Increasing workflow development speed and reproducibility with Vectools.
Ontology highlight
ABSTRACT:
SUBMITTER: Weirick T
PROVIDER: S-EPMC6281013 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:YAMP ("Yet Another Metagenomics Pipeline") is a user-friendly workflow that enables the analysis of whole shotgun metagenomic data while using containerization to ensure computational reproducibility and facilitate collaborative research. YAMP can be executed on any UNIX-like system and offers seamless support for multiple job schedulers as well as for the Amazon AWS cloud. Although YAMP was developed to be ready to use by nonexperts, bioinformaticians will appreciate its flexibility, modularization, and simple customization.
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Project description:Although motor imagery has been pointed as a promising strategy for the rehabilitation of children with neurological disorders, information on their development throughout childhood and adolescence is still scarce. For instance, it is still unclear at what age they reach a development comparable to the motor imagery performance observed in adults. Herein we used a mental rotation task to assess motor imagery in 164 typically developing children and adolescents, which were divided into four age groups (6-7, 8-9, 10-11, and 12-13 years) and 30 adults. The effects of biomechanical constraints, accuracy, and reaction time of the mental rotation task were considered. ANOVA showed that all groups had the effect of biomechanical restrictions of the mental rotation task. We found a group effect for accuracy [F (4, 180) = 17,560; p < 0.00; ?2 = 3.79] and reaction time [F (4, 180) = 17.5; p < 0.001, ?2 = 0.615], with the results of children groups 6-7 and 8-9 years being significantly lower than the other groups (p < 0.05). In all the analyses, there were no differences regarding accuracy and reaction time among the participants of the age groups 10-11 and 12-13 years and adults (p > 0.05). Concluding, children aged 6-7 years were able to perform motor imagery, motor imagery ability improved as the participants' ages increased, and children aged 10 and over-performed similarly to adults.
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Project description:Advances in high-throughput methods have brought new challenges for biological data analysis, often requiring many interdependent steps applied to a large number of samples. To address this challenge, workflow management systems, such as Watchdog, have been developed to support scientists in the (semi-)automated execution of large analysis workflows. Here, we present Watchdog 2.0, which implements new developments for module creation, reusability, and documentation and for reproducibility of analyses and workflow execution. Developments include a graphical user interface for semi-automatic module creation from software help pages, sharing repositories for modules and workflows, and a standardized module documentation format. The latter allows generation of a customized reference book of public and user-specific modules. Furthermore, extensive logging of workflow execution, module and software versions, and explicit support for package managers and container virtualization now ensures reproducibility of results. A step-by-step analysis protocol generated from the log file may, e.g., serve as a draft of a manuscript methods section. Finally, 2 new execution modes were implemented. One allows resuming workflow execution after interruption or modification without rerunning successfully executed tasks not affected by changes. The second one allows detaching and reattaching to workflow execution on a local computer while tasks continue running on computer clusters. Watchdog 2.0 provides several new developments that we believe to be of benefit for large-scale bioinformatics analysis and that are not completely covered by other competing workflow management systems. The software itself, module and workflow repositories, and comprehensive documentation are freely available at https://www.bio.ifi.lmu.de/watchdog.
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Project description:MotivationA challenge for computational biologists is to make our analyses reproducible-i.e. to rerun, combine, and share, with the assurance that equivalent runs will generate identical results. Current best practice aims at this using a combination of package managers, workflow engines, and containers.ResultsWe present BioNix, a lightweight library built on the Nix deployment system. BioNix manages software dependencies, computational environments, and workflow stages together using a single abstraction: pure functions. This lets users specify workflows in a clean, uniform way, with strong reproducibility guarantees.Availability and implementationBioNix is implemented in the Nix expression language and is released on GitHub under the 3-clause BSD license: https://github.com/PapenfussLab/bionix (biotools:BioNix) (BioNix, RRID:SCR_017662).
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Project description:ViralFlow: an automated workflow for SARS-CoV-2 genome assembly, lineage assignment, mutations and intrahost variants detection
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Project description:The three-dimensional conformation of genomes is an essential component of their biological activity. The advent of the Hi-C technology enabled an unprecedented progress in our understanding of genome structures. However, Hi-C is subject to systematic biases that can compromise downstream analyses. Several strategies have been proposed to remove those biases, but the issue of abnormal karyotypes received little attention. Many experiments are performed in cancer cell lines, which typically harbor large-scale copy number variations that create visible defects on the raw Hi-C maps. The consequences of these widespread artifacts on the normalized maps are mostly unexplored. We observed that current normalization methods are not robust to the presence of large-scale copy number variations, potentially obscuring biological differences and enhancing batch effects. To address this issue, we developed an alternative approach designed to take into account chromosomal abnormalities. The method, called OneD, increases reproducibility among replicates of Hi-C samples with abnormal karyotype, outperforming previous methods significantly. On normal karyotypes, OneD fared equally well as state-of-the-art methods, making it a safe choice for Hi-C normalization. OneD is fast and scales well in terms of computing resources for resolutions up to 5 kb.
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Project description:Biomarkers have revolutionized scientific research on neurodegenerative diseases, in particular Alzheimer's disease, transformed drug trial design, and are also increasingly improving patient management in clinical practice. A few key cerebrospinal fluid biomarkers have been robustly associated with neurodegenerative diseases. Several novel biomarkers are very promising, especially blood-based markers. However, many biomarker findings have had low reproducibility despite initial promising results. In this perspective, we identify possible sources for low reproducibility of studies on fluid biomarkers for neurodegenerative diseases, with a focus on Alzheimer's disease. We suggest guidelines for researchers and journal editors, with the aim to improve reproducibility of findings.
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Project description:Application of high-throughput sequencing technologies to microsatellite genotyping (SSRseq) has been shown to remove many of the limitations of electrophoresis-based methods and to refine inference of population genetic diversity and structure. We present here a streamlined SSRseq development workflow that includes microsatellite development, multiplexed marker amplification and sequencing, and automated bioinformatics data analysis. We illustrate its application to five groups of species across phyla (fungi, plant, insect and fish) with different levels of genomic resource availability. We found that relying on previously developed microsatellite assay is not optimal and leads to a resulting low number of reliable locus being genotyped. In contrast, de novo ad hoc primer designs gives highly multiplexed microsatellite assays that can be sequenced to produce high quality genotypes for 20-40 loci. We highlight critical upfront development factors to consider for effective SSRseq setup in a wide range of situations. Sequence analysis accounting for all linked polymorphisms along the sequence quickly generates a powerful multi-allelic haplotype-based genotypic dataset, calling to new theoretical and analytical frameworks to extract more information from multi-nucleotide polymorphism marker systems.
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Project description:A major limitation of gene expression biomarker studies is that they are not reproducible as they simply do not generalize to larger, real-world, heterogeneous populations. Frequentist multi-cohort gene expression meta-analysis has been frequently used as a solution to this problem to identify biomarkers that are truly differentially expressed. However, the frequentist meta-analysis framework has its limitations-it needs at least 4-5 datasets with hundreds of samples, is prone to confounding from outliers and relies on multiple-hypothesis corrected p-values. To address these shortcomings, we have created a Bayesian meta-analysis framework for the analysis of gene expression data. Using real-world data from three different diseases, we show that the Bayesian method is more robust to outliers, creates more informative estimates of between-study heterogeneity, reduces the number of false positive and false negative biomarkers and selects more generalizable biomarkers with less data. We have compared the Bayesian framework to a previously published frequentist framework and have developed a publicly available R package for use.
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Project description:Radiomics-the high throughput extraction of quantitative features from medical images and their correlation with clinical and biological endpoints- is the subject of active and extensive research. Although the field shows promise, the generalizability of radiomic signatures is affected significantly by differences in scan acquisition and reconstruction settings. Previous studies reported on the sensitivity of radiomic features (RFs) to test-retest variability, inter-observer segmentation variability, and intra-scanner variability. A framework involving robust radiomics analysis and the application of a post-reconstruction feature harmonization method using ComBat was recently proposed to address these challenges. In this study, we investigated the reproducibility of RFs across different scanners and scanning parameters using this framework. We analysed thirteen scans of a ten-layer phantom that were acquired differently. Each layer was subdivided into sixteen regions of interest (ROIs), and the scans were compared in a pairwise manner, resulting in seventy-eight different scenarios. Ninety-one RFs were extracted from each ROI. As hypothesized, we demonstrate that the reproducibility of a given RF is not a constant but is dependent on the heterogeneity found in the data under analysis. The number (%) of reproducible RFs varied across the pairwise scenarios investigated, having a wide range between 8 (8.8%) and 78 (85.7%) RFs. Furthermore, in contrast to what has been previously reported, and as hypothesized in the robust radiomics analysis framework, our results demonstrate that ComBat cannot be applied to all RFs but rather on a percentage of those-the "ComBatable" RFs-which differed depending on the data being harmonized. The number (%) of reproducible RFs following ComBat harmonization varied across the pairwise scenarios investigated, ranging from 14 (15.4%) to 80 (87.9%) RFs, and was found to depend on the heterogeneity in the data. We conclude that the standardization of image acquisition protocols remains the cornerstone for improving the reproducibility of RFs, and the generalizability of the signatures developed. Our proposed approach helps identify the reproducible RFs across different datasets.
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