The main way of analyzing biological sequences is by comparing and aligning them to each
other. It remains difficult, however, to compare modern multi-billionbase DNA data sets. The …

Motivation Although modern high-throughput biomolecular technologies produce various
types of data, biosequence data remain at the core of bioinformatic analyses. However …

Motivation: Alignment-free methods for sequence comparison are increasingly used for
genome analysis and phylogeny reconstruction; they circumvent various difficulties of …

Motivation: Metagenomics is a powerful approach to study genetic content of environmental
samples, which has been strongly promoted by next-generation sequencing technologies …

Motivation Analysis of genetic sequences is usually based on finding similar parts of
sequences, eg DNA reads and/or genomes. For big data, this is typically done via 'seeds' …

Cytosines in genomic DNA are sometimes methylated. This affects many biological
processes and diseases. The standard way of measuring methylation is to use bisulfite …

Seed design is important for sequence similarity search applications such as read mapping
and average nucleotide identity (ANI) estimation. Although k-mers and spaced k-mers are …

The suffix array and its variants are text-indexing data structures that have become
indispensable in the field of bioinformatics. With the uninitiated in mind, we provide an …

Multiple spaced seeds represent the current state-of-the-art for similarity search in
bioinformatics, with applications in various areas such as sequence alignment, read …

Many algorithms for sequence analysis rely on word matching or word statistics. Often, these
approaches can be improved if binary patterns representing match and don't-care positions …