Identifying cancer biomarkers from transcriptomics data is of importance to cancer research. However, transcriptomics data are often complex and heterogeneous, which complicates the identification of cancer biomarkers in practice. Currently, the heterogeneity still remains a challenge for detecting subtle but consistent changes of gene expression in cancer cells.In this paper, we propose to adaptively capture the heterogeneity of expression across samples in a gene regulation space instead of in a gene expression space. Specifically, we transform gene expression profiles into gene regulation profiles and mathematically formulate gene regulation probabilities (GRPs)-based statistics for characterizing differential expression of genes between tumor and normal tissues. Finally, an unbiased estimator (aGRP) of GRPs is devised that can interrogate and adaptively capture the heterogeneity of gene expression. We also derived an asymptotical significance analysis procedure for the new statistic. Since no parameter needs to be preset, aGRP is easy and friendly to use for researchers without computer programming background. We evaluated the proposed method on both simulated data and real-world data and compared with previous methods. Experimental results demonstrated the superior performance of the proposed method in exploring the heterogeneity of expression for capturing subtle but consistent alterations of gene expression in cancer.Expression heterogeneity largely influences the performance of cancer biomarker identification from transcriptomics data. Models are needed that efficiently deal with the expression heterogeneity. The proposed method can be a standalone tool due to its capacity of adaptively capturing the sample heterogeneity and the simplicity in use.https://github.com/hqwang126/aGRP.The source code of aGRP can be downloaded from The online version of this article (10.1186/s12859-018-2437-2) contains supplementary material, which is available to authorized users. Cancer is generally thought of to be driven by a series of genetic mutations of gene markers induced by selection pressures of carcinogenesis inside or outside cells , 2. Sucht-statistic and its various variants  reflects how likely the gene is differently regulated between the two conditions: The larger the absolute value of T the higher the likelihood of differential expression, and positive Ts mean that an up-regulation event possibly occur in cancer while negative Ts mean that a down-regulation event possibly occur in cancer. Biologically, genes with a positive T would be onco-gene-like while those with a negative T would be tumor suppressor-like. Note that T reflects an absolute quantity of regulation probability and can be completely rewritten as T = (P(U)-P(N))-(P(D)-P(N)) if considering the probability of non-significant regulation event (P(N)). We can estimate the two probabilities, P(U) and P(D), in a regulatory space in what follows.The statistic n tumor samples and m normal samples. Let ai1 denote the expression level of gene g in the ith tumor, i = 1, 2, …, n, and aj2 the expression level in the jth normal sample, the expression profile of gene g can be denoted as y = . We map the expression profile y into a tri-state regulation space as follows:For simplicity, consider a regulation space consisting of three statuses, i.e., up-regulated (1), down-regulated (− 1), and non-regulated (0). Assume ith tumor sample with expression level ai1, the regulation status can be calculated asai1 in the total m normal samples, I(·) is an indicator whose value is 1 if the condition is true and 0 else, and the parameter τ, 0.5 ≤ τ ≤ 1, can be referred to as regulation confidence cutoff. Different values of τ can be preset to capture the varying heterogeneity of gene expression in practice.For the ith normal sample with expression level ai2, the regulation status can be calculated as.ai2 in the total n tumor samples. As a result, a regulation profile of gene g across all the samples can be represented asSimilarly, for the P(U) and P(D), using the total probability theorem. Take P(U) as example. Let Y1 and Y2 represent the sample spaces of tumor and normal classes respectively, we haveP(Y1) and P(Y2) are the prior probabilities of tumor and normal classes respectively, and the two conditional probabilities, P(U|Y1) and P(U|Y2), can be estimated based on the regulation profile in Eq. and P(D), denoted by S, depends on the hard regulation confidence cutoff τ, i.e., S = 1 at τ = 0.5 but S < 1 at 0.5 < τ ≤ 1, and drops as τ increases.Then, we haveT in a regulation probability space as follows. For calculating P(U), by removing the hard cutoff, we rewrite the conditional probabilities in Eq.(The simple GRP estimator in Eq. uses a hes in Eq. as10\docP(N) ≡ 0. As a result, an unbiased estimator of the occurring probability of the up-regulation event can be obtained, i.e.,P(U) as expected. Finally, according to Eq.(T can be obtained:P(U) + P(D) ≡ 1. The statistic in Eq.(aGRP), which explores more details on regulation information and can capture the intra-class or inter-class heterogeneity of expression in an adaptive way.Compared with Eq., Eq.10)10) skipsaGRP. Supposing that the two groups of samples come from two normal distributions, i.e.,P(U) and P(D), and the aGRP statistic all follow a normal distribution  and the same deviation (0.1). Considering the influence of sample size, we varied the sample size of each class n = 6, 10, 20, 50, and in each scenario, twenty data sets were randomly generated and used for avoiding randomness on algorithm evaluation.We first evaluated the proposed method on simple simulation data. The simulation data contain two groups of genes:Group I consists of aGRP models on the simulation data. To investigate the property of P(U) and P(D), we plotted P(U) against P(D) for each gene on the simulation data. Results  and P(D): P(U) + P(D) =1 at τ = 0.5 but P(U) + P(D) < 1 at 1 ≥ τ > 0.5, and drops as τ increases, and in contrast aGRP favored a line P(U) + P(D) =1 as expected, suggesting the more favorable performance of aGRP. To examine the asymptotical significance analysis procedure of aGRP, we then compared the resulting p-values with those empirically estimated by permutation tests with randomly shuffled sample labels. Note that we considered B = 10, 50, 100, 1000 permutations of sample labels in the permutation tests respectively to gradually approximate the null distribution. It was revealed that the permutated p-values become closer to the asymptotic estimator as B increases .We then investigated the type-I errors and power of the aGRP on complex data, we next simulated gene expression data by revising the procedure in the reference  as follows: 1) randomly forming gene clumps of size m∈{1, 2, 3, ⋯, 100} and clump-wise correlation ρ from U. 2) generating noise vectors e.j of dimension m × 1 from NIm + ρ1m1’m) for sample j, j = 1,2, …,2n, and obtaining the background values of the m genes in the clump x.j=μ + diag(ω)e.j, where μ and ω are an m × 1 vector of elements μgωg = eβ0/2μgβ1/2 respectively. The correlation background increases the variability of data and makes the expression patterns heterogeneous. In the experiment, we set the parameters β0 = − 5, β1 = 2, and rendered the true expression ratios of DEGs to vary among δg~U. To investigate the effect of sample size, we considered the four sample sizes n = 6, 10, 20 and 50, and as a result, four simulation data scenarios were obtained. In each scenario, 20 random data sets were generated and their average results were used for algorithm evaluation to overcome randomness.To evaluate the performance of eference . The simaGRP at an ad hoc p-value cutoff of 0.05 in different scenarios of the simulated gene expression data. For comparison, we also applied previous methods, GRP models, Limma  into five intervals, , η = 0.5,0.6,0.7,0.8,0.9, and determined the genes whose absolute aGRP/GRP fall within each interval. Figure aGRP and GRPs with τ = 0.5, 0.7, 0.9. From this figure, we can clearly see that both aGRP and GRP had a tendency of the proportion of common genes gradually increasing with η, showing the reasonability of regulation-based statistics. Compared with GRP, aGRP led to the higher proportions, irrespective of interval used, suggesting the better consistency of results by aGRP. We further compared the proportions of genes with a same regulation direction in the common genes across the three data sets between aGRP and GRPs in each interval, as shown in Fig. aGRP achieved the proportions larger than 94.44% (at η = 0.01) on all the intervals, confirming the consistency of the results by aGRP. Although the GRP model with τ= 0.9 had all the proportions of one, the proportions of common genes obtained by it were far lower than those by aGRP in all the intervals  is the third leading cause of cancer-related deaths. We downloaded a HCC RNA-seq data set from the GEO database: Yang’s data (GSE77509) , which waGRP and DEGSeq on the RNA-seq data. As a result, the Spearman correlation of the aGRP statistic and log2 fold change from DEGSeq and the Spearman correlation of p-values derived from aGRP and p-values derived using DESeq are 0.86 and 0.617, respectively. Both of the correlations are not equal to zero at a significance level of < 2.2e-16 (t-test), respectively. Then, we compared the numbers of DEGs called by aGRP and the previous methods at an ad hoc BH-adjusted p-value cutoff of 0.01, as shown in Fig. aGRP still called more DEGs than those by previous methods, GRP (0.9), Rankprod, DEGSeq, DESeq2 and edgeR, on the RNA-Seq data, consistent with the results on the simulation gene expression and the three lung cancer microarray data, confirming the especial power of aGRP in identifying subtle but consistent expression changes. Among the 7234 DEGs identified by aGRP, there are totally 3548  is a DNA repair gene playing important role in Nucleotide Excision Repair (NER) pathway, whose single nucleotide polymorphism, rs3740526 has been reported to significantly distinguish adenocarcinoma with squamous cell carcinoma and whose expression levels are clinically related with ACT benefit of resected non-small cell lung cancer patients [aGRP = 0.66) has been previously evidenced to be associated with tumorigeneses of various types of cancer, e.g., leukemia and colorectal cancer [aGRP = 0.645) has been extensively observed to be highly expressed in lung adenocarcinoma and promote DNA replication in lung cancer development [aGRP DEGs, hormone receptor PGRMC2 (aGRP = − 0.635) was previously reported to be a tumor suppressor and an inhibitor of migration of cancer cell [We first examined the similarity between the statistics of patients , 44. TRIl cancer , 46. Espl cancer  computatelopment . In addicer cell . Recentlcer cell  also obsCurrently, the expression heterogeneity remains challenging in transcriptomics data analysis. Ignoring the heterogeneity often leads to inconsistent and non-reproducible identification of cancer biomarkers across studies. To our knowledge, there do not exist computational models that are dedicated to address the problem of expression heterogeneity. Compared with previous methods, aGRP operates in a regulation space but not in the expression space. This makes it possible to interrogate and adaptively capture the inter- or intra-class heterogeneity of expression for biologically meaningful identification of cancer biomarkers, as demonstrated in experiments on two types of simulation data Fig. . The advaGRP, for cancer biomarker identification. It aims to deal with the problem of expression heterogeneity that complicates the identification of cancer biomarkers. Specifically, two regulation events were defined between tumor and normal tissues, whose occurring probabilities were estimated in an unbiased way, and were used to transform the expression profiles of samples to a regulation profile. With the regulation profiles, a new GRP-based statistic were finally formulated for characterizing different expression of genes along with an asymptotic estimator of significance. aGRP automatically interrogates and adaptively captures the heterogeneity of gene expression so that subtle but consistent expression changes can be detected in a flexible and robust way. aGRP is also simple and easy to use in practice. Comparison experiments with current state-of-the-art methods on two simulation data sets and three real-world lung cancer expression data sets and one RNA-seq HCC data set demonstrated the effectiveness and efficiency of aGRP in identifying cancer biomarkers from transcriptomics data. Future work will be extending the proposed method to assessment of the heterogeneity of gene sets and meta-analysis of multiple transcriptomics data sets for cancer biomarker identification.In this paper, we have presented a novel computational method, Additional file 1:Figures S1-S2). (DOCX 880 kb)A proof of the significance estimator of aGRP and three supplemental figures Additional file 3:Table S2. List of 869 DEGs identified on the three lung cancer data sets with positive aGRP statistics. (XLS 263 kb)Additional file 4:Table S3. List of 38 KEGG pathways that are reported by DAVID to significantly enriched in the DEG list. (XLS 45 kb)Additional file 5:Table S4. List of 3548 DEGs identified on the HCC RNA-Seq data set with positive aGRP statistics. (CSV 194 kb)Additional file 6:Table S5. List of 3686 DEGs identified on the HCC RNA-Seq data set with negative aGRP statistics. (CSV 205 kb)


The rapid expansion of transcriptomics and affordability of next-generation sequencing (NGS) technologies generate rocketing amounts of gene expression data across biology and medicine, including cancer research. Concomitantly, many bioinformatics tools were developed to streamline gene expression and quantification. We tested the concordance of NGS RNA sequencing (RNA-seq) analysis outcomes between two predominant programs for read alignment, HISAT2, and STAR, and two most popular programs for quantifying gene expression in NGS experiments, edgeR and DESeq2, using RNA-seq data from breast cancer progression series, which include histologically confirmed normal, early neoplasia, ductal carcinoma in situ and infiltrating ductal carcinoma samples microdissected from formalin fixed, paraffin embedded (FFPE) breast tissue blocks. We identified significant differences in aligners’ performance: HISAT2 was prone to misalign reads to retrogene genomic loci, STAR generated more precise alignments, especially for early neoplasia samples. edgeR and DESeq2 produced similar lists of differentially expressed genes, with edgeR producing more conservative, though shorter, lists of genes. Gene Ontology (GO) enrichment analysis revealed no skewness in significant GO terms identified among differentially expressed genes by edgeR versus DESeq2. As transcriptomics of FFPE samples becomes a vanguard of precision medicine, choice of bioinformatics tools becomes critical for clinical research. Our results indicate that STAR and edgeR are well-suited tools for differential gene expression analysis from FFPE samples. After next-generation sequencing (NGS) technology was introduced in 2005, development of many high-throughput bioinformatics tools ensued, such as Bowtie, Tophat, Cufflinks, and CuffDiff “Tuxedo Suite” . One of For an ideal RNA-seq experiment, researchers and clinicians would acquire freshly frozen tissue samples with minimal inclusion of non-target tissues, such as blood and fat, the most common contaminants. In reality, majority of clinical research relies on either archived tissue specimens, mainly as formalin-fixed, paraffin-embedded (FFPE) biopsies, which have increased RNA degradation and decreased poly(A) binding affinity ,9,10, orWe focused on two of the most popular sequence aligners, HISAT2 , and STARNA-seq data used in this study are publicly available (BioProject ID: PRJNA205694 ). The daWe used two programs, STAR and HISAT2, which utilize different strategies to align the RNA-seq reads to human genome assembly. To improve alignment accuracy, both programs use a dataset of known splice sites to correctly identify potential spliced sequencing reads among RNA-seq data. This dataset, in “gene transfer format” (gtf), was obtained from ENSEMBL . Reads were aligned to the human reference genome assembly (hg19).–seedSearchStartLmax 50 –seedSearchStartLmaxOverLread 1.0 –seedSearchLmax 0 –seedMultimapNmax 10000 –seedPerReadMax 1000 –seedPerWindowMax 50 –seedNonoLociPerWindow 10 –alignIntronMin 21 –alignIntronMax 0 –alignMatesGapMax 0 –alignSJoverhangMin 5 –alignSJDBoverhangmin 3 –alignSpliceMateMapLmin 0–alignSplicedMateMapeLminOverLmate 0.66 –alignWindowsPerReadNmax 10000 –alignTranscriptsPerWindowNmax 100 –alignTranscriptsPerReadNmax 10000 –alignEndsType LocalSTAR’s algorithm  uses a t–mp MX=6, MN=2 –sp MX=2,MN=1 –np 1 –rdg 5,3 –rfg 5,3 –score-min L,0,-0.2 –pen-cansplice 0 –pen-noncansplice 12 –pen-canintronlen G,-8,1 –pen-noncanintronlen G,-8,1 –min-intronlen 20–max-intronlen 500000HISAT2 uses the Bowtie2  algorith–t ‘exon’ –g ‘gene_id’ –M –fraction –Q 12 –minOverlap 30.The simplest method for estimating transcript expression is to count the raw reads for each annotated gene locus in the genome assembly. This approach uses a gtf file containing genomic coordinates, such as gene nomenclature, positions in the chromosome for each exon, transcription start site, transcription termination site, etc. We used FeatureCounts , to extriCPM is a CPM value of a gene in a biological replicate; iR is the number of reads mapping to all exons of this gene in this biological replicate; RaT is the total number of reads aligned (anywhere in the genome) from this biological replicate . This procedure also transforms data from counts to a continuous scale.To account for the depth of sequencing impacts, which affect read numbers of individual transcripts, we use normalized expression data, specifically counts per million (CPM) , to perfFor quality control, we used ClustVis , a statiBoth tools are R packages and require raw read counts in a data matrix, which are normalized to account for differences in sequencing depth, and low count variability. Both tools assume RNA-seq data display overdispersion with variance greater than expected for random sampling. Both programs also assume RNA-seq data conform to a negative binomial distribution and employ Bayesian methods to fit raw gene expression counts into this distribution. To display differential expression outputs uniformly, we used the R software package Visualization of Differential Gene Expression using R (ViDGER) .2-fold change (LFC) estimates.This program, DESeq2, was used to detect differential expression in RNA-seq data. DESeq2 normalizes the counts of each gene employing a generalized linear model . AfterwaThe other popular program to detect differential expression, edgeR, uses a default method of normalization called trimmed mean of M-values, (TMM), which is obtained with the function: calcNormFactors. This method of normalization estimates the ratio of RNA production through a weighted trimmed mean of the log expression ratios. There are alternative normalization methods available in edgeR to account for data that fail to conform to a negative binomial distribution, which is assumed with TMM. To control for false discovery rate (FDR) we applied the estimateDisp function.VLAD , accessiRNA-seq data were analyzed using two different read aligners, STAR and HISAT2, to compare potential impacts of read mapping to the genome assembly upon the ultimate outputs of differential gene expression. Briefly, RNA-seq output reads were aligned to the genome assembly (hg19) by either HISAT2 or STAR, reads mapped to genes were counted for each individual gene to yield raw counts, subsequently count data were normalized, assessed for quality control using Principal Component Analysis using ClustVis, and sample outliers removed before performing differential gene expression analysis by two different programs, edgeR and DESeq2, to identify significant gene expression changes across breast cancer stages .To eliminate sample outlier biases, we performed Principal Component Analysis (PCA) of gene expression counts for each sample by each stage for both aligners. Gene expression counts were collected using featureCounts and normalized to the total number of aligned reads for each sample, and PCA completed on these data using ClustVis large edition . For allAll reads for all samples were aligned to the human genome assembly (hg19). Overall, STAR significantly outperformed HISAT2 in aligning the FASTQ reads to the genome . The genTo determine how concordant the alignment tools were in mapping the reads to the genome, we compared the highest expressed genes that correspond to 50% of all reads mapped to exons. In the normal samples 50% of the mapped reads came from 330 and 305 genes for STAR and HISAT2 respectively and they shared 263 of those genes . In atypProcessed pseudogenes, sometimes referred to as non-functional retrogenes, are intronless gene copies produced by reverse transcription of an original “parent” gene mRNA and insertion of the resulting cDNA copy elsewhere in the genome. Processed pseudogenes are non-functional and comprise the bulk of the known loci in the broad category of “pseudogenes”, i.e., genomic loci harboring similarity to a protein-coding gene but not having any recognized biological function. The sequence similarity among processed pseudogenes and their parent genes poses a problem for aligners, whose algorithms have to decide when assigning a read to a particular locus in the genome. To determine the underlying factors causing the different alignments, we analyzed the numbers of reads mapped to pseudogenes by HISAT2 and STAR. Between the two aligners tested, HISAT2 consistently had significantly higher amounts of reads aligned to pseudogenes when compared to STAR A. FurtheThe differential expression comparison on data from different alignment tools was done to further explore the consequences of previously described alignment tool biases, as well as to compare the two popular tools used for the purpose of identification and quantification of gene expression differences between conditions, edgeR and DESeq2.2 fold change LFC ≥1 or LFC ≤−1   for genes found significantly downregulated in pairwise comparisons of cancer stages  to normal samples by each program , using gene expression data only from STAR alignments because of the global misalignment to pseudogenes by HISAT2. The pathways represented in the GO category Molecular Function, with data from expression significantly decreased in DCIS or IDC compared to normal samples, indicate phosphatidylinositol kinase pathways A, which BRCA1 and BRCA2, which is inadequate to achieve the full promise of personalized medicine. A promising alternative to this genetic approach to personalized medicine is transcriptome-based identification of altered pathways of the diseased tissue to target its underlying disrupted cellular and molecular machinery. Identification of affected pathways using bioinformatics pipelines for RNA-seq data empowers clinicians to make a focused and informed decision on specific altered genes and pathways as potential therapeutic targets in precision medicine for individual patients. The goal of our studies was to unveil potential hidden biases arising from different bioinformatics analysis pipelines, and to find practical solutions to circumvent and mitigate these biases that impact the downstream analyses in transcriptomics experiments. Our results indicate that STAR and edgeR are robust and well-suited bioinformatics tools for transcriptomics pipelines of the most commonly used clinical specimens, biopsies from FFPE tissues, to precisely and accurately align and detect differential gene expression.Personalized medicine is a data science approach that promises focused treatments based on an individual’s sequenced genome to precisely target pathways underlying disease or its symptoms. To date only a few genotypes are robustly identified in breast cancer , such asAfter initial quality control checks on the raw output from the sequencer, alignment is the first step in RNA-seq analysis and all subsequent analysis relies profoundly upon this initial step . TypicalThe next step in bioinformatics pipelines, quantification of gene expression, is commonly performed with DESeq2 or edgeR programs ,15. A coIn our study, STAR had the highest average rates of mapped reads for each stage, whereas HISAT2 aligned fewer reads and importantly, had increased rates of alignment to pseudogenes instead of genes, which clearly compromised alignment fidelity, leading to skewed gene expression counts and likely erroneous outputs with either edgeR or DESseq2 programs, albeit the latter produced more expanded lists of differentially expressed genes. For these FFPE specimens, STAR alignment yielded more precise and accurate results with the fewest misalignments to pseudogenes compared to HISAT2. We speculate the preferential alignment to pseudogene regions of the genome is caused by the innate bias from 3′-end mRNA enriched libraries derived from FFPE samples contain a large proportion of reads corresponding to 3′ ends of mRNAs, which span into the poly(A) tails. Consequently, these reads better match to processed pseudogenes, which contain poly(A) stretches absent in genes . Indeed,Our results clearly demonstrate alignment impacts and play a large role in bioinformatics analysis outcomes, especially to detect and identify differential gene expression. Our data, together with other comparative tests of different programs ,35, indiA recent study  comparedOur study clearly demonstrates the need for heedful intent and meticulous review executing a bioinformatics pipeline to assess differential expression of RNA-seq data for clinical diagnoses, prognosis, and choice of treatment. Bioinformaticians need to be aware of the biological and clinical impacts, and limitations, of the specimen collection and preservation techniques for samples used for RNA-seq when creating custom transcriptomic pipelines, especially employing an SOP “standard ‘omics pipeline” in a genomics core setting. Due to the increased use of RNA-seq to diagnose, prognose, and generate therapeutic options clinicians and biomedical researchers need to more closely obtain a stronger foundation and understanding of the bioinformatics tools and pipelines analyzing RNA-seq data and generating the results. This study highlights possible limitations of this version of HISAT2 for some RNA-seq read generation technologies, poor quality samples, and short RNA seq reads, thus providing clinicians with insights for choosing the right bioinformatics tools for the job. In cases when paired  analysis is possible, it can be a valuable tool when between-analysis produces no significant result. However, in the breast cancer progression dataset only 3 of 25 patients had all stages of breast lesions  represented. Therefore, we used between analysis, which produces a more conservative statistical cutoff than a paired analysis. In our ClustVis quality control analysis, we found no evidence for batch effects in the analyzed dataset, albeit when batch effects are detected, additional modules can be used in edgeR, or adjust data model in DeSeq2, to extract significant gene expression differences. A recent review has an excellent discussion of possible interventions for batch effects when detected in RNA-seq data .Application of transcriptomics can facilitate the exploration of underlying pathogenic mechanisms, identification of genetic variants, and determination of treatment effects, including screening for molecular biomarkers. Importantly, expression signatures in diseased phenotypes may pinpoint precise interventions required to alleviate the disease state, a goal of precision medicine, without a need for the cost-prohibitive “personalized” assembly and analysis of patient’s own genome. Thus, transcriptomics can classify individuals, while simultaneously facilitating discovery, testing, and validation of new therapeutics for breast cancer patients, defined at the cellular and molecular levels.Transcriptomics is an effective tool for both diagnostics and discovery science, exposing novel cellular and molecular mechanisms in clinical and translational models to yield robust targets for drug discovery to identify and test novel therapeutics. The cost and time required for transcriptome analysis have been greatly reduced by the development of next generation sequencing. Our results underscore the essential importance to investigate the impacts of bioinformatics tools for sequence alignment and differential expression on accuracy of results and interpretation of transcriptome studies collected from FFPE specimens, which was the goal of our study. This paper also serves to point out the pragmatic shortcomings of universally applying a rigid “standard operating procedure” set of bioinformatics tools to RNA-seq data, by demonstrating the impacts and limitations of biological conditions, especially in sample processing and choice of programs.


Gene duplications are a major source of raw material for evolution and a likely contributor to the diversity of life on earth. Duplicate genes  may retain their ancestral function, sub- or neofunctionalize, or be lost entirely. A primary way that duplicate genes evolve new functions is by altering their expression patterns. Comparing the expression patterns of duplicate genes gives clues as to whether any of these evolutionary processes have occurred.Mimulus luteus). We assess the sensitivity of this test to different levels of homeolog expression bias and compare the method to several alternatives.We develop a likelihood ratio test for the analysis of the expression ratios of duplicate genes across two conditions . We demonstrate an application of this test by comparing homeolog expression patterns of 1448 homeologous gene pairs using RNA-seq data generated from leaves and petals of an allotetraploid monkeyflower (The likelihood ratio test derived here is a direct, transparent, and easily implemented method for detecting changes in homeolog expression bias that outperforms alternative approaches. While our method was derived with homeolog analysis in mind, this method can be used to analyze changes in the ratio of expression levels between any two genes in any two conditions. Gene duplications are a major source of raw material for evolution and a likely contributor to the diversity of life on earth –9. Gene eologs.There are multiple scenarios in which genes can be duplicated, ranging from small regional gene duplications to massive whole genome duplications (WGDs). The term polyploid refers to cells or organisms that have undergone a WGD event and contain more than two paired sets of chromosomes. Each complete set of chromosomes is referred to as a subgenome. Homologous genes located on separate subgenomes are referred to as homXenopus, commonly used as an experimental model system and extensively studied in developmental biology, includes species ranging from diploid to dodecaploid  in steps of 0.1. For each value of x, we generated 10,000 sets of data from a negative binomial distribution for N=3,6,12 and 24 replicates. We fixed the parameter r=10 for simplicity; this is within the range of values typically observed in RNA-seq data.To explore this question, we generated simulated data with one expression level fixed at a constant value, Figure ΔHEB to different levels of bias shift, we created a similar data set. This time, we set 3 of the expression levels equal x∈ in steps of 0.1. The aggregation parameter was again fixed at r=10. For each value of x, 10,000 sets of data were generated from a negative binomial distribution for N=3,6,12 and 24 replicates.To assess the sensitivity of ΔHEB on this simulated data set. The results are similar to those for HEB, with the test for ΔHEB being slightly less sensitive than the test for HEB. For ΔHEB, a 4-fold change in bias is detected more than 95% of the time when N≥6. As with the test for HEB, the ability to detect smaller changes increases significantly with the number of replicates.Figure ΔHEB, and requires a minimal number of assumptions; nevertheless we wished to investigate whether other methods could achieve similar results. Because we found only one method analogous to ours in the literature . Three replicates were generated from negative binomial distributions, and this was repeated 50 times for each value of ΔHEB .Our method is transparent, derived specifically for the analysis of omeoroq, ), we dev2-fold changes between conditions 1 and 2. Next, we ran DESeq2 and extracted the estimated shrunken log2-fold changes and their standard errors, and performed a z-test . Unsurprisingly, the naive methods (standard t- and z- tests) underperformed the LRT, with area under the ROC curve (ROC area) typically less than the LRT by ≈0.05 to 0.36.First, we took a naive approach and performed t-tests and z-tests on the ratio of logΔHEB=8 and 16 . In all cases, the Homeoroq method significantly underperformed both alternatives.The LRT outperformed DEZ for ΔHEB=8 and 16, while for ΔHEB=2 both methods performed poorly, with DEZ marginally better  across any two conditions.In this paper we have developed a robust statistical framework specifically designed for the comparison of duplicate gene expression patterns. Importantly, this technique is consistent and reproducible. Through analysis of simulated data we have shown that these methods perform well, especially given the small sample sizes typical of RNA-seq experiments. We have shown that the ability to detect small differences in expression levels increases as a function of sample size, a fact that can be used to aid experimental design. Other authors have noted this in the context of traditional differential expression analysis and made similar recommendations –43. MoreDue to the typically small number of replicates in RNA-seq experiments, accurate estimation of the aggregation parameter is not realistic on a gene-by-gene basis , 44. Insr for each experimental replicate, after rescaling to account for each replicates sequencing depth.to compute an aggregation parameter jth pair of homeologous genes obtained for experimental replicate i∈{1,2,…,n}. For each of the n replicates, we produce an auxiliary data set k, In brief, let j), we compute a scaled mean  and variance i). To obtain the aggregation parameter rk, we perform a nonlinear least squares fit of the observed mean-variance relation across all genes. That is, rk minimizes the sum of squares error, For each gene  to  through For the analysis of both HEB and v= lnλ+y and y=(lnω)/2. This ensures positivity of λ and ω and leads to a system of equations that is symmetric in λa⇔λb. The new variable v is the logarithm of the geometric mean of the expression levels λa=λ and λb=ωλ, that is, λa=λ=ev−y and λb=ωλ=ev+y. The transformed partial derivatives used to maximize the log-likelihood that is, nℒ1 Eqs. –8 are 2where y=0 in Eq. The transformed partial derivative used to maximize ΔHEB, the partial derivatives used to maximize k=1 and 2), For the analysis of where y2=y1=y we numerically solve a system of three equations, including For the null hypothesis k=1 and 2. These are coupled via for Mimulus luteus. All plants were grown in a greenhouse under a 16 h light regiment at 21°C and 30% humidity. Petal tissue was collected from the corolla of a flower bud near blooming, and leaf tissue came from young leaves adjacent to the stem apical meristem. Five replicates of each tissue type were collected, at the same time of day, from different individuals. Approximately 100–200 mg of plant tissue was immediately placed into liquid nitrogen. RNA was extracted by grinding frozen tissue with pestles in PureLink Plant RNA Reagent from Ambion. Column isolation of RNA was subsequently performed using Direct-zol RNA MiniPrep Plus Kit from Zymo Research. Libraries were constructed using KAPA Stranded mRNA-Seq Kit. During library construction, sequence specific Illumina TruSeq ÂⓇ adapters were added to distinguish each library. Using an Agilent 2100 Bioanalyzer, average fragment lengths were determined to be between 230 and 300 bp. Libraries were then pooled and sequenced by the Duke Center for Genomic and Computational Biology on an Illumina HiSeq 2500 instrument. The resulting reads  were mapped to the M. luteus genome using bowtie2 [Plant tissues were collected from second generation inbred  bowtie2  with the bowtie2  with the


Reverse transcription quantitative real-time polymerase chain reaction (RT-qPCR) is an accurate and fast method to measure gene expression. Reproducibility of the analyses is the main limitation of RT-qPCR experiments. Galaxy is an open, web-based, genomic workbench for a reproducible, transparent, and accessible science. Our aim was developing a new Galaxy tool for the analysis of RT-qPCR expression data. Our tool was developed using Galaxy workbench version 19.01 and functions implemented in several R packages. We developed PIPE-T, a new Galaxy tool implementing a workflow, which offers several options for parsing, filtering, normalizing, imputing, and analyzing RT-qPCR data. PIPE-T requires two input files and returns seven output files. We tested the ability of PIPE-T to analyze RT-qPCR data on two example datasets available in the gene expression omnibus repository. In both cases, our tool successfully completed execution returning expected results. PIPE-T can be easily installed from the Galaxy main tool shed or from Docker. Source code, step-by-step instructions, and example files are available on GitHub to assist new users to install, execute, and test PIPE-T. PIPE-T is a new tool suitable for the reproducible, transparent, and accessible analysis of RT-qPCR expression data. Reverse transcription qPCR (RT-qPCR) is an accurate, sensitive, and fast method to quantify gene expression from qPCR experiments2, and is widely accepted as the Golden Standard for the analysis of gene expression3. Briefly, RT-qPCR measures the expression of a set of target RNAs through repeated cycles of sequence-specific amplification followed by expression measurements4. The cycle at which the observed expression first exceeds a user-specified threshold is commonly called the threshold cycle (Ct) or quantification cycle. The Ct values of the target RNAs represent a quantitative assessment of gene expression and are often treated as the raw data for subsequent analyses4. Two methods can be used to quantify gene expression from the Ct value: the absolute and the relative quantification3. In the absolute quantification, a standard curve is used as reference calibrator. In the relative quantification, the signal is related to the expression of a user-specified group3. Therefore, the difference between the two approaches depends on the data used as reference calibrator to which relating the signal.Quantitative real-time polymerase chain reaction (qPCR) is a routinely used technique for the detection of specific nucleic acids, RNA expression profiling, quantification of DNA and DNA methylation, and validation of microarray hybridization data5. Handling missing data is a crucial step in the analysis of RT-qPCR experiments because procedures used in the subsequent analyses of these data are based on statistics that are unable to handle both numeric and missing values4. Imputation is an established technique to solve the problem6. Imputation substitutes a missing value with a rationally selected numeric value4. K-nearest neighbors (KNN)6, maximum Ct plus one cycle (Mestdagh)7, and cubic spline interpolation (Cubic)1 are known methods to impute missing values in RT-qPCR data6.In many RT-qPCR experiments not all Ct values can be numerically defined. For example, when the starting RNA abundance is too low, or an off-target product is amplified, or no reliable Ct can be determined, the corresponding Ct value cannot be quantified numerically and is flagged as missing value6. Data normalization is expected to reduce/eliminate any technical variability without affecting the true biological results6. Global mean8, DeltaCt based on universal normalizers9, Modified global mean10, Quantile9, and Rank Invariant9 are among the most accepted methods used for RT-qPCR data normalization5.Another key step in the analysis of RT-qPCR data is the assessment of true biological changes associated with the phenomenon or disease of interest. In fact, biological changes are often masked by nonspecific technical variability introduced in the data during the experimental procedure9. Plates have been used in several explorative studies to find novel biomarkers from the analysis of different diseases, tissues, experimental conditions, and cell types6. The large number of studies published in the literature stimulated companies to develop commercial technologies to perform RT-qPCR experiments3. For each experiment, these technologies generate textual reports summarizing a number of experimental parameters and data such as feature name, quality control flags, and Ct values. Different technologies generate reports that can be of different format. According to our experience, SDS, EDS, and OpenArray are among the most used file formats for reporting results of RT-qPCR experiments.RT-qPCR experiments allow measuring the expression of several transcripts in parallel using high-density plates6. Furthermore producing high quality publications and reproducible data are among the most critical pitfalls of qPCR experiments11.Although the computational procedures and technologies for analyzing RT-qPCR data are well established, the heterogeneity of the assays employed in RT-qPCR experiments and the lack of a consensus on the best normalization system and on the missing values imputation approach to adopt makes it hard to set up a standardized analysis procedure1. HTqPCR is a well-known open source R\Bioconductor package for the high-throughput analysis of RT-qPCR data9. It provides several functions and parameter options for assessing the quality of the experiment, filtering unreliable data, normalizing raw data, finding potential candidate biomarkers, and visualizing RT-qPCR data9. However, R-based analysis suffers from some known limitations. First of all, analysis procedures are implemented in several packages lacking a unified framework. Second, users with biological background who want to use the functionalities of R packages need non-trivial coding skills. Furthermore, the lack of a simple framework for reusing, sharing, and communicating experimental procedures and results limits reproducibility, transparency, and accessibility of R-based analysis12.Several open-access software packages, tools, and web applications, such as R packages, have been proposed in the last years for the analysis of RT-qPCR data12. Galaxy provides a very active developer community. More than 6746 public tools and workflows are freely available in the Galaxy Tool Shed repositories12. New tools and workflows are easily deployable in the Galaxy repositories. To this purpose, Galaxy offers fresh installations of R and Python environments, a fast dependency resolver, a step-by-step documentation, a simple graphical interface, and GitHub integration13. However, to the best of our knowledge, no Galaxy tool or workflow has been reported to date for analyzing RT-qPCR data.Galaxy is an open, collaborative, web-based, genomic workbench for a reproducible, transparent, and accessible sciencepipette (PIPE-T), a new tool for analyzing RT-qPCR expression data integrating the functionalities implemented in various R packages into one unified, reusable, transparent, accessible, and easy to use Galaxy wrapper.In the present work, we developed 14.PIPE-T implements the relative quantification method using the R language and computing environmentA List collection of tab-separated text files for all samples generated as report of the RT-qPCR experiment (ListOfFile).A tab-separated text file associating each filename in ListOfFile with a treatment group (FileTreatment).To start a PIPE-T analysis, users must upload two input files:Five distinct computational procedures are implemented in PIPE-T. Procedures are summarized in Fig. A tab-separated text file containing the raw Ct values for every sample and transcriptA PNG file showing the distribution of the Ct values of every samples obtained after the Ct filtering and categorization step visualized as sequence of boxplots.A tab-separated text file containing the normalized Ct valuesA PNG file showing the cumulative distribution plot before and after data normalization of the coefficient of variation of every transcript.A PNG file showing the distribution of the normalized Ct values visualized as sequence of boxplots.A tab-separated text file containing data after imputationA tab-separated text file containing the results of the differential expression analysis.The execution of PIPE-T outputs the following output files:Heterogeneity of assays quantifying RT-qPCR gene expression is often associated with heterogeneity of the file formats reporting data summarizing the results of the RT-qPCR experiment. Hence, it is crucial that the user uploads files whose content is compliant with the file format parsable by PIPE-T before running any PIPE-T analysis.15.“Upload File from your computer” is a Galaxy tool that allows uploading files into Galaxy. This tool is available on any fresh Galaxy instance or on the main Tool Shed repositoryApplied Biosystems Sequence Detection Systems (SDS)ThermoFisher Experiment Detection Systems (EDS)Applied Biosystems OpenArray (OpenArray)Roche LightCycler (LightCycler)Bio-Rad CFX (CFX)Fluidigm Biomark Table format (BioMark)User-formatted plain text (Plain)PIPE-T processes tab-separated text files containing a dot as decimal separator uploaded with “Upload File from your computer” tool. The formats supported by PIPE-T are:9 parsable file formats. We updated the parsing procedure to adapt it working with R 3.5.0 and tab-separated text files. We extended the list of the parsable file formats including the possibility of processing EDS format, which is one of the most used by Thermo Fisher Scientific real-time qPCR instruments.SDS, OpenArray, LightCycler, CFX, BioMark, and Plain are HTqPCR R packageFileTreatment should have only two columns named SampleName and Treatment. The column named SampleName lists the name and the extension of the files uploaded into the ListOfFile collection. The column named Treatment associates each sampleName with an experimental condition or group of interest. Group specification is necessary since PIPE-T implements the relative quantification method to analyze data from RT-qPCR experiments. PIPE-T admits the specification of two treatment groups. In the GitHub documentation we provided a checklist of recommendations to help users formatting their input files and checking that these files contain sufficient data to run PIPE-T without errors.Raw Ct\Cq value,Value of the internal quality control flag,Transcript and sample names,FeatureCategoryIf file format is correct, PIPE-T populates a qPCRset object containing the following data for each transcript and sample:readCtData function of the HTqPCR R package9.Data parsing and qPCRset object generation are carried out using the 9. HTqPCR package defines three possible categories: “Undertermined”, “Unreliable”, and “OK”9. “Undetermined” is used to flag Ct values above a user-defined threshold, and “Unreliable” indicates Ct values that are so low as to be estimated by the user to be problematic9.Feature categorization is a procedure for describing the level of reliability of a transcript and can be used to filter out features whose expression is not sufficiently reliable9.By default, only Ct values labeled as “undetermined” in the input data files are placed into the “Undetermined” category, and the rest are classified as “OK”9:Range of Ct values. Some Ct values might be too high or too low to be considered a reliable measure of gene expression in the sample and, therefore, should not be marked as “OK”.Flags. Depending on the qPCR input, the values might have associated flags, such as “Passed” or “Failed”, which are used for assigning categories.The FeatureCategory for a transcript can be altered on the basis of two criteriaPIPE-T implements the two criteria allowing users to set up a range of Ct values and a List button. Any Ct value exceeding the user-defined range is categorized as “Unreliable”. Users can force PIPE-T to check internal control flag status. In this case, the FeatureCategory for a transcript is replaced by an “Undetermined” if the transcript did not pass internal quality control.PIPE-T uses FeatureCategory labels to replace any Ct values corresponding to “Undertermined” and “Unreliable” with a not accessible value (NA).setCategory and filterCategory functions of HTqPCR package9.These operations are carried out using 16.Data normalization allows to minimize unwanted systematic technical and experimental variation in the data for better appreciating true biological changes8Global mean9DeltaCt10Modified global mean9Quantile9Norm Rank Invariant9.Scale rank invariantPIPE-T offers six different normalization options that are listed below:9. However, as Norm Rank Invariant and Scale rank invariant worked only if missing values were absent, we extended the procedure substituting any missing value with a numeric value using the na.spline function implemented in the zoo R package17. D’haene and collegues showed the benefits of using the geometric mean for the normalization of microRNA expression data by introducing the so-called modified global mean method10. For these reasons, we integrated the modified global mean method in PIPE-T.Global mean, quantile, norm rank invariant, and scale rank invariant were already implemented in HTqPCR R package18. When geNorm is selected, PIPE-T identifies candidate normalizers taking those transcripts whose stability was greater than 1.5 as reported by Vandesompele and collegues19.PIPE-T supports the deltaCt method. Housekeeping genes can be specified by the user or can be estimated by the geNorm or NormFinder methods implemented in the NormqPCR R\Bioconductor packagenormalizeCtData of the HTqPCR R package9.Newly implemented normalization methods have been integrated in PIPE-T as an updated version of the function 6. The simplest solution for handling missing values would be to exclude from the analysis any transcript with at least one missing value. In such a case, missing values do not represent a problem anymore because they are removed from the analysis. However, this approach could filter out a considerable number of potential useful transcripts. Another solution would be to take every transcript no matter of the number of missing values. In such a case, all potential useful transcripts are taken into account for subsequent analysis, but the probability of making an error increases with the number of missing values6. In the literature, there is a wide accepted approach that consists in keeping transcripts with a reasonable number of missing values and filtering out those exceeding this threshold6. Transcripts that do not exceed the threshold are imputed using a suitable method. In the literature, several imputation methods have been proposed20.High-throughput data may often contain missing values. For this reason, handling missing values is a crucial step of any RT-qPCR analysisfilterCtData function of the HTqPCR package9.PIPE-T offers a slider that the user can move to specify the maximum percentage of missing values admissible for a specific transcript. PIPE-T allows filtering transcripts using a user-defined percentage of missing values and/or a user-defined list of transcripts to be removed by using the KNNMestdaghCubicIn addition, PIPE-T gives the possibility of selecting one of three well-known imputation methods. These methods are:impute and zoo R packages.KNN and Cubic imputation methods were already implemented in the 7. This method has already been described in other reports5. This method assumes that missing values depends on the low or null abundance of the transcript in the sample.Mestdagh is an imputation method that substitutes a missing Ct value with a numeric value obtained adding one cycle to the highest Ct value across samples21, PIPE-T offers the possibility of choosing between three approaches:21.T-test21.Two sample Wilcoxon test22.Rank ProductDifferential expression is a very popular analysis for identifying candidate transcripts whose expression can discriminate between two predefined conditions. Among the methods eligible for a differential expression analysis21. Tests are implemented by ttestCtData and mannwhitneyCtData functions of the HTqPCR R package9. For the t-test and the two sample Wilcoxon test, PIPE-T offers the possibility of setting up six distinct parameters, which include: the types of alternative hypothesis to assess significance, the choice of a paired or an unpaired analysis, the presence in the data of replicated transcripts, the choice of a more or less stringent analysis, and the choice of the method for adjusting p-values in case of multiple hypothesis testing.T-test and two sample Wilcoxon test are among the most used statistical tests to perform a differential expression analysis22. Rank Product is carried out using RP function of RankProd R package23.Rank Product is a popular method originating from a biological reasoningIf users do not specify any differential expression analysis method, PIPE-T allows them to select an option named NONE. In this case, no differential expression analysis is performed on the data.2. HTqPCR provides several visualization options for assessing the quality of qPCR data, which include histograms, boxplots, density distributions, and scatter plots9. PIPE-T uses two boxplot visualizations showing the distribution of the expression values across all samples. The boxplots show the distribution of expression values before and after data normalization, respectively. The visual inspection of the two boxplots is used as qualitative assessment of the normalization procedure because boxplots show the noise reduction comparing the data before and after data normalization8. Empirical Cumulative Distribution Function (ECDF) is also used in the literature for measuring noise reduction as an effect of data normalization10. PIPE-T computes and plots ECDF before and after data normalization by using ecdf function of the stats R package14. The significance of the difference between the two ECDF curves is estimated by Kolmogorov-Smirnov test and p-value is reported on top of the figure and in the standard output.Quality assessment of RT-qPCR data is crucial for enhancing the accuracy of the results and the reliability of the conclusionsTabular output files include raw data, filtered data, imputed data and statistics to assess differential expression. A detailed description of the row and column names can be found in HTqPCR and RankProd R packages documentation. A detailed description of visualization, sharing, and workflow integration using Galaxy graphical interface can be found in the Galaxy documentation.24 and non-small cell lung (NSCL) cancers25. The first study reported the results of the analysis of sixteen different tumors including Lung, Renal, Colon, Sarcoma, Ovarian, and Head and neck squamous cell carcinoma24. The second study reported the results of the analysis of forty-four NSCL tumor samples25. We carried out PIPE-T analysis of both datasets on a test Galaxy instance version 19.01, installed in a local Linux machine. Parameter settings for the two analyses have been taken from the original publications when available. When the parameters were not specified we selected them arbitrarily.We tested the ability of PIPE-T of analyzing RT-qPCR data using two example datasets whose tab-separated text files were available in the Gene Expression Omnibus (GEO) with accession identifiers GSE25552 and GSE43000. Datasets were relative to two published studies on various metastatic tumorsWe downloaded input tab-delimited files from GEO and we added a SDS version 2.4 format header to each of these files because it lacked. Input files contained experimental data for 384 microRNAs. We coupled RT-qPCR data with information about tumor status, which was oligometastatic (OLIGO) for ten out of sixteen patients and polymetastatic (POLY) for the remaining six patients. File names and tumor status were organized into a tab-delimited text file. The newly created file and the sixteen tab-separated text files were uploaded in Galaxy as fileTreatment and ListOfFile through “Upload File from your computer” tool. Analysis was carried out with parameters settings reported in Fig. Our tool successfully completed the execution, returning seven output files  and twenty-four squamous cell lung cancer (SCLC). File names and tumor subtypes were organized into a text file. We uploaded the newly created file as fileTreatment, and the forty-four tab-separated text files as ListOfFile. Analysis was carried out with the parameter settings reported in Fig. NSCL input files were compliant with SDS format version 2.3 and reported experimental data for 381 microRNAs. Since the downloaded files used a comma as decimal separator, each comma was replaced with a dot before running PIPE-T. RT-qPCR data were coupled with histological data provided in the original publicationOur tool successfully completed the execution returning seven output files 26Project home page: Operating system(s): Linux , and platform independentProgramming language: ROther requirements: GalaxyLicense: GNU GPL15 at the link27, on the Docker28 at the link29 and on the web30 at the link31. PIPE-T code is freely available on GitHub at the link https://github.com/igg-molecular-biology-lab/pipe-t (2019)26.PIPE-T is available on the Main Tool ShedPIPE-T has the following dependencies:<requirements><requirement type = “package” version = “3.5.0”>r-base</requirement><requirement type = “package” version = “7.2.0”>libgcc</requirement><requirement type = “package” version = “1.36.0”>bioconductor-htqpcr</requirement><requirement type = “package” version = “3.8.0”>bioconductor-rankprod</requirement><requirement type = “package” version = “1.56.0”>bioconductor-impute</requirement><requirement type = “package” version = “1.11.0”>r-bbmisc</requirement><requirement type = “package” version = “1.8.4”>r-psych</requirement><requirement type = “package” version = “1.8_3”>r-zoo</requirement></requirements>32 is installed and enabled, Galaxy locates and resolves any tool dependencies automatically during tool installation.If Condahttps://github.com/igg-molecular-biology-lab/pipe-t (2019)26.The tab-separated text files included in the ListOfFile collections of the two example applications are available in GEO repository with accession numbers: GSE25552 and GSE43000. A detailed documentation, step-by-step tool installation instructions, configuration, example applications are available on GitHub at the link Supplementary informationSupplementary Dataset


While large-scale genetics studies have identified genomic regions associated with disease risk, less is known about the molecular mechanisms by which risk alleles with small effects lead to schizophrenia and bipolar disorder. In order to fill this gap between genetics and disease phenotype, we have undertaken a multi-cohort genomics study of postmortem brains from controls, individuals with schizophrenia and bipolar disorder. Here we present a public resource of functional genomic data from the dorsolateral prefrontal cortex  of 986 individuals from 4 separate brain banks, including 353 diagnosed with schizophrenia and 120 with bipolar disorder. The genomic data include RNA-seq and SNP genotypes on 980 individuals, and ATAC-seq on 269 individuals, of which 264 are a subset of individuals with RNA-seq. We have performed extensive preprocessing and quality control on these data so that the research community can take advantage of this public resource available on the Synapse platform at  Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.9747479 There has been much recent progress in understanding the genetic basis for schizophrenia8. Yet understanding the molecular mechanisms linking risk loci to disease phenotypes, and characterizing disease pathways remains an open challenge. Here we have compiled a large-scale functional genomics resource in order to understand the interplay between genetic regulation, gene expression, chromatin accessibility and disease in the human brain. We have generated RNA-seq (n = 991) and ATAC-seq (n = 269) profiles from the dorsolateral prefrontal cortex specimens and SNP genotypes (n = 1076) from 4 separate brain banks. The genotype data includes 98 samples without RNA- or ATAC-seq data released with the anticipation of including functional data for these in a future release.Schizophrenia and bipolar disorder are serious mental illnesses (SMI) that affect more than 2% of adultshttp://CommonMind.org) of processed and quality controlled data, we aim to empower other researchers to apply novel methods and perform integrative analyses. Moreover, we provide raw and aligned data to enable custom reprocessing.By providing an extensive public resource , item 65 of Federal regulations and University policy.Brain specimens are obtained during routine autopsies conducted at the Allegheny County Office of the Medical Examiner (Pittsburgh) following the consent of the next of kin. An independent committee of experienced research clinicians makes consensus DSM-IV diagnoses for all subjects on the basis of medical records and structured diagnostic interviews conducted with the decedent’s family members. All procedures for Pitt samples have been approved by the University of Pittsburgh’s Committee for the Oversight of Research involving the Dead and Institutional Review Board for Biomedical Research.Brain specimens are obtained under protocols approved by the CNS IRB (NCT03092687), with the permission of the next-of-kin (NOK) through the Offices of the Chief Medical Examiners (MEOs) in the District of Columbia, Northern Virginia and Central Virginia. All specimens were characterized neuropathologically, clinically and toxicologically. A clinical diagnosis was obtained through family interviews and review of medical records by two psychiatrists based on DSM-IV criteria. Non-psychiatric controls were defined as having no history of a psychiatric condition or substance use disorder and negative toxicology measures.This study includes data from the dorsolateral prefrontal cortex provided by the MSSM, Penn, and Pitt brain banks. Tissue for the study was dissected at each brain bank and shipped to the Icahn School of Medicine at Mount Sinai (ISMMS) for nucleotide isolation and data generation in one facility in order to reduce site-specific sources of technical variation. Postmortem tissue from schizophrenia and bipolar disorder cases were included if they met the diagnostic criteria in DSM-IV for schizophrenia or schizoaffective disorder, or for bipolar disorder, as determined in consensus conferences after review of medical records, direct clinical assessments, and interviews of care providers. Cases that had a history of Alzheimer’s disease, and/or Parkinson’s disease, or acute neurological insults  immediately prior to death, or were on ventilators near the time of death, were excluded. Eight samples were classified as a mood disorder other than bipolar post sample collection and have been labeled as AFF in the clinical file.This study includes data from the dorsolateral prefrontal cortex from the NIMH HBCC brain bank. Tissue for the study was dissected at the NIMH HBCC and shipped to the Icahn School of Medicine at Mount Sinai (ISMMS) for nucleotide isolation and data generation in one facility in order to reduce site-specific sources of technical variation. Tissue pH, RNA integrity number (RIN), and postmortem interval (PMI) were ascertained.MSSM: All samples for the study were dissected from the left hemisphere of fresh frozen coronal slabs cut at autopsy from the dorsolateral prefrontal cortex from Brodmann areas 9 or 46. Immediately after dissection, samples were cooled to −190 °C and dry homogenized to a fine powder using a L-N2 cooled mortar and pestle. Tissue was transferred on dry ice to ISMMS as a dry powder for DNA and RNA extraction.Penn: At autopsy, right or left hemisphere of each brain was blocked into coronal slabs, which were immediately frozen and stored at −80 °C. For this study, the Brodmann areas 9 or 46 were dissected from either the left or right hemisphere and pulverized in liquid nitrogen. The tissue was shipped in tubes appropriate for DNA or RNA extraction to ISMMS as homogenized tissue in trizol for RNA extraction and dry pulverized tissue for DNA extraction.Pitt: At autopsy, the right hemisphere of each brain was blocked coronally, immediately frozen and stored at −80 °C. Samples for the study contained only the gray matter of DLPFC where Brodmann area 9 from the right hemisphere was cut on a cryostat and collected in tubes appropriate for DNA or RNA extraction. The DNA and RNA tubes were shipped on dry ice to ISMMS as homogenized tissue in trizol for RNA extraction and thinly sliced tissue for DNA extraction. Specimens from Pitt were provided as matched case/control pairs. These were perfectly matched for sex, and as closely as possible for age and race. Members of a pair were always processed together.Samples were dissected at each of the brain banks and shipped to Icahn School of Medicine - Mt Sinai (ISMMS) for sample preparation, genotyping, and RNA-sequencing. Dissection protocols were as follows:Samples for the study were dissected from either the left or right hemisphere of fresh frozen coronal slabs cut and frozen and stored at −80 °C. Brodmann areas 9 and 46 on the dorsolateral surface of the prefrontal cortex were dissected from the frozen slabs under the direction of a neuropathologist. The samples were shipped on dry ice to ISMMS as homogenized tissue in Trizol for RNA extraction.Dorsolateral Prefrontal Cortex: Total RNA from 670 samples was isolated from approximately 50 mg homogenized tissue in Trizol using the RNeasy kit according to manufacturer’s protocol. Samples from all brain banks were processed together in batches of 12 and the Pitt matched case/control pairs were always processed in the same batch. 10 control samples were processed as part of both a schizophrenia and bipolar pair and therefore found as duplicate samples in the dataset. The order of extraction for schizophrenia-affected and control samples was assigned randomly with respect to brain bank, diagnosis and all other sample characteristics. Because the bipolar-affected and matched controls were not available until after the processing of the schizophrenia and controls was underway, these samples were randomized among the remaining 132 schizophrenia and control samples for extraction. The mean total RNA yield was 15.3 ug. The RNA Integrity Number (RIN) was determined by fractionating RNA samples on the 6000 Nano chip (Agilent Technologies) on the Agilent 4200 TapeStation. Samples with RIN > =5.5 were included in the sequencing set, resulting in 46 samples with RIN < 5.5 to be excluded from the study. An additional 18 samples were removed post sequencing due to sample genotype variant inconsistencies and low reads resulting in a final dataset of 606 samples.Dorsolateral Prefrontal Cortex: Total RNA from 469 HBCC samples was isolated from approximately 100 mg pulverized tissue from each sample by Trizol/chloroform extraction and purification with the Qiagen RNeasy kit (Cat #74106) according to manufacturer’s protocol. Samples were processed in randomized batches of 12. The order of extraction was assigned randomly with respect to diagnosis and all other sample characteristics. The mean total RNA yield was 24.2 ug. The RNA Integrity Number (RIN) was determined by fractionating RNA samples on the 4200 Agilent TapeStation System. Samples with RIN > =5.5 were included in the sequencing set, resulting in 69 samples with RIN < 5.5 to be excluded from the study. An additional 11 samples were removed post sequencing due to sample genotype variant inconsistencies, and 4 duplicate samples were removed (the sample with the highest RIN was kept) resulting in a final dataset of 385 samples.RNA sequencing raw and gene counts is provided for 991 samples originating from 981 unique donors, where 10 ‘Pitt’ control samples were run in duplicate. Data was generated, QC’ed, processed and quantified as follows:RNA library preparation and sequencing: Processing order was randomized prior to ribosomal RNA depletion, and samples were processed in batches of 8. In order to expedite sequencing, processing began before extraction was complete and randomization occurred among all available extracted samples in sets of 120 to 226. Briefly, rRNA was depleted from about 1 ug of total RNA using Ribozero Magnetic Gold kit (Illumina/Epicenter Cat #MRZG12324) to enrich polyadenylated coding RNA and non-coding RNA. The Pitt case/control pairs were batched together in each processing step, including Ribozero depletion, sequence library preparation and sequencing lane. The sequencing library was prepared using the TruSeq RNA Sample Preparation Kit v2 (RS-122-2001-48 reactions) in batches of 24 samples. The insert size and DNA concentration of the sequencing library was determined on Agilent Bioanalyzer and Qubit, respectively. A pool of 10 barcoded libraries were layered on a random selection of two of the eight lanes of the Illumina flow cell at appropriate concentration and bridge amplified to ~250 million raw clusters. One-hundred base pair paired end reads were obtained on a HiSeq 2500. The sequencing data generated was simultaneously transferred  to storage computer cluster and then transferred to high performance computer cluster. The sequence data was processed for primary analysis to generate QC values. Data is provided for those samples that passed all of the following QC filters: samples were required to have had a minimum of 25 million read pairs and less than 5% rRNA alignment. This gives a mean of 40.1 million, median of 39 million and maximum of 103.9 million read pairs.RNA library preparation and sequencing: All samples submitted to the New York Genome Center for RNA-seq were prepared for sequencing in randomized batches of 94. The sequencing libraries were prepared using the KAPA Stranded RNA-seq Kit with RiboErase (KAPA Biosystems). rRNA was depleted from 1ug of RNA using the KAPA RiboErase protocol that is integrated into the KAPA Stranded RNA-seq Kit. The insert size and DNA concentration of the sequencing library was determined on Fragment Analyzer Automated CE System  and Quant-iT PicoGreen (ThermoFisher) respectively. A pool of 10 barcoded libraries were layered on a random selection of two of the eight lanes of the Illumina flow cell at appropriate concentration and bridge amplified to ~250 million raw clusters. One-hundred base pair paired end reads were obtained on a HiSeq 2500. Data is provided for those samples that passed all of the following QC filters: samples were required to have had a minimum of 25 million read pairs and less than 5% rRNA alignment. This gives a mean of 56.5 million, median of 56.4 million and maximum of 74.4 million read pairs.10 and then mapped to human reference genome GRCh38.v24 (ftp://ftp.ebi.ac.uk/pub/databases/gencode/Gencode_human/release_24/GRCh38.primary_assembly.genome.fa.gz) using STAR (v2.5.3a)11. The BAM files that were generated contain the mapped paired-end reads, including those spanning splice junctions. Following read alignment, expression quantification was performed at the transcript isoform level using RSEM (v1.3.0)12 and then summarized at the gene level. Gene quantifications correspond to GENCODE v27 (ftp://ftp.ebi.ac.uk/pub/databases/gencode/Gencode_human/release_27/gencode.v27.annotation.gtf.gz). Quality control metrics were reported with RNA-SeqQC (v1.1.7)13. All analysis used log2 counts per million (CPM) following TMM normalization14 implemented in edgeR (v3.22.5)15. Correction for GC content bias was performed with cqn (v1.26.0)16. Genes with over 1 CPM in at least 50% of the experiments were retained.Mapping and quantification of genes, exons and transcripts: The raw reads were trimmed with Trimmomatic (v0.36)DNA was isolated from approximately 10 mg dry homogenized tissue coming from the same dissected samples as the RNA isolation. The thinly sliced tissue from Pitt was homogenized before DNA isolation. All DNA isolation was done using the Qiagen DNeasy Blood and Tissue Kit according to manufacturer’s protocol. DNA yield was quantified using Thermo Scientific’s NanoDrop. The mean yield was 12.6 ug (+/−4.6), the mean ratio of 260/280 was 2.0 (+/−0.1) and the mean ratio of 260/230 was 1.9 (+/−2.3).Varying amounts of pulverized cerebellar tissue were used (45 to 80 mg) for DNA extraction. The QIAamp DNA mini Kit (Qiagen) method was used. The tissue was initially lysed using Tissue Lyser (Qiagen) and extractions proceeded according to manufacturer’s protocol. DNA was captured in 500uL elution buffer. The concentrations were measured using Thermo Scientific’s NanoDrop 1000/NanoDrop ONE. The mean yield was 128.85 uG (+/−79.48), the mean ratio of 260/280 was 1.87 (+/−0.105), and the mean ratio of 260/230 was 2.48 (+/−1.75).9. The HBCC samples were genotyped on one of 3 different Illumina gene chips: HumanHap650Y, Human1M-Duo, and HumanOmni5M-Quad using the manufacturer’s protocol as follows: Approximately, 400 ng DNA was used and each DNA sample was QC tested for 260/280 ratio by nanodrop and DNA band intactness on 2% agarose gel. Briefly, the samples were whole-genome amplified, fragmented, precipitated and resuspended in appropriate hybridization buffer. Denatured samples were hybridized on prepared Illumina Quad Bead Chips. After hybridization, the Bead Chip oligonucleotides were extended by a single fluorescent labeled base, which was detected by fluorescence imaging with an Illumina Bead Array Reader, iScan.For the MSSM/Penn/Pitt cohorts, genotyping was performed on the Illumina Infinium HumanOmniExpressExome 8 v 1.1b chip  using the manufacturer’s protocol as previously described17 to remove markers with: zero alternate alleles, genotyping call rate ≤ 0.98, Hardy-Weinberg P value < 5 × 10−5, and individuals with genotyping call rate < 0.90. Samples were then imputed to HRC (r1.1 2016)18, as follows: if necessary, marker positions were lifted-over to GRCh37 and aligned to the HRC loci using HRC-1000G-check-bim-v4.2 (https://www.well.ox.ac.uk/~wrayner/tools/), which checks the strand, alleles, position, reference/alternate allele assignments and frequencies of the markers, removing A/T & G/C single nucleotide polymorphisms (SNPs) with minor allele frequency (MAF) > 0.4, SNPs with differing alleles, SNPs with > 0.2 allele frequency difference between the genotyped samples and the HRC samples, and SNPs not in reference panel. Imputation was performed via the Michigan Imputation Server19 using Eagle v2.320 as the phasing algorithm. Since all imputation was performed using the GRCh37, the genotype data was subsequently lifted over to GRCh38 for downstream integrating with functional genomics assays.Genotype calling, QC and imputation proceeded separately by gene chip set. Normalized bead intensity data obtained for each sample were called using Illumina Genome Studio with cluster position files provided by Illumina, and fluorescence intensities were converted into SNP genotypes separately for each gene chip. Initial QC was performed using PLINK21. Briefly, 20 mg of tissue was pulverized in liquid nitrogen and thawed in 1 ml of nuclear isolation buffer . Samples were mixed by inversion, filtered from large pieces of tissue through Miracloth, centrifuged at 1100 × g for 10 min at 4 °C, pellets washed with 50 μl Reduced Swing buffer, centrifuged again, and supernatants were removed. The nuclear pellets were resuspended in Tn5 transposase reaction mix, barcoded, combined into pools, and used for sequencing22. Each pool contained eight randomly selected samples that were balanced by case–control status and gender. Then, each pool was sequenced at Duke Sequencing and Genomic Technologies shared resource on two lanes of Illumina HiSeq 2000 or HiSeq 4000 obtaining 2 × 125 or 2 × 151 single- or paired-end reads. Since only eight samples were sequenced in the single-end mode and all showed different epigenomics profile based on multidimensional scaling analysis, we decided to exclude them. After additional quality controls (see below), 269 ATAC-seq libraries were retained21.A total number of 314 frozen pulverized brain samples were received from the Mt. Sinai Brain Repository, and processed for ATAC-seq as described10 to remove low-quality base pairs and sequence adapters. Reads were subsequently aligned to the GRCh38  analysis set reference genome with the pseudoautosomal region masked on chromosome Y with the STAR aligner11. This yielded for each sample a BAM file of mapped paired-end reads sorted by genomic coordinates. From these files, reads that mapped to multiple loci or to the mitochondrial genome were removed using samtools23 and duplicated reads were removed with PICARD (http://broadinstitute.github.io/picard).Each set of pair-end reads was processed by Trimmomatichttps://raw.githubusercontent.com/mills-lab/svelter/master/Support/GRCh38/Exclude.GRCh38.bed). For each peak, we assigned the closest gene and the genomic context of an ATAC-seq OCR using ChIPSeeker24; transcript database was built by GenomicFeatures25 upon ENSEMBL genes.To create a final peakset, we subsampled and merged BAM-files separately for schizophrenia and control samples. For each, a total of one billion paired-end reads were used, corresponding to 6.99 million read pairs sample per schizophrenia sample and 7.94 million read pairs per control sample. We called peaks separately on these two merged BAM files, and merging these two peaksets into a single consensus peakset. Finally, we removed peaks overlapping ENCODE blacklisted regions of low read mappability or repeated sequence , 10 low doses of haloperidol (0.14 mg/kg/d), 9 clozapine (5.2 mg/kg/d), and 8 vehicle  and pulverized. Samples were shipped to ISMMS for sample preparation and RNA-sequencing.Dorsolateral Prefrontal Cortex: Total RNA was isolated from approximately 100 mg homogenized tissue in Trizol using the RNeasy kit according to manufacturer’s protocol and processed together in treatment and sex randomized batches of 12 samples. The mean total RNA yield was 28.5 ug. The RNA Integrity Number (RIN) was determined by fractionating RNA samples on the 6000 Nano chip (Agilent Technologies) on the Agilent 2100 Bioanalyzer. Samples with RIN > =5.5 were included in the sequencing set, resulting in 2 samples with RIN < 5.5 being excluded from the study. Among the remaining samples the mean RIN was 7.3 and the mean ratio of 260/280 was 2.1.Raw and quantified RNA-seq expression data is provided for 32 samples consisting of data from 32 unique animals. Data was generated, QC’ed, processed and quantified as follows:Processing order was randomized prior to ribosomal RNA depletion, and samples were processed in batches of 9 or 8. rRNA was depleted from about 1 ug of total RNA using Ribozero Magnetic Gold kit (Illumina/Epicenter Cat #MRZG12324) to enrich polyadenylated coding RNA and non-coding RNA. The sequencing library was prepared using the TruSeq RNA Sample Preparation Kit v2 (RS-122-2001-48 reactions) in batches of 17 samples. The insert size and DNA concentration of the sequencing library was determined on Agilent Bioanalyzer and Qubit, respectively. A pool of 8 or 9 barcoded libraries were layered on a random selection of two of the eight lanes of the Illumina flow cell at appropriate concentration and bridge amplified to ~250 million raw clusters. One-hundred base pair paired end reads were obtained on a HiSeq 2500. The sequencing data were simultaneously transferred  to storage computer cluster and then transferred to high performance computer cluster. The sequence data was processed for primary analysis to generate QC values. All data provided passed all of the following QC filters: samples were required to have a minimum of 50 million total reads and less than 5% rRNA alignment.11 and featureCounts27.Reads were mapped to the macaque reference genome and transcriptome (mmul1) using STARhttp://CommonMind.org. This includes a complete table of files described in this manuscript is available on Synapse. Data is either open, where the only requirement is to acknowledge data contributors in publications, or controlled. Controlled data access application must be placed through the NIMH Repository and Genomics Resources (https://www.nimhgenetics.org/resources/commonmind). The CommonMind Consortium resource includes processed data as well as raw FASTQ and BAM files. A tutorial on how to access the data using the Synapse platform is available at https://docs.synapse.org/articles/downloading_data.html.RNA-seq, ATAC-seq, SNP genotypes, and sample metadata is available from the CommonMind Consortium Knowledge Portal using the Synapse platform at http://CommonMind.org:A complete list of files available through the consortium, including information about access type (open or controlled), can be found on Synapse at 28ATAC-seq29RNA-seq30Genotypes31ClinicalSample metadata files:32Genotypes QCd - CMC study33Genotypes QCd - CMC_HBCC study34Genotypes Imputed - CMC/CMC_HBCC35RNA-seq expression quantifications36RNA-seq BAM files37RNA-seq FASTQ files with remaining unmapped reads38ATAC-seq read count matrix39ATAC-seq fastq40ATAC-seq consensus BigWig41ATAC-seq BigWig per sample42ATAC-seq consensus peaks43ATAC-seq peaks per sample44ATAC-seq BAM files26Rhesus macaques metadata45Rhesus macaque RNA-seq expression quantifications46Rhesus macaque RNA-seq BAM files47Rhesus macaque RNA-seq files with remaining unmapped readsImportant files for this resource include:et al.9. The original analysis results are available on from this Synapse project.We note that this data release supersedes the initial data release from Fromer, 9.We note that qPCR validation of gene expression levels of 13 genes in 57 schizophrenia and 57 matched control individuals from the University of Pittsburgh brain bank was performed in the original paperAll RNA-seq samples were integrated into a single analysis across the 4 brain banks in order to perform a joint quality control. Retained samples had acceptable values for RIN , intergenic rate , total read pairs , intronic rate , mapped read pairs  and ribosomal RNA rate  Fig. . As expeRNA-seq data was also used to confirm that the expression of genes on sex chromosomes is consistent with the reported sex. UTY and XIST were selected as represented genes, and samples show distinct clustering by reported sex after problematic samples were removed Fig. 48 based on the comparison of allele heterogeneity.Confirmation of the identity of samples and estimation of their contamination was performed by VerifyBamID (v1.1.3)49. For the purpose of quality control analysis, the consensus peakset was created from the individual peaks that were subsequently merged across all samples, retaining only peaks found in at least 2 samples. Subsequently, we counted how many reads for each sample overlapped each consensus peak using featureCounts27 in the Rsubread package50. Finally, we counted fragments defined from paired-end reads that overlapped with the final consensus set of peaks.To evaluate the overall quality of samples, the peaks of open chromatin regions (OCRs) were called using MACS (v1.4.2)51. (3) Read counts in OCRs on chromosome Y outside the pseudoautosomal region.The sex of the samples was assessed using three metrics: (1) the heterozygosity rate of chromosome X genotype calls outside the pseudoautosomal regions. For this, we discarded variants with MAF < 5%. In male samples, a high heterozygosity rate can indicate sample contamination, sex mismatch, or chromosomal abnormalities like Klinefelter’s syndrome. (2) The read counts of OCRs adjacent to FIRRE and XIST, which are only, or predominantly, accessible in femalesOn average, we obtained 28.8 million uniquely mapped reads per sample after removing duplicate reads  and those aligned to the mitochondrial genome . Analyses included autosomes and sex chromosomes unless stated otherwise. The comparison of fractions of fragment in peaks (FRiP) was used to mark the replicates of lower quality for nine individuals that have more than one sample present in the dataset. Since we requested only one sample per individual, those nine replicates with lower FRiP were excluded. Using the sex check pipeline, two individuals were found to be genetically females though initially reported as males in the sample description Fig. . However52 on autosomes and showed good concordance with reported ethnicity.Genotype data was analyzed to confirm reported sex and reported ethnicity Fig. . As expe48 was used to match RNA-seq data to SNP genotyping. Using the flags --best--ignoreRG--verbose, we identified each RNA-seq sample’s best genotyping match, and removed potentially contaminated samples, as judged by the chipmix and freemix estimates. If a BAM file matched to the expected individual in the SNP genotype data, the sample was accepted if its chipmix and freemix parameters were both below 0.2  or haloperidol (N = 17), compared to untreated monkeys (N = 8)  Table . For the9. This CommonMind Consortium resource has RNA-seq data from postmortem brains from 351 schizophrenia cases and 500 controls. Researchers can perform differential expression analysis to identify genes whose expression differs significantly between cases and controls53. The 4 cohorts can be used to evaluate how well signals from one cohort replicate in an independent cohort. A meta-analysis can be performed by analyzing each cohort separately combining the results of the statistical test, or a mega-analysis can be performed by combining all cohorts into a single analysis.Dysregulation of gene expression is of primary interest in understanding the molecular mechanisms of schizophrenia biology21. This resource includes ATAC-seq data from 126 schizophrenia cases and 127 controls. Researchers can perform statistical analysis of differential chromatin accessibility between cases and controls. These results can then be compared to the differential analysis of gene expression to examine the shared and unique components of these assays. Moreover, the chromatin accessibility data can be directly integrated with gene expression data where both assays were performed on the same samples.The role of epigenetic variation and in particular the variation in chromatin accessibility between schizophrenia cases and controls is of particular interest for its role in mediating downstream gene expression56.Variants associated with schizophrenia are thought to mediate disease risk by regulating chromatin accessibility and then gene expression. By integrating genome-wide SNP genotyping data with RNA-seq from 981 individuals, researchers can identify eQTL’s. By integrating SNP genotyping data with ATAC-seq data, researchers can identify chromatin accessibility QTL’s (caQTL’s). These eQTL/caQTL results can then be integrated with summary statistics from genome-wide association studies (GWAS) of schizophrenia or other neuropsychiatric diseases to identify genes, or ATAC-seq peaks than mediate disease risk57. This integrated framework can be used to understand epigenetic regulation of gene expression and further prioritize disease genes.Integrated analysis of caQTLs, eQTLs, and GWAS summary statistics can trace the impact of risk variants from their downstream effect on a specific open chromatin region to the effect on gene expression and finally to disease riskSupplementary Information