Review of sample-based methods used in an analysis of multistable dynamical systems.

Sample-based methods are a useful tool in analyzing the global behavior of multi-stable systems originating from various branches of science. Classical methods, such as bifurcation diagrams, Lyapunov exponents, and basins of attraction, often fail to analyze complex systems with many coexisting attractors. Thus, we have to apply a different strategy to understand the dynamics of such systems. We can distinguish basin stability, extended basin stability, constrained basin stability, basin entropy, time dependent stability margin, and survivability among sample-based methods. Each method has specific properties and gives us important data about the behavior of the analyzed system. However, none of the methods provides complete information. Hence, to have a full overview of the dynamics, one has to collect data from two or more approaches. This study describes the sample-based methods and presents their advantages and disadvantages for the archetypal nonlinear oscillator with multiple coexisting attractors. Hence, we give helpful information in selecting the best method or methods for analyzing the dynamical system.

MBGC: Multiple Bacteria Genome Compressor.

BACKGROUND: Genomes within the same species reveal large similarity, exploited by specialized multiple genome compressors. The existing algorithms and tools are however targeted at large, e.g., mammalian, genomes, and their performance on bacteria strains is rather moderate. RESULTS: In this work, we propose MBGC, a specialized genome compressor making use of specific redundancy of bacterial genomes. Its characteristic features are finding both direct and reverse-complemented LZ-matches, as well as a careful management of a reference buffer in a multi-threaded implementation. Our tool is not only compression efficient but also fast. On a collection of 168,311 bacterial genomes, totalling 587 GB, we achieve a compression ratio of approximately a factor of 1,265 and compression (respectively decompression) speed of ∼1,580 MB/s (respectively 780 MB/s) using 8 hardware threads, on a computer with a 14-core/28-thread CPU and a fast SSD, being almost 3 times more succinct and >6 times faster in the compression than the next best competitor.

PgRC: pseudogenome-based read compressor.

MOTIVATION: The amount of sequencing data from high-throughput sequencing technologies grows at a pace exceeding the one predicted by Moore's law. One of the basic requirements is to efficiently store and transmit such huge collections of data. Despite significant interest in designing FASTQ compressors, they are still imperfect in terms of compression ratio or decompression resources. RESULTS: We present Pseudogenome-based Read Compressor (PgRC), an in-memory algorithm for compressing the DNA stream, based on the idea of building an approximation of the shortest common superstring over high-quality reads. Experiments show that PgRC wins in compression ratio over its main competitors, SPRING and Minicom, by up to 15 and 20% on average, respectively, while being comparably fast in decompression. AVAILABILITY AND IMPLEMENTATION: PgRC can be downloaded from https://github.com/kowallus/PgRC. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.