The SoftWipe tool and benchmark for assessing coding standards adherence of scientific software

Zapletal, Adrian; Höhler, Dimitri; Sinz, Carsten; Stamatakis, Alexandros

doi:10.1038/s41598-021-89495-8

Cited by 11 publications

(8 citation statements)

References 14 publications

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“…We checked the correctness of our implementations on simulated data with 100 taxa and alignment lengths ranging from 1000 to 100 000 sites ( Supplementary Fig. S1 ) and assessed the code quality of DecentTree using SoftWipe ( Zapletal et al 2021 ). The overall score of DecentTree was 5.7, slightly higher than the average score of 5.6 for software evaluated by Zapletal et al (2021) .…”

Section: Methodsmentioning

confidence: 99%

DecentTree: scalable Neighbour-Joining for the genomic era

Wang,

Barbetti,

Wong

et al. 2023

Bioinformatics

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Motivation Neighbour-Joining is one of the most widely used distance-based phylogenetic inference methods. However, current implementations do not scale well for datasets with more than 10,000 sequences. Given the increasing pace of generating new sequence data, particularly in outbreaks of emerging diseases, and the already enormous existing databases of sequence data for which NJ is a useful approach, new implementations of existing methods are warranted. Results Here we present DecentTree, which provides highly optimised and parallel implementations of Neighbour-Joining and several of its variants. DecentTree is designed as a stand-alone application and a header-only library easily integrated with other phylogenetic software (e.g., it is integral in the popular IQ-TREE software). We show that DecentTree shows similar or improved performance over existing software (BIONJ, Quicktree, FastME, and RapidNJ), especially for handling very large alignments. For example, DecentTree is up to 6-fold faster than the fastest existing Neighbour-Joining software (e.g., RapidNJ) when generating a tree of 64,000 SARS-CoV-2 genomes. Availability DecentTree is open source and freely available at https://github.com/iqtree/decenttree. All code and data used in this analysis are available on Github (https://github.com/asdcid/Comparison-of-neighbour-joining-software). Supplementary information Supplementary data are available at Bioinformatics online.

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Section: Methodsmentioning

confidence: 99%

DecentTree: scalable Neighbour-Joining for the genomic era

Wang,

Barbetti,

Wong

et al. 2023

Bioinformatics

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“…Finally, Phylourny has a software quality score of 7.7 as rated by the software quality analysis tool SoftWipe (Zapletal et al. 2021 ), which places Phylourny in the top 10% of scientific software tools included in the SoftWipe benchmark. Version v1.2.1 was used to perform the uncertainty analyses presented here.…”

Section: Methodsmentioning

confidence: 99%

Phylourny: efficiently calculating elimination tournament win probabilities via phylogenetic methods

2023

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The prediction of knockout tournaments represents an area of large public interest and active academic as well as industrial research. Here, we show how one can leverage the computational analogies between calculating the phylogenetic likelihood score used in the area of molecular evolution to efficiently calculate, instead of approximate via simulations, the exact per-team tournament win probabilities, given a pairwise win probability matrix between all teams. We implement and make available our method as open-source code and show that it is two orders of magnitude faster than simulations and two or more orders of magnitude faster than calculating the exact per-team win probabilities naïvely, without taking into account the substantial computational savings induced by the tournament tree structure. Furthermore, we showcase novel prediction approaches that now become feasible due to this order of magnitude improvement in calculating tournament win probabilities. We demonstrate how to quantify prediction uncertainty by calculating 100,000 distinct tournament win probabilities for a tournament with 16 teams under slight variations of a reasonable pairwise win probability matrix within one minute on a standard laptop. We also conduct an analogous analysis for a tournament with 64 teams.

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“…To build the software, the only requirements are a C++ compiler, and CMake. Optionally, Lagrange-NG can be built with the respective system versions of the Intel Math Kernel Library (MKL) [1] and NLOpt [9, 5]. If a system version of MKL is not present, Lagrange-NG will build with OpenBLAS [2] instead.…”

Section: Software Descriptionmentioning

confidence: 99%

Lagrange-NG: The next generation of Lagrange

Bettisworth

Smith

Stamatakis

2022

Preprint

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Computing ancestral ranges via the Dispersion Extinction and Cladogensis (DEC) model of biogeography is characterized by an exponential number of states relative to the number of regions considered. This is because the DEC model requires computing a large matrix exponential, which typically accounts for up to 80% of overall runtime. Therefore, the kinds of biogeographical analyses that can be conducted under the DEC model are limited by the number of regions under consideration. In this work, we present a completely redesigned efficient version of the popular tool Lagrange which is up to 2.5 times faster, which we call Lagrange-NG (Next Generation). We further reduce time-to-completion by introducing a multi-grained parallelization approach, achieving a total parallel speedup of 8.5 over Lagrange on a machine with 8 cores. In order to validate the correctness of Lagrange-NG, we also introduce a novel metric on range distributions for trees in order to assess the difference between any two range inferences. Finally, Lagrange-NG exhibits substantially higher adherence to coding quality standards. It improves a respective software quality indicator as implemented in the SoftWipe tool from average (5.5; Lagrange) to high (7.8; Lagrange-NG). Lagrange-NG is freely available under GPL2.

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The SoftWipe tool and benchmark for assessing coding standards adherence of scientific software

Cited by 11 publications

References 14 publications

DecentTree: scalable Neighbour-Joining for the genomic era

DecentTree: scalable Neighbour-Joining for the genomic era

Phylourny: efficiently calculating elimination tournament win probabilities via phylogenetic methods

Lagrange-NG: The next generation of Lagrange

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