Armin Töpfer

Probabilistic inference of viral quasispecies subject to recombination.

RNA viruses exist in their hosts as populations of different but related strains. The virus population, often called quasispecies, is shaped by a combination of genetic change and natural selection. Genetic change is due to both point mutations and recombination events. We present a jumping hidden Markov model that describes the generation of viral quasispecies and a method to infer its parameters from next-generation sequencing data. The model introduces position-specific probability tables over the sequence alphabet to explain the diversity that can be found in the population at each site. Recombination events are indicated by a change of state, allowing a single observed read to originate from multiple sequences. We present a specific implementation of the expectation maximization (EM) algorithm to find maximum a posteriori estimates of the model parameters and a method to estimate the distribution of viral strains in the quasispecies. The model is validated on simulated data, showing the advantage of explicitly taking the recombination process into account, and applied to reads obtained from a clinical HIV sample.

Full-length haplotype reconstruction to infer the structure of heterogeneous virus populations

Next-generation sequencing (NGS) technologies enable new insights into the diversity of virus populations within their hosts. Diversity estimation is currently restricted to single-nucleotide variants or to local fragments of no more than a few hundred nucleotides defined by the length of sequence reads. To study complex heterogeneous virus populations comprehensively, novel methods are required that allow for complete reconstruction of the individual viral haplotypes. Here, we show that assembly of whole viral genomes of ∼8600 nucleotides length is feasible from mixtures of heterogeneous HIV-1 strains derived from defined combinations of cloned virus strains and from clinical samples of an HIV-1 superinfected individual. Haplotype reconstruction was achieved using optimized experimental protocols and computational methods for amplification, sequencing and assembly. We comparatively assessed the performance of the three NGS platforms 454 Life Sciences/Roche, Illumina and Pacific Biosciences for this task. Our results prove and delineate the feasibility of NGS-based full-length viral haplotype reconstruction and provide new tools for studying evolution and pathogenesis of viruses.

Viral Quasispecies Assembly via Maximal Clique Enumeration

Genetic variability of virus populations within individual hosts is a key determinant of pathogenesis, virulence, and treatment outcome. It is of clinical importance to identify and quantify the intra-host ensemble of viral haplotypes, called viral quasispecies. Ultra-deep next-generation sequencing NGS of mixed samples is currently the only efficient way to probe genetic diversity of virus populations in greater detail. Major challenges with this bulk sequencing approach are i to distinguish genetic diversity from sequencing errors, ii to assemble an unknown number of different, unknown, haplotype sequences over a genomic region larger than the average read length, iii to estimate their frequency distribution, and iv to detect structural variants, such as large insertions and deletions indels that are due to erroneous replication or alternative splicing. Even though NGS is currently introduced in clinical diagnostics, the de-facto standard procedure to assess the quasispecies structure is still single-nucleotide variant SNV calling. Viral phenotypes cannot be predicted solely from individual SNVs, as epistatic interactions are abundant in RNA viruses. Therefore, reconstruction of long-range viral haplotypes has the potential to be adopted, as data is already available.

BioXSD: the common data-exchange format for everyday bioinformatics web services

Motivation: The world-wide community of life scientists has access to a large number of public bioinformatics databases and tools, which are developed and deployed using diverse technologies and designs. More and more of the resources offer programmatic web-service interface. However, efficient use of the resources is hampered by the lack of widely used, standard data-exchange formats for the basic, everyday bioinformatics data types. Results: BioXSD has been developed as a candidate for standard, canonical exchange format for basic bioinformatics data. BioXSD is represented by a dedicated XML Schema and defines syntax for biological sequences, sequence annotations, alignments and references to resources. We have adapted a set of web services to use BioXSD as the input and output format, and implemented a test-case workflow. This demonstrates that the approach is feasible and provides smooth interoperability. Semantics for BioXSD is provided by annotation with the EDAM ontology. We discuss in a separate section how BioXSD relates to other initiatives and approaches, including existing standards and the Semantic Web. Availability: The BioXSD 1.0 XML Schema is freely available at http://www.bioxsd.org/BioXSD-1.0.xsd under the Creative Commons BY-ND 3.0 license. The http://bioxsd.org web page offers documentation, examples of data in BioXSD format, example workflows with source codes in common programming languages, an updated list of compatible web services and tools and a repository of feature requests from the community. Contact: matus.kalas@bccs.uib.no; developers@bioxsd.org; support@bioxsd.org

Challenges in RNA Virus Bioinformatics.

Abstract Motivation: Computer-assisted studies of structure, function and evolution of viruses remains a neglected area of research. The attention of bioinformaticians to this interesting and challenging field is far from commensurate with its medical and biotechnological importance. It is telling that out of >200 talks held at ISMB 2013, the largest international bioinformatics conference, only one presentation explicitly dealt with viruses. In contrast to many broad, established and well-organized bioinformatics communities (e.g. structural genomics, ontologies, next-generation sequencing, expression analysis), research groups focusing on viruses can probably be counted on the fingers of two hands. Results: The purpose of this review is to increase awareness among bioinformatics researchers about the pressing needs and unsolved problems of computational virology. We focus primarily on RNA viruses that pose problems to many standard bioinformatics analyses owing to their compact genome organization, fast mutation rate and low evolutionary conservation. We provide an overview of tools and algorithms for handling viral sequencing data, detecting functionally important RNA structures, classifying viral proteins into families and investigating the origin and evolution of viruses. Contact: manja@uni-jena.de Supplementary information: Supplementary data are available at Bioinformatics online. The references for this article can be found in the Supplementary Material.

A Comprehensive Analysis of Primer IDs to Study Heterogeneous HIV-1 Populations.

Determining the composition of viral populations is becoming increasingly important in the field of medical virology. While recently developed computational tools for viral haplotype analysis allow for correcting sequencing errors, they do not always allow for the removal of errors occurring in the upstream experimental protocol, such as PCR errors. Primer IDs (pIDs) are one method to address this problem by harnessing redundant template resampling for error correction. By using a reference mixture of five HIV-1 strains, we show how pIDs can be useful for estimating key experimental parameters, such as the substitution rate of the PCR process and the reverse transcription (RT) error rate. In addition, we introduce a hidden Markov model for determining the recombination rate of the RT PCR process. We found no strong sequence-specific bias in pID abundances (the same RT efficiencies as compared to commonly used short, specific RT primers) and no effects of pIDs on the estimated distribution of the references viruses.

Viral quasispecies assembly from paired-end reads

Viruses can display high intra-patient genetic diversity. A single infected individual hosts billions of virus particles that can be summarized as a set of genetically different strains, called haplotypes, with their respective frequencies. The haplotype distribution, also known as a viral quasispecies, is a key determinent of virulence, pathogenesis, and treatment outcome.

Probing of viral diversity by global haplotype prediction

The focus in clinical virology shifts from well-established identification of single nucleotide variants (SNV) to probing of individual viral RNA strains, called haplotypes. The success of antiretroviral treatment of HIV infection heavily depends on the knowledge of an intra-patients viral population heterogeneity, because diversity and in particular, low frequency variants affect virulence, immune escape, and drug resistance.