dataesr/scientific-paragraphs-categorization huggingface dataset:
Figure1Resistance to TEV and LMV depends on different isoforms of eIF4E. A, Phylogenetic tree based on full length eIF4E1 and eIFiso4E protein sequences from Lycopersicon esculentum (Le), Capsicum annum (Ca), Lactuva sativa (Ls), Pisum sativum (Ps), Prunus domestica (Pd) and Arabidopsis thaliana (At). See methods for accession numbers. Bootstrap values over 0. 6 supporting the branchpoints are represented. Resistance to potyviruses that have been reported both in Arabidopsis and in another plant species, namely TEV, LMV, ClYVV and PPV, are represented by a boxed virus abbreviation next to the 4E protein it has been shown to rely on (See Table1for references). B, Protein sequence alignment of Capsicum annuum and Arabidopsis thaliana eIF4E1 and eIFiso4E. Amino acids identical or similar among at least 3 sequences are highlighted in black and grey, respectively.
ECM molecules further induce ADSC cardiac commitment To assess the influence of LM and FN in combination with the GF cocktail on the cardiac commitment of ADSCs, the mRNA expression of several cardiac markers was studied. For the qRT-PCR analysis we used GAPDH as a reference and ADSCs were treated for 1 and 2 weeks. At week 1 we did not observe any Nkx2. 5 expression (data not shown), nor significant changes in GATA4 mRNA levels with FN, LM, GFs alone or with the combination of FN (GF-FN) (Fig. 2 ). Importantly, GATA4 was very highly expressed in the GF-LM condition (450-fold increase compared with the GAPDH mRNA expression), suggesting an early engagement towards a cardio-muscular lineage. Furthermore, in the same conditions the mRNA of MEF2C (10-fold increase) and SERCA2a (sixfold increase) were highly expressed.
5 was found at both time-points in the cytoplasm [Fig. 3(C, D )]. In addition, MEF2C appears to be already in the nucleus of some cells the first week and to remain in the same compartment at week 2 in the majority of the cells [Fig. 3(C, D )]. Moreover, using the LM substrate, both GATA4 and cTnI were observed at 2 weeks [Fig. 3(F)] , suggesting that this ECM molecule better induces the cardiogenic differentiation of ADSCs. Finally, using a cell proliferation assay (CyquantV R Invitrogen), a higher number of cells with FN compared with LM were measured (data not shown).
Five microli- Abcys) in DPBS, 4% BSA, 0. 2% TritonX-100 for 1 h. Finally, Gene Full Name NM Accession Number after rinsing again and following incubation with streptavidin-Sequences FITC (1:500, Dako) or streptavidin-rhodamin (1:500, Dako) in GATA4 MEF2C GATA binding protein 4 Myocyte enhancer factor 2C NM_002052. 3 DPBS for 40 min, the slides were mounted (Mounting Media, Fwd: AGATGCGTCCCATCAAGACG Rev: GGAGCTGGTCTGTGGAGACT NM_001131005. 2 Rev: TTTTTCTCCCCATAGTCCCCG Fwd: CTAATCTGATCGGGTCTTCCTTCAT Dako Nkx2. 5 NK2 homeobox 5 NM_004387. 3 Fwd: CTATCCACGTGCCTACAGCG Rev: GCCGCTCCAGTTCATAGACC SERCA2a ATPase, Ca 11 transporting, cardiac muscle, slow NM_001681. 3 Fwd: ACCTGGAACCTGCAATACTGG Rev: TGCACAGGGTTGGTAGATGTG twitch 2 (ATP2A2) variant a MLC-2V Myosin light chain 2, cardiac, slow NM_000432.
A robust and reproducible comparison of such methods requires the definition of a framework for generating realistic copy number profiles, and a framework for assessing methods' performance. • A data generation framework based on resampling from real data makes it possible to compare different methods across a large number of different realistic scenarios. • The performance of segmentation methods is mainly driven by biological parameters such as the proportion of tumor cells in the sample and the proportion of heterozygous markers. • Using the open source and cross-platform R package jointseg, the present comparison study may be reproduced either on the data sets provided or on other data sets.
Background Changes in DNA copy numbers are a hallmark of cancer cells  [1]. Therefore, the accurate detection and interpretation of such changes are two important steps toward improved diagnosis and treatment. The analysis of copy number profiles measured from high-throughput technologies such as array-comparative genomic hybridization (array-CGH), Single Nucleotide Polymorphism array (SNP array) or high-throughput DNA sequencing data raises a number of statistical and bioinformatic challenges. Various methods have been proposed in the past decade for analyzing such data. From a practitioner's point of view, it is quite difficult to find which method is best for a given scientific question. In fact, it is likely that the overall difficulty of the problem depends on the context (technology, type of cancer, percentage of tumor cells).
data with known "truth"; 2. methods to be compared; 3. criteria for performance assessment. In this paper, we propose such a definition and illustrate how it may be used to compare segmentation methods. The main contributions of this work are • a framework to generate realistic DNA copy-number profiles with known "truth". This framework is generic and may be applied to any copy number data set; • a framework to address the question of which SNP array data segmentation method performs best, depending on biologically relevant parameters. These frameworks are implemented in the R package jointseg. The rest of this paper is organized as follows. We start by giving some background on DNA copy number segmentation (Section 2) and describe our proposed data generation framework (Section 3). Then, we describe the pipeline we use for evaluating segmentation methods (Section 4).
DNA copy number data Normal cells have two copies of DNA, inherited from each biological parent of the individual. In tumor cells, parts of a chromosome of various sizes (from kilobases to a chromosome arm) may be deleted, or copied several times. As a result, DNA copy numbers in tumor cells are piecewise constant along the genome. Copy numbers can be measured using microarray or sequencing experiments. For illustration, Figure  1  displays an example of copy number signals that may be obtained from SNP-array data. Red vertical lines represent change points. In this particular example, the first region [0-2200] is normal, the second one [2200-6100] is a region where one of the parental chromosomes has been duplicated, and the third one [6100-10000] is a region of uniparental disomy, that is, a region where one of the parental chromosomes has been duplicated and the other one deleted.
After this transformation, DNA copy numbers can be considered as a bivariate, piecewise-constant signal, as illustrated by Figure 2. It should be emphasized at this stage that because the proportion of heterozygous markers among SNPs is generally of the order of 1/3 for a given sample, the number of informative markers is several times larger for (c) than for (d). This feature of SNP array data has implications in terms of speed and performance of segmentation methods, which will be explained in detail later in the paper.
This data set is freely distributed on CRAN 1. Based on this large data set with known truth, the authors have performed a comprehensive comparison of segmentation methods for array-CGH data based on ROC curves. A drawback of this evaluation framework is that once a particular data set is chosen, it is not possible to tune the signal-to-noise ratio of the problem. Moreover, annotating a new data set is a challenging task, because it has to be large enough to contain a set of change-points that discriminate between competing segmentation methods. A simulation model  [16]. The authors designed a complex simulation model to generate "realistic" copy-number profiles. This model is implemented in the R package CnaGen, which is available from the authors' web page  2. The simulation model depends on 24 parameters 3.
Compared segmentation methods We evaluated different types of methods belonging to the different classes described in Section 2. 2: multiple change-point, recursive, fused, and HMMbased methods. These methods are described in Table  2 , where we mention which of them are able to process both signal dimensions (c and d) or only one of them. Not all of these methods were implemented in R. We ported from Matlab GFLseg 4  to R the implementation of multi-dimensional dynamic programming and the group-fused LARS  [29] , and we implemented recursive binary segmentation  [27]  in R. In practice, as recommended by  [27, 28, 29] , both group-fused LARS and recursive binary segmentation are used to quickly identify a list of candidate change points, which is then pruned using dynamic programming. All of the compared methods are reasonably fast and memory-efficient, except those based on two-dimensional dynamic programming (DP): cnaStruct and our implementation of DP in R.
Indeed, two-dimensional DP is quadratic in time and memory and thus cannot handle profiles of size n = 10 5. It may be surprising that the two-dimensional version of GFLars is faster than its one-dimensional counterpart. This is a consequence of the fact that the number of informative markers is several times larger for (c) than for (d) (as explained in Section 2. 1). As the implementation of GFLars does not handle missing values, the 2d version of GFLars was applied to non-missing entries in (c, d), while the 1d version was applied to a much longer signal (all Time (s) Name R package function dims n=10 (c) entries). This phenomenon does not happen for other two-dimensional segmentation methods as their implementation does handle missing values.
For a given DNA sample, the fraction of heterozygous SNPs among those present on the microarray is typically close to 1/3; moreover, data set 1 contains not only SNP probes but also non-polymorphic loci, with a 1:1 ratio. As a result, the fraction n i /n i is approximately 1/6 for data set 1 and 1/3 for data set 2. A natural extension of this definition of SNR to the two-dimensional case of the statistic (c, d) is SNR(c, d) = c0 -c1 , d0 -d1 (S 0 + S 1 ) -1 c0 -c1 , d0 -d1 , (3) where S i is the population covariance matrix of the bivariate vector (c, d), that is S i = σ 2 c,i /n i τ cd,i /n i τ cd,i /n i σ 2 d,i /n i with τ cd,i the covariance between c and d in state i. In practice, the population parameters for copy-number state i (that is, σ d,i , τ cd,i , and σ d,i ) are calculated from the annotated data.
Summary and discussion We have developed a framework to assess the performance of various DNA copy number segmentation methods. A critical aspect of this framework is that it generates realistic copy-number profiles by resampling real SNP array data. This allows us to study a large number of scenarios without relying on a particular statistical model. It is our opinion that this framework is simple to use as it depends on few parameters, all of which have a straightforward biological interpretation. An R package is available and we believe that our proposed data generation scheme can be used readily as well as applied to other data sets and technologies. It is also possible to extend the set of segmentation methods compared, as explained in the package documentation. In this paper, we illustrated the usage of this framework on two SNP array data sets from Affymetrix and Illumina.
1 a. u. was applied and the pictures were created for an isosurface value of s ¼ 0. 5 and colored in the [À0. 03,0. 03] a. u. sign (l 2 )r range. Integrations described in the ESI † were carried out with an in-house program for cubic grids with a 0. 1 a. u. increments along each side and re-scaled with respect to the total cube volume. Each data table or data gure is assigned a DOI in the Figshare repository (see footnotes), all retrievable as e. g. qd8. A 3D printable full-colour model of the lowest energy transition state for R ¼ Ph can be obtained at http:// shpws. me/pstF.
In addition, algorithmic considerations that may be important for computational efficiency are reported on. The latter is inevitably computing platform dependent. The ensemble of results from a wide variety of schemes presented here helps shed light on the ability of the test case suite diagnostics and flow settings to discriminate between algorithms and provide insights into accuracy in the context of global atmospheric/ocean modeling. A library of benchmark results is provided to facilitate scheme intercomparison and model development. Simple software and data sets are made available to facilitate the process of model evaluation and scheme intercomparison. It is the purpose of this paper to provide a library of benchmark results for the LSPT2012 standard test case suite. The data were provided by the participants of the 2011 workshop on transport schemes held at the National Center for Atmospheric Research (NCAR) in Boulder (Colorado, USA), 30-31 March 2011.
Specific details on time step, native grid resolutions used to match test case specifications on resolution, viscosity coefficients (if applicable), etc. are given in the Appendix for each scheme. The results for the LSPT2012 test case suite are presented in Sect. 3. It has been a challenge to present effectively and concisely the results graphically given the large number of schemes. We have found it most effective to depict most of the data in histogram format. The complete histogram data sets are made available as supplemental material for the interested reader. Scripts and data to produce convergence plots (Figs. 1  and 2 ), filament diagnostic plot (Fig. 5 ), contour plots  (Figs. 7, 8, 9, 10) , scatterplots  (Figs. 11, 12, 13 and 14) , and histogram plots  (Figs. 3, 4, 6, 15 and 16)  are also made available in the Supplement.
Wave-propagation algorithm Related to the "swept area" approaches described above, in the sense that this algorithm has some conceptual similarities, is the wave-propagation algorithm of  LeVeque (2002). The specific version of this algorithm is referred to as CLAW as it is implemented in the general Clawpack package  (LeVeque, 2006). The wave-propagation algorithm can be viewed as a scheme that propagates information (i. e. , waves) first in a direction normal to a given cell interface, and then in a direction transverse to this interface effectively approximating "swept area" fluxes (see, e. g. ,  Fig. 5. 22 in Durran, 2010). CLAW is based on a first-order donor cell upwind method (first-order waves) composed of one-dimensional flux-divergence operators with "correction" terms to take into account traverse flow of waves and/or higher order waves.
In addition to assessing the consistency of the coupling, that accuracy of the coupling between air and tracer mass is assessed. Normalized error norms ( 2 , ∞ , φ min , φ max ) at λ ≈ 1. 5 resolutions are shown in the histogram in Fig. 16. The minimum (φ min ) and maximum norms (φ max ) are defined in LSPT2012. Although LSPT2012 also requested these error norms at λ ≈ 0. 75 and λ ≈ λ m , we did not find intriguing insights by analyzing these data, and for brevity the histograms for this data are omitted (the data are available in the supplemental material). Except for CAM-FV and FAR-SIGHT, the divergent data are ordered similarly to λ m in terms of magnitude  (Fig. 3, top). Note that schemes based on FCT limiting in general improve accuracy when shape preservation is enforced, whereas schemes based on reconstruction limiting degrade the error norms.
(A4) For the results presented here, we set ᾱ = 0. 95. Clawpack does not make use of any explicit diffusion parameters or artificial viscosity. The Fortran code and Python scripts for running the benchmark examples, and Matlab scripts for visualization can all be downloaded from the author's web page (http://math. boisestate. edu/~calhoun/www_personal/ research/NCAR_workshop/).
Peter H. Lauritzen  thanks  Björn Stevens  and  Marco Giorgetta  ( Max Planck Institute for Meteorology, Hamburg, Germany ) for funding a collaborative visit during which part of this paper was written. Peter Bosler  thanks  NSF  grant # AGS-0723440  and  ONR  grant # N00014-12-1-0509. The research for HOMME and CAM-SE schemes was supported by the  Department of Energy Office of Biological and Environmental Research , work package  12-015335 , " Applying Computationally Efficient Schemes for BioGeochemical Cycles " and work package  09-014487 , " Climate Impacts Analysis ". Andrew Conley  is funded by  Department of Energy (DOE) Office of Biological & Environmental Research  under grant number  SC0006747. Thanks to  Francis Witt  ( NCAR ) for the software engineering support in implementing the test case suite in CAM-SE.
Identification of candidate disease genes and RRAS mutation analysis While the core of the machinery implicated in RAS signalling has been characterized comprehensively, signal propagation through this network is likely to include a larger number of proteins playing a modulatory or structural role  (14) , whose aberrant or defective function is expected to perturb development and contribute to oncogenesis. Based on this assumption, we used a protein interaction/functional association network analysis to select a panel of genes encoding proteins functionally linked to the RAS signalling network as candidates for NS or a related RASopathy  (15). Candidate gene selection was based on the use of the previously identified RASopathy genes as 'seed' proteins (i. e. proteins used to build the interaction/functional networks), and considering a panel of databases to construct functional subnetworks (Supplementary material, Table  S1  and Fig.
wa shington. edu/EVS/). Of note, similar frequencies have been reported in the same database for recurrent RASopathy-causing mutations (e. g. c. 922A. G in PTPN11, and c. 1259G. A in CBL). Mutation analysis was extended to additional 408 patients with NS or a clinically related phenotype tested negative for mutations in the major NS disease genes (see Materials and Methods), allowing to identify one sporadic case heterozygous for a threenucleotide duplication (c. 116_118dup, p. Gly39dup) (Supplementary material, Fig. S2 ). Parental DNA sequencing of the relevant exon demonstrated the de novo origin of the variant, and STR genotyping confirmed paternity. In this subject, the duplication was documented in DNA obtained from skin fibroblasts, excluding a somatic event restricted to haematopoietic cells.
Disease-associated RRAS mutations affected residues highly conserved among orthologs and paralogs (Supplementary material, Fig. S4 ) residing in the GTP-binding pocket (Fig. 1C ) and were predicted to be damaging with high confidence (Supplementary material, Table  S4 ). Among them, Gln 87 , homolog of Gln 61 in RAS proteins, is directly involved in catalysis  (18, 19). The p. Gln87Leu substitution had previously been reported as a rare somatic event in lung carcinoma, and mutations affecting Gln 61 are among the most recurrent oncogenic lesions in RAS genes (COSMIC database, http://cancer. sanger. ac. uk/ca ncergenome/projects/cosmic/). Likewise, p. Gly39dup altered the G1 motif participating in GTP/GDP binding and GTPase activity (Fig. 1B ). Within this motif, Gly 12 and Gly 13 (Gly 38 and Gly 39 in RRAS) represent major mutation hot-spots in human cancer (COSMIC database) and account for the majority of germline HRAS mutations causing Costello syndrome  (20).
Selection of RASopathy candidate genes A web-based tool, Genes2FANs (http://actin. pharm. mssm. edu/ genes2FANs), using a large-scale protein -protein interaction network coupled to a panel of functional association networks (FANs) was utilized to build a subnetwork connecting proteins to the known RASopathy genes (i. e. PTPN11, SOS1, NF1, SPRED1, CBL, NRAS, KRAS, HRAS, RAF1, BRAF, SHOC2, MAP2K1 and MAP2K2), as seed proteins. Gene Ontology (biological process tree), mammalian phenotype browser, and Connectivity Map (drug-associated gene expression signatures), ChEA and TRANSFAC (transcription factor networks) databases were selected to construct the functional subnetworks utilized for prioritization of candidates  (15). The programme allows to calculate z-scores for intermediate nodes (i. e.
Exome sequencing Targeted enrichment and massively parallel sequencing were performed on genomic DNA extracted from circulating leukocytes and fibroblasts of patient 9802. Exome capture was carried out using the SureSelect Human All Exon V4+UTRs (Agilent), and sequencing with a HiSeq2000 instrument (Illumina). Image analysis and base calling were performed using the Real Time Analysis (RTA) pipeline v. 1. 14 (Illumina). Paired-end reads alignment to the reference human genome (UCSC GRCh37/hg19) and variant calling were carried out using the CASAVA v. 1. 8 pipeline (Illumina). Variant annotation, SNP filtering (dbSNP135, 1000 Genomes, HapMap and IntegraGen Exome databases) and patient-matched germline variant filtering were attained using an in-house pipeline by Inte-graGen (Evry, France).
Molecular dynamics analyses Starting coordinates for MD simulations were obtained from the RRAS crystallographic structure in complex with GDP and Mg 2+ (PDB: 2FN4; RCSB Protein Data Bank, http://www. rcsb. org/pdb/home/home. do). The N-terminus and C-terminus of RRAS, absent in the crystal, were not considered in simulations. Deep View software was used to add atoms missing in the PDB file, belonging to residues 31, 96, 114, 121 and 192 as well as to substitute residue 55 (simulation of the p. Val55Met mutant)  (59). All MD simulations were performed with GROMACS 4. 5 package, by using the GROMOS96 43a1 force field parameters for the protein. Parameters for GDP were taken from the GROMACS website (http://www. groma cs. org). Simulations were performed as previously described  (60, 61) , except for some details.
(C) Conformation of the loop comprised between Val 55 /Met 55 and Asp 59 in wild-type (red) and mutant (blue) RRAS/GDP complexes obtained from MD simulations. GDP is reported as semi-transparent yellow surface. Superimposed conformations of the corresponding loop (residues 29-33) in GDP-bound HRAS (violet) (PDB: 4Q21) and GDP-bound HRAS complexed with SOS1 (cyan) (PDB: 1BKD) are shown for comparison. The side chains of Tyr 58 and the corresponding residue in HRAS, Tyr 32 , are displayed as sticks.
ACKNOWLEDGEMENTS We are grateful to the participating patients and their families. We thank  Serenella Venanzi  ( Istituto Superiore di Sanita, Rome, Italy ),  Michela Bonaguro  ( Policlinico S. Orsola-Malpighi ,  Bologna, Italy) ,  Federica Consoli  ( Istituto Mendel, Rome, Italy ) and Cedric Vignal and  Sabrina Pereira  ( Hopital Robert Debre, Paris, France ) for skilful technical assistance, and the  Open Laboratory (IGB-CNR, Naples, Italy)  for experimental support. We also thank  Paolo Bazzicalupo  ( IGB-CNR ) for critical reading of the manuscript, paediatricians from the  SocieteFrancaise des Cancers de l'Enfant (SFCE)  for providing biological material from their patients and CINECA for computational resources. Some nematode strains used in this work were provided by the  Caenorhabditis Genetics Center  ( University of Minnesota, Minneapolis, MN, USA ) funded by the  NIH Office of Research Infrastructure Programs  ( P40OD010440 ).
INTRODUCTION Comparative sequence analysis and phylogeny reconstruction are traditionally based on pairwise or multiple se-quence alignments. These classical approaches are wellestablished and regarded as standard methods of sequence analysis. There are, however, various limitations associated with these approaches if large data sets are to be analysed. Aligning whole genomes of higher eukaryotes requires excessive computation time and the reliability of such global sequence alignments is often limited. Moreover, because of genome duplications and rearrangements it is often not possible to align entire genomes, so alignable homologous portions of the genomes under study have to be identified in a first step. With the growing amount of sequence data in public and private databases, produced by next-generation sequencing technologies, there is now a strong demand for faster sequence-analysis algorithms.