STORMSeq: an open-source, user-friendly pipeline for processing personal genomics data in the cloud.

The increasing public availability of personal complete genome sequencing data has ushered in an era of democratized genomics. However, read mapping and variant calling software is constantly improving and individuals with personal genomic data may prefer to customize and update their variant calls. Here, we describe STORMSeq (Scalable Tools for Open-Source Read Mapping), a graphical interface cloud computing solution that does not require a parallel computing environment or extensive technical experience. This customizable and modular system performs read mapping, read cleaning, and variant calling and annotation. At present, STORMSeq costs approximately $2 and 5-10 hours to process a full exome sequence and $30 and 3-8 days to process a whole genome sequence. We provide this open-access and open-source resource as a user-friendly interface in Amazon EC2.

Using molecular features of xenobiotics to predict hepatic gene expression response.

Despite recent advances in molecular medicine and rational drug design, many drugs still fail because toxic effects arise at the cellular and tissue level. In order to better understand these effects, cellular assays can generate high-throughput measurements of gene expression changes induced by small molecules. However, our understanding of how the chemical features of small molecules influence gene expression is very limited. Therefore, we investigated the extent to which chemical features of small molecules can reliably be associated with significant changes in gene expression. Specifically, we analyzed the gene expression response of rat liver cells to 170 different drugs and searched for genes whose expression could be related to chemical features alone. Surprisingly, we can predict the up-regulation of 87 genes (increased expression of at least 1.5 times compared to controls). We show an average cross-validation predictive area under the receiver operating characteristic curve (AUROC) of 0.7 or greater for each of these 87 genes. We applied our method to an external data set of rat liver gene expression response to a novel drug and achieved an AUROC of 0.7. We also validated our approach by predicting up-regulation of Cytochrome P450 1A2 (CYP1A2) in three drugs known to induce CYP1A2 that were not in our data set. Finally, a detailed analysis of the CYP1A2 predictor allowed us to identify which fragments made significant contributions to the predictive scores.

A novel signal detection algorithm for identifying hidden drug-drug interactions in adverse event reports.

OBJECTIVE: Adverse drug events (ADEs) are common and account for 770 000 injuries and deaths each year and drug interactions account for as much as 30% of these ADEs. Spontaneous reporting systems routinely collect ADEs from patients on complex combinations of medications and provide an opportunity to discover unexpected drug interactions. Unfortunately, current algorithms for such "signal detection" are limited by underreporting of interactions that are not expected. We present a novel method to identify latent drug interaction signals in the case of underreporting. MATERIALS AND METHODS: We identified eight clinically significant adverse events. We used the FDA's Adverse Event Reporting System to build profiles for these adverse events based on the side effects of drugs known to produce them. We then looked for pairs of drugs that match these single-drug profiles in order to predict potential interactions. We evaluated these interactions in two independent data sets and also through a retrospective analysis of the Stanford Hospital electronic medical records. RESULTS: We identified 171 novel drug interactions (for eight adverse event categories) that are significantly enriched for known drug interactions (p=0.0009) and used the electronic medical record for independently testing drug interaction hypotheses using multivariate statistical models with covariates. CONCLUSION: Our method provides an option for detecting hidden interactions in spontaneous reporting systems by using side effect profiles to infer the presence of unreported adverse events.

Bioinformatics challenges for personalized medicine.

MOTIVATION: Widespread availability of low-cost, full genome sequencing will introduce new challenges for bioinformatics. RESULTS: This review outlines recent developments in sequencing technologies and genome analysis methods for application in personalized medicine. New methods are needed in four areas to realize the potential of personalized medicine: (i) processing large-scale robust genomic data; (ii) interpreting the functional effect and the impact of genomic variation; (iii) integrating systems data to relate complex genetic interactions with phenotypes; and (iv) translating these discoveries into medical practice. CONTACT: russ.altman@stanford.edu

Proteomic analysis of active multiple sclerosis lesions reveals therapeutic targets.

Understanding the neuropathology of multiple sclerosis (MS) is essential for improved therapies. Therefore, identification of targets specific to pathological types of MS may have therapeutic benefits. Here we identify, by laser-capture microdissection and proteomics, proteins unique to three major types of MS lesions: acute plaque, chronic active plaque and chronic plaque. Comparative proteomic profiles identified tissue factor and protein C inhibitor within chronic active plaque samples, suggesting dysregulation of molecules associated with coagulation. In vivo administration of hirudin or recombinant activated protein C reduced disease severity in experimental autoimmune encephalomyelitis and suppressed Th1 and Th17 cytokines in astrocytes and immune cells. Administration of mutant forms of recombinant activated protein C showed that both its anticoagulant and its signalling functions were essential for optimal amelioration of experimental autoimmune encephalomyelitis. A proteomic approach illuminated potential therapeutic targets selective for specific pathological stages of MS and implicated participation of the coagulation cascade.

Genome-wide network analysis reveals the global properties of IFN-beta immediate transcriptional effects in humans.

IFN-beta effectively controls clinical exacerbations and magnetic resonance imaging activity in most multiple sclerosis patients. However, its mechanism of action has not been yet fully elucidated. In this study we used DNA microarrays to analyze the longitudinal transcriptional profile of blood cells within a week of IFN-beta administration. Using differential expression and gene ontology analyses we found evidence of a general decrease in the cellular activity of T lymphocytes resembling the endogenous antiviral response of IFNs. In contrast, most of the differentially expressed genes (DEGs) from untreated individuals were involved in cellular physiological processes. We then used mutual information (MI) to build networks of coregulated genes in both treated and untreated individuals. Interestingly, the connectivity distribution (k) of networks generated with high MI values displayed scale-free properties. Conversely, the observed k for networks generated with suboptimal MI values approximated a Poisson distribution, suggesting that MI captures biologically relevant interactions. Gene networks from individuals treated with IFN-beta revealed a tight core of immune- and apoptosis-related genes associated with higher values of MI. In contrast, networks obtained from untreated individuals primarily reflected cellular housekeeping functions. Finally, we trained a neural network to reverse engineer the directionality of the main interactions observed at the biological process level. This is the first study that incorporates network analysis to investigate gene regulation in response to a therapeutic drug in humans. Implications of this method in the creation of personalized models of response to therapy are discussed.

Mapping gene activity in complex disorders: Integration of expression and genomic scans for multiple sclerosis.

Genetic predisposition contributes to the pathogenesis of most common diseases. Genetic studies have been extremely successful in the identification of genes responsible for a number of Mendelian disorders. However, with a few exceptions, genes predisposing to diseases with complex inheritance remain unknown despite multiple efforts. In this article we collected detailed information for all genome-wide genetic screens performed to date in multiple sclerosis (MS) and in its animal model experimental autoimmune encephalomyelitis (EAE), and integrated these results with those from all high throughput gene expression studies in humans and mice. We analyzed a total of 55 studies. We found that differentially expressed genes (DEG) are not uniformly distributed in the genome, but rather appear in clusters. Furthermore, these clusters significantly differ from the known heterogeneous organization characteristic of eukaryotic gene distributions. We also identified regions of susceptibility that overlapped with clusters of DEG leading to the prioritization of candidate genes. Integration of genomic and transcriptional information is a powerful tool to dissect genetic susceptibility in complex multifactorial disorders like MS.