Consistent predictive performance was found in different multivariate models in increased patient population (p = 0.002). The survival signature predicted a late metastatic feature through 5-year disease free survival (p = 0.006). We identified subtypes within the lymph node positive (p < 0.001) and ER positive (p = 0.01) patients that best reflected the invasive breast cancer biology. In conclusion using the Common Probe Approach, we present a novel prognostic signature as a predictor in breast cancer late recurrences.Clinical applications of gene expression signatures in breast cancer prognosis still remain limited due to poor predictive strength of single training datasets and appropriate invariable platforms. We proposed a gene expression signature by reducing baseline differences and analyzing common probes among three recent Affymetrix U133 plus 2 microarray data sets. Using a newly developed supervised method, a 92-probe signature found in this study was associated with overall survival. It was robustly validated in four independent data sets and then repeated on three subgroups by incorporating 17 breast cancer microarray datasets. The signature was an independent predictor of patients' survival in univariate analysis [(HR) 1.927, 95% CI (1.237–3.002);  Breast cancer is the leading cause of cancer-related deaths amongst women worldwide  and it iIn the present study, we identified a novel probe signature by reducing baseline differences, incorporating a large number of probes and patients (677), and updated primary breast cancer datasets to improve predictor performance. In addition, we tested whether the identified gene signature could robustly validate in independent and combined data sets. Finally, we attempted to demonstrate whether this signature could distinguish subtypes of breast cancer reflecting the biological and clinical characteristics of the disease.n > 100), treatment regimen (2/3rd untreated before surgery) and previous survival association. Detailed information and distribution of several clinical variables for these data sets are shown in Tables 2 scale with at least five observations that represented the same probe expression level. As a result, 810, 1024 and 918 probe sets were identified from data sets 1, 2, and 3, respectively, and were passed through a Venn diagram generator that produced 408 common probe sets. The heatmap of these common probe sets for the three data sets is shown in We selected three recent microarray data of primary human breast cancer considering both high and moderate quality of gene expression, cancer cells content (>60%), patients number n > 00, treatth percentile (median) cutoff of the prognostic index (−0.272144). To evaluate patient's prognosis, Kaplan-Meier plots were drawn and the log-rank test showed significant differences in all prognostic variables including OS and relapse free survival (RFS) in the training group . Individual Kaplan-Meier graphs were then evaluated and the significant 92 probe sets were finally considered the prognostic signature Figure . These 9−3p = 1.12 × 10, −3p = 3.16 × 10, −5p = 1.2 × 10 and −2p = 1.37 × 10, respectively; Figure −4p = 4.66 × 10, −11p = 6.04 × 10 and −9p = 7.32 × 10, respectively; Figure To evaluate the prognostic performance of the newly developed 92-probe signature, validations were first done on independent datasets 2, 3, 4 and 5. Data set 1 from Ireland cohorts was used for training of classifiers to validate all the datasets. To begin with, the 92 probes of the training cohorts were combined with corresponding probes from each of the validation sets. All the genes in the signature were submitted into the prediction algorithms CCP, LDA, 1NN, 3NN, NC and SVM for the validation of the datasets. Performance of the gene signature was assessed by leave-one-out cross-validation (LOOCV) to obtain the accuracy, sensitivity and specificity. During LOOCV, the specificity for predicting high risk in dataset 2, 3 and 5 was 1.0, while that for dataset 4 was 0.93. On the other hand, the sensitivity of the corresponding datasets was 0.902, 0.961, 0.922 and 0.872, respectively. The area under curve (AUC) during cross validation was 0.999 for the data set 2, 3, and 5; while 0.967 for data set 4 (data not shown). The Kaplan-Meier plots predicted by CCP showed significant difference with prognosis in all independent datasets -status, lymph node, grade and the absence or presence of the 92-probe signature. In the first model, lymph node and grade were significantly correlated with patients' survival (p < 0.01)  1.927 1.237–3.002); 7–3.002; 1) Table . When thp < 0.05 and < 0.001 for ER-and lymph node positive respectively; Figure p < 0.05, p < 0.01; data sets 3 and 5 respectively; Figure p < 0.001, dataset 3 and combined cohorts AL; p < 0.05, Canada cohorts; Figure Based on the available clinical information and sufficient patients' number, subset analyses were performed within the ER and lymph node status. The gene expression signature successfully identified patients with poor survival among those with ER-positive and positive axillary lymph node involvement in breast cancer within the training data set 1 (p < 0.001) in which 9 genes were adenylate cyclase 1 (ADCY1), erb-b2 receptor tyrosine kinase 4 (ERBB4), estrogen receptor 1 (ESR1), GATA binding protein 3 (GATA3), insulin-like growth factor 1 receptor (IGF1R), neuropeptide Y receptor Y1 (NPY1R), ras-related and estrogen-regulated growth inhibitor-like protein (RERG), serpin peptidase inhibitor clade A member 1 (SERPINA1), and transforming growth factor beta receptor III (TGFBR3). Two additional hormonal processes, namely responses to steroid hormone stimulus and estrogen stimulus, were found. While cell motion was the least significant process (p < 0.05), it consisted of 6 genes such as axonemal dynein light intermediate polypeptide 1 (DNALI1), forkhead box C1 (FOXC1), insulin-like growth factor 1 (IGF1), ret proto-oncogene (RET), S100 calcium binding protein A9 (S100A9), and TGFBR3. Other important processes like gland development, response to organic substance, regulation of epithelial cell proliferation, cell maturation, mesenchymal cell differentiation, regulation of cell migration along with some signaling pathways such as protein tyrosine kinase signaling and second-messenger-mediated signaling pathways were found to be considerably predominating biological processes. Genes involved in multiple biological processes were FOXC1, TGFBR3, IGF1R, IGF1, RET, NPY1R and forkhead box A1 (FOXA1), which participated in 7 to 15 processes while protein phosphatase 1 regulatory subunit 1B (PPP1R1B) and zinc finger protein ZIC 1 (ZIC1) included only single cell behavior process. A network analysis of the 92-probes showed cytochrome P450 2B6 (CYP2B6) is linked to the strongest protein-protein interaction especially with cytochrome P450 4X1 (CYP4X1), FOXA1, IGF1, nephronectin (NPNT), and transcription factor SOX-11 (SOX11). In addition, an unknown gene (AA588092), anterior gradient homolog 3 (AGR3), angiotensin II receptor type 1 (AGTR1), chloride intracellular channel protein 6 (CLIC6), C-X-C motif chemokine 14 (CXCL14), ectonucleotide pyrophosphatase/phospho-diesterase 5 (ENPP5), FOXC1, signal peptide-CUB-EGF-like domain containing protein 2 (SCUBE2), and SERPINA1 were also found to be strongly connected within the signature  and specificity (>90%) of all the prediction models within dataset 1 and a significant association of predicted outcomes was found with patient prognosis in all independent datasets 2, 3, 4, and 5) . This is consistent with elevated cell proliferation and loss of cell cycle control associated with poor outcomes , raw CEL files and clinical survival information. Multiple data from the same institution were excluded, except for three datasets, GSE9195, GSE20711 and GSE16391, from Princess Margaret Research, Canada. The raw CEL files were preprocessed with robust multiarray average (RMA) algorithm [http://www.bioconductor.org). Based on the U133 plus 2.0 platform and patients characteristics, five datasets were mainly considered in the study. To identify the survival gene candidates, dataset 1 (GSE42568) from Dublin City University (Ireland) was used as a training cohort consisted of 121 patients, 17 of whom were excluded due to normal breast tissue. Datasets 1 to 3 were used for the identification of common genes while datasets 2 to 5 were used as independent testing samples for validation  of the identified signature. The first validation dataset 2  was published by the Koo Foundation SYS Cancer Center, Taiwan. Dataset 3  was taken from the Institut Paoli-Calmettes, France, however, only 246 patients that provided detailed survival and clinical information were analyzed in this study. Dataset 4 (GSE12276) was collected from the Netherlands Erasmus Medical Centre and 195 patients out of 204 were analyzed in the present study. Finally, dataset 5  was used as the last validation set from the Cathay General Hospital SiJhih, Taiwan. Details about the patients' characteristics were described in Table In the current analysis, 18 different breast cancer patient datasets were studied. Gene expression datasets were downloaded from the GEO repository (lgorithm  using R http://www.pangloss.com/seidel/Protocols./venn.cgi). The univariate Cox analysis, based on hazards model and Wald statistics [The following three steps were included in the identification of the prognostic signature: (1) selection of the common probes using the Venn diagram method (2) determination of the optimal survival gene by the univariate Cox and (3) prognostic prediction between gene expression and patient's survival by supervised principle component method. At first, the 54675 probe sets from data sets 1 to 3 were filtered by the Gene filtration method and the common probes were identified by a Venn diagram generator . Under this condition, gene expression data from different cohorts were adjusted individually by subtracting the median expression value across the samples before combining them. Six different prediction methods were applied for the validation of all datasets which included compound covariate predictor (CCP), linear discriminant analysis (LDA), support vector machine (SVM), nearest neighbor 1NN, 3NN and nearest centroid (NC) [The validation of the survival gene signature was accomplished on independent and combined datasets. For the combined validation, three subgroups were made depending on the chip versions and the internal data sets. These included 573 breast cancer patients from the internal test sets, 492 from external test sets and 2351 from the affymatrix U133A version Figure . For lymoid (NC) . The robhttp://david.abcc.ncifcrf.gov/) [Gene ontology (GO) biological process enrichment analysis was carried out using the database for Annotation, Visualization and Integrated Discovery (DAVID) bioinformatics tool (rf.gov/) . A connerf.gov/) .http://linus.nci.nih.gov/BRB-ArrayTools.html) [http://www.r-project.org) and Statistical Package for Social Sciences (SPSS) software . All comparisons of Kaplan-Meier survival analysis were performed by the log rank test. Cluster analysis was performed with Cluster and Tree View (http://bonsai.hgc.jp/~mdehoon/software/cluster/software.htm#ctv) [p value of less than 0.05 was considered statistically significant.Microarray data were analyzed using BRB-Array Tools Version 3.0 (ls.html) . All othhtm#ctv) . p value


CRN has a user-friendly web interface designed to facilitate cancer research and personalized medicine. It is an open resource for intuitive data exploration, providing coding-transcript/lncRNA expression profiles to support researchers generating new hypotheses in cancer research and personalized medicine.The genome-wide transcriptome profiling of cancerous and normal tissue samples can provide insights into the molecular mechanisms of cancer initiation and progression. RNA Sequencing (RNA-Seq) is a revolutionary tool that has been used extensively in cancer research. However, no existing RNA-Seq database provides all of the following features: (i) large-scale and comprehensive data archives and analyses, including coding-transcript profiling, long non-coding RNA (lncRNA) profiling and coexpression networks; (ii) phenotype-oriented data organization and searching and (iii) the visualization of expression profiles, differential expression and regulatory networks. We have constructed the  Over the past two decades, gene expression data generated by microarray technology has been widely used to study the causes and therapies of cancers. Research on the human transcriptome has produced a large amount of expression data at the gene level. Many gene-expression databases have been developed for cancer research, such as Oncomine (public version) , NextBiohttp://cancergenome.nih.gov/) database stores genomics data (including RNA-Seq data) for a variety of cancer types, but it only contains data generated by TCGA consortium. The RNA-Seq Atlas   and the eq Atlas  attemptseq Atlas , and pereq Atlas . Neverthhttp://syslab4.nchu.edu.tw/CRN.This paper presents the Cancer RNA-Seq Nexus (CRN), the first public database providing phenotype-specific coding-transcript/lncRNA expression profiles and mRNA–lncRNA coexpression networks in cancer cells. CRN includes cancer RNA-Seq data sets in the TCGA, SRA and GEO databases. Figure http://cancergenome.nih.gov/). The final database consists of 28 TCGA data sets and 26 GEO/SRA data sets.We collected a total of 54 human cancer data sets and 11 030 samples from major cancer RNA-Seq data portals, including the Gene Expression Omnibus (GEO) , SequencWe obtained annotation data for the RNA-Seq data sets from GEO, and downloaded RNA-Seq reads from SRA. Each data set consists of several subsets, where each subset is a group of RNA-Seq samples associated with a specific phenotype trait or cancer condition. For example, the subsets can be identified with a specific stage of cancer, a disease state, a cell line, a type of tissue or a genotype. We manually created these subsets from the raw data and annotations, assigning samples based on their textual descriptions. The GEO/SRA data yielded a total of 142 subsets and 1337 samples. To obtain expression profiles for both coding transcripts and lncRNAs, we downloaded the entire human transcriptome with Ensembl ID, including 93 139 protein-coding and 26 414 lncRNA transcripts, from GENCODE   as a refhttp://cancergenome.nih.gov/). We defined subsets for all patient samples based on TCGA clinical information, e.g. primary tumor, metastatic, recurrent tumor, adjacent normal tissue or the pathologic tumor stage. Since the number of adjacent normal samples for each tumor stage is small, we pull all the adjacent normal samples from various tumor stages into a normal subset for each TCGA data set. This analysis resulted in a total of 239 subsets and 10,270 samples. The TCGA expression data contain 67 119 transcripts with UCSC KnownGene IDs that are given gene symbol assignments according to the UCSC KnownGene annotation (6 to obtain transcripts per million (TPM).We collected all Level 3 RNA-Seq version 2 data sets and clinical information from the TCGA portal : 444 ‘cancer versus cancer’ and 157 ‘cancer versus normal’ subset pairs.To identify phenotype-specific differentially expressed (DE) protein-coding transcripts and DE lncRNAs, within each data set we selected all subsets with at least three samples, then converted the expression value to logjustment ,19 to car to a P-value using the following method: given a sample size n, we calculated the t-value  > 0.5), then calculated correlations between every pair of selected mRNA and lncRNA expression profiles. The samples of the subset pair were used for the correlation analysis. To assess the significance of these connections, we transferred Pearson's correlation coefficient  t-value :\documeP-value was calculated using this t-value for the t-distribution with n−2 degrees of freedom. The significant correlations between protein-coding transcripts and lncRNAs  were collected for network construction. In the web interface, Cytoscape  protein-coding transcripts, sorted by the significance level  between two subsets. Users can filter DE transcripts based on their adjusted P-value threshold or up/down regulation. The interface displays the rank, the adjusted P-value, the average expression values, the transcript ID and the gene symbol for each transcript. The transcript ID represents Ensembl transcript ID for GEO/SRA data sets and it represents UCSC KnownGene ID for TCGA data sets.DE lncRNAs. Given a subset pair, CRN visualizes the expression profiles of DE lncRNAs sorted by the significance level between two subsets. Users can filter DE lncRNAs based on an adjusted P-value threshold or up/down regulation. CRN displays the rank, the adjusted P-value, the expression values of two subsets, the lncRNA ID and the lncRNA name.mRNA–lncRNA coexpression network. Given a subset pair, CRN visualizes an mRNA–lncRNA co-expression network, displaying coding transcripts and lncRNAs with significant correlations between coding transcripts and lncRNAs  for the given gene or the given lncRNA.Search for coding transcripts and lncRNAs. Given a gene symbol, CRN provides a search function on the expression profiles of all transcripts associated with the given gene, allowing users to investigate the differential expressions of its various isoforms. An auto-complete function provides suggestions for gene or transcript symbols as the user types, quickly searching and displaying partially matched terms. The auto-complete function not only helps users search efficiently, but also make a quick filtering  the upstream promoter of the gene generates TAp63 isoforms containing the N-terminal transactivation (TA) domain, and (ii) an alternative internal promoter generates ΔNp63 isoforms lacking the TA domain  with various C-terminal tails are generated through alternative splicing at the 3′ end. It has been suggested that ΔNp63 isoforms are highly specific for squamous cells: they are overexpressed in squamous cell carcinoma (SCC)  –25, whil and SCC . Therefoherapies ,28. As sTAp63α and ΔNp63α isoforms (those with the longest 3′-terminus) in lung SCC and lung adenocarcinoma. The expression of ΔNp63α is extremely high (Average TPM = 109.61) and significantly overexpressed  in lung SCC, compared with normal samples (Average TPM = 1.205). The expression of ΔNp63α is very low  and has no significant differential expression  in lung adenocarcinoma. The expression of TAp63α is extremely low in both lung SCC and lung adenocarcinoma (Average TPM = 0.239–0.833), consistent with the previous reports (Table  reports .CCAT1) lncRNA is significantly up-regulated in colon and colorectal cancer compared with the normal human tissues  in cancer subsets (average TPM = 4.53–9.89) with respect to normal subsets (average TPM = 0.318)  in seven cancer subsets (average TPM = 4.43–10.17) with respect to normal subsets (average TPM = 1.575)  Table . Additio5) Table . InteresPVT1 lncRNA is induced by MYC, and is involved in the suppression of apoptosis  in kidney renal clear cell carcinoma Stage I  with respect to the adjacent normal subset . PVT1 lncRNA also has two isoforms, uc003ysl.2 and uc010mdp.1, and both of them are significantly overexpressed  in the cancer subset  with respect to adjacent normal tissues . As shown in Figure MYC isoform uc003ysi.2 and PVT1 isoform uc010mdp.1.The overexpression of poptosis ,34. PVT1C copies . Figure We processed massive data sets collected from NCBI GEO and TCGA in order to obtain complete transcriptome profiles, extract the differentially expressed protein-coding transcripts and lncRNAs involved in various cancer types and infer the lncRNA regulatory network. Although CRN was developed with the goal of collecting, processing, analyzing and visualizing all publically available cancer RNA-seq data, it is still limited by the characteristics of these data. Firstly, different subsets from the same RNA-Seq data set have different sizes, which can influence the power of statistical analysis. Secondly, the cancer RNA-Seq data were generated using different experiment designs, sequencing platforms, cDNA library protocols, genetic backgrounds (cell lines or patients) and so on. It is a great challenge to integrate such diverse data resources. We have therefore applied the strategy of only comparing subsets from the same data set. Thirdly, some clinical information has not yet been integrated into CRN yet, such as treatments, pharmaceutical therapies and survival data.As more and more cancer RNA-Seq data sets accumulate in the TCGA and NCBI GEO databases, we will update our CRN database to ensure that it can empower biologists with comprehensive cancer transcriptome knowledge.


The choice of stochasticity distribution for modeling the noise distribution is a fundamental assumption for the analysis of sequencing data and consequently is critical for the accurate assessment of biological heterogeneity and differential expression. The stochasticity of RNA sequencing has been assumed to follow Poisson distributions. We collected microRNA sequencing data and observed that its stochasticity is better approximated by gamma distributions, likely because of the stochastic nature of exponential PCR amplification. We validated our findings with two independent datasets, one for microRNA sequencing and another for RNA sequencing. Motivated by the gamma distributed stochasticity, we provided a simple method for the analysis of RNA sequencing data and showed its superiority to three existing methods for differential expression analysis using three data examples of technical replicate data and biological replicate data. Next-generation sequencing is a stochastic, or “noisy”, processIn the literature the intrinsic stochasticity for RNA sequencing has been assumed to follow a Poisson distribution. For example, a Poisson distribution is assumed for modeling technical variations in popular tools for identifying differentially expressed genes  on the basis of data from technical replicates encompassing both the library preparation step and the read generation step. We collected miRNA sequencing data for two sarcomas: a myxofibrosarcoma (MXF) and a pleomorphic malignant fibrous histiocytoma (PMFH), each subjected to library preparation and sequencing six times using uniform experimental handling. We observed that the stochasticity for miRNA sequencing data is more consistent with a gamma distribution and provided a biological interpretation based on the exponential stochastic growth of PCR amplifications. We further validated this observation in two independent datasets, one for miRNA sequencing and another for RNA sequencing. Motivated by the gamma distributed stochasticity, we provided a simple and powerful method  for analyzing RNA sequencing data and showed its superiority to three existing methods for differential expression analysis using three data examples of technical replicate data and biological replicate data.In addition to our own data, we also observed the same mean-variance relationship in miRNA sequencing data from the miRNA Quality Control StudyTo demonstrate the importance of the stochasticity assumption in the analysis of sequencing data, we investigated its effect on the analysis of differential expression when only technical replicates are available . Because such experiments typically involve only a small number of technical replicates, the variance is typically approximated as a function of the mean under the constraint imposed by a distribution, rather than being estimated empirically, to avoid the statistically undesirable consequences of estimating both mean and variance from sparse data. Our analysis compared the mean expression level between MXF and PMFH for each of their 352 shared high-read miRNAs  and apprThe gamma-distributed nature of RNA-seq data can be explained by the use of PCR amplification in library preparation and in the initial step of read generation. PCR amplifies a molecule exponentially: with each cycle, a molecule is doubled with a certain probability Although our method of analyzing the gamma distribution under the generalized linear model requires statistical expertise, gamma distributions can be analyzed simply and accurately by the use of cubic root transformation and normal-distribution–based methods, such as the t-test for two-group comparison1315Statistical methods such as edgeRWe hypothesized that cubic root transformation can also help normalize biological replicate data and subsequently allow the use of normal-distribution-based methods such as the two-sample t-test for differential expression analysis. We examined this hypothesis in two public miRNA-seq datasets with biological replicates, one from The Cancer Genome Atlas (TCGA) ovarian cancer studyTaken together, for the analysis of both technical replicates and biological replicates, our method provides a good balance between the over-liberal tendency of edgeR and the over-conservativeness of DESeqIn summary, we have made three contributions towards the understanding of fundamental stochastic properties of RNA sequencing: (1) we have provided empirical evidence that the stochastic distribution of RNA sequencing is gamma for high-read genes and Poisson for low-read genes, (2) we have offered a biological interpretation for the gamma distribution by recognizing its connection with exponential PCR amplifications, and (3) we have proposed a simple, powerful, and versatile solution using cubic root transformation and normal-distribution-based methods for the analysis of sequencing data, which is readily assessable to biomedical researchers.Our finding of the gamma-distributed nature of intrinsic stochasticity has broad implications not only for the assessment of differential expression, but also broadly for applications of deep RNA sequencing and other quantitative methods that involve PCR amplification. In addition, our findings highlight the importance of studying the stochasticity of a technology using technical replications for the entire experiment.Two pooled soft tissue sarcoma (STS) samples were used in our study. One was from pooling 27 primary myxofibrosarcoma (MXF) tumor tissue samples and another from pooling 27 primary pleomorphic malignant fibrous histiocytoma (PMFH) tumor tissue samples. These tumor samples, all from newly diagnosed, previously untreated tumors, were collected at Memorial Sloan Kettering Cancer Center (MSKCC) between 2000 and 2012. Detailed description of these 54 tumors will be provided in a separate manuscript that is under preparation. Human tumor tissues used in this study were obtained from participants who provided informed consent in written form and their use in our study was approved by the MSKCC Institutional Review Board. The methods in our study were carried out in accordance with the approved guidelines.Sample preparation used strict quality control on the specimens. Freshly harvested tissue was snap-frozen for eventual cryomold embedding and RNA extraction for sequencing analysis. Cryomolds (0.5 × 1 × 1 cm) were macrodissected under the supervision of a dedicated soft tissue sarcoma pathologist to ensure subtype uniformity and to eliminate necrotic/normal tissue as describedEach pooled STS sample was sequenced six times using consistent experimental handling. We used a barcoded small RNA sequencing approachFor each gene in each of the two STS pooled samples, we calculated the mean and variance of the reads count across the six technical replicates. For each sample, we then plotted the mean plus one versus the variance plus one among the genes on the logarithmic scale. To evaluate the evidence of goodness-of-fit for an assumed probability distribution, for each gene in each pooled sample, we performed the Kolmogorov–Smirnov test and calculated the p-value, under the assumption of a Poisson distribution and under the assumption of a gamma distribution13Scatterplots of miRNA-specific variance versus the miRNA-specific mean number of reads were drawn for data from the miRQC studyWe performed differential miRNA expression, using cubic root transformation followed by t-test, edgeR, DESeq, and voom, using data from the TCGA ovarian cancer studyStatistical analyses were conducted using RHow to cite this article: Qin, L.-X. et al. Empirical insights into the stochasticity of small RNA sequencing. Sci. Rep. 6, 24061; doi: 10.1038/srep24061 (2016).


Human methylome mapping in health and disease states has largely relied on Illumina Human Methylation 450k array (450k array) technology. Accompanying this has been the necessary evolution of analysis pipelines to facilitate data processing. The majority of these pipelines, however, cater for experimental designs where matched ‘controls’ or ‘normal’ samples are available. Experimental designs where no appropriate ‘reference’ exists remain challenging. Herein, we use data generated from our study of the inheritance of methylome profiles in families to evaluate the performance of eight normalisation pre-processing methods. Fifty individual samples representing four families were interrogated on five 450k array BeadChips. Eight normalisation methods were tested using qualitative and quantitative metrics, to assess efficacy and suitability.p < 0.01 to p = 0.97 after stratified QN and ComBat. Median absolute differences between replicated samples were the lowest after stratified QN and ComBat as were the standard error measures on known imprinted regions. Biological information was preserved after normalisation as indicated by the maintenance of a significant association between a known mQTL and methylation (p = 1.05e-05).Stratified quantile normalisation combined with ComBat were consistently found to be the most appropriate when assessed using density, MDS and cluster plots. This was supported quantitatively by ANOVA on the first principal component where the effect of batch dropped from A strategy combining stratified QN with ComBat is appropriate for use in the analyses when no reference sample is available but preservation of biological variation is paramount. There is great potential for use of 450k array data to further our understanding of the methylome in a variety of similar settings. Such advances will be reliant on the determination of appropriate methodologies for processing these data such as established here.The online version of this article (doi:10.1186/s13148-016-0241-2) contains supplementary material, which is available to authorized users. DNA methylation, the covalent addition of a methyl group to a cytosine base, usually in a cytosine-guanine pair (CpG), remains the most widely studied epigenetic modification in disease. While around 70 % of CpG dinucleotides are methylated in mammals, when clustered in groups or ‘islands’ (CGIs) they are generally unmethylated . These iHNF1B locus in ovarian cancer are associated with altered methylation and consequent expression of HNF1B. Also, it has been proposed that at least a proportion of unexplained Lynch syndrome cases are likely to be due to epigenetic silencing of mismatch repair genes. Indeed, it has been shown that the inheritance of the c.-27C>A germ-line variant in the 5′ UTR leads to epigenetic silencing MLH1 in Lynch syndrome [Deepening the complexities surrounding the regulatory roles of CpG dinucleotides located in regions adjacent to promoters, ‘shores’ and gene bodies is the knowledge that sequence variation has a strong influence on methylation. Gertz et al.  examinedsyndrome . Thus, tGenome-wide epigenetic studies have thus far largely focused on epigenetic alterations that occur in diseased tissues, where epigenetic changes across the genome are mapped through comparing ‘normal’ and affected tissues from the same individual. Indeed, epigenetic drugs, currently in clinical use, are designed to correct the epigenetic alterations acquired during disease development . The assA number of challenges exist in the analysis of genome-wide methylation mapping in samples and these include technical challenges dealing with batch effects and the underlying biochemistry employed by the array methods. This has necessitated the development of numerous pre-processing quality control methods to ensure reliable, high-quality data generation. As most studies examining epigenetic profiles have typically examined differences between two distinct groups , the majority of normalisation methods for the 450k array are designed for these types of data, frequently requiring two data groups to normalise negative and positive control probes or genomic regions. Such methods are incompatible with pedigree data, which lack a distinct second group for normalisation. In response to the absence of appropriate strategies, we have developed a pipeline for optimal normalisation and pre-processing of familial-based methylation array data.Fifty peripheral blood samples were collected from individuals representing clusters of densely aggregated cases of affected men and close relatives from the Tasmanian Familial Prostate Cancer study. A diagrammatic representation of the family pedigrees is shown in Fig. iControl software, with all further analysis carried out in the R environment [minfi [methylumi [ChAMP [IDAT files into R and perform basic quality control. Different normalisation methods require the data to be in different formats which cannot be subsequently modified once loaded into R. As such, a number of different packages were used to load data, with the chosen package dependent on the normalisation method tested. Methylumi was used to read data into R in the correct format for quantile normalisation in the lumi R package. The minfi package provides a quality control report based on inbuilt control probes on the array  as well as the ability to exclude probes and samples based on probe signal intensity. Samples failing this initial quality control were excluded from further analysis. Replicate samples across batches were included on the beadchips to allow assessment of quality control and technical bias. Of the 50 unique samples and 8 replicates initially interrogated, 45 unique and 5 replicate samples passed quality control metrics and were used for further analysis. Following sample quality control, the recommended quality thresholds in ChAMP were employed to exclude poor quality probes, with a minimum detection p value of 0.05 in more than one sample removing 6740 probes and a bead count threshold of <3 in 5 % of samples removing a further 478 probes. To account for sex differences in methylation, driven particularly by dosage compensation by X-inactivation, probes on the sex chromosomes were removed prior to normalisation. While ChAMP includes this option as default when loading data, most packages require manual separation, normalisation and recombination of sex chromosomes or their complete manual removal. Thus, to permit appropriate comparison of normalisation methods, a homogenous set of loci across all packages was required; therefore, sex chromosomes were removed at this stage of analysis and not re-introduced.IDAT files containing the raw intensity signals from red and green colour channels were generated using Illumina’s ironment . A combit [minfi , methyluethylumi  and ChAMi [ChAMP , were usEight normalisation techniques were applied to the whole dataset, as detailed in Table β = M/(M + U + 100), where M is the methylated signal and U unmethylated. In some normalisation methods, the offset of 100 is included to regularise scores when both methylated and unmethylated values are very low. While the β value is more biologically intuitive , it suffers from severe heteroskedasticity at very high or low values [M value removes this unequal variance. Thus wherever possible, calculations in this study have been performed on the M values and transformed back to β values if required for biological interpretation. Eight performance metrics were then used to compare methods and determine the optimal normalisation approach for familial datasets. Visual tools such as density and MDS plots and unsupervised hierarchical clustering were used to compare the various methods between all samples and particularly replicate samples. See Table Data are presented for each method except RUV, for which the results were not resolvable using the data generated in this study. These methods involve various degrees of type I and II probe scaling to account for underlying technical differences between the probe types, background and dye bias correction and initial between array batch correction. Depending on the normalisation method, data was either used in the red/green signal format (RGset), converted into methylated and unmethylated values (MethylSet) or converted to β values by the function w values . Logit tsva package [Since an obvious batch effect remained after normalisation, the ComBat function from the  package  was usedGenomeStudio Software.DNA from a subset of samples was extracted as described above and interrogated on Illumina’s HumanOmni2.5-8 Beadchip according to the manufacturer’s instructions. Quality control was performed with Illumina’s Eight methods, as described in Table hclust function using the Euclidean distance between from the default R package, stats. Cluster dendrograms group samples by differences, with similar samples grouping together.For a qualitative measure to examine effectiveness of between array normalisation, hierarchical cluster dendrograms were generated using all probes with the aov and prcomp functions in the stats core R package. p values are displayed on the MDS plots in Fig. p value indicates that clustering is more significantly explained by batch or family, with a larger p value after normalisation indicating a reduction in technical bias.MDS plots were clustered by batch or family; then, analysis of variance was performed on the first principal component from a PCA on the 1000 most variable beta values using the Six replicate sample pairs were used to quantitatively assess the performance of the normalisation methods, as one sample from each pair was interrogated on a separate batch. The median absolute difference between each pair was calculated by first taking the absolute difference at each probe between the two replicates and then taking the median of the differences. A lower median difference indicates less technical bias, as the samples are biologically identical.wateRmelon as a quality control metric [wateRmelon package. The dmrse_row function was used to measure dispersion of methylation between samples for each normalisation method. A lower value indicates methylation values are more tightly aligned with expected methylation levels.There are 227 known imprinted regions (iDMRs) on the 450k array, and these have previously been employed in analysis packages such as l metric . These rcis [GenABEL R package [While evidence of clustering according to familial relationships following normalisation correction provides some confidence that biological integrity of the data is preserved, to further test the preservation of biologically relevant information, we examined detectable associations of known meQTLs in our data. Shoemaker and colleagues have previously identified 736 CpG sites to be associated with SNPs in cis . Here, w package . The resp values of the models plotted against −log10 expected p values as QQ plots.To further demonstrate biological information is preserved after normalisation and batch correction, the association between age and epigenome-wide methylation was compared for raw data, stratified QN normalised data and ComBat-corrected stratified QN data. Linear regression models were fitted with age as the explanatory variable and methylation as the outcome variable, with −log10 Data generated from whole genome methylation analysis employing array technology generates an output necessitating application of normalisation methods to correct for possible bias arising from within and between array variation. Herein eight different methodologies Table  were exaBatch effect (between array variation) was examined and the density distribution plot Fig.  of the rStratified QN also performs best at removing within array biases as the distribution of probe I and II types become more uniform Fig. . This biIn contrast, the density plots of β values for other normalisation (SWAN and FunNorm) methods do not improve to the same degree and in some cases greater variation is introduced  plots. These permitted the visualisation of the two-dimensional projection of the differences between samples. For each plot, the 1000 most variable probes were selected, as these represent the most pertinent biological differences between samples. w values . Again, M values from the top 1000 most variable sites. Consistent with the visualised MDS plot, the p value was highly significant demonstrating the significant association of batch in M value in raw and stratified QN data (p < 0.01) but was not significant following correction using ComBat (p = 0.97).This efficacy of normalisation methods in reducing clustering of samples by batch was assessed quantitatively by ANOVA to test the effect of batch on the first principal component. The ANOVA was repeated for each normalisation method, using For a final qualitative measure to examine effectiveness of between array normalisation, hierarchical cluster dendrograms were generated. Application of stratified QN and ComBat , noob (0.0052) and functional normalisation (0.0056). The remaining normalisation methods generated reduced DMSRE values with stratified QN with ComBat batch correction again producing the smallest error values at 0.0012. See Additional file p value = 7.29e-06), increasing markedly after stratified QN . After ComBat (C), there was a drop in significance compared to stratified QN and raw, yet the p value was still highly significant  indicating preservation of the biological information of interest. The drop in significance after batch correction may be explained as confounding between batch and family, which is removed after ComBat. Ideally, samples would be randomised across experiments; however, the nature of familial studies is such that this is not always possible, as samples are collected at different time points, often across generations. To maintain maximum power, the inclusion of all available samples is essential and, therefore, data processing methods capable of dealing with non-ideal datasets are required.Critical to any normalisation method is the maintenance of true biological differences between samples. As described in the statistical analysis section of the methods, a previously identified meQTL was selected to perform association analysis with prior to and following normalisation. Following Bonferroni correction, a significant association was detected in the raw data , with an increase from 0.838 to 1.402. There is another small increase in significance after ComBat batch correction  is a limitation of this study, therefore additional familial studies would aid in validating the pipeline.There is currently a plethora of pre-processing methods and R packages available for analysis of 450k array data, and comprehensive review articles evaluating their utility have been published –26. The A fundamental requirement for processing methylation array data is effective adjustment for technical bias, including batch effects and adjusting for the two-probe biochemistry of the array. Batch effects may be introduced through bisulphite conversion or downstream processing or variation in array quality. Various methods have been developed to adjust for these effects, mostly involving variations in quantile normalisation, a technique commonly used in analysis of microarray datasets to align two different distributions so they result in identical statistical properties –29.BMIQ and functional normalisation have been advocated as the preferred methods for cancer studies as they are more specific in design than quantile normalisation and have been shown to be more effective at removing unwanted technical bias , 29. HowwateRmelon package.The inherent strengths of familial data could be further exploited by a normalisation technique that accounts for known relationships between samples. Such a method could draw on pedigree information to ensure normalisation has effectively removed technical bias while maintaining known biologically relevant information such as relatedness and familial clustering by methylation. A diagnostic metric accounting for a known relationship could be used to test the efficacy of pre-processing methods in a similar manner to the standard error associated with iDMRs from the It may also be of importance for researchers to consider the undesirable effect of non-specific binding and the presence of SNPs in the probe body. A study from the Weksberg lab found around 6 % of probes on the array cross-hybridised to non-targeted genomic regions . They haTo overcome this issue, Zhi and colleagues suggest an elegant approach to examine the effect of meSNPs on methylation without the potential bias introduced by SNPs altering probe binding . The typPreservation of the biological integrity of information from methylation array data is imperative and requires appropriate pre-processing to minimise technical errors, which will be dictated by the type of data. Stratified QN in combination with ComBat batch correction performed the best of those methods tested for normalising familial data interrogated on 450k array. This method was observed to remove technical biases while maintaining biologically relevant information; allowing true biological differences and similarities to inform our search for the role of methylation patterns driving disease processes. The workflow presented in this paper (highlighted in Fig. CpG, cytosine-guanine pair; meQTL, methylation quantitative trait loci; MDS, multidimensional scaling; meSNPs, methylation single nucleotide polymorphisms


For higher replicate numbers, minimizing false positives is more important and DESeq marginally outperforms the other tools.RNA-seq is now the technology of choice for genome-wide differential gene expression experiments, but it is not clear how many biological replicates are needed to ensure valid biological interpretation of the results or which statistical tools are best for analyzing the data. An RNA-seq experiment with 48 biological replicates in each of two conditions was performed to answer these questions and provide guidelines for experimental design. With three biological replicates, nine of the 11 tools evaluated found only 20%–40% of the significantly differentially expressed (SDE) genes identified with the full set of 42 clean replicates. This rises to >85% for the subset of SDE genes changing in expression by more than fourfold. To achieve >85% for all SDE genes regardless of fold change requires more than 20 biological replicates. The same nine tools successfully control their false discovery rate at ≲5% for all numbers of replicates, while the remaining two tools fail to control their FDR adequately, particularly for low numbers of replicates. For future RNA-seq experiments, these results suggest that at least six biological replicates should be used, rising to at least 12 when it is important to identify SDE genes for all fold changes. If fewer than 12 replicates are used, a superior combination of true positive and false positive performances makes  RNA-seq has now supplanted microarrays as the technology of choice for genome-wide differential gene expression (DGE) experiments. In any experimental design, selecting the appropriate number of biological replicates is a trade-off between cost and precision. For microarray methods it has been shown that low replicate experiments often have insufficient statistical power to call DGE correctly  and cannThe rise of RNA-seq technology has led to the development of many tools for analyzing DGE from these data e.g., . Each toedgeR represents the best compromise between accuracy and speed. limma and DESeq for data with fewer than five replicates per condition, finding that edgeR is “oversensitive” and suffers from high variability in its results while SAMSeq suffers from a lack of statistical power with few replicates. The idea of combining DGE methods is implemented in the novel tool PANDORA, which weights the results of different DGE tools according to their performance on test data and performs at least as well as the constituent tools , limma, NOISeq, PoissonSeq, and SAMSeq  and a Δsnf2 mutant. Quality control and data processing steps reject several replicates from each condition resulting in 42 WT and 44 Δsnf2 biological replicates of “clean” data totaling ∼889M aligned reads . The data used for the performance comparison here represents a best-case scenario for the DGE tools since biological variation within conditions is low . In contrast, the mean Pearson's correlation (±1 SD) between replicates using the count data for four RNA-seq studies from the ReCount project .The performance of each DGE tool as a function of replicate number and expression fold change was evaluated by comparing the DGE results from subsets of these replicates against the “gold standard” set of DGE results obtained for each tool with the full set of clean replicates. The tool-specific gold standards were computed by running the tool on the read-count-per-gene measurements from the full set of clean data and marking as “significantly differentially expressed” (SDE) those differentially expressed genes with multiple testing corrected embl v68  S. cerevi repeated subselections drawn from the set of clean replicates (without replacement). For each of the tools, bootstrap runs were performed with i = 100 iterations and nr = 2,…,40 replicates in each condition (cuffdiff was significantly slower than the other tools so the number of iterations was reduced to i = 30 for this tool). For a given value of nr, the mean log2 transformed fold change [log2(FC)] and median adjusted P-value or FDR calculated across all the bootstrap iterations was considered representative of the measured behavior for each individual gene. Again, genes were marked as SDE when the adjusted P-value or FDR was ≤0.05. From these results, true positive, true negative, false positive, and false negative rates  were then calculated as a function of nr for four arbitrary fold-change thresholds  . The fraction of all genes edgeR (exact) calls as SDE increases as a function of nr and the impact of sampling effects on this fraction shrinks as nr increases (edgeR (exact) successfully controls its FDR for all combinations of both nr and T and the primary effect of increasing the number of replicates or imposing a fold-change threshold is to increase the sensitivity of the tool, converting false negatives to true positives  is ∼20%–40% for all the tools except NOISeq and DEGSeq, indicating that with this few replicates these experiments were unable to identify the majority of DE genes regardless of the tool used to analyze the data . For DEGSeq in particular this originates from overestimating the number of SDE genes regardless of the number of replicates . Excluding DEGSeq, the TPR performance for all the remaining tools is a strong function of fold change . For the highest fold-change genes (T = 2), these tools show TPRs ≳85% and with the exception of cuffdiff also show FPRs consistent with zero . Increasing the number of replicates has a dramatic effect on the detection rate of genes with smaller fold changes. Reducing the fold-change threshold reduces the TPR independently of replicate number for all the tools except DEGSeq (edgeR (exact) for fold-change thresholds of T = 1, 0.3, and 0 requires ∼9, 11, and 26 replicates, respectively . The transition between these two regions is a function of both the tool and the number of replicates. For edgeR (exact) with nr = 3, this transition fold-change threshold is ∼0.5 and drops to ∼0.25 and ∼0.15 for nr = 10 and 30, respectively , highlighting again that the primary effect of increasing replicate number is to increase the sensitivity of these tools, converting false negatives to true positives . Other tools are not so successful in this regard but a detailed interpretation of the FPR from this test is complicated by the fact that each tool is tested against its own gold standard. A more robust method for probing the FPR performance of DGE tools is presented below.The best performing tools, P-value or FDR threshold ≤0.05) by the tool or not. The vectors for each tool or test were ordered by gene id and then hierarchically clustered by correlation distance with complete linkage  form a distinct independent cluster . For each tool or test, a 7126-element long vector of 1s and 0s was constructed representing whether each gene in the annotation was called as SDE (adjusted  linkage  using th(pvclust . pvclustnr replicates were randomly selected (without replacement) from the WT condition. Under the null hypothesis that there is no expression change between these two sets, every gene identified as SDE is, by definition, a false positive. For each bootstrap run, the fraction of the total gene set identified as SDE was computed. The distribution of this false positive fraction as a function of the number of replicates, for each differential expression tool, is shown in DEGSeq, NOISeq, and SAMSeq perform poorly even with a large number of replicates. DEGSeq, in particular, has poor false positive performance with every bootstrap iteration identifying >5% of all genes as false positives (FPs) and a median FPR of ∼50% irrespective of the number of replicates. Approximately 10% of cuffdiff, PoissonSeq, and ∼40% of SAMSeq bootstrap iterations identify >5% of all genes as FPs, suggesting that these tools are also not controlling their FPR well. BaySeq, DESeq, and EBSeq perform particularly well in this test with edgeR, DESeq2, and limma also performing adequately.Perhaps the most important performance measure for RNA-seq differential expression tools is their false detection rate. The large number of replicates in this study permits a simple test of the FPR for each of the tools. Two sets of EBSeq, edgeR (exact), DESeq, DESeq2, and limma show excellent performance in the tests presented here. Reassuringly, edgeR and DESeq are the most widely used of the tools tested here as measured by citations (n ≲ 12), the data presented here suggest edgeR (exact) or DESeq2 in preference to the other tools due to their superior TP identification rate and well-controlled FDR at lower fold changes. All the tools perform well for experiments with sufficient numbers of replicates to ensure that the majority of the true SDE is already being captured ; however, the marginally better FPR performance of DESeq suggests it should be the tool of choice in this regime. Conversely, baySeq, cuffdiff, DEGSeq, NOISeq, PoissonSeq, and SAMSeq all show inferior performance in one or more areas. In this work, the performance of eleven popular RNA-seq DGE tools has been evaluated using a highly replicated two-condition RNA-seq experiment designed specifically for the purpose of benchmarking RNA-seq DGE tools on genuine biological replicate data. Five of the 11 tools, itations , suggestDESeq2 includes an option to specify a fold-change threshold for the null hypothesis being tested. In this mode the tool tests whether the measured gene fold changes are consistent with being below this threshold value (rather than being consistent with zero), providing a natural mechanism for incorporating a fold-change threshold in a statistically meaningful way. As expected, this reduces the number of genes called as SDE. Setting nr = 10 and running 100 DESeq2 bootstraps, the number of SDE genes called is reduced from 3470 to 1277 by including a null hypothesis testing the fold-change threshold of 0.5.It is clear from the benchmarking runs that even the best tools have limited statistical power with few replicates in each condition, unless a stringent fold-change threshold is imposed . For allT = 1), three clean replicates per condition may be sufficient. However, this is not the same as conducting the experiment with a total of three replicates, because there is a significant minority chance that one or more replicates within each condition should be rejected  or DESeq2.For experiments with <12 replicates per condition; use DESeq.For experiments with >12 replicates per condition; use T ≤ 0.5  and Δsnf2 mutant in the same genetic background. Asynchronous WT and Δsnf2 mutant strains were streaked out on rich media (YPAD) to get individual colonies. For 48 replicates in both strains, single colonies were inoculated to 15 mL cultures and cells were grown to an OD600 of 0.7–0.8 (corresponding to approximately 106 cells) at 30°C. RNA was isolated using the hot-phenol method (P = 0.16) indicating no bias in RNA content between WT and Δsnf2 mutant samples.The l method  and cleasnf2 mutant samples and 49–96 to the WT strain. For each batch the Illumina TruSeq protocol was used to prepare the sequencing library, with the addition of the ERCC spike-in standard (Ambion)  for accurate aliquoting for cluster generation and appropriate lane loading. Seven independent pools of the 96 barcoded samples were prepared and loaded onto seven lanes of an Illumina HiSeq 2000. Thus, each lane contains all 96 samples prepared in four batches with different spike-in mixes in each strain. The flow-cell was run for 51 cycles single-end.The RNA-seq experiment described here implements a “balanced block design” in order to control for technical artifacts such as library batch effects , barcodi(Ambion) . Briefly9 reads were reported with each technical replicate having between 0.8 and 2.8 × 106 reads. Aggregating the technical replicates across lanes results in ∼107 reads per biological replicate. First pass quality control of the reads was performed with fastQC (http://www.bioinformatics.bbsrc.ac.uk/projects/fastqc) for each technical replicate. The reads from each technical replicate were then aligned to the Ensembl release 68 (S. cerevisiae genome with bowtie2 (v2.0.0-beta7) (TopHat2 (v2.0.5)   and TopH(v2.0.5)  using th2FC) and a statistical significance of differential expression for each gene. The fold change is based on the mean count across replicates in each condition, and for many of the tools this includes a calculation of sample-specific normalization factors based on the gene read-count data. For this study, the default normalization factors were used for each of the tools assessed. While there are differences between the normalizations used by these tools, it has been suggested that the details of which method is used to normalize the data does not significantly alter the downstream DGE results , beta-binomial (BBSeq), binomial (DEGSeq), Poisson (PoissonSeq), and log-normal (limma) distributions. A few algorithms make no assumptions about the read-count distribution and instead take nonparametric approaches to testing for DGE (NOISeq and SAMSeq). n ≳ 12), the mean and variance estimators can be accurately computed directly on the data. However, many RNA-seq DGE studies rely on a low number of replicates per condition (n ≲ 3), so several of the DGE tools  compensate for the lack of replication by modeling the mean-variance relation and borrowing information across genes to shrink the given gene's variance toward the common model  an assumption about the probability distribution that underlies the raw read-count measurements, and (ii) being able to accurately measure the mean count and variance for each gene. Different tools assume different forms for the underlying read-count distribution including the negative binomial . This bootstrapping procedure includes applying the default normalization for each tool where relevant and possible  and the full output for each tool was stored in a local sqlite database, including the log2 transformed fold change and the statistical significance for every expressed gene in the S. cerevisiae annotation. Most of the tools return Benjamini–Hochberg . Supplemental Figure S1 shows an example of the output mean log2FC and median P-value data for the tool edgeR (exact) with nr = 3.A utility pipeline was written to automate the process of running each DGE algorithm iteratively on nr, for four arbitrary absolute log2 fold-change thresholds, T∈{0,0.3,1,2}. A reference fold change was used for deciding whether each gene falls above the threshold T because the measured values of mean nr. These reference fold changes were defined independently of the tools by applying DESeq normalization , each gene was called as true/false positive/negative by comparing whether it was called as SDE in the bootstrap run, and whether it was called as SDE in the corresponding tool-specific “gold standard.” Then, taking each fold-change threshold in turn, the mean of the number of true/false positives/negatives  for genes with reference fold changes above this threshold was calculated across all the individual DGE calculations within a bootstrap run. This results in a TPR, TNR, FPR, and FNR for a tool, for a given nr and for a given T  replicates for gene g and condition k, When assessing the performance of each DGE tool on the full set of clean data, we compare the tools not only within themselves, but also to the following set of standard statistical tests. For the following mathematical descriptions, t-test is that the given gene under two conditions has the same mean count, H0 : µg1 = µg2. We used the test statisticv = n1 + n2 − 2.The null hypothesis in the t-test is more appropriate for log-normally distributed data. The null hypothesis is lnµg1 = lnµg2. The test statistic,t-distribution with n1 + n2 − 2 degrees of freedom  test is a nonparametric test assessing if count rate in a gene under one condition tends to be larger than under the other. The null hypothesis is ximation  and takiximation . The MW xg =  and then randomly resampled B times without replacement from xg, using the original sizes, n1 and n2. For the b-th random permutation P-value is the fraction of cases where the resampled statistic exceeds the observed one, In the permutation test, counts from both conditions are pooled together (for each gene), n1 and n2, under the null hypothesis of the common mean. Data are resampled with replacement to estimate the significance level. For the bth bootstrap, P-value is the fraction of cases where the resampled statistic exceeds the observed one, The Studentized bootstrap test described by Equation . As in thttp://www.ebi.ac.uk/ena/data/view/ERX425102). All the code for this work is publicly available (https://github.com/bartongroup/profDGE48).The data sets supporting the results of this article are available in the European Nucleotide Archive repository (ENA) (PRJEB5348, Supplemental material is available for this article.


Dreissena polymorpha) to heavy metals  was analyzed by quantitative Real-Time Polymerase Chain Reaction (qRT-PCR) to study the coordinated regulation of different metal-, oxidative stress- and xenobiotic defence-related genes in gills and digestive gland. Regulatory network analyses allowed the comparison of this response between different species and taxa.The transcriptional response of adult zebra mussels  with the correlations observed in the still poorly-known, invasive zebra mussel.Our analyses found a general conservation of regulation genes and of their interactions among the different considered species, and may serve as a guide to extrapolate regulatory data from model species to lesser-known environmentally  relevant species.The online version of this article (doi:10.1186/s12864-015-1567-4) contains supplementary material, which is available to authorized users. The coordinated activation of different stress mechanisms is a fundamental element of the overall response to pollutants and to other potentially deleterious external inputs . On the cibility ,6. In adcibility . Howevercibility -14.Dreissena polymorpha) associated with environmental stresses, such as the presence of pollutants, are investigated by means of chemometric analysis of qRT-PCR data. This freshwater mussel species has been selected due to its invasive character, which brought it to expand from its natural geographic distribution in the Caspian and Black seas to a real worldwide distribution in the last few decades [In this work, variations in the gene expression of the zebra mussel  . SeveralFigure Relationships between genes from the same and different tissues were investigated. Correlation matrix plot between the 18 considered genes is shown in Figure Correlation diagrams showed relationships already commented above, but a deeper analysis of the data was attempted to extract more information. A representation of the correlation matrix as a gene network is shown in Figure Similar results were obtained when experimental data were analysed by means of unsupervised hierarchical clustering. In this case, no previous information was provided to the algorithm and genes were clustered iteratively in an agglomerative manner using Ward’s  and EuclIn dendrogram of Figure As in the previous case, Figure In this figure, clusters built up by the genes were most strongly correlated in both gills and digestive glands. SOD, COI, GST and CAT formed a group because of their correlation in both tissues. Relations between genes in both tissues can be observed at the edges that connect nodes of gills and digestive glands. For instance, the relationship between SOD genes in gills and GPx genes in digestive glands were stronger than other relations observed for genes of the same tissues. Finally, HSP70 and GPx in gills and HSP70 in digestive glands were confirmed not to have any correlation with the other investigated genes.When interpretation of these results using non-supervised data analysis methods is complemented with the information available in the literature, it is observed that genes with stronger correlations in both gills and digestive glands  are known to be related to the oxidative metabolism which is significantly affected by heavy metals exposure.Principal component analysis (PCA) is used to extract relevant information about samples clustering and effects of metal exposure treatments. The information given by PCA analysis will be compared with that obtained through graphical means in the previous section. First principal component, PC1, already explained 45.1% of the observed variance of the experimental data. The rest of principal components explained a lower amount of experimental variance: PC2 – 19.5%, PC3 – 10.5% and PC4 – 7.7%. From PC5, the amount of explained variance was lower than 5%.Figure vs. PC1 plots, Cu – Hg – Cd could be differentiated along PC2 . If PC3 was considered, a cluster of Cd treated samples was separated from the rest of samples.From the analysis of these plots, it can be observed the influence of the exposure time on PC1 Figures a. Samplevs. PC1 scatter plot , the exposure time separated each metal cluster into two sub-clusters within each type of metal group. For instance, in the PC2 t Figure c the thrnd plot, and Figures In Figure In Figure vs. PC1 scores plot enabled the differentiation among samples as a function of heavy metal treatment.Summarizing PCA results, PC1 could be related with the exposure time of samples. Among genes that mostly contributed to PC1, the HSP70 gene could be identified as the one showing higher positive score values (in both tissues). This indicates that this gene allowed differentiating samples according to accumulative toxic effects across time. On the same manner, the diagonal trend in PC2 From gene loadings, HSP90 and CAT of the digestive glands were correlated with copper treated samples while HSP70 of gills was mainly correlated to cadmium treated samples. The determination of the genes most correlated with the Hg treated samples was not straightforward due to their closeness to the origin.From PC2 loadings plots differentiation of genes according to tissue type was possible. Samples behaved differently and only in the case of cadmium treated samples, a cluster was identified. Cd treated samples with one-day of exposure time showed negative values of PC2 which could be related to gills genes expression, while Cd treated samples with one-week of exposure time showed positive PC2 values which could be linked with digestive gland genes.For ANOVA simultaneous component analysis (ASCA), the data matrix was rearranged as can be seen in Figure Xe (ptime = 0.00505) and treatment Xt (ptreatment = 0.00003) were significant, and allow rejecting the null hypothesis H0. In all the other cases, the null hypothesis (H1) was accepted (there was no significant effect of the considered factor or interaction). The triple interaction treatment-tissue-time and the double interactions tissue-time, tissue-treatment, and time-treatment provided p-values rather close to 1. Finally, individual effects of tissue (XT) was not considered statistically significant (ptissue = 0.4540).Statistical significance of these factors was estimated by using a permutation test approach. In this work, the number of permutations was set to 100000. Only individual effects of exposure time Figure For metal treatment, three principal components were needed. Effects in projected scores Figure d and loaPartial least squares discriminant analysis models were used to identify the more discriminant variables among different type of samples considering exposure time and metal treatment as possible factors.In the case of exposure time, two PLS-DA models were built up: one for the discrimination of one-day samples and the other for the discrimination of one-week samples. In both cases, discrimination between samples classes achieved by the PLS-DA model was good as can be seen in the obtained sensitivity, specificity, and accuracy parameters . Figures of merit of PLS-DA models shown in Table D. polymorpha , whereas COI and P-gp1 genetic interaction with other stress genes was rarely (if ever) observed in the other species , so D. polymorpha than mercury. This conclusion was also drawn from the preliminary analysis of this data [D. polymorpha populations along a pollution gradient in the Ebro River . The pa . It is In this work, the application of different chemometric methods allowed the extraction of relevant information from qRT-PCR data. Results from different methods appeared to be complementary focusing on various data features. Information provided by gene network diagrams can make rather easy the interpretation of the possible correlations between investigated genes.D. polymorpha, for which very little genetic information is available.Chemometric results showed that genes were clustered according to the type of tissue, and separation of samples was achieved according to their time evolution (one-day versus one-week treatment) and heavy metal treatment. It is remarkable the conservation of at least some of the regulatory networks within Metazoans, and the ability of the presented method to define these genetic interactions using only a limited number of experiments and conditions in species, such as p). Above this threshold value, the fluorescence of the samples is considered to be above the background contribution [p value is related to the initial concentration of RNA that allows its absolute quantification, according to Equation 1:A short introduction about qRT-PCR is presented below. qRT-PCR measures the fluorescence of the PCR reaction products during a cycle threshold  DNA sequence ,29:2\docIn this work, qRT-PCR measurements allowed building up a data matrix of 120 rows (samples) and 24 columns (variables). These 120 rows included 3 technical replicates of 20 different mussel samples  measured at two different treatment times , respectively. For each sample, 24 measurements were obtained corresponding to the expression responses of 12 selected genes . In both cases, the best housekeeping gene was S3. Other genes that showed minor variations across samples were EF1 or BAct, but they were not selected as a reference gene and therefore discarded for further analysis. Final size of experimental data matrix was 40 rows (20 samples after one day and 20 samples after one week of exposure) and 18 columns (9 genes for gills and 9 genes for digestive gland). A schematic representation of the dataset built up is shown in Additional file For ASCA analysis, this data matrix was rearranged with the goal of obtaining more information from the study. Gene measurements from different tissues were considered as different samples generating a final matrix of 80 rows (40 samples for gills and 40 samples for digestive glands) and 9 columns (corresponding to the 9 different genes)Finally, data were mean centred prior to chemometric analysis. Figure D. polymorpha  in five reference model species: human, Homo sapiens; mouse, Mus musculus; zebrafish, Danio rerio; the fruit fly Drosophila melanogaster; and the nematode Caenorhabditis elegans, were identified by the BLAST algorithm at NCBI server, (http://www.ncbi.nlm.nih.gov/blast/Blast.cgi) using the complete sequences of the corresponding D. polymorpha genes (Listed in Table http://genemania.org) [Putative orthologues for the nine stress genes analysed in nia.org) .qgraph tool of the R environment [Initially, the gene correlation data matrix was investigated. Two visualization tools were used in order to extract the main relevant information from this correlation matrix in a natural and intuitive manner. On one side, the heat map graphical representation of the correlation matrix was considered. An ellipse-based color codification was used to indicate if correlation values are positive (blue) or negative (red). Moreover, the length of the minor axis of the ellipse indicates the strength of the correlation  . On the ironment  was usedSecondly, non-supervised hierarchical clustering analysis (HCA) was used to display correlations between genes. Cluster analysis is used to classify objects, characterized by the values of a set of variables, into clusters or groups ; in suchTwo different display outputs can be obtained using this clustering process, the dendrogram, which is a tree-like diagram illustrating HCA clusters and the matrix of gene distances values . DistancPrincipal component analysis (PCA) is based on the fulfilment of a bilinear model that decomposes experimental data in the product of two factor matrices related respectively to sample (rows) and variable (columns) contributions, using a minimum number of components to explain most of the data variance :3\documeX is the experimental data matrix of size m samples (rows) and n genes , T is the factor matrix related to sample contributions  of size m number of samples and Ns number of principal components selected in the analysis, PT corresponds to the matrix related to the gene contributions  of size Ns number of principal components and n number of genes. Finally, matrix E (of size m samples and n genes) contains the variance not explained by the bilinear model for the considered number of principal components, Ns. Every one of these Ns components is characterized by two vector profiles related respectively to the individual samples responses and with its gene expression. Hence, from these two profiles associated with each principal component, biological interpretation of each one of these components can be inferred [In this Equation PCA, inferred . In summThere are different data analysis approaches that combine the power of ANOVA with that of PCA to identify and separate variance sources ,24. In tX is split into effect matrices containing the level averages for each factor and interaction matrices that describes the interaction between the considered factors [In ASCA, SCA (similar to previously described PCA) is applied separately to each effect matrix and to all possible interaction matrices. First, the data matrix  factors . In the X is the experimental data matrix of i rows and j variables, XT is the effect of tissue factor (gills or digestive glands), Xe is the effect of exposure time factor (one-day or one-week), Xt is the effect of metal treatment factor , XTe is the interaction of tissue and exposure time factors, XTt is the interaction of tissue and metal treatment factors, Xet is the interaction of exposure time, and metal treatment factors and XTet is the global interaction of tissue, exposure time and metal treatment factors. In addition, 1 is a vector of ones of i rows and m is a vector of the overall means of the experimental matrix (j rows). For each submodel of factors or interactions, there are the associated component scores  and component loadings , residual matrix (E) and loadings (Pk) are used to obtain the projection matrix.The projection for each factor 0 of no experimental effect (no difference between the level averages of the effect matrices) against the alterative hypothesis of the presence of an experimental effect with a p confidence level. The estimation of this p-value is obtained by a permutation test, in which the original data matrix is permuted a number of times and the sum of the squares (SSQ) of the k effect matrix is recalculated (i.e. 100000 permutations) [i) correspond to the total number of samples (N) and the second summation (m) to the considered principal component (maximum 2). The probability p-value is estimated from the number of permutations that give an SSQ value that is larger than the SSQ obtained for the experimental data.The assessment of the statistically significance of the effects of all factors and of their interactions is checked under the null hypothesis Htations) :6\documeY, is a vector or matrix that codifies the pertinence or not of a given sample to a particular sample class or type. In this method, X contains the input information about the gene expression samples response (qRT-PCR data) after the different considered treatments . Internal cross-validation by random subsets of the samples was used to evaluate the reliability of the obtained model. The PLS method constructs a set of loading weights (or weights) W, which give the relationships between X and Y during the regression process. Each one of the wi vectors is orthogonal from each other and characterize the PLS component direction in the X-space, which is optimally correlated with the variation in Y [Partial least squares discriminant analysis (PLS-DA) is the application of PLS method for discrimination purposes . In PLS-ion in Y ,42.j, are defined as:m corresponds to the total number of variables, p is the number of latent variables, wjk is the j-th element of vector wk and bk is the regression weight for the k-th latent variable.From PLS weight vectors, Variable Importance on Projection (VIP) can be calculated to facilitate feature selection. VIP values provide a score value for each variable and rank them according to their significance in the projection used by the PLS model ,44. In tIn this work, PLS-DA has been applied to discriminate samples according to two types of factors: exposure time (with classes: one-day exposure time and one-week exposure time) and heavy metal treatment type . PLS-DA results provide information about which are the most useful variables for the discrimination between the considered classes, knowledge that can be deduced from the VIP scores.Data pretreatment, hierarchical clustering, PCA, PLS-DA and ASCA analysis have been carried out using the Eigenvector PLS Toolbox (version 7.8.2) for the MATLAB® environment (2013b Release). Correlation Maps with information based on elliptical shapes (corrplot/plotcorr)  and Gene


Identification of noncoding drivers from thousands of somatic alterations in a typical tumor is a difficult and unsolved problem. We report a computational framework, FunSeq2, to annotate and prioritize these mutations. The framework combines an adjustable data context integrating large-scale genomics and cancer resources with a streamlined variant-prioritization pipeline. The pipeline has a weighted scoring system combining: inter- and intra-species conservation; loss- and gain-of-function events for transcription-factor binding; enhancer-gene linkages and network centrality; and per-element recurrence across samples. We further highlight putative drivers with information specific to a particular sample, such as differential expression. FunSeq2 is available from funseq2.gersteinlab.org.The online version of this article (doi:10.1186/s13059-014-0480-5) contains supplementary material, which is available to authorized users. TERT) promoter shows that regulatory variants may constitute driver events [Cancer genome sequencing generally identifies thousands of somatic alterations in individual tumor genomes. A few of them, called drivers, contribute to oncogenesis, whereas the majority are passenger mutations accumulated during cancer progression . Systemar events –10. WithIn contrast to coding variants, the functional impact of noncoding variants is more difficult to evaluate, due to the lack of knowledge about noncoding regions. The important role of regulatory variants in various diseases has generated a great deal of interest in studying noncoding sequences –14. ProjWhile much work has been done for germline variants, this is not the case for cancer somatic mutations. Through analyzing the variation patterns of natural polymorphisms, we have published a prototype approach (FunSeq) to identify potential noncoding drivers . Here, wTERT promoter, no other regulatory variants have been functionally characterized as cancer drivers. Thus, due to the lack of a gold standard for regulatory cancer drivers, we used recurrent somatic mutations and known germline pathogenic variants to evaluate the performance of our method. Our method has good prediction power for both recurrent somatic and germline pathogenic regulatory variants, and more importantly it contains multiple cancer-specific features, such as differential gene expression detection between tumor and matched normal samples. As a test case, we also applied our method to an individual tumor genome with a known TERT promoter mutation. Our method is able to prioritize the variant and provides a hypothesis for its potential functional impact. This shows that our method can help researchers and clinicians to prioritize a few somatic regulatory mutations for further studies.Besides mutations in the High-throughput technologies have generated huge amount of genomics data in the past few decades. How to mine and integrate these data to tackle particular scientific question remains a challenge. In this study, we first build an organized data context by processing large-scale genomics and cancer resources into small-scale informative data and then use them to annotate and prioritize cancer somatic alterations. The workflow is depicted in Figure Yip et al. [Regulatory elements, especially promoters and enhancers, are capable of regulating the expression of specific genes. We collected functional annotations from ENCODE  , whereas that with germline allele is not. As discussed later, our scheme is validated by the detection of motif-gaining events caused by the two driver mutations in TERT promoter   with publicly available cancer whole-genome sequencing data. Currently, we have identified recurrent loci or sites from 570 samples of 10 tumor types –40 and fAll of the above features are used to annotate and score variants Figure . To inteInterpretation of the functional impact of noncoding variants can be greatly enhanced if the function of its target protein-coding gene is known. Many cancer genes are known to play a crucial role in cell proliferation and DNA repair. We integrated prior knowledge of genes, such as known cancer-driver genes ,42, geneVariants in regulatory elements may disrupt the expression of coding genes. Differential expression of target genes in cancer samples indicates the potential effect of noncoding variants. We provide a ‘differential gene expression analysis’ module Figure  to deteccis-acting effects that control the spatial and temporal patterns of gene expression [We also provide an option for users to incorporate their own annotations. Impact of variants in regulatory regions is generally restricted to pression . Activatpression . For exaAll features used in our method and corresponding details are described in Additional file P value <2.2 e-16; Figure P value <2.2e-16; Figure Currently, only two known regulatory variants are thought to act as cancer drivers. Hence, to evaluate the performance of our scoring scheme, we used recurrence to approximate the deleteriousness of somatic variants. Recurrence is considered as one potential sign of positive selection among tumors and is more likely to be associated with driver events . We examWe note that cancer is a very heterogeneous disease and distinct molecular subtypes may involve unique oncogenic mechanisms. Thus, tumor samples from different patients may involve different driver events. These unique drivers would not show recurrence across samples. Furthermore, in the absence of large sample sizes, it might be impossible to detect recurrence of mutations. Our method would be especially useful in such scenarios, since it has the ability to prioritize deleterious variants in each tumor genome. Moreover, the functional relevance and hence the biological mechanism by which drivers act is largely unknown. Our method provides an in-depth annotation of such variants, including the relative contribution of each feature to its deleteriousness. This knowledge would greatly help understand the potential oncogenic mechanisms.Ritchie et al. [P value <2.2e-16; Disease studies have identified many noncoding pathogenic variants. Designed primarily for somatic mutations, our framework contains several features that are applicable to germline variants. We tested the ability of our method to distinguish germline pathogenic variants from neutral ones. We also did possible comparisons with other germline variant prioritization tools ,25. We oe et al.  using thTERT promoter mutations indicates their important roles in tumorigenesis [TERT promoter mutation (chr5: 1295228). We used one Medulloblastoma sample as an example to prioritize regulatory variants from whole-genome sequencing. Among the 2,183 somatic single nucleotide variants, the TERT promoter mutation ranks second (0.09%). Our method further suggests potential functional impact of this variant. As shown in Table TERT. It is also found in another five liver samples and 54 COSMIC samples in our recurrence database. Besides DNA sequences, epigenome or transcriptome could also be altered in cancer genomes. These data provide sample-specific activation or inactivation signatures of regulatory sequences. If provided, our framework is flexible in integrating those data into our annotation scheme  and GWAVA ranked it 25th (1.15%) with the matched region model  to rebuild the data context based on pre-processed genomics data, such as ENCODE peak calls. The data context will be updated regularly to keep it up-to-date. Users can input additional data to customize the data context upon the existing one. Variant prioritization step is quite efficient. It takes about 2 to 3 min to prioritize one genome with thousands of variants on a QEMU Virtual CPU version (cpu64-rhel6) @ 2.24-GHz 1 processor Linux PC with 20 GB RAM, and a 500 GB local disk. Time comparison with CADD and GWAVA is in Additional file We have developed a method integrating various genomic and cancer resources to prioritize cancer somatic variants, especially regulatory noncoding mutations. User data can be easily integrated into the framework. We believe that the software will be useful for researchers to identify a few somatic events among thousands for further in-depth analysis to understand the mechanisms underlying oncogenesis.Vogelstein et al., cancer gene consensus, and COSMIC [Gerstein et al. [Lin et al. [Alexandrov et al. [We collected polymorphisms from 1000 Genomes Project Phase 1 , GERP scd COSMIC ,41,42. Dd COSMIC ,44. Binad COSMIC  and HINTd COSMIC . Regulatn et al. , and Linn et al. , respectv et al.  and 64 pv et al. ,40.User-input variants are first filtered against natural polymorphisms based on user-defined minor allele frequency (MAF) threshold to get rid of unlikely somatic variants (hg19). Currently, SNVs and small indels (<=20 bp) will be analyzed.When variants hit transcription factor binding motifs under ENCODE Chip-Seq peaks, we examined their motif breaking or conserving effect using position weight matrixes (PWM). Motif-breaking events are defined as variants decreasing the PWM scores, whereas motif-conserving events are those that do not change or increase the PWM scores   or regulatory elements significantly associated with genes. For each variant, +/- 29 bp are concatenated from the human reference genome . For each PWM, we scanned the 59 bp sequence. For each candidate motif encompassing the variant, we evaluated the sequence scores using TFM-Pvalue [We define both proximal and distal associations. For proximal associations, we assign variants in gene promoters, introns, or UTRs to their nearby genes. For distal associations, in addition to those identified in , we furtWe started with a list of regulatory regions from three different types, namely transcription factor binding peaks (TFP), DNase I hypersensitive sites (DHS), and Segway/ChromHMM-predicted enhancers. All regulatory regions at least 1 kb from the closest gene according to the Gencode v7 annotation  were defWe grouped different transcripts of a gene sharing the same transcription start site as a transcription start site expression unit (tssEU). For each DRM, we first considered all tssEUs within 1 Mb from it as its candidate targets. We then correlated some activity/inactivity signals at a DRM and the expression of its candidate target tssEUs, and called the ones with significant correlation values as potential DRM-target pairs as follows.i within a DRM, we counted the number of reads that support the methylation of it (mi), and the total number of reads covering it (ni). The methylation level of the DRM was then defined as the ratio of their sums across all CpG sites in the DRM, At the DRMs, we considered the enhancer marks H3K4me1 and H3K27ac as two types of activity signals, and DNA methylation as an inactivity signal. The activity level of each DRM was defined as the number of sequencing reads aligned to the DRM from the corresponding ChIP-seq experiments. The methylation level of a DRM was defined as follows. For each CpG site 1). We considered 19 tissue types with data for both the activity signals and gene expression, and 20 tissue types with data for both the inactivity signal and gene expression. For RNA-seq, we used the paired-end 100 bp Poly-A enriched datasets. For experiments with replicates, we used the mean value across the replicates as the expression level of a gene.We collected all bisulfite sequencing, ChIP-Seq, and RNA-Seq data from the Roadmap Epigenomics project website   if the samples are drawn independently from normal distributions. The Fisher’s Z transformation was used to compute the P values. For Spearman correlation, the P value was computed based on a procedure proposed by Hollander and Wolfe [P values were then adjusted for multiple hypotheses testing using the Bonferroni, Holm, Benjamini-Hochberg (BH), or Benjamini-Yekutieli (BY) methods.For each DRM-candidate target pair, we computed the correlations of their activity/inactivity and expression levels across the different tissue types. We computed both value-based Pearson correlation and rank-based Spearman correlation. The statistical significance of each correlation value was evaluated by computing a nd Wolfe . For actWe incorporated a module to detect differentially expressed genes in cancer samples  from RNA-Seq data. When provided with gene expression files, our module calls NOISeq  when havFor each variant associated with genes, we examined its topological properties in various networks. For each network, we calculated the centrality position  of the associated gene. If one variant is associated with multiple genes or the associated gene participates in multiple networks, the maximum cumulative probability is used as the continuous value for centrality score. Scripts are provided to calculate network centralities  are analyzed with VAT (variant annotation tool) . VariantFeatures used to score noncoding variants are shown in Additional file d, we calculated the probability pd that overlaps a natural polymorphism. Then we computed 1-Shannon entropy .We weighted each feature based on the mutation patterns observed in the 1000 Genomes polymorphisms. We randomly selected 10% of the 1000 Genomes Phase 1 SNPs (approximately 3.7 M) and ran them through our pipeline. For each discrete feature c, which is associated with a score vc , we calculated feature weights for each vc. In particular, we discretized at each vc and computed 1-Shannon entropy using  using the actual vc corresponding to the variant.The situation is more complex for continuous features, as different feature values have different probabilities of being observed in natural polymorphisms. Thus, one weight cannot suffice for varied feature values. For a continuous feature py using . Then wev, we calculated the probability of observing motif-breaking scores ≥ v in polymorphism data, then used .We obtained noncoding somatic variants from COSMIC (version 68). Recurrent variants  are defined as identified in whole-genome sequencing and observed in at least two samples. All other variants  are non-recurrent ones. After excluding variants in coding regions (GENCODE 16) and mitochondrion, there are 956 variants occurring in more than two samples, 8,932 variants in two samples, and 1,305,699 non-recurrent variants. Because the same sample from different papers may have multiple ids, we also defined recurrence based on number of studies. Study-based recurrent variants also have higher scores than non-recurrent ones  and 126,217 variants in recurrent and non-recurrent regulatory elements, respectively. The feature of recurrence is not considered in the weighted scoring scheme for variants in sections 1 and 2.Genome locations of pathogenic regulatory variants  We downloaded pre-calculated CADD scores for 1000 Genomes variants and extracted corresponding scores for control sets. For HGMD variants, we used the online CADD server to obtain the scores.We compared the prediction scores of HGMD variants with three sets of controls using various measures - TPR (true positive rate), FPR , precision, and recall. We treated HGMD as positive set and controls as negative sets. For each possible score, we draw the cutoff to make predictions and calculated - TPR = TP/(TP + FN); FPR = FP/(FP + TN); Precision = TP/(TP + FP); Recall = TP/(TP + FN) . AUC score is the cumulative area under the curve of TPR and FPR.We also tested our method with GWAS SNPs  and matched controls  from . Allele User data can be easily incorporated into our framework. Cancer sample-specific studies, such as histone modifications and gene expression, are especially useful in evaluating variants’ impact. Please refer to ‘Additional file