COSMOS: Python library for massively parallel workflows

Gafni, Erik; Luquette, Lovelace J.; Lancaster, Alex K.; Hawkins, Joseph E.; Jung, Jae-Yoon; Souilmi, Yassine; Wall, Dennis P.; Tonellato, Peter J.

doi:10.1093/bioinformatics/btu385

Cited by 27 publications

(23 citation statements)

References 7 publications

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“…We measured the performance of MC-GenomeKey on AWS, Google, and Azure using the following two datasets:A whole genome dataset of 113 GB from [23]. The NGS reads come from Illumina NGS machines and cover all genomic regions with an average depth of about 30X.…”

Section: Resultsmentioning

confidence: 99%

“…The NGS reads come from Illumina NGS machines and cover all genomic regions with an average depth of about 30X.An exome sequence dataset (~9.2 GB), also from [23]. The NGS reads come also from an Illumina NGS machine and cover only the exons of the genome.…”

Section: Resultsmentioning

confidence: 99%

“…A whole genome dataset of 113 GB from [23]. The NGS reads come from Illumina NGS machines and cover all genomic regions with an average depth of about 30X.…”

Section: Resultsmentioning

confidence: 99%

“…An exome sequence dataset (~9.2 GB), also from [23]. The NGS reads come also from an Illumina NGS machine and cover only the exons of the genome.…”

Section: Resultsmentioning

confidence: 99%

“…MC-GenomeKey is a package implementing the variant detection and analysis workflow, based on the python-based Cosmos workflow engine [23]. Its basic features can be categorized into three groups: 1) Variant Analysis Workflow Specifications, 2) Workflow engine and parallelization schemes and 3) Cloud Support.…”

Section: Methodsmentioning

confidence: 99%

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MC-GenomeKey: a multicloud system for the detection and annotation of genomic variants

et al. 2017

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BackgroundNext Generation Genome sequencing techniques became affordable for massive sequencing efforts devoted to clinical characterization of human diseases. However, the cost of providing cloud-based data analysis of the mounting datasets remains a concerning bottleneck for providing cost-effective clinical services. To address this computational problem, it is important to optimize the variant analysis workflow and the used analysis tools to reduce the overall computational processing time, and concomitantly reduce the processing cost. Furthermore, it is important to capitalize on the use of the recent development in the cloud computing market, which have witnessed more providers competing in terms of products and prices.ResultsIn this paper, we present a new package called MC-GenomeKey (Multi-Cloud GenomeKey) that efficiently executes the variant analysis workflow for detecting and annotating mutations using cloud resources from different commercial cloud providers. Our package supports Amazon, Google, and Azure clouds, as well as, any other cloud platform based on OpenStack. Our package allows different scenarios of execution with different levels of sophistication, up to the one where a workflow can be executed using a cluster whose nodes come from different clouds. MC-GenomeKey also supports scenarios to exploit the spot instance model of Amazon in combination with the use of other cloud platforms to provide significant cost reduction. To the best of our knowledge, this is the first solution that optimizes the execution of the workflow using computational resources from different cloud providers.ConclusionsMC-GenomeKey provides an efficient multicloud based solution to detect and annotate mutations. The package can run in different commercial cloud platforms, which enables the user to seize the best offers. The package also provides a reliable means to make use of the low-cost spot instance model of Amazon, as it provides an efficient solution to the sudden termination of spot machines as a result of a sudden price increase. The package has a web-interface and it is available for free for academic use.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

“…A whole genome dataset of 113 GB from [23]. The NGS reads come from Illumina NGS machines and cover all genomic regions with an average depth of about 30X.…”

Section: Resultsmentioning

confidence: 99%

“…An exome sequence dataset (~9.2 GB), also from [23]. The NGS reads come also from an Illumina NGS machine and cover only the exons of the genome.…”

Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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MC-GenomeKey: a multicloud system for the detection and annotation of genomic variants

et al. 2017

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Algorithms and design strategies towards automated glycoproteomics analysis

Khatri

Zaia

2016

Mass Spectrometry Reviews

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Glycoproteomics involves the study of the glycosylation events on protein sequences ranging from purified proteins to whole proteome scales. Understanding these complex post-translational modification (PTM) events requires elucidation of the glycan moieties (monosaccharide sequences and glycosidic linkages between residues), protein sequences, as well as site-specific attachment of glycan moieties onto protein sequences, in a spatial and temporal manner in a variety of biological contexts. Compared with proteomics, bioinformatics for glycoproteomics is immature and many researchers still rely on tedious manual interpretation of glycoproteomics data. As sample preparation protocols and analysis techniques have matured, the number of publications on glycoproteomics bioinformatics has increased substantially; however, the lack of consensus on tool development and code reuse limits the dissemination of bioinformatics tools because it requires significant effort to migrate a computational tool tailored for one method design to alternative methods. This review discusses algorithms and methods in glycoproteomics, and refers to the general proteomics field for potential solutions. It also introduces general strategies for tool integration and pipeline construction in order to better serve the glycoproteomics community.

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