ]; and the  French Anti-Cancer League.
Fibrinogen is a large heterogeneous aggregation/degradation-prone protein playing a central role in blood coagulation and associated pathologies, whose structure is not completely resolved. When a high-molecular-weight fraction was analyzed by size-exclusion high-performance liquid chromatography/smallangle X-ray scattering (HPLC-SAXS), several composite peaks were apparent and because of the stickiness of fibrinogen the analysis was complicated by severe capillary fouling. Novel SAS analysis tools developed as a part of the UltraScan Solution Modeler (US-SOMO; http://somo. uthscsa. edu/), an opensource suite of utilities with advanced graphical user interfaces whose initial goal was the hydrodynamic modeling of biomacromolecules, were implemented and applied to this problem. They include the correction of baseline drift due to the accumulation of material on the SAXS capillary walls, and the Gaussian decomposition of non-baseline-resolved HPLC-SAXS elution peaks.
It was thus possible to resolve at least two species co-eluting under the fibrinogen main monomer peak, probably resulting from in-column degradation, and two others under an oligomers peak. The overall and cross-sectional radii of gyration, molecular mass and mass/length ratio of all species were determined using the manual or semi-automated procedures available within the US-SOMO SAS module. Differences between monomeric species and linear and sideways oligomers were thus identified and rationalized. This new US-SOMO version additionally contains several computational and graphical tools, implementing functionalities such as the mapping of residues contributing to particular regions of P(r), and an advanced module for the comparison of primary I(q) versus q data with model curves computed from atomic level structures or bead models.
Among the intermediateresolution techniques, those utilizing the small-angle scattering from X-rays (SAXS) or neutrons (SANS) have the distinct advantage of allowing samples to be examined in nearphysiological conditions, though they only provide ensembleaveraged data and thus often require extensive modeling for correct interpretation  (Mertens & Svergun, 2010; Putnam et al. , 2007). A popular suite of computer programs covering from data reduction to modeling for both SAXS and SANS is the ATSAS suite developed by the Svergun group at EMBL in Hamburg, Germany (see  Petoukhov et al. , 2012;  http:// www. embl-hamburg. de/biosaxs/software. html). However, the SAS field is undergoing rapid developments, such as the availability of high-performance liquid chromatography (HPLC)-SAXS setups (e.
uthscsa. edu/), a suite of open-source computer programs under a graphical user interface (GUI) that was originally developed for computing conformational and hydrodynamic parameters of biomacromolecules starting from their three-dimensional atomic structure  (Brookes, Demeler, Rosano & Rocco, 2010). US-SOMO's hydrodynamic modeling is based on accurate methods developed by the Rocco and Byron laboratories, preserving the correspondence between the atoms in the original biomacromolecule and the low-resolution beads used to represent it  (Byron, 1997; Rai et al. , 2005). The new SAS tools have evolved from a small initial nucleus that we had previously reported  (Brookes, Demeler & Rocco, 2010) , providing an integrated framework with existing hydrodynamics tools. On the data analysis side, prominent is a module for the conversion of HPLC-SAXS scattering intensity as a function of the scattering vector [I(q) versus q] data frames into scattering intensity as a function of elution frame/ time [I(t) versus t] at each scattering vector magnitude q (q = 4 sin /, with 2 the scattering angle and the incident radiation wavelength).
1 below. The HPLC-SAXS US-SOMO module was first tested on SE-HPLC-SAXS data collected on a crude bovine serum albumin sample containing a large number of trimer and dimer species that was used to verify the SE columns' performance, and then applied to the hpHMW-FG data. In both cases baseline correction and Gaussian decomposition were employed. In particular, the analysis allowed us to distinguish between side-by-side and linear aggregates in the hpHMW-FG oligomers peaks, and to characterize the two components of the main peak as having nearly identical conformation while probably differing by the presence/ absence of a relevant portion of the C domains.
tosohbioscience. com/) connected in series, protected by a 6 Â 40 mm guard column filled with G3000PW resin (Tosoh). The Agilent chromatographic system of the SWING beamline at the synchrotron SOLEIL  (David & Perez, 2009 ) was operated at 0. 35 ml min À1 flow rate. The columns and the SAXS flow cell were maintained at 293. 2 AE 0. 1 K. BSA was dissolved at 9 mg ml À1 in TBS, centri-filtered at 12 000 r min À1 over 0. 22 mm cellulose acetate filters (Costar Spin-X, Sigma-Aldrich), and 60 ml (two replicates) were then injected into the SE columns. The hpHMW-FG concentration was 17. 3 mg ml À1 in TBS, and after centri-filtration, 20 or 50 ml were injected into the SE columns. SAXS data ( = 1. 03 A ˚) were collected at a $4 m sample-detector distance, accessing a q range of 0. 0023-0. 2750 A ˚À1 , normalized to the intensity of the transmitted beam, background subtracted on the SWING beamline using the local dedicated program Foxtrot, and put on an absolute scale using the scattering by water within the US-SOMO SAS module.
Software implementation The US-SOMO technical specifications have already been described  (Brookes, Demeler, Rosano & Rocco, 2010 , 2012). The current software is a GUI application written in C++ utilizing Qt (http://qt-project. org/). The code is multi-platform, with binaries available for Linux, Mac OSX and Windows. The source code is available via a wiki integrated subversion repository, which can be found from the main US-SOMO web page. The current user base includes $700 registered individual researchers and 56 registered laboratories worldwide.
Main panel The new GUI of the US-SOMO SAS module is shown in Fig. 1(a). It is divided in two halves, the top one for reciprocalspace operations and the bottom one for real-space operations. Among the reciprocal-space operations, I(q) versus q SAXS and SANS curves can be computed from atomic level structures, either with explicit hydration (which should be externally provided; see e. g. Poitevin et al. , 2011)  using the Debye equation  (Glatter & Kratky, 1982)  and its variant computed with spherical harmonics  (Stuhrmann, 1970; Stuhrmann et al. , 1977; Svergun & Stuhrmann, 1991) , or with implicit hydration, as in Crysol  (Svergun et al. , 1995) , Cryson  (Svergun et al. , 1998)  and a fast Debye method based on the FoXS concept  (Schneidman-Duhovny et al. , 2010). Guinier analyses can be performed in manual or semi-automatic mode.
A primary data reduction utility with the ability to perform buffer subtractions, normalization and curve joining is also present (to be described in a future publication). In addition, we have developed a novel module for the processing of HPLC-SAXS data, allowing a first-order correction for spurious background intensity arising from capillary fouling, and application of Gaussian decomposition to non-baselineresolved SAXS peaks (see below, and the supplementary material 1  ). In the real-space section, P(r) versus r curves can be computed directly from atomic level structures for both SAXS and SANS approaches, and compared with data derived by inverse Fourier transformation of reciprocal-space data. To help in understanding how the distribution of residues in a macromolecule affects the P(r) versus r distribution, a novel tool was developed, allowing visualization (using RasMol;  Sayle & Milner-White, 1995)  of the structure with its residues color coded according to their contribution to a particular distance range.
In Fig. 1 (b), a BSA structure is visualized, colour coded to show which residues contribute the most to the P(r) versus r curve in the 45-55 A ˚range (yellow to blue in decreasing order; gray, no contribution). All synthetic curves can be ranked against or combined to yield a bestfitting curve with experimentally derived data using a nonnegative least-squares fitting routine. Both reciprocal-and real-space curves can also be computed starting from lower-resolution bead models. Finally, conformational variability and local or segmental flexibility can be taken into account by using a discrete molecular dynamics  (Ding & Dokholyan, 2006; Dokholyan et al. , 1998)  utility running remotely on several supercomputer clusters. The supplementary material contains a full description of the US-SOMO SAS module's many features.
Testing the US-SOMO HPLC-SAXS module with BSA data A relatively crude, old BSA sample having a substantial number of dimers and trimers was run before the hpHMW-FG samples, mainly to test the columns' efficiency. The SE-HPLC-SAXS data acquired on this BSA sample were then used to verify the performance of the US-SOMO HPLC-SAXS module, and many images taken from a typical processing run are used in the supplementary material to describe the module (Figs. S12-S15, S19, S21, S22 and S24-S30). Since a typical HPLC-SAXS experiment produces a series of I(q) versus q data collected at some time interval ('frames'), they can be inserted into a two-dimensional matrix where each line corresponds to a frame number (or time value) and the columns contain the intensities I(q) and their associated standard deviations (SDs) at the various scattering angles q.
In such cases, as a first approximation we can assume a linear increase over time of the material deposited on the capillary. This allows the definition of baselines for each q-value chromatogram (see Fig. S14 ), which can then be subtracted (see Fig. S15 ). If necessary, Gaussian decomposition can be performed on this baseline-subtracted data set (see Figs. S19, S21, S22 and S24-S27). SVD can also be performed either on (a) The renewed GUI of the US-SOMO SAS module main panel. In the graphic windows, the I(q) versus q and the P(r) versus r curves computed from the BSA crystal structure 4f5s  (Bujacz, 2012)  using Crysol and the US-SOMO internal SAXS method, respectively, are shown. (b) A snapshot of the RasMol-produced BSA structure with the residues contributing to the chosen P(r) versus r range, 45-55 A ˚, color coded from yellow to blue in order of decreasing importance.
the original or, if baseline subtraction was performed, on a reconstructed I(q) versus q data set, for instance to decide how many Gaussians should be used to decompose each peak (see the hpHMW-FG section; not needed in this very simple situation). Importantly, the position and width of each Gaussian in a 'family' (i. e. the Gaussians in all q-value chromatograms fitting a particular chromatographic peak in the time or frame domain) must have the same values, and only the amplitudes are fitted. This is done by first optimizing these parameters on a subset of the q-value chromatograms (see Figs. S21, S22 and S24), with optional SD weighting (recommended), and then globally applying them to all q-value chromatograms (see Figs. S25-S26 ). In Fig. S25 , the nonrandom distribution of residuals around frames 130-150 arises from the tail ends of the chromatograms that are not well fitted by pure Gaussian functions.
Either right after baseline correction (if necessary) or after Gaussian decomposition, it is possible to back generate I(q) versus q data sets for each Gaussian in each frame, by back transposition of the data matrix. Generating data directly from the Gaussians produces smoothed data sets, which might hide potential problems. Therefore, the default option in the US-SOMO HPLC-SAXS module is to produce data as a percentage of the original curve based on the contribution of each Gaussian to that particular point in the I(t) versus t curves, with SDs also assigned proportionally. Finally, if a concentration curve and its Gaussian decomposition have been associated with the I(t) versus t data, it is possible to compute the fractional concentration for each resulting I(q) versus q decomposed frame.
2  After baseline definition and subtraction (not shown), SVD followed by Gaussian analysis were performed. SVD was done on a reconstructed I(q) versus q data set, to avoid fitting the baseline drift. As shown in Fig. 4 (a), at least four components, possibly five, are making relevant contributions to the data. In the end, however, six Gaussians were found to be necessary to produce a reasonably good fit of the I(t) versus t chromatograms. In Fig. 4 (b), the results for a single q value are shown, and the contribution of the five 'major' G-pks (#1-4, #6) is evident. However, without the small G-pk (#5) positioned between the two principal G-pks (#4 and #6) under the main chromatographic peak, the fit is significantly worse (data not shown). The results of the global fit and global Gaussian operations have produced very nicely fitting Gaussians for all the peaks in all the I(t) versus t chromatograms examined (q range 0.
Conclusions The structure of fibrinogen, a protein of relevant biomedical/ biotechnological interest, is not fully known. Given the presence of a probably mostly disordered large portion of the A chains, multi-resolution studies will be necessary to have a complete three-dimensional picture of FG. An important contribution could come from SAXS data, but aggregation/ degradation issues had previously limited their utility. In this article, we have described and applied to this problem the recent developments of an enhanced SAS module of the US-SOMO suite, producing much improved data that could be utilized in modeling studies. US-SOMO has undergone a (still ongoing) major expansion with the aim of providing a multiresolution platform for easy combination of scattering data and hydrodynamics results with bead as well as all-atom molecular modeling tools.
It has been designed as a hub that allows a variety of operations to be performed, from primary data reduction and analysis to complex modeling approaches. It makes use of several widely used, publicly available software packages from other groups, such as Gnom, Crysol(n) and FoXS. It also offers original tools that, to our knowledge, are not yet available elsewhere, such as the mapping over the molecule structure of the relative contributions to a particular distance range in P(r) versus r and, most relevant here, the baseline correction and Gaussian decomposition of SE-HPLC-SAXS data sets. We believe that the latter allows the experimentalist to make the best use of the recorded frames, as illustrated by the reported BSA example and hpHMW-FG application, and may in the future become part and parcel of the SE-HPLC-SAXS data handling and analysis package.
Related literature The supplementary material contains a detailed description of the software and references the following additional literature. For details on the Rayleigh structure factors for spheres, see  Rayleigh (1911). The inverse Fourier transform of the I(q) data to produce a pairwise distance distribution curve is achieved using the indirect transform method  (Glatter, 1977)  as implemented in the packages ATSAS  (Svergun & Koch, 2003)  and Irena  (Ilavsky & Jemian, 2009) , and the Bayesian method described by  Hansen (2000). For the five exponential terms used in the atomic form factors, see  Waasmaier & Kirfel (1991).
Transcription Activators-Like Effectors (TALEs) belong to a family of virulence proteins from the Xanthomonas genus of bacterial plant pathogens that are translocated into the plant cell. In the nucleus, TALEs act as transcription factors inducing the expression of susceptibility genes. A code for TALE-DNA binding specificity and high-resolution three-dimensional structures of TALE-DNA complexes were recently reported. Accurate prediction of TAL Effector Binding Elements (EBEs) is essential to elucidate the biological functions of the many sequenced TALEs as well as for robust design of artificial TALE DNA-binding domains in biotechnological applications. In this work a program with improved EBE prediction performances was developed using an updated specificity matrix and a position weight correction function to account for the matching pattern observed in a validation set of TALE-DNA interactions.