In the past decade, next-generation sequencing (NGS) enabled the generation of genomic data in a cost-effective, high-throughput manner. The most recent third-generation sequencing technologies produce longer reads; however, their error rates are much higher, which complicates the assembly process. This generates time- and space- demanding long-read assemblers. Moreover, the advances in these technologies have allowed portable and real-time DNA sequencing, enabling in-field analysis. In these scenarios, it becomes crucial to have more efficient solutions that can be executed in computers or mobile devices with minimum hardware requirements. We re-implemented an existing assembler devoted for long reads, more concretely Flye, using compressed data structures. We then compare our version with the original software using real datasets, and evaluate their performance in terms of memory requirements, execution speed, and energy consumption. The assembly results are not affected, as the core of the algorithm is maintained, but the usage of advanced compact data structures leads to improvements in memory consumption that range from 22% to 47% less space, and in the processing time, which range from being on a par up to decreases of 25%. These improvements also cause reductions in energy consumption of around 3-8%, with some datasets obtaining decreases up to 26%.

Even though the field of spatial databases is more than 40 years old, most existing logical data models are highly focused either on spatial objects (vector data models) or spatial fields (raster data models). Furthermore, spatial index structures and query algorithms are still proposed for one of the approaches and little research work has been dedicated to index structures and query algorithms where both types of information are needed. However, due to the current high availability of different types of data, it is much more common nowadays that applications require querying vector and raster data at the same time. This paper presents a method to perform a spatial query between a vector data set represented using an R-tree and a raster data set represented using a compact and space-efficient data structure called k²-tree that saves main memory space. Therefore, the method described in this paper solves two problems: first, it can be used to evaluate queries between vector and raster data without having to convert one of the data sets to the other data model; and second, it saves main memory space, thus obtaining a more scalable system.

Motivation: RNA viruses exhibit a high mutation rate and thus they exist in infected cells as a population of closely related strains called viral quasispecies. The viral quasispecies assembly problem asks to characterize the quasispecies present in a sample from high-throughput sequencing data. We study the de novo version of the problem, where reference sequences of the quasispecies are not available. Current methods for assembling viral quasispecies are either based on overlap graphs or on de Bruijn graphs. Overlap graph-based methods tend to be accurate but slow, whereas de Bruijn graph-based methods are fast but less accurate.Results: We present viaDBG, which is a fast and accurate de Bruijn graph-based tool for de novo assembly of viral quasispecies. We first iteratively correct sequencing errors in the reads, which allows us to use large k-mers in the de Bruijn graph. To incorporate the paired-end information in the graph, we also adapt the paired de Bruijn graph for viral quasispecies assembly. These features enable the use of long-range information in contig construction without compromising the speed of de Bruijn graph-based approaches. Our experimental results show that viaDBG is both accurate and fast, whereas previous methods are either fast or accurate but not both. In particular, viaDBG has comparable or better accuracy than SAVAGE, while being at least nine times faster. Furthermore, the speed of viaDBG is comparable to PEHaplo but viaDBG is able to retrieve also low abundance quasispecies, which are often missed by PEHaplo.Availability and implementation: viaDBG is implemented in C++ and it is publicly available at https://bitbucket.org/bfreirec1/viadbg. All datasets used in this article are publicly available at https://bitbucket.org/bfreirec1/data-viadbg/.Supplementary information: Supplementary data are available at Bioinformatics online.