Large-Scale Sequence Comparison

Long-term virus evolution in nature

Viruses spread to give rise to epidemics and pandemics, and some key parameters that include virus and host population numbers determine virus persistence or extinction in nature. Viruses evolve at different rates depending on the polymerase copying fidelity during genome replication and a number of environmental influences. Calculated rates of evolution in nature vary depending on the time interval between virus isolations. In particular, intrahost evolution is generally more rapid that interhost evolution, and several possible mechanisms for this difference are considered. The mechanisms by which the error-prone viruses evolve are very unlikely to render the operation of a molecular clock (constant rate of incorporation of mutations in the evolving genomes), although a clock is assumed in many calculations. Several computational tools permit the alignment of viral sequences and the establishment of phylogenetic relationships among viruses. The evolution of the virus in the form of dynamic mutant clouds in each infected individual, together with multiple environmental parameters renders the emergence and reemergence of viral pathogens an unpredictable event, another facet of biological complexity.

Upgrading Affinity Screening Experiments by Analysis of Next-Generation Sequencing Data

Computational analysis of next-generation sequencing data (NGS; also termed deep sequencing) enables the analysis of affinity screening procedures (or biopanning experiments) in an unprecedented depth and therewith improves the identification of relevant peptide or antibody ligands with desired binding or functional properties. Virtually any selection methodology employing the direct physical linkage of geno- and phenotype to select for desired properties can be leveraged by computational analysis. This article describes a concept how relevant ligands can be identified by harnessing NGS data. Thereby, the focus lays on improved ligand identification and describes how NGS data can be structured for single-round analysis as well as for comparative analysis of multiple selection rounds. Especially, the comparative analysis opens new avenues in the field of ligand identification. The concept of computational analysis is described at the example of the software tool "AptaAnalyzer ^TM ." This intuitive tool was developed for scientists without special computer skills and makes the computational approach accessible to a broad user range.