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CONCLUSIONS The new automated Spatial Transcriptomics protocol using the Agilent Bravo Automated Liquid Handling Platform rapidly generates high-quality Spatial Transcriptomics libraries. Given the wide use of the Agilent Bravo Automated Liquid Handling Platform in research laboratories and facilities, this will allow many researchers to quickly create robust Spatial Transcriptomics libraries.BACKGROUND Cancer is caused by genetic mutations, but not all somatic mutations in human DNA drive the emergence or growth of cancers. While many frequently-mutated cancer driver genes have already been identified and are being utilized for diagnostic, prognostic, or therapeutic purposes, identifying driver genes that harbor mutations occurring with low frequency in human cancers is an ongoing endeavor. Typically, mutations that do not confer growth advantage to tumors - passenger mutations - dominate the mutation landscape of tumor cell genome, making identification of low-frequency driver mutations a challenge. [https://www.selleckchem.com/products/epz-5676.html https://www.selleckchem.com/products/epz-5676.html] The leading approach for discovering new putative driver genes involves analyzing patterns of mutations in large cohorts of patients and using statistical methods to discriminate driver from passenger mutations. RESULTS We propose a novel cancer driver gene detection method, QuaDMutNetEx. QuaDMutNetEx discovers cancer drivers with low mutation frequency by giving preference to genes encoding proteins that are connected in human protein-protein interaction networks, and that at the same time show low deviation from the mutual exclusivity pattern that characterizes driver mutations occurring in the same pathway or functional gene group across a cohort of cancer samples. CONCLUSIONS Evaluation of QuaDMutNetEx on four different tumor sample datasets show that the proposed method finds biologically-connected sets of low-frequency driver genes, including many genes that are not found if the network connectivity information is not considered. Improved quality and interpretability of the discovered putative driver gene sets compared to existing methods shows that QuaDMutNetEx is a valuable new tool for detecting driver genes. QuaDMutNetEx is available for download from https//github.com/bokhariy/QuaDMutNetExunder the GNU GPLv3 license.BACKGROUND Progesterone receptor membrane component 1 (PGRMC1) is often elevated in cancers, and exists in alternative states of phosphorylation. A motif centered on PGRMC1 Y180 was evolutionarily acquired concurrently with the embryological gastrulation organizer that orchestrates vertebrate tissue differentiation. RESULTS Here, we show that mutagenic manipulation of PGRMC1 phosphorylation alters cell metabolism, genomic stability, and CpG methylation. Each of several mutants elicited distinct patterns of genomic CpG methylation. Mutation of S57A/Y180/S181A led to increased net hypermethylation, reminiscent of embryonic stem cells. Pathways enrichment analysis suggested modulation of processes related to animal cell differentiation status and tissue identity, as well as cell cycle control and ATM/ATR DNA damage repair regulation. We detected different genomic mutation rates in culture. CONCLUSIONS A companion manuscript shows that these cell states dramatically affect protein abundances, cell and mitochondrial morphology, and glycolytic metabolism. We propose that PGRMC1 phosphorylation status modulates cellular plasticity mechanisms relevant to early embryological tissue differentiation.BACKGROUND Animal coloration is usually an adaptive attribute, under strong local selection pressures and often diversified among species or populations. The strawberry poison frog (Oophaga pumilio) shows an impressive array of color morphs across its distribution in Central America. Here we quantify gene expression and genetic variation to identify candidate genes involved in generating divergence in coloration between populations of red, green and blue O. pumilio from the Bocas del Toro archipelago in Panama. RESULTS We generated a high quality non-redundant reference transcriptome by mapping the products of genome-guided and de novo transcriptome assemblies onto a re-scaffolded draft genome of O. pumilio. We then measured gene expression in individuals of the three color phenotypes and identified color-associated candidate genes by comparing differential expression results against a list of a priori gene sets for five different functional categories of coloration - pteridine synthesis, carotenoid synthesis the three morphs. However, the finding of differentially expressed genes across pathways and functional categories suggests that multiple mechanisms are responsible for the coloration differences, likely involving both pigmentary and structural coloration. In addition to regulatory differences, we found potential evidence of differential selection acting at the protein sequence level in several color-associated loci, which could contribute to the color polymorphism.BACKGROUND Multiple co-inertia analysis (mCIA) is a multivariate analysis method that can assess relationships and trends in multiple datasets. Recently it has been used for integrative analysis of multiple high-dimensional -omics datasets. However, its estimated loading vectors are non-sparse, which presents challenges for identifying important features and interpreting analysis results. We propose two new mCIA methods 1) a sparse mCIA method that produces sparse loading estimates and 2) a structured sparse mCIA method that further enables incorporation of structural information among variables such as those from functional genomics. RESULTS Our extensive simulation studies demonstrate the superior performance of the sparse mCIA and structured sparse mCIA methods compared to the existing mCIA in terms of feature selection and estimation accuracy. Application to the integrative analysis of transcriptomics data and proteomics data from a cancer study identified biomarkers that are suggested in the literature related with cancer disease. CONCLUSION Proposed sparse mCIA achieves simultaneous model estimation and feature selection and yields analysis results that are more interpretable than the existing mCIA. Furthermore, proposed structured sparse mCIA can effectively incorporate prior network information among genes, resulting in improved feature selection and enhanced interpretability.