Tutorial 4: Genetic Diversity

Inferring Genetic Diversity from Next-generation Sequencing Data: Computational Methods and Biomedical Applications

Niko Beerenwinkel, ETH Zurich and SIB.
Karin J. Metzner, University Hospital Zurich.
Volker Roth, University of Basel

 [Download tutorial slides here]

Genetic diversity is a hallmark of evolution and it plays a key role in the pathogenesis and treatment of rapidly evolving cancer cells and pathogens, such as viruses and bacteria. With high-coverage next-generation sequencing (NGS), the genetic diversity of mixed samples can be probed at an unprecedented level of detail in a cost-effective manner. However, reads are erroneous and relatively short, complicating the detection of low-frequency variants and the reconstruction of long haplotype sequences. In this tutorial, we will introduce new computational biology problems associated with genetic diversity estimation from NGS data. We discuss computational and statistical approaches to their solution based on probabilistic graphical models and combinatorial optimization, and we present several real-world biomedical applications, including HIV and cancer. The tutorial addresses both, potential or actual method developers as well as users of software for inferring genetic diversity from NGS data.

Motivation

Genetic diversity is abundant between species, among individuals of the same species, and also among the cells of single multicellular organisms. It plays a particularly important role in pathogen populations, where it often drives disease progression and the development of drug resistance. For example, tumors are large ensembles of genetically inhomogeneous cancer cells and the pre-existing diversity is a major factor for resistance to chemotherapy. Similarly, infectious agents, including bacteria and viruses, exist as heterogeneous populations. Prime examples are retroviruses, which include HIV, where genetic diversity is fostered by high mutation and recombination rates, resulting in so-called viral quasispecies. The diversity of viral quasispecies has been linked to disease progression, virulence, immune escape, and drug resistance.

In the past, assessing the diversity of a mixed sample has been very difficult and labor-intensive, relying either on cloning individual genomes followed by Sanger sequencing or on targeted PCR- or hybridization-based technologies. With recent NGS technologies, however, heterogeneous samples can be probed at an unprecedented level of detail in a cost-effective manner. This approach is based on direct, high-coverage sequencing of the mixed sample followed by computational and statistical analysis of the resulting read data. The data analysis and interpretation step is critical, because all NGS platforms produce reads that (i) contain errors, which limits the ability to detect low-frequency variants, and (ii) are relatively short, which complicates inference of long haplotypes and the detection of co-occurring variants.

Thus, computational and statistical methods for error correction and haplotype reconstruction are essential for inferring genetic diversity from NGS data. The development of such methods and their implementation in software tools is an emerging topic in bioinformatics. It is methodologically challenging and involves concepts from probabilistic graphical models, Markov chains, and combinatorial optimization. The tutorial aims at introducing these new computational biology problems and approaches to their solution to the bioinformatics community, including both developers and users. We will discuss experimental procedures, computational and statistical methods, and biomedical applications by means of selected case studies.

Overall Goals

The tutorial will provide an introduction to computational methods for inferring genetic diversity from NGS data. Specifically, participants will

• become acquainted with important biomedical applications of genetic diversity estimation, including the somatic evolution of cancer (intra-tumor diversity) and the evolution of viral quasispecies (intra-patient viral diversity);
• understand the different tasks associated with diversity estimation, including inference of low-frequency single nucleotide variants (SNVs) and haplotype reconstruction;
• appreciate the opportunities and limitations of different NGS technologies for diversity inference;
• survey existing approaches and software; and
• understand the basic computational and statistical principles underlying error correction and haplotype inference.

 
To follow the tutorial, basic statistics and algorithms are required on the level covered by any bioinformatics or systems biology Master program.

Tutorial Outline

Tutors