WABI: Workshop on Algorithms in Bioinformatics Olivera Perunkovska

Summary This is the 16th Workshop on Algorithms in Bioinformatics (WABI). Held on August 22-24 in Aarhus, Denmark. 56 submitted. 25 papers accepted. It covers researches in all aspects of algorithms in bioinformatics, computational biology and systems biology.

A Linear Time Approximation Algorithm for the DCJ Distance for Genomes with Bounded Number of Duplicates Large-scale mutations or rearrangements can produce complex changes and structural variations in genomes. Rearrangements can be represented by the double cut and join (DCJ) operation These operations cuts a genome in two different positions, creating four open ends, and joins these open ends in a different way The minimum number of rearrangements that transform one genome into the other and to find the rearrangement distance between two given genomes is a basic task in comparative genomics

For genomes without duplicate genes, there are linear time algorithms to compute the distance allowing only DCJ operations Computing the DCJ distance between two genomes with the same content and possibly duplicate genes, with some restriction. This paper describing a linear time approximation algorithm with approximation factor O(k), where k is the maximum number of duplicates of any gene in the input genomes

Gerbil: A Fast and Memory-Efficient k-mer Counter with GPU-Support* A basic task in bioinformatics is the counting of k-mers in genome strings. Counting of k-mers becomes a challenging problem for large instances, when its aims to be resource and time-efficient. Existing tools for k-mer counting are often optimized for k < 32 and lack good performance for larger k. Working with long sequencing reads helps to improve accuracy and counting assembly Among the first software tools that succeeded in counting the k-mers of large genome data sets was Jellysh which uses a lock-free hash table that allows parallel insertion.

Gerbil open source k-mer counting tool. High performance for large values of k. This software is the result of an extensive process of algorithm engineering that tried to bring together the best ideas from the literature. Time efficient and memory frugal. Optionally use GPUs to accelerate the counting step. For large values of k, it reduces the runtime by up to a factor of four.

Copy-Number Evolution Problems: Complexity and Algorithms Computational inference of phylogenetic trees - fundamental problem in species evolution studied for tumor. Cancer is an evolutionary process characterized by the accumulation of somatic mutations in a population of cells that form a tumor. A tumor is made of clones, which are subpopulations of cells sharing a unique combination of somatic mutations. Evolutionary history of these clones can be described by the phylogenetic trees (leaves - clones edges - mutations).

Consider 2 problems in the evolutionary analysis of copy-number profiles: Copy-Number Triplet (CN3) - linear in n Copy-Number Tree (CNT) – NP - hard CN3 aims to find a parental profile that minimizes the sum of distances to its children CNT asks to construct a phylogenetic tree whose k leaves are labeled by the k given profiles, and to assign profiles to the internal vertices so that the sum of distances over all edges is minimum.