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Financiado para publicación en acceso aberto: Universidade da Coruña/CISUG [Abstract]: The increase in the size of data repositories has forced the design of new computing paradigms to be able to process large volumes of data in a reasonable amount of time. One of them is in-memory computing, which advocates storing all the data in main memory to avoid the disk I/O bottleneck. Compression is one of the key technologies for this approach. For raster data, a compact data structure, called (Formula presented.) -raster, have been recently been proposed. It compresses raster maps while still supporting fast retrieval of a given datum or a portion of the data directly from the compressed data. (Formula presented.) -raster's original work introduced several queries in which it was superior to competitors. However, to be used as the basis of an in-memory system for raster data, it is mandatory to demonstrate its efficiency when performing more complex operations such as the map algebra operators. In this work, we present the algorithms to run a set of these operators directly on (Formula presented.) -raster without a decompression procedure. This work was supported by the National Natural Science Foundation of China (Grant Nos. 31171944, 31640068), Anhui Provincial Natural Science Foundation (Grant No. 2019B319), Earmarked Fund for Anhui Science and Technology Major Project (202003b06020016). Information CITIC, Ministerio de Ciencia e Innovación, Grant/Award Numbers: PID2020-114635RB-I00; PDC2021-120917-C21; PDC2021-121239-C31; PID2019-105221RB-C41; TED2021-129245-C21; Xunta de Galicia, Grant/Award Numbers: ED431C 2021/53; IN852D 2021/3 (CO3)This work was partially supported by CITIC, CITIC is funded by the Xunta de Galicia through the collaboration agreement between the Department of Culture, Education, Vocational Training and Universities and the Galician universities for the reinforcement of the research centers of the Galician University System (CIGUS). IN852D 2021/3(CO3): partially funded by UE, (ERDF), GAIN, convocatoria Conecta COVID. GRC: ED431C 2021/53: partially funded by GAIN/Xunta de Galicia. TED2021-129245B-C21; PDC2021-121239-C31; PDC2021-120917-C21: partially funded by MCIN/AEI/10.13039/501100011033 and “NextGenerationEU”/PRTR. PID2020-114635RB-I00; PID2019-105221RB-C41: partially funded by MCIN/AEI/10.13039/501100011033. Funding for open access charge: Universidadeda Coruña/CISUG. Xunta de Galicia; ED431C 2021/53 Xunta de Galicia; IN852D 2021/3 (CO3) National Natural Science Foundation of China; 31171944 National Natural Science Foundation of China; 31640068 Anhui Provincial Natural Science Foundation; 2019B319

During viral infection, intrahost mutation and recombination can lead to significant evolution, resulting in a population of viruses that harbor multiple haplotypes. The task of reconstructing these haplotypes from short-read sequencing data is called viral quasispecies assembly, and it can be categorized as a multiassembly problem. We consider the de novo version of the problem, where no reference is available. We present ViQUF, a de novo viral quasispecies assembler that addresses haplotype assembly and quantification. ViQUF obtains a first draft of the assembly graph from a de Bruijn graph. Then, solving a min-cost flow over a flow network built for each pair of adjacent vertices based on their paired-end information creates an approximate paired assembly graph with suggested frequency values as edge labels, which is the first frequency estimation. Then, original haplotypes are obtained through a greedy path reconstruction guided by a min-cost flow solution in the approximate paired assembly graph. ViQUF outputs the contigs with their frequency estimations. Results on real and simulated data show that ViQUF is at least four times faster using at most half of the memory than previous methods, while maintaining, and in some cases outperforming, the high quality of assembly and frequency estimation of overlap graph-based methodologies, which are known to be more accurate but slower than the de Bruijn graph-based approaches., This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible

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.

[Abstract]: LiDAR devices are capable of acquiring clouds of 3D points reflecting any object around them, and adding additional attributes to each point such as color, position, time, etc. LiDAR datasets are usually large, and compressed data formats (e.g. LAZ) have been proposed over the years. These formats are capable of transparently decompressing portions of the data, but they are not focused on solving general queries over the data. In contrast to that traditional approach, a new recent research line focuses on designing data structures that combine compression and indexation, allowing directly querying the compressed data. Compression is used to fit the data structure in main memory all the time, thus getting rid of disk accesses, and indexation is used to query the compressed data as fast as querying the uncompressed data. In this paper, we present the first data structure capable of losslessly compressing point clouds that have attributes and jointly indexing all three dimensions of space and attribute values. Our method is able to run range queries and attribute queries up to 100 times faster than previous methods. Secretara Xeral de Universidades; [ED431G 2019/01] Ministerio de Ciencia e Innovacion; [PID2020-114635RB-I00] Ministerio de Ciencia e Innovacion; [PDC2021-120917C21] Ministerio de Ciencia e Innovación; [PDC2021-121239-C31] Ministerio de Ciencia e Innovación; [PID2019-105221RB-C41] Xunta de Galicia; [ED431C 2021/53] Xunta de Galicia; [IG240.2020.1.185]

Financiado para publicación en acceso aberto: Universidade da Coruña/CISUG [Abstract] The new opportunities generated by the data-driven economy in the manufacturing industry have causedmany companies opt for it. However, the size of time series data that need to be captured creates theproblem of having to assume high storage costs. Moreover, these costs, which are constantly growing,begin to have an impact on the profitability of companies. Thus, in this scenario, the need arises to developtechniques that allow obtaining reduced representations of the time series. In this paper, we present alossless compression method for industrial time series that allows an efficient access. That is, our aim goesbeyond pure compression, where the usual way to access the data requires a complete decompressionof the dataset before processing it. Instead, our method allows decompressing portions of the dataset,and moreover, it allows direct querying the compressed data. Thus, the proposed method combines theefficient access, typical of lossy methods, with the lossless compression. Xunta de Galicia; ED431G 2019/01 Xunta de Galicia; IG240. 2020.1.185 Xunta de Galicia; IN852A 2018/14 Gobierno Vasco; IT1330-19 For the A Coruña team: This work was supported by CITIC, as Research Center accredited by Galician University System, is funded by “Consellería de Cultura, Educación e Universidade from Xunta de Galicia”, supported in an 80% through ERDF Funds, ERDF Operational Programme Galicia 2014-2020, and the remaining 20% by “Secretaría Xeral de Universidades” (Grant ED431G 2019/01), Xunta de Galicia/FEDER-UE under Grants [IG240.2020.1.185; IN852A 2018/14] and Ministerio de Ciencia, Innovación under Grants [TIN2016-78011-C4-1-R; RTC-2017-5908-7]. For the Basque team: Ministerio de Ciencia, Innovación y Universidades under Grant [FEDER/TIN2016-78011-C4-2-R] and the Basque Government under Grant No. [IT1330-19]. Funding for open access charge: Universidade da Coruña/CISUG.

We introduce a compressed data structure for the storage of free trajectories of moving objects (such as ships and planes) that efficiently supports various spatio-temporal queries. Our structure, dubbed GraCT, stores the absolute positions of all the objects at regular time intervals (snapshots) using a $k^2$-tree, which is a space- and time-efficient version of a region quadtree. Positions between snapshots are represented as logs of relative movements and compressed using Re-Pair, a grammar-based compressor. The nonterminals of this grammar are enhanced with MBR information to enable fast queries. The GraCT structure of a dataset occupies less than the raw data compressed with a powerful traditional compressor such as p7zip. Further, instead of requiring full decompression to access the data like a traditional compressor, GraCT supports direct access to object trajectories or to their position at specific time instants, as well as spatial range and nearest-neighbor queries on time instants and/or time intervals. Compared to traditional methods for storing and indexing spatio-temporal data, GraCT requires two orders of magnitude less space, and is competitive in query times. In particular, thanks to its compressed representation, the GraCT structure may reside in main memory in situations where any classical uncompressed index must resort to disk, thereby being one or two orders of magnitude faster., This research has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sk{\l}odowska-Curie Actions H2020-MSCA-RISE-2015 BIRDS GA No. 690941