GigaDB: promoting data dissemination and reproducibility

Sneddon, Tam P.; Xiao, Sizhe; Edmunds, Scott; Li, Peter; Goodman, Laurie; Hunter, Chris

doi:10.1093/database/bau018

Cited by 24 publications

(20 citation statements)

References 22 publications

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“…Sequences of P. nigra [21], P. deltoides, P. tremula and P. tremuloides were obtained from PlantGDB [22] and sequence read archive (SRA) of national center for biotechnology information (NCBI) database. The cDNA sequences of P. trichocarpa (v3.1) [1], P. euphratica [2], P. pruinosa [3], and S. purpurea (v1.0) were downloaded from genome projects of GigaDB [23], NCBI and the United States department of energy joint genome institute (JGI) [24]. SRA datasets with FASTQ format were filtered to remove raw reads of low quality.…”

Section: Data Sourcesmentioning

confidence: 99%

Comparative Genome and Transcriptome Analysis Reveals Gene Selection Patterns Along with the Paleo-Climate Change in the Populus Phylogeny

Zhao

Han

Cao

et al. 2019

Forests

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Poplars are widely distributed in the northern hemisphere and have good adaptability to different living environments. The accumulation of genome and transcriptome data provides a chance to conduct comparative genomics and transcriptomics analyses to elucidate the evolutionary patterns of Populus phylogeny. Transcript sequences of eight Salicaceae species were downloaded from public databases. All of the pairwise orthologues were identified by comparative transcriptome analysis in these species, from which we constructed a phylogenetic tree and estimated the rate of divergence. The divergence times of the phylogenetic clades were mainly estimated during the Middle Miocene Climate Transition (MMCT) to Quaternary Ice Age. We also identified all of the fast-evolving sequences of positive selection and found some resistance genes that were related to environmental factors. Our results suggest that drought-, H2O2- and cold-stress genes are involved in positive selection along with the paleoclimate change. These data are useful in elucidating the evolutionary patterns and causes of speciation in the Populus lineage.

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Section: Data Sourcesmentioning

confidence: 99%

Comparative Genome and Transcriptome Analysis Reveals Gene Selection Patterns Along with the Paleo-Climate Change in the Populus Phylogeny

Zhao

Han

Cao

et al. 2019

Forests

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“…Even if the raw data are deposited in public archives, the essential analysis intermediaries, scripts or software are frequently not made available, meaning the science is not reproducible [75]. One approach to transfer research results are Research Objects (RO [76]), which allow scientists to group and associate the resources and data used to generate a scientific result.…”

Section: Sharing Reproducible Simulation Studiesmentioning

confidence: 99%

How Modeling Standards, Software, and Initiatives Support Reproducibility in Systems Biology and Systems Medicine

Waltemath

Wolkenhauer

2016

IEEE Trans. Biomed. Eng.

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“…For metabolomics‐scale, “big data” handling, dissemination, and archiving, although the role of GigaDB , the European COordination of Standards in MetabOlomicS (COSMOS) consortium , and MetaboLights as front runners is well known. MetaboLights is the first comprehensive, cross‐species, cross‐platform metabolomics database that can hold complete metabolomics experiments.…”

Section: Metabolomics Libraries Databases Experiments Repositories mentioning

confidence: 99%

Updates in metabolomics tools and resources: 2014–2015

2015

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acquisition. Improved machinery in metabolomics generate increasingly complex data sets which 6 create the need for more and better processing and analysis software and in-silico approaches to 7 understand the resulting data. However, a comprehensive source of information describing the 8 utility of the most recently developed and released metabolomics resources --in the form of 9 tools, software, and databases -is currently lacking. Thus, here we provide an overview of 10 freely-available, open-source, tools, algorithms and frameworks to make both upcoming and 11 established metabolomics researchers aware of the recent developments in an attempt to advance 12 and facilitate data processing workflows in their metabolomics research. The major topics 13 include tools and researches for data processing, data annotation, and data visualization in MS 14 and NMR based metabolomics. Most in this review described tools are dedicated to untargeted 15 metabolomics workflows; however, some more specialist tools are described as well. All tools 16 and resources described including their analytical and computational platform dependencies are 17 summarized in an overview Table. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 In metabolomics, data processing and interpretation represent some of the most 2 challenging and time-consuming steps in the high throughput process, regardless of the 3 analytical platform used for data generation. The exponentially growing volume of generated 4 data has triggered the research and development of tools, software, programs, databases, and 5 applications to facilitate the robust understanding of metabolic processes of biological systems. 6For instance, natural products discovery has greatly benefited from the development of open-7 access spectral and chemical databases [1]. In addition, a large number of chemoinformatics 8 tools are finding direct application in handling metabolomics data or helping in annotation. 9Targeted metabolomics investigations obtain quantitative data on a predefined set of compounds, 10while untargeted metabolomics studies provide a broader exploration of metabolites with the 11 goal of identifying new compounds [2]. Untargeted metabolomic studies are characterized by 12simultaneous qualitative and quantitative analysis of a large number of metabolites in samples. 13Currently, untargeted metabolomics is being increasingly applied in diverse areas of research however, all these applications share the same bottlenecks: i.e., the harmonization and coverage 17of existing analytical methods, the lack of automation of spectral data processing, and the data 18 interpretation [3]. These limitations are the major driving force providing impetus for the 19 development of a huge plethora of metabolomics tools, software, programs, and databases. 20Although excellent compilations are available for M...

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GigaDB: promoting data dissemination and reproducibility

Cited by 24 publications

References 22 publications

Comparative Genome and Transcriptome Analysis Reveals Gene Selection Patterns Along with the Paleo-Climate Change in the Populus Phylogeny

Comparative Genome and Transcriptome Analysis Reveals Gene Selection Patterns Along with the Paleo-Climate Change in the Populus Phylogeny

How Modeling Standards, Software, and Initiatives Support Reproducibility in Systems Biology and Systems Medicine

Updates in metabolomics tools and resources: 2014–2015

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