MOTIVATION: Unique marker sequences are highly sought after in molecular diagnostics. Nevertheless, there are only few programs available to search for marker sequences, compared to the many programs for similarity search. We therefore wrote the program Fur for Finding Unique genomic Regions. RESULTS: Fur takes as input a sample of target sequences and a sample of closely related neighbors. It returns the regions present in all targets and absent from all neighbors. The recently published program genmap can also be used for this purpose and we compared it to fur. When analyzing a sample of 33 genomes representing the major phylogroups of E.coli, fur was 40 times faster than genmap but used three times more memory. On the other hand, genmap yielded three times more markers, but they were less accurate when tested in silico on a sample of 237 E.coli genomes. We also designed phylogroup-specific PCR primers based on the markers proposed by genmap and fur, and tested them by analyzing their virtual amplicons in GenBank. Finally, we used fur to design primers specific to a Lactobacillus species, and found excellent sensitivity and specificity in vitro. AVAILABILITY AND IMPLEMENTATION: Fur sources and documentation are available from https://github.com/evolbioinf/fur. The compiled software is posted as a docker container at https://hub.docker.com/r/haubold/fox. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: Tracking disease outbreaks by whole-genome sequencing leads to the collection of large samples of closely related sequences. Five years ago, we published a method to accurately compute all pairwise distances for such samples by indexing each sequence. Since indexing is slow, we now ask whether it is possible to achieve similar accuracy when indexing only a single sequence. RESULTS: We have implemented this idea in the program phylonium and show that it is as accurate as its predecessor and roughly 100 times faster when applied to all 2678 Escherichia coli genomes contained in ENSEMBL. One of the best published programs for rapidly computing pairwise distances, mash, analyzes the same dataset four times faster but, with default settings, it is less accurate than phylonium. AVAILABILITY AND IMPLEMENTATION: Phylonium runs under the UNIX command line; its C++ sources and documentation are available from github.com/evolbioinf/phylonium. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Information integration and workflow technologies for data analysis have always been major fields of investigation in bioinformatics. A range of popular workflow suites are available to support analyses in computational biology. Commercial providers tend to offer prepared applications remote to their clients. However, for most academic environments with local expertise, novel data collection techniques or novel data analysis, it is essential to have all the flexibility of open-source tools and open-source workflow descriptions. Workflows in data-driven science such as computational biology have considerably gained in complexity. New tools or new releases with additional features arrive at an enormous pace, and new reference data or concepts for quality control are emerging. A well-abstracted workflow and the exchange of the same across work groups have an enormous impact on the efficiency of research and the further development of the field. High-throughput sequencing adds to the avalanche of data available in the field; efficient computation and, in particular, parallel execution motivate the transition from traditional scripts and Makefiles to workflows. We here review the extant software development and distribution model with a focus on the role of integration testing and discuss the effect of common workflow language on distributions of open-source scientific software to swiftly and reliably provide the tools demanded for the execution of such formally described workflows. It is contended that, alleviated from technical differences for the execution on local machines, clusters or the cloud, communities also gain the technical means to test workflow-driven interaction across several software packages.

MOTIVATION: In many organisms, including humans, recombination clusters within recombination hotspots. The standard method for de novo detection of recombinants at hotspots is sperm typing. This relies on allele-specific PCR at single nucleotide polymorphisms. Designing allele-specific primers by hand is time-consuming. We have therefore written a package to support hotspot detection and analysis. RESULTS: hotspot consists of four programs: asp looks up SNPs and designs allele-specific primers; aso constructs allele-specific oligos for mapping recombinants; xov implements a maximum-likelihood method for estimating the crossover rate; six, finally, simulates typing data. AVAILABILITY AND IMPLEMENTATION: hotspot is written in C. Sources are freely available under the GNU General Public License from http://github.com/evolbioinf/hotspot/ CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: A standard approach to classifying sets of genomes is to calculate their pairwise distances. This is difficult for large samples. We have therefore developed an algorithm for rapidly computing the evolutionary distances between closely related genomes. RESULTS: Our distance measure is based on ungapped local alignments that we anchor through pairs of maximal unique matches of a minimum length. These exact matches can be looked up efficiently using enhanced suffix arrays and our implementation requires approximately only 1 s and 45 MB RAM/Mbase analysed. The pairing of matches distinguishes non-homologous from homologous regions leading to accurate distance estimation. We show this by analysing simulated data and genome samples ranging from 29 Escherichia coli/Shigella genomes to 3085 genomes of Streptococcus pneumoniae. AVAILABILITY AND IMPLEMENTATION: We have implemented the computation of anchor distances in the multithreaded UNIX command-line program andi for ANchor DIstances. C sources and documentation are posted at http://github.com/evolbioinf/andi/ CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.