Performance of 3D-space-based atoms-in-molecules methods for electronic delocalization aromaticity indices

Several definitions of an atom in a molecule (AIM) in three-dimensional (3D) space, including both fuzzy and disjoint domains, are used to calculate electron sharing indices (ESI) and related electronic aromaticity measures, namely, Iringand multicenter indices (MCI), for a wide set of cyclic planar aromatic and nonaromatic molecules of different ring size. The results obtained using the recent iterative Hirshfeld scheme are compared with those derived from the classical Hirshfeld method and from Bader's quantum theory of atoms in molecules. For bonded atoms, all methods yield ESI values in very good agreement, especially for C-C interactions. In the case of nonbonded interactions, there are relevant deviations, particularly between fuzzy and QTAIM schemes. These discrepancies directly translate into significant differences in the values and the trends of the aromaticity indices. In particular, the chemically expected trends are more consistently found when using disjoint domains. Careful examination of the underlying effects reveals the different reasons why the aromaticity indices investigated give the expected results for binary divisions of 3D spaceM.S. is grateful for the nancial help furnished by the Spanish MICINN Project No. CTQ2008-03077/BQU and by the Catalan DIUE through project No. 2009SGR63

Estudio de los colegios invisibles en la revista "Apunts" (1964-1993)

En el presente trabajo estudiamos la estructura de los grupos de autores de la revista Apunts, así como los temas sobre los que escribieron estos profesionales, procedentes de diferentes disciplinas del mundo del deporte, entre los años 1964 y 1993 en la revista de estudio. En nuestro interés por conocer la evolución de la Educación Física y las Ciencias del Deporte a través de una revista elegimos de entre las de mayor relevancia y más tiempo de presencia: Apunts. Elegimos 1993 como el último año de estudio, el año posterior a la Olimpíada de Barcelona-92. La revista contaba con dos momentos claves, 1964 año de inicio de Apuntes de Medicina Deportiva y 1985, año de inicio de la Revista Apunts. Educació Física. En nuestro recorrido de estudio contábamos por consiguiente con un período en el que la revista es propiamente de Medicina Deportiva, ciencia pionera entre las Ciencias del Deporte en nuestro país, en la que se publicaban también artículos de otras ciencias de deporte (Educación Física, Psicología, Sociología, etc.) y el período en el que se inicia Apunts. Educació Física. En este segundo período los artículos que se publican son fundamentalmente los de Educación Física, y además como en el período anterior, los de otras ciencias del deporte (Medicina, Psicología, etc.). Hemos considerado como "colegio invisible" el conjunto de autores que aparecen ligados entre sí como consecuencia de haber firmado conjuntamente artículos en Apunts. En el análisis de los colegios invisibles en la revista Apunts, hemos realizado una búsqueda bibliográfica utilizando los artículos publicados en Apuntes de Medicina Deportiva y Apunts. Educació Física i Esport. El período de estudio abarca desde el nacimiento en 1964, hasta el año 1993, tras la finalización de la Olimpíada de Barcelona-92. El indicador que hemos utilizado para detectar los "colegios invisibles" es el de la colaboración que presentan los autores en los trabajos publicados. Los resultados obtenidos muestran la existencia de colegios invisibles cuyo número de miembros oscila entre dos y noventa y cinco. Destacando entre las características más notables de éstos, que en la primera cabecera casi la práctica totalidad de ellos son médicos, y en la segunda, se observa la presencia además, de profesores de educación física, entrenadores y psicólogos. La mayoría son catalanes, desarrollan su labor profesional en instituciones catalanas, y las cabezas visibles de los principales colegios invisibles han formado parte en algún momento del equipo editorial de la revista

Optimal gap-affine alignment in O (s) space

Altres ajuts: DRAC project [001-P-001723]Pairwise sequence alignment remains a fundamental problem in computational biology and bioinformatics. Recent advances in genomics and sequencing technologies demand faster and scalable algorithms that can cope with the ever-increasing sequence lengths. Classical pairwise alignment algorithms based on dynamic programming are strongly limited by quadratic requirements in time and memory. The recently proposed wavefront alignment algorithm (WFA) introduced an efficient algorithm to perform exact gap-affine alignment in time, where s is the optimal score and n is the sequence length. Notwithstanding these bounds, WFA's memory requirements become computationally impractical for genome-scale alignments, leading to a need for further improvement. In this article, we present the bidirectional WFA algorithm, the first gap-affine algorithm capable of computing optimal alignments in memory while retaining WFA's time complexity of . As a result, this work improves the lowest known memory bound to compute gap-affine alignments. In practice, our implementation never requires more than a few hundred MBs aligning noisy Oxford Nanopore Technologies reads up to 1 Mbp long while maintaining competitive execution times. All code is publicly available at . Supplementary data are available at Bioinformatics online

An FPGA accelerator of the wavefront algorithm for genomics pairwise alignment

In the last years, advances in next-generation sequencing technologies have enabled the proliferation of genomic applications that guide personalized medicine. These applications have an enormous computational cost due to the large amount of genomic data they process. The first step in many of these applications consists in aligning reads against a reference genome. Very recently, the wavefront alignment algorithm has been introduced, significantly reducing the execution time of the read alignment process. This paper presents the first FPGA- based hardware/software co-designed accelerator of such relevant algorithm. Compared to the reference WFA CPU-only implementation, the proposed FPGA accelerator achieves performance speedups of up to 13.5× while consuming up to 14.6× less energy.ed medicine. These applications have an enormous computational cost due to the large amount of genomic data they process. The first step in many of these applications consists in aligning reads against a reference genome. Very recently, the wavefront alignment algorithm has been introduced, significantly reducing the execution time of the read alignment process. This paper presents the first FPGA- based hardware/software co-designed accelerator of such relevant algorithm. Compared to the reference WFA CPU-only imple- mentation, the proposed FPGA accelerator achieves performance speedups of up to 13.5× while consuming up to 14.6× less energy.This work has been supported by the European HiPEAC Network of Excellence, by the Spanish Ministry of Science and Innovation (contract PID2019-107255GB-C21/AEI/10.13039/501100011033), by the Generalitat de Catalunya (contracts 2017-SGR-1414 and 2017-SGR-1328), by the IBM/BSC Deep Learning Center initiative, and by the DRAC project, which is co-financed by the European Union Regional Development Fund within the framework of the ERDF Operational Program of Catalonia 2014-2020 with a grant of 50% of total eligible cost. Ll. Alvarez has been partially supported by the Spanish Ministry of Economy, Industry and Competitiveness under the Juan de la Cierva Formacion fellowship No. FJCI-2016-30984. M. Moreto has been partially supported by the Spanish Ministry of Economy, Industry and Competitiveness under Ramon y Cajal fellowship No. RYC-2016-21104.Peer ReviewedPostprint (author's final draft

AnchorWave: Sensitive alignment of genomes with high sequence diversity, extensive structural polymorphism, and whole-genome duplication

Millions of species are currently being sequenced, and their genomes are being compared. Many of them have more complex genomes than model systems and raise novel challenges for genome alignment. Widely used local alignment strategies often produce limited or incongruous results when applied to genomes with dispersed repeats, long indels, and highly diverse sequences. Moreover, alignment using many-to-many or reciprocal best hit approaches conflicts with well-studied patterns between species with different rounds of whole-genome duplication. Here, we introduce Anchored Wavefront alignment (AnchorWave), which performs whole-genome duplication–informed collinear anchor identification between genomes and performs base pair–resolved global alignment for collinear blocks using a two-piece affine gap cost strategy. This strategy enables AnchorWave to precisely identify multikilobase indels generated by transposable element (TE) presence/absence variants (PAVs). When aligning two maize genomes, AnchorWave successfully recalled 87% of previously reported TE PAVs. By contrast, other genome alignment tools showed low power for TE PAV recall. AnchorWave precisely aligns up to three times more of the genome as position matches or indels than the closest competitive approach when comparing diverse genomes. Moreover, AnchorWave recalls transcription factor–binding sites at a rate of 1.05- to 74.85-fold higher than other tools with significantly lower false-positive alignments. AnchorWave complements available genome alignment tools by showing obvious improvement when applied to genomes with dispersed repeats, active TEs, high sequence diversity, and whole-genome duplication variation.This project is supported by the United States Department of Agriculture Agricultural Research Service, NSF No. 1822330, NSF No. 1854828, the European Union's Horizon 2020 Framework Programme under the DeepHealth project [825111], the European Union Regional Development Fund within the framework of The European Regional Development Fund Operational Program of Catalonia 2014 to 2020 with a grant of 50% of total cost eligible under the DRAC project [001-P-001723], and National Natural Science Foundation of China No. 31900486. M.C.S. was supported by NSF Postdoctoral Research Fellowship in Biology No. 1907343. M.M. was partially supported by the Spanish Ministry of Economy, Industry, and Competitiveness under Ramón y Cajal (RYC) fellowship number RYC-2016-21104.Peer ReviewedPostprint (published version

Accelerating edit-distance sequence alignment on GPU using the wavefront algorithm

Sequence alignment remains a fundamental problem with practical applications ranging from pattern recognition to computational biology. Traditional algorithms based on dynamic programming are hard to parallelize, require significant amounts of memory, and fail to scale for large inputs. This work presents eWFA-GPU, a GPU (graphics processing unit)-accelerated tool to compute the exact edit-distance sequence alignment based on the wavefront alignment algorithm (WFA). This approach exploits the similarities between the input sequences to accelerate the alignment process while requiring less memory than other algorithms. Our implementation takes full advantage of the massive parallel capabilities of modern GPUs to accelerate the alignment process. In addition, we propose a succinct representation of the alignment data that successfully reduces the overall amount of memory required, allowing the exploitation of the fast shared memory of a GPU. Our results show that our GPU implementation outperforms by 3- 9× the baseline edit-distance WFA implementation running on a 20 core machine. As a result, eWFA-GPU is up to 265 times faster than state-of-the-art CPU implementation, and up to 56 times faster than state-of-the-art GPU implementations.This work was supported in part by the European Unions’s Horizon 2020 Framework Program through the DeepHealth Project under Grant 825111; in part by the European Union Regional Development Fund within the Framework of the European Regional Development Fund (ERDF) Operational Program of Catalonia 2014–2020 with a Grant of 50% of Total Cost Eligible through the Designing RISC-V-based Accelerators for next-generation Computers Project under Grant 001-P-001723; in part by the Ministerio de Ciencia e Innovacion (MCIN) Agencia Estatal de Investigación (AEI)/10.13039/501100011033 under Contract PID2020-113614RB-C21 and Contract TIN2015-65316-P; and in part by the Generalitat de Catalunya (GenCat)-Departament de Recerca i Universitats (DIUiE) (GRR) under Contract 2017-SGR-313, Contract 2017-SGR-1328, and Contract 2017-SGR-1414. The work of Miquel Moreto was supported in part by the Spanish Ministry of Economy, Industry and Competitiveness under Ramon y Cajal Fellowship under Grant RYC-2016-21104.Peer ReviewedPostprint (published version

OpenCL-based FPGA accelerator for semi-global approximate string matching using diagonal bit-vectors

An FPGA accelerator for the computation of the semi-global Levenshtein distance between a pattern and a reference text is presented. The accelerator provides an important benefit to reduce the execution time of read-mappers used in short-read genomic sequencing. Previous attempts to solve the same problem in FPGA use the Myers algorithm following a column approach to compute the dynamic programming table. We use an approach based on diagonals that allows for some resource savings while maintaining a very high throughput of 1 alignment per clock cycle. The design is implemented in OpenCL and tested on two FPGA accelerators. The maximum performance obtained is 91.5 MPairs/s for 100 × 120 sequences and 47 MPairs/s for 300 × 360 sequences, the highest ever reported for this problem.This research was supported by the EU Regional Development Fund under the DRAC project [001-P-001723], by the MINECO-Spain (contract TIN2017-84553-C2-1-R), by the MICIU-Spain (contract RTI2018-095209-B-C22) and by the Catalan government (contracts 2017-SGR-1624, 2017-SGR313, 2017-SGR-1328). M.M. was partially supported by the MINECO under RYC-2016-21104. We thank Intel for granting us access to the DevCloud system and let us join the HARP research program. The presented HARP-2 results were obtained on resources hosted at the Paderborn Center for Parallel Computing (PC2) in the Intel Hardware Accelerator Research Program (HARP2).Peer ReviewedPostprint (author's final draft

Sargantana: A 1 GHz+ in-order RISC-V processor with SIMD vector extensions in 22nm FD-SOI

The RISC-V open Instruction Set Architecture (ISA) has proven to be a solid alternative to licensed ISAs. In the past 5 years, a plethora of industrial and academic cores and accelerators have been developed implementing this open ISA. In this paper, we present Sargantana, a 64-bit processor based on RISC-V that implements the RV64G ISA, a subset of the vector instructions extension (RVV 0.7.1), and custom application-specific instructions. Sargantana features a highly optimized 7-stage pipeline implementing out-of-order write-back, register renaming, and a non-blocking memory pipeline. Moreover, Sar-gantana features a Single Instruction Multiple Data (SIMD) unit that accelerates domain-specific applications. Sargantana achieves a 1.26 GHz frequency in the typical corner, and up to 1.69 GHz in the fast corner using 22nm FD-SOI commercial technology. As a result, Sargantana delivers a 1.77× higher Instructions Per Cycle (IPC) than our previous 5-stage in-order DVINO core, reaching 2.44 CoreMark/MHz. Our core design delivers comparable or even higher performance than other state-of-the-art academic cores performance under Autobench EEMBC benchmark suite. This way, Sargantana lays the foundations for future RISC-V based core designs able to meet industrial-class performance requirements for scientific, real-time, and high-performance computing applications.This work has been partially supported by the Spanish Ministry of Economy and Competitiveness (contract PID2019- 107255GB-C21), by the Generalitat de Catalunya (contract 2017-SGR-1328), by the European Union within the framework of the ERDF of Catalonia 2014-2020 under the DRAC project [001-P-001723], and by Lenovo-BSC Contract-Framework (2020). The Spanish Ministry of Economy, Industry and Competitiveness has partially supported M. Doblas and V. Soria-Pardos through a FPU fellowship no. FPU20-04076 and FPU20-02132 respectively. G. Lopez-Paradis has been supported by the Generalitat de Catalunya through a FI fellowship 2021FI-B00994. S. Marco-Sola was supported by Juan de la Cierva fellowship grant IJC2020-045916-I funded by MCIN/AEI/10.13039/501100011033 and by “European Union NextGenerationEU/PRTR”, and M. Moretó through a Ramon y Cajal fellowship no. RYC-2016-21104.Peer ReviewedPostprint (author's final draft