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Standardized and accessible multi-omics bioinformatics workflows through the NMDC EDGE resource

COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL 2024年 23卷 3575-3583页

作者： Kelliher, Julia M. Xu, Yan Flynn, Mark C. Babinski, Michal Canon, Shane Cavanna, Eric Clum, Alicia Corilo, Yuri E. Fujimoto, Grant Giberson, Cameron Johnson, Leah Y. D. Li, Kaitlyn J. Li, Po-E Li, Valerie Lo, Chien-Chi Lynch, Wendi Piehowski, Paul Prime, Kaelan Purvine, Samuel Rodriguez, Francisca Roux, Simon Shakya, Migun Smith, Montana Sarrafan, Setareh Cholia, Shreyas Mccue, Lee Ann Mungall, Chris Hu, Bin Eloe-Fadrosh, Emiley A. Chain, Patrick S. G. Los Alamos Natl Lab Biosci Div Los Alamos NM 87545 USA Lawrence Berkeley Natl Lab Environm Genom & Syst Biol Div Berkeley CA USA Pacific Northwest Natl Lab Richland WA USA

Accessible and easy-to-use standardized bioinformatics workflows are necessary to advance microbiome research from observational studies to large-scale, data-driven approaches. Standardized multi-omics data enables comparative studies, data reuse, and applications of machine learning to model biological processes. To advance broad accessibility of standardized multi-omics bioinformatics workflows, the National Microbiome Data Collaborative (NMDC) has developed the Empowering the Development of Genomics Expertise (NMDC EDGE) resource, a user-friendly, open-source web application (https://***). Here, we describe the design and main functionality of the NMDC EDGE resource for processing metagenome, metatranscriptome, natural organic matter, and metaproteome data. The architecture relies on three main layers (web application, orchestration, and execution) to ensure flexibility and expansion to future workflows. The orchestration and execution layers leverage best practices in software containers and accommodate high-performance computing and cloud computing services. Further, we have adopted a robust user research process to collect feedback for continuous improvement of the resource. NMDC EDGE provides an accessible interface for researchers to process multi-omics microbiome data using production-quality workflows to facilitate improved data standardization and interoperability.

关键词： Microbiome Multi-omics bioinformatics workflows Standardization Software Open-source

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bioinformatics workflows With NoSQL Database in Cloud Computing

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EVOLUTIONARY bioinformatics 2019年 15卷 1176934319889974页

作者： Wercelens, Polyane da Silva, Waldeyr Hondo, Fernanda Castro, Klayton Walter, Maria Emilia Araujo, Aleteia Lifschitz, Sergio Holanda, Maristela Univ Brasilia Dept Comp Sci Brasilia DF Brazil Fed Inst Goias IFG NEPBIO Grp Biol Studies & Res Corrado Formosa Go Brazil Pontifical Catholic Univ Rio de Janeiro Rio De Janeiro Brazil

Scientific workflows can be understood as arrangements of managed activities executed by different processing entities. It is a regular bioinformatics approach applying workflows to solve problems in Molecular Biology, notably those related to sequence analyses. Due to the nature of the raw data and the in silico environment of Molecular Biology experiments, apart from the research subject, 2 practical and closely related problems have been studied: reproducibility and computational environment. When aiming to enhance the reproducibility of bioinformatics experiments, various aspects should be considered. The reproducibility requirements comprise the data provenance, which enables the acquisition of knowledge about the trajectory of data over a defined workflow, the settings of the programs, and the entire computational environment. Cloud computing is a booming alternative that can provide this computational environment, hiding technical details, and delivering a more affordable, accessible, and configurable on-demand environment for researchers. Considering this specific scenario, we proposed a solution to improve the reproducibility of bioinformatics workflows in a cloud computing environment using both Infrastructure as a Service (IaaS) and Not only SQL (NoSQL) database systems. To meet the goal, we have built 3 typical bioinformatics workflows and ran them on 1 private and 2 public clouds, using different types of NoSQL database systems to persist the provenance data according to the Provenance Data Model (PROV-DM). We present here the results and a guide for the deployment of a cloud environment for bioinformatics exploring the characteristics of various NoSQL database systems to persist provenance data.

关键词： bioinformatics workflows reproducibility data provenance cloud computing NoSQL

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OnTimeURB: Multi-Cloud Resource Brokering for bioinformatics workflows

OnTimeURB: Multi-Cloud Resource Brokering for Bioinformatics...

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IEEE International Conference on bioinformatics and Biomedicine (BIBM)

作者： Pandey, Ashish Lyu, Zhen Joshi, Trupti Calyam, Prasad Univ Missouri Columbia Columbia MO 65211 USA

ISBN: (纸本)9781728118673

Scientific workflows due to their data and memory intensive requirements are among the prime applications which benefit by leveraging cloud computing. However, Cloud service providers (CSPs) have distinct policies and service dynamics that present a problem of excess choice for users. Performance and cost of the cloud services are among the principal factors in CSP selection for scientific bioinformatics workflows. The workflows typically are based on private data, and require diverse cloud resources, thus often requiring synergistic services from multiple CSPs. In this paper, we address this challenge of multi-cloud resource selection using cloud template solutions based on user specifications. We propose an optimizer that incorporates a combinatorial optimization model built on performance, cost and CSPs interoperability factors. The optimizer is integrated within a novel resource broker (i.e., OnTimeURB) for prescriptive recommendations of template solutions with intuitive choices for users. We implement and evaluate the OnTimeURB recommendations framework with a catalog of bioinformatics workflow applications integrated within a KBCommons science gateway. The evaluation considered four CSP resources featuring more than 300 different machine configuration instances. Our evaluation results show that our OnTimeURB creates consistently more economical, performance optimized and practical cloud solutions compared to a k-nearest neighbors (k-NN) approach.

关键词： Multi-Cloud interoperability Cost Optimization Custom templates bioinformatics workflows

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A Resource Manager for Maximizing the Performance of bioinformatics workflows in Shared Clusters 1

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17th International Conference on Algorithms and Architectures for Parallel Processing (ICA3PP)

作者： Badosa, Ferran Acevedo, Cesar Espinosa, Antonio Vera, Gonzalo Ripoll, Ana Univ Autonoma Barcelona Bellaterra Spain Ctr Res Agr Genom Bellaterra Spain

ISBN: (数字)9783319654829

ISBN: (纸本)9783319654829;9783319654812

In order for bioinformatics workflows to achieve good performance when running on shared clusters, resources must be properly allocated, adjusting to the needs of the bioinformatics applications within. Time-changing cluster status, caused by the dynamic workload, must also be considered. Users of bioinformatics applications are prompted with the dilemma of providing adequate job description, without prior hint of the resources used by their applications. As a result, naive approaches are taken and both platform efficiency and users' goals, such as makespan or cost, are compromised. To prevent that, we propose a Resource Manager (RM) for bioinformatics workflows running in shared clusters, capable of improving platform efficiency and reducing average makespan of queued applications. Our RM contains a predictor that generates multiple job performance predictions, under different combinations of resources. We also included a shared-resource model, that considers the degree of multiprogramming of the nodes (DP), and determines which applications are more compatible for sharing same-node resources. With this information, we developed a scheduling algorithm capable of operating in compliance with the cluster's default manager, i.e. SLURM. At the end, our RM is tested on a set of queued workflows, formed by multiple applications each. We prove that a 28% makespan reduction, and a 75% resource efficiency improvement, can be achieved.

关键词： bioinformatics workflows Resource sharing Multivariate regression prediction Makespan Scheduling

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Classification of bioinformatics workflows using weighted versions of partitioning and hierarchical clustering algorithms

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BMC bioinformatics 2015年第1期16卷 1-19页

作者： Lord, Etienne Diallo, Abdoulaye Banire Makarenkov, Vladimir Univ Quebec Dept Informat Montreal PQ H3C 3P8 Canada Univ Montreal Dept Sci Biol Montreal PQ H3C 3J7 Canada

Background: workflows, or computational pipelines, consisting of collections of multiple linked tasks are becoming more and more popular in many scientific fields, including computational biology. For example, simulation studies, which are now a must for statistical validation of new bioinformatics methods and software, are frequently carried out using the available workflow platforms. workflows are typically organized to minimize the total execution time and to maximize the efficiency of the included operations. Clustering algorithms can be applied either for regrouping similar workflows for their simultaneous execution on a server, or for dispatching some lengthy workflows to different servers, or for classifying the available workflows with a view to performing a specific keyword search. Results: In this study, we consider four different workflow encoding and clustering schemes which are representative for bioinformatics projects. Some of them allow for clustering workflows with similar topological features, while the others regroup workflows according to their specific attributes (e.g. associated keywords) or execution time. The four types of workflow encoding examined in this study were compared using the weighted versions of k-means and k-medoids partitioning algorithms. The Calinski-Harabasz, Silhouette and logSS clustering indices were considered. Hierarchical classification methods, including the UPGMA, Neighbor Joining, Fitch and Kitsch algorithms, were also applied to classify bioinformatics workflows. Moreover, a novel pairwise measure of clustering solution stability, which can be computed in situations when a series of independent program runs is carried out, was introduced. Conclusions: Our findings based on the analysis of 220 real-life bioinformatics workflows suggest that the weighted clustering models based on keywords information or tasks execution times provide the most appropriate clustering solutions. Using datasets generated by the Armadillo

关键词： bioinformatics workflows Hierarchical clustering k-means partitioning Scientific workflows Workflow clustering

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Building flexible and robust analysis frameworks for molecular subtyping of cancers

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MOLECULAR ONCOLOGY 2024年第3期18卷 606-619页

作者： Pedersen, Christina Bligaard Campos, Benito Rene, Lasse Wegener, Helene Scheel Krishnan, Neeraja M. Panda, Binay Vitting-Seerup, Kristoffer Rossing, Maria Bagger, Frederik Otzen Olsen, Lars Ronn Tech Univ Denmark Dept Hlth Technol Lyngby Denmark Copenhagen Univ Hosp Rigshosp Ctr Genom Med Copenhagen Denmark Jawaharlal Nehru Univ Sch Biotechnol New Delhi India Jawaharlal Nehru Univ Special Ctr Syst Med New Delhi India Tech Univ Denmark Dept Hlth Technol Anker Engelunds Vej 1 DK-2800 Lyngby Denmark

Molecular subtyping is essential to infer tumor aggressiveness and predict prognosis. In practice, tumor profiling requires in-depth knowledge of bioinformatics tools involved in the processing and analysis of the generated data. Additionally, data incompatibility (e.g., microarray versus RNA sequencing data) and technical and uncharacterized biological variance between training and test data can pose challenges in classifying individual samples. In this article, we provide a roadmap for implementing bioinformatics frameworks for molecular profiling of human cancers in a clinical diagnostic setting. We describe a framework for integrating several methods for quality control, normalization, batch correction, classification and reporting, and develop a use case of the framework in breast cancer. We provide a generally applicable roadmap for implementing data and bioinformatics tools for robust inference of cancer subtypes in a clinical setting, both from raw and processed RNA sequencing data and on a per-sample basis. We apply the resulting framework to the PAM50 and CIT breast cancer ***

关键词： bioinformatics workflows clinical bioinformatics molecular subtyping sample classification

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Knowledge-Engineered Multi-Cloud Resource Brokering for Application Workflow Optimization

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IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT 2023年第3期20卷 3072-3088页

作者： Pandey, Ashish Calyam, Prasad Lyu, Zhen Wang, Songjie Chemodanov, Dmitrii Joshi, Trupti Univ Missouri Dept Elect Engn & Comp Sci Columbia MO 65211 USA Univ Missouri Dept Hlth Management & Informat Columbia MO 65211 USA

Data-intensive application workflows benefit by leveraging cloud services to decrease execution times and increase data sharing. Cloud service providers (CSPs) have distinct capabilities and policies, and performance/cost of the cloud services are amongst the prime factors for CSP selection. However, workflow users who need brokering of cloud resources often lack expert guidance to handle the problem of overwhelming choice in CSP selection, and optimization to compensate for service dynamics. In this paper, we address the optimal resource selection problem using a multi-cloud resource broker viz., OnTimeURB that uses knowledge-engineering of user requirements and service capabilities across multiple CSPs. OnTimeURB is powered by integer linear programming and a Naive Bayes classifier to recommend optimal cloud template solutions by weighting performance, agility, cost, and security (PACS) factors. We evaluate the OnTimeURB recommendations with a catalog of bioinformatics application workflows using four CSP resources featuring more than 300 different instance configurations. Our evaluation results show the efficacy of OnTimeURB in creating consistently cost-effective and agile solutions compared to a state-of-the-art k-nearest neighbors (k-NN) approach. We also show that OnTimeURB has 91% success rate improvement in workflow execution times via cloud template recommendations over approaches that do not use knowledge-engineered multi-CSP resource brokering.

关键词： Cloud resource recommendation cloud interoperability infrastructure agility performance optimization custom templates bioinformatics workflows machine learning

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Reducing Theory to Practice: Designing Training Modules and Bco Tools for Adaptation of BioCompute by Novice Audiences

Reducing Theory to Practice: Designing Training Modules and ...

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作者： Patel, Janisha A. The George Washington University

学位级别：M.S., Master of Science/Master of Surgery

In recent years, the field of bioinformatics has seen an exponential growth of biological data generation and pipeline development due to the unprecedented scale in the advancement of high-throughput technologies. This increasing growth and complexity of bioinformatics research is accompanied by the urgent need to capture the data generated, analysis mechanisms, procedures used, and various environments of execution. Currently, computational researchers use a variety of bioinformatics software with different environments and parameter settings, which leads to variation in research protocols and difficulty reproducing results. While there is no shortage of open source information on bioinformatics workflows and pipelines, there is a lack of standardized documentation and accessibility. BioCompute is a standardized mechanism of analysis communication, built specifically for collaboration, reproducibility, and recording provenance, and which extends to both data analysis workflows and the development of knowledgebases. BioCompute Objects (BCO) are the artifacts of the BioCompute Framework; they are machine and human readable data objects that adhere to the BioCompute Specification. This curated audit trail of bioinformatics processes is able to support the dynamic elements, complexities and variations intrinsic to bioinformatics research. This level of communication promotes the accessibility and reproducibility of novel and existing pipelines. This transparency and accessibility will further enable interoperability between the diverse groups in academia, industry, and regulatory bodies that exist within the scientific community. To evaluate and further refine the BCO Specification and to make robust for more use cases, training modules and BCO tools can be leveraged. This thesis explores the development of user-friendly platform-dependent and platform-independent BCO creation tools (HIVE BCO App and BCO Editor) and training modules used in the implementation of BioCom

关键词： BioCompute bioinformatics workflows Community engagement High-throughput sequencing Standardization Training modules

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A History-Based Resource Manager for Genome Analysis workflows Applications on Clusters with Heterogeneous Nodes

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INTERNATIONAL JOURNAL OF PARALLEL PROGRAMMING 2019年第2期47卷 317-342页

作者： Badosa, Ferran Espinosa, Antonio Acevedo, Cesar Vera, Gonzalo Ripoll, Ana Univ Autonoma Barcelona Comp Architecture & Operat Syst Dept Bellaterra Spain Ctr Res Agr Genom Bellaterra Spain

bioinformatics workflows require large amounts of resources and are commonly executed in clusters. Determining the adequate amount of resources for bioinformatics applications is a tricky matter, since the resource usage of a single application might vary substantially from one execution to the next. Resource management systems in clusters don't consider these variations and subsequent needs. As a result, the computing power offered by clusters is not harnessed properly, compromising both application performance and resource efficiency. To tackle these issues, we propose a History-Based Resource Manager for bioinformatics workflows applications running on clusters with heterogeneous nodes. The proposed resource manager features a prediction model that generates multiple performance predictions for each job under different combinations of cluster resources. Furthermore, the proposed resource manager includes a scheduling algorithm that considers the degree of multiprogramming of the nodes, scheduling combinations of applications for simultaneous same-node execution upon their compatibility. To test the proposed resource manager, we process two workloads formed by different amounts of workflows made up by common bioinformatics applications. Results prove that for the given cases, the proposed resource manager improves the performance obtained with SLURM, using First Come First Served policy. The proposal shows an average workflow makespan improvement range between 28 and 35%, averaging 32%, an average workflow efficiency improvement range between 75 and 83%, averaging 79%, and an average resource usage improvement range between 96 and 101%, averaging 99%. Furthermore, the proposed scheduling algorithm can improve the average workflow makespan by a range of values between 26 and 36%, averaging 31%, compared to Max-Min and Min-Min algorithms.

关键词： Resource manager bioinformatics workflows Multivariate regression prediction Scheduling algorithm Resource sharing Slowdown Makespan

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A (fire)cloud-based DNA methylation data preprocessing and quality control platform

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BMC bioinformatics 2019年第1期20卷 160-160页

作者： Kangeyan, Divy Dunford, Andrew Iyer, Sowmya Stewart, Chip Hanna, Megan Getz, Gad Aryee, Martin J. Harvard TH Chan Sch Publ Hlth Dept Biostat Boston MA 02115 USA Broad Inst MIT & Harvard Cambridge MA 02142 USA Massachusetts Gen Hosp Dept Pathol Boston MA 02114 USA Harvard Med Sch Dept Pathol Boston MA 02115 USA Massachusetts Gen Hosp Canc Ctr Boston MA 02114 USA

Background: Bisulfite sequencing allows base-pair resolution profiling of DNA methylation and has recently been adapted for use in single-cells. Analyzing these data, including making comparisons with existing data, remains challenging due to the scale of the data and differences in preprocessing methods between published datasets. Results: We present a set of preprocessing pipelines for bisulfite sequencing DNA methylation data that include a new R/Bioconductor package, scmeth, for a series of efficient QC analyses of large datasets. The pipelines go from raw data to CpG-level methylation estimates and can be run, with identical results, either on a single computer, in an HPC cluster or on Google Cloud Compute resources. These pipelines are designed to allow users to 1) ensure reproducibility of analyses, 2) achieve scalability to large whole genome datasets with 100 GB+ of raw data per sample and to single-cell datasets with thousands of cells, 3) enable integration and comparison between user-provided data and publicly available data, as all samples can be processed through the same pipeline, and 4) access to best-practice analysis pipelines. Pipelines are provided for whole genome bisulfite sequencing (WGBS), reduced representation bisulfite sequencing (RRBS) and hybrid selection (capture) bisulfite sequencing (HSBS). Conclusions: The workflows produce data quality metrics, visualization tracks, and aggregated output for further downstream analysis. Optional use of cloud computing resources facilitates analysis of large datasets, and integration with existing methylome profiles. The workflow design principles are applicable to other genomic data types.

关键词： DNA methylation Cloud computing bioinformatics workflows Quality control analysis

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