nestly—a framework for running software with nested parameter choices and aggregating results

McCoy, Connor O.; Gallagher, Aaron; Hoffman, Noah G.; Matsen, F. A.

doi:10.1093/bioinformatics/bts696

Cited by 8 publications

(6 citation statements)

References 6 publications

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“…All models were implemented in Python 3.6 using Keras 2.2.4 (Chollet, 2015) and the Tensorflow 1.11.0 backend (Abadi et al, 2015). Our pipeline is written with SCons (https://scons.org) and nestly (https://pythonhosted.org/nestly/; McCoy et al, 2013). The sumrep package depends heavily on the Immcantation framework (https://immcantation.readthedocs.io/; Gupta et al, 2015).…”

Section: Methodsmentioning

confidence: 99%

Deep generative models for T cell receptor protein sequences

Davidsen

Olson

DeWitt

et al. 2019

eLife

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Probabilistic models of adaptive immune repertoire sequence distributions can be used to infer the expansion of immune cells in response to stimulus, differentiate genetic from environmental factors that determine repertoire sharing, and evaluate the suitability of various target immune sequences for stimulation via vaccination. Classically, these models are defined in terms of a probabilistic V(D)J recombination model which is sometimes combined with a selection model. In this paper we take a different approach, fitting variational autoencoder (VAE) models parameterized by deep neural networks to T cell receptor (TCR) repertoires. We show that simple VAE models can perform accurate cohort frequency estimation, learn the rules of VDJ recombination, and generalize well to unseen sequences. Further, we demonstrate that VAE-like models can distinguish between real sequences and sequences generated according to a recombination-selection model, and that many characteristics of VAE-generated sequences are similar to those of real sequences.

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Section: Methodsmentioning

confidence: 99%

Deep generative models for T cell receptor protein sequences

Davidsen

Olson

DeWitt

et al. 2019

eLife

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“…Bpipe (Sadedin, Pope, & Oshlack, 2012) focuses on the management of running tasks in a simple and flexible style. The Python package nestly (McCoy et al, 2013) allows nested combinations of parameters/inputs as well as aggregation of output results. Tools published recently also added support for parallelization and scaling of the pipeline (K€ oster & Rahmann, 2012;Gafni et al, 2014;Cingolani, Sladek, & Blanchette, 2015), which provide users with the options to analyze large datasets using different high performance computing infrastructures such as traditional cluster or cloud.…”

Section: Available Tools Towards Pipeline Deploymentmentioning

confidence: 99%

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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“…They let users run individual steps or entire pipelines on a remote compute system with the framework keeping track of the executed analysis. Scripting frameworks like bpipe [ 6 ], Ruffus [ 7 ], nestly [ 8 ], NGSANE [ 9 ], Makeflow [ 10 ], and Snakemake [ 11 ], let users build bioinformatics pipelines in a command line fashion. Given the different types of user interactions, the former solutions are more targeted for the experienced biologists or the application-oriented bioinformaticians while the latter address the needs of bioinformaticians who are more inclined to programming and high-throughput analysis of many datasets.…”

Section: Introductionmentioning

confidence: 99%

SUSHI: an exquisite recipe for fully documented, reproducible and reusable NGS data analysis

et al. 2016

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BackgroundNext generation sequencing (NGS) produces massive datasets consisting of billions of reads and up to thousands of samples. Subsequent bioinformatic analysis is typically done with the help of open source tools, where each application performs a single step towards the final result. This situation leaves the bioinformaticians with the tasks to combine the tools, manage the data files and meta-information, document the analysis, and ensure reproducibility.ResultsWe present SUSHI, an agile data analysis framework that relieves bioinformaticians from the administrative challenges of their data analysis. SUSHI lets users build reproducible data analysis workflows from individual applications and manages the input data, the parameters, meta-information with user-driven semantics, and the job scripts. As distinguishing features, SUSHI provides an expert command line interface as well as a convenient web interface to run bioinformatics tools. SUSHI datasets are self-contained and self-documented on the file system. This makes them fully reproducible and ready to be shared. With the associated meta-information being formatted as plain text tables, the datasets can be readily further analyzed and interpreted outside SUSHI.ConclusionSUSHI provides an exquisite recipe for analysing NGS data. By following the SUSHI recipe, SUSHI makes data analysis straightforward and takes care of documentation and administration tasks. Thus, the user can fully dedicate his time to the analysis itself. SUSHI is suitable for use by bioinformaticians as well as life science researchers. It is targeted for, but by no means constrained to, NGS data analysis. Our SUSHI instance is in productive use and has served as data analysis interface for more than 1000 data analysis projects. SUSHI source code as well as a demo server are freely available.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-016-1104-8) contains supplementary material, which is available to authorized users.

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nestly—a framework for running software with nested parameter choices and aggregating results

Abstract: Source, documentation and tutorial examples are available at http://github.com/fhcrc/nestly. nestly can be installed from the Python Package Index via pip; it is open source (MIT license).

Cited by 8 publications

References 6 publications

Deep generative models for T cell receptor protein sequences

Deep generative models for T cell receptor protein sequences

Algorithms and design strategies towards automated glycoproteomics analysis

SUSHI: an exquisite recipe for fully documented, reproducible and reusable NGS data analysis

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