| 1. |
- Grüning, Björn A., et al.
(författare)
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Software engineering for scientific big data analysis
- 2019
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Ingår i: GigaScience. - : Oxford University Press (OUP). - 2047-217X. ; 8:5
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Forskningsöversikt (refereegranskat)abstract
- The increasing complexity of data and analysis methods has created an environment where scientists, who may not have formal training, are finding themselves playing the impromptu role of software engineer. While several resources are available for introducing scientists to the basics of programming, researchers have been left with little guidance on approaches needed to advance to the next level for the development of robust, large-scale data analysis tools that are amenable to integration into workflow management systems, tools, and frameworks. The integration into such workflow systems necessitates additional requirements on computational tools, such as adherence to standard conventions for robustness, data input, output, logging, and flow control. Here we provide a set of 10 guidelines to steer the creation of command-line computational tools that are usable, reliable, extensible, and in line with standards of modern coding practices.
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| 2. |
- Lampa, Samuel, et al.
(författare)
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SciPipe : A workflow library for agile development of complex and dynamic bioinformatics pipelines
- 2019
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Ingår i: GigaScience. - : Oxford University Press (OUP). - 2047-217X. ; 8:5
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Tidskriftsartikel (refereegranskat)abstract
- Background: The complex nature of biological data has driven the development of specialized software tools. Scientific workflow management systems simplify the assembly of such tools into pipelines, assist with job automation, and aid reproducibility of analyses. Many contemporary workflow tools are specialized or not designed for highly complex workflows, such as with nested loops, dynamic scheduling, and parametrization, which is common in, e.g., machine learning. Findings: SciPipe is a workflow programming library implemented in the programming language Go, for managing complex and dynamic pipelines in bioinformatics, cheminformatics, and other fields. SciPipe helps in particular with workflow constructs common in machine learning, such as extensive branching, parameter sweeps, and dynamic scheduling and parametrization of downstream tasks. SciPipe builds on flow-based programming principles to support agile development of workflows based on a library of self-contained, reusable components. It supports running subsets of workflows for improved iterative development and provides a data-centric audit logging feature that saves a full audit trace for every output file of a workflow, which can be converted to other formats such as HTML, TeX, and PDF on demand. The utility of SciPipe is demonstrated with a machine learning pipeline, a genomics, and a transcriptomics pipeline. Conclusions: SciPipe provides a solution for agile development of complex and dynamic pipelines, especially in machine learning, through a flexible application programming interface suitable for scientists used to programming or scripting.
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| 3. |
- Nowell, Reuben W., et al.
(författare)
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A high-coverage draft genome of the mycalesine butterfly Bicyclus anynana
- 2017
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Ingår i: GigaScience. - : Oxford University Press (OUP). - 2047-217X. ; 6:7
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Tidskriftsartikel (refereegranskat)abstract
- The mycalesine butterfly Bicyclus anynana, the Squinting bush brown, is a model organism in the study of lepidopteran ecology, development, and evolution. Here, we present a draft genome sequence for B. anynana to serve as a genomics resource for current and future studies of this important model species. Seven libraries with insert sizes ranging from 350 bp to 20 kb were constructed using DNA from an inbred female and sequenced using both Illumina and PacBio technology; 128 Gb of raw Illumina data was filtered to 124 Gb and assembled to a final size of 475 Mb (similar to x260 assembly coverage). Contigs were scaffolded using mate-pair, transcriptome, and PacBio data into 10 800 sequences with an N50 of 638 kb (longest scaffold 5 Mb). The genome is comprised of 26% repetitive elements and encodes a total of 22 642 predicted protein-coding genes. Recovery of a BUSCO set of core metazoan genes was almost complete (98%). Overall, these metrics compare well with other recently published lepidopteran genomes. We report a high-quality draft genome sequence for Bicyclus anynana. The genome assembly and annotated gene models are available at LepBase (http://ensembl.lepbase.org/index.html).
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| 4. |
- Olsen, Remi-Andre, et al.
(författare)
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De novo assembly of Dekkera bruxellensis : a multi technology approach using short and long-read sequencing and optical mapping
- 2015
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Ingår i: GigaScience. - : Oxford University Press (OUP). - 2047-217X. ; 4
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Tidskriftsartikel (refereegranskat)abstract
- Background: It remains a challenge to perform de novo assembly using next-generation sequencing (NGS). Despite the availability of multiple sequencing technologies and tools (e.g., assemblers) it is still difficult to assemble new genomes at chromosome resolution (i.e., one sequence per chromosome). Obtaining high quality draft assemblies is extremely important in the case of yeast genomes to better characterise major events in their evolutionary history. The aim of this work is two-fold: on the one hand we want to show how combining different and somewhat complementary technologies is key to improving assembly quality and correctness, and on the other hand we present a de novo assembly pipeline we believe to be beneficial to core facility bioinformaticians. To demonstrate both the effectiveness of combining technologies and the simplicity of the pipeline, here we present the results obtained using the Dekkera bruxellensis genome. Methods: In this work we used short-read Illumina data and long-read PacBio data combined with the extreme long-range information from OpGen optical maps in the task of de novo genome assembly and finishing. Moreover, we developed NouGAT, a semi-automated pipeline for read-preprocessing, de novo assembly and assembly evaluation, which was instrumental for this work. Results: We obtained a high quality draft assembly of a yeast genome, resolved on a chromosomal level. Furthermore, this assembly was corrected for mis-assembly errors as demonstrated by resolving a large collapsed repeat and by receiving higher scores by assembly evaluation tools. With the inclusion of PacBio data we were able to fill about 5 % of the optical mapped genome not covered by the Illumina data.
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| 5. |
- Smolander, Olli Pekka, et al.
(författare)
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Improved chromosome-level genome assembly of the Glanville fritillary butterfly (Melitaea cinxia) integrating Pacific Biosciences long reads and a high-density linkage map
- 2022
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Ingår i: GigaScience. - : Oxford University Press (OUP). - 2047-217X. ; 11
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Tidskriftsartikel (refereegranskat)abstract
- Background: The Glanville fritillary (Melitaea cinxia) butterfly is a model system for metapopulation dynamics research in fragmented landscapes. Here, we provide a chromosome-level assembly of the butterfly's genome produced from Pacific Biosciences sequencing of a pool of males, combined with a linkage map from population crosses. Results: The final assembly size of 484 Mb is an increase of 94 Mb on the previously published genome. Estimation of the completeness of the genome with BUSCO indicates that the genome contains 92-94% of the BUSCO genes in complete and single copies. We predicted 14,810 genes using the MAKER pipeline and manually curated 1,232 of these gene models. Conclusions: The genome and its annotated gene models are a valuable resource for future comparative genomics, molecular biology, transcriptome, and genetics studies on this species.
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| 6. |
- Spjuth, Ola, et al.
(författare)
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Recommendations on e-infrastructures for next-generation sequencing
- 2016
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Ingår i: GigaScience. - : Oxford University Press (OUP). - 2047-217X. ; 5
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Forskningsöversikt (refereegranskat)abstract
- With ever-increasing amounts of data being produced by next-generation sequencing (NGS) experiments, the requirements placed on supporting e-infrastructures have grown. In this work, we provide recommendations based on the collective experiences from participants in the EU COST Action SeqAhead for the tasks of data preprocessing, upstream processing, data delivery, and downstream analysis, as well as long-term storage and archiving. We cover demands on computational and storage resources, networks, software stacks, automation of analysis, education, and also discuss emerging trends in the field. E-infrastructures for NGS require substantial effort to set up and maintain over time, and with sequencing technologies and best practices for data analysis evolving rapidly it is important to prioritize both processing capacity and e-infrastructure flexibility when making strategic decisions to support the data analysis demands of tomorrow. Due to increasingly demanding technical requirements we recommend that e-infrastructure development and maintenance be handled by a professional service unit, be it internal or external to the organization, and emphasis should be placed on collaboration between researchers and IT professionals.
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