dataesr/scientific-paragraphs-categorization huggingface dataset:
(3)  Recently, we developed a GWAS analysis method called DEPTH (DEPendency of association on the number of Top Hits) to identify regions potentially associated with disease by considering overlapping groups of contiguous markers across the genome. (4) DEPTH is a machine learning algorithm for feature ranking of ultra-high dimensional datasets. It is built from well-established statistical tools, such as bootstrapping, penalised regression and decision trees, which are utilised to determine which exposures in a dataset are associated with the outcome variable. (5) (6) (7)  Unlike marginal regression, which considers each SNP individually, the key concept behind DEPTH is to rank groups of SNPs in terms of their joint strength of association with the outcome. Currently, there are two implementations of the DEPTH algorithm: (i) an IBM BlueGene/Q supercomputer version which is written in C/C++ and uses the Eigen library for numerical computing (parametric version), and (ii) a MATLAB implementation (non-parametric version).
The IBM BlueGene/Q version of DEPTH is intended to be used for datasets where the sample size (N) and the number of predictors (P) are extremely large (e. g. , N > 50,000 or P>1,000,000). The MATLAB version, which is preferred for smaller datasets as it can be run on a commodity PC, is based on decision trees, a nonparametric technique commonly used in regression and classification problems. Decision trees are estimated using minimum message length, a well-established information theoretic approach to model selection and parameter estimation. The DEPTH algorithm grows decision trees using a sliding window of SNPs and provides a measure of association for each window of SNPs under consideration. The statistic of association is equivalent to the Bayesian posterior log-odds in favour of association, which is driven by the ability of the SNPs to discriminate between cases and controls.
One can, of course, investigate the directions of associations for individual SNPs by examining the estimates for SNPs in a window from a fitted model. However, because the model fits marginal and joint interaction effects between the SNPs within the window, the direction of association for a given SNP could depend on other SNPs in the region. The size of the sliding window can be defined in terms of genetic distance (e. g. , 100 Kb) or a fixed number of variants (e. g. , 100 SNPs) as arguments in the main command line. Our aim was to compare conventional logistic regression GWAS analysis with the MATLAB implementation of the DEPTH algorithm using two previously analysed prostate cancer case-control datasets.
Materials and Methods Two previously analysed datasets were used in this analysis, a Stage 1 GWAS of the UK Genetic Prostate Cancer Study (UKGPCS; henceforth referred to as the UK GWAS dataset) and a Stage 2 custom array UK and Australian dataset (referred to as the iCOGS dataset). All study participants were self-reported as Caucasian and gave written informed consent. Both studies were approved by the appropriate national ethics committees. For the UK GWAS dataset, a total of 1,854 prostate cancer cases and 1,894 controls were selected from the UKGPCS. (8)  Cases were diagnosed at or before the age of 60 years or had a first-or second-degree relative with prostate cancer. Controls were men aged 50 years or older with a PSA of <0. 5ng/ml, frequency matched to the geographical distribution of the cases.
A total of 541,129 SNPs were genotyped using the Illumina Infinium HumanHap550 array (version 1). This dataset is described in detail elsewhere. (9)  For the iCOGS dataset, a total of 4,544 cases and 3,376 controls were selected from the UK and Australia. The 2,859 prostate cancer cases and 2,193 controls from the UK were selected from the UKGPCS and were not participants in the aforementioned UK GWAS dataset. (10)  Blood DNA was collected from prostate cancer cases aged ≤ 60 years at diagnosis across the UK and from a systematic series of cases attending the prostate cancer clinic at The Royal Marsden NHS Foundation Trust. Diagnosis was confirmed from medical record or death certificate, 60% were clinically detected. Controls with normal PSA levels (<3ng/ml) were selected from the same GP register and five-year age band as the cases.
00001). SNPs added to the iCOGS array for fine mapping (see  (10)  for details) were also excluded, but otherwise SNPs were not pruned based on linkage disequilibrium. This left 508,932 (UK GWAS dataset) and 173,524 (iCOGS dataset) SNPs available for analysis. Of the 100 SNPs that have been identified as being associated with prostate cancer susceptibility((1), 67 (UK GWAS dataset) and 68 (iCOGS dataset) were directly genotyped in both datasets. We present results from the DEPTH algorithm using a 100 Kb sliding window of SNPs from which the posterior log-odds in favour of association for each window was calculated. Note, the posterior log-odds in favour of association are not directly comparable with P values derived from logistic regression. The null distribution was empirically estimated using 1,000 bootstrap iterations.
The maximum 95 th percentile of the null distribution equalled approximately 1. 0 across the genome for both datasets. To reduce the possibility of identifying false-positive regions, we defined a "risk-associated region" in the UK GWAS dataset as having a peak with a magnitude of at least 1 unit above the 95 th percentile of the null distribution, extending from both sides of the peak until the signal drops below the null distribution; see discussion about this below. Regions were deemed confirmed by the same criteria in the iCOGS dataset (i. e. , greater than 1 unit above the 95 th percentile of the null distribution). Figure  1  shows a sample of the output generated from chromosome 8 using the DEPTH software. In this example, only the region at the 23. 0-23. 6 Mb position (shown with black shading) was classified as risk-associated as the signal was greater than 1 unit above the 95 th percentile of the null distribution.
Peaks such as the one at 20. 0-20. 2 (shown with only light grey shading) were less than 1 unit above the 95 th percentile of the null distribution and thus were not considered to be risk-associated. Signals that were less than 0 are denoted as 0 in the output for ease of visual interpretation. Conventional logistic regression analyses were computed for each SNP using the software package PLINK v1. 9 (http://pngu. mgh. harvard. edu/purcell/plink/). (  16 ) As in an earlier publication that analysed the UK GWAS dataset,(9) all SNPs with a P < 10 -6 based on a 1 degree of freedom trend test were deemed significant, while all SNPs with a P < 0. 002 and in the same direction (based on a Bonferroni adjustment for 50 SNPs) in the iCOGS dataset were deemed to be confirmed.
Results From the DEPTH analysis of the UK GWAS dataset, we identified 137 prostate cancer riskassociated regions with maximum posterior log-odds in favour of association greater than 1 unit above the 95 th percentile of the null distribution. Twenty-five of these regions contained 33 of the previously identified 100 independent prostate cancer susceptibility SNPs. The remaining 112 regions that were not confirmed by this criterion in the iCOGS dataset represent potential novel susceptibility regions (results not shown). The number of measured SNPs within the 137 regions depended on whether they were previously identified prostate cancer susceptibility regions or not. For example, across the 25 regions that contained at least one previously identified susceptibility SNP, there was an average of 65 and 101 genotyped SNPs per region for the UK GWAS and iCOGS datasets, respectively.
On the other hand, for the remaining 112 regions, the average number of SNPs genotyped per region was 48 for the UK GWAS dataset, but only 16 for the iCOGS chip. Of the 137 susceptibility regions identified from the DEPTH analysis of the UK GWAS dataset, we confirmed 14 from the DEPTH analysis of the iCOGS dataset (Table  1 ). All 14 confirmed regions contained at least one previously identified prostate cancer susceptibility SNP. Table  1  shows that four of these regions (#2, #3, #6, #9) did not contain any SNPs with P < 10 -6 when analysed using standard logistic regression. Three of these regions (#2, #6, #9) were subsequently identified in a third-stage analysis involving an additional 16,229 cases and 14,821 controls from 21 studies  (17) , while region #3 was identified using 25,074 prostate cancer cases and 24,272 controls from the international PRACTICAL Consortium.
(10)  After performing conventional logistic regression analyses of the UK GWAS dataset, we identified 50 SNPs that were significant at the P < 10 -6 level (Table  2 ). We found confirmatory evidence (P < 0. 002) for 40 of the 44SNPs that were genotyped in the iCOGS dataset (the six SNPs that were not genotyped were all located in the 8q24 region). These 40 SNPs were located in 11 regions, and these regions were also confirmed by DEPTH analyses as being risk-associated. Two of the four SNPs that were not confirmed by logistic regression analyses (rs2660753 and rs2659056) were located very close to at least one other SNP that was confirmed by logistic regression; therefore, we considered that these two regions were confirmed by logistic regression. The remaining two SNPs (rs9364554 and rs902774), however, did not have any other confirmed SNPs located nearby.
We found, that the region encompassing rs9364554 on chromosome 6 was confirmed by DEPTH analyses as being risk-associated. This region, therefore, presents an additional region to the four regions identified from the DEPTH analyses that logistic regression would not have found. On the other hand, we found no confirmatory evidence using either analysis method for the region on chromosome 12 that contained the SNP rs902774, but this may be due to the disease characteristics of the iCOGS dataset as this SNP was originally identified from an analysis of 2891 advanced prostate cases and 4592 controls of European ancestry. ( 18 ).
Discussion Using DEPTH analysis we identified 14 regions associated with prostate cancer risk that had been reported previously; five of which would not have been identified using conventional logistic regression on these datasets. We also identified 112 novel putative susceptibility regions that were not identified using logistic regression. As the iCOGS chip was developed as a custom genotyping array, the design focused on previously known risk loci and did not include a GWAS backbone. We were, therefore, unable to confirm any of the 112 novel risk-associated regions detected by DEPTH, primarily due to insufficient numbers of iCOGS array SNPs in those regions. While using imputation could be a solution, it does not provide independent measures of SNPs. Increasing SNP density increases the chance of discovering associations, but generally will result in larger stretches of the same signal on the DEPTH plot, and has little effect on the ranking process or the generation of the empirical null distribution.
We also plan to implement continuous phenotypes in future papers. At present, the non-parametric version of DEPTH does not allow for principal components adjustments. We intend to implement this feature in a future version of DEPTH. While ethnic background of the UK participants was fairly homogeneous, this was not the case for the Australian participants who predominantly included Australian born men of northern European background, but also included southern European migrants. In sensitivity analyses, we observed that P values from the logistic regression analyses were similar when using the restricted iCOGS dataset compared with the full iCOGS dataset after adjustment for principal components (results not shown). While it is preferable to utilise the full dataset, the similar results from the sensitivity analyses suggest it is unlikely that the results from our non-parametric DEPTH analyses would change appreciably after adjustment for principal components, but further work is needed in this regard.
From our study of two existing prostate cancer datasets, we have identified and confirmed 14 regions that have been previously reported to be associated with prostate cancer risk, five of which would not have been identified using the conventional approach that considers each SNP individually. This study demonstrates that the DEPTH algorithm can be applied to existing and future datasets to identify additional genetic susceptibility regions that merit further investigation. Sample output obtained from the DEPTH software using the UK GWAS dataset. The solid line represents the raw signal, the light grey shading represents signal above the 95th percentile of the null distribution, and the black shaded area represents the "risk-associated region" (i. e. , 1 unit above the 95th percentile of the null distribution).
Acknowledgments Financial support:  J. L. Hopper ,  G. Severi ,  E. Makalic ,  D. F. Schmidt ,  Q. M. Bui ,  G. Qian ,  D. J. Park ,  A. Kowalczyk  received support from the  National Health and Medical Research Council Australia  project grant ( 1033452 ). R. A. Eeles and  Z. Kote-Jarai  received support from  Cancer Research UK  (grant numbers  C5047/A7357 ,  C1287/ A10118 ,  C1287/A5260 ,  C5047/A3354 ,  C5047/A10692 ,  C16913/A6135  and  C16913/A6835 ),  Prostate Research Campaign UK (now Prostate Cancer UK) ,  The Institute of Cancer Research and The Everyman Campaign, The National Cancer Research Network UK, The National Cancer Research Institute (NCRI) UK. R. A. Eeles and Z.
In our approach, this phase is partitioned into two subprocesses: one at design time and another one at runtime. At design time, a resolution model is specified to configure the variability model. This resolution model is extended from the CVL resolution meta-model for generating the necessary elements in an ASA. Generating the architecture is realized by our Adaptive Software Architecture Generation (AdapSwAG) tool. As depicted in Figure  2 , the input of this tool is the CVL model and its output is a particular architecture (the product model). Compared to the CVL execution of the CVL approach, the software architecture generated by AdapSwAG tool includes part of its initial variability, and is specified in the same language as the base model. Based on the product model and a given target platform, a text generation module generates implementation artifacts skeleton.
VI. CONCLUSION In this paper, we have presented a variability-driven development process for building an ASA. The process is based on CVL tools and meta-models to model variability of the software architecture but identifies an ordered set of tasks to be performed by engineers from variability specification up to the software architecture. In order to validate our process, we have implemented a simple client-server application. The server is implemented with two different versions and can dynamically switch. In order to represent the variability of this application, a VSpec tree in CVL that conforms to the CVL meta-model is finally specified. Its architecture (base model) is specified by using ACME -an ADL. In order to generate a product, a resolution model is specified. Then, AdapSwAG tool generates a product model as an ASA.
In this example, we have used the iPOJO component model and the Apache CXF framework to develop components. Based on their specification, a text generation module is implemented by using Xpand generator framework to generate implementation artifacts skeleton that includes packages. Each of them corresponds to a component specified in the product model and consists of component implementations, a component specification represented in XML documents, and a configuration file to create iPOJO components. To this end, the generated packages can be manually completed by an engineer, or the implementation artifacts developed independently by an engineer are integrated into them to build the iPOJO components of the adaptive product. Due to space constraints we can not give more details of our implementation, but the example is available at https://github.
Such abilities are related to the secretion of a wide range of enzymes that may be exploited to improve the degradation of complex plant biomass for carbon recycling and biofuel production (e. g. ,  Couturier et al. , 2012). The genus Pycnoporus is known for the efficient degradation of lignin, and the use of the fungi for biotechnological applications has been reviewed  (Eggert et al. , 1997; Gupta et al. , 2011; Lomascolo et al. , 2011). Enzymes active on plant biomass polymers including glycoside hydrolases (GH), carbohydrate esterases (CE), polysaccharide lyases (PL), and auxiliary activity enzymes (AA) are classified in sequence-based families in the Carbohydrate Active Enzyme database (CAZy 1 ,  Levasseur et al. , 2013; Lombard et al. , 2014). The volume of next-generation sequencing (NGS) data has been increasing rapidly.
, 2015) , Pycnoporus sanguineus  (Miyauchi et al. , in preparation) , and Pycnoporus cinnabarinus  (Levasseur et al. , 2014). We performed information mining of the fungal omics data from both transcriptomic and secretomic point of view. Our approach was evaluated by examining the concordance of the resultant gene/protein clusters in terms of biological relevance. Also, our method was compared to the model-based clustering (i. e. , MBCluster. Seq;  Si et al. , 2014). To the best of our knowledge, this is the first study in which the genome-wide transcriptome and secretome of fungi were systematically clustered to generate layers of omics topographies for biological extrapolations.
RESULTS Three fungal strains, Pycnoporus coccineus CIRM-BRFM 310 (Pycco 310), Pycnoporus cinnabarinus CIRM-BRFM 137 (Pycci 137), and Pycnoporus sanguineus CIRM-BRFM 1264 (Pycsa 1264) were grown on each of the five carbon sources selected. Two carbon sources were used as control conditions; maltose, which is easily assimilated by fungi and Avicel, which was used as a cellulose model substrate. Ground wheat straw, pine wood, and aspen wood were used as models for gramineae biomass, softwood biomass, and hardwood biomass, respectively (Figure  1 ). A visualization workflow for omics integration was developed for the datasets to identify fungal genes and proteins involved in lignocellulose deconstruction. The integrated omics model consists of; (1) genes with similar transcription patterns being clustered into nodes; (2) the calculation of the node-wise mean of the normalized transcript read counts, which reflects the transcription level in response to each condition; (3) the selection of condition-specific highly transcribed gene clusters; and (4) the count of secreted proteins detected from the culture medium (Figure  1 ).
Comparison of the Clustering Methods We measured the biological relevance of our model in comparison to MBCluster using the Pycco 310 data. Correlations were estimated between the average transcription levels of the individual gene clusters and the corresponding count of the secreted proteins (Supplementary Data S3). Given that in general a higher gene transcription level corresponds to a higher expression of gene products, we expected some positive correlation if a clustering method is biologically relevant. Self-organizing map and MBCluster were used separately to generate 320 gene clusters. This number of clusters was optimal for SOM and gave a resolution of about 35 genes/cluster. The correlation coefficients were 0. 55 and 0. 24 (p < 0. 001) for SOM and MBCluster, respectively, suggesting that SOM combined the transcriptomic and secretomic data more effectively than MBCluster.
The layers of genome-wide complementary omics information revealed a global picture of the fungal response to the growth conditions. The transcriptomic and secretomic topographies of the three fungal strains were moderately correlated on the genome scale (Supplementary Table  S1 ). Thus, our method allowed us to exploit the interconnected omics information by selecting nodes of interest containing co-regulated genes and corresponding gene products. We demonstrated two different approaches to data mine genes/proteins of interest. The co-regulated genes/proteins were determined by either transcriptomic or secretomic patternoriented selection. The former approach unveiled the dynamics of genome-wide transcription patterns in response to the substrates. The latter approach was suitable for identifying enzymes synergistically involved in decomposition of the substrates.
MATERIALS AND METHODS Fungal Strains, Genomes, RNA Extraction, RNA-Sequencing, Protein Extraction, and Detection Pycnoporus coccineus CIRM-BRFM 310 (herein called Pycco310), Pycnoporus cinnabarinus CIRM-BRFM 137 (Pycci 137), and Pycnoporus sanguineus CIRM-BRFM 1264 (Pycsa 1264) strains were obtained from the CIRM collection 2  at the National Institute of Agricultural Research. All three genomes were sequenced by US Department of Energy Joint Genome Institute and annotated using the Joint Genome Institute (JGI) Annotation Pipeline  (Grigoriev et al. , 2014). Transcriptome and secretome data were collected from triplicated independent three day-cultures in the presence of either 20 g. l -1 maltose, 15 g. l -1 Avicel, 15 g. l -1 ground wheat straw, 15 g. l -1 pine wood, or 15g. l -1 aspen wood as the sole carbon source.
Three day-cultures were chosen because, by this stage, most of the three day-cultures grown under the all conditions had gone through DNA synthesis, indicating that the fungi were in comparable physiological states  (Miyauchi et al. , in preparation). The high concentrations of the substrates provided were excess to the growth requirements of the fungal cultures  (Miyauchi et al. , in preparation). RNA libraries were prepared and sequenced on Illumina HighSeq-2500  (Couturier et al. , 2015). The sequence data are available on NCBI (GEO accession: GSE82486). The transcriptome response of Pycsa1264 to pine could not be analyzed due to poor quality of the extracted RNAs from these samples. Secreted proteins were collected from the same cultures, dia-filtered and identified by ESI-MS/MS  (Couturier et al.
Data Preparation and Manipulations The obtained Illumina RNA-Seq 150 bp paired end reads were filtered and trimmed using the JGI QC pipeline. QCed reads were aligned to the genomes using TopHat 2  (Kim et al. , 2013) , counted with HTSeq  (Anders et al. , 2014) , normalized with dsNorm from the DESeq2 Bioconductor package, and log2 transformed  (Love et al. , 2014). The reliability of the data was verified using a selection of genes by Real Time quantitative PCR  (Couturier et al. , 2015). Transcriptomic and secretomic data were combined for the data processing. R was used for data manipulations using our customized scripts (R Core Team, 2013).
SOMs for the Integrated Omics Models Self-organizing maps were constructed with the R package kohonen  (Wehrens and Buydens, 2007). The genes showing similar transcription levels were grouped into nodes of SOMs. It was empirically found that a target of 35 genes per single node of the SOM gave the best resolution of the gene clusters. The default parameters were used for initialization, learning rate, and radius. Hexagonal SOM models were applied to have six neighboring nodes. The map units used were 320, 285, 340 for Pycco 310, Pycci 137, and Pycsa 1264, respectively. The number of iterations used was 100 times more than the map units in order to minimize the Euclidian distances between the nodes for the optimal convergence.