A case of a small-sized cavernous hemangioma in the right ventricle - an incidental finding.

A cardiac cavernous hemangioma is a rare, primary, benign tumor that is usually diagnosed in young or middle-aged patients. In this article, we report the case of a 71-year-old male patient whose doctors incidentally discovered a heart tumor on his transthoracic echocardiography. Triple-phase computed tomography (CT) (pre-contrast, arterial and portal venous) missed the lesion, and magnetic resonance imaging (MRI) revealed a small, oval tumor attached to the wall of the right ventricle. The tumor was successfully removed surgically, and the patient recovered after 2 weeks. A histopathological examination resulted in the diagnosis of a benign cavernous hemangioma.

De novo ChIP-seq analysis.

Methods for the analysis of chromatin immunoprecipitation sequencing (ChIP-seq) data start by aligning the short reads to a reference genome. While often successful, they are not appropriate for cases where a reference genome is not available. Here we develop methods for de novo analysis of ChIP-seq data. Our methods combine de novo assembly with statistical tests enabling motif discovery without the use of a reference genome. We validate the performance of our method using human and mouse data. Analysis of fly data indicates that our method outperforms alignment based methods that utilize closely related species.

Integrating sequence, expression and interaction data to determine condition-specific miRNA regulation.

MOTIVATION: MicroRNAs (miRNAs) are small non-coding RNAs that regulate gene expression post-transcriptionally. MiRNAs were shown to play an important role in development and disease, and accurately determining the networks regulated by these miRNAs in a specific condition is of great interest. Early work on miRNA target prediction has focused on using static sequence information. More recently, researchers have combined sequence and expression data to identify such targets in various conditions. RESULTS: We developed the Protein Interaction-based MicroRNA Modules (PIMiM), a regression-based probabilistic method that integrates sequence, expression and interaction data to identify modules of mRNAs controlled by small sets of miRNAs. We formulate an optimization problem and develop a learning framework to determine the module regulation and membership. Applying PIMiM to cancer data, we show that by adding protein interaction data and modeling cooperative regulation of mRNAs by a small number of miRNAs, PIMiM can accurately identify both miRNA and their targets improving on previous methods. We next used PIMiM to jointly analyze a number of different types of cancers and identified both common and cancer-type-specific miRNA regulators. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MicroRNA regulation and its effects on cellular transcriptome in human immunodeficiency virus-1 (HIV-1) infected individuals with distinct viral load and CD4 cell counts.

BACKGROUND: Disease progression in the absence of therapy varies significantly in HIV-1 infected individuals. Both viral and host cellular molecules are implicated; however, the exact role of these factors and/or the mechanism involved remains elusive. To understand how microRNAs (miRNAs), which are regulators of transcription and translation, influence host cellular gene expression (mRNA) during HIV-1 infection, we performed a comparative miRNA and mRNA microarray analysis using PBMCs obtained from infected individuals with distinct viral load and CD4 counts. METHODS: RNA isolated from PBMCs obtained from HIV-1 seronegative and HIV-1 positive individuals with distinct viral load and CD4 counts were assessed for miRNA and mRNA profile. Selected miRNA and mRNA transcripts were validated using in vivo and in vitro infection model. RESULTS: Our results indicate that HIV-1 positive individuals with high viral load (HVL) showed a dysregulation of 191 miRNAs and 309 mRNA transcripts compared to the uninfected age and sex matched controls. The miRNAs miR-19b, 146a, 615-3p, 382, 34a, 144 and 155, that are known to target innate and inflammatory factors, were significantly upregulated in PBMCs with high viral load, as were the inflammatory molecules CXCL5, CCL2, IL6 and IL8, whereas defensin, CD4, ALDH1, and Neurogranin (NRGN) were significantly downregulated. Using the transcriptome profile and predicted target genes, we constructed the regulatory networks of miRNA-mRNA pairs that were differentially expressed between control, LVL and HVL subjects. The regulatory network revealed an inverse correlation of several miRNA-mRNA pair expression patterns, suggesting HIV-1 mediated transcriptional regulation is in part likely through miRNA regulation. CONCLUSIONS: Results from our studies indicate that gene expression is significantly altered in PBMCs in response to virus replication. It is interesting to note that the infected individuals with low or undetectable viral load exhibit a gene expression profile very similar to control or uninfected subjects. Importantly, we identified several new mRNA targets (Defensin, Neurogranin, AIF) as well as the miRNAs that could be involved in regulating their expression through the miRNA-mRNA interaction.

Probabilistic error correction for RNA sequencing.

Sequencing of RNAs (RNA-Seq) has revolutionized the field of transcriptomics, but the reads obtained often contain errors. Read error correction can have a large impact on our ability to accurately assemble transcripts. This is especially true for de novo transcriptome analysis, where a reference genome is not available. Current read error correction methods, developed for DNA sequence data, cannot handle the overlapping effects of non-uniform abundance, polymorphisms and alternative splicing. Here we present SEquencing Error CorrEction in Rna-seq data (SEECER), a hidden Markov Model (HMM)-based method, which is the first to successfully address these problems. SEECER efficiently learns hundreds of thousands of HMMs and uses these to correct sequencing errors. Using human RNA-Seq data, we show that SEECER greatly improves on previous methods in terms of quality of read alignment to the genome and assembly accuracy. To illustrate the usefulness of SEECER for de novo transcriptome studies, we generated new RNA-Seq data to study the development of the sea cucumber Parastichopus parvimensis. Our corrected assembled transcripts shed new light on two important stages in sea cucumber development. Comparison of the assembled transcripts to known transcripts in other species has also revealed novel transcripts that are unique to sea cucumber, some of which we have experimentally validated. Supporting website: http://sb.cs.cmu.edu/seecer/.

Comparative expression profile of miRNA and mRNA in primary peripheral blood mononuclear cells infected with human immunodeficiency virus (HIV-1).

Host cells respond to exogenous infectious agents such as viruses, including HIV-1. Studies have evaluated the changes associated with virus infection at the transcriptional and translational levels of the cellular genes involved in specific pathways. While this approach is useful, in our view it provides only a partial view of genome-wide changes. Recently, technological advances in the expression profiling at the microRNA (miRNA) and mRNA levels have made it possible to evaluate the changes in the components of multiple pathways. To understand the role of miRNA and its interplay with host cellular gene expression (mRNA) during HIV-1 infection, we performed a comparative global miRNA and mRNA microarray using human PBMCs infected with HIV-1. The PBMCs were derived from multiple donors and were infected with virus generated from the molecular clone pNL4-3. The results showed that HIV-1 infection led to altered regulation of 21 miRNAs and 444 mRNA more than 2-fold, with a statistical significance of p<0.05. Furthermore, the differentially regulated miRNA and mRNA were shown to be associated with host cellular pathways involved in cell cycle/proliferation, apoptosis, T-cell signaling, and immune activation. We also observed a number of inverse correlations of miRNA and mRNA expression in infected PBMCs, further confirming the interrelationship between miRNA and mRNA regulation during HIV-1 infection. These results for the first time provide evidence that the miRNA profile could be an early indicator of host cellular dysfunction induced by HIV-1.

Cross-species queries of large gene expression databases.

MOTIVATION: Expression databases, including the Gene Expression Omnibus and ArrayExpress, have experienced significant growth over the past decade and now hold hundreds of thousands of arrays from multiple species. Since most drugs are initially tested on model organisms, the ability to compare expression experiments across species may help identify pathways that are activated in a similar way in humans and other organisms. However, while several methods exist for finding co-expressed genes in the same species as a query gene, looking at co-expression of homologs or arbitrary genes in other species is challenging. Unlike sequence, which is static, expression is dynamic and changes between tissues, conditions and time. Thus, to carry out cross-species analysis using these databases, we need methods that can match experiments in one species with experiments in another species. RESULTS: To facilitate queries in large databases, we developed a new method for comparing expression experiments from different species. We define a distance metric between the ranking of orthologous genes in the two species. We show how to solve an optimization problem for learning the parameters of this function using a training dataset of known similar expression experiments pairs. The function we learn outperforms previous methods and simpler rank comparison methods that have been used in the past for single species analysis. We used our method to compare millions of array pairs from mouse and human expression experiments. The resulting matches can be used to find functionally related genes, to hypothesize about biological response mechanisms and to highlight conditions and diseases that are activating similar pathways in both species. AVAILABILITY: Supporting methods, results and a Matlab implementation are available from http://sb.cs.cmu.edu/ExpQ/.

Cache-oblivious dynamic programming for bioinformatics.

We present efficient cache-oblivious algorithms for some well-studied string problems in bioinformatics including the longest common subsequence, global pairwise sequence alignment and three-way sequence alignment (or median), both with affine gap costs, and RNA secondary structure prediction with simple pseudoknots. For each of these problems, we present cache-oblivious algorithms that match the best-known time complexity, match or improve the best-known space complexity, and improve significantly over the cache-efficiency of earlier algorithms. We present experimental results which show that our cache-oblivious algorithms run faster than software and implementations based on previous best algorithms for these problems.

Pattern identification in biogeography.

Identifying common patterns among area cladograms that arise in historical biogeography is an important tool for biogeographical inference. We develop the first rigorous formalization of these pattern-identification problems. We develop metrics to compare area cladograms. We define the maximum agreement area cladogram (MAAC) and we develop efficient algorithms for finding the MAAC of two area cladograms, while showing that it is NP-hard to find the MAAC of several binary area cladograms. We also describe a linear-time algorithm to identify if two area cladograms are identical.