Motivation: Quantitative real-time PCR (qPCR) is one of the most widely used methods to measure gene expression. Despite extensive research in qPCR laboratory protocols, normalization and statistical analysis, little attention has been given to qPCR non-detects—those reactions failing to produce a minimum amount of signal.Results: We show that the common methods of handling qPCR non-detects lead to biased inference. Furthermore, we show that non-detects do not represent data missing completely at random and likely represent missing data occurring not at random. We propose a model of the missing data mechanism and develop a method to directly model non-detects as missing data. Finally, we show that our approach results in a sizeable reduction in bias when estimating both absolute and differential gene expression.Availability and implementation: The proposed algorithm is implemented in the R package, . This package also contains the raw data for the three example datasets used in this manuscript. The package is freely available at http://mnmccall.com/software and as part of the Bioconductor project.Contact: mccallm@gmail.com

The mitochondrial unfolded protein response (UPRmt) is a cytoprotective signaling pathway triggered by mitochondrial dysfunction. UPRmt activation upregulates chaperones, proteases, antioxidants, and glycolysis at the gene level to restore proteostasis and cell energetics. Activating transcription factor 5 (ATF5) is a proposed mediator of the mammalian UPRmt. Herein, we hypothesized pharmacological UPRmt activation may protect against cardiac ischemia-reperfusion (I/R) injury in an ATF5-dependent manner. Accordingly, in vivo administration of the UPRmt inducers oligomycin or doxycycline 6 h before ex vivo I/R injury (perfused heart) was cardioprotective in wild-type but not global Atf5−/− mice. Acute ex vivo UPRmt activation was not cardioprotective, and loss of ATF5 did not impact baseline I/R injury without UPRmt induction. In vivo UPRmt induction significantly upregulated many known UPRmt-linked genes (cardiac quantitative PCR and Western blot analysis), and RNA-Seq revealed an UPRmt-induced ATF5-dependent gene set, which may contribute to cardioprotection. This is the first in vivo proof of a role for ATF5 in the mammalian UPRmt and the first demonstration that UPRmt is a cardioprotective drug target. NEW & NOTEWORTHY Cardioprotection can be induced by drugs that activate the mitochondrial unfolded protein response (UPRmt). UPRmt protection is dependent on activating transcription factor 5 (ATF5). This is the first in vivo evidence for a role of ATF5 in the mammalian UPRmt.

A challenge of next generation sequencing is read contamination. We use Genotype-Tissue Expression (GTEx) datasets and technical metadata along with RNA-seq datasets from other studies to understand factors that contribute to contamination. Here we report, of 48 analyzed tissues in GTEx, 26 have variant co-expression clusters of four highly expressed and pancreas-enriched genes (PRSS1, PNLIP, CLPS, and/or CELA3A). Fourteen additional highly expressed genes from other tissues also indicate contamination. Sample contamination is strongly associated with a sample being sequenced on the same day as a tissue that natively expresses those genes. Discrepant SNPs across four contaminating genes validate the contamination. Low-level contamination affects ~40% of samples and leads to numerous eQTL assignments in inappropriate tissues among these 18 genes. This type of contamination occurs widely, impacting bulk and single cell (scRNA-seq) data set analysis. In conclusion, highly expressed, tissue-enriched genes basally contaminate GTEx and other datasets impacting analyses.

Cell morphological phenotypes, including shape, size, intensity, and texture of cellular compartments have been shown to change in response to perturbation with small molecule compounds. Image-based cell profiling or cell morphological profiling has been used to associate changes of cell morphological features with alterations in cellular function and to infer molecular mechanisms of action. Recently, the Library of Integrated Network-based Cellular Signatures (LINCS) Project has measured gene expression and performed image-based cell profiling on cell lines treated with 9515 unique compounds. These data provide an opportunity to study the interdependence between transcription and cell morphology. Previous methods to investigate cell phenotypes have focused on targeting candidate genes as components of known pathways, RNAi morphological profiling, and cataloging morphological defects; however, these methods do not provide an explicit model to link transcriptomic changes with corresponding alterations in morphology. To address this, we propose a cell morphology enrichment analysis to assess the association between transcriptomic alterations and changes in cell morphology. Additionally, for a new transcriptomic query, our approach can be used to predict associated changes in cellular morphology. We demonstrate the utility of our method by applying it to cell morphological changes in a human bone osteosarcoma cell line.

A lack of knowledge of the cellular origin of miRNAs has greatly confounded functional and biomarkers studies. Recently, three studies characterized miRNA expression patterns across >78 human cell types. These combined data expand our knowledge of miRNA expression localization and confirm that many miRNAs show cell type-specific expression patterns.