Biological Database of Images and Genomes: tools for community annotations linking image and genomic information

T., Oberlin, Andrew; Jurkovic, Dominika A.; Balish, Mitchell F.; Friedberg, Iddo

doi:10.1093/database/bat016

Cited by 8 publications

(10 citation statements)

References 38 publications

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“…MaizeDIG is an easy to use and easily extensible web-based resource to link genes, alleles and genomes to phenotypes in images. MaizeDIG is based on the GMOD tool BioDIG (32), but has been enhanced to support multiple genomes and integrated with genome browsers to make tracks showing mutant phenotypes images within their genomic context. The ability to view images of mutant phenotypes at the genes’ position on our genome browser is a new and unique feature at MaizeGDB.…”

Section: New Tools and Featuresmentioning

confidence: 99%

MaizeGDB 2018: the maize multi-genome genetics and genomics database

Portwood

Woodhouse

Cannon

et al. 2018

Nucleic Acids Research

265

211

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Since its 2015 update, MaizeGDB, the Maize Genetics and Genomics database, has expanded to support the sequenced genomes of many maize inbred lines in addition to the B73 reference genome assembly. Curation and development efforts have targeted high quality datasets and tools to support maize trait analysis, germplasm analysis, genetic studies, and breeding. MaizeGDB hosts a wide range of data including recent support of new data types including genome metadata, RNA-seq, proteomics, synteny, and large-scale diversity. To improve access and visualization of data types several new tools have been implemented to: access large-scale maize diversity data (SNPversity), download and compare gene expression data (qTeller), visualize pedigree data (Pedigree Viewer), link genes with phenotype images (MaizeDIG), and enable flexible user-specified queries to the MaizeGDB database (MaizeMine). MaizeGDB also continues to be the community hub for maize research, coordinating activities and providing technical support to the maize research community. Here we report the changes MaizeGDB has made within the last three years to keep pace with recent software and research advances, as well as the pan-genomic landscape that cheaper and better sequencing technologies have made possible. MaizeGDB is accessible online at https://www.maizegdb.org.

show abstract

Section: New Tools and Featuresmentioning

confidence: 99%

MaizeGDB 2018: the maize multi-genome genetics and genomics database

Portwood

Woodhouse

Cannon

et al. 2018

Nucleic Acids Research

265

211

View full text Add to dashboard Cite

show abstract

“…An important field of research in phenomics is the development of high-throughput technology analogous to high-throughput sequencing in genetics and genomic studies, to enable the collection of large-scale data with minimal efforts. Many phenotypic traits could be recorded with images, and databases such as BioDIG [ 28 ] make the connection of such image data with genomic information, providing genetics researchers with tools to examine the relationship between the two types of data directly. Hence, the computation and manipulation of such phenomic image data becomes essential.…”

Section: Discussionmentioning

confidence: 99%

Crowdsourcing image analysis for plant phenomics to generate ground truth data for machine learning

et al. 2018

Self Cite

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The accuracy of machine learning tasks critically depends on high quality ground truth data. Therefore, in many cases, producing good ground truth data typically involves trained professionals; however, this can be costly in time, effort, and money. Here we explore the use of crowdsourcing to generate a large number of training data of good quality. We explore an image analysis task involving the segmentation of corn tassels from images taken in a field setting. We investigate the accuracy, speed and other quality metrics when this task is performed by students for academic credit, Amazon MTurk workers, and Master Amazon MTurk workers. We conclude that the Amazon MTurk and Master Mturk workers perform significantly better than the for-credit students, but with no significant difference between the two MTurk worker types. Furthermore, the quality of the segmentation produced by Amazon MTurk workers rivals that of an expert worker. We provide best practices to assess the quality of ground truth data, and to compare data quality produced by different sources. We conclude that properly managed crowdsourcing can be used to establish large volumes of viable ground truth data at a low cost and high quality, especially in the context of high throughput plant phenotyping. We also provide several metrics for assessing the quality of the generated datasets.

show abstract

“…An important field of research in phenomics is the development of high-throughput technology analogous to high-throughput sequencing in genetics and genomic studies, to enable the collection of large-scale data with minimal efforts. A lot of phenotypic traits could be recorded with images, and databases such as BioDIG (17) makes the connection of such image data with genomic information, providing genetics researchers with tools to examine the relationship between the two types of data directly. Hence, the computation and manipulation of such phenomic image data becomes essential.…”

Section: Discussionmentioning

confidence: 99%

Crowdsourcing Image Analysis for Plant Phenomics to Generate Ground Truth Data for Machine Learning

Siegel

Zhou

Zarecor

et al. 2018

Preprint

Self Cite

View full text Add to dashboard Cite

The accuracy of machine learning tasks is critically dependent on high quality ground truth data. Therefore, in many cases, producing good ground truth data typically involves trained professionals; however, this can be costly in time, effort, and money. Here we explore the use of crowdsourcing to generate a large number of training data points of good quality. We explore an image analysis task involving the segmentation of corn tassels from images taken in a field setting. We explore the accuracy, speed and other quality metrics when this task is performed by students for academic credit, Amazon MTurk workers, and Master Amazon MTurk workers. We conclude that the Amazon MTurk and Master Mturk workers perform significantly better than the for-credit students, with no significant difference between the two MTurk worker types. The quality of the segmentation produced by Amazon MTurk workers rivals that of an expert worker. We provide best practices to assess the quality of ground truth data, and to compare data quality produced by different sources. We conclude that properly managed crowdsourcing can be used to establish large volumes of viable ground truth data at a low cost and high quality, especially in the context of high throughput plant phenotyping. We also provide several metrics for assessing the quality of the generated datasets.

show abstract

Biological Database of Images and Genomes: tools for community annotations linking image and genomic information

Cited by 8 publications

References 38 publications

MaizeGDB 2018: the maize multi-genome genetics and genomics database

MaizeGDB 2018: the maize multi-genome genetics and genomics database

Crowdsourcing image analysis for plant phenomics to generate ground truth data for machine learning

Crowdsourcing Image Analysis for Plant Phenomics to Generate Ground Truth Data for Machine Learning

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