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Here, we investigate the potential of epidermal growth factor receptor (EGFR) inhibition to prevent vascular calcification, a leading indicator of and contributor to cardiovascular morbidity and mortality. EGFR interacts and affects the trafficking of the plasma membrane scaffolding protein caveolin-1. Previous studies reported a key role for caveolin-1 in the development of specialized extracellular vesicles that mediate vascular calcification; however, no role of EGFR has been reported. We demonstrated that EGFR inhibition modulates caveolin-1 trafficking and hinders calcifying extracellular vesicle formation, which prevents vascular calcification. Given that EGFR inhibitors are clinically approved for other indications, this may represent a novel therapeutic strategy for vascular calcification.

Long non-coding RNAs (lncRNAs) orchestrate various biological processes and regulate the development of cardiovascular diseases. Their potential therapeutic benefit to tackle disease progression has recently been extensively explored. Our study investigates the role of lncRNA Nudix Hydrolase 6 (NUDT6)and its antisense target Fibroblast Growth Factor 2 (FGF2)in two vascular pathologies: abdominal aortic aneurysms (AAA) and carotid artery disease. Using tissue samples from both diseases, we detected a substantial increase ofNUDT6, whereasFGF2was downregulated. TargetingNudt6 in vivowith antisense oligonucleotides in three murine and one porcine animal models of carotid artery disease and AAA limited disease progression. Restoration of FGF2 uponNudt6knockdown improved vessel wall morphology and fibrous cap stability. Overexpression ofNUDT6 in vitroimpaired smooth muscle cell (SMC) migration, while limiting their proliferation and augmenting apoptosis. By employing RNA pulldown followed by mass spectrometry as well as RNA immunoprecipitation, we identified Cysteine and Glycine Rich Protein 1 (CSRP1) as another directNUDT6interaction partner, regulating cell motility and SMC differentiation. Overall, the present study identifiesNUDT6as a well-conserved antisense transcript ofFGF2. NUDT6silencing triggers SMC survival and migration and could serve as a novel RNA-based therapeutic strategy in vascular diseases.

Currently, cardiovascular disease (CVD) drug discovery has focused primarily on addressing the inflammation and immunopathology aspects inherent to various CVD phenotypes such as cardiac fibrosis and coronary artery disease. However, recent findings suggest new biological pathways for cytoskeletal and extracellular matrix (ECM) regulation across diverse CVDs, such as the roles of matricellular proteins (e.g. tenascin-C) in regulating the cellular microenvironment. The success of anti-inflammatory drugs like colchicine, which targets microtubule polymerization, further suggests that the cardiac cytoskeleton and ECM provide prospective therapeutic opportunities. In this Abstract, we will present genetic validation evidence supporting the efficacy of pharmaceutical modulation of the cytoskeleton and the associated ECM that supports its cellular architecture to find new computationally-derived therapeutics in the treatment of CVD using integrated in-silico, in-vitro, and in-vivo methods. Our focus will be on the dynamic role that the cytoskeleton and ECM play in CVD pathophysiology, highlighting how novel target discovery in cytoskeletal and ECM-related genes enables new therapeutics development to alter the regulation of cellular architecture in plaque formation and rupture, cardiac contractility, and other molecular mechanisms.

Background and aims The atherosclerotic plaque microenvironment is highly complex, and selective agents that modulate plaque stability are not yet available. We sought to develop a scRNA-seq analysis workflow to investigate this environment and uncover potential therapeutic approaches. We designed a user-friendly, reproducible workflow that will be applicable to other disease-specific scRNA-seq datasets. Methods Here we incorporated automated cell labeling, pseudotemporal ordering, ligand-receptor evaluation, and drug-gene interaction analysis into a ready-to-deploy workflow. We applied this pipeline to further investigate a previously published human coronary single-cell dataset by Wirka et al. Notably, we developed an interactive web application to enable further exploration and analysis of this and other cardiovascular single-cell datasets. Results We revealed distinct derivations of fibroblast-like cells from smooth muscle cells (SMCs), and showed the key changes in gene expression along their de-differentiation path. We highlighted several key ligand-receptor interactions within the atherosclerotic environment through functional expression profiling and revealed several avenues for future pharmacological development for precision medicine. Further, our interactive web application, PlaqView ( www.plaqview.com ), allows lay scientists to explore this and other datasets and compare scRNA-seq tools without prior coding knowledge. Conclusions This publicly available workflow and application will allow for more systematic and user-friendly analysis of scRNA datasets in other disease and developmental systems. Our analysis pipeline provides many hypothesis-generating tools to unravel the etiology of coronary artery disease. We also highlight potential mechanisms for several drugs in the atherosclerotic cellular environment. Future releases of PlaqView will feature more scRNA-seq and scATAC-seq atherosclerosis-related datasets to provide a critical resource for the field, and to promote data harmonization and biological interpretation.

BackgroundChronic kidney disease (CKD) increases the risk of cardiovascular disease, including vascular calcification, leading to higher mortality. Release of calcifying extracellular vesicles (EVs) by vascular smooth muscle cells (VSMCs) promotes the ectopic mineralization of vessel walls. Caveolin-1 (CAV1) protein plays a key role in formation of calcifying EVs in VSMCs. Epidermal growth factor receptor (EGFR) co-localizes with and influences the intracellular trafficking of CAV1. MethodsWe computationally analyzed 7651 individuals in the Multi-Ethnic Study of Atherosclerosis (MESA) and Framingham cohorts to assess potential correlations between coronary artery calcium and single nucleotide polymorphisms (SNPs) associated with elevated serum levels of EGFR. Using a diet-induced mouse model of CKD, we measured serum EGFR and assessed the potential of EGFR inhibition to prevent vascular calcification. ResultsIndividuals in the MESA and Framingham cohorts with SNPs associated with increased serum EGFR exhibit elevated coronary artery calcium. Mice with CKD developed widespread vascular calcification, which associated with increased serum levels of EGFR. In both the CKD mice and human VSMC culture, EGFR inhibition significantly reduced vascular calcification by mitigating the release of CAV1-positive calcifying EVs. EGFR inhibition also increased bone mineral density in CKD mice. ConclusionGiven that EGFR inhibitors exhibit clinical safety and efficacy in other pathologies, the current data suggest that EGFR may be an ideal target to prevent pathological vascular calcification. Subject termsGrowth Factors, Vascular Biology, Vascular Disease, Translational Study

Machine reading (MR) is essential for unlocking valuable knowledge contained in millions of existing biomedical documents. Over the last two decades1,2, the most dramatic advances in MR have followed in the wake of critical corpus development3. Large, well-annotated corpora have been associated with punctuated advances in MR methodology and automated knowledge extraction systems in the same way that ImageNet4 was fundamental for developing machine vision techniques. This study contributes six components to an advanced, named entity analysis tool for biomedicine: (a) a new, Named Entity Recognition Ontology (NERO) developed specifically for describing textual entities in biomedical texts, which accounts for diverse levels of ambiguity, bridging the scientific sublanguages of molecular biology, genetics, biochemistry, and medicine; (b) detailed guidelines for human experts annotating hundreds of named entity classes; (c) pictographs for all named entities, to simplify the burden of annotation for curators; (d) an original, annotated corpus comprising 35,865 sentences, which encapsulate 190,679 named entities and 43,438 events connecting two or more entities; (e) validated, off-the-shelf, named entity recognition (NER) automated extraction, and; (f) embedding models that demonstrate the promise of biomedical associations embedded within this corpus.

Machine reading is essential for unlocking valuable knowledge contained in the millions of existing biomedical documents. Over the last two decades1,2, the most dramatic advances in machine-reading have followed in the wake of critical corpus development3. Large, well-annotated corpora have been associated with punctuated advances in machine reading methodology and automated knowledge extraction systems in the same way that ImageNet4was fundamental for developing machine vision techniques. This study contributes six components to an advanced, named-entity analysis tool for biomedicine: (a) a new, Named-Entity Recognition Ontology (NERO) developed specifically for describing entities in biomedical texts, which accounts for diverse levels of ambiguity, bridging the scientific sublanguages of molecular biology, genetics, biochemistry, and medicine; (b) detailed guidelines for human experts annotating hundreds of named-entity classes; (c) pictographs for all named entities, to simplify the burden of annotation for curators; (d) an original, annotated corpus comprising 35,865 sentences, which encapsulate 190,679 named entities and 43,438 events connecting two or more entities; (e) validated, off-the-shelf, named-entity recognition automated extraction, and; (f) embedding models that demonstrate the promise of biomedical associations embedded within this corpus.

In this study, we investigate how an organism’s codon usage bias levels can serve as a predictor and classifier of various genomic and evolutionary features across the three kingdoms of life (archaea, bacteria, eukarya). We perform secondary analysis of existing genetic datasets to build several artificial intelligence (AI) and machine learning models trained on over 13,000 organisms that show it is possible to accurately predict an organism’s DNA type (nuclear, mitochondrial, chloroplast) and taxonomic identity simply using its genetic code (64 codon usage frequencies). By leveraging advanced AI and machine learning methods to accurately identify evolutionary origins and genetic composition from codon usage patterns, our study suggests that the genetic code can be utilized to train accurate machine learning classifiers of taxonomic and phylogenetic features. Our dataset and analyses are made publicly available on Github and the UCI Machine Learning Repository (https://archive.ics.uci.edu/ml/datasets/Codon+usage) to facilitate open-source reproducibility and community engagement.

Chronic suppurative otitis media (CSOM) is a widespread, debilitating problem with poorly understood immunology. Here, we assess the host response to middle ear infection over the course of a month post-infection in a mouse model of CSOM and in human subjects with the disease. Using multiparameter flow cytometry and a binomial generalized linear machine learning model, we identified Ly6G, a surface marker of mature neutrophils, as the most informative factor of host response driving disease in the CSOM mouse model. Consistent with this, neutrophils were the most abundant cell type in infected mice and Ly6G expression tracked with the course of infection. Moreover, neutrophil-specific immunomodulatory treatment using the neutrophil elastase inhibitor GW 311616A significantly reduces bacterial burden relative to ofloxacin-only treated animals in this model. The levels of dsDNA in middle ear effusion samples are elevated in both humans and mice with CSOM and decreased during treatment, suggesting that dsDNA may serve as a molecular biomarker of treatment response. Together these data strongly implicate neutrophils in the ineffective immune response to P. aeruginosa infection in CSOM and suggest that immunomodulatory strategies may benefit drug-tolerant infections for chronic biofilm-mediated disease.

Cardiovascular disease (CVD) is the leading cause of death worldwide for both genders and across most racial and ethnic groups. However, different races and ethnicities exhibit different rates of cardiovascular disease and its related cardiorenal and metabolic co-morbidities, suggesting differences in genetic predisposition and risk of onset, as well as socioeconomic and lifestyle factors (diet, exercise, etc.) that act upon an individual’s unique underlying genetic background. Here we present HeartBioPortal2.0, a major update to HeartBioPortal, the world’s largest CVD genetics data precision medicine platform for harmonized CVD-relevant genetic variants, which now enables search and analysis of human genetic information related to heart disease across ethnically diverse populations and cardiovascular/renal/metabolic quantitative traits pertinent to CVD pathophysiology. HeartBioPortal2.0 is structured as a cloud-based computing platform and knowledge portal that consolidates a multitude of CVD-relevant next-generation sequencing data modalities into a single powerful query and browsing interface between data and user via a user-friendly web application publicly available to the scientific research community. Since its initial release, HeartBioPortal2.0 has added new cardiovascular/renal/metabolic disease relevant gene expression data as well as genetic association data from numerous large-scale genome-wide association study (GWAS) consortiums such as CARDIoGRAMplusC4D, TOPMed, FinnGen, AFGen, MESA, MEGASTROKE, UK Biobank, CHARGE, Biobank Japan, MyCode, among other studies. In addition, HeartBioPortal2.0 now includes support for quantitative traits and ethnically diverse populations, allowing users to investigate the shared genetic architecture of any gene or its variants across the continuous cardiometabolic spectrum from health (e.g., blood pressure traits) to disease (hypertension), facilitating the understanding of CVD trait genetics that inform health-to-disease transitions and endophenotypes. Custom visualizations in the new and improved user interface (UI), including performance enhancements and new security features such as user authentication collectively re-imagine HeartBioPortal’s user experience and provide a data commons that co-locates data, storage and computing infrastructure in the context of studying the genetic basis behind the leading cause of global mortality.Database URLhttps://www.heartbioportal.com/

Cardiovascular disease (CVD) is the leading cause of death worldwide, causing over 17 million deaths per year, which outpaces global cancer mortality rates. Despite these sobering statistics, most bioinformatics and computational biology research and funding to date has been concentrated predominantly on cancer research, with a relatively modest footprint in CVD. In this paper, we review the existing literary landscape and critically assess the unmet need to further develop an emerging field at the multidisciplinary interface of bioinformatics and precision cardiovascular medicine, which we refer to as ‘cardioinformatics’.

Cardiovascular disease (CVD) is the leading cause of death worldwide, causing over 17M deaths per year, which outpaces global cancer mortality rates. Despite these sobering statistics, the state-of-the-art in computational infrastructure to study datasets associated with CVD has lagged far behind public resources widely available in the oncology field, where improved data science and visualization methods have led to the development of large-scale cancer genomics resources like MSKCC9s cBioPortal or NCI9s Genomic Data Commons (GDC) Portal. Developing a similar user-friendly computational platform could significantly lower the barriers between complex CVD data and researchers who want rapid, intuitive, and high-quality visual access to molecular profiles and clinical attributes from existing CVD projects. Here we present HeartBioPortal: a publicly available web application that provides intuitive visualization, analysis, and downloads of large-scale CVD data currently focused on gene expression, genetic association, and ancestry information. By democratizing access to anonymized CVD data, HeartBioPortal9s aim is to integrate relevant omics and clinical information across the biological dataverse to support CVD clinicians and researchers.

We present a rationale for expanding the presence of the Lisp family of programming languages in bioinformatics and computational biology research. Put simply, Lisp-family languages enable programmers to more quickly write programs that run faster than in other languages. Languages such as Common Lisp, Scheme and Clojure facilitate the creation of powerful and flexible software that is required for complex and rapidly evolving domains like biology. We will point out several important key features that distinguish languages of the Lisp family from other programming languages, and we will explain how these features can aid researchers in becoming more productive and creating better code. We will also show how these features make these languages ideal tools for artificial intelligence and machine learning applications. We will specifically stress the advantages of domain-specific languages (DSLs): languages that are specialized to a particular area, and thus not only facilitate easier research problem formulation, but also aid in the establishment of standards and best programming practices as applied to the specific research field at hand. DSLs are particularly easy to build in Common Lisp, the most comprehensive Lisp dialect, which is commonly referred to as the ‘programmable programming language’. We are convinced that Lisp grants programmers unprecedented power to build increasingly sophisticated artificial intelligence systems that may ultimately transform machine learning and artificial intelligence research in bioinformatics and computational biology.

Epigenetic processes have been implicated in the pathophysiology of alcohol dependence, but the specific molecular mechanisms mediating dependence-induced neuroadaptations remain largely unknown. Here, we found that a history of alcohol dependence persistently decreased the expression of Prdm2, a histone methyltransferase that monomethylates histone 3 at the lysine 9 residue (H3K9me1), in the rat dorsomedial prefrontal cortex (dmPFC). Downregulation of Prdm2 was associated with decreased H3K9me1, supporting that changes in Prdm2 mRNA levels affected its activity. Chromatin immunoprecipitation followed by massively parallel DNA sequencing showed that genes involved in synaptic communication are epigenetically regulated by H3K9me1 in dependent rats. In non-dependent rats, viral-vector-mediated knockdown of Prdm2 in the dmPFC resulted in expression changes similar to those observed following a history of alcohol dependence. Prdm2 knockdown resulted in increased alcohol self-administration, increased aversion-resistant alcohol intake and enhanced stress-induced relapse to alcohol seeking, a phenocopy of postdependent rats. Collectively, these results identify a novel epigenetic mechanism that contributes to the development of alcohol-seeking behavior following a history of dependence. Funding Agencies|NIAAA division of Intramural Research; Swedish Research Council; NIAAA R01 [1R01AA023781-01A1]; United States Department of Defense (DoD), through the National Defense Science and Engineering Graduate Fellowship (NDSEG) Program; US National Institute of Health [DA035592, MH084880, NS071674]; DoD; Army Research Office (ARO); National Defense Science and Engineering Graduate (NDSEG) Fellowship [32 CFR 168a]

Epigenetic enzymes oversee long‐term changes in gene expression by integrating genetic and environmental cues. While there are hundreds of enzymes that control histone and DNA modifications, their potential roles in substance abuse and alcohol dependence remain underexplored. A few recent studies have suggested that epigenetic processes could underlie transcriptomic and behavioral hallmarks of alcohol addiction. In the present study, we sought to identify epigenetic enzymes in the brain that are dysregulated during protracted abstinence as a consequence of chronic and intermittent alcohol exposure. Through quantitative mRNA expression analysis of over 100 epigenetic enzymes, we identified 11 that are significantly altered in alcohol‐dependent rats compared with controls. Follow‐up studies of one of these enzymes, the histone demethylase KDM6B, showed that this enzyme exhibits region‐specific dysregulation in the prefrontal cortex and nucleus accumbens of alcohol‐dependent rats. KDM6B was also upregulated in the human alcoholic brain. Upregulation of KDM6B protein in alcohol‐dependent rats was accompanied by a decrease of trimethylation levels at histone H3, lysine 27 (H3K27me3), consistent with the known demethylase specificity of KDM6B. Subsequent epigenetic (chromatin immunoprecipitation [ChIP]–sequencing) analysis showed that alcohol‐induced changes in H3K27me3 were significantly enriched at genes in the IL‐6 signaling pathway, consistent with the well‐characterized role of KDM6B in modulation of inflammatory responses. Knockdown of KDM6B in cultured microglial cells diminished IL‐6 induction in response to an inflammatory stimulus. Our findings implicate a novel KDM6B‐mediated epigenetic signaling pathway integrated with inflammatory signaling pathways that are known to underlie the development of alcohol addiction., Through quantitative mRNA expression analysis of over 100 epigenetic enzymes, we identified 11 that are dysregulated in the brain of alcohol dependent rats. The histone 3, lysine 27 (H3K27me3) demethylase KDM6B, was found to have region‐specific dysregulation in the prefrontal cortex and nucleus accumbens and also in human alcoholic brain tissues. A ChIP‐sequencing analysis showed that alcohol‐induced changes in H3K27me3 were enriched at genes in the IL‐6 signaling pathway, thereby implicating a novel KDM6B‐mediated epigenetic signaling pathway integrated with inflammatory signaling pathways that underlie alcohol addiction.

DNA encodes protein primary structure using 64 different codons to specify 20 different amino acids and a stop signal. Frequencies of codon occurrence when ordered in descending sequence provide a global characterization of a genome’s preference (bias) for using the different codons of the redundant genetic code. Whereas frequency/rank relations have been described by empirical expressions, here we propose a statistical model in which two different forms of codon usage co-exist in a genome. We investigate whether such a model can account for the range of codon usages observed in a large set of genomes from different taxa. The differences in frequency/rank relations across these genomes can be expressed in a single parameter, the proportion of the two codon compartments. One compartment uses different codons with weak bias according to a Gaussian distribution of frequency, the other uses different codons with strong bias. In prokaryotic genomes both compartments appear to be present in a wide range of proportions, whereas in eukaryotic genomes the compartment with Gaussian distribution tends to dominate. Codon frequencies that are Gaussian-distributed suggest that many evolutionary conditions are involved in shaping weakly-biased codon usage, whereas strong bias in codon usage suggests dominance of few evolutionary conditions.

A biological dataset’s metadata profile (e.g., study description, organism name, sequencing type, etc.) typically contains terse but descriptive textual information that can be used to link it with other similar biological datasets for the purpose of integrating omics data of different types to inform hypotheses and biological questions. Here we present Biochat, a database containing a multi-omics data integration support system to aid in cross-linking Gene Expression Omnibus (GEO) records to each other by metadata similarity through a user-friendly web application. Biochat is publicly available at: http://www.biochat.ai. Biochat source code is hosted at: https://github.com/Bohdan-Khomtchouk/Bio-chat.Database URLhttps://github.com/Bohdan-Khomtchouk/Bio-chat

Cardiovascular disease (CVD) is the leading cause of death worldwide, causing over 17M deaths per year, which outpaces global cancer mortality rates. Despite these sobering statistics, the state-of-the-art in computational infrastructure to study datasets associated with CVD has lagged far behind public resources widely available in the oncology field, where improved data science and visualization methods have led to the development of large-scale cancer genomics resources like MSKCC's cBioPortal or NCI's Genomic Data Commons (GDC) Portal. Developing a similar user-friendly computational platform could significantly lower the barriers between complex CVD data and researchers who want rapid, intuitive, and high-quality visual access to molecular profiles and clinical attributes from existing CVD projects. Here we present HeartBioPortal: a publicly available web application that provides intuitive visualization, analysis, and downloads of large-scale CVD data currently focused on gene expression, genetic association, and ancestry information. By democratizing access to anonymized CVD data, HeartBioPortal's aim is to integrate relevant omics and clinical information across the biological dataverse to support CVD clinicians and researchers.

Background Ischemic preconditioning ( IPC ) protects the heart from prolonged ischemic insult and reperfusion injury through a poorly understood mechanism. Post‐translational modifications of histone residues can confer rapid and drastic switches in gene expression in response to various stimuli, including ischemia. The aim of this study was to investigate the effect of histone methylation in the response to cardiac ischemic preconditioning. Methods and Results We used cardiac biopsies from mice subjected to IPC to quantify global levels of 3 of the most well‐studied histone methylation marks (H3K9me2, H3K27me3, and H3K4me3) with Western blot and found that H3K9me2 levels were significantly increased in the area at risk compared to remote myocardium. In order to assess which genes were affected by the increase in H3K9me2 levels, we performed Ch IP ‐Seq and transcriptome profiling using microarray. Two hundred thirty‐seven genes were both transcriptionally repressed and enriched in H3K9me2 in the area at risk of IPC mice. Of these, Mtor (Mechanistic target of rapamycin) was chosen for mechanistic studies. Knockdown of the major H3K9 methyltransferase G9a resulted in a significant decrease in H3K9me2 levels across Mtor , increased Mtor expression, as well as decreased autophagic activity in response to rapamycin and serum starvation. Conclusions IPC confers an increase of H3K9me2 levels throughout the Mtor gene—a master regulator of cellular metabolism and a key player in the cardioprotective effect of IPC —leading to transcriptional repression via the methyltransferase G9a. The results of this study indicate that G9a has an important role in regulating cardiac autophagy and the cardioprotective effect of IPC .

MotivationDifferent ChIP-seq peak callers often produce different output results from the same input. Since different peak callers are known to produce differentially enriched peaks with a large variance in peak length distribution and total peak count, accurately annotating peak lists with their nearest genes can be an arduous process. Functional genomic annotation of histone modification ChIP-seq data can be a particularly challenging task, as chromatin marks that have inherently broad peaks with a diffuse range of signal enrichment (e.g., H3K9me1, H3K27me3) differ significantly from narrow peaks that exhibit a compact and localized enrichment pattern (e.g., H3K4me3, H3K9ac). In addition, varying degrees of tissue-dependent broadness of an epigenetic mark can make it difficult to accurately and reliably link sequencing data to biological function. Thus, there exists an unmet need to develop a software program that can precisely tailor the computational analysis of a ChIP-seq dataset to the specific peak coordinates of the data and its surrounding genomic features.ResultsgeneXtendeRoptimizes the functional annotation of ChIP-seq peaks by exploring relative differences in annotating ChIP-seq peak sets to variable-length gene bodies. In contrast to prior techniques,geneXtendeRconsiders peak annotations beyond just the closest gene, allowing users to investigate peak summary statistics for the first-closest gene, second-closest gene, …,nth-closest gene whilst ranking the output according to biologically relevant events and iteratively comparing the fidelity of peak-to-gene overlap across a user-defined range of upstream and downstream extensions on the original boundaries of each gene’s coordinates. We testedgeneXtendeRon 547 human transcription factor ChIP-seq ENCODE datasets and 198 human histone modification ChIP-seq ENCODE datasets, providing the analysis results as case studies.AvailabilityThegeneXtendeRR/Bioconductor package (including detailed introductory vignettes) is available under the GPL-3 Open Source license and is freely available to download from Bioconductor at:https://bioconductor.org/packages/geneXtendeR/Author summarygeneXtendeRmakes functional annotation of ChIP-seq data more robust and precise, regardless of peak variability attributable to parameter tuning or peak caller algorithmic differences. Since different ChIP-seq peak callers produce differentially enriched peaks with large variance in peak length distribution and total peak count, annotating peak lists with their nearest genes can often be a noisy process where an adjacent second or third-closest gene may constitute a more viable biological candidate, e.g., during cases of linked genes that are located close to each other. As such, the goal ofgeneXtendeRis to robustly link differentially enriched peaks with their respective genes, thereby aiding experimental follow-up and validation in designing primers for a set of prospective gene candidates during qPCR.

Codon usage in 2730 genomes is analyzed for evolutionary patterns in the usage of synonymous codons and amino acids across prokaryotic and eukaryotic taxa. We group genomes together that have similar amounts of intra-genomic bias in their codon usage, and then compare how usage of particular different codons is diversified across each genome group, and how that usage varies from group to group. Inter-genomic diversity of codon usage increases with intra-genomic usage bias, following a universal pattern. The frequencies of the different codons vary in robust mutual correlation, and the implied synonymous codon and amino acid usages drift together. This kind of correlation indicates that the variation of codon usage across organisms is chiefly a consequence of lateral DNA transfer among diverse organisms. The group of genomes with the greatest intra-genomic bias comprises two distinct subgroups, with each one restricting its codon usage to essentially one unique half of the genetic code table. These organisms include eubacteria and archaea thought to be closest to the hypothesized last universal common ancestor (LUCA). Their codon usages imply genetic diversity near the hypothesized base of the tree of life. There is a continuous evolutionary progression across taxa from the two extremely diversified usages toward balanced usage of different codons (as approached, e.g. in mammals). In that progression, codon frequency variations are correlated as expected from a blending of the two extreme codon usages seen in prokaryotes.AUTHOR SUMMARYThe redundancy intrinsic to the genetic code allows different amino acids to be encoded by up to six synonymous codons. Genomes of different organisms prefer different synonymous codons, a phenomenon known as ‘codon usage bias.’ The phenomenon of codon usage bias is of fundamental interest for evolutionary biology, and is important in a variety of applied settings (e.g., transgene expression). The spectrum of codon usage biases seen in current organisms is commonly thought to have arisen by the combined actions of mutations and selective pressures. This view focuses on codon usage in specific genomes and the consequences of that usage for protein expression.Here we investigate an unresolved question of molecular genetics: are there global rules governing the usage of synonymous codons made by genomic DNA across organisms? To answer this question, we employed a data-driven approach to surveying 2730 species from all kingdoms of the ‘tree of life’ in order to classify their codon usage. A first major result was that the large majority of these organisms use codons rather uniformly on the genome-wide scale, without giving preference to particular codons among possible synonymous alternatives. A second major result was that two compartments of codon usage seem to co-exist and to be expressed in different proportions by different organisms. As such, we investigate how individual different codons are used in different organisms from all taxa. Whereas codon usage is generally believed to be the evolutionary result of both mutations and natural selection, our results suggest a different perspective: the usage of different codons (and amino acids) by different organisms follows a superposition of two distinct patterns of usage. One distinction locates to the third base pair of all different codons, which in one pattern is U or A, and in the other pattern is G or C. This result has two major implications: (1) the variation of codon usage as seen across different organisms is best accounted for by lateral gene transfer among diverse organisms; (2) the organisms that are by protein homology grouped near the base of the ‘tree of life’ comprise two genetically distinct lineages.We find that, over evolutionary time, codon usages have converged from two distinct, non-overlapping usages (e.g., as evident in bacteria and archaea) to a near-uniform, balanced usage of synonymous codons (e.g., in mammals). This shows that the variations of codon (and amino acid) biases reveal a distinct evolutionary progression. We also find that codon usage in bacteria and archaea is most diverse between organisms thought to be closest to the hypothesized last universal common ancestor (LUCA). The dichotomy in codon (and amino acid usages) present near the origin of the current ‘tree of life’ might provide information about the evolutionary development of the genetic code.

We propose a search engine and file retrieval system for all bioinformatics databases worldwide. PubData searches biomedical data in a user-friendly fashion similar to how PubMed searches biomedical literature. PubData is built on novel network programming, natural language processing, and artificial intelligence algorithms that can patch into the file transfer protocol servers of any user-specified bioinformatics database, query its contents, retrieve files for download, and adapt to the user’s search preferences.PubData is hosted as a user-friendly, cross-platform graphical user interface program developed using PyQt:http://www.pubdata.bio. The methods are implemented in Python, and are available as part of the PubData project at:https://github.com/Bohdan-Khomtchouk/PubData.

DNA encodes protein primary structure using 64 different codons to specify 20 different amino acids and a stop signal. To uncover rules of codon use, ranked codon frequencies have previously been analyzed in terms of empirical or statistical relations for a small number of genomes. These descriptions fail on most genomes reported in the Codon Usage Tabulated from GenBank (CUTG) database. Here we model codon usage as a random variable. This stochastic model provides accurate, one-parameter characterizations of 2210 nuclear and mitochondrial genomes represented with > 104 codons/genome in CUTG. We show that ranked codon frequencies are well characterized by a truncated normal (Gaussian) distribution. Most genomes use codons in a nearuniform manner. Lopsided usages are also widely distributed across genomes but less frequent. Our model provides a universal framework for investigating determinants of codon use.

Heatmaps are an indispensible visualization tool for examining large-scale snapshots of genomic activity across various types of next-generation sequencing datasets. However, traditional heatmap software do not typically offer multi-scale insight across multiple layers of genomic analysis (e.g., differential expression analysis, principal component analysis, gene ontology analysis, and network analysis) or multiple types of next-generation sequencing datasets (e.g., ChIP-seq and RNA-seq). As such, it is natural to want to interact with a heatmap’s contents using an extensive set of integrated analysis tools applicable to a broad array of genomic data types. We propose a user-friendly ChIP-seq and RNA-seq software suite for the interactive visualization and analysis of genomic data, including integrated features to support differential expression analysis, interactive heatmap production, principal component analysis, gene ontology analysis, and dynamic network analysis. MicroScope is hosted online as an R Shiny web application based on the D3 JavaScript library: http://microscopebioinformatics.org/ . The methods are implemented in R, and are available as part of the MicroScope project at: https://github.com/Bohdan-Khomtchouk/Microscope .

The graphical visualization of gene expression data using heatmaps has become an integral component of modern-day medical research. Heatmaps are used extensively to plot quantitative differences in gene expression levels, such as those measured with RNAseq and microarray experiments, to provide qualitative large-scale views of the transcriptonomic landscape. Creating high-quality heatmaps is a computationally intensive task, often requiring considerable programming experience, particularly for customizing features to a specific dataset at hand. Software to create publication-quality heatmaps is developed with the R programming language, C++ programming language, and OpenGL application programming interface (API) to create industry-grade high performance graphics. We create a graphical user interface (GUI) software package called HeatmapGenerator for Windows OS and Mac OS X as an intuitive, user-friendly alternative to researchers with minimal prior coding experience to allow them to create publication-quality heatmaps using R graphics without sacrificing their desired level of customization. The simplicity of HeatmapGenerator is that it only requires the user to upload a preformatted input file and download the publicly available R software language, among a few other operating system-specific requirements. Advanced features such as color, text labels, scaling, legend construction, and even database storage can be easily customized with no prior programming knowledge. We provide an intuitive and user-friendly software package, HeatmapGenerator, to create high-quality, customizable heatmaps generated using the high-resolution color graphics capabilities of R. The software is available for Microsoft Windows and Apple Mac OS X. HeatmapGenerator is released under the GNU General Public License and publicly available at: http://sourceforge.net/projects/heatmapgenerator/ . The Mac OS X direct download is available at: http://sourceforge.net/projects/heatmapgenerator/files/HeatmapGenerator_MAC_OSX.tar.gz/download . The Windows OS direct download is available at: http://sourceforge.net/projects/heatmapgenerator/files/HeatmapGenerator_WINDOWS.zip/download .