SUMMARY: Third-generation genome sequencing technologies have led to a sharp increase in the number of high-quality genome assemblies. This allows the comparison of multiple assembled genomes of individual species and demands new tools for visualizing their structural properties. Here, we present plotsr, an efficient tool to visualize structural similarities and rearrangements between genomes. It can be used to compare genomes on chromosome level or to zoom in on any selected region. In addition, plotsr can augment the visualization with regional identifiers (e.g. genes or genomic markers) or histogram tracks for continuous features (e.g. GC content or polymorphism density). AVAILABILITY AND IMPLEMENTATION: plotsr is implemented as a python package and uses the standard matplotlib library for plotting. It is freely available under the MIT license at GitHub (https://github.com/schneebergerlab/plotsr) and bioconda (https://anaconda.org/bioconda/plotsr). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Genome assembly is one of the most important problems in computational genomics. Here, we suggest addressing the scaffolding phase, in which contigs need to be linked and ordered to obtain larger pseudo-chromosomes, by means of a second incomplete assembly of a related species. The idea is to use alignments of binned regions in one contig to find the most homologous contig in the other assembly. We show that ordering the contigs of the other assembly can be expressed by a new string problem, the longest run subsequence problem (LRS). We show that LRS is NP-hard and present reduction rules and two algorithmic approaches that, together, are able to solve large instances of LRS to provable optimality. In particular, they can solve realistic instances resulting from partial Arabidopsis thaliana assemblies in short computation time. Our source code and all data used in the experiments are freely available.

Motivation: Analyzing k-mer frequencies in whole-genome sequencing data is becoming a common method for estimating genome size (GS). However, it remains uninvestigated how accurate the method is, especially if it can capture intra-species GS variation. Results: We present findGSE, which fits skew normal distributions to k-mer frequencies to estimate GS. findGSE outperformed existing tools in an extensive simulation study. Estimating GSs of 89 Arabidopsis thaliana accessions, findGSE showed the highest capability in capturing GS variations. In an application with 71 female and 71 male human individuals, findGSE delivered an average of 3039 Mb as haploid human GS, while female genomes were on average 41 Mb larger than male genomes, in astonishing agreement with size difference of the X and Y chromosomes. Further analysis showed that human GS variations link to geographical patterns and significant differences between populations, which can be explained by variable abundances of LINE-1 retrotransposons. Availability and implementation: R package of findGSE is freely available at https://github.com/schneebergerlab/findGSE and supported on linux and Mac systems. Contact: [email protected]. Supplementary information: Supplementary data are available at Bioinformatics online.

MOTIVATION: Repeat sequences in ESTs are a source of problems, in particular for clustering. ESTs are therefore commonly masked against a library of known repeats. High quality repeat libraries are available for the widely studied organisms, but for most other organisms the lack of such libraries is likely to compromise the quality of EST analysis. RESULTS: We present a fast, flexible and library-less method for masking repeats in EST sequences, based on match statistics within the EST collection. The method is not linked to a particular clustering algorithm. Extensive testing on datasets using different clustering methods and a genomic mapping as reference shows that this method gives results that are better than or as good as those obtained using RepeatMasker with a repeat library. AVAILABILITY: The implementation of RBR is available under the terms of the GPL from http://www.ii.uib.no/~ketil/bioinformatics CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.