Dataset Information

Small Genomes and Big Data: Adaptation of Plastid Genomics to the High-Throughput Era.

ABSTRACT: Plastid genome sequences are becoming more readily available with the increase in high-throughput sequencing, and whole-organelle genetic data is available for algae and plants from across the diversity of photosynthetic eukaryotes. This has provided incredible opportunities for studying species which may not be amenable to in vivo study or genetic manipulation or may not yet have been cultured. Research into plastid genomes has pushed the limits of what can be deduced from genomic information, and in particular genomic information obtained from public databases. In this Review, we discuss how research into plastid genomes has benefitted enormously from the explosion of publicly available genome sequence. We describe two case studies in how using publicly available gene data has supported previously held hypotheses about plastid traits from lineage-restricted experiments across algal and plant diversity. We propose how this approach could be used across disciplines for inferring functional and biological characteristics from genomic approaches, including integration of new computational and bioinformatic approaches such as machine learning. We argue that the techniques developed to gain the maximum possible insight from plastid genomes can be applied across the eukaryotic tree of life.

SUBMITTER: Klinger CM

PROVIDER: S-EPMC6723049 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature

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Small Genomes and Big Data: Adaptation of Plastid Genomics to the High-Throughput Era.

Klinger Christen M CM Richardson Elisabeth E

Biomolecules 20190724 8

Plastid genome sequences are becoming more readily available with the increase in high-throughput sequencing, and whole-organelle genetic data is available for algae and plants from across the diversity of photosynthetic eukaryotes. This has provided incredible opportunities for studying species which may not be amenable to in vivo study or genetic manipulation or may not yet have been cultured. Research into plastid genomes has pushed the limits of what can be deduced from genomic information, ...[more]

PMID: 31344945

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Project description:BACKGROUND:The emergence of high throughput technologies that produce vast amounts of genomic data, such as next-generation sequencing (NGS) is transforming biological research. The dramatic increase in the volume of data, the variety and continuous change of data processing tools, algorithms and databases make analysis the main bottleneck for scientific discovery. The processing of high throughput datasets typically involves many different computational programs, each of which performs a specific step in a pipeline. Given the wide range of applications and organizational infrastructures, there is a great need for highly parallel, flexible, portable, and reproducible data processing frameworks. Several platforms currently exist for the design and execution of complex pipelines. Unfortunately, current platforms lack the necessary combination of parallelism, portability, flexibility and/or reproducibility that are required by the current research environment. To address these shortcomings, workflow frameworks that provide a platform to develop and share portable pipelines have recently arisen. We complement these new platforms by providing a graphical user interface to create, maintain, and execute complex pipelines. Such a platform will simplify robust and reproducible workflow creation for non-technical users as well as provide a robust platform to maintain pipelines for large organizations. RESULTS:To simplify development, maintenance, and execution of complex pipelines we created DolphinNext. DolphinNext facilitates building and deployment of complex pipelines using a modular approach implemented in a graphical interface that relies on the powerful Nextflow workflow framework by providing 1. A drag and drop user interface that visualizes pipelines and allows users to create pipelines without familiarity in underlying programming languages. 2. Modules to execute and monitor pipelines in distributed computing environments such as high-performance clusters and/or cloud 3. Reproducible pipelines with version tracking and stand-alone versions that can be run independently. 4. Modular process design with process revisioning support to increase reusability and pipeline development efficiency. 5. Pipeline sharing with GitHub and automated testing 6. Extensive reports with R-markdown and shiny support for interactive data visualization and analysis. CONCLUSION:DolphinNext is a flexible, intuitive, web-based data processing and analysis platform that enables creating, deploying, sharing, and executing complex Nextflow pipelines with extensive revisioning and interactive reporting to enhance reproducible results.

Dataset Information

Small Genomes and Big Data: Adaptation of Plastid Genomics to the High-Throughput Era.

Publications

Small Genomes and Big Data: Adaptation of Plastid Genomics to the High-Throughput Era.

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