Predator: Predicting the Impact of Cancer Somatic Mutations on Protein-Protein Interactions.

Since many biological processes are governed by protein-protein interactions, understanding which mutations lead to a disruption in these interactions is profoundly important for cancer research. Most of the existing methods focus on the stability of the protein without considering the specific effects of a mutation on its interactions with other proteins. Here, we focus on somatic mutations that appear on the interface regions of the protein and predict the interactions that would be affected by a mutation of interest. We build an ensemble model, Predator, that classifies the interface mutations as disruptive or nondisruptive based on the predicted effects of mutations on specific protein-protein interactions. We show that Predator outperforms existing approaches in literature in terms of prediction accuracy. We then apply Predator on various TCGA cancer cohorts and perform comprehensive analysis at cohort level, patient level, and gene level in determining the genes whose interface mutations tend to yield a disruption in its interactions. The predictions obtained by Predator shed light on interesting patterns on several genes for each cohort regarding their potential as cancer drivers. Our analyses further reveal that the identified genes and their frequently disrupted partners exhibit patterns of mutually exclusivity across cancer cohorts under study.

The P-body protein 4E-T represses translation to regulate the balance between cell genesis and establishment of the postnatal NSC pool.

Here, we ask how developing precursors maintain the balance between cell genesis for tissue growth and establishment of adult stem cell pools, focusing on postnatal forebrain neural precursor cells (NPCs). We show that these NPCs are transcriptionally primed to differentiate and that the primed mRNAs are associated with the translational repressor 4E-T. 4E-T also broadly associates with other NPC mRNAs encoding transcriptional regulators, and these are preferentially depleted from ribosomes, consistent with repression. By contrast, a second translational regulator, Cpeb4, associates with diverse target mRNAs that are largely ribosome associated. The 4E-T-dependent mRNA association is functionally important because 4E-T knockdown or conditional knockout derepresses proneurogenic mRNA translation and perturbs maintenance versus differentiation of early postnatal NPCs in culture and in vivo. Thus, early postnatal NPCs are primed to differentiate, and 4E-T regulates the balance between cell genesis and stem cell expansion by sequestering and repressing mRNAs encoding transcriptional regulators.

miR-223 Exerts Translational Control of Proatherogenic Genes in Macrophages.

BACKGROUND: A significant burden of atherosclerotic disease is driven by inflammation. Recently, microRNAs (miRNAs) have emerged as important factors driving and protecting from atherosclerosis. miR-223 regulates cholesterol metabolism and inflammation via targeting both cholesterol biosynthesis pathway and NFkB signaling pathways; however, its role in atherosclerosis has not been investigated. We hypothesize that miR-223 globally regulates core inflammatory pathways in macrophages in response to inflammatory and atherogenic stimuli thus limiting the progression of atherosclerosis. METHODS AND RESULTS: Loss of miR-223 in macrophages decreases Abca1 gene and protein expression as well as cholesterol efflux to apoA1 (Apolipoprotein A1) and enhances proinflammatory gene expression. In contrast, overexpression of miR-223 promotes the efflux of cholesterol and macrophage polarization toward an anti-inflammatory phenotype. These beneficial effects of miR-223 are dependent on its target gene, the transcription factor Sp3. Consistent with the antiatherogenic effects of miR-223 in vitro, mice receiving miR223-/- bone marrow exhibit increased plaque size, lipid content, and circulating inflammatory cytokines (ie, IL-1ß). Deficiency of miR-223 in bone marrow-derived cells also results in an increase in circulating pro-atherogenic cells (total monocytes and neutrophils) compared with control mice. Furthermore, the expression of miR-223 target gene (Sp3) and pro-inflammatory marker (Il-6) are enhanced whereas the expression of Abca1 and anti-inflammatory marker (Retnla) are reduced in aortic arches from mice lacking miR-223 in bone marrow-derived cells. In mice fed a high-cholesterol diet and in humans with unstable carotid atherosclerosis, the expression of miR-223 is increased. To further understand the molecular mechanisms underlying the effect of miR-223 on atherosclerosis in vivo, we characterized global RNA translation profile of macrophages isolated from mice receiving wild-type or miR223-/- bone marrow. Using ribosome profiling, we reveal a notable upregulation of inflammatory signaling and lipid metabolism at the translation level but less significant at the transcription level. Analysis of upregulated genes at the translation level reveal an enrichment of miR-223-binding sites, confirming that miR-223 exerts significant changes in target genes in atherogenic macrophages via altering their translation. CONCLUSIONS: Our study demonstrates that miR-223 can protect against atherosclerosis by acting as a global regulator of RNA translation of cholesterol efflux and inflammation pathways.

PersonaDrive: a method for the identification and prioritization of personalized cancer drivers.

MOTIVATION: A major challenge in cancer genomics is to distinguish the driver mutations that are causally linked to cancer from passenger mutations that do not contribute to cancer development. The majority of existing methods provide a single driver gene list for the entire cohort of patients. However, since mutation profiles of patients from the same cancer type show a high degree of heterogeneity, a more ideal approach is to identify patient-specific drivers. RESULTS: We propose a novel method that integrates genomic data, biological pathways and protein connectivity information for personalized identification of driver genes. The method is formulated on a personalized bipartite graph for each patient. Our approach provides a personalized ranking of the mutated genes of a patient based on the sum of weighted 'pairwise pathway coverage' scores across all the samples, where appropriate pairwise patient similarity scores are used as weights to normalize these coverage scores. We compare our method against five state-of-the-art patient-specific cancer gene prioritization methods. The comparisons are with respect to a novel evaluation method that takes into account the personalized nature of the problem. We show that our approach outperforms the existing alternatives for both the TCGA and the cell line data. In addition, we show that the KEGG/Reactome pathways enriched in our ranked genes and those that are enriched in cell lines' reference sets overlap significantly when compared to the overlaps achieved by the rankings of the alternative methods. Our findings can provide valuable information toward the development of personalized treatments and therapies. AVAILABILITY AND IMPLEMENTATION: All the codes and data are available at https://github.com/abu-compbio/PersonaDrive, and the data underlying this article are available in Zenodo, at https://doi.org/10.5281/zenodo.6520187. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

A Network-Centric Framework for the Evaluation of Mutual Exclusivity Tests on Cancer Drivers.

One of the key concepts employed in cancer driver gene identification is that of mutual exclusivity (ME); a driver mutation is less likely to occur in case of an earlier mutation that has common functionality in the same molecular pathway. Several ME tests have been proposed recently, however the current protocols to evaluate ME tests have two main limitations. Firstly the evaluations are mostly with respect to simulated data and secondly the evaluation metrics lack a network-centric view. The latter is especially crucial as the notion of common functionality can be achieved through searching for interaction patterns in relevant networks. We propose a network-centric framework to evaluate the pairwise significances found by statistical ME tests. It has three main components. The first component consists of metrics employed in the network-centric ME evaluations. Such metrics are designed so that network knowledge and the reference set of known cancer genes are incorporated in ME evaluations under a careful definition of proper control groups. The other two components are designed as further mechanisms to avoid confounders inherent in ME detection on top of the network-centric view. To this end, our second objective is to dissect the side effects caused by mutation load artifacts where mutations driving tumor subtypes with low mutation load might be incorrectly diagnosed as mutually exclusive. Finally, as part of the third main component, the confounding issue stemming from the use of nonspecific interaction networks generated as combinations of interactions from different tissues is resolved through the creation and use of tissue-specific networks in the proposed framework. The data, the source code and useful scripts are available at: https://github.com/abu-compbio/NetCentric.

Ranking cancer drivers via betweenness-based outlier detection and random walks.

BACKGROUND: Recent cancer genomic studies have generated detailed molecular data on a large number of cancer patients. A key remaining problem in cancer genomics is the identification of driver genes. RESULTS: We propose BetweenNet, a computational approach that integrates genomic data with a protein-protein interaction network to identify cancer driver genes. BetweenNet utilizes a measure based on betweenness centrality on patient specific networks to identify the so-called outlier genes that correspond to dysregulated genes for each patient. Setting up the relationship between the mutated genes and the outliers through a bipartite graph, it employs a random-walk process on the graph, which provides the final prioritization of the mutated genes. We compare BetweenNet against state-of-the art cancer gene prioritization methods on lung, breast, and pan-cancer datasets. CONCLUSIONS: Our evaluations show that BetweenNet is better at recovering known cancer genes based on multiple reference databases. Additionally, we show that the GO terms and the reference pathways enriched in BetweenNet ranked genes and those that are enriched in known cancer genes overlap significantly when compared to the overlaps achieved by the rankings of the alternative methods.

Sparse data embedding and prediction by tropical matrix factorization.

BACKGROUND: Matrix factorization methods are linear models, with limited capability to model complex relations. In our work, we use tropical semiring to introduce non-linearity into matrix factorization models. We propose a method called Sparse Tropical Matrix Factorization (STMF) for the estimation of missing (unknown) values in sparse data. RESULTS: We evaluate the efficiency of the STMF method on both synthetic data and biological data in the form of gene expression measurements downloaded from The Cancer Genome Atlas (TCGA) database. Tests on unique synthetic data showed that STMF approximation achieves a higher correlation than non-negative matrix factorization (NMF), which is unable to recover patterns effectively. On real data, STMF outperforms NMF on six out of nine gene expression datasets. While NMF assumes normal distribution and tends toward the mean value, STMF can better fit to extreme values and distributions. CONCLUSION: STMF is the first work that uses tropical semiring on sparse data. We show that in certain cases semirings are useful because they consider the structure, which is different and simpler to understand than it is with standard linear algebra.

DriveWays: a method for identifying possibly overlapping driver pathways in cancer.

The majority of the previous methods for identifying cancer driver modules output nonoverlapping modules. This assumption is biologically inaccurate as genes can participate in multiple molecular pathways. This is particularly true for cancer-associated genes as many of them are network hubs connecting functionally distinct set of genes. It is important to provide combinatorial optimization problem definitions modeling this biological phenomenon and to suggest efficient algorithms for its solution. We provide a formal definition of the Overlapping Driver Module Identification in Cancer (ODMIC) problem. We show that the problem is NP-hard. We propose a seed-and-extend based heuristic named DriveWays that identifies overlapping cancer driver modules from the graph built from the IntAct PPI network. DriveWays incorporates mutual exclusivity, coverage, and the network connectivity information of the genes. We show that DriveWays outperforms the state-of-the-art methods in recovering well-known cancer driver genes performed on TCGA pan-cancer data. Additionally, DriveWay's output modules show a stronger enrichment for the reference pathways in almost all cases. Overall, we show that enabling modules to overlap improves the recovery of functional pathways filtered with known cancer drivers, which essentially constitute the reference set of cancer-related pathways.

MEXCOwalk: mutual exclusion and coverage based random walk to identify cancer modules.

MOTIVATION: Genomic analyses from large cancer cohorts have revealed the mutational heterogeneity problem which hinders the identification of driver genes based only on mutation profiles. One way to tackle this problem is to incorporate the fact that genes act together in functional modules. The connectivity knowledge present in existing protein-protein interaction (PPI) networks together with mutation frequencies of genes and the mutual exclusivity of cancer mutations can be utilized to increase the accuracy of identifying cancer driver modules. RESULTS: We present a novel edge-weighted random walk-based approach that incorporates connectivity information in the form of protein-protein interactions (PPIs), mutual exclusivity and coverage to identify cancer driver modules. MEXCOwalk outperforms several state-of-the-art computational methods on TCGA pan-cancer data in terms of recovering known cancer genes, providing modules that are capable of classifying normal and tumor samples and that are enriched for mutations in specific cancer types. Furthermore, the risk scores determined with output modules can stratify patients into low-risk and high-risk groups in multiple cancer types. MEXCOwalk identifies modules containing both well-known cancer genes and putative cancer genes that are rarely mutated in the pan-cancer data. The data, the source code and useful scripts are available at: https://github.com/abu-compbio/MEXCOwalk. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

RBPSponge: genome-wide identification of lncRNAs that sponge RBPs.

SUMMARY: Long non-coding RNAs (lncRNAs) can act as molecular sponge or decoys for an RNA-binding protein (RBP) through their RBP-binding sites, thereby modulating the expression of all target genes of the corresponding RBP of interest. Here, we present a web tool named RBPSponge to explore lncRNAs based on their potential to act as a sponge for an RBP of interest. RBPSponge identifies the occurrences of RBP-binding sites and CLIP peaks on lncRNAs, and enables users to run statistical analyses to investigate the regulatory network between lncRNAs, RBPs and targets of RBPs. AVAILABILITY AND IMPLEMENTATION: The web server is available at https://www.RBPSponge.com. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MITF-MIR211 axis is a novel autophagy amplifier system during cellular stress.

Macroautophagy (autophagy) is an evolutionarily conserved recycling and stress response mechanism. Active at basal levels in eukaryotes, autophagy is upregulated under stress providing cells with building blocks such as amino acids. A lysosome-integrated sensor system composed of RRAG GTPases and MTOR complex 1 (MTORC1) regulates lysosome biogenesis and autophagy in response to amino acid availability. Stress-mediated inhibition of MTORC1 results in the dephosphorylation and nuclear translocation of the TFE/MITF family of transcriptional factors, and triggers an autophagy- and lysosomal-related gene transcription program. The role of family members TFEB and TFE3 have been studied in detail, but the importance of MITF proteins in autophagy regulation is not clear so far. Here we introduce for the first time a specific role for MITF in autophagy control that involves upregulation of MIR211. We show that, under stress conditions including starvation and MTOR inhibition, a MITF-MIR211 axis constitutes a novel feed-forward loop that controls autophagic activity in cells. Direct targeting of the MTORC2 component RICTOR by MIR211 led to the inhibition of the MTORC1 pathway, further stimulating MITF translocation to the nucleus and completing an autophagy amplification loop. In line with a ubiquitous function, MITF and MIR211 were co-expressed in all tested cell lines and human tissues, and the effects on autophagy were observed in a cell-type independent manner. Thus, our study provides direct evidence that MITF has rate-limiting and specific functions in autophagy regulation. Collectively, the MITF-MIR211 axis constitutes a novel and universal autophagy amplification system that sustains autophagic activity under stress conditions. Abbreviations: ACTB: actin beta; AKT: AKT serine/threonine kinase; AKT1S1/PRAS40: AKT1 substrate 1; AMPK: AMP-activated protein kinase; ATG: autophagy-related; BECN1: beclin 1; DEPTOR: DEP domain containing MTOR interacting protein; GABARAP: GABA type A receptor-associated protein; HIF1A: hypoxia inducible factor 1 subunit alpha; LAMP1: lysosomal associated membrane protein 1; MAP1LC3B/LC3B: microtubule associated protein 1 light chain 3 beta; MAPKAP1/SIN1: mitogen-activated protein kinase associated protein 1; MITF: melanogenesis associated transcription factor; MLST8: MTOR associated protein, LST8 homolog; MRE: miRNA response element; MTOR: mechanistic target of rapamycin kinase; MTORC1: MTOR complex 1; MTORC2: MTOR complex 2; PRR5/Protor 1: proline rich 5; PRR5L/Protor 2: proline rich 5 like; RACK1: receptor for activated C kinase 1; RPTOR: regulatory associated protein of MTOR complex 1; RICTOR: RPTOR independent companion of MTOR complex 2; RPS6KB/p70S6K: ribosomal protein S6 kinase; RT-qPCR: quantitative reverse transcription-polymerase chain reaction; SQSTM1: sequestosome 1; STK11/LKB1: serine/threonine kinase 11; TFE3: transcription factor binding to IGHM enhancer 3; TFEB: transcription factor EB; TSC1/2: TSC complex subunit 1/2; ULK1: unc-51 like autophagy activating kinase 1; UVRAG: UV radiation resistance associated; VIM: vimentin; VPS11: VPS11, CORVET/HOPS core subunit; VPS18: VPS18, CORVET/HOPS core subunit; WIPI1: WD repeat domain, phosphoinositide interacting 1.

A Translational Repression Complex in Developing Mammalian Neural Stem Cells that Regulates Neuronal Specification.

The mechanisms instructing genesis of neuronal subtypes from mammalian neural precursors are not well understood. To address this issue, we have characterized the transcriptional landscape of radial glial precursors (RPs) in the embryonic murine cortex. We show that individual RPs express mRNA, but not protein, for transcriptional specifiers of both deep and superficial layer cortical neurons. Some of these mRNAs, including the superficial versus deep layer neuron transcriptional regulators Brn1 and Tle4, are translationally repressed by their association with the RNA-binding protein Pumilio2 (Pum2) and the 4E-T protein. Disruption of these repressive complexes in RPs mid-neurogenesis by knocking down 4E-T or Pum2 causes aberrant co-expression of deep layer neuron specification proteins in newborn superficial layer neurons. Thus, cortical RPs are transcriptionally primed to generate diverse types of neurons, and a Pum2/4E-T complex represses translation of some of these neuronal identity mRNAs to ensure appropriate temporal specification of daughter neurons.

Predicting clinical outcomes in neuroblastoma with genomic data integration.

BACKGROUND: Neuroblastoma is a heterogeneous disease with diverse clinical outcomes. Current risk group models require improvement as patients within the same risk group can still show variable prognosis. Recently collected genome-wide datasets provide opportunities to infer neuroblastoma subtypes in a more unified way. Within this context, data integration is critical as different molecular characteristics can contain complementary signals. To this end, we utilized the genomic datasets available for the SEQC cohort patients to develop supervised and unsupervised models that can predict disease prognosis. RESULTS: Our supervised model trained on the SEQC cohort can accurately predict overall survival and event-free survival profiles of patients in two independent cohorts. We also performed extensive experiments to assess the prediction accuracy of high risk patients and patients without MYCN amplification. Our results from this part suggest that clinical endpoints can be predicted accurately across multiple cohorts. To explore the data in an unsupervised manner, we used an integrative clustering strategy named multi-view kernel k-means (MVKKM) that can effectively integrate multiple high-dimensional datasets with varying weights. We observed that integrating different gene expression datasets results in a better patient stratification compared to using these datasets individually. Also, our identified subgroups provide a better Cox regression model fit compared to the existing risk group definitions. CONCLUSION: Altogether, our results indicate that integration of multiple genomic characterizations enables the discovery of subtypes that improve over existing definitions of risk groups. Effective prediction of survival times will have a direct impact on choosing the right therapies for patients. REVIEWERS: This article was reviewed by Susmita Datta, Wenzhong Xiao and Ziv Shkedy.

Modeling Gene Regulation in Liver Hepatocellular Carcinoma with Random Forests.

Liver hepatocellular carcinoma (HCC) remains a leading cause of cancer-related death. Poor understanding of the mechanisms underlying HCC prevents early detection and leads to high mortality. We developed a random forest model that incorporates copy-number variation, DNA methylation, transcription factor, and microRNA binding information as features to predict gene expression in HCC. Our model achieved a highly significant correlation between predicted and measured expression of held-out genes. Furthermore, we identified potential regulators of gene expression in HCC. Many of these regulators have been previously found to be associated with cancer and are differentially expressed in HCC. We also evaluated our predicted target sets for these regulators by making comparison with experimental results. Lastly, we found that the transcription factor E2F6, one of the candidate regulators inferred by our model, is predictive of survival rate in HCC. Results of this study will provide directions for future prospective studies in HCC.

A comprehensive database of high-throughput sequencing-based RNA secondary structure probing data (Structure Surfer).

BACKGROUND: RNA molecules fold into complex three-dimensional shapes, guided by the pattern of hydrogen bonding between nucleotides. This pattern of base pairing, known as RNA secondary structure, is critical to their cellular function. Recently several diverse methods have been developed to assay RNA secondary structure on a transcriptome-wide scale using high-throughput sequencing. Each approach has its own strengths and caveats, however there is no widely available tool for visualizing and comparing the results from these varied methods. METHODS: To address this, we have developed Structure Surfer, a database and visualization tool for inspecting RNA secondary structure in six transcriptome-wide data sets from human and mouse ( http://tesla.pcbi.upenn.edu/strucuturesurfer/ ). The data sets were generated using four different high-throughput sequencing based methods. Each one was analyzed with a scoring pipeline specific to its experimental design. Users of Structure Surfer have the ability to query individual loci as well as detect trends across multiple sites. RESULTS: Here, we describe the included data sets and their differences. We illustrate the database's function by examining known structural elements and we explore example use cases in which combined data is used to detect structural trends. CONCLUSIONS: In total, Structure Surfer provides an easy-to-use database and visualization interface for allowing users to interrogate the currently available transcriptome-wide RNA secondary structure information for mammals.

Chemogenomics and orthology-based design of antibiotic combination therapies.

Combination antibiotic therapies are being increasingly used in the clinic to enhance potency and counter drug resistance. However, the large search space of candidate drugs and dosage regimes makes the identification of effective combinations highly challenging. Here, we present a computational approach called INDIGO, which uses chemogenomics data to predict antibiotic combinations that interact synergistically or antagonistically in inhibiting bacterial growth. INDIGO quantifies the influence of individual chemical-genetic interactions on synergy and antagonism and significantly outperforms existing approaches based on experimental evaluation of novel predictions in Escherichia coli Our analysis revealed a core set of genes and pathways (e.g. central metabolism) that are predictive of antibiotic interactions. By identifying the interactions that are associated with orthologous genes, we successfully estimated drug-interaction outcomes in the bacterial pathogens Mycobacterium tuberculosis and Staphylococcus aureus, using the E. coli INDIGO model. INDIGO thus enables the discovery of effective combination therapies in less-studied pathogens by leveraging chemogenomics data in model organisms.

Inferring RBP-Mediated Regulation in Lung Squamous Cell Carcinoma.

RNA-binding proteins (RBPs) play key roles in post-transcriptional regulation of mRNAs. Dysregulations in RBP-mediated mechanisms have been found to be associated with many steps of cancer initiation and progression. Despite this, previous studies of gene expression in cancer have ignored the effect of RBPs. To this end, we developed a lasso regression model that predicts gene expression in cancer by incorporating RBP-mediated regulation as well as the effects of other well-studied factors such as copy-number variation, DNA methylation, TFs and miRNAs. As a case study, we applied our model to Lung squamous cell carcinoma (LUSC) data as we found that there are several RBPs differentially expressed in LUSC. Including RBP-mediated regulatory effects in addition to the other features significantly increased the Spearman rank correlation between predicted and measured expression of held-out genes. Using a feature selection procedure that accounts for the adaptive search employed by lasso regularization, we identified the candidate regulators in LUSC. Remarkably, several of these candidate regulators are RBPs. Furthermore, majority of the candidate regulators have been previously found to be associated with lung cancer. To investigate the mechanisms that are controlled by these regulators, we predicted their target gene sets based on our model. We validated the target gene sets by comparing against experimentally verified targets. Our results suggest that the future studies of gene expression in cancer must consider the effect of RBP-mediated regulation.

Quaking promotes monocyte differentiation into pro-atherogenic macrophages by controlling pre-mRNA splicing and gene expression.

A hallmark of inflammatory diseases is the excessive recruitment and influx of monocytes to sites of tissue damage and their ensuing differentiation into macrophages. Numerous stimuli are known to induce transcriptional changes associated with macrophage phenotype, but posttranscriptional control of human macrophage differentiation is less well understood. Here we show that expression levels of the RNA-binding protein Quaking (QKI) are low in monocytes and early human atherosclerotic lesions, but are abundant in macrophages of advanced plaques. Depletion of QKI protein impairs monocyte adhesion, migration, differentiation into macrophages and foam cell formation in vitro and in vivo. RNA-seq and microarray analysis of human monocyte and macrophage transcriptomes, including those of a unique QKI haploinsufficient patient, reveal striking changes in QKI-dependent messenger RNA levels and splicing of RNA transcripts. The biological importance of these transcripts and requirement for QKI during differentiation illustrates a central role for QKI in posttranscriptionally guiding macrophage identity and function.

Modeling the combined effect of RNA-binding proteins and microRNAs in post-transcriptional regulation.

Recent studies show that RNA-binding proteins (RBPs) and microRNAs (miRNAs) function in coordination with each other to control post-transcriptional regulation (PTR). Despite this, the majority of research to date has focused on the regulatory effect of individual RBPs or miRNAs. Here, we mapped both RBP and miRNA binding sites on human 3'UTRs and utilized this collection to better understand PTR. We show that the transcripts that lack competition for HuR binding are destabilized more after HuR depletion. We also confirm this finding for PUM1(2) by measuring genome-wide expression changes following the knockdown of PUM1(2) in HEK293 cells. Next, to find potential cooperative interactions, we identified the pairs of factors whose sites co-localize more often than expected by random chance. Upon examining these results for PUM1(2), we found that transcripts where the sites of PUM1(2) and its interacting miRNA form a stem-loop are more stabilized upon PUM1(2) depletion. Finally, using dinucleotide frequency and counts of regulatory sites as features in a regression model, we achieved an AU-ROC of 0.86 in predicting mRNA half-life in BEAS-2B cells. Altogether, our results suggest that future studies of PTR must consider the combined effects of RBPs and miRNAs, as well as their interactions.

JBASE: Joint Bayesian Analysis of Subphenotypes and Epistasis.

MOTIVATION: Rapid advances in genotyping and genome-wide association studies have enabled the discovery of many new genotype-phenotype associations at the resolution of individual markers. However, these associations explain only a small proportion of theoretically estimated heritability of most diseases. In this work, we propose an integrative mixture model called JBASE: joint Bayesian analysis of subphenotypes and epistasis. JBASE explores two major reasons of missing heritability: interactions between genetic variants, a phenomenon known as epistasis and phenotypic heterogeneity, addressed via subphenotyping. RESULTS: Our extensive simulations in a wide range of scenarios repeatedly demonstrate that JBASE can identify true underlying subphenotypes, including their associated variants and their interactions, with high precision. In the presence of phenotypic heterogeneity, JBASE has higher Power and lower Type 1 Error than five state-of-the-art approaches. We applied our method to a sample of individuals from Mexico with Type 2 diabetes and discovered two novel epistatic modules, including two loci each, that define two subphenotypes characterized by differences in body mass index and waist-to-hip ratio. We successfully replicated these subphenotypes and epistatic modules in an independent dataset from Mexico genotyped with a different platform. AVAILABILITY AND IMPLEMENTATION: JBASE is implemented in C++, supported on Linux and is available at http://www.cs.toronto.edu/∼goldenberg/JBASE/jbase.tar.gz. The genotype data underlying this study are available upon approval by the ethics review board of the Medical Centre Siglo XXI. Please contact Dr Miguel Cruz at mcruzl@yahoo.com for assistance with the application. CONTACT: anna.goldenberg@utoronto.ca SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.