The robustness was demonstrated through a high correlation between metabolite concentrations measured using our method and the NIST reference values (R2 = 0.967), including cross-platform comparability against the BIOCRATES AbsoluteIDQp180 kit (R2 = 0.975) and NMR analyses (R2 = 0.884). We have shown that our method can be successfully applied in many biomedical research fields and clinical trials, including epidemiological studies for biomarker discovery. In summary, a thorough validation demonstrated that our method is reproducible, robust, reliable, and suitable for metabolomics studies.The use of metabolomics profiling to understand the metabolism under different physiological states has increased in recent years, which created the need for robust analytical platforms. Here, we present a validated method for targeted and semiquantitative analysis of 102 polar metabolites that cover major metabolic pathways from 24 classes in a single 17.5-min assay. The method has been optimized for a wide range of biological matrices from various organisms, and involves automated sample preparation and data processing using an inhouse developed R-package. To ensure reliability, the method was validated for accuracy, precision, selectivity, specificity, linearity, recovery, and stability according to European Medicines Agency guidelines. We demonstrated an excellent repeatability of retention times (CV < 4%), calibration curves (R Metabolomics has a great influence on many disciplines, as metabolites are intermediates or end products of cellular functions. Hence, metabolomics can be used as a powerful tool in understanding, diagnosing, and managing different pathophysiological conditions. It is therefore essential to be able to identify and measure metabolites from different biological matrices . AlthougAn appropriate sample pretreatment is required to obtain reproducible and high-quality quantitative data in targeted metabolomics. However, metabolites are present in a wide dynamic range with great diversity in physicochemical properties in the biological matrices . Recent Secondly, a robust analytical methodology is required for an accurate quantification of metabolites with good reproducibility over an extended period of time . MoleculEven though liquid chromatography-MS (LC-MS) is a method of choice in targeted metabolomics, obtaining an accurate quantification and long-term data reproducibility remains an analytical challenge. This is due to limitations such as matrix effect, MS performance drift, and LC column contamination and aging . FinallyWe have previously developed a robust, reproducible, and high-throughput targeted method for measuring 102 polar metabolites from various biological classes semiquantitatively in a single injection ,18. FurtThe primary objective of this work is to show the robustness of our previously developed analytical method through a thorough validation according to European Medicines Agency (EMA) guidelines. We also demonstrate the automation of tedious and manual data-processing tasks in high-throughput metabolomics analyses using an inhouse developed R-package. The R-package automates various corrections and normalization steps to convert the raw peak area data to molecular concentrations for each compound in each sample and also provides quality evaluation of the data and reduces the manual workload significantly.All metabolite standards were purchased from Sigma-Aldrich . Internal standards were ordered from Cambridge Isotope Laboratory. Inc. . LC-MS-grade solvents, 2-proponol, acetonitrile, and methanol (HiPerSolv) were obtained from VWR International . Analytical-grade chemicals  were obtained from Sigma-Aldrich. Deionized water (18 MΩ·cm at 25 °C) used for solution preparation was made using a Milli-Q water purification system . Mouse tissues, including heart, liver, brain, spleen, and muscles were obtained from Innovative Research Laboratory . The whole blood, from which serum was prepared during method optimization and validation, was obtained from the Finnish Red Cross blood service . Cell samples were provided by our research collaborators. NIST Standard reference material (SRM) 1950 plasma was purchased from Sigma-Aldrich .2O + 1% formic acid was used instead of 80/20% ACN/H2O + 1% formic acid during the second step of extraction. Additionally, during cell pellet sample extraction, 80/20% ACN/H2O + 1% formic acid was replaced with 90/10% ACN/H2O + 1% formic acid. After optimization, we used the tissue protocol for analysis of various biological matrices, such as heart, liver, placenta, brain, muscles, spleen tissues, dental carries, and Drosophila larvae (weight 10–20 ± 5 mg), C. elegans (2000–4000 worms/sample), dried blood spots (12–15 punched spots), and fecal samples (weight 20–50 ± 5 mg). The cell pellet protocol was used for all types of adherent cells (around 1 million cells/sample) and E. coli  and S. cerevisiae (OD600nm ~ 2.2) samples. The biofluid protocol was used for all types of biofluids, such as blood, plasma, serum, cell culture supernatant, CSF, and urine (sample volume 50–100 µL).All metabolites were extracted, separated with HILIC , and analyzed with a Waters Xevo TQ-S triple quadrupole mass spectrometer using our previously published protocol . The proValidation of the method was performed to verify various parameters and the reliability of the developed method for the analysis of a large number of samples. The method was validated according to EMA guidelines for bioanalytical method validation in terms of selectivity, specificity, linearity, accuracy, precision, extraction recovery, matrix effect, and stability . In addiThe selectivity and specificity for each metabolite were investigated using serum-spiked samples (N = 6) with known amount of standard. Chromatographic interferences from other endogenous compounds of the biological matrix at the retention time of the target analyte for a particular metabolite were verified. The chromatographic peaks from spiked samples were compared with the standards by the retention times and if required from their respective MRM spectra.To assess the linearity, accuracy, and precision, six replicates of spiked QC samples at high, medium, and low concentrations along with calibration curve standards were injected on three separate days. Calibration curve standards of 11 points were prepared via serial dilution and each calibration curve has over 1000-fold dynamic concentration range. The curve was plotted by using the peak area response ratios (standard/labeled standard) versus the concentrations of the individual metabolites. Each calibration curve was statistically evaluated and constructed using appropriate regression models, weighing factors and transformations. The accuracy was calculated as the measured value divided by the nominal value at each concentration level of the calibration curve standards in all three batches. Inter- and intrabatch variability was calculated by measuring coefficient of variation (%CV) at each QC concentration level.The recovery efficiencies for each analyzed metabolite were determined by comparing analytical results from QC samples spiked with a standard mixture before and after extraction using different concentrations. The spike concentrations covered the calibration range. The matrix effect  was determined by comparing the analytical response of the QC samples that were spiked after extraction with the analytical response of aqueous spiked samples (diluent spiked with respective concentrations of QCs). Since there were endogenous metabolites, we subtracted the endogenous concentrations from the samples that were spiked. This experiment was performed using 6 QC replicates.Wet extract, freeze-thaw, and stock solution stability for all metabolites were determined to check the integrity of the analytes in solvents and in QC samples at different conditions. To determine the wet extract stability, six replicates of extracted QC samples were kept in the autosampler at 5 °C. The same samples in the same sequence were reinjected with freshly extracted QC samples and the results were compared.Freeze-thaw stability was evaluated up to three cycles by freezing and thawing the spiked QC samples stored at –80 °C (for 12–16 h) and comparing the concentrations against the freshly thawed and spiked QC samples.Long-term stock solution stability for metabolite stock solutions and intermediate solutions were checked by comparing the mean peak area of freshly prepared solutions with stored solutions at 4 °C. All stability experiments were performed with 6 replicates of QCs.The carryover was evaluated by injecting the highest standard concentration (ULOQ) of the metabolites in the calibration curve followed by a series of blank injections and lowest standard concentration (LLOQ). The blank samples were evaluated for any signal at the retention time of particular metabolites and signal intensities of the blank samples were compared with the LLOQ samples. The acceptance criteria for carryover was set at 20% of the peak area corresponding to the LLOQ level as per the EMA guidelines for bioanalysis.Internal QC samples were prepared after separating serum from pooled healthy human blood samples. A volume of 350 µL of serum was aliquoted and stored at –80 °C after providing a lot number and QC number. The concentration of QC samples that were incorporated in batches during the metabolomics studies was calculated for all the metabolites along with the experimental samples. Average concentrations (μmol/L) and %CV of the QC samples were calculated for each metabolite. The data were saved along with QC lot numbers, batch name, and run date. The QC data were collected from six different lots for a period of 5.5 years (N = 539 replicates). An internal QC database has been maintained and used for quality checks.To evaluate the performance of our semiquantitative method, commercially available standard-reference plasma (NIST SRM 1950)  was analTo further evaluate the robustness and performance of our method, we performed a cross-platform comparison using two completely different analytical platforms: (1) the commercially available AbsoluteIDQ p180 targeted metabolomics assay kit using LC-MS/MS and (2) a nuclear magnetic resonance (NMR) platform. We sent our internal QC samples to the BIOCRATES Life Sciences AG  (N = 3 replicates) and to the NMR Metabolomics Laboratory, School of Pharmacy, University of Eastern Finland  (N = 3 replicates). Our QC samples were extracted and analyzed as described previously for the AbsoluteIDQ p180 kit  and for 2 was obtained from the linear regression between the variables. Coefficient of variation (CV) percentages was calculated as a measure of variability. To automate the downstream processing of data produced by the instrument vendor software , we built a data-processing package called “Unlynx” in R statistical programming language. The R-package is available upon request.To estimate the median concentration values (μmol/L) of the metabolites from the QC samples (N = 539), we fitted a linear mixed model with the MCMCglmm R-package  using anThe “Unlynx” package parses the output of TargetLynx software  and produces a processed dataset in an Excel spreadsheet after performing a series of preprocessing operations.(i)Molecular weight normalization, in which the ppb values are normalized by the molecular weight of each compound, thereby converting the data from ppb units to µmoles.(ii)Process efficiency correction for the semiquantification of metabolites without internal standards.(iii)Normalization using dilution factor for specific sample type if dilution was needed.(iv)Cell number normalization (for cell samples) to convert the concentration values per million cells.(v)Calculation of mean, standard deviation, and relative standard deviation (RSD) of molecular concentrations (resulting from the previous steps) for each phenotypic group.(vi)Outlier detection in each phenotypic group; if the concentration value of a compound in a sample is more than one or two standard deviations (SD) away from the mean of the phenotypic group, then it is marked as an outlier in the Excel data set in two different colors.(vii)QC check by comparing the RSD of QC samples in the current dataset against the internal database of QC sample RSDs (based on interday RSDs recorded over one year).The preprocessing steps included the following:The primary objective of this work was to optimize and validate our previously published protocol for different types of biological matrices. For tissue samples , the sample volumes of the tissues and extraction solvent volumes were optimized to fit the concentrations of most of the metabolites within the linearity of calibration curve for reliable results. We observed that most of the metabolites could be semiquantified within the calibration curve range with 20 ± 5 mg of sample weight.Furthermore, we optimized the protocol with extraction solvent for tissues and adherent cells. Some of the metabolites  had poor separation and irreproducible chromatography. Interference of isobaric compounds with other metabolites was also observed due to poor separation. Thus, different compositions of the extraction solvent were assessed to achieve the acceptable chromatography. We observed that modification of acetonitrile content from 80% to 90% and applying longer equilibration time for the HILIC column yielded acceptable chromatography and also good separation for most of the metabolites.E. coli, and S. cerevesiae) and other sample types such as fecal samples, dried blood samples, dental carries, biofilm, drosophila (larvae and whole flies), and C. elegans.We also optimized the extraction protocol for different sample types from various organisms , such as tissue types , biofluids , cell types  is shown in There were no significant interference peaks from the matrix components in their respective retention-time windows, indicating the selectivity of the metabolites in our method. We repeated the injections for five times from all different serum samples and confirmed every peak to have eluted only from the target analyte, indicating that they are specific to their corresponding MRM transitions. The chromatograms for all the 102 metabolites of QC sample were given in 2) value for each metabolite was greater than 0.980 at their respective concentration range, except for some metabolites such as aspartate, uracil, 2-deoxyuridine sucrose, and chenodeoxycholic acid  and transformations (log-log) were used . The coeThe concentration precision for QC samples was calculated by measuring %CV at high, medium, and low concentration level of QCs (N = 6 replicates). In general, intra- and interday precision (CV) values were within 15% for 94 metabolites except acetoacetic acid, folic acid, sucrose, homoserine, 2-deoxyuridine, and cholic acid at high concentration . Howeverd-ribose 5-phosphate, NAD, AMP, homocysteine, carnosine, and glutathione had recoveries less than 30% in serum. However, CV of recoveries at low, medium, and high concentration levels was within 25% except for cGMP in serum. Metabolites such as 1-methylhistamine, aspartate, glutamine, adenosine, and glutathione had recoveries over 120%. Histidine, ornithine, cystathionine, 3-OH-dl-kynurenine, carnosine, AMP, NAD, cGMP, IMP, and UDP-glucose had less than 30% recovery in some tissues types, indicating matrix effect or degradation  in both biofluid (serum) and tissues . However, compounds such as UDP-glucose, IMP, cGMP, radation . HoweverThe matrix effect values were observed to be within the range of 0.6 to 1.8 (below 1 indicates ion suppression and above 1 indicates ion enhancement) for the metabolites in serum and tissues. The challenge of the matrix effect can be overcome by having individual isotope-labeled internal standards for each individual compound for true quantification. However, this is not practically possible for high-throughput metabolomics analyses. This is due to high costs and also because not all internal standards are commercially available. In our method, we selected 12 labeled internal standards , which rSome of the endogenous metabolites are not stable due to degradation or conversion reactions. Hence, the stabilities of all metabolites were assessed under different conditions. For wet extract stability, approximately 90% of the metabolites were stable (stabilities range between 85 and 115%) for 35 h at 5 °C in the autosampler A.For freeze and thaw cycle stability, most of the metabolites were stable even after three freeze and thaw cycles, with the exception of cGMP, succinate, glutathione, and homocysteine  was observed, and excellent reproducibility was observed for R2 values of calibration curves (CV < 3%)  . On the To obtain reproducible and accurate data, we set up a strict quality management and electronic lab notebook system. To reduce the bias from sample analysis, we always double-randomized the samples . For stabilization of response and retention time, we always verified a few runs of highest calibration level 11 before injecting the experimental samples. During the stabilization process, we also verified the chromatography, including peak shape, retention time, and response of all the metabolites. Any significant changes in the intensity, peak shape, retention time, and system pressure were thoroughly investigated and corrected by resolving the problems before injection of experimental samples.To ensure the integrity of LCMS runs, QC samples were run at every tenth experimental sample and a blank sample at every fifth run during all the metabolomics studies within a batch. Furthermore, chromatography and response of QC samples (including chromatography of some metabolites and IS response variation) and blank runs were always verified after completion of the runs and before starting data processing. In case of any abnormality observed for particular samples, those samples were reinjected or reanalyzed. Only after passing these quality checks we proceeded further to process the data. This included verifying the accuracy of calibration curve standards, chromatography peak integrations, IS response variation, and verifying LLOQ and ULOQ for each sample for all metabolites within a batch. The high-throughput targeted metabolomics workflow is shown in We collected concentration values (μmol/L) for our QC samples within metabolomics studies conducted over a period of 5.5 years from six different lots (N = 539 replicates). The median values of each metabolite together with a 95% credibility interval are presented in To verify the performance of our method, we analyzed the NIST standard reference material SRM 1950 plasma. The correlation coefficient for 17 matched metabolites between the given reference values and from our semiquantitative method was 0.967, indicating the high performance of our method A.2 = 0.975) (2 = 0.884)  in our QC samples. We obtained a high correlation coefficient for matched 38 metabolites measured using BIOCRATES AbsoluteIDQ p180 kit and our method (R= 0.975) B and for= 0.884) C. These After the raw data processing using the instrument-coupled software (TargetLynx), an unstructured flat text file containing information such as sample ids, file name, and concentrations (PPB) for all metabolites is generated in a complex format. For example, in a single-batch run of 85 samples, after the data processing, the concentration values for all samples are separately obtained under each metabolite. This means that, if a data matrix of 85 samples × 100 metabolites is desired, each individual concentration value must be copied and pasted in another sheet in a tabular format. Then, the PPB values are to be converted into µm and corrected for process efficiencies and dilution factors (if any) and normalized with tissue weight or cell number depending on the sample type. Apart from these steps, as the samples are analyzed in a randomized manner, the next steps would include rearranging the experimental samples according to phenotypic group and separating the QC samples from the study samples. This manual data processing creates a ready-to-use data matrix for visualization and for downstream statistical analyses, but is tedious, time-consuming, and, more importantly, prone to errors.2 values for each metabolite. With the automated data processing, we reduced a two-day manual workload to a few minutes.To automate the manual data processing, we have implemented a software package “Unlynx” in R statistical language. This package takes the raw data produced by TargetLynx software as input and produces processed data into ready-to-use spreadsheets. We use Unlynx to covert the PPB values to µmol/L, µmol/g, or µmol/million cells by dividing with molecular weight of the respective metabolites and other correction factors  appropriate for the sample type. In addition, mean concentrations and %CV of QC samples for all the metabolites are calculated and used to evaluate quality by comparing with the inhouse QC database constructed based on inter-day %CV. Furthermore, Unlynx retrieves LLOQ, ULOQ, and outlier values in each phenotypic group according to one and two SDs, retention time values, and RThus, after processing the data using TargetLynx, we routinely exported all results for the automated data processing using Unlynx. Typically, the data resulting from such automated data processing undergoes more specialized data analyses  aimed at answering specific scientific questions related to the study design.We have applied our fully validated analytical methodology in various international and national biomedical research projects, epidemiological studies, clinical studies including dietary interventions, and clinical trials. We have successfully implemented our technology in the following research fields, including but not limited to: mitochondrial metabolism/disorders ,23,24,25(i)Optimization: well-characterized protocols for various biological matrices from different organisms enabled to study wide variety of research projects.(ii)Accuracy/Precision: the targeted and semiquantitative analysis using 102 external 11-point calibration curves, including 12 labeled internal standards in every analysis, made it possible to compare the data within and between the studies.(iii)Quality management: standard operating protocols, good laboratory practices, strict quality-management system, and proper documentation using electronic laboratory notebook enabled to check/retrieve very old data.(iv)High-throughput: automated sample preparation and short analysis time (17.5 min), enabled high-throughput capabilities, which is the most desired feature for large-scale analyses.(v)Stability: long-term stability studies in stock and intermediate solutions, wet extract, and freeze-thaw stability studies, critical for projects based on clinical/biobank samples.(vi)Automation: downstream data processing steps in an automated manner reduces the interbatch variation and human errors, valuable for analyzing population cohorts and in epidemiological studies.(vii)Reproducibility: low %CV of concentrations, retention times, correlation coefficient of calibration curves for an extended period of time, very important in metabolomics studies when comparing the data produced at different points of time.(viii)Reliability: as shown by the excellent correlation between metabolite concentrations measured using our method and the NIST SRM plasma reference values, our method serves a standardised and reliable platform for metabolomics studies.(ix)Robustness: Our results demonstrate an excellent cross-platform comparability with two completely different analytical platforms, a highly desirable criterion in multicenter studies when comparing the data across different laboratories using different instrumentation, protocols and analytical platforms.(x)Data sharing: the huge QC sample database of healthy adults (N = 539) collected for six years and shared with the scientific community, provide normal reference values such as those provided in the HMDB database.We validated our high-throughput targeted and semiquantitative analytical method according to the EMA guidelines for bioanalytical methods. The key features of our method include,The above-mentioned aspects are critical in the metabolomics analysis, as the International Metabolomics Community has been putting a lot of efforts in data standardization, QC, reproducibility, robustness, and data sharing with the goal of moving towards applicability and integration of metabolomics data in precision/personalised medicine. In this context, our method provides a very timely and important contribution to the field. Furthermore, we have widely applied this method in many biomedical research projects, clinical trials, epidemiological studies, and in biomarker discovery.


All the risk scores from the gene expression and CNA data and their concatenated one were significantly associated with breast cancer survival. The patients with the high-risk group were significantly associated with patients’ worse outcomes . The concatenated risk score was enriched by the AMP-activated protein kinase (AMPK) signaling pathway, the regulation of DNA-templated transcription, the regulation of nucleic acid-templated transcription, the regulation of apoptotic process, the positive regulation of gene expression, the positive regulation of cell proliferation, heart morphogenesis, the regulation of cellular macromolecule biosynthetic process, with FDR  less than 0.05. We confirmed DAs can effectively extract meaningful genomic features from genomic data and concatenating multiple data sources can improve the significance of the features associated with breast cancer patients’ clinical characteristics and outcomes.Artificial intelligence-based unsupervised deep learning (DL) is widely used to mine multimodal big data. However, there are few applications of this technology to cancer genomics. We aim to develop DL models to extract deep features from the breast cancer gene expression data and copy number alteration (CNA) data separately and jointly. We hypothesize that the deep features are associated with patients’ clinical characteristics and outcomes. Two unsupervised denoising autoencoders (DAs) were developed to extract deep features from TCGA (The Cancer Genome Atlas) breast cancer gene expression and CNA data separately and jointly. A heat map was used to view and cluster patients into subgroups based on these DL features. Fisher’s exact test and Pearson’ Chi-square test were applied to test the associations of patients’ groups and clinical information. Survival differences between the groups were evaluated by Kaplan–Meier (KM) curves. Associations between each of the features and patient’s overall survival were assessed using Cox’s proportional hazards (COX-PH) model and a risk score for each feature set from the different omics data sets was generated from the survival regression coefficients. The risk scores for each feature set were binarized into high- and low-risk patient groups to evaluate survival differences using KM curves. Furthermore, the risk scores were traced back to their gene level DAs weights so that the three gene lists for each of the genomic data points were generated to perform gene set enrichment analysis. Patients were clustered into two groups based on concatenated features from the gene expression and CNA data and these two groups showed different overall survival rates ( Advanced hardware technologies have highly increased computational power, which makes the implementation of computation-consuming algorithms possible. At the same time, the development of biological technologies has greatly reduced the cost of genomic sequencing, which produced a huge amount of high-dimensional genomic data. Under these circumstances, bioinformatics becomes an exciting research field for researchers to explore the possibility to interpret genomic data using advanced computational technologies .Different types of high-dimensional genomic data have been associated with cancer clinical characteristics and outcomes. The most commonly used ones are gene expression data and copy number alteration (CNA) data . The actTraditional ML methods such as artificial neural networks (ANN) and support vector machines (SVM) may suffer some problems in dealing with the high-dimensional, noisy and massive genomic data . RecentlVincent, et al. brought the concept of DAs into DL and built a specialized feature extraction DL architecture . The keyDL as a special case of ML and ANN has been applied to mine deep information from complex genomic data and has generated interesting results . Its higThis study aims to extract the integrated features from both the gene expression and CNA data by a concatenated DAs model. As a comparison, we also built a standard DAs model to extract deep features from gene expression and CNA data separately. The comparisons were made in terms of the performance in association analysis as well as prognosis analysis. The study design and analysis procedures are shown in Datasets used in this study came from The Cancer Genome Atlas (TCGA) , which iFor gene expression data, the sequencing, alignment, quality control and quantification were performed previously . Using tSimilar to the gene expression data, upstream processes of CNA data were done previously as well . Using tAfter normalization and preprocessing, there were 18,163 genes from each of the 1095 patients left for gene expression data and 23,563 genes from the 1098 patients left for CNA data. To keep the gene dimension and scale matched in the two data sources, both of them were linearly transformed into a range between 0 and 1, resulting in the decreasing of the data dimension to 16,197 (genes) × 1085 (patients) for both data sources.Two DAs models were developed using Keras  with TenW is the weight metrics between the layers with the size of 16,197 × 100, b is the bias for each node, and the sigmoid function is sigmoid (x) = 1 / (1 + −xe). The counterparts with the superscript refer to the transpose metrics. A dropout layer was added after the encode layer, which randomly set 50% of the output of encode layer to 0 to prevent overfitting. The encode item was chosen to be the activity values of the deep features in this model.This architecture was composed of one input layer, one fully connected encode hidden layer with 100 nodes which were chosen to be the deep features used in this study and one decode layer which uses the transpose of encoding layer’ weights. This procedure can be formulated as below:1input_1W, 2input_1W, and 2concate_W are the weight metrics between the layers with the size of 16,197 × 1000, 16,197 × 1000, 2000 × 100 respectively. The 1input_1b, 2input_1b, and 2concate_b are the biases for each node. The counterparts with superscript refer to the transpose metrics. A dropout layer was added after concate_encode2 layer, which randomly set 50% of the output of that layer to 0. The concate_encode2 was chosen to be the activity values of the deep features in this model.Literally, the two-input DAs model contained two input layers, followed by one encode layer with 1000 nodes for each input layer, then followed by a concatenated layer, and another encode layer with 100 nodes which were chosen to be the deep concatenated features. Finally, there were two decode layers. The procedure can be formulated as follow:L  is the binary cross-entropy, K is the index of batches, N is the total number of batches. Thus, the training task is to minimize the L .Before the training process, the input data sets were disrupted by a noise factor of 0.25, which is the proportion of the number of genes in the data sources. These genes were selected randomly and their values were set to 0. The binary cross-entropy function shown below was used to measure the difference between the input layer and the output layer:For the optimizer, e.g., the strategy to update the weights and bias so that the minima could be found, we selected stochastic gradient descent (SGD), which has several arguments to be set freely. After having different trials, the learning rate was finally set to 0.1; the batch size and epoch were set to 64 and 100 respectively. The models were finally trained under the parameters mentioned above. The activity values and weight metrics related to deep features were read out.Heatmap3  was usedTo test whether the identified patient clusters are associated with known clinical and molecular characteristics, we applied both Fisher’s exact test and Pearson’ Chi-square test.t represents the survival time. b is the coefficient which measures the impact of the covariate x. Later, a risk score for each feature set was generated from the COX-PH coefficients:r is the risk score, ib is the coefficient from the COX-PH model and ia is the related activity value of the given feature. Afterward, the risk scores were binarized into high-risk and low-risk groups using R package xtile function with a prob parameter set to 0.55, which means we use the 55% quantile as the cutoff to bin the patients into the high-risk and low-risk groups. Finally, the survival differences between these two groups were evaluated by the KM curve.Survival differences between the identified patients groups were evaluated by Kaplan–Meier (KM) curves. Furthermore, associations between each deep feature in the three feature sets  and patient’s overall survival was assessed using Cox’s proportional hazards (COX-PH) model . The hazW is the 16,197 × 100 dimensional weights that were extracted from a given DAs model previously. B is the vector of COX-PH coefficients. The gene-specific weights gW were filtered by a cutoff 0.01, which resulted in the three selected gene lists with 6954, 5381 and 6297 genes, respectively. Finally, the three gene lists were used to perform gene set enrichment analysis (GSEA) by the Enricr tool [For each of the three DAs models for gene expression, CNA and their concatenated one, we traced back their gene-level weights based onicr tool  to identicr tool  and Geneicr tool  BiologicBased on the normalized and processed breast cancer genomic data, our models were trained and the activity values of the 100 deep features for each of the three data sets as well as the weights matrices were extracted . Then wep-values 0.049 and 0.002, respectively, which mean that the concatenated features have learned the ER information and performed well in predicting patient’s prognosis. The odds ratio of ER status is 0.626, indicating that the second group tends to be associated with ER-negative patients. From the KM plot  signaling pathway in the KEGG family was significantly down-regulated with a false discovery rate (FDR) less than 0.05, and several GO-based regulation processes, such as the regulation of DNA-templated transcription, the regulation of nucleic acid-templated transcription, the regulation of apoptotic process, the positive regulation of gene expression, the positive regulation of cell proliferation, and the regulation of cellular macromolecule biosynthetic process were significantly enriched as well, with an FDR less than 0.05 .AMPK is an important cellular metabolism and energy homeostasis regulator in mammalian tissues. It is situated in the center of a signaling network which contains tumor suppressors such as LKB1, TSC2 and p53 . Some evIn building the DA model, one of the key parameters we need to set up is the noise level used to partially destroy the inputs. We tried to add different levels of noise  into the DA model. Similar to the observations made by Vincent et al. , we alsoComparing with conventional breast cancer biomarkers, such as CA15-3 for measuring how breast cancer treatment is working and looking for cancer that has come back or recurred, after treatment , and NCCIn order to extract robust deep features using the proposed DA model, we took a strategy to add noise into the input genomic data by the partial corruption of the input pattern. It is expected that the learned deep features from the partially destroyed inputs can yield almost the same representation of the raw genomic data. In order to further boost the performance of using the learned deep features to predict breast cancer outcome or traits, another interesting strategy is to incorporate the prior knowledge about breast cancer hallmarks, which can be represented by a few molecular or signaling networks , into thIn this study, we showed that unsupervised DAs as an effective model to extract meaningful deep genomic features from either single- or multi- genomic sources from breast cancer patients. These features were significantly associated with the breast cancer ER status and had the prognosis significance. We also showed that the concatenated deep features were enriched by breast cancer relevant pathways.This study can be improved in two potential ways. The first one is to develop new DAs model structures such as stacking more layers into DAs or adding a regression layer to make it supervised . The sec


Deregulation of transcription factors (TFs) is an important driver of tumorigenesis, but non-invasive assays for assessing transcription factor activity are lacking. Here we develop and validate a minimally invasive method for assessing TF activity based on cell-free DNA sequencing and nucleosome footprint analysis. We analyze whole genome sequencing data for >1,000 cell-free DNA samples from cancer patients and healthy controls using a bioinformatics pipeline developed by us that infers accessibility of TF binding sites from cell-free DNA fragmentation patterns. We observe patient-specific as well as tumor-specific patterns, including accurate prediction of tumor subtypes in prostate cancer, with important clinical implications for the management of patients. Furthermore, we show that cell-free DNA TF profiling is capable of detection of early-stage colorectal carcinomas. Our approach for mapping tumor-specific transcription factor binding in vivo based on blood samples makes a key part of the noncoding genome amenable to clinical analysis. Deregulation of transcription factors is frequently observed in cancer. Here, the authors develop a blood-based approach to map transcription factor binding, revealing both patient-specific and tumour-specific aberrations TF binding is often correlated with nucleosome occupancy4. It has recently been shown that nucleosome positioning can be inferred from cell-free DNA (cfDNA) in plasma5, suggesting that it may be possible to infer not only gene expression6 but also TF binding in tumor samples from circulating tumor DNA. Accurate inference of TF binding from cfDNA would have substantial diagnostic potential in cancer and potentially other diseases, but to date TFs cannot be evaluated noninvasively.Transcription factors (TFs) modulate the expression of their target genes and often play a key role in development and differentiation10. Moreover, we generate shallow WGS data for 229 plasma samples from patients with the aforementioned tumor entities  and we furthermore use 769 plasma samples from patients with colon cancer (n = 592) and healthy controls (n = 177) (~238 billion mapped plasma sequence reads in addition).We thus develop an approach capable of providing insights into single TFs directly from nucleosome footprints to objectively assess and compare TF-binding site (TFBS) accessibility in different plasma samples. To validate our method, we produce deep whole-genome sequencing (WGS) data for 24 plasma samples from healthy donors, i.e., subjects without known current disease, and for 15 plasma samples of patients with metastasized prostate, colon or breast cancer, where cfDNA also comprises circulating tumor DNA (ctDNA)Our approach allows identification of lineage-specific TFs and profiling of individual TFs from cfDNA. We demonstrate two relevant clinical applications: first, our TF-based cfDNA assays are capable of distinguishing between prostate adenocarcinoma and small-cell neuroendocrine prostate cancer, a distinction that has important therapeutic implications. Second, the large colon cancer cohort enabled us to accurately establish resolution limits and to explore the use of TF-based plasma analyses for detection of early cancer stages.http://gtrd.biouml.org)11 , breast cancer (BRCA), and colon adenocarcinoma (COAD) and calculated individual TF accessibilities (see the Methods section). As expected, we observed different tissue-dependent TF accessibility patterns 15 showed a reverse pattern with an increased accessibility in hematopoietic lineages , where the vast majority (>90%) of cfDNA is derived from apoptosis of white blood cells with minimal contribution from other tissuesWe found evidence that nucleosome plasma footprints are informative regarding TFBSs. For example, in healthy individuals, binding sites of the hematopoietic TF LYL1 were surrounded by arrays of strongly positioned nucleosomes yielding a high-amplitude oscillating coverage pattern Fig. . HoweverWe next tested whether TF accessibilities as established from ATAC-seq data can be inferred from the cfDNA nucleosome occupancy patterns. However, currently no means of assessing TF accessibility and changes thereof in cfDNA exist. To implement such an approach, we first investigated TF-specific nucleosome coverage profiles, which led us to conduct calculations separately for TFBSs within and outside of TSSs  as compared with the accessibility of expressed  TFs    Fig. . These dst) Fig. .We then used overall z-scores, i.e., the rank differences between a tumor sample and the healthy controls, and defined detection thresholds for TFBS accessibility differences from the mean of rank in normal samples by >5 standard deviations . Histogram analysis and a QQ-plot showed no relevant deviation from a normal distribution , and PU.1 were reduced in all tumor samples , for colon (n = 100), and for breast (n = 60) cancer cases ×31, thatTo address the question whether TF accessibility remains stable over time, we analyzed two samples each from 4 patients  and found no changes in three of the four plasma sample pairs   and an increase of NSE (neuron-specific enolase)33. The t-SCNC is no longer an androgen-dependent stage of prostate cancer34 and, accordingly, accessibility of AR binding sites was substantially reduced in sample P148_3  Fig. . Within EST Fig. . To empl32. Furthermore, the involvement of TFs in this transdifferentiation process has been extensively studied37  mechanism in the development of treatment resistance and has clinically significant implications because it requires a change in therapyd37 Fig. .34. These proof-of-concept analyses suggest that inference of transcription factor binding from cfDNA may enable tumor subtype prediction.Therefore, we added plasma samples from four further clinically proven t-SCNCs cases  (Supplementary Table n = 197) or stage II (n = 280) disease  and stage II (n = 280). Using our cross-validated test-set values, we found that this approach is capable of identifying ctDNA in patients with stage I colon cancer with a 74% precision (95% CI: 0.53–0.90), 71% sensitivity  (95% CI: 0.54–0.87), and 72% specificity ) , while performance metrics were calculated on the withheld samples (10%). We applied this approach to all available samples and to those samples derived from patients with stage I and stage II disease combined 5, whereas we utilized recently generated ATAC-seq data13 and profiled numerous individual TFs, which enabled us to establish lineage-specific TFs for clinical applications. Second, we introduce a metric, the accessibility score, which, by enabling the objective comparison of TF binding events in various plasma samples, paves the way for entirely new diagnostic procedures. In fact, the accessibility score represents a measure of the strength of nucleosome phasing at the binding sites of a TF and reflects the strength of TF binding. Third, we were able to use cfDNA to show TFBS plasticity during a disease course, such as reprogramming to a different cell lineage in prostate cancer. Such a dynamic view of TF activity (vis-à-vis the static view obtained from tissue13) is a unique feature of cfDNA analyses. Fourth, we demonstrate that our cfDNA TFBS bioinformatics pipeline allows subclassification of tumor entities, and hence may address an important diagnostic dilemma in the managing of patients with prostate cancer32. Fifth, whereas Snyder et al. required 1.5 billion reads per plasma sample5, which is prohibitive for routine clinical use from a cost-perspective, we were able to conduct in-depth TF analysis with ~50 million reads, making our approach more amenable to clinical applications. Finally, we used a large cohort of patients with colon cancer to establish the resolution limits and provide evidence that our approach is suitable for detection of early-stage cancer. As such, our approach may enable the detection of cancer at stages when the disease is most treatable/curable and thereby help to reduce cancer mortality rates40.Compared with a previous publication by Snyder et al.41 and comparisons with other studies are limited by differences in methodology and study design. The recent CancerSEEK study assessed levels of circulating proteins and mutations in plasma and included 388 patients with colorectal cancer, with a lower percentage of stage I and stage II patients than our study 42. Whereas we achieved a better sensitivity , the CancerSEEK study reported a high specificity for the entire study cohort of 99%42. Another study, which employed size profiling of ctDNA molecules and machine learning, described a lesser specificity than ours of 65% in samples from low-ctDNA cancers 43. In future applications, it will be interesting to test whether multiparameter strategies, combining different, orthogonal approaches, will result in an improved sensitivity and specificity of plasma-based early cancer detection10.At the same time, cancer tests should have a high specificity to avoid potentially unsettling healthy individuals with a false positive test result, who would then be subjected to unnecessary follow-up procedures. At present, early cancer detection using liquid biopsies is in its infancyAnother limitation is that our TF nucleosome interaction maps are inevitably heterogeneous, comprising signals of all cell types that give rise to cfDNA. Furthermore, using all 504 TFs in our logistic regression model does not make our strategy specific for colon cancer. Further work will be required to identify distinct TFs subsets that are specific for different tumor types.Nevertheless, tumor studies lack dynamic models, and, in particular, dynamic profiling of clinical samples, for exploring transitions and interplays between pathways. Because of the potential of TFs regulating gene transcription throughout the genome and their often exquisite lineage specificity, their detailed noninvasive analyses based on cfDNA offer a unique opportunity to improve clinical diagnostics. Our data also provide the foundation for further dissection of the noncoding genome through means of transcription regulation profiling.The study was approved by the Ethics Committee of the Medical University of Graz , conducted according to the Declaration of Helsinki and written informed consent was obtained from all patients and healthy probands, respectively.Retrospective human plasma samples from patients with colon cancer (Freenome cohort) were acquired from five biobanks for patients diagnosed with COAD and healthy controls. All plasma samples used in this analysis were de-identified prior to receipt, with no key available to re-identify.31.Peripheral blood was collected from patients with metastatic prostate, breast, and colon cancer at the Department of Oncology and from anonymous healthy donors without known chronic or malignant disease at the Department of Hematology at the Medical University of Graz. CfDNA was isolated from plasma using the QIAamp Circulating Nucleic Acids kit  in accordance with the manufacturer’s protocol. Regarding library preparation for WGS, we generated shotgun libraries using the TruSeq DNA Sample preparation Kit  according to the manufacturer’s protocol with the following modifications: (i) we used 5–10 ng of input DNA; (ii) due to the length of ctDNA we omitted, the fragmentation step; (iii) for selective amplification of the library fragments, we used 20–25 PCR cycles. Libraries were quality checked on a Bioanalyzer (Agilent) and quantified using qPCRFor the Freenome cohort, cfDNA was extracted from 250 μl plasma using the MagMAX cfDNA Isolation Kit (Applied Biosystems) and converted into libraries using the NEBNext Ultra II DNA Library Prep Kit (New England Biolabs).39 from read counts in 50-kilobase (Kb) bins across the entire genome. In addition 50 kb-bin counts were GC-normalized and normalized by a panel of normal samples using tangent normalization.Tumor fraction was estimated in each sample using ichorCNAControl and high-coverage tumor samples were sequenced on the Illumina NovaSeq S4 flowcell at 2 × 150 bp by the Biomedical Sequencing Facility at CeMM, Vienna, Austria. For the control samples, an average of 435,135,450  paired-end reads were obtained. For the tumor samples , an average of 688,482,253 reads  were sequenced. Additional samples were sequenced using the Illumina NextSeq platform .Low-coverage tumor samples which were used to create single-entity pools were sequenced on either the Illumina Next-Seq or MiSeq platform. This resulted in 382,306,130 reads from 69 prostate cancer samples; 254,490,128 reads from 60 breast cancer samples and 604,080,473 reads from 100 colon cancer samples.Samples from the Freenome cohort were paired-end sequenced on the Illumina NovaSeq platform.6 and P40, P147, and P14833 were previously analyzed within other studies. From these analyses, we had information regarding mutations, specific SCNAs, and tumor content of the plasma samples based on the algorithm ichorCNA39.Some plasma samples, i.e., of patients B7 and B13P40: Mutations: BRCA1: NM_007294: Q975R; specific SCNAs: TMPRSS2-ERG fusion; AR amplification in sample 2; chr12 amplification ; tumor content: P40_1: 30%; P40_2: 24%;P147: Mutations: BRCA2: T298fs; TP53: F338I; specific SCNAs: RET amplification in sample 3; AR amplification; BRAF amplification (7q34); PTEN loss; tumor content: P147_1: 52%; P147_3: 73%;P148: Mutations: TP53: R213X; specific SCNAs: MYC amplification; PTEN loss; FOXP1, RYBP, SHQ1 loss; TMPRSS2-ERG fusion; AR amplification (lost in P148_3); tumor content: P148_1: 38%; 148_3: 49%.C2: specific SCNAs: high-level amplification on chromosome 12 (KRAS) in C2_6, not visible in C2_7; tumor content: C2_6: 18%; C2_7: 28%.http://gtrd.biouml.org)11, which provides detailed TFBS information based on ChIP-seq data for a variety of tissue samples. We annotated these TFs with an up-to-date curated list of 1639 known or likely human TFs 1. Because of the potentially high number of TFBSs per TF, we employed stringent criteria and restricted our analyses to TFs with 1000 TFBSs which were supported by the majority of tissue samples in the GTRD. In total, 172 TFs had fewer than 1000 defined sites in the GTRD and were thus omitted, resulting in 504 TFs amenable to analysis , and individual BED files per TF were extracted. The position was recalculated by focusing on the reported point where the meta-cluster has the highest ChIP-seq signal. The top 1000 sites that were supported by most of the analyzed samples were extracted (1000-msTFBSs). All BED files were then converted to hg19  using the liftOver tool provided by UCSC.The data from the GTRD database were downloaded . The full count matrix was divided by sample. Next, all reads of bins that overlap defined binding sites of a respective transcription factor were summed and divided by the number of total reads within this sample.https://api.gdc.cancer.gov/data/f0094e76-4a80-4ee1-9ad0-8ffb94aff5f7). Again, data were divided by sample and bins that overlap a specific transcription factor are summed up and divided by the total amount of reads for a specific sample.Raw ATAC-seq data matrices of TCGA samples were downloaded for colon adenocarcinoma, prostate carcinoma and breast carcinoma samples , transcription factor accessibilities were compared with hematological samples by two-tailed Mann–Whitney U tests, mean ratios, and effect sizes (by Cohen’s D).In order to enhance the nucleosome signal, reads were trimmed to remove parts of the sequencing read that are associated with the linker region. Hence, forward reads were trimmed to only contain base 53–113 . Reads were then aligned to the human hg19 genome using bwa and PCR duplicates were removed using samtools rmdup. Average coverage is calculated by bedtools genomecov.For the Freenome cohort, reads were aligned to the human hg38 genome using BWA-MEM 0.7.15. Midpoints of paired-end reads were extracted and average midpoint counts at transcription factor binding sites (+/−1250 bp) were calculated per transcription factor. In order to better compare the data to the aforementioned samples, a running median (window size = 30) was applied to the data using the numpy.convolve function in a single dimension.BAM files of MNase-seq experiments of GM12878 were downloaded from the ENCODE portal. Reads in BAM files were trimmed directly from the BAM file using pysam. In brief, left-most alignment positions in the BAM file were shifted 53 bp in the respective direction, and the sequence length was adjusted to 60 bp. The coverage patterns were then calculated in the same way as the trimmed cfDNA sequencing data.For every transcription factor in the GTRD, coverage patterns were calculated. To this end, coverage data were extracted for every region using pysam count_coverage in a region ±1000 bp around the defined binding sites. Coverage data at every site were normalized by regional copy-number variation and by mean coverage. For every position around the TFBS, coverage was averaged, and 95% confidence intervals were calculated.As we hypothesized that two distinct signals make up the coverage pattern, two signals of different frequencies were extracted. The lower range frequency data were extracted by a Savitzky–Golay filter . A high-frequency signal was extracted by a different Savitzky–Golay filter . The high-frequency signal was then normalized by division by the results of the low-frequency signal. Subsequently, the data range of the high-frequency signal was recorded. Since coverage profiles from TFs with few described binding sites are inherently noisier, a LOESS smoothing was performed over the signal range and the amount of described binding sites. The range values were corrected by the smoothed LOESS, and ranks of the adjusted range were calculated.In order to compare tumor and control samples, the ranks of the respective TFs in the adjusted range values were compared. Rank differences were calculated between a tumor sample and every control sample, and mean rank differences were recorded. Moreover, z-scores were calculated for every transcription factor from the accessibility ranks by taking the respective rank and subtracting the mean rank of the control samples and dividing by the standard deviation of the transcription factor ranks of the control samples (RankDiff z-scores). In another round, z-scores were calculated for the overall deviation of transcription factor accessibility in two samples. To this end, accessibility values of each of the 24 healthy samples were compared with the remaining 23, and the rank difference was recorded. The rank differences were used to estimate a normal distribution, and z-scores for the rank differences over all TFs were calculated .In order to compare subsequent samples from the same patient, we performed pairwise comparisons of every combination of the 24 healthy samples in order to estimate the variability of paired comparisons. The mean and standard deviation of these differences were then used to calculate z-scores of accessibilities of paired samples. Briefly, rank differences between the paired samples were calculated and z-scores of this difference were calculated.BAM_files from DNase hypersensitivity experiments were downloaded from the ENCODE database for GM12878, LNCaP, and HCT116 cell lines. Binding site regions of a transcription factor were increased by 25 bp on either side using bedtools slop. Coverage at the respective binding sites was extracted using mosdepth and normalized by million mapped reads per sample.31 was applied to the subsampled FastQ files and the low-coverage data of the cancer samples, respectively. In brief, reads were aligned to the human hg19 genome, and reads were counted within pre-specified bins. The bin size was determined by the amount of theoretically mappable positions to account for differences in mappability throughout the genome. Read counts were normalized for total amount of reads, and GC content of bins were corrected for by LOESS smoothing over the GC spectrum. Moreover, corrected read counts were normalized by the mean read counts of noncancer controls per bin to control for additional positional variation.For control data, paired-end alignments were subsampled using samtools view to only include 2% of the initial alignments and converted to FastQ using samtools fastq. For the cancer samples, separate low-coverage WGS was performed. Plasma-Seq39.Accessibilities in colon cancer samples were compared with healthy samples for TFs where evidence of tumor-specific increases and decreases in accessibility was observed in former analyses. Two-tailed Mann–Whitney U tests were performed to compare healthy and cancer samples, as well as subsamples of the cancer data set, based on the respective tumor fraction, as estimated by ichorCNAIn order to classify samples, logistic regression was applied to the accessibility values of all 504 TFs in 971 samples (373 control samples and 598 samples of cancer patients). To this end, the LogisticRegressionCV from the scikit-learn package was applied using fivefold cross-validation and balanced class weights to correct for the slightly unbalanced sample set. In 100 permutations, samples were split into training and test sets. Training samples were used to establish the model whereas (held-out) test sets were used to estimate the actual performance of the model. Mean performance metrics, as well as ROC curves (based on the prediction probabilities of the LogisticRegression) of the models were calculated from the 100 permutations.To address the question whether TF accessibility remains stable over time, we also analyzed two samples each from patients P40, P147, and C2. However, with our very stringent criteria, we did not observe significant differences in these plasma sample pairs  had evolved, which was reflected in a high-level amplification of the AR gene45. However, if AR expanded its repertoire of transcriptional targets, it did not become apparent  with a range from LLD of 0.050 to ULD of 370 ng/ml and a reference range from 15.7–17 ng/ml. Inter- and intraassay variances were described with 3.9 and 2.5%, respectively.PSA was determined by the commercial ARCHITECT Total PSA Assay (Abbot G47859R06), according to the manufacturer’s instructions.Further information on research design is available in the Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Reporting Summary


RNA degradation affects RNA-seq quality when profiling transcriptional activities in cells. Here, we show that transcript degradation is both gene- and sample-specific and is a common and significant factor that may bias the results in RNA-seq analysis. Most existing global normalization approaches are ineffective to correct for degradation bias. We propose a novel pipeline named DegNorm to adjust the read counts for transcript degradation heterogeneity on a gene-by-gene basis while simultaneously controlling for the sequencing depth. The robust and effective performance of this method is demonstrated in an extensive set of simulated and real RNA-seq data.The online version of this article (10.1186/s13059-019-1682-7) contains supplementary material, which is available to authorized users. M values [RNA-seq is currently the most prevailing method for profiling transcriptional activities using high-throughput sequencing technology . The seqM values , and uppM values . The secM values , 7, and M values . SystemaM values , 10.Another type of bias arises from cDNA fragmentation and mRNA degradation. The RNA-seq assay requires fragmenting the cDNA (reversely transcribed from mRNA) or mRNA for high-throughput sequencing. Ideally, for a complete, non-degraded transcript, if the fragmentation is completely random, we expect to see reads uniformly distributed along the transcript. Nevertheless, the fragmentation by random priming is not truly random due to primer specificity –13. ConsWhile the major impact of RNA degradation on gene expression analysis has been well recognized , 18, metTo reduce the degradation effect, Finotello et al. proposed to quantify the exon-level expression by the maximum of its per-base counts instead of the raw read counts . If a giAlternative splicing is frequently observed in higher organisms, and it further complicates the gene expression estimation in RNA-seq . In the In this paper, we propose a novel data-driven method to quantify the transcript degradation in a generalized sense for each gene within each sample. Using the estimated degradation index scores, we build a normalization pipeline named DegNorm to correct for degradation bias on a gene-by-gene basis while simultaneously controlling the sequencing depth. The performance of the proposed pipeline is investigated using simulated data, and an extensive set of real data that came from both cell line and clinical samples sequenced in poly(A)+ or Ribo-Zero protocol.We consider six RNA-seq data sets that were generated from cell lines or clinical samples under either the mainstream poly(A) enrichment (mRNA-seq) or ribosomal RNA depletion protocol (Ribo-Zero-seq). The first one was from a brain glioblastoma (GBM) cell line study of a human for the impact of RNA degradation on gene expression analysis . TechnicThe second set contained 32 single-end mRNA-seq samples from human peripheral blood mononuclear cells (PBMC) of 4 different subjects: S00, S01, S02, and S03 . The extThe third set was from Sequencing Quality Control (SEQC) Consortium , 28 and The fourth data set contained RNA-seq data from dorsolateral prefrontal cortex (DLPFC) tissue of five brains—three controls and two schizophrenia cases . Each tiThe fifth data set originated from three pairs of matched fresh-frozen (FF) and formalin-fixed paraffin-embedded (FFPE) tissues of three breast tumor patients  with a moderate archival time of about 4–5 years . The FFPThe last data set arose from a clinical study on how AMP kinase (AMPK) promotes glioblastoma bioenergetics and tumor growth . It was We define a total transcript as the concatenation of all annotated exons from the same gene. The read coverage score at a given location within the transcript is defined as the total number of reads (single-end) or DNA fragments (paired-end) that cover this position  [We set out to evaluate how the proposed DegNorm pipeline may improve differential expression analysis by comparing it with other seven normalization methods including UQ , TIN 1313, RUVr,es (TMM) , relatives (TMM) , and totes (TMM) . The RUVWe first examine the five data sets that originated from the samples that had no true biological difference between conditions under test . RNA degradation induces bias and thus may cause extra variance. Severe degradation may even result in a difference of transcript abundance for some genes when they are prepared for RNA-seq. Thus, we investigate how different methods may reduce variance by plotting the coefficient of variation (CV) of normalized read count vs. mean read count in log scale  were input into the edgeR package  for DE ap value than UQ in Ribo-Zero  to correct multiple comparison errors. Thus, we further compared the false-positive rate of different methods by controlling the nominal FDR under the criterion of q value ≤ 0.05 using q-value package [p value ECDF curves in Fig. The  package –33. For The SEQC-AB data presents an atypical example containing an unusually large number of differentially expressed genes , 34. DegThe RUVr and TIN methods both showed pronouncedly lower power than DegNorm and UQ, but due to different reasons Fig. d–f. The q value, which quantifies the average FDR for all genes with smaller p value than the given one, lfdr is more appropriate to quantify the false discovery rate associated with any individual p value. The MMP14 gene in the GBM-R10vsR4 comparison displayed a clear degradation in the 5′ end in R4 samples . DegNorm compensated for the degraded portion of R4 samples and returned negative test result   from q-value package –33 to qules Fig. a and tes02) Fig. c while np value than UQ and RUVr methods  vs. 4 treatment (B) samples in 4 different degradation settings. Each sample of 20,000 genes was simulated with a random sequencing depth of 40–60 million reads, 5% of which were chosen to be upregulated and another 5% for downregulated in expression. In the first setting, all genes had no degradation whereas in the rest 3 settings, 80% of the genes were randomly selected for degradation. In the second setting, for a gene selected for degradation, either 3, 4, or 5 samples out of the 8 were randomly chosen for degradation, whereas in the third, either all 4 control samples or 4 treatment samples were randomly chosen for degradation. In both second and third settings, the degradation extent for each degraded gene was random but following the same distribution. In the fourth setting, for each gene selected to degrade, 2 control samples were randomly selected for degradation with the same expected severity, while all treatment samples had a sample-specific systematic difference in expected severity. The simulation details are described in the “p value ECDF and ROC curves of DegNorm almost perfectly overlap with the latent and UQ curves , and a nearly symmetric distribution of data points around the diagonal line in the scatter plot of normalized vs. latent read counts . One complexity is that the true biological difference is often confounded with degradation  and other unwanted variation. The factor analysis cannot well separate the unwanted from the wanted variation, and it may even remove the true biological difference of interest. This confounding issue was illustrated in the SEQC-AB data by the high within-condition and low between-condition correlation of DI scores  on a 22-core node on the Linux cluster. We have implemented an MPI release through parallel computing that can reduce the time by a factor of n where n is the number of nodes used.There are a few limitations of the DegNorm method. First, like any other normalization method, DegNorm is a post hoc approach that is designed to alleviate the issues due to degradation heterogeneity between samples/genes and thus improve the accuracy of DE analysis. It cannot completely remove the bias for every single gene. In particular, cautions must be taken when testing DE for samples that have a dramatic difference in degradation , as it may change the true abundance level of transcripts of interest and lead to the excess of false positive or false negatives. High-quality RNA samples are always desirable in RNA-seq. Furthermore, cross-platform DE analysis is not recommendable. Second, the core component of DegNorm is a matrix factorization over-approximation algorithm aiming to correct for the degradation bias that commonly exists in RNA samples, even for the high-quality SEQC data. DegNorm tends to result in a pronounced over-estimation bias in DI scores for genes with low read counts regardless of whether degradation is present (simulations II–IV) or absent (simulation I). We showed in simulation I that this bias is not a big concern in DE analysis as it tends to be homogeneous across all samples for all non-degraded genes. Third, DegNorm has only been tested in this paper on the bulk RNA-seq data generated from a cell line or clinical samples (FF or FFPE) under mRNA-seq or Ribo-Zero-seq protocol. The effectiveness of DegNorm for RNA-seq data from 3′ end sequencing (3seq) or other variants needs to be further investigated in the future. Lastly, DegNorm is computing intensive due to parsing read alignment results, calculation of read coverage curves, and repeated non-negative matrix factorization of large matrices. We have implemented DegNorm in a Python package available at In summary, we conclude DegNorm provides a pipeline for informative quantification of gene-/sample-specific transcript degradation pattern and for effective correction of degradation bias in RNA-seq. We intend for DegNorm to serve as a general normalization method to improve the accuracy in the gene expression differentiation analysis.p samples and n genes, Xij is the read count for gene i in sample j. For simplicity of notation, we first illustrate the proposed method by focusing on one gene. Let fij(x), x = 1, … , Li; i = 1, … , n; j = 1, … , p be the read coverage score for transcript i of length Li from sample j. When different isoforms are present, the Li positions represent the assembly of all expressed exons in the sequential order. We assume there is an envelope function ei(x) that defines the ideal shape of read coverage curve for gene i if no degradation exists. The actual ideal coverage curve for the given gene in the jth sample is kijei(x), where kij denotes the confounded effect of sequencing depth and relative abundance of gene i in sample j.Suppose we have fij(x) to deviate downward from kijei(x) in degraded region(s). Clearly, sampling error can cause random fluctuation of fij(x) from the ideal curve kijei(x)  ≥ fij(x) for all j and x. The difference between kijei(x) and fij(x) provides an estimate of degraded portion of read count. We propose a method that allows to estimate kij and ei(x) to quantify the degradation extent of each gene within each sample while simultaneously controlling the sequencing depth.Degradation causes (x) Fig. a, b. We fij = (fij(1),  … , fij(Li))T, j = 1, … , p and Fi = T. Let Ki = T, Ei = (ei(1),  … , ei(Li))T. We propose to estimate Ki and Ei by minimizing the following quadratic loss function subject to some constraint:Let ∙‖2 stands for the element-wise quadratic norm , and ≤ and ≥ for element-wise logical comparison. We call this a rank-one non-negative matrix factorization over-approximation (NMF-OA) problem as Ki and Ei both have rank 1 and KiEiT ≥ Fi. The proposed iterative algorithm is described in details in the Additional file We can configure this problem into a non-negative matrix factorization problem , 36 as fmentclass2pt{minimQ, an fij of larger magnitude tends to have more influence on the estimation of envelope function. A dominant scale in fij may force the algorithm to fit an envelope function that resembles fij to minimize the loss. Thus, a good scale normalizing factor for sequencing depth is important to yield a good estimate of the envelope function ei(x). With gene-specific and sample-specific degradation, the total number of reads may not provide a reliable measure of sequencing depth.The NMF-OA optimization algorithm provides an approximate solution to this problem. However, in the RNA-seq data, the performance of the solution can be affected by two confounding factors, the degradation extent and the sequencing depth. In our quadratic objective function Fi that is appropriately normalized for sequencing depth, the scale factor Ki should reflect the relative abundance of the gene in the non-degraded region of each sample (to be referred to as the baseline region below)  as follows:Ki,Ei) for i = 1, … , n, define the degradation index (DI) score as:Sequencing depth adjustment: given the current estimate of  Fig. a. The noρij stands for the fraction of the total area under the curve kijei(x) but above fij(x)  Fig. e. The DINext, we calculate the sequencing depth scaling factor using the degradation-corrected total number of read count:sj based on the degradation-adjusted read count 2.sj from step 1, adjust the coverage curves as follows:Degradation estimation: given the estimated sequencing depth In this paper, the results presented were all based on this scale normalization. Alternatively, we can use other normalization methods like TMM or UQ to calculate the normalizing constant Fi = T. Run NMF-OA for each gene on updated coverage curves Fi and obtain the estimate of .Let Ri = KiEiT − Fi . We identify a subset of bins on which Fi preserves the most similar shape as the envelope function Ei across all samples by progressively dropping bins that have the largest sum of squares of normalized residuals (Ri/Fi) and repeatedly applying NMF-OA to the remaining bins. This step stops if the maximum DI score obtained from the remaining bins is ≤ 0.1 or if 80% bins have been dropped  for up- or downregulated genes, respectively, where γ was simulated randomly from an exponential distribution with mean = 1. In the second step, we simulated the degradation given the latent read count (simulations II–IV). For a given gene, we first chose a Gaussian mixture distribution that covers the entire range of total transcript to model the read start position distribution. For simplicity, we only consider 5′ end degradation. Each read from a gene that was pre-selected for degradation degraded (or was disregarded) with the probability that depended on the start position of the read, defined by a cumulative distribution function (CDF) of a lognormal distribution. The degradation pattern and extent can be tuned by varying the parameters in the lognormal distribution  in both control and treatment samples were randomly simulated from the negative binomial distribution with the same mean and same dispersion parameters that were randomly chosen from the fitted values of SEQC-AB data. For a gene with DE, the latent counts for the control samples were first simulated as above, and those for the treatment samples were simulated from the negative binomial with mean parameter increased/decreased by a factor of 1.5 + .5 + γ foAdditional file 1:Supplementary methods. (DOCX 109 kb)Additional file 2:Supplementary figures and figure legends. (PDF 69270 kb)Additional file 3:Table S1. This table summarizes the number of false positives claimed in differential expression analysis by edgeR from different normalization methods at q value threshold = 0.05 for data sets including SEQC-AA, GBM RIN10 vs. RIN4, GBM RIN6 vs. RIN4, PBMC-S01, DLPFC Br1729 Ribo-zero, DLPFC Br1729 poly(A)+, and breast tumor FF vs. (XLSX 9 kb)Additional file 4:Table S2. This table compares the p values and q values of differential expression analysis by edgeR from different normalization methods for 12 genes which were PCR-verified positives in the AMPK data. (XLSX 12 kb)Additional file 5:Table S3. This table compares the sensitivity (true-positive rate) achieved by different normalization methods in the differential expression analysis at FPR  threshold = 0.05 for simulations I–IV. (XLSX 8 kb)Additional file 6:Review history. (DOCX 53 kb)


How gene expression correlates with schizophrenia across individuals is beginning to be examined through analyses of RNA-seq from postmortem brains of individuals with disease and control brains. Here we focus on variation in allele-specific expression, following up on the CommonMind Consortium (CMC) RNA-seq experiments of nearly 600 human dorsolateral prefrontal cortex (DLPFC) samples. Analyzing the extent of allelic expression bias—a hallmark of imprinting—we find that the number of imprinted human genes is consistent with lower estimates , and thus contradicts much higher estimates. Moreover, the handful of putatively imprinted genes are all in close genomic proximity to known imprinted genes. Joint analysis of the imprinted genes across hundreds of individuals allowed us to establish how allelic bias depends on various factors. We find that age and genetic ancestry have gene-specific, differential effect on allelic bias. In contrast, allelic bias appears to be independent of schizophrenia. This study analyzes allelic expression bias in post-mortem brains of healthy individuals and those diagnosed with schizophrenia or bipolar disorder. The study shows that the number of imprinted genes is consistent with low estimates, and that allelic bias is independent of psychiatric disease status. As it is known that the extent of imprinting of individual genes varies over different tissues, we chose to analyze the dorsolateral prefrontal cortex (DLPFC) region, which controls complex cognitive and executive functions, and is known to display functional abnormalities in SCZ.The observation3, but followup studies using mouse or human subjects arrived at estimates that are lower with an order of magnitude9.A related question is the number of imprinted genes in the human brain. Some 1300 genes were estimated to be imprinted in the mouse brain8 (http://www.synapse.org/CMC) and analyzed allele-specific expression with the idea of (i) identifying imprinted genes in the adult human brain and (ii) explaining the variability in allelic bias across 579 individuals in terms of their psychiatric diagnosis, age at death, etc. This was facilitated by the balanced case–control groups  and the large age variability in the cohort.We obtained DLPFC RNA-seq data from the CMCi and gene g we quantified allelic bias based on RNA-seq reads using a statistic called read count ratio Sig .For each individual Sig Fig. , MethodsSg across all individuals given each gene g. The observed wide Sg distributions suggest large across-individuals variation of allelic bias for all genes, even if a substantial component of the Sg variation originates from technical sources. Still, as expected, for many genes known to be imprinted in mice or in other human tissues  the distribution of Sg was shifted to the right signaling strong allelic bias . Figure g as the fraction of individuals i for whom Sg >  0.9. We ranked all 5307 genes according to their score . We combined this prior expectation with two tests based on our data to distinguish imprinting from alternative causes of high read count ratio such as mapping bias and cis-eQTL effects9 . The results of both tests . Such joint models can capture much of the complex pattern of dependencies in genomic data, including those we observed within and between technical and biological explanatory variables  we could formally reject the hypotheses that read count ratio depends on diagnosis as either main effect or interaction see term 1|Dx) and (1|Dx):Gene in Table |Dx and , (Ancestry.3|Gene), and (1|Gender:Gene)  and if we make the same distributional assumption as above, namely that pig→1. We took advantage of this theoretical result in that we subjected only those genes to statistical inference, whose read count ratio was found to be high and, therefore, whose pig is expected to be high as well.We quantified allelic bias based on RNA-seq reads using a statistic called read count ratio Sig for the combination of two hypothetical genes, g1,g2, and two individuals, i1,i2. It also shows an example for the less likely event that the lower rather than the higher read count corresponds to the SNP variant tagging the higher expressed allele .Figure Before we carried out our read count ratio-based analyses, however, we cleaned our RNA-seq data by quality-filtering and by improving the accuracy of SNP calling with the use of DNA SNP array data and imputation. In the following subsections of “Methods” we describe the data, these procedures, as well as our regression models in detail.Human RNA samples were collected from the DLPFC of the CommonMind consortium from a total of 579 individuals after quality control. Subjects included 267 control individuals, as well as 258 with SCZ, and 54 with affective spectrum disorder (AFF). RNA-seq library preparation uses Ribo-Zero  to prepare the RNA, followed by Illumina paired end library generation. RNA-seq was performed on Illumina HiSeq 2000.We mapped 100 bp, paired-end RNA-seq reads (≈50 million reads per sample) using Tophat to Ensembl gene transcripts of the human genome  with default parameters and 6 mismatches allowed per pair . We required both reads in a pair to be successfully mapped, and we removed reads that mapped to >1 genomic locus. Then, we removed PCR replicates using the Samtools rmdup utility; around one-third of the reads mapped . We used Cufflinks to determine gene expression of Ensembl genes, using default parameters. Using the BCFtools utility of Samtools, we called SNPs . Then, we invoked a quality filter requiring a Phred score >20 .We annotated known SNPs using dbSNP . Considering all 579 samples, we find 936,193 SNPs in total, 563,427 (60%) of which are novel. Further filtering of this SNP list removed the novel SNPs and removed SNPs that either did not match the alleles reported in dbSNP or had more than 2 alleles in dbSNP. We also removed SNPs without at least 10 mapped reads in at least one sample. Read depth was measured using the Samtools Pileup utility. After these filters were applied, 364,509 SNPs remained in 22,254 genes. These filters enabled use of data with low coverage. For the 579 samples, there were 203 million reads overlapping one of the 364,509 SNPs defined above. Of those, 158 million (78%) had genotype data available from either SNP array or imputation.DNA samples were genotyped using the Illumina Infinium SNP array. We used PLINK with default parameters to impute genotypes for SNPs not present on the Infinium SNP array using 1000 genomes data. We calibrated the imputation parameters to find a reasonable balance between the number of genes assessable for allelic bias and the number false positive calls, since the latter can arise if a SNP is incorrectly called heterozygous.Lhet of 1, separately from RNA seq data overlapping imputed heterozygous SNPs, where the Lhet score could range from 0 to 1. After testing different thresholds, we selected an Lhet cutoff of 0.95 , and a minimal coverage of 7 reads per SNP. With these parameters, the discordance rate  was 0.71% for array-called SNPs and 3.2% for imputed SNPs.We first examined how many SNPs were heterozygous in DNA calls and had a discordant RNA call  using different imputation parameters. Known imprinted genes were excluded. We examined RNA-seq reads overlapping array-called heterozygous SNPs, which we assigned a heterozygosity score g and individual i jointly. Second, we excluded any individual, for which one or more SNPs supported biallelic expression.The higher rate of discordance for the imputed SNPs is due to imputation error. These were taken into account in two ways. First, we considered all imputed SNPs for a gene i,g) of individual i and genes g for which the total read count Tig < trc, where the read count threshold trc was set to 15. The individual-based filter removes any genes g  if read count data involving g are available for less than tind number of individuals, set to 25. These final filtering procedures decreased the number of genes in the data from 15,584 to n = 5307.Two kind of data filters were applied sequentially: (1) a read count-based and (2) an individual-based. The read count-based filter removes any such pair . Thus for each SNP we assumed that the number of individuals for whom the reference allele is associated to the higher read count is binomially distributed with probability parameter 0.5. Thus, we calculated the fraction of informative SNPs for which the null hypothesis can be rejected at 0.05 significance level and used this information to decide whether the compound null hypothesis for the gene itself can be rejected.For a given gene the compound null hypothesis is that the observed high read count ratio is due only to imprinting. For each informative SNP this hypothesis means that the reference and nonreference allele are associated with equal probability to the higher read countg and individual i is drawn from a binomial distribution with a probability parameter pig ≈ 0.5 suggesting nearly unbiased allelic expression. More specifically, the test was defined by the criteriaig for the expected read count ratio pig was calculated assuming that the higher read count zp is the p quantile of the standard normal distribution.The null hypothesis of this test is that the higher read count We found transformations of the read count ratio data to be useful for fitting our statistical models . We used either (or none) of the following two transformations:Sig and Tig mean read count ratio and total read count for individual i and gene g; log means natural logarithm (base e); c is a pseudo read count set to 1 in order to avoid zero in the parenthesis since the log function is undefined at 0.1. The quasi-log transformation, defined as2. The rank transformationj may equal i in Eq. i among all individuals j and then scales the ranks between 0 and 100.Note that We modeled the dependence of read count ratio of imprinted genes jointly on all biological and technical explanatory variables  for gene-specific coefficients (parameters), which the less powerful fixed models do provide . GLMs in general describe a conditional distribution of a response variable entclass1pt{minimaentclass1pt{minimaη = Xβ + Zb and in fixed GLMS η = Xβ, where X,Z are design matrices containing data on explanatory variables, whereas β and b are fixed and random vectors of regression coefficients that mediate fixed and random effects, respectively .Besides the random effects term Q of read count ratio as this combination was found to provide the best fit . Simply put: errors and random coefficients are all normally distributed.Here we describe the detailed syntax and semantics of the normal linear mixed models combined with a quasi-log transformation To clarify the semantics of Eq. k = 1 can be expanded into First note that the random effect term labeled with y and second as a gene-specific random slope effect, denoted as (Age|Gene). The random effect appears to be supported by Fig. We see that age appears twice: first as a fixed slope effect on Now we are ready to write the toy model as an expanded special case of Eq. k superscripts on the random regression coefficients As in the earlier R formalism the terms of the linear predictor are grouped into fixed and random effects. Within the latter group we have two batches of terms indicated by the kth batch Eq. ith observation; these levels are denoted as genei and Dxi:Genei, respectively. Implicitly however, Eq. n = 30 intercept coefficients Jk coefficients in the kth batch, where Jk is the product of the number of factor levels and one plus the number of random slope variables. This way we can provide the expansion of the general formula Eq. Within the Eq. χ2-test on the degrees of freedom that correspond the evaluated term. If fit improved the term was added otherwise it was omitted. Next, further terms were tested. This iterative procedure lead to the following model.We used a heuristic search strategy in order to restrict the vast model space to a relatively small subset of plausible models. The search was started at a model whose relatively simple linear predictor was composed of terms using our prior results based on fixed effects models. The same results suggested a sequence in which further terms were progressively added to the model to test if they improve fit. Improvement was assessed by ΔAIC and the https://github.com/attilagk/monoallelic-brain-notebook The corresponding lab notebook can be browsed as a website at:https://attilagk.github.io/monoallelic-brain-notebookAll code developed by A. Gulyás-Kovács is available at:Data and analytical results generated through the CommonMind Consortium are available through the CommonMind Consortium Knowledge Portal: 10.7303/syn2759792. Intermediate results leading to the final results published here are available from the authors at request.Supplementary InformationPeer Review File