Volume 58, Issue 3 - Bioinformatics Challenges of Next Generation Sequencing / Guest Editors: Ivo Grosse and Katrin Hoffmann

The Read Mapping problem asks for the exact origin of a nucleotide sequence in a reference genome. It translates to a conceptually simple approximate string matching problem. The practical difficulty, however, arises from the typical size of the data sets produced by modern high throughput sequencing technologies, from the biological processes involved in derivation of the query molecule from its genomic source, and from the technical processes of the sequencing technology itself.

DNA sequence assembly is a rich combinatorial problem that arose with the first DNA sequencing projects in the early 80's. Here we give a short history of the progression of algorithmic ideas used to solve the de novo problem of inferring a genome given a large sampling of substrings covering it. This classic inverse problem is compounded by a variety of experimental features and artifacts that must be considered in any realistic solution. While current methods produce very good results, the perfect assembler has yet to be built.

High throughput next generation sequencing technologies have enabled cultivation-independent approaches to study microbial communities in environmental samples. To date much of functional metagenomics has been limited to the gene or pathway level. Recent breakthroughs in metagenome binning have made it feasible to reconstruct high quality, individual microbial genomes from complex communities with thousands of species. In this review we aim to compare several automated metagenome binning software tools for their performance, and provide a practical guide for the metagenomics research community to carry out successful binning analyses.

With every additional individual whose genome is sequenced thousands of novel variants enter the scene. It is these variants of unknown clinical significance, VUCS, that represent a great challenge to geneticists, who are dealing with high-throughput sequencing data sets. Especially in diagnostics of patients with unknown monogenic disease the joint effort of geneticists is required to find new disease gene associations. For this purpose, online platforms for matchmaking have been developed that allow clinician scientists to collaborate worldwide and to share medically relevant data. However, for a success of these tools, skills in deep phenotyping as well as new statistical approaches will be required.

For successful software system, maintenance efforts dominate initial development costs by far. However, research and practice still place most of the attention on the initial development phase. In consequence, companies suffer mission-critical systems that are extremely hard to maintain. To better support practitioners in writing maintainable software, we both need an empirically sound understanding of software properties that help or hinder maintenance and tools that are useful in practice. This requires work that includes both research and practitioner perspectives. In this article, I outline my work towards this goal.

Brain-Computer Interfacing (BCI) is a promising technology for patients that are severely motor-disabled, because it enables them to communicate and interact with the environment. A BCI system decodes user's intentions from brain signals, typically recorded with electroencephalography (EEG), and transmits them to a computer application that, e.g., controls a wheelchair. The efficiency of the system largely depends upon a reliable extraction of informative features from the high-dimensional EEG signal. Spatial filtering is a crucial step in this protocol, however, current approaches are prone to errors when data is contaminated by artifacts or is nonstationary. This article provides an overview of a dissertation, which has addressed the problem of robust spatial filtering in BCI. The contributions of the thesis range from the development of regularization schemes and a robust parameter estimator for spatial filtering, to the formulation of an information geometric view on the spatial filtering problem and the proposal of a new family of algorithms based on robust divergences. The developed methods and concepts are applicable to a variety of problems in machine learning and signal processing.