How data analysis affects power, reproducibility and biological insight of RNA-seq studies in complex datasets

Serotonin Transporter-dependent Histone Serotonylation in Placenta Contributes to the Neurodevelopmental Transcriptome

Brain development requires appropriate regulation of serotonin (5-HT) signaling from distinct tissue sources across embryogenesis. At the maternal-fetal interface, the placenta is thought to be an important contributor of offspring brain 5-HT and is critical to overall fetal health. Yet, how placental 5-HT is acquired, and the mechanisms through which 5-HT influences placental functions, are not well understood. Recently, our group identified a novel epigenetic role for 5-HT, in which 5-HT can be added to histone proteins to regulate transcription, a process called H3 serotonylation. Here, we show that H3 serotonylation undergoes dynamic regulation during placental development, corresponding to gene expression changes that are known to influence key metabolic processes. Using transgenic mice, we demonstrate that placental H3 serotonylation is dependent on 5-HT uptake by the serotonin transporter (SERT/SLC6A4). SERT deletion robustly reduces enrichment of H3 serotonylation across the placental genome, and disrupts neurodevelopmental gene networks in early embryonic brain tissues. Thus, these findings suggest a novel role for H3 serotonylation in coordinating placental transcription at the intersection of maternal physiology and offspring brain development.

standR: spatial transcriptomic analysis for GeoMx DSP data

To gain a better understanding of the complexity of gene expression in normal and diseased tissues it is important to account for the spatial context and identity of cells in situ. State-of-the-art spatial profiling technologies, such as the Nanostring GeoMx Digital Spatial Profiler (DSP), now allow quantitative spatially resolved measurement of the transcriptome in tissues. However, the bioinformatics pipelines currently used to analyse GeoMx data often fail to successfully account for the technical variability within the data and the complexity of experimental designs, thus limiting the accuracy and reliability of the subsequent analysis. Carefully designed quality control workflows, that include in-depth experiment-specific investigations into technical variation and appropriate adjustment for such variation can address this issue. Here, we present standR, an R/Bioconductor package that enables an end-to-end analysis of GeoMx DSP data. With four case studies from previously published experiments, we demonstrate how the standR workflow can enhance the statistical power of GeoMx DSP data analysis and how the application of standR enables scientists to develop in-depth insights into the biology of interest.

Ambiguous genes due to aligners and their impact on RNA-seq data analysis

The main scope of the study is ambiguous genes, i.e. genes whose expression is difficult to estimate from the data produced by next-generation sequencing technologies. We focused on the RNA sequencing (RNA-Seq) type of experiment performed on the Illumina platform. It is crucial to identify such genes and understand the cause of their difficulty, as these genes may be involved in some diseases. By giving misleading results, they could contribute to a misunderstanding of the cause of certain diseases, which could lead to inappropriate treatment. We thought that the ambiguous genes would be difficult to map because of their complex structure. So we looked at RNA-seq analysis using different mappers to find genes that would have different measurements from the aligners. We were able to identify such genes using a generalized linear model with two factors: mappers and groups introduced by the experiment. A large proportion of ambiguous genes are pseudogenes. High sequence similarity of pseudogenes to functional genes may indicate problems in alignment procedures. In addition, predictive analysis verified the performance of difficult genes in classification. The effectiveness of classifying samples into specific groups was compared, including the expression of difficult and not difficult genes as covariates. In almost all cases considered, ambiguous genes have less predictive power.

A Global Transcriptional Atlas of the Effect of Sleep Deprivation in the Mouse Frontal Cortex

Sleep deprivation (SD) has negative effects on brain function. Sleep problems are prevalent in neurodevelopmental, neurodegenerative and psychiatric disorders. Thus, understanding the molecular consequences of SD is of fundamental importance in neuroscience. In this study, we present the first simultaneous bulk and single-nuclear (sn)RNA sequencing characterization of the effects of SD in the mouse frontal cortex. We show that SD predominantly affects glutamatergic neurons, specifically in layers 4 and 5, and produces isoform switching of thousands of transcripts. At both the global and cell-type specific level, SD has a large repressive effect on transcription, down-regulating thousands of genes and transcripts; underscoring the importance of accounting for the effects of sleep loss in transcriptome studies of brain function. As a resource we provide extensive characterizations of cell types, genes, transcripts and pathways affected by SD; as well as tutorials for data analysis.

Serotonin transporter-dependent histone serotonylation in placenta contributes to the neurodevelopmental transcriptome

Brain development requires appropriate regulation of serotonin (5-HT) signaling from distinct tissue sources across embryogenesis. At the maternal-fetal interface, the placenta is thought to be an important contributor of offspring brain 5-HT and is critical to overall fetal health. Yet, how placental 5-HT is acquired, and the mechanisms through which 5-HT influences placental functions, are not well understood. Recently, our group identified a novel epigenetic role for 5-HT, in which 5-HT can be added to histone proteins to regulate transcription, a process called H3 serotonylation. Here, we show that H3 serotonylation undergoes dynamic regulation during placental development, corresponding to gene expression changes that are known to influence key metabolic processes. Using transgenic mice, we demonstrate that placental H3 serotonylation largely depends on 5-HT uptake by the serotonin transporter (SERT/SLC6A4). SERT deletion robustly reduces enrichment of H3 serotonylation across the placental genome, and disrupts neurodevelopmental gene networks in early embryonic brain tissues. Thus, these findings suggest a novel role for H3 serotonylation in coordinating placental transcription at the intersection of maternal physiology and offspring brain development.

Activity-induced gene expression in the human brain

Activity-induced gene expression underlies synaptic plasticity and brain function. Here, using molecular sequencing techniques, we define activity-dependent transcriptomic and epigenomic changes at the tissue and single-cell level in the human brain following direct electrical stimulation of the anterior temporal lobe in patients undergoing neurosurgery. Genes related to transcriptional regulation and microglia-specific cytokine activity displayed the greatest induction pattern, revealing a precise molecular signature of neuronal activation in the human brain.

The circadian clock is disrupted in pancreatic cancer

Disruption of the circadian clock is linked to cancer development and progression. Establishing this connection has proven beneficial for understanding cancer pathogenesis, determining prognosis, and uncovering novel therapeutic targets. However, barriers to characterizing the circadian clock in human pancreas and human pancreatic cancer-one of the deadliest malignancies-have hindered an appreciation of its role in this cancer. Here, we employed normalized coefficient of variation (nCV) and clock correlation analysis in human population-level data to determine the functioning of the circadian clock in pancreas cancer and adjacent normal tissue. We found a substantially attenuated clock in the pancreatic cancer tissue. Then we exploited our existing mouse pancreatic transcriptome data to perform an analysis of the human normal and pancreas cancer samples using a machine learning method, cyclic ordering by periodic structure (CYCLOPS). Through CYCLOPS ordering, we confirmed the nCV and clock correlation findings of an intact circadian clock in normal pancreas with robust cycling of several core clock genes. However, in pancreas cancer, there was a loss of rhythmicity of many core clock genes with an inability to effectively order the cancer samples, providing substantive evidence of a dysregulated clock. The implications of clock disruption were further assessed with a Bmal1 knockout pancreas cancer model, which revealed that an arrhythmic clock caused accelerated cancer growth and worse survival, accompanied by chemoresistance and enrichment of key cancer-related pathways. These findings provide strong evidence for clock disruption in human pancreas cancer and demonstrate a link between circadian disruption and pancreas cancer progression.

Design and off-target prediction for antisense oligomers targeting bacterial mRNAs with the MASON web server

Antisense oligomers (ASOs), such as peptide nucleic acids (PNAs), designed to inhibit the translation of essential bacterial genes, have emerged as attractive sequence- and species-specific programmable RNA antibiotics. Yet, potential drawbacks include unwanted side effects caused by their binding to transcripts other than the intended target. To facilitate the design of PNAs with minimal off-target effects, we developed MASON (make antisense oligomers now), a web server for the design of PNAs that target bacterial mRNAs. MASON generates PNA sequences complementary to the translational start site of a bacterial gene of interest and reports critical sequence attributes and potential off-target sites. We based MASON's off-target predictions on experiments in which we treated Salmonella enterica serovar Typhimurium with a series of 10-mer PNAs derived from a PNA targeting the essential gene acpP but carrying two serial mismatches. Growth inhibition and RNA-sequencing (RNA-seq) data revealed that PNAs with terminal mismatches are still able to target acpP, suggesting wider off-target effects than anticipated. Comparison of these results to an RNA-seq data set from uropathogenic Escherichia coli (UPEC) treated with eleven different PNAs confirmed that our findings are not unique to Salmonella We believe that MASON's off-target assessment will improve the design of specific PNAs and other ASOs.

MyBrain-Seq: A Pipeline for MiRNA-Seq Data Analysis in Neuropsychiatric Disorders

High-throughput sequencing of small RNA molecules such as microRNAs (miRNAs) has become a widely used approach for studying gene expression and regulation. However, analyzing miRNA-Seq data can be challenging because it requires multiple steps, from quality control and preprocessing to differential expression and pathway-enrichment analyses, with many tools and databases available for each step. Furthermore, reproducibility of the analysis pipeline is crucial to ensure that the results are accurate and reliable. Here, we present myBrain-Seq, a comprehensive and reproducible pipeline for analyzing miRNA-Seq data that incorporates miRNA-specific solutions at each step of the analysis. The pipeline was designed to be flexible and user-friendly, allowing researchers with different levels of expertise to perform the analysis in a standardized and reproducible manner, using the most common and widely used tools for each step. In this work, we describe the implementation of myBrain-Seq and demonstrate its capacity to consistently and reproducibly identify differentially expressed miRNAs and enriched pathways by applying it to a real case study in which we compared schizophrenia patients who responded to medication with treatment-resistant schizophrenia patients to obtain a 16-miRNA treatment-resistant schizophrenia profile.

Conditioned place avoidance is associated with a distinct hippocampal phenotype, partly preserved pattern separation, and reduced reactive oxygen species production after stress

Stress is associated with contextual memory deficits, which may mediate avoidance of trauma-associated contexts in posttraumatic stress disorder. These deficits may emerge from impaired pattern separation, the independent representation of similar experiences by the dentate gyrus-Cornu Ammonis 3 (DG-CA3) circuit of the dorsal hippocampus, which allows for appropriate behavioral responses to specific environmental stimuli. Neurogenesis in the DG is controlled by mitochondrial reactive oxygen species (ROS) production, and may contribute to pattern separation. In Experiment 1, we performed RNA sequencing of the dorsal hippocampus 16 days after stress in rats that either develop conditioned place avoidance to a predator urine-associated context (Avoiders), or do not (Non-Avoiders). Weighted genome correlational network analysis showed that increased expression of oxidative phosphorylation-associated gene transcripts and decreased expression of gene transcripts for axon guidance and insulin signaling were associated with avoidance behavior. Based on these data, in Experiment 2, we hypothesized that Avoiders would exhibit elevated hippocampal (HPC) ROS production and degraded object pattern separation (OPS) compared with Nonavoiders. Stress impaired pattern separation performance in Non-Avoider and Avoider rats compared with nonstressed Controls, but surprisingly, Avoiders exhibited partly preserved pattern separation performance and significantly lower ROS production compared with Non-Avoiders. Lower ROS production was associated with better OPS performance in Stressed rats, but ROS production was not associated with OPS performance in Controls. These results suggest a strong negative association between HPC ROS production and pattern separation after stress, and that stress effects on these outcome variables may be associated with avoidance of a stress-paired context.

On data normalization and batch-effect correction for tumor subtyping with microRNA data

The discovery of new tumor subtypes has been aided by transcriptomics profiling. However, some new subtypes can be irreproducible due to data artifacts that arise from disparate experimental handling. To deal with these artifacts, methods for data normalization and batch-effect correction have been utilized before performing sample clustering for disease subtyping, despite that these methods were primarily developed for group comparison. It remains to be elucidated whether they are effective for sample clustering. We examined this issue with a re-sampling-based simulation study that leverages a pair of microRNA microarray data sets. Our study showed that (i) normalization generally benefited the discovery of sample clusters and quantile normalization tended to be the best performer, (ii) batch-effect correction was harmful when data artifacts confounded with biological signals, and (iii) their performance can be influenced by the choice of clustering method with the Prediction Around Medoid method based on Pearson correlation being consistently a best performer. Our study provides important insights on the use of data normalization and batch-effect correction in connection with the design of array-to-sample assignment and the choice of clustering method for facilitating accurate and reproducible discovery of tumor subtypes with microRNAs.

Ontogenesis of the molecular response to sleep loss

Sleep deprivation (SD) results in profound cellular and molecular changes in the adult mammalian brain. Some of these changes may result in, or aggravate, brain disease. However, little is known about how SD impacts gene expression in developing animals. We examined the transcriptional response in the prefrontal cortex (PFC) to SD across postnatal development in male mice. We used RNA sequencing to identify functional gene categories that were specifically impacted by SD. We find that SD has dramatically different effects on PFC genes depending on developmental age. Gene expression differences after SD fall into 3 categories: present at all ages (conserved), present when mature sleep homeostasis is first emerging, and those unique to certain ages. Developmentally conserved gene expression was limited to a few functional categories, including Wnt-signaling which suggests that this pathway is a core mechanism regulated by sleep. In younger ages, genes primarily related to growth and development are affected while changes in genes related to metabolism are specific to the effect of SD in adults.

Ontogenesis of the molecular response to sleep loss

Sleep deprivation (SD) results in profound cellular and molecular changes in the adult mammalian brain. Some of these changes may result in, or aggravate, brain disease. However, little is known about how SD impacts gene expression in developing animals. We examined the transcriptional response in the prefrontal cortex (PFC) to SD across postnatal development in male mice. We used RNA sequencing to identify functional gene categories that were specifically impacted by SD. We find that SD has dramatically different effects on PFC genes depending on developmental age. Gene expression differences after SD fall into 3 categories: present at all ages (conserved), present when mature sleep homeostasis is first emerging, and those unique to certain ages in adults. Developmentally conserved gene expression was limited to a few functional categories, including Wnt-signaling which suggests that this pathway is a core mechanism regulated by sleep. In younger ages, genes primarily related to growth and development are affected while changes in genes related to metabolism are specific to the effect of SD in adults.

Removing unwanted variation from large-scale RNA sequencing data with PRPS

Accurate identification and effective removal of unwanted variation is essential to derive meaningful biological results from RNA sequencing (RNA-seq) data, especially when the data come from large and complex studies. Using RNA-seq data from The Cancer Genome Atlas (TCGA), we examined several sources of unwanted variation and demonstrate here how these can significantly compromise various downstream analyses, including cancer subtype identification, association between gene expression and survival outcomes and gene co-expression analysis. We propose a strategy, called pseudo-replicates of pseudo-samples (PRPS), for deploying our recently developed normalization method, called removing unwanted variation III (RUV-III), to remove the variation caused by library size, tumor purity and batch effects in TCGA RNA-seq data. We illustrate the value of our approach by comparing it to the standard TCGA normalizations on several TCGA RNA-seq datasets. RUV-III with PRPS can be used to integrate and normalize other large transcriptomic datasets coming from multiple laboratories or platforms.

Systematic evaluation of genome-wide metabolic landscapes in lactic acid bacteria reveals diet- and strain-specific probiotic idiosyncrasies

Lactic acid bacteria (LAB) are well known to elicit health benefits in humans, but their functional metabolic landscapes remain unexplored. Here, we analyze differences in growth, intestinal persistence, and postbiotic biosynthesis of six representative LAB and their interactions with 15 gut bacteria under 11 dietary regimes by combining multi-omics and in silico modeling. We confirmed predictions on short-term persistence of LAB and their interactions with commensals using cecal microbiome abundance and spent-medium experiments. Our analyses indicate that probiotic attributes are both diet and species specific and cannot be solely explained using genomics. For example, although both Lacticaseibacillus casei and Lactiplantibacillus plantarum encode similarly sized genomes with diverse capabilities, L. casei exhibits a more desirable phenotype. In addition, "high-fat/low-carb" diets more likely lead to detrimental outcomes for most LAB. Collectively, our results highlight that probiotics are not "one size fits all" health supplements and lay the foundation for personalized probiotic design.

Iodoacetic acid exposure alters the transcriptome in mouse ovarian antral follicles

Iodoacetic acid (IAA) is an unregulated water disinfection byproduct that is an ovarian toxicant. However, the mechanisms of action underlying IAA toxicity in ovarian follicles remain unclear. Thus, we determined whether IAA alters gene expression in ovarian follicles in mice. Adult female mice were dosed with water or IAA (10 or 500 mg/L) in the water for 35-40 days. Antral follicles were collected for RNA-sequencing analysis and sera were collected to measure estradiol. RNA-sequencing analysis identified 1063 differentially expressed genes (DEGs) in the 10 and 500 mg/L IAA groups (false discovery rate FDR < 0.1), respectively, compared to controls. Gene Ontology Enrichment analysis showed that DEGs were involved with RNA processing and regulation of angiogenesis (10 mg/L) and the cell cycle and cell division (500 mg/L). Pathway Enrichment analysis showed that DEGs were involved in the phosphatidylinositol 3-kinase and protein kinase B (PI3K-Akt), gonadotropin-releasing hormone (GnRH), estrogen, and insulin signaling pathways (10 mg/L). Pathway Enrichment analysis showed that DEGs were involved in the oocyte meiosis, GnRH, and oxytocin signaling pathways (500 mg/L). RNA-sequencing analysis identified 809 DEGs when comparing the 500 and 10 mg/L IAA groups (FDR < 0.1). DEGs were related to ribosome, translation, mRNA processing, oxidative phosphorylation, chromosome, cell cycle, cell division, protein folding, and the oxytocin signaling pathway. Moreover, IAA exposure significantly decreased estradiol levels (500 mg/L) compared to control. This study identified key candidate genes and pathways involved in IAA toxicity and can help to further understand the molecular mechanisms of IAA toxicity in ovarian follicles.

A comparison of strategies for generating artificial replicates in RNA-seq experiments

Due to the overall high costs, technical replicates are usually omitted in RNA-seq experiments, but several methods exist to generate them artificially. Bootstrapping reads from FASTQ-files has recently been used in the context of other NGS analyses and can be used to generate artificial technical replicates. Bootstrapping samples from the columns of the expression matrix has already been used for DNA microarray data and generates a new artificial replicate of the whole experiment. Mixing data of individual samples has been used for data augmentation in machine learning. The aim of this comparison is to evaluate which of these strategies are best suited to study the reproducibility of differential expression and gene-set enrichment analysis in an RNA-seq experiment. To study the approaches under controlled conditions, we performed a new RNA-seq experiment on gene expression changes upon virus infection compared to untreated control samples. In order to compare the approaches for artificial replicates, each of the samples was sequenced twice, i.e. as true technical replicates, and differential expression analysis and GO term enrichment analysis was conducted separately for the two resulting data sets. Although we observed a high correlation between the results from the two replicates, there are still many genes and GO terms that would be selected from one replicate but not from the other. Cluster analyses showed that artificial replicates generated by bootstrapping reads produce it p values and fold changes that are close to those obtained from the true data sets. Results generated from artificial replicates with the approaches of column bootstrap or mixing observations were less similar to the results from the true replicates. Furthermore, the overlap of results among replicates generated by column bootstrap or mixing observations was much stronger than among the true replicates. Artificial technical replicates generated by bootstrapping sequencing reads from FASTQ-files are better suited to study the reproducibility of results from differential expression and GO term enrichment analysis in RNA-seq experiments than column bootstrap or mixing observations. However, FASTQ-bootstrapping is computationally more expensive than the other two approaches. The FASTQ-bootstrapping may be applicable to other applications of high-throughput sequencing.

Endoplasmic reticulum chaperone genes encode effectors of long-term memory

The mechanisms underlying memory loss associated with Alzheimer's disease and related dementias (ADRD) remain unclear, and no effective treatments exist. Fundamental studies have shown that a set of transcriptional regulatory proteins of the nuclear receptor 4a (Nr4a) family serve as molecular switches for long-term memory. Here, we show that Nr4a proteins regulate the transcription of genes encoding chaperones that localize to the endoplasmic reticulum (ER). These chaperones fold and traffic plasticity-related proteins to the cell surface during long-lasting forms of synaptic plasticity and memory. Dysregulation of Nr4a transcription factors and ER chaperones is linked to ADRD, and overexpressing Nr4a1 or the chaperone Hspa5 ameliorates long-term memory deficits in a tau-based mouse model of ADRD, pointing toward innovative therapeutic approaches for treating memory loss. Our findings establish a unique molecular concept underlying long-term memory and provide insights into the mechanistic basis of cognitive deficits in dementia.

DJExpress: An Integrated Application for Differential Splicing Analysis and Visualization

RNA-seq analysis of alternative pre-mRNA splicing has facilitated an unprecedented understanding of transcriptome complexity in health and disease. However, despite the availability of countless bioinformatic pipelines for transcriptome-wide splicing analysis, the use of these tools is often limited to expert bioinformaticians. The need for high computational power, combined with computational outputs that are complicated to visualize and interpret present obstacles to the broader research community. Here we introduce DJExpress, an R package for differential expression analysis of transcriptomic features and expression-trait associations. To determine gene-level differential junction usage as well as associations between junction expression and molecular/clinical features, DJExpress uses raw splice junction counts as input data. Importantly, DJExpress runs on an average laptop computer and provides a set of interactive and intuitive visualization formats. In contrast to most existing pipelines, DJExpress can handle both annotated and de novo identified splice junctions, thereby allowing the quantification of novel splice events. Moreover, DJExpress offers a web-compatible graphical interface allowing the analysis of user-provided data as well as the visualization of splice events within our custom database of differential junction expression in cancer (DJEC DB). DJEC DB includes not only healthy and tumor tissue junction expression data from TCGA and GTEx repositories but also cancer cell line data from the DepMap project. The integration of DepMap functional genomics data sets allows association of junction expression with molecular features such as gene dependencies and drug response profiles. This facilitates identification of cancer cell models for specific splicing alterations that can then be used for functional characterization in the lab. Thus, DJExpress represents a powerful and user-friendly tool for exploration of alternative splicing alterations in RNA-seq data, including multi-level data integration of alternative splicing signatures in healthy tissue, tumors and cancer cell lines.

An omic and multidimensional spatial atlas from serial biopsies of an evolving metastatic breast cancer

Mechanisms of therapeutic resistance and vulnerability evolve in metastatic cancers as tumor cells and extrinsic microenvironmental influences change during treatment. To support the development of methods for identifying these mechanisms in individual people, here we present an omic and multidimensional spatial (OMS) atlas generated from four serial biopsies of an individual with metastatic breast cancer during 3.5 years of therapy. This resource links detailed, longitudinal clinical metadata that includes treatment times and doses, anatomic imaging, and blood-based response measurements to clinical and exploratory analyses, which includes comprehensive DNA, RNA, and protein profiles; images of multiplexed immunostaining; and 2- and 3-dimensional scanning electron micrographs. These data report aspects of heterogeneity and evolution of the cancer genome, signaling pathways, immune microenvironment, cellular composition and organization, and ultrastructure. We present illustrative examples of how integrative analyses of these data reveal potential mechanisms of response and resistance and suggest novel therapeutic vulnerabilities.

Pharmahuasca and DMT Rescue ROS Production and Differentially Expressed Genes Observed after Predator and Psychosocial Stress: Relevance to Human PTSD

Post-traumatic stress disorder (PTSD) is associated with cognitive deficits, oxidative stress, and inflammation. Animal models have recapitulated features of PTSD, but no comparative RNA sequencing analysis of differentially expressed genes (DEGs) in the brain between PTSD and animal models of traumatic stress has been carried out. We compared DEGs from the prefrontal cortex (PFC) of an established stress model to DEGs from the dorsolateral PFC (dlPFC) of humans. We observed a significant enrichment of rat DEGs in human PTSD and identified 20 overlapping DEGs, of which 17 (85%) are directionally concordant. N,N-dimethyltryptamine (DMT) is a known indirect antioxidant, anti-inflammatory, and neuroprotective compound with antidepressant and plasticity-facilitating effects. We tested the capacity of DMT, the monoamine oxidase inhibitor (MAOI) harmaline, and "pharmahuasca" (DMT + harmaline) to reduce reactive oxygen species (ROS) production and inflammatory gene expression and to modulate neuroplasticity-related gene expression in the model. We administered DMT (2 mg/kg IP), harmaline (1.5 mg/kg IP), pharmahuasca, or vehicle every other day for 5 days, following a 30 day stress regiment. We measured ROS production in the PFC and hippocampus (HC) by electron paramagnetic resonance spectroscopy and sequenced total mRNA in the PFC. We also performed in vitro assays to measure the affinity and efficacy of DMT and harmaline at 5HT_2AR compared to 5-HT. DMT and pharmahuasca reduced ROS production in the PFC and HC, while harmaline had mixed effects. Treatments normalized 9, 12, and 14 overlapping DEGs, and pathway analysis implicated that genes were involved in ROS production, inflammation, growth factor signaling, neurotransmission, and neuroplasticity.

Estimating and accounting for unobserved covariates in high-dimensional correlated data

Many high dimensional and high-throughput biological datasets have complex sample correlation structures, which include longitudinal and multiple tissue data, as well as data with multiple treatment conditions or related individuals. These data, as well as nearly all high-throughput 'omic' data, are influenced by technical and biological factors unknown to the researcher, which, if unaccounted for, can severely obfuscate estimation of and inference on the effects of interest. We therefore developed CBCV and CorrConf: provably accurate and computationally efficient methods to choose the number of and estimate latent confounding factors present in high dimensional data with correlated or nonexchangeable residuals. We demonstrate each method's superior performance compared to other state of the art methods by analyzing simulated multi-tissue gene expression data and identifying sex-associated DNA methylation sites in a real, longitudinal twin study.

cdev: a ground-truth based measure to evaluate RNA-seq normalization performance

Normalization of RNA-seq data has been an active area of research since the problem was first recognized a decade ago. Despite the active development of new normalizers, their performance measures have been given little attention. To evaluate normalizers, researchers have been relying on ad hoc measures, most of which are either qualitative, potentially biased, or easily confounded by parametric choices of downstream analysis. We propose a metric called condition-number based deviation, or cdev, to quantify normalization success. cdev measures how much an expression matrix differs from another. If a ground truth normalization is given, cdev can then be used to evaluate the performance of normalizers. To establish experimental ground truth, we compiled an extensive set of public RNA-seq assays with external spike-ins. This data collection, together with cdev, provides a valuable toolset for benchmarking new and existing normalization methods.

Application of miRNA-seq in neuropsychiatry: A methodological perspective

MiRNAs are emerging as key molecules to study neuropsychiatric diseases. However, despite the large number of methodologies and software for miRNA-seq analyses, there is little supporting literature for researchers in this area. This review focuses on evaluating how miRNA-seq has been used to study neuropsychiatric diseases to date, analyzing both the main findings discovered and the bioinformatics workflows and tools used from a methodological perspective. The objective of this review is two-fold: first, to evaluate current miRNA-seq procedures used in neuropsychiatry; and second, to offer comprehensive information that can serve as a guide to new researchers in bioinformatics. After conducting a systematic search (from 2016 to June 30, 2020) of articles using miRNA-seq in neuropsychiatry, we have seen that it has already been used for different types of studies in three main categories: diagnosis, prognosis, and mechanism. We carefully analyzed the bioinformatics workflows of each study, observing a high degree of variability with respect to the tools and methods used and several methodological complexities that are identified and discussed in this review.

Global RNA profiles show target selectivity and physiological effects of peptide-delivered antisense antibiotics

Antisense peptide nucleic acids (PNAs) inhibiting mRNAs of essential genes provide a straight-forward way to repurpose our knowledge of bacterial regulatory RNAs for development of programmable species-specific antibiotics. While there is ample proof of PNA efficacy, their target selectivity and impact on bacterial physiology are poorly understood. Moreover, while antibacterial PNAs are typically designed to block mRNA translation, effects on target mRNA levels are not well-investigated. Here, we pioneer the use of global RNA-seq analysis to decipher PNA activity in a transcriptome-wide manner. We find that PNA-based antisense oligomer conjugates robustly decrease mRNA levels of the widely-used target gene, acpP, in Salmonella enterica, with limited off-target effects. Systematic analysis of several different PNA-carrier peptides attached not only shows different bactericidal efficiency, but also activation of stress pathways. In particular, KFF-, RXR- and Tat-PNA conjugates especially induce the PhoP/Q response, whereas the latter two additionally trigger several distinct pathways. We show that constitutive activation of the PhoP/Q response can lead to Tat-PNA resistance, illustrating the utility of RNA-seq for understanding PNA antibacterial activity. In sum, our study establishes an experimental framework for the design and assessment of PNA antimicrobials in the long-term quest to use these for precision editing of microbiota.

MOCCASIN: a method for correcting for known and unknown confounders in RNA splicing analysis

The effects of confounding factors on gene expression analysis have been extensively studied following the introduction of high-throughput microarrays and subsequently RNA sequencing. In contrast, there is a lack of equivalent analysis and tools for RNA splicing. Here we first assess the effect of confounders on both expression and splicing quantifications in two large public RNA-Seq datasets (TARGET, ENCODE). We show quantification of splicing variations are affected at least as much as those of gene expression, revealing unwanted sources of variations in both datasets. Next, we develop MOCCASIN, a method to correct the effect of both known and unknown confounders on RNA splicing quantification and demonstrate MOCCASIN's effectiveness on both synthetic and real data. Code, synthetic and corrected datasets are all made available as resources.

Best practices on the differential expression analysis of multi-species RNA-seq

Advances in transcriptome sequencing allow for simultaneous interrogation of differentially expressed genes from multiple species originating from a single RNA sample, termed dual or multi-species transcriptomics. Compared to single-species differential expression analysis, the design of multi-species differential expression experiments must account for the relative abundances of each organism of interest within the sample, often requiring enrichment methods and yielding differences in total read counts across samples. The analysis of multi-species transcriptomics datasets requires modifications to the alignment, quantification, and downstream analysis steps compared to the single-species analysis pipelines. We describe best practices for multi-species transcriptomics and differential gene expression.

A gene expression atlas for different kinds of stress in the mouse brain

Stressful experiences are part of everyday life and animals have evolved physiological and behavioral responses aimed at coping with stress and maintaining homeostasis. However, repeated or intense stress can induce maladaptive reactions leading to behavioral disorders. Adaptations in the brain, mediated by changes in gene expression, have a crucial role in the stress response. Recent years have seen a tremendous increase in studies on the transcriptional effects of stress. The input raw data are freely available from public repositories and represent a wealth of information for further global and integrative retrospective analyses. We downloaded from the Sequence Read Archive 751 samples (SRA-experiments), from 18 independent BioProjects studying the effects of different stressors on the brain transcriptome in mice. We performed a massive bioinformatics re-analysis applying a single, standardized pipeline for computing differential gene expression. This data mining allowed the identification of novel candidate stress-related genes and specific signatures associated with different stress conditions. The large amount of computational results produced was systematized in the interactive “Stress Mice Portal”.

The CBP KIX domain regulates long-term memory and circadian activity

Background

CREB-dependent transcription necessary for long-term memory is driven by interactions with CREB-binding protein (CBP), a multi-domain protein that binds numerous transcription factors potentially affecting expression of thousands of genes. Identifying specific domain functions for multi-domain proteins is essential to understand processes such as cognitive function and circadian clocks. We investigated the function of the CBP KIX domain in hippocampal memory and gene expression using CBP^KIX/KIX mice with mutations that prevent phospho-CREB (Ser133) binding.

Results

We found that CBP^KIX/KIX mice were impaired in long-term memory, but not learning acquisition or short-term memory for the Morris water maze. Using an unbiased analysis of gene expression in the dorsal hippocampus after training in the Morris water maze or contextual fear conditioning, we discovered dysregulation of CREB, CLOCK, and BMAL1 target genes and downregulation of circadian genes in CBP^KIX/KIX mice. Given our finding that the CBP KIX domain was important for transcription of circadian genes, we profiled circadian activity and phase resetting in CBP^KIX/KIX mice. CBP^KIX/KIX mice exhibited delayed activity peaks after light offset and longer free-running periods in constant dark. Interestingly, CBP^KIX/KIX mice displayed phase delays and advances in response to photic stimulation comparable to wildtype littermates. Thus, this work delineates site-specific regulation of the circadian clock by a multi-domain protein.

Conclusions

These studies provide insight into the significance of the CBP KIX domain by defining targets of CBP transcriptional co-activation in memory and the role of the CBP KIX domain in vivo on circadian rhythms.

Graphical abstract

Impact of RNA-seq data analysis algorithms on gene expression estimation and downstream prediction

To use next-generation sequencing technology such as RNA-seq for medical and health applications, choosing proper analysis methods for biomarker identification remains a critical challenge for most users. The US Food and Drug Administration (FDA) has led the Sequencing Quality Control (SEQC) project to conduct a comprehensive investigation of 278 representative RNA-seq data analysis pipelines consisting of 13 sequence mapping, three quantification, and seven normalization methods. In this article, we focused on the impact of the joint effects of RNA-seq pipelines on gene expression estimation as well as the downstream prediction of disease outcomes. First, we developed and applied three metrics (i.e., accuracy, precision, and reliability) to quantitatively evaluate each pipeline's performance on gene expression estimation. We then investigated the correlation between the proposed metrics and the downstream prediction performance using two real-world cancer datasets (i.e., SEQC neuroblastoma dataset and the NIH/NCI TCGA lung adenocarcinoma dataset). We found that RNA-seq pipeline components jointly and significantly impacted the accuracy of gene expression estimation, and its impact was extended to the downstream prediction of these cancer outcomes. Specifically, RNA-seq pipelines that produced more accurate, precise, and reliable gene expression estimation tended to perform better in the prediction of disease outcome. In the end, we provided scenarios as guidelines for users to use these three metrics to select sensible RNA-seq pipelines for the improved accuracy, precision, and reliability of gene expression estimation, which lead to the improved downstream gene expression-based prediction of disease outcome.

Identification of population-level differentially expressed genes in one-phenotype data

Motivation

For some specific tissues, such as the heart and brain, normal controls are difficult to obtain. Thus, studies with only a particular type of disease samples (one phenotype) cannot be analyzed using common methods, such as significance analysis of microarrays, edgeR and limma. The RankComp algorithm, which was mainly developed to identify individual-level differentially expressed genes (DEGs), can be applied to identify population-level DEGs for the one-phenotype data but cannot identify the dysregulation directions of DEGs.

Results

Here, we optimized the RankComp algorithm, termed PhenoComp. Compared with RankComp, PhenoComp provided the dysregulation directions of DEGs and had more robust detection power in both simulated and real one-phenotype data. Moreover, using the DEGs detected by common methods as the ‘gold standard’, the results showed that the DEGs detected by PhenoComp using only one-phenotype data were comparable to those identified by common methods using case-control samples, independent of the measurement platform. PhenoComp also exhibited good performance for weakly differential expression signal data.

Availability and implementation

The PhenoComp algorithm is available on the web at https://github.com/XJJ-student/PhenoComp.

Supplementary information

Supplementary data are available at Bioinformatics online.

Minority Gene Expression Profiling: Probing the Genetic Signatures of Pathogenesis Using Ribosome Profiling

Minority Gene Expression Profiling (MGEP) refers to a scenario where the expression profiles of specific genes of interest are concentrated in a small cellular pool that is embedded within a larger, non-expressive pool. An example of this is the analysis of disease-related genes within sub-populations of blood or biopsied tissues. These systems are characterized by low signal-to-noise ratios that make it difficult, if not impossible, to uncover the desired signatures of pathogenesis in the absence of lengthy, and often problematic, technical manipulations. We have adapted ribosome profiling (RP) workflows from the Illumina to the Ion Proton platform and used them to analyze signatures of pathogenesis in an MGEP model system consisting of human cells eliciting <3% productive dengue infection. We find that RP is powerful enough to identify relevant responses of differentially expressed genes, even in the presence of significant noise. We discuss how to deal with sources of unwanted variation, and propose ways to further improve this powerful approach to the study of pathogenic signatures within MGEP systems.

Circadian analysis of the mouse retinal pigment epithelium transcriptome

The presence of a phagocytic peak of photoreceptor outer segments by the retinal pigment epithelium (RPE) one or 2 h after the onset of light has been reported for several diurnal and nocturnal species. This peak in phagocytic activity also persists under constant lighting conditions (i.e., constant light or dark) thus demonstrating that the timing of this peak is driven by a circadian clock. The aim of this study was to investigate the change in RPE whole transcriptome at two different circadian times (CT; 1 h before (CT23) and 1 h after (CT1) subjective light onset). C57BL/6J male mice were maintained in constant dark conditions for three days and euthanized under red light (<1 lux) at CT23 and CT1. RPE was isolated from whole eyes for RNA library preparation and sequencing on an Illumina HiSeq4000 platform. 14,083 mouse RPE transcripts were detected in common between CT23 and CT1. 12,005 were protein coding transcripts and 2078 were non-protein coding transcripts. 2421 protein coding transcripts were significantly upregulated whereas only 3 transcripts were significantly downregulated and 12 non-protein coding transcripts were significantly upregulated and 31 non-protein coding transcripts were significantly downregulated at CT1 when compared to CT23 (p < 0.05, fold change ≥ ±2.0). Of the protein coding transcripts, most of them were characterized as: enzymes, kinases, and transcriptional regulators with a large majority of activity in the cytoplasm, nucleus, and plasma membrane. Non-protein coding transcripts included biotypes such as long-non coding RNAs and pseudogenes. Gene ontology analysis and ingenuity pathway analysis revealed that differentially expressed transcripts were associated with integrin signaling, oxidative phosphorylation, protein phosphorylation, and actin cytoskeleton remodeling suggesting that these previously identified phagocytic pathways are under circadian control. Our analysis identified new pathways (e.g., increased mitochondrial respiration via increased oxidative phosphorylation) that may be involved in the circadian control of phagocytic activity. In addition, our dataset suggests a possible regulatory role for the identified non-protein coding transcripts in mediating the complex function of RPE phagocytosis. Finally, our results also indicate, as seen in other tissues, about 20% of the whole RPE transcriptome may be under circadian clock regulation.

Rigor and reproducibility in rodent behavioral research

Behavioral neuroscience research incorporates the identical high level of meticulous methodologies and exacting attention to detail as all other scientific disciplines. To achieve maximal rigor and reproducibility of findings, well-trained investigators employ a variety of established best practices. Here we explicate some of the requirements for rigorous experimental design and accurate data analysis in conducting mouse and rat behavioral tests. Novel object recognition is used as an example of a cognitive assay which has been conducted successfully with a range of methods, all based on common principles of appropriate procedures, controls, and statistics. Directors of Rodent Core facilities within Intellectual and Developmental Disabilities Research Centers contribute key aspects of their own novel object recognition protocols, offering insights into essential similarities and less-critical differences. Literature cited in this review article will lead the interested reader to source papers that provide step-by-step protocols which illustrate optimized methods for many standard rodent behavioral assays. Adhering to best practices in behavioral neuroscience will enhance the value of animal models for the multiple goals of understanding biological mechanisms, evaluating consequences of genetic mutations, and discovering efficacious therapeutics.

Early-life adversity and neurological disease: age-old questions and novel answers

Neurological illnesses, including cognitive impairment, memory decline and dementia, affect over 50 million people worldwide, imposing a substantial burden on individuals and society. These disorders arise from a combination of genetic, environmental and experiential factors, with the latter two factors having the greatest impact during sensitive periods in development. In this Review, we focus on the contribution of adverse early-life experiences to aberrant brain maturation, which might underlie vulnerability to cognitive brain disorders. Specifically, we draw on recent robust discoveries from diverse disciplines, encompassing human studies and experimental models. These discoveries suggest that early-life adversity, especially in the perinatal period, influences the maturation of brain circuits involved in cognition. Importantly, new findings suggest that fragmented and unpredictable environmental and parental signals comprise a novel potent type of adversity, which contributes to subsequent vulnerabilities to cognitive illnesses via mechanisms involving disordered maturation of brain 'wiring'.

Dynamic- and Frequency-Specific Regulation of Sleep Oscillations by Cortical Potassium Channels

Primary electroencephalographic (EEG) features of sleep arise in part from thalamocortical neural assemblies, and cortical potassium channels have long been thought to play a critical role. We have exploited the regionally dynamic nature of sleep EEG to develop a novel screening strategy and used it to conduct an adeno-associated virus (AAV)-mediated RNAi screen for cellular roles of 31 different voltage-gated potassium channels in modulating cortical EEG features across the circadian sleep-wake cycle. Surprisingly, a majority of channels modified only electroencephalographic frequency bands characteristic of sleep, sometimes diurnally or even in specific vigilance states. Confirming our screen for one channel, we show that depletion of the KCa1.1 (or "BK") channel reduces EEG power in slow-wave sleep by slowing neuronal repolarization. Strikingly, this reduction completely abolishes transcriptomic changes between sleep and wake. Thus, our data establish an unexpected connection between transcription and EEG power controlled by specific potassium channels. We postulate that additive dynamic roles of individual potassium channels could integrate different influences upon sleep and wake within single neurons.

Integrating -Omics Approaches into Human Population-Based Studies of Prenatal and Early-Life Exposures

PURPOSE OF REVIEW

We present the study design and methodological suggestions for population-based studies that integrate molecular -omics data and highlight recent studies that have used such data to examine the potential impacts of prenatal environmental exposures on fetal health.

RECENT FINDINGS

Epidemiologic studies have observed numerous relationships between prenatal exposures (smoking, toxic metals, endocrine disruptors) and fetal and early-life molecular profiles, though such investigations have so far been dominated by epigenomic association studies. However, recent transcriptomic, proteomic, and metabolomic studies have demonstrated their promise for the identification of exposure and response biomarkers. Molecular -omics have opened new avenues of research in environmental health that can improve our understanding of disease etiology and contribute to the development of exposure and response biomarkers. Studies that incorporate multiple -omics data from different molecular domains in longitudinally collected samples hold particular promise.

Shank3 modulates sleep and expression of circadian transcription factors

Autism Spectrum Disorder (ASD) is the most prevalent neurodevelopmental disorder in the United States and often co-presents with sleep problems. Sleep problems in ASD predict the severity of ASD core diagnostic symptoms and have a considerable impact on the quality of life of caregivers. Little is known, however, about the underlying molecular mechanisms of sleep problems in ASD. We investigated the role of Shank3, a high confidence ASD gene candidate, in sleep architecture and regulation. We show that mice lacking exon 21 of Shank3 have problems falling asleep even when sleepy. Using RNA-seq we show that sleep deprivation increases the differences in prefrontal cortex gene expression between mutants and wild types, downregulating circadian transcription factors Per3, Bhlhe41, Hlf, Tef, and Nr1d1. Shank3 mutants also have trouble regulating wheel-running activity in constant darkness. Overall, our study shows that Shank3 is an important modulator of sleep and clock gene expression.

A complete statistical model for calibration of RNA-seq counts using external spike-ins and maximum likelihood theory

A fundamental assumption, common to the vast majority of high-throughput transcriptome analyses, is that the expression of most genes is unchanged among samples and that total cellular RNA remains constant. As the number of analyzed experimental systems increases however, different independent studies demonstrate that this assumption is often violated. We present a calibration method using RNA spike-ins that allows for the measurement of absolute cellular abundance of RNA molecules. We apply the method to pooled RNA from cell populations of known sizes. For each transcript, we compute a nominal abundance that can be converted to absolute by dividing by a scale factor determined in separate experiments: the yield coefficient of the transcript relative to that of a reference spike-in measured with the same protocol. The method is derived by maximum likelihood theory in the context of a complete statistical model for sequencing counts contributed by cellular RNA and spike-ins. The counts are based on a sample from a fixed number of cells to which a fixed population of spike-in molecules has been added. We illustrate and evaluate the method with applications to two global expression data sets, one from the model eukaryote Saccharomyces cerevisiae, proliferating at different growth rates, and differentiating cardiopharyngeal cell lineages in the chordate Ciona robusta. We tested the method in a technical replicate dilution study, and in a k-fold validation study.

Targeted expression profiling by RNA-Seq improves detection of cellular dynamics during pregnancy and identifies a role for T cells in term parturition

Development of maternal blood transcriptomic markers to monitor placental function and risk of obstetrical complications throughout pregnancy requires accurate quantification of gene expression. Herein, we benchmark three state-of-the-art expression profiling techniques to assess in maternal circulation the expression of cell type-specific gene sets previously discovered by single-cell genomics studies of the placenta. We compared Affymetrix Human Transcriptome Arrays, Illumina RNA-Seq, and sequencing-based targeted expression profiling (DriverMap^TM) to assess transcriptomic changes with gestational age and labor status at term, and tested 86 candidate genes by qRT-PCR. DriverMap identified twice as many significant genes (q < 0.1) than RNA-Seq and five times more than microarrays. The gap in the number of significant genes remained when testing only protein-coding genes detected by all platforms. qRT-PCR validation statistics (PPV and AUC) were high and similar among platforms, yet dynamic ranges were higher for sequencing based platforms than microarrays. DriverMap provided the strongest evidence for the association of B-cell and T-cell gene signatures with gestational age, while the T-cell expression was increased with spontaneous labor at term according to all three platforms. We concluded that sequencing-based techniques are more suitable to quantify whole-blood gene expression compared to microarrays, as they have an expanded dynamic range and identify more true positives. Targeted expression profiling achieved higher coverage of protein-coding genes with fewer total sequenced reads, and it is especially suited to track cell type-specific signatures discovered in the placenta. The T-cell gene expression signature was increased in women who underwent spontaneous labor at term, mimicking immunological processes at the maternal-fetal interface and placenta.

Exploring the effect of library preparation on RNA sequencing experiments

RNA sequencing (RNA-seq) has become the widely preferred choice for surveying the genome-wide transcriptome complexity in many organisms. However, the broad adaptation of this methodology into the clinic still needs further evaluation of potential effect of sample preparation factors on its analytical reliability using patient samples. In this study, we examined the impact of three major sample preparation factors (i.e., cDNA library storage time, the quantity of input RNA, and cryopreservation of cell samples) on sequence biases, gene expression profiles, and enriched biological functions using RNAs isolated from primary B cell and CD4+ cell blood samples of healthy subjects. Our comprehensive comparison results suggested that different cDNA library storage time, quantity of input RNA, and cryopreservation of cell samples did not significantly alter gene transcriptional expression profiles generated by RNA-seq experiments. These findings shed new lights on the potential applications of RNA-seq technique to patient samples in a regular clinical setting.

Synthetic data sets for the identification of key ingredients for RNA-seq differential analysis

Numerous statistical pipelines are now available for the differential analysis of gene expression measured with RNA-sequencing technology. Most of them are based on similar statistical frameworks after normalization, differing primarily in the choice of data distribution, mean and variance estimation strategy and data filtering. We propose an evaluation of the impact of these choices when few biological replicates are available through the use of synthetic data sets. This framework is based on real data sets and allows the exploration of various scenarios differing in the proportion of non-differentially expressed genes. Hence, it provides an evaluation of the key ingredients of the differential analysis, free of the biases associated with the simulation of data using parametric models. Our results show the relevance of a proper modeling of the mean by using linear or generalized linear modeling. Once the mean is properly modeled, the impact of the other parameters on the performance of the test is much less important. Finally, we propose to use the simple visualization of the raw P-value histogram as a practical evaluation criterion of the performance of differential analysis methods on real data sets.

An integrated genomic analysis of anaplastic meningioma identifies prognostic molecular signatures

Anaplastic meningioma is a rare and aggressive brain tumor characterised by intractable recurrences and dismal outcomes. Here, we present an integrated analysis of the whole genome, transcriptome and methylation profiles of primary and recurrent anaplastic meningioma. A key finding was the delineation of distinct molecular subgroups that were associated with diametrically opposed survival outcomes. Relative to lower grade meningiomas, anaplastic tumors harbored frequent driver mutations in SWI/SNF complex genes, which were confined to the poor prognosis subgroup. Aggressive disease was further characterised by transcriptional evidence of increased PRC2 activity, stemness and epithelial-to-mesenchymal transition. Our analyses discern biologically distinct variants of anaplastic meningioma with prognostic and therapeutic significance.

Recent Developments in Single-Cell RNA-Seq of Microorganisms

Single-cell transcriptome analysis through next-generation sequencing (single-cell RNA-seq) has been used broadly to address important biological questions. It has proved to be very powerful, and many exciting new biological discoveries have been achieved in the last decade. Its application has greatly improved our understanding of diverse biological processes and the underlying molecular mechanisms, an understanding that would not have been achievable by conventional analysis based on bulk populations. However, so far, single-cell RNA-seq analysis has been used mostly for higher organisms. For microorganisms, single-cell RNA-seq has not been widely used, mainly because the stiff cell wall prevents effective lysis, much less starting RNA material is obtained, and the RNA lacks polyadenylated tails for universal priming of mRNA molecules. In general, the detection efficiency of current single-cell RNA-seq technologies is very low, and further development or improvement of these technologies is required for exploring the microbial world at single-cell resolution. Here, we briefly review recent developments in single-cell RNA-seq of microorganisms and discuss current challenges and future directions.

Gene Expression Detection Assay for Cancer Clinical Use

Cancer is a genetic disease where genetic variations cause abnormally functioning genes that appear to alter expression. Proteins, the final products of gene expression, determine the phenotypes and biological processes. Therefore, detecting gene expression levels can be used for cancer diagnosis, prognosis, and treatment prediction in a clinical setting. In this review, we investigated six gene expression assay systems (qRT-PCR, DNA microarray, nCounter, RNA-Seq, FISH, and tissue microarray) that are currently being used in clinical cancer studies. Some of these methods are also commonly used in a modified way; for example, detection of DNA content or protein expression. Herein, we discuss their principles, sample preparation, design, quantification and sensitivity, data analysis, time for sample preparation and processing, and cost. We also compared these methods according to their sample selection, particularly for the feasibility of using formalin-fixed paraffin-embedded (FFPE) samples, which are routinely archived for clinical cancer studies. We intend to provide a guideline for choosing an assay method with respect to its oncological applications in a clinical setting.

Identifying differentially expressed genes from cross-site integrated data based on relative expression orderings

It is a basic task in high-throughput gene expression profiling studies to identify differentially expressed genes (DEGs) between two phenotypes. But the weakly differential expression signals between two phenotypes are hardly detectable with limited sample sizes. To solve this problem, many researchers tried to combine multiple independent datasets using meta-analysis or batch effect adjustment algorithms. However, these algorithms may distort true biological differences between two phenotypes and introduce unacceptable high false rates, as demonstrated in this study. These problems pose critical obstacles for analyzing the transcriptional data in The Cancer Genome Atlas where there are many small-scale batches of data. Previously, we developed RankComp to detect DEGs for individual disease samples through exploiting the incongruous relative expression orderings between two phenotypes and further improved it here to identify DEGs using multiple independent datasets. We demonstrated the improved RankComp can directly analyze integrated cross-site data to detect DEGs between two phenotypes without the need of batch effect adjustments. Its usage was illustrated in detecting weak differential expression signals of breast cancer drug-response data using combined datasets from multiple experiments.

Learning-dependent chromatin remodeling highlights noncoding regulatory regions linked to autism

Autism spectrum disorder (ASD) is a prevalent neurodevelopmental disorder that is associated with genetic risk factors. Most human disease-associated single-nucleotide polymorphisms (SNPs) are not located in genes but rather are in regulatory regions that control gene expression. The function of regulatory regions is determined through epigenetic mechanisms. Parallels between the cellular basis of development and the formation of long-term memory have long been recognized, particularly the role of epigenetic mechanisms in both processes. We analyzed how learning alters chromatin accessibility in the mouse hippocampus using a new high-throughput sequencing bioinformatics strategy we call DEScan (differential enrichment scan). DEScan, which enabled the analysis of data from epigenomic experiments containing multiple replicates, revealed changes in chromatin accessibility at 2365 regulatory regions-most of which were promoters. Learning-regulated promoters were active during forebrain development in mice and were enriched in epigenetic modifications indicative of bivalent promoters. These promoters were disproportionally intronic, showed a complex relationship with gene expression and alternative splicing during memory consolidation and retrieval, and were enriched in the data set relative to known ASD risk genes. Genotyping in a clinical cohort within one of these promoters (SHANK3 promoter 6) revealed that the SNP rs6010065 was associated with ASD. Our data support the idea that learning recapitulates development at the epigenetic level and demonstrate that behaviorally induced epigenetic changes in mice can highlight regulatory regions relevant to brain disorders in patients.

Swarm Intelligence-Enhanced Detection of Non-Small-Cell Lung Cancer Using Tumor-Educated Platelets

Blood-based liquid biopsies, including tumor-educated blood platelets (TEPs), have emerged as promising biomarker sources for non-invasive detection of cancer. Here we demonstrate that particle-swarm optimization (PSO)-enhanced algorithms enable efficient selection of RNA biomarker panels from platelet RNA-sequencing libraries (n = 779). This resulted in accurate TEP-based detection of early- and late-stage non-small-cell lung cancer (n = 518 late-stage validation cohort, accuracy, 88%; AUC, 0.94; 95% CI, 0.92-0.96; p < 0.001; n = 106 early-stage validation cohort, accuracy, 81%; AUC, 0.89; 95% CI, 0.83-0.95; p < 0.001), independent of age of the individuals, smoking habits, whole-blood storage time, and various inflammatory conditions. PSO enabled selection of gene panels to diagnose cancer from TEPs, suggesting that swarm intelligence may also benefit the optimization of diagnostics readout of other liquid biopsy biosources.

Estimation of random effects and identifying heterogeneous genes in meta-analysis of gene expression studies

Combining effect sizes from individual studies using random-effects meta-analysis models are commonly applied in high-dimensional gene expression data. However, unknown study heterogeneity can arise from inconsistencies in sample quality and experimental conditions. High heterogeneity of effect sizes can reduce statistical power of the models. In this study, we describe three hypothesis-testing frameworks for meta-analysis of microarray data, and review several existing meta-analytic techniques that have been used in the genomic setting. These include P-value-based methods, rank-based methods and effect-size-based methods. We then discuss limitations of some of these methods and describe random-effects-based methods in detail. We introduce two methods for estimating the inter-study variance in random-effects meta-analytic models and another method for identifying heterogeneous genes for gene expression data. We compared various methods with the standard and existing meta-analytic techniques in the genomic framework. We demonstrate our results through a series of simulations and application in Alzheimer's gene expression data.