dataesr/scientific-paragraphs-categorization huggingface dataset:
This first set of simulations has been performed with the potential (29) V (x 1 ) = sin(2πx 1 ). Note that, according to Definition 10, for such potential we have u crit 0 = u crit 1 = 1. The initial datum u in (x 1 ) is chosen as a linear function interpolating the values u 0 and u 1 and the evolution of such datum towards the asymptotic, stationary solution (dotted black curve) is illustrated for different choices of u 0 and u 1. In panels (a), (b), (c) and (d) we have chosen, respectively, (u 0 , u 1 ) = (1. 2, 2. 2), (u 0 , u 1 ) = (1. 2, 1. 2), (u 0 , u 1 ) = (1. 2, 0. 2), (u 0 , u 1 ) = (0. 6, 1. 2). Then, in cases (a) and (b) both data are supercritical while, in case (c), u 1 is subcritical and, in case (d), u 0 is subcritical. We recall that Theorem 11 guarantees the wellposedness of the stationary problem if at least one of the Dirichlet data is supercritical.
Summary Statistics As SENCA has many free parameters, we developed three summary statistics to estimate the overall role played by each layers based on classical codon usage statistics: GC and GC3 composition, and ENC  (Wright 1990 ). First, we deduced from equation (4) the GC equilibrium frequency for each layer, respectively, noted GC ? N ; GC ? C , and GC ? A in order to estimate the influence of each layer on the equilibrium genome composition. Similarly, we estimated the equilibrium frequency at the third position within codons, denoted GC3 ? for each layer. Because the redundancy in the genetic code is greater at this position compared with others, GC3 is often used as a proxy for underlying mutational bias in prokaryotic genomes  (Muto and Osawa 1987). As genomes with very different CUB can be similar in terms of GC ? and GC3 ? , we used the same approach by computing ENC ? statistics for each layer.
Intraspecies Data Set Our data set came from  Lassalle et al. (2015) , see table  1. We used coding DNA sequences from the core genomes of 20 bacterial pathogens and of one archeal group. These species were chosen because they encompass the diversity of genome composition among prokaryotes and that we could select nonrecombinant genes. We obtained between 6 and 35 strains per species. For each species, we built codonwise nucleotide alignments using seqinR package in R  (Charif and Lobry 2007)  to translate nucleotide sequences, ClustalW  (Larkin et al. 2007 ) to align protein sequences, and PAL2NAL  (Suyama et al. 2006)  to retrieve nucleic alignments. Within a species, we sorted genes by increasing ENC values and concatenated them by groups of around 50 genes, to ensure we had enough data for precise parameter estimation.
In total, we obtained 166 concatenates (from 1 to 16 per species, see table  1 ). Then, we computed a phylogenetic tree for each concatenate using CodonPhyML  (Gil et al. 2013) , with an Nearest Neighbor Interchange (NNI) tree topology search and the GY + W+K + F, F3x4 model. We selected one tree per species, as trees topologies were consistent within each species (see supplementary fig. S1  for topology, Supplementary Material online). We rooted our trees using TPMS  (Bigot et al. 2013 ) and a reference species tree built with BIONJ  (Gascuel 1997 ) from a distance matrix of the complete genomes of HOGENOM V6 database  (Penel et al. 2009).
It suggests that explicitly taking into account the structure of the genetic code in the substitution process is an important modeling feature. It is interesting to understand how these results depend on the evolutionary scale. In particular, intraspecific results for Escherichia and Salmonella can be compared with those of the interspecific study (fig. 3 ) which includes these species. For global GC content, results are quite similar between and inside species, with YN98 still closer to a uniform GC content relative to SENCA. For GC3, the equilibrium estimates both by SENCA and YN98 are higher than the intraspecific estimates, which reveals the difference between studies at inter-versus intraspecific scales, where synonymous mutations may still be polymorphic. We can see that the difference is more marked for SENCA, which we attribute to a better capacity to grasp the evolutionary signal at the third position in the intraspecific study.
Discussion In sequence evolution, several biological processes act together at nucleotides, codons, and amino acids scales. In order to quantify the effects of mutation and selection at each of these scales, we developed an evolutionary model, SENCA, divided into three layers: nucleotide (N), codon (C), and amino acid (A). SENCA, by construction, is very flexible, and can be employed to tackle a variety of biological questions. As an example, we can set each layer to be stationary or not in function of the data. The decomposition of evolutionary signals in different layers allows for treating each layer separately; for example, by using specific amino acid substitution models for the A layer, or specific nucleotide substitution models for the N layer. Moreover, because the genetic code is explicit in this model, selection on CUB and on nonsynonymous substitutions can be studied simultaneously.
Acknowledgments This work was supported by  French National Research Agency (ANR)  grant  Ancestrome  ( ANR-10-BINF-01-01 ). F. P. received a doctoral scholarship from  Ecole Normale Suprieure de Lyon  (http://www. ens-lyon. eu/). This work was performed using the computing facilities of the  CC LBBE/PRABI. The authors thank  Will Pett  for expert English corrections,  Bastien Boussau  for comments, and  Vincent Miele  for providing the species tree of HOGENOM complete genomes to process the rooting of our trees. They also thank the anonymous reviewers for their relevant comments that helped us to improve this work greatly.
The tracers gradually lose their mode water characteristics as they are advected, but we refer to the tracers as mixing parameterization schemes. The Southern Ocean State Estimate (SOSE) offers one such set of fields  [Mazloff et al. , 2010]. SOSE is a collection of state estimate products that are constructed by bringing a numerical ocean circulation model (MITgcm) into consistency with a suite of available observations, including data from Argo floats, satellite altimetry, and hydrographic profiles. SOSE approaches consistency by iteratively reducing a measure of disagreement between the model and observations using an adjoint method  [Mazloff et al. , 2010]. The adjoint method is used to map out how the initial conditions and the surface forcing (e. g. , wind stress, atmospheric temperature) can be perturbed to reduce the disagreement between the model and observations over the length of the simulation.
Acknowledgments This work was supported by the  Natural Environment Research Council  ( NERC grant   NE/J007757/1 ) and used the  ARCHER UK National Supercomputing Service  (http://www. archer. ac. uk). Matt Mazloff acknowledges the  National Science Foundation  ( NSF ) for support of this research through grants  OCE-1234473  and  PLR-1425989. The authors thank  Lynne Talley ,  Jinbo Wang , and an anonymous reviewer for comments that greatly improved the quality of this paper. The model used (MITgcm) is available at http://mitgcm. org/, and other data are available on the SOSE website (http://sose. ucsd. edu/)and in the references of this paper. Journal of Geophysical Research: Oceans 10. 1002/2016JC011680.
This approach takes into account depth of coverage by normal reads and abnormalities in read pair mappings. To estimate the model likelihood, SV-Bay considers GC-content and read mappability of the genome, thus making important corrections to the expected read count. For the detection of somatic variants, SV-Bay makes use of a matched normal sample when it is available. We validated SV-Bay on simulated datasets and an experimental mate-pair dataset for the CLB-GA neuroblastoma cell line. The comparison of SV-Bay with several other methods for structural variant detection demonstrated that SV-Bay has better prediction accuracy both in terms of sensitivity and false-positive detection rate.
DOC-based methods detect regions of gain and loss in the genome using DOC normalized for GC-content bias  (Boeva et al. , 2011 (Boeva et al. , , 2012;; Yoon et al. , 2009). A deviation from the expected DOC suggests putative gain or loss of genomic material. DOC-based methods do not provide information about the adjacency of DNA regions involved in copy number changes. Thus, such methods are not able to indicate the type of SV (e. g. tandem duplication, fragment reinsertion, translocation) causing genomic loss or gain. Additionally, the resolution of such methods rather is low for low DOC datasets: a 30Â coverage dataset allows approximately a resolution of 1 kb for rearrangement breakpoints. Split-read-based methods use partial read alignments for SV detection  (Schro ¨der et al. , 2014; Trappe et al.
, 2011; Layer et al. , 2014; Oesper et al. , 2012; Qi and Zhao, 2011; Sindi et al. , 2012). However, most of these methods do not take into account certain important parameters that affect read count for both normal and abnormal mappings: GC-content and read mappability. Another general drawback of the majority of these methods is their lack of ability to detect all possible types of SV that can be present in cancer data including co-amplifications, tandem duplications with inversions, linking insertions, etc. Here, we propose a Bayesian framework for SV detection using paired-end or mate-pair libraries, implemented as the software SV-Bay. In this framework, we combine both PEM signatures and information about changes in DOC in regions flanking each candidate rearrangement. Our method takes into account GC-content and mappability.
To include this intuition, we introduce a user-defined parameter for the expected number of true SVs in the dataset (E SV , default value 1000). Then, the probabilities PðM c>0 Þ and P M c¼0 À Á are assigned as follows: P M c>0 À Á ¼ minðE SV =N links ; 1Þ and P M c¼0 À Á ¼ 1 À P M c>0 À Á , where N links is the total number of links. In the general case (A 1 , A 2 , B 1 , B 2 are not empty and do not overlap), the conditional probability P DjM ð Þcan be factorized: P DjM a1;a2;b 1 ;b 2 ;c À Á ¼ P n A1 ja 1 ð ÞÁP n A2 ja 2 ð ÞÁP n B1 jb 1 ð ÞÁP n B2 jb 2 ð ÞÁP n C jc ð Þ: (10) To calculate these probabilities, we assume that the read count follows a Poisson distribution with the mean equal to the expected number of reads per region, calculated by formulas (1) and (3) for normal read pairs in flanking regions A 1 , A 2 , B 1 , B 2 , and formulas (6) and (  7 ) for abnormal read pairs in the link: P n A1 ja 1 ð Þ¼PoisðE A1;a1 ; n A1 Þ; P n A2 ja 2 ð Þ¼PoisðE A2;a2 ; n A2 Þ; (11) P n B1 jb 1 ð Þ¼PoisðE B1;b 1 ; n B1 Þ; P n B2 jb 2 ð Þ¼Pois E B2;b 2 ; n B2 À Á ; (12) and P n C jc ð Þ¼PoisðE x break A ;x break B ;c ; n C Þ; (13) where Poisðk; kÞ ¼ k k e Àk =k! is the probability density function for the Poisson distribution with mean k.
Results We applied SV-Bay to simulated and experimental datasets and were able to demonstrate that it had a higher prediction accuracy than four other published methods: GASVPro  (Sindi et al. , 2012) , Lumpy  (Layer et al. , 2014)  and the two most popular methods for SV detection, BreakDancer (BreakDancerMax)  (Chen et al. , 2009)  and DELLY  (Rausch et al. , 2012). We have selected GASVPro and Lumpy, since like SV-Bay, these tools take into consideration information about DOC changes in the proximity of candidate breakpoints. However, unlike SV-Bay, they consider only short proximity of breakpoints while SV-Bay calculates read counts in maximally large regions around the breakpoint, which increases the statistical power of its probabilistic approach. In addition, GASVPro, Lumpy and DELLY integrate split-reads into the analysis.
The version of each software and parameters used are summarized in Supplementary Table  S1. Lumpy was applied to paired-end data only as it does not provide an option to run on mate-pair datasets. In the analysis below, we assumed that an SV calling method correctly detected a given SV when (i) predicted breakpoints laid within a certain distance d from the correct breakpoints, and (ii) there was a match in read orientation. Distance d corresponded to the maximal fragment length, i. e. 5179 bp for simulated matepair data, 446 bp for simulated paired-end data, and 3542 bp for the experimental mate-pair data.
SV-bay performance on simulated paired-end and mate-pair datasets To simulate tumor datasets, we used a combination of TGSim, software we developed to simulate a tumor genome (https://github. com/ InstitutCurie/TGSim), and read simulation software PIRS  (Hu et al. , 2012)  (code. google. com/p/pirs/). To create a nucleotide sequence corresponding to a normal control diploid genome, we modified the reference human genome GRCh38/hg38 by adding 3 million heterozygous SNPs, 315 000 small indels and 972 small inversions. To simulate a matched tumor genome, we applied a sequence of genomic rearrangements to the nucleotide sequence of the diploid normal genome using TGSim. TGSim inserted into the normal genome 'standard' SVs (deletions, tandem duplications, insertions of random sequences, inversions, translocations) but also "complex" SVs, which are usually missed or incorrectly assembled by SV calling tools (co-amplifications, tandem duplications with an inversion of the duplicated unit, linking insertions and linking re-insertions, Fig.
In addition to the represented SVs, SV-Bay is able to recognize balanced and unbalanced translocations (with and without inversion), amplifications and co-amplifications, simple inversions, direct tandem duplications, and so on. (Supplementary Fig. S1 ) algorithm allowed us to take into account GC-content bias and experimental read error profile along the reads  (Hu et al. , 2012). The corresponding parameters were taken from experimental neuroblastoma WGS datasets  (Boeva et al. , 2013). We also generated matched normal datasets (Supplementary Table  S4 ). In the simulations, we expected that the average number of abnormal read pairs required to confirm each novel genomic adjacency (physical coverage) would be higher for mate-pair than for pairedend data (Supplementary Table  S4 ). Thus, despite the fact that the mate-pair library contained from 5 to 10 times less reads than the paired-end one, we observed that in all cases but one all methods tested were able to identify more correct SVs in the mate-pair dataset (Fig.
3 , Supplementary Tables  S2  and S3 ). This observation supports the common choice of mate-pairs for annotation of structural variants in tumor genomes, even though creating a mate-pair library requires a more elaborate protocol. SV-Bay achieved maximal recall on all the four simulated datasets and detected 125/147, 129/147, 34/62 and 59/62 correct novel genomic adjacencies for T2_PE, T2_MP, T4_PE and T4_ME, respectively (Fig. 3 , Supplementary Tables  S2  and S3 ). Of note, to construct precision/recall curves, we run each method only once with the most relaxed parameters; then, we used a threshold on the quality of predicted SVs provided by each tool. We confirm that the choice of user-defined value for the expected number of true SVs in the dataset (E SV ) plays a limited role in the prediction accuracy by SV-Bay (Supplementary Fig.
S4 ). However, the user should consider to change the value of this parameter to higher values in case of interest in germline events or when analyzing chromothripsis cases. In many cases, the recall provided by BreakDancer and DELLY on simulated datasets was almost as high as the recall provided by SV-Bay (Supplementary Tables  S2  and S3 ). However, both BreakDancer and DELLY gave a large number of false-positive predictions (Fig. 3 ). Overall, on simulated data SV-Bay demonstrated better prediction accuracy than other tools both in terms of detection precision and recall. SB-Bay can recognize and infer from novel genomic adjacencies more types of complex SVs than other methods (Fig. 2 ). For instance, using mate-pair data for the T4 genome all tools we tested were able to identify novel genomic adjacencies corresponding to a co-amplification (50 times) of several regions on chromosome 13.
SV-Bay performance on a neuroblastoma mate-pair dataset To investigate the performance of SV-Bay on an experimental dataset, we selected a mate-pair dataset from a neuroblastoma diploid cell line CLB-GA recently sequenced using a mate-pair protocol together with a corresponding normal control  (Boeva et al. , 2013). For this dataset, we recently performed SNP6 array experiment to characterize genotype and copy number alterations independently from WGS data. Also, for this cancer cell line, we had a set of 11 SVs validated by PCR and Sanger sequencing (Supplementary Table  S5 ). Most of the 11 validated SVs correspond each to two breakpoints in the SNP array copy number profile. The following SVs correspond to only one breakpoint: (i) the SV between the ALK gene (chromosome 2p, 29 Mb) and a repetitive peri-telomeric sequence; the exact position of the latter could not be defined; (ii) the SV between chromosomes 12q and 20q, as it corresponds to a more complex SV on chromosome 12q (Fig.
4 ), and (iii) the inverted duplication at chromosome 5q. Both validated SVs and 27 breakpoints obtained by the analysis of Affymetrix SNP6. 0 datasets were further used to check the performance of SV-Bay and compare it with BreakDancer, DELLY and GASVPro. We excluded Lumpy from this test as it is not able to analyze mate-pair data. The total number of SVs predicted on this dataset by DELLY, GASVPro and BreakDancer was significantly higher than the number of SVs predicted by SV-Bay (62822, 1648 and 5543 versus 765). S2  and S3  for more detail Only 88 SVs were predicted by all the methods (Supplementary Fig. S5 ). First, we correlated these SVs with breakpoints in the copy number profile calculated using an Affymetrix SNP6. 0 array for CLB-GA using the genotyping software GAP  (Popova et al.
SV-Bay is written in Python with the possibility to parallelize the analysis for different chromosomes. However, even without parallelization, SV-Bay demonstrated a very reasonable execution time (Table  2 ). BreakDancer and DELLY were the fastest tools among the five. For both paired-end and mate-pair simulated datasets, SV-Bay showed the third best execution time: less than 2-4 h for matepair and paired-end data, respectively. GASVPro took more than 12 days to analyze the mate-pair dataset. The reason for this may be the long insert size of mate-pair data (more than 4 kb in our case) and thus the range of all possible breakpoint positions per SV was extremely large and required a significant amount of time to be analyzed. SV-Bay also attempts to predict the most likely breakpoint position based on the data for each SV.
many artefact clusters of mismapped read pairs. Indeed, in comparison with other methods, SV-Bay demonstrated a noticeably better SV detection accuracy both for simulated and experimental datasets. SV-Bay not only detects novel genomic adjacencies but also, where possible, groups them into more complex SVs such as co-amplifications, linking insertions, tandem duplications with inversion, etc. Overall, SV-Bay allows the user to skip such data post-processing steps like filtering out links with low number of fragments that do not correspond to copy number changes, filtering out events present both in the tumor and the matched normal control (artefacts and germline SVs), and performing manual inference of complex SV from the detected genomic adjacencies. SV-Bay does not use split reads to improve the resolution of predicted breakpoints.
Conclusion We have presented SV-Bay, a computational method and software to detect structural variants in cancer using whole genome sequencing data with or without matched normal control sample. SV-Bay does not only use information about abnormal read mappings but also assesses changes in the copy number profile and tries to associate these changes with candidate SVs. The likelihood of each novel genomic adjacency is evaluated using a Bayesian model. In its final step, SV-Bay annotates genomic adjacencies according to their type and, where possible, groups detected genomic adjacencies into complex SVs as balanced translocations, co-amplifications, and so on. A comparison of SV-Bay with BreakDancer, Lumpy, DELLY and GASVPro demonstrated its superior performance on both simulated and experimental datasets.
(in prep)  and  Hollowood et al. (in prep). The layout of this paper is as follows. In Section 2 we describe the DES Science Verification and SDSS DR8 data used in this work. Section 3 describes the updates to the redMaPPer algorithm since the RM1 and RM4 papers. Section 4 describes the cluster catalogs, as well as the photometric redshift performance on DES and SDSS data. In Section 5 we detail the effects of star/galaxy separation and small-scale masking on the cluster properties, and in Section 6 we compare the redMaPPer catalog with X-ray and SZ clusters in the DES SVA1 footprint. Finally, in Section 7 we summarize our results. When necessary, distances are estimated assuming a flat ΛCDM model with Ω m = 0. 30. For consistency with previous redMaPPer work, we use h = 1. 0 when quoting distances (h -1 Mpc) and h = 0.
Finally, there are smaller discontinguous regions targeting massive clusters  (Melchior et al. 2015)  and the COS-MOS field  (Scoville et al. 2007 ). We utilize this DES SV data set to construct the first DES redMaPPer cluster catalog. The redMaPPer footprint used in this paper is the same as that used for the associated redMaGiC (red-sequence matched-filter Galaxies Catalog) of red galaxies with well-behaved photo-z performance  (Rozo et al. 2015c, henceforth RM15). The DES SV data was processed by the DES Data Management (DESDM) infrastructure  (Gruendl et al, in prep) , which includes image detrending, astrometric registration, global calibration, image coaddition, and object catalog creation. Details of the DES single-epoch and coadd processing can be found in  Sevilla et al. (2011)  and  Desai et al.
SDSS DR8 In addition to our new catalog on DES SVA1 data, we have updated the redMaPPer catalog for SDSS DR8 photometric data  (Aihara et al. 2011) , which remains the most recent photometric data release from SDSS. The DR8 galaxy catalog contains ∼ 14000 deg 2 of imaging, which we cut to the 10401 deg 2 of contiguous high quality observations using the mask from the Baryon Acoustic Oscillation Survey (BOSS,  Dawson et al. 2013 ). The mask is further extended to exclude all stars in the Yale Bright Star Catalog  (Hoffleit & Jaschek 1991) , as well as the area around objects in the New General Catalog  (NGC Sinnott 1988). The resulting mask is that used by RM1 to generate the SDSS DR8 redMaPPer catalog. We refer the reader to that work for further discussion on the mask, as well as object and flag selection.
UPDATES TO THE redMaPPer ALGORITHM RedMaPPer is a matched-filter red-sequence photometric cluster finding algorithm with three filters based on galaxy color, position, and luminosity. The most important filter characterizes the color of red-sequence galaxies as a function of redshift. This filter is a linear red-sequence model in color-magnitude space (with slope and intercept) in n col dimensions, where n col is the number of independent colors in the input dataset. The filter also incorporates the intrinsic scatter, C int , which is the n col × n col covariance matrix assuming Gaussian errors in photometric magnitudes. This filter is self-calibrated by making use of clusters with known spectroscopic redshifts. The additional two filters are the radial filter, comprised of a projected NFW profile  (Navarro et al.