Frequent Pattern Finding in Integrated Biological Networks

Biomedical research is undergoing a revolution with the advance of high-throughput technologies. A major challenge in the post-genomic era is to understand how genes, proteins and small molecules are organized into signaling pathways and regulatory networks. To simplify the analysis of large complex molecular networks, strategies are sought to break them down into small yet relatively independent network modules, e.g. pathways and protein complexes. In fulfillment of the motivation to find evolutionary origins of network modules, a novel strategy has been developed to uncover duplicated pathways and protein complexes. This search was first formulated into a computational problem which finds frequent patterns in integrated graphs. The whole framework was then successfully implemented as the software package BLUNT, which includes a parallelized version. To evaluate the biological significance of the work, several large datasets were chosen, with each dataset targeting a different biological question. An application of BLUNT was performed on the yeast protein-protein interaction network, which is described. A large number of frequent patterns were discovered and predicted to be duplicated pathways. To explore how these pathways may have diverged since duplication, the differential regulation of duplicated pathways was studied at the transcriptional level, both in terms of time and location. As demonstrated, this algorithm can be used as new data mining tool for large scale biological data in general. It also provides a novel strategy to study the evolution of pathways and protein complexes in a systematic way. Understanding how pathways and protein complexes evolve will greatly benefit the fundamentals of biomedical research

Compression of next-generation sequencing reads aided by highly efficient de novo assembly

We present Quip, a lossless compression algorithm for next-generation sequencing data in the FASTQ and SAM/BAM formats. In addition to implementing reference-based compression, we have developed, to our knowledge, the first assembly-based compressor, using a novel de novo assembly algorithm. A probabilistic data structure is used to dramatically reduce the memory required by traditional de Bruijn graph assemblers, allowing millions of reads to be assembled very efficiently. Read sequences are then stored as positions within the assembled contigs. This is combined with statistical compression of read identifiers, quality scores, alignment information, and sequences, effectively collapsing very large datasets to less than 15% of their original size with no loss of information. Availability: Quip is freely available under the BSD license from http://cs.washington.edu/homes/dcjones/quip

Host cell transcriptional profiling during malaria liver stage infection reveals a coordinated and sequential set of biological events

<p>Abstract</p> <p>Background</p> <p><it>Plasmodium </it>sporozoites migrate to the liver where they traverse several hepatocytes before invading the one inside which they will develop and multiply into thousands of merozoites. Although this constitutes an essential step of malaria infection, the requirements of <it>Plasmodium </it>parasites in liver cells and how they use the host cell for their own survival and development are poorly understood.</p> <p>Results</p> <p>To gain new insights into the molecular host-parasite interactions that take place during malaria liver infection, we have used high-throughput microarray technology to determine the transcriptional profile of <it>P. berghei</it>-infected hepatoma cells. The data analysis shows differential expression patterns for 1064 host genes starting at 6 h and up to 24 h post infection, with the largest proportion correlating specifically with the early stages of the infection process. A considerable proportion of those genes were also found to be modulated in liver cells collected from <it>P. yoelii-</it>infected mice 24 and 40 h after infection, strengthening the data obtained with the <it>in vitro </it>model and highlighting genes and pathways involved in the host response to rodent <it>Plasmodium </it>parasites.</p> <p>Conclusion</p> <p>Our data reveal that host cell infection by <it>Plasmodium </it>sporozoites leads to a coordinated and sequential set of biological events, ranging from the initial stage of stress response up to the engagement of host metabolic processes and the maintenance of cell viability throughout infection.</p

Computational identification of hepatitis C virus associated microRNA-mRNA regulatory modules in human livers

<p>Abstract</p> <p>Background</p> <p>Hepatitis C virus (HCV) is a major cause of chronic liver disease by infecting over 170 million people worldwide. Recent studies have shown that microRNAs (miRNAs), a class of small non-coding regulatory RNAs, are involved in the regulation of HCV infection, but their functions have not been systematically studied. We propose an integrative strategy for identifying the miRNA-mRNA regulatory modules that are associated with HCV infection. This strategy combines paired expression profiles of miRNAs and mRNAs and computational target predictions. A miRNA-mRNA regulatory module consists of a set of miRNAs and their targets, in which the miRNAs are predicted to coordinately regulate the level of the target mRNA.</p> <p>Results</p> <p>We simultaneously profiled the expression of cellular miRNAs and mRNAs across 30 HCV positive or negative human liver biopsy samples using microarray technology. We constructed a miRNA-mRNA regulatory network, and using a graph theoretical approach, identified 38 miRNA-mRNA regulatory modules in the network that were associated with HCV infection. We evaluated the direct miRNA regulation of the mRNA levels of targets in regulatory modules using previously published miRNA transfection data. We analyzed the functional roles of individual modules at the systems level by integrating a large-scale protein interaction network. We found that various biological processes, including some HCV infection related canonical pathways, were regulated at the miRNA level during HCV infection.</p> <p>Conclusion</p> <p>Our regulatory modules provide a framework for future experimental analyses. This report demonstrates the utility of our approach to obtain new insights into post-transcriptional gene regulation at the miRNA level in complex human diseases.</p

A new approach to bias correction in RNA-Seq

Motivation: Quantification of sequence abundance in RNA-Seq experiments is often conflated by protocol-specific sequence bias. The exact sources of the bias are unknown, but may be influenced by polymerase chain reaction amplification, or differing primer affinities and mixtures, for example. The result is decreased accuracy in many applications, such as de novo gene annotation and transcript quantification

Infection with Mers-Cov Causes Lethal Pneumonia in the Common Marmoset

The availability of a robust disease model is essential for the development of countermeasures for Middle East respiratory syndrome coronavirus (MERS-CoV). While a rhesus macaque model of MERS-CoV has been established, the lack of uniform, severe disease in this model complicates the analysis of countermeasure studies. Modeling of the interaction between the MERS-CoV spike glycoprotein and its receptor dipeptidyl peptidase 4 predicted comparable interaction energies in common marmosets and humans. The suitability of the marmoset as a MERS-CoV model was tested by inoculation via combined intratracheal, intranasal, oral and ocular routes. Most of the marmosets developed a progressive severe pneumonia leading to euthanasia of some animals. Extensive lesions were evident in the lungs of all animals necropsied at different time points post inoculation. Some animals were also viremic; high viral loads were detected in the lungs of all infected animals, and total RNAseq demonstrated the induction of immune and inflammatory pathways. This is the first description of a severe, partially lethal, disease model of MERS-CoV, and as such will have a major impact on the ability to assess the efficacy of vaccines and treatment strategies as well as allowing more detailed pathogenesis studies

Eccentricity-paced monsoon variability on the northeastern Tibetan Plateau in the Late Oligocene high CO 2 world

Constraining monsoon variability and dynamics in the warm unipolar icehouse world of the Late Oligocene can provide important clues to future climate responses to global warming. Here, we present a ~4-thousand year (ka) resolution rubidium-to-strontium ratio and magnetic susceptibility records between 28.1 and 24.1 million years ago from a distal alluvial sedimentary sequence in the Lanzhou Basin (China) on the northeastern Tibetan Plateau margin. These Asian monsoon precipitation records exhibit prominent short (~110-ka) and long (405-ka) eccentricity cycles throughout the Late Oligocene, with a weak expression of obliquity (41-ka) and precession (19-ka and 23-ka) cycles. We conclude that a combination of eccentricity-modulated low-latitude summer insolation and glacial-interglacial Antarctic Ice Sheet fluctuations drove the eccentricity-paced precipitation variability on the northeastern Tibetan Plateau in the Late Oligocene high CO2 world by governing regional temperatures, water vapor loading in the western Pacific and Indian Oceans, and the Asian monsoon intensity and displacement