Dataset Information

Glbase: a framework for combining, analyzing and displaying heterogeneous genomic and high-throughput sequencing data.

ABSTRACT: Genomic datasets and the tools to analyze them have proliferated at an astonishing rate. However, such tools are often poorly integrated with each other: each program typically produces its own custom output in a variety of non-standard file formats. Here we present glbase, a framework that uses a flexible set of descriptors that can quickly parse non-binary data files. glbase includes many functions to intersect two lists of data, including operations on genomic interval data and support for the efficient random access to huge genomic data files. Many glbase functions can produce graphical outputs, including scatter plots, heatmaps, boxplots and other common analytical displays of high-throughput data such as RNA-seq, ChIP-seq and microarray expression data. glbase is designed to rapidly bring biological data into a Python-based analytical environment to facilitate analysis and data processing. In summary, glbase is a flexible and multifunctional toolkit that allows the combination and analysis of high-throughput data (especially next-generation sequencing and genome-wide data), and which has been instrumental in the analysis of complex data sets. glbase is freely available at http://bitbucket.org/oaxiom/glbase/.

SUBMITTER: Hutchins AP

PROVIDER: S-EPMC4230833 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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glbase: a framework for combining, analyzing and displaying heterogeneous genomic and high-throughput sequencing data.

Hutchins Andrew Paul AP Jauch Ralf R Dyla Mateusz M Miranda-Saavedra Diego D

Cell regeneration (London, England) 20140124 1

Genomic datasets and the tools to analyze them have proliferated at an astonishing rate. However, such tools are often poorly integrated with each other: each program typically produces its own custom output in a variety of non-standard file formats. Here we present glbase, a framework that uses a flexible set of descriptors that can quickly parse non-binary data files. glbase includes many functions to intersect two lists of data, including operations on genomic interval data and support for th ...[more]

PMID: 25408880

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Project description:BackgroundY-chromosome DNA (Y-DNA) has been used for tracing paternal lineages and offers a clear path from an individual to a known, or likely, direct paternal ancestor. The advance of next-generation sequencing (NGS) technologies increasingly improves the resolution of the non-recombining region of the Y-chromosome (NRY). However, a lack of suitable computer tools prevents the use of NGS data from the Y-DNA studies.ResultsWe developed Y-LineageTracker, a high-throughput analysis framework that not only utilizes state-of-the-art methodologies to automatically determine NRY haplogroups and identify microsatellite variants of Y-chromosome on a fine scale, but also optimizes comprehensive Y-DNA analysis methods for NGS data. Notably, Y-LineageTracker integrates the NRY haplogroup and Y-STR analysis modules with recognized strategies to robustly suggest an interpretation for paternal genetics and evolution. NRY haplogroup module mainly covers haplogroup classification, clustering analysis, phylogeny construction, and divergence time estimation of NRY haplogroups, and Y-STR module mainly includes Y-STR genotyping, statistical calculation, network analysis, and estimation of time to the most recent common ancestor (TMRCA) based on Y-STR haplotypes. Performance comparison indicated that Y-LineageTracker outperformed existing Y-DNA analysis tools for the high performance and satisfactory visualization effect.ConclusionsY-LineageTracker is an open-source and user-friendly command-line tool that provide multiple functions to efficiently analyze Y-DNA from NGS data at both Y-SNP and Y-STR level. Additionally, Y-LineageTracker supports various formats of input data and produces high-quality figures suitable for publication. Y-LineageTracker is coded with Python3 and supports Windows, Linux, and macOS platforms, and can be installed manually or via the Python Package Index (PyPI). The source code, examples, and manual of Y-LineageTracker are freely available at https://www.picb.ac.cn/PGG/resource.php or CodeOcean ( https://codeocean.com/capsule/7424381/tree ).

Project description:BackgroundExplosive growth of next-generation sequencing data has resulted in ultra-large-scale data sets and ensuing computational problems. Cloud computing provides an on-demand and scalable environment for large-scale data analysis. Using a MapReduce framework, data and workload can be distributed via a network to computers in the cloud to substantially reduce computational latency. Hadoop/MapReduce has been successfully adopted in bioinformatics for genome assembly, mapping reads to genomes, and finding single nucleotide polymorphisms. Major cloud providers offer Hadoop cloud services to their users. However, it remains technically challenging to deploy a Hadoop cloud for those who prefer to run MapReduce programs in a cluster without built-in Hadoop/MapReduce.ResultsWe present CloudDOE, a platform-independent software package implemented in Java. CloudDOE encapsulates technical details behind a user-friendly graphical interface, thus liberating scientists from having to perform complicated operational procedures. Users are guided through the user interface to deploy a Hadoop cloud within in-house computing environments and to run applications specifically targeted for bioinformatics, including CloudBurst, CloudBrush, and CloudRS. One may also use CloudDOE on top of a public cloud. CloudDOE consists of three wizards, i.e., Deploy, Operate, and Extend wizards. Deploy wizard is designed to aid the system administrator to deploy a Hadoop cloud. It installs Java runtime environment version 1.6 and Hadoop version 0.20.203, and initiates the service automatically. Operate wizard allows the user to run a MapReduce application on the dashboard list. To extend the dashboard list, the administrator may install a new MapReduce application using Extend wizard.ConclusionsCloudDOE is a user-friendly tool for deploying a Hadoop cloud. Its smart wizards substantially reduce the complexity and costs of deployment, execution, enhancement, and management. Interested users may collaborate to improve the source code of CloudDOE to further incorporate more MapReduce bioinformatics tools into CloudDOE and support next-generation big data open source tools, e.g., Hadoop BigTop and Spark.AvailabilityCloudDOE is distributed under Apache License 2.0 and is freely available at http://clouddoe.iis.sinica.edu.tw/.

Dataset Information

Glbase: a framework for combining, analyzing and displaying heterogeneous genomic and high-throughput sequencing data.

Publications

glbase: a framework for combining, analyzing and displaying heterogeneous genomic and high-throughput sequencing data.

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