Summary: With the rapid advances and prevalence of high-throughput genomic technologies, integrating information of multiple relevant genomic studies has brought new challenges. Microarray meta-analysis has become a frequently used tool in biomedical research. Little effort, however, has been made to develop a systematic pipeline and user-friendly software. In this article, we present MetaOmics, a suite of three R packages MetaQC, MetaDE and MetaPath, for quality control, differentially expressed gene identification and enriched pathway detection for microarray meta-analysis. MetaQC provides a quantitative and objective tool to assist study inclusion/exclusion criteria for meta-analysis. MetaDE and MetaPath were developed for candidate marker and pathway detection, which provide choices of marker detection, meta-analysis and pathway analysis methods. The system allows flexible input of experimental data, clinical outcome (case–control, multi-class, continuous or survival) and pathway databases. It allows missing values in experimental data and utilizes multi-core parallel computing for fast implementation. It generates informative summary output and visualization plots, operates on different operation systems and can be expanded to include new algorithms or combine different types of genomic data. This software suite provides a comprehensive tool to conveniently implement and compare various genomic meta-analysis pipelines. Availability:  http://www.biostat.pitt.edu/bioinfo/software.htm Contact:  ctseng@pitt.edu Supplementary Information:  Supplementary data are available at Bioinformatics online.

The brain transcriptional profile of MDD differs greatly by sex, with multiple transcriptional changes in opposite directions between men and women with MDD.

Recent advances in large-scale gene expression profiling necessitate concurrent development of biostatistical approaches to reveal meaningful biological relationships. Most analyses rely on significance thresholds for identifying differentially expressed genes. We use an approach to compare gene expression datasets using ‘threshold-free’ comparisons. Significance cut-offs to identify genes shared between datasets may be too stringent and may miss concordant patterns of gene expression with potential biological relevance. A threshold-free approach gaining popularity in several research areas, including neuroscience, is Rank–Rank Hypergeometric Overlap (RRHO). Genes are ranked by their p-value and effect size direction, and ranked lists are compared to identify significantly overlapping genes across a continuous significance gradient rather than at a single arbitrary cut-off. We have updated the previous RRHO analysis by accurately detecting overlap of genes changed in the same and opposite directions between two datasets. Here, we use simulated and real data to show the drawbacks of the previous algorithm as well as the utility of our new algorithm. For example, we show the power of detecting discordant transcriptional patterns in the postmortem brain of subjects with psychiatric disorders. The new R package, RRHO2, offers a new, more intuitive visualization of concordant and discordant gene overlap.

Background: Fusion transcripts are formed by either fusion genes (DNA level) or trans-splicing events (RNA level). They have been recognized as a promising tool for diagnosing, subtyping and treating cancers. RNA-seq has become a precise and efficient standard for genome-wide screening of such aberration events. Many fusion transcript detection algorithms have been developed for paired-end RNA-seq data but their performance has not been comprehensively evaluated to guide practitioners. In this paper, we evaluated 15 popular algorithms by their precision and recall trade-off, accuracy of supporting reads and computational cost. We further combine top-performing methods for improved ensemble detection.Results: Fifteen fusion transcript detection tools were compared using three synthetic data sets under different coverage, read length, insert size and background noise, and three real data sets with selected experimental validations. No single method dominantly performed the best but SOAPfuse generally performed well, followed by FusionCatcher and JAFFA. We further demonstrated the potential of a meta-caller algorithm by combining top performing methods to re-prioritize candidate fusion transcripts with high confidence that can be followed by experimental validation.Conclusion: Our result provides insightful recommendations when applying individual tool or combining top performers to identify fusion transcript candidates.

Schizophrenia is associated with disrupted cognitive control and sleep-wake cycles. Here we identify diurnal rhythms in gene expression in the human dorsolateral prefrontal cortex (dlPFC), in schizophrenia and control subjects. We find significant diurnal (24 h) rhythms in control subjects, however, most of these transcripts are not rhythmic in subjects with schizophrenia. Instead, subjects with schizophrenia have a different set of rhythmic transcripts. The top pathways identified in transcripts rhythmic only in subjects with schizophrenia are associated with mitochondrial function. Importantly, these rhythms drive differential expression patterns of these and several other genes that have long been implicated in schizophrenia (including BDNF and GABAergic-related transcripts). Indeed, differential expression of these transcripts is only seen in subjects that died during the night, with no change in subjects that died during the day. These data provide insights into a potential mechanism that underlies changes in gene expression in the dlPFC with schizophrenia.

Background Impairments in certain cognitive processes (e.g., working memory) are typically most pronounced in schizophrenia (SZ), intermediate in bipolar disorder (BP) and least in major depressive disorder (MDD). Given that working memory depends, in part, on neural circuitry that includes pyramidal cells in layer 3 (L3) and layer 5 (L5) of the dorsolateral prefrontal cortex (DLPFC), we sought to determine if transcriptome alterations in these neurons were shared or distinctive for each diagnosis. Methods Pools of L3 and L5 pyramidal cells in the DLPFC were individually captured by laser-microdissection from 19 matched tetrads of unaffected comparison, SZ, BP and MDD subjects and the mRNA was subjected to transcriptome profiling by microarray. Results In DLPFC L3 and L5 pyramidal cells, transcriptome alterations were numerous in SZ subjects, but rare in BP and MDD subjects. The leading molecular pathways altered in SZ subjects involved mitochondrial energy production and the regulation of protein translation. In addition, we did not find any significant transcriptome signatures related to psychosis or suicide. Conclusions In concert, these findings suggest that molecular alterations in DLPFC L3 and L5 pyramidal cells might be characteristic of the disease process(es) operative in individuals diagnosed with SZ and thus might contribute to the circuitry alterations underlying cognitive dysfunction in individuals with this disorder.

Schizophrenia is associated with dysfunction of the dorsolateral prefrontal cortex (DLPFC). This dysfunction is manifest as cognitive deficits that appear to arise from disturbances in gamma frequency oscillations. These oscillations are generated in DLPFC layer 3 via reciprocal connections between pyramidal cells and parvalbumin (PV)-containing interneurons. The density of cortical PV neurons is not altered in schizophrenia, but expression levels of several transcripts involved in PV cell function, including PV, are lower in the disease. However, the transcriptome of PV cells has not been comprehensively assessed in a large cohort of subjects with schizophrenia. In this study, we combined an immunohistochemical approach, laser microdissection, and microarray profiling to analyze the transcriptome of DLPFC layer 3 PV cells in 36 matched pairs of schizophrenia and unaffected comparison subjects. Over 800 transcripts in PV neurons were identified as differentially-expressed in schizophrenia subjects; most of these alterations have not previously been reported. The altered transcripts were enriched for pathways involved in mitochondrial function and tight junction signaling. Comparison with the transcriptome of layer 3 pyramidal cells from the same subjects revealed both shared and distinct disease-related effects on gene expression between cell types. Furthermore, network structures of gene pathways differed across cell types and subject groups. These findings provide new insights into cell type-specific molecular alterations in schizophrenia which may point toward novel strategies for identifying therapeutic targets.

The development of Alzheimer's dementia (AD) accompanies both central and peripheral metabolic disturbance, but the metabolic basis underlying AD and metabolic markers predictive of AD risk remain to be determined. It is also unclear whether the metabolic changes in peripheral blood and brain are overlapping in relation to AD. The current study addresses these questions by targeted metabolomics in both ante-mortem blood and post-mortem brain samples in two community-based longitudinal cohorts of aging and dementia. We found that higher serum levels of three acylcarnitines, including decanoylcarnitine [C10], pimelylcarnitine [C7-DC], and tetradecadienylcarnitine [C14:2], significantly predicts a lower risk of incident AD (composite HR = 0.368, 95% CI [0.207, 0.653]) after an average of 4.5-year follow-up, independent of age, sex, and education. In addition, baseline serum levels of ten glycerophospholipids, one amino acid, and five acylcarnitines predict the longitudinal change in cognitive functions. Moreover, 28 brain metabolites were associated with AD phenotypes. Of the putative metabolites identified in serum and brain, four metabolites (3 glycerophospholipids [PC aa C30:0, PC ae C34:0, PC ae C36:1] and 1 acylarnitine [C14:2]) were present in both post-mortem brain and ante-mortem blood, but only one metabolite (C14:2) was associated with AD in the same direction (i.e., protective). Partial correlation and network analyses suggest a potential tissue-specific regulation of metabolism, although other alternatives exist. Together, we identified significant associations of both central and peripheral metabolites with AD phenotypes, but there seems to be little overlap between the two tissues.