The power of human induced pluripotent stem cell (hiPSC)-based studies to resolve the smaller effects of common variants within the size of cohorts that can be realistically assembled remains uncertain. We identified and accounted for a variety of technical and biological sources of variation in a large case/control schizophrenia (SZ) hiPSC-derived cohort of neural progenitor cells and neurons. Reducing the stochastic effects of the differentiation process by correcting for cell type composition boosted the SZ signal and increased the concordance with post-mortem data sets. We predict a growing convergence between hiPSC and post-mortem studies as both approaches expand to larger cohort sizes. For studies of complex genetic disorders, to maximize the power of hiPSC cohorts currently feasible, in most cases and whenever possible, we recommend expanding the number of individuals even at the expense of the number of replicate hiPSC clones. Induced pluripotent stem cell (hiPSC)-based models have inherent variations in their cellular and molecular output and readouts. Here, Hoffman and colleagues devise a method to account for gene expression variations in hiPSC-derived neurons from patients with childhood-onset schizophrenia. We and others have previously shown that hiPSC-derived neural progenitor cells (NPCs) and neurons generated from patients with schizophrenia (SZ) show altered gene expression4, which may underlie observed in vitro phenotypes such as aberrant hiPSC-NPC polarity5 and migration6, as well as deficits in hiPSC-neuron connectivity and function7. Altogether, such hiPSC-based approaches seem to capture aspects of SZ biology identified through post-mortem studies and animal models8. Nonetheless, mechanistic studies to date have tended to focus on rare variants5; the ability of an hiPSC-based approach to resolve the much smaller effects of common variants remained uncertain.A growing number of studies have demonstrated that human induced pluripotent stem cells (hiPSCs) can serve as cellular models of both syndromic and idiopathic forms of a variety of neurodevelopmental disorders  cohort, a subset of SZ patients defined by onset, severity and prognosis. COS patients have a more salient genetic risk, with a higher rate of SZ-associated copy number variants (CNVs)9 and stronger common SZ polygenic risk scores10. Overall, across 94 RNA-Seq samples, we observed many sources of variation reflecting both biological  and technical effects. By systematically accounting for covariates and adjusting for heterogeneity in neural differentiation, we improved our ability to resolve the disease-relevant signal. Our bioinformatic pipeline reduces the risk of false positives arising from the small sample sizes of hiPSC-based approaches and we hope it can help guide data analysis in similar hiPSC-based disease studies.We established a case-control SZ cohort structure designed to capture a broad range of rare and common variants that might underlie SZ risk, in order to address and quantify the intra- and inter-individual variability inherent in this approach and uncover to what extent hiPSC-based models can identify common pathways underlying such different genetic risk factors. Because hiPSC-neurons are likely best suited for the study of disease predisposition10 (see Supplementary Data NRXN1 deletion (2p16.3)11 and/or idiopathic genetics with a strong family history of SZ, 12 controls were identified as being most appropriately matched for sex, age, and ethnicity , quantitative polymerase chain reaction (qPCR) and karyotype assays to assess the quality of the hiPSCs  and unrelated age- and sex-matched controls  Fig. . BrieflySCs Fig. , we sele6, once hiPSCs had been passaged ~10 times. hiPSC-NPCs with normal morphology and robust protein levels of NESTIN and SOX2 by FACS and/or immunocytochemistry  were selected for further differentiation to 6-week-old forebrain neuron populations   following ribosomal RNA (rRNA) depletion  between populations of hiPSC-NPCs and hiPSC-neurons. In the following sections, we discuss our strategy to address these sources of variation.In addition to SZ diagnosis-dependent effects, gene expression between hiPSC-NPCs and hiPSC-neurons was expected to vary as a result of technical16 and/or residual Sendai virus expression were flagged.We implemented an extensive quality control pipeline to detect, minimize and account for many possible sources of technical variation Fig. . SamplesXIST on chrX and the expression of six genes on chrY , this analysis identified 2 mislabeled males that show a female expression pattern and 15 female samples that have expression patterns intermediate between males and females  were validated for subsequent analysis  quantified the number of reads corresponding to residual Sendai expression in each NPC and neuron sample. Although Sendai viral vectors are widely assumed to be lost within 11 hiPSC passages19, and that on average our hiPSCs were passaged > 10–15 times and our hiPSC-NPCs > 5 times, we identified Sendai viral transcripts in a subset of our samples. While the majority   of RNA-Seq samples did not contain any reads that mapped to the Sendai viral genome, 17  samples   used in hiPSC reprogramming, expression of these four genes was not associated with Sendai expression , and eliminating these factors as possible explanations for this expression variation ultimately improved our ability to resolve SZ-relevant biology in our data set.22, which is consistent with previous reports26. hiPSC-NPCs and hiPSC-neurons, as well as post-mortem brain samples, cluster separately from hiPSCs, ESCs, fibroblasts and whole blood27. Despite being reprogrammed and differentiated through different methodologies, hiPSC-NPCs and hiPSC-neurons from the current study cluster with hiPSC-NPCs and hiPSC-neurons, respectively, generated previously in the same lab28 and with hiPSC-NPCs and hiPSC-neurons from others29, although some hiPSC-neurons3 are more similar to prenatal brain samples from multiple brain regions22. Consistent with a differentiation paradigm from hiPSC to NPC to neuron, multidimensional scaling analysis , as well as to post-mortem brain, we compared our data set to publicly available hiPSC, hiPSC-derived NPCs/neurons, and post-mortem brain homogenate expression data sets Fig. . Hierarcsis Fig.  indicateGenome-wide, hiPSC-NPCs and hiPSC-neurons express a common set of genes, so that expression differences between these cell types appear as changes in expression magnitude rather than activation of entirely different transcriptional modules  correct for variation in differentiation efficiencies and account for some of the intra-individual expression variation.Given the substantial variability we observed between hiPSC-NPCs and hiPSC-neurons, even from the same individual  , consistent with a “stemness” signal    of the expression data indicates that hiPSC-NPCs and hiPSC-neurons separate along the first principal component (PC), explaining 25.8% of the variance, and that the cell types have distinct CTC scores Fig. . As expeel) Fig. , while hce) Fig. . Unexpecel) Fig. . Rather 33), but both skin fibroblast preparations34 and hiPSC-derived NPCs37 show evidence of mesenchymal and/or neural crest contaminants. Therefore, it is important to consider the fibroblast1 and fibroblast2 signatures only as a tool with which to assess the variability in differentiation quality; high values for the “fibroblast signature” may well imply the presence of non-fibroblast contaminant(s) such as neural crest and/or mesenchymal cells. Supplementary Fig. 39 and mesenchymal40 genes in our hiPSC-NPC and hiPSC-neuron data sets, as well as the reference panels.Not only is there significant overlap between fibroblast, mesenchymal and neural crest gene expression signatures , when included in our model, most explained the variance in our samples. PCA on the residuals from a model including fibroblastnce Fig. . Moreovence Fig. . Finally41  is impacted by a number of biological and technical factors. By properly attributing multiple sources of expression variation, it is possible to  correct for some variables. To decompose gene expression into the percentage attributable to multiple biological and technical sources of variation, we applied variancePartition41 Fig. . Overall41 Fig. . Express41 Fig. , especia1: 3.3%, fibroblast2: 3.2%). The median observed variation across donor is 2.2%, substantially lower than reported in other data sets from hiPSCs42 and other cell types41. By considering CTC in our model, the percentage of variation explained by donor significantly increased , indicating that cell type heterogeneity is an important source of intra-donor expression variation that obscures some inter-donor variation  of particular biological interest. Critically, there is no apparent diagnosis dependent variation in CTC 48 to identify modules of genes with shared expression patterns  and hiPSC-neurons , indicating that in this data set, differentially expressed genes did not form a coherent structure but are instead widely distributed. Genes identified by genetic studies  and case/control signatures from two post-mortem data sets (the CommonMind Consortium (CMC)21 and the NIMH Human Brain Collection core (HBCC)) showed moderate enrichment in many modules, but did not strongly overlap with the gray modules enriched for differentially expressed genes from this study. Finally, gene sets corresponding to the neural proteome show the strongest enrichment in the brown module from hiPSC-neurons, including, the targets of FMRP  and genes involved in post-synaptic density .Genes with similar functions are known to share regulatory mechanisms and so are often coexpressedrns Fig. . Genes t49. This approach is widely used to control the false positive rate in studies of repeated measures and its importance in hiPSC data sets was recently emphasized15.The central objective of this study was to determine if a gene expression signature of SZ could be detected in an experimentally tractable cell culture model Fig. . Due to Differential expression analysis between cases and controls in hiPSC-NPCs Fig.  identifiFZD6 and QPCT were differentially expressed, gene set enrichment testing did not implicate a coherent set of pathways , we failed to identify a coherent biological enrichment. Nonetheless, there was an unexpected concordance in the differential expression analysis between COS and control hiPSC-NPCs and hiPSC-neurons, which showed remarkably similar log2 fold changes  by computing the correlation in t-statistics from the differential expression analysis between cases and controls.While it is well-understood that all hiPSC-based studies of SZ remain under-powered due to small sample sizes and polygenic disease architecture, what is less appreciated is that post-mortem approaches are similarly constrained. Using allele frequencies from the Psychiatric Genetics Consortium data, the median number of subjects needed to obtain 80% power to resolve genome-wide expression differences in SZ cases was estimated to be ~28,500, well beyond any existing data setp < 4.6e−40 and 7.8e−12 for CMC and HBCC, respectively; and for hiPSC-neurons: p < 6.7e−61 and 1.6e−20 respectively (Spearman correlation test). Similar results were obtained by using Pearson correlation and by evaluating the concordance using the log2 fold changes from each data set , the concordance with HBCC was only apparent when correcting for the fibroblastGiven the degree of concordance in the SZ differentially expressed genes between the hiPSC-NPCs, hiPSC-neurons, CMC and NIMH HBCC data sets Fig. , the lac11 and coding mutations55, as well as common single nucleotide polymorphisms (SNPs) with small effect sizes56. The strongest finding to date from these genetic studies is that SZ-associated variants are enriched for pathways primarily associated with synaptic biology57. Although > 50 post-mortem gene expression studies of SZ have been reported, the results have been inconsistent, likely owing to the small sample sizes involved21. The largest of these, comparing brain tissue from 258 subjects with SZ and 279 controls did not find evidence for case–control differential expression among the implicated SZ risk genes; moreover, by modeling both the allele frequencies and the predicted allelic effects on gene expression, they predicted the median number of subjects needed to obtain genome-wide power (80%) to be ~28,50021. This issue of small sample sizes is not unique to post-mortem studies, and may be exacerbated in hiPSC-based experiments through the variability that arises as a result of the reprogramming and differentiation processes. We established an hiPSC cohort of COS patients62, testing our ability to model gene expression changes associated with both common and rare variants in vitro. While other studies have focused on SZ cohorts comprised of relatively few individuals with rare mutations5, we sought to determine to what extent a larger cohort captured the expression signature of polygenic SZ, focusing on COS due to the higher genetic burden of both rare and common variants in these patients.SZ is a complex genetic disease arising through a combination of rare and common variants. Recent large-scale genotyping studies have begun to reveal the extent to which SZ risk reflects rare copy number variants (CNVs)55 and 48.8%12 of expression variation, while the effect of donor was much smaller (2.2%) in this study. We hypothesize that donor effects are reduced due to stochastic noise in the differentiation from hiPSCs to neurons; it remains to be established whether different hiPSC-derived cell types will retain more or less donor signal over the course of differentiation. In our data set, while genes with high expression variation across donors were enriched for eQTLs detected in post-mortem brain, substantial expression variation within donors obscured some biological signal. In order to identify biological or technical variations that explained this intra-donor expression variation, we implemented a quality control pipeline to detect sample mislabeling, cell culture contamination, residual Sendai virus expression, incomplete X-inactivation and batch effects in sample processing; however, it was only accounting for variation in CTC that significantly decreased intra-donor variation.The goal of studying patient-derived cell culture models is to develop an experimentally tractable platform that recapitulates a donor-specific gene expression signature. Retaining this donor-specific signature is essential to studying case-control differences. In two recent studies of hiPSCs, variance across donors explained a median of ~6c-MYC, despite the use of sendai viral non-integrative methods has been previously reported64 and may reflect the variation of vector replication between cell lines, as well as a potential growth advantage of c-MYC expressing cells64. Although standard non-integrative methodologies rely upon passive and inefficient omission for the loss of sendai viral vectors64, new methods, such as auto-erasable Sendai virus vectors65, should facilitate the generation of truly transgene-free hiPSCs.The persistent expression of exogenous reprogramming factors, particularly 42 affects the contribution of each additional sample to the ESS  addresses this issue directly and indicates that the tradeoff is dependent on the cost per donor and per hiPSC line in addition to the fraction of expression variation explained by donor . The generation of single-cell sequencing data sets from hiPSC-NPCs and/or hiPSC-neurons will further yield a custom reference panel with which to improve CTC deconvolution. In fact, our results suggest that to maximize ESS while minimizing associated costs, it may be sufficient to focus on a single cell type, hiPSC-neurons rather than hiPSC-NPCs. Given our improved understanding of the inherent challenges associated with studying highly polygenic diseases as well as the biological constraints encountered with hiPSC-based models here, disease signal will be further improved by reducing disease heterogeneity through focusing on cohorts of patients with shared genetic variants and/or the genetic engineering of isogenic hiPSC lines to introduce or repair SZ-relevant variants.21, an effect that was strongest in hiPSC-neurons. Yet this shared biology did not yield enrichments at the pathway or network level in the diagnosis-dependent differentially expressed genes observed between hiPSC-NPCs and hiPSC-neurons with either post-mortem data set. Moving forward, increasing the sample size of hiPSC-based cohorts may improve this concordance and biological coherence. Alternatively, it is possible that many SZ-associated processes are not present in simple monolayer hiPSC-NPC and hiPSC-neuron populations; relevant aspects of SZ biology may only be detected through activity-dependent processes arising from complex neuronal circuitry, following oligodendrocyte myelination, astrocyte support or microglia pruning, or after exposure to neuroinflammation or environmental stimuli. While the best strategy to improve biological significance is to strive to enhance the complexity of hiPSC-based models, the surest approach to improve the power of case-control comparisons is to integrate a growing number of post-mortem and hiPSC studies. In either case, to facilitate improved sharing between stem cell laboratories, all hiPSCs have already been deposited at a repository. We urge widespread sharing of all RNA-Seq data and reproducible scripts, and so make ours available.Despite our relatively small sample size, we were able to identify a subtle but statistically significant concordance between both COS hiPSC-NPCs and hiPSC-neurons with two recent SZ post-mortem cohorts68; there is no clinical, neuroimaging, pharmacological or genetic evidence to suggest that COS is a distinct disorder (reviewed71). This is an unusually well-characterized cohort, with medication-free in-patient observation used for diagnosis72. The following clinical information was collected: gender, age at biopsy, developmental history, age of symptom onset, IQ, number of hospitalizations as a measure of disease severity, positive and negative symptom scale, diagnostic screening by Comprehensive Assessment of Symptoms and History (CASH), attention tests, current antipsychotic treatment, clozapine responsiveness, and substance abuse history.Description of COS cohort: Childhood-onset-schizophrenia (COS) is reliably diagnosed in children using unmodified DSM criteria10; many had skin biopsies completed. The Rapoport laboratory generously provided fibroblasts, from which 14 cases and 12 controls were reprogrammed. COS cases: NSB499 , NSB581 , NSB676 , NSB1251 , NSB1275 , NSB1358 , NSB1442 , NSB1804 , NSB2011 , NSB2476 , NSB2484 , NSB2513 , NSB2620 , NSB2962 . Controls: NSB553 , NSB690 , NSB2607 , NSB3084 , NSB3113 , NSB3121 , NSB3130 , NSB3158 , NSB3182 , NSB3183 , NSB3188 , NSB3234 . All fibroblast samples had IlluminaOmni 2.5 bead chip genotyping10, PsychChip and exome sequencing completed. The PsychChip genotyping data were used to calculate the polygenic risk score for each individual in this study; polygenic risk scores and SZ-relevant CNVs are listed in Supplementary Data Patients with COS, unaffected family members, and unrelated controls were recruited into a longitudinal study by Dr Judith Rapoport at the NIMHOCT4, SOX2, KLF4 and c-MYC, as per lot specifications. Cells from a single well of a six-well plate were split 1:2, 1:3 and 1:10 onto 10-cm plates containing 1×106 mouse embryonic fibroblasts . Cells were switched to hiPSC media , 1% (v/v) GlutaMAX, 1% (v/v) nonessential amino acids (NEAA), 55 μM β‐mercaptoethanol (β‐me)  and 20 ng ml−1 FGF2 ) and fed daily. hiPSC colonies were manually picked and clonally plated onto 24-well mEF-coated plates. hiPSC lines were maintained on mEFs in hiPSC media; at early passages, hiPSCs were split using manual passaging and at higher passages, hiPSC were enzymatically passaged with Collagenase (1 mg/ml in DMEM) (Sigma) until cryopreservation in cold freezing media (hiPSC media containing 10% DMSO). Individual hiPSC lines were validated using TRA-1-60 and SSEA-4 flow cytometry and NANOG, TRA-1-60, SOX2 and OCT4 immunocytochemistry. G-banded karyotyping was performed by WiCell Cytogenetic Services. The differentiation potential of the hiPSCs derived in this study was confirmed by RT-PCR for markers of the three germ layers following spontaneous differentiation of a subset of the lines into embryoid bodies. Routine (every 2–4 weeks) mycoplasma testing was conducted using the MycoAlert Mycoplasma detection kit (Lonza); all cells used in this study and were found to be negative.HFs were cultured on 0.1% (w/v) gelatin coated plates in HF medium (DMEM (ThermoFisher Scientific), 20% (v/v) FBS (Corning)). Replicating 90% confluent HFs were reprogrammed using a modified protocol; briefly, HFs were transduced with Cytotune® Sendai viruses (ThermoFisher Scientific) expressing 6 and maintained at high density, grown either growth factor reduced Matrigel (BD Biosciences) coated plates in NPC media (Dulbecco’s Modified Eagle Medium/Ham’s F12 Nutrient Mixture (ThermoFisher Scientific), 1x N2, 1x B27-RA (ThermoFisher Scientific) and 20 ng ml−1 FGF2 and split 1:3 every week with Accutase . NPCs were dissociated with Accutase and plated at 2.0×105 cells per cm−2 in NPC media onto growth factor reduced Matrigel-coated plates. For neuronal differentiation, media was changed to neural differentiation medium , 20 ng ml−1 GDNF (Peprotech), 1 mM dibutyryl-cyclic AMP (Sigma), 200 nM ascorbic acid (Sigma) and 1 μg ml−1 laminin (ThermoFisher Scientific) 1–2 days later. NPC-derived neurons were differentiated for 6 weeks.NPCs were generated from unique hiPSC lines with had normal karyotypes, and then passed NPC quality control based on immunocytochemistry and FACS for SOX2 and NESTIN levels. NPCs were derived, as previously described6 cells, BioLegend #330613) and SSEA4-647  in 1% (w/v) BSA for 45 min at 4 °C before being washed with 1× PBS and resuspended in FACS buffer (1× PBS (no Mg2+/Ca2) containing 1% (v/v) BSA and TO-PRO®3  and filtered using a 40 μm filter (BD Biosciences). NPCs were dissociated using Accutase, fixed for 10 min in 4% paraformaldehyde (PFA), permeabilized and blocked with 0.5% (v/v) Triton (Sigma)/1% (w/v) bovine serum albumin  in PBS and labeled with NESTIN-647  and SOX2-488  antibodies overnight at 4 °C before being washed with PBS and resuspended in FACS buffer (1× PBS (no Mg2+/Ca2) containing 1% (v/v) BSA and TO-PRO®3  and filtered using a 40 μm filter (BD Biosciences). Cytometry was performed using a LSR-II or FACS Canto (BD Biosciences) and analysis was performed using Flowjo .hiPSCs were labeled with TRA-1-60-488 , NESTIN , SYN1 , ACTIN , GAPDH . Data were analyzed using GraphPad PRISM 6 software. Values are expressed as mean ± SEM.Total RNA was extracted using Trizol following the manufactures instructions. Transcript analysis was carried out using a QuantStudio™ 7 Flex Real-Time PCR System using the Power SYBR green RNA-to-Ct RT-qPCR kit for primers . Around 50 ng of RNA template was added to the PCR mix. (ThermoFisher Scientific). qPCR conditions were as follows, 48 °C for 15 min, 95 °C for 10 min followed by 40 cycles . Primers used as follows: RNA Sequencing libraries were prepared using the Kapa Total RNA library prep kit with ribo-depletion and strand specific cDNA library construction (Kappa Biosystems). Paired-end sequencing reads (125 bp) were generated on an Illumina HiSeq2000 platform (New York Genome Center).73. Uniquely mapping reads overlapping genes were counted with featureCounts v1.4.474 using annotations from ENSEMBL v70. All analysis used log2 counts per million (CPM) following TMM normalization75 implemented in edgeR v3.14.076 unless stated otherwise. Genes with over > 1 counts per million in at least 30% of the experiments were retained.RNA-Seq reads were aligned to GRCh37 with STAR v2.4.0g177 following best practices to produce gVCF files. These files were merged using the GATK CombineVCFs functionality. The resulting VCF was then merged with variants from whole exome sequencing and PsychChip array from the same donors. Variant concordance between all pairs of samples was evaluated with bcftools v1.3. Discordant samples were relabeled when possible, otherwise they were excluded from downstream analysis.Variant concordance analysis was performed to identify instances where samples labeled as being from the same donor are discordant based on variant calls. Variants were called from the RNA-Seq BAM files using GATK 3.417 compares a BAM file from a sequencing experiment  to a set of reference genotypes to identify sample contamination. The software estimates the contamination percentage for each sample using a sophisticated statistical model. Each RNA-Seq BAM file was analyzed with verifyBamID using a VCF from either the PsychChip and whole exome sequencing data as the reference set. Results from both analyses were very similar. This method was originally designed for DNA sequencing where variants calls have a much lower error rate than from RNA-Seq. For this reason, multipotency data are expected to have increased contamination estimates even under the null model of no contamination.VeryifyBamID2 CPM for each in a linear model in order to remove the effect of cell type. Z-scores for each gene were computed by subtracting the mean and dividing by the standard deviation for each gene. Expression outliers were identified based on extreme z-scores.CNV coordinates where stored in a BED file and genes overlapping these regions were identified in R. Gene expression residuals were computed by fitting log18 was used to perform de novo assembly of these reads. Trinity was run with flags—no_run_chrysalis—no_run_butterfly and otherwise with default settings were used. This produced a FASTA file of de novo contigs for each RNA-Seq experiment. Bowtie2 v2.1.078 was used to index this FASTA file and TopHat2 v2.0.679 was used to align reads from the RNA-Seq FASTQ to the de novo contigs. This step quantifies how many reads correspond to each contig. Next, each contig was aligned to a database of complete viral genes from NCBI using BLAST80. The results were filtered to retain only contigs aligning to the Sendai virus genome sequence (GenBank: AB855655.1). Note that the specific Sendai virus used in the iPSC reprogramming has been engineered to incorporate four human transcription factors, and the genome sequence is not available. Therefore we used AB855655.1 as a proxy. Finally, reads corresponding to contigs that align to the Sendai genome were counted for each RNA-Seq experiment and these values were included in downstream analysis.Reads that did not map to the human genome with STAR were saved in a separate FASTQ file and Trinity30 using default settings on the web interface. CIBERSORT uses a machine learning approach to estimate the cellular composition of each sample based on the expression profiles of a set of reference cell types. The reference set was constructed based on biological expectations of the constituent cell types.31: Astrocytes, Neuron, Oligodendrocyte Precursor Cell, Newly Formed Oligodendrocyte, Myelinating Oligodendrocytes, Microglia, Endothelial CellsSingle cell RNA-Seq from mouse brain32. We included untreated mouse embryonic fibroblast (here termed fibroblast2). Cells were transformed with Ascl1, Brn2 and Myt1l and cultured for 22 days. Single cells were sequenced, clustered computationally, and annotated based on characterizing gene expression patterns. Cells with annotated based on expression of know genes as either: Neuron, Myocyte, Fibroblast (here termed fibroblast1).Single cell RNA-Seq from mouse cell culture from direct reprogramming from mouse embryonic fibroblast to neuron27.Bulk RNA-Seq from hiPSCCell type composition scores were computed using CIBERSORT v1.04Since single-cell expression data can be very noise, multiple examples of each cell type were included in the reference panel and the component scores were summed for each cell type. This analysis was performed multiple times with different representatives of each cell type included each time to ensure the results were robust.For each sample, CIBERSORT reports the estimated percent composition for each cell type in the reference panel. However, the scale of these percentages is sensitive to the other cell types included in the panel and is often not biologically plausible. For example, while the hiPSC composition of NPCs is estimated to be ~35–57%, this not biologically realistic because (i) hiPSCs cannot survive in NPC culture conditions, (ii) hiPSCs replicate more quickly than NPCs, (iii) colonies of hiPSCs would be immediately visually obvious in NPC or neuron cultures, and (iv) NPCs do not show strong expression of critical hiPSC markers such as NANOG, OCT4 or TRA-1-60. Instead, we treat these results as “composition scores” and ignore the scale, focusing on comparing scores for a given cell type across all samples. In this context, the high hiPSC composition score for NPCs likely indicates a “stemness” signal that would be expected to be evident in NPCs and be much lower in neurons.41. The results were visualized using the package’s build-in functions. Categorical variables  were modeled as random effects and continuous variables  were modeled as fixed effects. Each gene was considered separately and the results for all genes were aggregated afterwards.The expression variance for each gene was partitioned into the variance attributable to each variable using a linear mixed model implemented in variancePartition v1.5.32 RPKM. Quantile normalization was performed on all samples using normalizeBetweenArrays in the limma package50.RNA-Seq data sets were obtained from GTEx , CommonMind (http://www.synapse.org/CMC), BrainSpan (http://www.brainspan.org/), and GEO (https://www.ncbi.nlm.nih.gov/geo/). Gene-level counts for Entrez or HGNC symbols were assigned to the corresponding ENSEMBL identifier. Genes with > 1 count per million in 10% of the samples in each data set were retained. This left 12,670 genes in common across all data sets. All expression values were converted to logt-statistics from differential expression analysis of SZ donors compared to controls in hiPSC-NPCs and hiPSC-neurons in the current analysis compared to differential expression t-statistics from five psychiatric diseases53 and nine cancer types : Analysis-ready standardized TCGA data from Broad GDAC Firehose 2016_01_28 run. Broad Institute of MIT and Harvard. Data set. https://doi.org/10.7908/C11G0KM9). Only cancers with at least 30 RNA-Seq experiments were considered.The correlation between Analysis was performed using cmdscale function in R based on the distance matrix computed from the pairwise correlation matrix based on all genes in the merged data set.Hierarchical clustering was implemented in R using complete linkage clustering. A pairwise distance matrix was computed for all samples, and the median distance between all samples in each category were used to create a summary distance matrix using to perform the final clustering.2 CPM values for the each cell type separately, the combined NPC+neuron data set, and the residuals from the combined NPC+neuron data set after removing the effect cell type composition scores.PCA was performed on the logThe gene expression data were adjusted for heterogeneity in cell type composition using a linear model by including the cell type composition score as a covariate. Residuals computed using fibroblast and MEF CTC-scores were used in principal components analysis.21 and genes exceeding a variance percentage cutoff for a particular variable in the current analysis is computed. This overlap is then compared to the overlap computed from randomly permutated variance percentages. Each gene is assigned a value based on the percentage of variance explained by a particular variable in the variancePartition analysis. At each of 40 cutoff values, the overlap between genes with values exceeding this cutoff and the 2000 genes with the smallest p-values from cis-eQTL analysis is evaluated. The overlap was computed for the observed data and 10,000 data sets with the variance percentages randomly permutated. At each cutoff where > 100 genes are represented, the fold enrichment is computed asThe overlap between eQTL genes from the CommonMind Consortium81.The mean enrichment value and the 90% confidence interval are shown in the plot. Since the most genes (80%) have genome-wide significant eQTLs in the CommonMind data set due to the large sample size, only a set of top genes were considered for enrichment. The top 2000 genes were used here, but the results are not sensitive to varying this number as long as  ≲ 10,000 genes are used. Permutation and overlap calculations were performed using regioneR50 using duplicateCorrelation to account for measuring multiple samples per donor. Hypothesis testing was performed using the Empirical Bayes procedure in limma. Analysis was corrected for multiple testing using qvalue82. Standard error of the log2 fold change estimates were computed by dividing the log2 fold change by the moderated t-statistic. Analysis included sex and the cell type composition scores as described above.Differential expression analysis was performed with limma/voom v3.28.1783, MAGMA84 and additional sets from Fromer et al.21Standard gene set enrichment tests was performed with a hypergeometric test using gene sets from MSigDB85.Due to the polygenic nature of COS and the lower power of this study due to its relatively small sample size, changes in gene expression are expected to by subtle and distributed across many genes. The differential expression analysis between SZ and controls did not produce strong results, so we performed extensive enrichment analysis downstream in order extract biological insight. The simplest analysis uses a hard cutoff and considers only genes that pass a given FDR threshold. Genes with FDR < 30% in either cell type were tested with EnrichRt-statistics of a differential expression test. These tests evaluate enrichment based on genes that are not genome-wide significant, and identify sets of genes for which the distribution of t-statistics differs from expectation. Moreover, these tests can use empirical permutations to address the multiple testing problem and determine the significance of gene set enrichments. This permutation approach increases power in small sample sizes with complex correlation structure between genes compared to the standard statistical methods for differential expression and multiple testing correction. This family of tests is well suited to a study of polygenic disease in an underpowered data set. These methods are available in the limma package50 and work directly on the result of a standard voom analysis49.Alternatively, more powerful enrichment analyses do not use a cutoff but instead consider the t-statistics of genes in a given set are higher, lower or deviate from zero in either direction more than expected. ROMER is similar to GSEA83, but uses a sophisticated permutation approach within a linear model framework to increase power.ROAST is a self-contained test that evaluates whether the We modified the standard R code for these methods in order to enable parallelized analysis on a multicore machine and increase the number of permutations. This allows us to run 10,000 permutations for ROAST and 100,000 permutations for ROMER.48 on the log2 CPM values for each cell type. NPC and forebrain neuron samples were analyzed separately and the results were combined downstream. Following standard procedure to ensure an approximately scale-free network, pairwise correlation matrices were raised to a power 9 for both cell types. Topological overlap matrices were computed for each cell type. Coexpression modules were identified with average linkage clustering followed by dynamic brank pruning using the cutreeDynamic function using the “tree” method with a minimum module size of 20 genes. Enrichment tests for gene sets in each coexpression model were performed with a hypergeometric test.Analysis was performed with WGCNAAll hiPSCs have already been deposited at the Rutgers University Cell and DNA Repository . RNA-Seq data and reproducible scripts are available at www.synapse.org/hiPSC_COS, as well as GSE106589. Owing to constraints reflecting the original patient consents, the raw RNA-Seq data will be made available by the authors upon reasonable request and IRB approval.Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Supplementary Data 6Supplementary Data 7Supplementary Data 8Supplementary Data 9


Detecting differentially expressed (DE) genes between disease and normal control group is one of the most common analyses in genome-wide transcriptomic data. Since most studies don’t have a lot of samples, researchers have used meta-analysis to group different datasets for the same disease. Even then, in many cases the statistical power is still not enough. Taking into account the fact that many diseases share the same disease genes, it is desirable to design a statistical framework that can identify diseases’ common and specific DE genes simultaneously to improve the identification power.We developed a novel empirical Bayes based mixture model to identify DE genes in specific study by leveraging the shared information across multiple different disease expression data sets. The effectiveness of joint analysis was demonstrated through comprehensive simulation studies and two real data applications. The simulation results showed that our method consistently outperformed single data set analysis and two other meta-analysis methods in identification power. In real data analysis, overall our method demonstrated better identification power in detecting DE genes and prioritized more disease related genes and disease related pathways than single data set analysis. Over 150% more disease related genes are identified by our method in application to Huntington’s disease. We expect that our method would provide researchers a new way of utilizing available data sets from different diseases when sample size of the focused disease is limited.The online version of this article (10.1186/s13040-018-0163-y) contains supplementary material, which is available to authorized users. High-throughput technology like microarray and next-generation sequencing (NGS) allows researchers measure thousands of gene or microRNA expression in one sample simultaneously. Detecting differentially expressed (DE) genes between disease and normal control group is one of the most common analyses in genome-wide transcriptomic data. Differentially expressed genes are potential disease-related genes and could be used for generating biological hypothesis of disease mechanism, developing potential clinical diagnosis tools and investigating potential drug targets. This approach has been successfully applied in many complex diseases like cancers , 13 and With the cost of microarray and next generation sequencing technique decreasing and stabilization of the experiment protocol, there are now over 1,000,000+ samples deposited in public databases such as Gene Expression Ominus (GEO) . With thlimma [Numerous cross-disease analyses have shown that similar diseases might share similar disease related genes. However, in these cross-disease comparison studies, they took a simple “disease-by-disease” approach: each disease was analyzed with traditional DE detection method like two-sample t-test or limma  separatelimma , 16, 30.Meta-analysis approach is a popular data integrating statistical methods used to analyze multiple public datasets of same biological conditions . They imMotivated by this, we propose an empirical Bayesian based mixture model which jointly analyzed multiple similar diseases to increase the identification power of common and disease-specific DE genes. The rest of paper is organized as follows. First, through a comprehensive simulation study, we compare our method with single data set analysis as well as two popular meta-analysis approaches with different underlying null hypothesis: minP, maxP , 32 and g  to obtain a differential test score within each data set i . In this study, we choose to use two sample t-statistic: tgi as the differential test score. We then transform tgi into a Z-score: Zgi according to McLachlan’s normal transformation [i where each gene’s hidden DE status variable Di is either DE (Di =1) or non-DE (Di =0). Then we assume two different conditional density distributions of Z-scores depending on a gene’s hidden status Di in data set i: f(Z| Di) where Di =1 or 0. By doing so, we model the study-specific variation of Z-scores observed within each data set i.Assume that there are N data sets. Each data set contains both disease and normal samples. Same G genes’ expressions are measured in each data set. In the proposed joint analysis framework, a differential test is first performed for each gene ormation  so that N disease datasets, we want to compute the posterior probability to infer the DE status of gene g in disease data set i which could be written as:Given observed expression difference According to Bayes’ Theorem, we could expand (1) into:Di is determined, written as:We further assume independence of conditional joint Z score distribution across data sets if the hidden status variable f(Z| Di) distribution is different from data set to data set, so it needs to be estimated separately for each data set. Here we apply the method of local false discovery rate  developed by Efron [f(Zgi) = f(Zgi| Di = 0) Pr(Di = 0) + f(Zgi| Di = 1) Pr(Di = 1) and Pr(Di = 1) =1 − Pr(Di = 0).by Efron  to estimf(Zgi) is estimated through fitting z-scores of all genes to a cubic spline. The conditional density f(Zgi| Di = 0) is assumed to be a normal distribution. Its mean and variance as well as the quantity Pr(Di = 0) are estimated through fitting the Z-scores in the central peak (around 0) by maximum likelihood estimation approach. This is a reasonable assumption because most Z-scores around the 0 should come from the non-DE distribution. Then through Eq. (f(Zgi| Di = 1). All the estimation procedures described above are done through the locfdr package in R [Di = 1| Zgi) = 1− localFDR(Zgi) computed by local FDR method as the inference result of single data set analysis for method comparison purpose.In the localFDR approach, the marginal density ough Eq. , we coulage in R . In thisD1, D2, …Di, …DN). The shared information between similar diseases is also modeled by this quantity. For example, we would expect that if one gene is DE in one disease, it is highly likely to be DE in another similar disease. In mathematics, we write this relation as Pr = Pr(D2 = 1| D1 = 1) Pr(D1 = 1) and Pr ≠ Pr(D1 = 1) Pr(D2 = 1). This prior probability could be estimated by using Expectation Maximization (EM) algorithm [Initialize the prior probability: s, compute the joint posterior probability of gene g given At iteration Finally, we need to estimate the only unknown parameter in Eq. , the prilgorithm  to maxim(3)Estimate the new prior probability at iteration s + 1 by averaging the joint probability calculated in step (2) over all genes: (4)Repeat step (2) and (3) until convergence.The proposed joint analysis framework is implemented under R statistical programming language.In real world, researchers often have limited samples for a specific disease and do not have other public data sets of the same disease while public data set of other similar diseases exists. We design a simulation study which mimics the real situation to test and compare the performance of our method with others. Our simulation study models this situation by generating different number of studies with similar but slightly different DE gene configuration in each disease.N studies with 15 disease and 15 control samples within each study. Each study here could be considered as a similar disease. There is a total of 10,000 genes expression value measured in each sample. We first need to determine the hidden DE status variable value for each gene in each study. We define shared percentage between study i and study j as the conditional probability of being DE in study j if the hidden DE status variable in study i is true, i.e. Pr(Dj = 1 ∣ Di =1). We further define “similarity” as the average shared percentage of DE genes between two studies, i.e. i =1) = 0.1. We finally define two diseases are “similar” if the similarity value between two diseases is higher than the expected similarity  = Pr(Di = 1) Pr(Dj = 1)). Therefore, once the DE status variable value of a gene in the reference study is known, we could generate DE status variables of this gene for all other studies. In this simulation, we assume study 1 is the reference study, the hidden DE status configuration of other studies is then generated for each gene based on the DE status variable in study. After the hidden DE status variable is determined, we generate the normally distributed expression value based on the DE status of each gene. The variance σ2gd of each gene g is assumed to be the same in each study d and is sampled from an inverse chi-square distribution with degrees of freedom 4 and scale parameter 0.02. We then generate gene ’s expression for every sample from N. If Di =1, we sample a μgd from N where wgd = 4 here and add it to the expression value of disease samples. By using this simulation setup, we mimic the real case when the sample size of target disease is small but studies of similar diseases exist in public database.To be more specific, the simulation is set to have N = 2 data sets, with same number of genes and samples setup described above. In the first data set, the first 1000 genes are assumed to be DE. In the second data set, we assume that first X genes are DE and for the rest of 1000-X genes, we ensure that they will not overlap with any DE genes in data set 1. Once the DE status of all genes are set in two data sets, gene expression values are generated with the same procedure described above. By setting so, the true prior probability is a fixed value. For example, if X = 600, then the prior probability will be Pr =0.06, Pr =0.04, Pr =0.04 and Pr = 0.86 respectively. By using this simulation setup, we are able to compare the estimated prior probability generated from joint analysis with the true value.We also design another simulation study by fixing the hidden DE status of each gene before generating the expression value. In this simulation study, we assume that there are p-value from each study as test statistics: SgmaxP = argmax(pgk). SgmaxP follows a beta distribution with degrees of freedom α = N and β = 1 under null hypothesis. The maxP method targets the DE genes with small p-values in all studies. The minP method takes the minimum p-value among the K studies as the test statistic: SgminP = argmin(pgk). It follows a beta distribution with degrees of freedom α = 1 and β = N under the null hypothesis. This method detects a DE gene whenever a small p-value exists in any one of all studies. All meta-analysis methods used in this paper are implemented through using metaRawdata function in metaDE R package [Two popular meta-analysis approaches are compared with our joint analysis method: minP and maxP . These t package , 32. TwoSix normalized microarray expression data sets representing different types of cancers are downloaded from GEO . Each dasva R package [Narayanan et al. conducted a co-expression network analysis between Alzheimer’s disease (AD) and Huntington’s disease (HD) using the prefrontal cortex region of postmortem brain samples consisting of 310 AD patients, 157 HD patients and 157 controls . The micg in each data set i, then the differential test statistics is transformed into a Z-score: Zgi. Then within each data set, estimate f(Z| Di) distribution with localFDR approach. After the conditional density value is obtained for each Zgi, the prior probability Pr is estimated through EM algorithm. Finally compute posterior probability defined in Eq.  = 0,0.1, 0.6, 0.7, 0.8, 0.9, 1. Every parameter combination is repeated for 100 times. In each run, we set a specified posterior probability cutoff in data set 1 which is considered as the disease data set of interest and report the average sensitivity as well as average false discovery rate (FDR) in study 1 as the result. The cutoff is set to 0.95.We begin by comparing the identification power between single data set analysis and joint analysis using simulation studies. Different simulated disease data sets are generated by varying the number of data sets and shared percentage among diseases as described in “Methods” section. The number of data sets is set for Figure N = 2, 4, 6 and the shared percentage between study 1 and other studies is set to Pr (Dj=1|D1 = 1) = 0.6, 0.8, 1. The results are shown in Fig. N = 2 minP and joint analysis have similar performance. When the shared percentage value increases to 0.8, joint analysis outforms other methods in top ranking below 800, minP method performs better in the rank range of top 800–1000 genes. When the shared percentage is 1, which means all measurement are based on same disease, minP has better performance. Overall, when the data sets are based on similar but different diseases, especially when more diseases are included, our joint analysis outperformed other methods.We then compared the proposed joint analysis framework with other two popular meta-analysis approaches: minP and maxP and use single data set analysis as a baseline of comparison. We evaluated the performance of different methods by plotting the top ranked genes against the average number of true DE genes identified in study 1 out of 100 runs with varying values of shared percentage and number of data sets. The number of data sets is set for X value was set to 600, 700, 800, 900 and 1000. Each parameter setup was repeated 10 times and the results were summarized in Table The estimated prior probability from joint analysis is evaluated because prior probability plays an important role in empirical Bayes framework. We first compared the estimated prior probability with true value in a two-dataset simulation study in which hidden DE status of genes were fixed. The details of the simulation was described in “Methods” section. Finally, the influence of sample size of a similar disease to be borrowed from is evaluated. To achieve this purpose, we first fix the target data set with 15 disease and 15 control samples. Then, we generate second similar (60% similarity) disease data sets with different sample sizes, each of which contains 5, 10 and 15 disease and control samples respectively. The mean and variance for each gene in each data set is fixed in this simulation. This simulation procedure is then repeated 100 times for each sample size parameter. After that, we apply both single and joint analysis on the simulated data sets and record the average sensitivity and FDR at specified cutoff = 0.95 for each sample size parameter. The result is shown in Fig. N = 6 and increased shared percentage parameter setup with cutoff = 0.95. The simulation study suggested that the joint analysis could identify most of genes which are identified by single data set analysis . The estimation of prior probability would become infeasible when the number reaches 20 or more. Some potential solution to this problem has been proposed in a recent paper [There would be several improvements for the proposed joint framework in the future. The first issue to be addressed is how to jointly analyze more disease data sets. As mentioned by one reviewer, the estimation of the prior probability in the proposed framework here is computationally intensive when the number of diseases to be jointly analyzed is large Additional file 2: Table S2. Comparison of estimated similarity from joint analysis and true similarity. (XLSX 8 kb)Additional file 3: Table S3. Complete DE gene lists identified by joint analysis. (XLSX 81 kb)Additional file 4: Table S4. Overlapped genes comparison between single data set and joint analysis. (XLSX 8 kb)


Colorectal cancer (CRC) is the third most common cancer in the world. A significant survival rate is achieved if it is detected at an early stage. A whole blood screening test, without any attempt to isolate blood fractions, could be an important tool to improve early detection of colorectal cancer. We searched for candidate markers with a novel approach based on the Transcriptome Mapper (TRAM), aimed at identifying specific RNAs with the highest differential expression ratio between colorectal cancer tissue and normal blood samples. This tool permits a large-scale systematic meta-analysis of all available data obtained by microarray experiments. The targeting of RNA took into consideration that tumour phenotypic variation is associated with changes in the mRNA levels of genes regulating or affecting this variation.TSPAN8, LGALS4, COL1A2 and CEACAM6 resulted as being statistically different.A real time quantitative reverse transcription polymerase chain reaction (qRT- PCR) was applied to the validation of candidate markers in the blood of 67 patients and 67 healthy controls. The expression of genes: TSPAN8 an AUC of 0.751 with a sensitivity of 83.6% and a specificity of 58.2% at a cut off of 10.85, while the panel of the two best genes showed an AUC of 0.861 and a sensitivity of 92.5% with a specificity of 67.2%.In particular ROC curves attested for Our preliminary study on a total of 134 subjects showed promising results for a blood screening test to be validated in a larger cohort with the staging stratification and in patients with other gastrointestinal diseases. Colorectal cancer (CRC) is the third most common cancer in the world, with nearly 1.4 million new cases detected in 2012. A significant survival rate is achieved if the primary tumour is detected at an early stage .Most CRC develops in a multistep process, starting with benign precancerous adenomas, which develop into aggressive metastatic carcinoma . This maANXA3), C-Type Lectin Domain Family 4, Member D (CLEC4D), Lamin B1 (LMNB1), Proline Rich Gla (G-Carboxyglutamic Acid) 4 (Transmembrane) (PRRG4), Tumor Necrosis Factor, Alpha-Induced Protein 6 (TNFAIP6), Vanin 1 (VNN1) and Interleukin 2 Receptor, Beta (IL2RB) has been proposed by Marshall et al. ”. The terms “Colon cancer” and “Rectal cancer” were also used. In addition, the search for “Whole blood” [Sample Source] AND Homo sapiens [ORGANISM]” was used to retrieve datasets in the same database, thereby generating a pool serving as a comparison set to highlight CRC-specific differential gene expression compared to blood cells. The searches were carried out until February 2012. Search results were then filtered using inclusion and exclusion criteria as explained below.The inclusion criteria of datasets in the analysis were: experiments of the type “Expression profiling by microarray”; primary tissue; any age or sex of the subject; cancer of any stage (for CRC); unfractioned peripheral blood; unstimulated blood cells; availability of the raw or pre-processed data for the single channel of the tissue of interest in the case of two-channel experiments.Exclusion criteria were: cell line samples; non malignant adenoma tissues; metastatic cells; patients on treatment; cord blood; blood fractions, e.g. peripheral blood mononuclear cells or blood samples subjected to globin mRNA reduction; blood from treated or non-healthy or paediatric subjects; exon arrays (hampering data elaboration by TRAM due to an exceedingly high number of data rows) or platforms using probes split into several distinct arrays for each sample ; platforms assaying an atypical number of genes (i.e. < 5.000 or > 60.000).In order to obtain a quantitative transcriptome map, values from each dataset were linearized when provided as logarithms.Finally, due to the timing of the TRAM elaboration process, for each series of GEO samples we randomly selected the first 10 listed samples when the sample number was greater than 10. This was done for a total of 349 CRC samples out of 2,532 found and 200 samples of normal blood out of 958 found.The 349 CRC subjects considered for our meta-analysis were randomly selected from 37 studies performed in different countries . Their clinical data are not completely available for all subjects, and the age range of patients was between 42 and 88 years old. Males and females were equally represented, all colon sites and Dukes stages were included. The 200 blood samples considered were obtained from 23 studies, which were also from different countries , and came from healthy subjects with no clinical history of neoplastic disease.TRAM (Transcriptome Mapper) software allows the import of gene expression data recorded in the NCBI GEO database in tab-delimited text format. It also allows the integration of all data by decoding probe set identifiers to gene symbols via UniGene data parsing, normalizing data from multiple platforms using intra-sample and inter-sample normalization  .n = 349; pool ‘B’, blood, n = 200) and selected for the study. The comparisons allowed us to analyse the differential transcriptome maps ‘A’ vs ‘B’, using the ratio of the mean expression values for each locus.We created a directory (folder) for each tissue, containing all the sample datasets related to the same source , instead of the original 2011 version. The data for 10 platforms used by some of the experiments selected but not provided by the default configuration of TRAM 1.1 (version for “Human”) were then uploaded . In briefly, gene expression values were assigned to individual loci via UniGene, intra-sample normalized as a percentage of the mean value and inter-sample normalized by scaled quantile.We used an updated version of TRAM, including enhanced resolution of gene identifiers and updated UniGene and Entrez Gene databases  has been estimated to be at least 64 healthy control subjects and 64 patients with diagnosed CRC  error = 0.05 and assuming a medium effect size at d = 0.5).In order to evaluate the proper number of subjects to question, we performed the analysis of statistical power of the study with the G*Power 3 software for Macintosh OS X system. The sample size needed to achieve a power of 0.80 when analysing results by The study was conducted following approval by the ethical committee of Sant'Orsola-Malpighi Hospital, Bologna, and complied with the Ethical Principles for Medical Research Involving Human Subjects of the Helsinki Declaration. All subjects involved were asked for informed written consent before taking part in the study.A peripheral blood sample (5 mL) was obtained from 67 healthy donors with no clinical history of neoplastic disease and from 67 unrelated patients with a histological confirmed diagnosis of CRC at any stage, before elective surgery  and total RNA was extracted according to the manufacturer's protocol. Total extracted RNA from 1 mL of blood was subjected to standard ethanol precipitation, and the pellet was dissolved in 15 μL RNase-free water to a final concentration of up to 0.5 μg/μL, and stored at −20°C.The concentration of all RNA samples was quantified by Nanodrop ND-2000 spectrophotomer .300 ng of RNA was reverse transcribed with the RevertAid First Strand cDNA Synthesis kit  and amplified using the EvaGreen system , according to the manufacturer's instructions. The list of primers  for candidate markers and the reference gene is reported in Table Real-time PCR reactions were performed using the CFX96 instrument , in duplicate, at 95°C for 10 min, followed by 40 cycles of 95°C for 15 sec and 60°C for 1 min, with melting curve analysis. Each qPCR run always included a negative control lacking cDNA template, and a positive control of cDNA derived from the HT-29 cell line, in which the gene of interest is known to be present. Reaction efficiency (E) was calculated from the slope of the standard curve generated from 10-fold serial dilutions of calibrator cDNA, according to the formula: E = [10 (−1/slope)−1] × 100.t-test was adopted for the comparison of the expression levels analysed between CRC cases and controls. ROC (Receiving Operating Characteristic) curve analysis was used to assess the accuracy with which the parameters diagnosed CRC, in order to discriminate between patients with CRC and controls. Calculation of both the area under the curve and the corresponding 95% confidence intervals was evaluated using MedCalc version 16 for statistical analyses. To determine the cut off of the markers that allows for the best discrimination between the two groups, the discriminant analysis was performed using SPSS statistical software, version 23. The sets of healthy and cancer patients were considered as grouping variable and the four independent markers grouped together as predicted variable for the panel.The Student's


Plasmodium falciparum parasitemia by measuring levels of thyroglobulin, ferritin, soluble transferrin receptor, retinol binding protein 4, α-1-acid glycoprotein, C-reactive protein, and histidine-rich protein II. This 7-plex immunoassay technique has potential as a rapid and effective tool for use in large-scale surveys and assessments of nutrition intervention programs in low- and middle-income countries.Deficiencies of vitamin A, iron, and iodine are major public health concerns in many low- and middle-income countries, but information on their status in populations is often lacking due to high costs and logistical challenges associated with assessing micronutrient status. Accurate, user-friendly, and low-cost analytical tools are needed to allow large-scale population surveys on micronutrient status. We present the expansion of a 7-plex protein microarray tool for the simultaneous measurement of up to seven biomarkers with relevance to the assessment of the key micronutrients iron, iodine, and vitamin A, and inflammation and malaria biomarkers: α-1-acid glycoprotein, C-reactive protein, ferritin, retinol binding protein 4, soluble transferrin receptor, thyroglobulin, and histidine-rich protein II. Assay performance was assessed using international reference standards and then verified by comparing the multiplexed and conventional immunoassay results on a training panel of plasma samples collected from US adults. These data were used to assign nominal concentrations to the calibrators of the assay to further improve performance which was then assessed by interrogating plasma samples from a cohort of pregnant women from Niger. The correlation between assays for each biomarker measured from this cohort was typically good, with the exception of thyroglobulin, and the sensitivity ranged from 74% to 93%, and specificity from 81% to 98%. The 7-Plex micronutrient assay has the potential for use as an affordable tool for population surveillance of vitamin A, iron, and iodine deficiencies as well as falciparum malarial parasitemia infectivity and inflammation. The assay is easy-to-use, requires minimal sample volume, and is scalable, rapid, and accurate—needing only a low-cost reader and basic equipment present in most reference laboratory settings and so may be employed by low and middle income countries for micronutrient surveillance to inform on status in key populations. Micronutrient deficiencies including iron, iodine, and vitamin A affect a significant portion of the world’s population. Efforts to assess the prevalence of these deficiencies in vulnerable populations are challenging, partly due to measurement tools that are inadequate for assessing multiple micronutrients in large-scale population surveys. We have developed a 7-plex immunoassay for the simultaneous measurement of seven biomarkers relevant to assessing iodine, iron, and vitamin A status, inflammation and  Plasmodium falciparum . To . To 5]. Summary statistics from the 7-Plex assays and conventional assays from both the training panel and the NiMaNu study specimens are shown in Reference materials measured in the 7-Plex and conventional immunoassays as compared to their certified values are shown in Using a method similar to inter-method harmonization, as described by Wilson, et al. , we modeSpearman correlations between the 7-Plex assays and original study results were high (0.840–0.987) for plasma AGP, CRP, ferritin, RBP and sTfR concentrations, and moderate for Tg (0.753) which compared 7-Plex plasma results to NiMaNu DBS results . HRP2 opA total of 1442 data points for the 7 biomarkers from the 206 NiMaNu samples were generated by the 7-Plex assay. From the initial 7-Plex assay, there were only 21 test results that were out of range (upper or lower) and only 4 samples that failed to meet quality control (QC) criteria of an intra-assay CV of <15% for duplicate sample wells. Only 3 assays were affected by being out of range limits: CRP , ferritin , and sTfR (1 lower). Some samples were out of range for more than one analyte, meaning that retesting a single sample permitted measurement of more than one biomarker that was out of range. Therefore, only 25 from 1442 test results (1.7% of the entire test panel) had to be reassayed.Further comparison of the data sets was made via scatter plots and Bland Altman analyses Figs . Each plOur initial evaluation of the panel showed significant differences in absolute values between results from an R&D Systems sTfR ELISA kit and the 7-Plex sTfR . The NiMFor Tg, the Spearman correlation between the 7-Plex and revised NiMaNu results was originally 0.753 (n = 199). Scatterplots  show ninSensitivity and specificity analysis was used to determine whether the data derived via the 7-Plex could be used correctly to categorize individual samples as deficient/sufficient , positive or negative for malaria parasitemia (HRP2), and positive or negative for acute inflammation (AGP and CRP), using the classification from the NiMaNu study as the gold standard . Using tP. falciparum. The sTfR assay was redesigned because an antibody used in the original design was no longer available. All of the assays were successfully integrated into an immuno-array and then further optimized to allow a single dilution of small sample volume for the simultaneous quantification of seven analytes using a traditional ELISA protocol and equipment, apart from the Q-viewer for plate reading and analysis. In addition, a reference spot was also added to the array in order to simplify the alignment of the data capture grid of the analysis tool with each well of the 96 well plate image. The Q-View software automatically fits calibration curves for all seven analytes using a log 5-parameter logistic model, thus simplifying analysis, as compared to conventional assays which may require the use of additional software to fit assay calibration curves and calculate concentration. Verification experiments demonstrated that the resulting 7-Plex panel met performance requirements for assay precision, linearity, and limits of detection. Good correlation, sensitivity and specificity in comparisons to results of conventional assay results for samples collected from a population at risk for deficiency, inflammation and malaria parasitemia suggest the 7-Plex assay offers a promising alternative to existing methods.This work was designed to expand upon a pre-existing multiplexed assay that we have previously developed by adding additional biomarkers for MN status assessment, and to make improvements to the original assays included in the Quansys Bioscience Q-Plex™ technology . New assIn our previous study, we used the Bio-Rad Liquichek 3 control as the calibrator for our reference curve values ,36. WhilA lack of harmonization between different ELISAs to the same target biomarker is commonly observed and their standardization remains a challenge ,53. Our We noted that the ferritin results differed significantly from the Ramco kit results when the 7-Plex calibrator was referenced to the NIBSC standard, with recovery averaging 12%. The NIBSC standard was intended to qualify the standard concentration but we found that it was poor when used with our assay. The NIBSC ferritin standard is a recombinant protein expressed from a bacterial source and therefore it may be possible that some epitopes are absent or altered in this molecule as compared to human ferritin. We hypothesize that one or both of the monoclonal antibodies used in the 7-Plex sandwich ELISA has reduced avidity to the ferritin standard used. However, an inter-laboratory evaluation of this standard showed generally good high concordance with the standard values . We thenSimilarly, the sTfR results were significantly different in the 7-Plex immunoassay versus the reference method (R&D or VitMin Labs). The challenges with standardization of sTfR immunoassays have been noted before with bias across methods prior to a reference standard being available; the NIBSC standard also uses a recombinant antigen . Even wiA key limitation of our previous study was that the panel used to verify the original 5-plex assay was derived from an adult US cohort, which does not represent the typical populations that the tool is designed to survey in terms of more variable MN status and increased exposure to disease and/or malaria. In this study, we also demonstrate the performance of the 7-Plex array on samples derived from pregnant women in Niger. This represents a key population group where surveillance can play a role in understanding MN status in populations at risk. This panel was ideal for the initial verification of the 7-Plex as the same biomarkers had all been qualified by independent methods, with the exception of seven data points for Tg, from samples that were not analyzed. Overall, the assays on the 7-Plex array had good concordance with the predicate ELISAs when analyzed by Spearman correlation, and scatter plots, and Bland Altman analysis reflect overall agreement in spite of some cases of systematic difference in absolute value. The Bland Altman analyses were key to highlighting that some highly discordant test data points were at either very low or high levels, and so while significantly different, they present the same test outcome . This was further confirmed by applying ROC analyses to determine the optimal cut-off values that still gave high sensitivity and specificity of the 7-Plex assay as compared to the NiMaNu cut off threshold to establish MN status as sufficient or deficient, inflammation status, or malarial infection. When considering all the biomarkers assessed from the NiMaNu panel, only 1.7% had to be retested due to being out of range or being where duplicate test results were variant by >15%, indicating that the combination of broad calibrator ranges and the sample dilution volume permit the detection of most biomarkers in one test run. This limits the need to run multiple repeat assays using different sample dilutions in order to acquire a complete data set saving money, reducing the need for extra sample volumes and the time to process discrepant samples.AGP had the poorest sensitivity in the NiMaNu samples of pregnant women via this analysis and this is largely in part to the few samples that had significantly elevated AGP levels with the majority of samples being tightly clustered at low concentrations. We hypothesize that AGP is prone to more significant variation within small sample dilutions as compared to the other biomarkers due to its relatively high concentration. For example, many conventional ELISA assays for AGP recommend a sample dilution of 1:10000 to prior to measurement of AGP, whereas the 7-Plex plasma sample volume is only diluted by 1:10. In some cases, the values for recovery were significantly different from the original assay while being concordant via Spearman and Bland Altman analysis.While MN deficiencies affect vulnerable population groups globally, LMICs are most significantly affected. Most LMICs attempt to address these deficiencies via supplementation, fortification, and dietary and/or agricultural interventions to improve micronutrient intakes. However, the challenge in assessing MN status at the population level has led to large data gaps in terms of general surveillance. This is due to a variety of reasons but primarily because MN measurements are costly, difficult and complex to perform in-country based upon the current methodologies. Our tool is specifically targeted toward users in LMICs, with the intent of developing a practical and relatively low-cost tool  to enable in-country surveillance of nutritional status across key populations and to measure the performance of ongoing nutritional interventions. We have developed a tool that can be used in a laboratory setting, does not need a technically complex reader, and requires only common laboratory equipment . The protocol follows a standard methodology for performing an ELISA assay and one trained laboratorian can process 10 plates in an 8-hour period .Further validation of the Human Micronutrient 7-Plex assay on other appropriate cohorts is necessary and is presently underway by our team and by other independent groups. We are currently investigating the use of dried blood or plasma spots as a sample type given that an assay only requires 16 μL of sample. We are also in the process of investigating the expansion of the range of assays hosted by the platform in order to assess other key MNs including folate, vitamin B12, and vitamin D.S1 FigConcentrations of each analyte as measured in the 7-Plex (x-axes) plotted against concentrations measured using conventional assays (y-axes) for 72 lithium heparin plasma specimens. Solid line is linear regression. AGP, α-1-acid glycoprotein; CRP, C-reactive protein; RBP, retinol binding protein 4; sTfR, soluble transferrin receptor; Tg, thyroglobulin.(TIF)Click here for additional data file.S2 FigBland-Altman plots showing percent difference between 7-Plex and conventional assay results on the y-axes plotted against average concentration on the x-axes for 72 lithium heparin plasma specimens. Heavy horizontal line and light horizontal lines indicate mean ± 2 standard deviations of percent difference.AGP, α-1-acid glycoprotein; CRP, C-reactive protein; RBP, retinol binding protein 4; sTfR, soluble transferrin receptor; Tg, thyroglobulin.(TIF)Click here for additional data file.S3 FigReceiver operating characteristic (ROC) curves plotting sensitivity (y-axis) against 1-specificity (x-axis) were generated from 7-Plex results of pregnant women participating in NiMaNu study to identify optimal threshold values for each analyte as measured in the 7-Plex to be used in specificity and sensitivity analysis. Optimal threshold values, defined as those resulting in maximal area under the ROC curves (AUC), are reported in (TIF)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.S2 TableMedian concentrations and interquartile range (IQR) of sTfR as measured by three immunoassay methods per sample set and comparisons between assays in absolute value (% recovery) and by Spearman correlation.(DOCX)Click here for additional data file.


H. sapiens data set; additional examples on a B. terrestris data set and on an A. thaliana data set are described in the Supplemental Information. A comparison with existing approaches is also included, which exemplifies some of the issues that need to be addressed for sRNA analysis and how the new pipeline may be used to do this.Recently, high-throughput sequencing (HTS) has revealed compelling details about the small RNA (sRNA) population in eukaryotes. These 20 to 25 nt noncoding RNAs can influence gene expression by acting as guides for the sequence-specific regulatory mechanism known as RNA silencing. The increase in sequencing depth and number of samples per project enables a better understanding of the role sRNAs play by facilitating the study of expression patterns. However, the intricacy of the biological hypotheses coupled with a lack of appropriate tools often leads to inadequate mining of the available data and thus, an incomplete description of the biological mechanisms involved. To enable a comprehensive study of differential expression in sRNA data sets, we present a new interactive pipeline that guides researchers through the various stages of data preprocessing and analysis. This includes various tools, some of which we specifically developed for sRNA analysis, for quality checking and normalization of sRNA samples as well as tools for the detection of differentially expressed sRNAs and identification of the resulting expression patterns. The pipeline is available within the UEA sRNA Workbench, a user-friendly software package for the processing of sRNA data sets. We demonstrate the use of the pipeline on a  RNA silencing is known to play a key role in the fine-tuning of gene expression in eukaryotes .The procFor most molecular biology experiments, an important question is how the observed phenotype or inherent differences  are reflected in the variation in expression of sRNAs, commonly referred to as differential expression analysis or DE analysis . IdentifBioinformatics methods developed for DE analysis have thus far largely focused on analyzing messenger RNA (mRNA) data, first from microarray experiments and now, more commonly, from RNA-seq (mRNA-seq) data sets . Many ofA common approach for HTS data analysis is to group several tools into a pipeline. As well as providing the ability to tailor pipelines to individual experiments, this enables researchers to configure the distinct stages of the analysis as required . After tH. sapiens data set, described in the main text, we demonstrate how this can be extended to multiple comparisons that can be used to group sequences with similar patterns across the whole experiment.Here we present a comprehensive, interactive processing pipeline for the analysis of sRNA-seq data sets included as part of the UEA small RNA Workbench . The pipH. sapiens, GSE47532  and in Supplemental Information 3 . The impact of the number of samples or available memory (RAM) on the runtime is discussed in Supplemental Information 2.In this section, we illustrate the features of our pipeline on a publically available data set in GSE47532  to highlThe pipeline is part of the existing UEA small RNA Workbench package , which pA standard pipeline takes as input sequence data in FASTA format with the adapters trimmed. The files can be generated using the adapter removal tool  which alAfter configuring the sample files, users can choose to begin the workflow immediately or enter each stage of the workflow and change the configurable parameters, as necessary. In addition, during the workflow, users can mark problematic replicates  or individual size classes for removal, then select up to six normalization methods to be investigated. The quality check reports are then recreated on the normalized data and can be inspected. Next, the user can select the method that best corrects the data artifacts based on the nuanced characteristics of the data set's expression distributions.The quality check, normalization, and DE steps are computationally intensive and pose significant demands on both processor and in particular memory (RAM). To counteract this issue, we developed a series of back end improvements, which enable users with a wide range of computing hardware to use the pipeline. More specifically, we used disk solutions based on relational database management interfaced with a Java front end and interacted via a JavaScript GUI . However, as the use of disk for runtime storage and calculations can have significant impacts on processing time, a RAM-only version of the software is also available for users with access to high-end computing hardware.H. sapiens data set (H data) were conducted both before and after aligning reads to the reference genome. The first step of the pipeline is to evaluate the overall features of the data being analyzed. The sequencing quality of individual sRNA-seq samples is initially assessed based on the positional nucleotide composition. Next, the total library size (redundant count) and the total number of unique sequences (non-redundant) count are compared across libraries to assess the variation in sequencing depth. The size class distributions for both redundant and non-redundant reads . Other options are rank-based quantile normalization adapted for sRNA-seq data  and subsThe analysis of the H data set highlights a common issue with normalization where two replicates are sequenced with different overall depths (mainly due to the characteristics of the sequencing platform used). To evaluate which method(s) are suitable for this data set, we tested all six normalization techniques described in Materials and Methods. We exemplify the DE analysis on the H data set for two comparisons: N00/H32 and H32/H48; the left-hand side is considered the reference sample.2 scale (LOFC) and confidence interval (CI) pattern approach—described in Materials and Methods, with two of the most widely used tools for detecting DE reads, DESeq2  we encourage the user to investigate their using the revised quality check plots.P-value threshold to assess DE genes, which often reports large numbers of significant genes often with a low difference in expression, we used a cut-off of 1 LOFC to filter the reported sequences. The cut-off can, however, be user defined in order to reduce or increase the number of reported sequences. Importantly, the ranking of sequences by LOFC is not populated with high but insignificant fold changes.When identifying DE transcripts in HTS data it is important to take into account the level of noise, a quantity that increases with the depth of sequencing. To account for this, we have implemented a user-friendly tool for the identification of a suitable offset, which estimates the abundance range of the reads lacking sRNA characteristics , taking into account the sequencing depth and the characteristics of the sRNA population present in the samples. We compared the results of our DE analysis (LOFC) to that of DESeq2 and edgeR DE packages to determine the level of overlap between other methods and our own. In lieu of a To further accommodate the variability between replicates we use CIs created over normalized replicate expression levels which produce more stringent lists of DE sequences between treatments. The method is also extended to multiple conditions by using pattern-based grouping of the sequences . The metDuring our analyses we observed that problematic data sets arise when whole size classes are affected by a condition, causing a high rate of DE for a large proportion of the sRNAs, e.g., RNAi mutants which cause the exclusion of a whole class of sRNAs or virus infections which produce a large set of viral siRNAs in the infected samples . To our In conclusion, we have described a user-friendly pipeline for sRNA DE analysis that allows the evaluation of a variety of techniques to identify the most suitable approach for a given data set. The workbench includes both established approaches and tools that we have specifically developed for sRNA sequence analysis and facilitates a coherent and informed analysis through linking the different aspects into a workflow. The UEA sRNA Workbench and the pipeline design devised for the data analysis may prove to be a valuable resource facilitating the expansion of our knowledge of sRNAs, especially for the study of new or less well characterized classes of sRNAs.H. sapiens data set referred to as “H” data (publicly available on Gene Expression Omnibus [GEO] under accession number GSE47532). This is an experiment on the effects of hypoxic conditions on MCF7 cells  consisting of two samples, with four biological replicates each  consisting of two samples, with three biological replicates each -based approach . For eac2 offset fold change (LOFC) method previously described in The amplitude of the difference in expression between conditions is considered on proximate extremes (the closest ends of the neighboring CI) of the reference and observed CIs and is only calculated on sequences that have been assigned a U or D descriptor. The amplitude is calculated using the logTo determine an appropriate offset for a data set, the pipeline can be used to estimate the abundance level around which the majority of noise-related reads lie. Previous studies have observed that low abundance regions/loci have a high strand bias (derived from the reduced number of reads), but loci within the noise-to-signal range have no preferred strand bias . Based ohttp://srna-workbench.cmp.uea.ac.uk. The license is a custom license written for the UEA sRNA Workbench and can be found in the Workbench installation directory or by visiting http://srna-workbench.cmp.uea.ac.uk/wp-content/uploads/2016/11/sRNA-WorkbenchEULA.pdf. There are no restrictions on use other than requiring citations to specific papers when conducting research with the software; specific details can be found on the website.The workbench and all the supporting data and tutorials are freely available from Supplemental material is available for this article.


Reconstructing transcript models from RNA-sequencing (RNA-seq) data and establishing these as independent transcriptional units can be a challenging task. Current state-of-the-art tools for long non-coding RNA (lncRNA) annotation are mainly based on evolutionary constraints, which may result in false negatives due to the overall limited conservation of lncRNAs.To tackle this problem we have developed the Zipper plot, a novel visualization and analysis method that enables users to simultaneously interrogate thousands of human putative transcription start sites (TSSs) in relation to various features that are indicative for transcriptional activity. These include publicly available CAGE-sequencing, ChIP-sequencing and DNase-sequencing datasets. Our method only requires three tab-separated fields  as input and generates a report that includes a detailed summary table, a Zipper plot and several statistics derived from this plot.Using the Zipper plot, we found evidence of transcription for a set of well-characterized lncRNAs and observed that fewer mono-exonic lncRNAs have CAGE peaks overlapping with their TSSs compared to multi-exonic lncRNAs. Using publicly available RNA-seq data, we found more than one hundred cases where junction reads connected protein-coding gene exons with a downstream mono-exonic lncRNA, revealing the need for a careful evaluation of lncRNA 5′-boundaries. Our method is implemented using the statistical programming language R and is freely available as a webtool.The online version of this article (doi:10.1186/s12859-017-1651-7) contains supplementary material, which is available to authorized users. The introduction of RNA-sequencing (RNA-seq) has revolutionized the field of molecular biology, revealing that up to 75% of the human genome is actively transcribed . The majState-of-the-art tools for lncRNA annotation based on evolutionary constraints such as PLAR (pipeline for lncRNA annotation from RNA-seq data)  and slncLncRNA transcript models can be refined and filtered by integrating complementary datasets on chromatin state (i.e. ChIP sequencing (ChIP-seq) for histone marks or DNase sequencing (DNase-seq)) and transcript boundaries (i.e. CAGE sequencing (CAGE-seq) to mark the transcription start site (TSS) or 3P-seq to mark the 3′ end of poly-adenylated transcripts) . TranscrGRIT  is a comTo tackle the challenge of establishing lncRNAs as independent transcriptional units we have created the Zipper plot, a novel visualization and analysis method available as a quick and user-friendly webtool  that empThe Zipper plot is freely available as a webtool (front-end) at  and has Due to memory constraints on our server, we limited the number of genomic features per input file to 20,000. However, to allow users to integrate our tool as part of bigger pipelines, we have made our scripts available at Github .To start using the webtool, the user only needs to upload a list of genomic features (one per line), each consisting of three tab-separated fields: chromosome, human genomic coordinate (hg19) of the TSS and strand. Optionally, users can provide an additional fourth column containing labels for the genomic features being studied.If the user has a file from another genomic build (e.g. hg38), we propose two alternatives to convert it to hg19: 1) hgLiftOver : a webtoImportantly, hgLiftOver can also be installed locally on unix-based systems by downloading the executable  and apprIn a second step, the user has to select the data type of interest among the ones available in our database  and Area Under the Zipper (AUZ) are calculated. ZH corresponds to the quotient between the number of genomic features with a peak overlapping with the TSS and the total number of genomic features being studied . The AUZ_global is computed as the sum of all the areas between the closest peak and the TSS of each genomic feature . The AUHowever, since the distribution of peaks upstream or downstream of the TSSs can be asymmetric, AUZleft  and AUZright  are considered independently  indicates an overlap between the closest peak and TSS for all the genomic features being studied. We have also incorporated the AUZ_window, which depends on the window size choice (by default +/− 5 kb) and is computed using only those peaks that lie within the window. The method virtually sets to 5 kb  all those distances that are located more than 5 kb away from the TSS. This allows a quick visual comparison between two Zipper plots built using the same window size. Following the same reasoning as the paragraph above, we have incorporated both AUZ_window_right and AUZ_window_left separately. Of note, ZH and AUZ are negatively correlated.p-value  is calculated by comparing the AUZ of the Zipper plot built with the user’s input to 100 (by default) or 1000 random Zipper plots created by selecting as many random locations as the number of genomic features supplied by the user while maintaining the same distribution of TSSs per chromosome. Since truly random locations picked uniformly along the length of each chromosome are not representative of possible lncRNA TSSs, we have excluded from the selection those genomic regions containing gaps, centromeres, telomeres, heterochromatin and repetitive regions from [p-value is computed dividing the number of random cases with AUZ values smaller than or equal to the AUZ for the user case by the total number of repetitions. The p-value represents the chance of finding a random Zipper plot with an AUZ_global smaller than or equal to the AUZ_global of the actual use case or, in other words, whether it is likely that the set of TSSs chosen by the user was randomly selected or not. Therefore, the smaller the p-value, the higher the likelihood your set of genomic features are truly independent transcriptional units.A one-sided ons from , 23 usinons from . The p-vp-value is calculated comparing how many tissues have a peak as close (or closer) to the TSS than the one found in the tissue chosen by the user.When evaluating genomic features in one sample type, the closest peaks in that sample type are retrieved for both the random TSSs and the user input. Optionally, the closest peak in each sample type can be retrieved for each TSS and, for each TSS, a TSS p-value is calculated similarly to the case where the user selects one sample type.On the other hand, if the user selects all sample types, the closest peaks among all possible sample types are retrieved for both the random TSS and the user input. AUZs are calculated and a Eventually, the knitr package  is used To assess the usefulness of our webtool, we first investigated a set of 36 well-characterized lncRNAs proposed by . The ZipAs a second example application of the Zipper plot, we evaluated the transcriptional independence of all human lncRNAs listed in Lncipedia 3.1 . We studWe hypothesized that at least a fraction of mono-exonic lncRNAs were actually extensions of UTRs from upstream protein coding genes or genomic DNA contamination. To further investigate this hypothesis, we first retrieved the intron lengths for all RefSeq protein coding genes  , 27 and If these lncRNAs were truly unannotated portions of upstream coding genes, we should find junction reads spanning one exon from a protein coding gene and another exon from a lncRNA. To evaluate this, we used RNA-seq data from The Cancer Genome Atlas (TCGA) , 29 and Strikingly, we found spanning reads for 135 out of the 536 cases (25.19%) based on the TCGA RNA-seq data and for 35 (6.53%) based on UHRR RNA-seq data  based on the TCGA RNA-seq data and for 33 (4.89%) based on UHRR RNA-seq data  .To further expand the applicability of our tool, we plan to integrate publicly available data from methods that detect nascent RNAs (GRO-seq and PRO-seq), to extend the number of samples when new data becomes available and to allow users to work with their own data.We have created the Zipper plot, a novel visualization and analysis method available as a webtool  that allWe also recognize a limitation in our webtool: the presence of a CAGE-peak and activating histone marks at the TSS is indicative of independent transcription, but the absence of such features does not imply the opposite. Low abundant transcripts may not show up in the CAGE-seq data because of too low sequencing depth or the expression of the lncRNA may be restricted to a tissue of cell type not (yet) included in the CAGE-seq, ChIP-seq and DNase-seq database. Importantly, TSSs of RNA transcripts reconstructed from RNA-seq data might appear several nucleotides downstream of a CAGE-seq peak. Particularly for low abundant RNA transcripts, this inconsistency may be the result of an incomplete transcript assembly due to non-uniformity of read coverage towards 5′ ends and should be carefully examined.ChIP-seq & DNase-seq from 127 consolidated human epigenomes already processed in the context of the Roadmap Epigenomics Project (111 from NIH Roadmap Epigenomics Mapping Consortium (Release 9 of the Human Epigenome Atlas)  and 16 cThese files contain lists of peaks that were obtained by a peak caller algorithm in the context of the Roadmap Epigenomics Project. The peak calling process identified regions in the genome that were enriched with aligned reads (“peaks”) as a consequence of the ChIP or DNase-seq experiment.We focused our filtering approach on the qValue, being a measurement of statistical significance for the signal enrichment of each peak using the false discovery rate (FDR). We set a FDR < = 0.05, implying that only those peaks with qValue < = 0.05 were retained in our database for downstream applications.The following activating marks  were useCAGE-seq expression data  for human samples was retrieved from the Functional Annotation of the Mammalian Genome FANTOM5) project , 40. CAG project One thousand four hundred sixty RNA-seq samples from TCGA across different cancer types , 29 Additional file 2: Table S1.Summary table for the set of 36 well-characterized lncRNAs using CAGE-seq data. (XLS 69 kb)Additional file 3: Table S2.Junction reads between protein coding genes and mono/multi-exonic lncRNAs based on RNA-seq data from TCGA and UHRR; nucleotides of junction read overlapping with lncRNA and protein coding gene exons. (XLS 65 kb)Additional file 4: Table S3.Correspondence between Roadmap Epigenomics names and actual sample types; number of peaks and number of epigenomes available for each case; peak width and peak enrichment distributions across chromosomes for narrow, broad and gapped peaks (for each mark). (XLS 109 kb)Additional file 5: Table S4.Correspondence between FANTOM5 names and actual sample types; number of CAGE-seq peaks per chromosome; peak width and tpm distributions across chromosomes. (XLS 101 kb)Additional file 6: Table S5.Cancer type and barcode for each sample from TCGA. (XLS 138 kb)Additional file 7: Table S6.HGNC, Ensembl ID, PMID, chromosome location, TSS and strand information for the set of 36 well-characterized lncRNAs. (XLS 26 kb)