Olga Glebova

Computational framework for next-generation sequencing of heterogeneous viral populations using combinatorial pooling

MOTIVATION Next-generation sequencing (NGS) allows for analyzing a large number of viral sequences from infected patients, providing an opportunity to implement large-scale molecular surveillance of viral diseases. However, despite improvements in technology, traditional protocols for NGS of large numbers of samples are still highly cost and labor intensive. One of the possible cost-effective alternatives is combinatorial pooling. Although a number of pooling strategies for consensus sequencing of DNA samples and detection of SNPs have been proposed, these strategies cannot be applied to sequencing of highly heterogeneous viral populations. RESULTS We developed a cost-effective and reliable protocol for sequencing of viral samples, that combines NGS using barcoding and combinatorial pooling and a computational framework including algorithms for optimal virus-specific pools design and deconvolution of individual samples from sequenced pools. Evaluation of the framework on experimental and simulated data for hepatitis C virus showed that it substantially reduces the sequencing costs and allows deconvolution of viral populations with a high accuracy. AVAILABILITY AND IMPLEMENTATION The source code and experimental data sets are available at http://alan.cs.gsu.edu/NGS/?q=content/pooling.

Improved transcriptome quantification and reconstruction from RNA-Seq reads using partial annotations

The paper addresses the problem of how to use RNA-Seq data for transcriptome reconstruction and quantification, as well as novel transcript discovery in partially annotated genomes. We present a novel annotation-guided general framework for transcriptome discovery, reconstruction and quantification in partially annotated genomes and compare it with existing annotation-guided and genome-guided transcriptome assembly methods. Our method, referred as Discovery and Reconstruction of Unannotated Transcripts (DRUT), can be used to enhance existing transcriptome assemblers, such as Cufflinks, as well as to accurately estimate the transcript frequencies. Empirical analysis on synthetic datasets confirms that Cufflinks enhanced by DRUT has superior quality of reconstruction and frequency estimation of transcripts.

Inferring metabolic pathway activity levels from RNA-Seq data

BackgroundAssessing pathway activity levels is a plausible way to quantify metabolic differences between various conditions. This is usually inferred from microarray expression data. Wide availability of NGS technology has triggered a demand for bioinformatics tools capable of analyzing pathway activity directly from RNA-Seq data. In this paper we introduce XPathway, a set of tools that compares pathway activity analyzing mapping of contigs assembled from RNA-Seq reads to KEGG pathways. The XPathway analysis of pathway activity is based on expectation maximization and topological properties of pathway graphs.ResultsXPathway tools have been applied to RNA-Seq data from the marine bryozoan Bugula neritina with and without its symbiotic bacterium “Candidatus Endobugula sertula”. We successfully identified several metabolic pathways with differential activity levels. The expression of enzymes from the identified pathways has been further validated through quantitative PCR (qPCR).ConclusionsOur results show that XPathway is able to detect and quantify the metabolic difference in two samples. The software is implemented in C, Python and shell scripting and is capable of running on Linux/Unix platforms. The source code and installation instructions are available at http://alan.cs.gsu.edu/NGS/?q=content/xpathway.

Detection of genetic relatedness between viral samples using EM-based clustering of next-generation sequencing data

We present a novel general tool for highly sensitive detection of genetic relatedness between highly heterogeneous viral samples based on the clustering of next-generation sequencing data. The tool may be used for detection of viral transmissions and outbreaks and for laboratory quality control.

Inference of genetic relatedness between viral quasispecies from sequencing data

BackgroundRNA viruses such as HCV and HIV mutate at extremely high rates, and as a result, they exist in infected hosts as populations of genetically related variants. Recent advances in sequencing technologies make possible to identify such populations at great depth. In particular, these technologies provide new opportunities for inference of relatedness between viral samples, identification of transmission clusters and sources of infection, which are crucial tasks for viral outbreaks investigations.ResultsWe present (i) an evolutionary simulation algorithm Viral Outbreak InferenCE (VOICE) inferring genetic relatedness, (ii) an algorithm MinDistB detecting possible transmission using minimal distances between intra-host viral populations and sizes of their relative borders, and (iii) a non-parametric recursive clustering algorithm Relatedness Depth (ReD) analyzing clusters’ structure to infer possible transmissions and their directions. All proposed algorithms were validated using real sequencing data from HCV outbreaks.ConclusionsAll algorithms are applicable to the analysis of outbreaks of highly heterogeneous RNA viruses. Our experimental validation shows that they can successfully identify genetic relatedness between viral populations, as well as infer transmission clusters and outbreak sources.

Deterministic regression algorithm for transcriptome frequency estimation

We present a deterministic version of our novel Monte-Carlo Regression based method MCReg [1] for transcriptome quantification from RNA-Seq reads. Experiments on simulated and real datasets demonstrate better transcriptome frequency estimation accuracy compared to that of the existing tools which tend to skew the estimated frequency toward super-transcripts.

Optimizing pooling strategies for the massive next-generation sequencing of viral samples

Next-generation sequencing (NGS) allows for analyzing a large number of viral sequences from infected patients, presenting novel prospects for studying the structure of viral populations and understanding virus evolution and epidemiology. It potentially provides an opportunity to implement large-scale molecular surveillance of viral diseases, which offers more precise estimations of epidemiological parameters, detection of transmissions and studying the structure of transmission networks, prediction of the epidemics progress and development of more effective vaccination strategies. A large-scale molecular surveillance requires sequencing of unprecedentedly large sets of viral samples. Although NGS has recently become less expensive and is expected to further decrease its cost in the future, massive NGS of tens of thousands of samples is still highly cost- and labor-intensive. Therefore it is highly important to develop a framework for identification of viral sequences from large number of samples using the smallest possible number of NGS runs.

Algorithms for prediction of viral transmission using analysis of intra-host viral populations

Molecular analysis has become one of the major tools used for viral outbreak investigation and transmission network inference. We present two novel methods for accurate identification of transmission clusters and sources of infection for highly heterogeneous viruses such as HIV and HCV. Validation on data obtained from HCV outbreaks shows that the proposed algorithms outperform the state-of-the-art consensus-based methods both in true and false positive rates for transmission prediction, as well as in accuracy of source identification for outbreaks.

Alignment of DNA Mass-Spectral Profiles Using Network Flows

Mass spectrometry (MS) of DNA fragments generated by base-specific cleavage of PCR products emerges as a cost-effective and robust alternative to DNA sequencing. MS has been successfully applied to SNP discovery using reference sequences, genotyping and detection of viral transmissions. Although MS is yet to be adapted for reconstruction of genetic composition of complex intra-host viral populations on the scale comparable to the next-generation DNA sequencing technologies, the MS profiles are rich sources of data reflecting the structure of viral populations and completely suitable for accurate assessment of genetic relatedness among viral strains. However, owing to a data structure, which is significantly different from sequences, application of MS profiles to genetic analyses remains a challenging task. Here, we develop a novel approach to aligning DNA MS profiles and assessment of genetic relatedness among DNA species using spectral alignments (MSA). MSA was formulated and solved as a network flow problem. It enables an accurate comparison of MS profiles and provides a direct evaluation of genetic distances between DNA molecules without invoking sequences. MSA may serve as accurately as sequence alignments to facilitate phylogenetic analysis and, as such, has numerous applications in basic research, clinical and public health settings.