medna-metadata: an open-source data management system for tracking environmental DNA samples and metadata

Kimble, Melissa; Allers, S; Campbell, Keith; Chen, C; Jackson, Leonard A.; King, Benjamin L.; Silverbrand, Samantha; York, Geneva; Beard, Karen H.

doi:10.1093/bioinformatics/btac556

Cited by 6 publications

(2 citation statements)

References 51 publications

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“…To remedy the state of the art, several authors have argued for the use of data sheets with at least minimum metadata to be associated with published HTS data [ 17 , 62 , 76 ] with a focus on eDNA approaches [ 68 , 75 , 77 ]. However, for large-scale analysis of insects, different amplicon-based methods can be used, and metadata storage should not exclusively focus on eDNA standards.…”

Section: Standardization Of Metadata Reportsmentioning

confidence: 99%

Three steps towards comparability and standardization among molecular methods for characterizing insect communities

Iwaszkiewicz-Eggebrecht,

Zizka,

Lynggaard

2024

Phil. Trans. R. Soc. B

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Molecular methods are currently some of the best-suited technologies for implementation in insect monitoring. However, the field is developing rapidly and lacks agreement on methodology or community standards. To apply DNA-based methods in large-scale monitoring, and to gain insight across commensurate data, we need easy-to-implement standards that improve data comparability. Here, we provide three recommendations for how to improve and harmonize efforts in biodiversity assessment and monitoring via metabarcoding: (i) we should adopt the use of synthetic spike-ins, which will act as positive controls and internal standards; (ii) we should consider using several markers through a multiplex polymerase chain reaction (PCR) approach; and (iii) we should commit to the publication and transparency of all protocol-associated metadata in a standardized fashion. For (i), we provide a ready-to-use recipe for synthetic cytochrome c oxidase spike-ins, which enable between-sample comparisons. For (ii), we propose two gene regions for the implementation of multiplex PCR approaches, thereby achieving a more comprehensive community description. For (iii), we offer guidelines for transparent and unified reporting of field, wet-laboratory and dry-laboratory procedures, as a key to making comparisons between studies. Together, we feel that these three advances will result in joint quality and calibration standards rather than the current laboratory-specific proof of concepts. This article is part of the theme issue ‘Towards a toolkit for global insect biodiversity monitoring’.

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Section: Standardization Of Metadata Reportsmentioning

confidence: 99%

Three steps towards comparability and standardization among molecular methods for characterizing insect communities

Iwaszkiewicz-Eggebrecht,

Zizka,

Lynggaard

2024

Phil. Trans. R. Soc. B

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“…For example, in metabolomics [ 35 ], Oliver Fiehn and colleagues [ 36 ] added mass spectrometry metadata to the physiological, clinical, and genomic data. Some bioinformatics teams developed structured metadata management tools, such as BioSamples [ 37 ] and medna-metadata [ 38 ].…”

Section: Introductionmentioning

confidence: 99%

Ten quick tips for avoiding pitfalls in multi-omics data integration analyses

2023

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Data are the most important elements of bioinformatics: Computational analysis of bioinformatics data, in fact, can help researchers infer new knowledge about biology, chemistry, biophysics, and sometimes even medicine, influencing treatments and therapies for patients. Bioinformatics and high-throughput biological data coming from different sources can even be more helpful, because each of these different data chunks can provide alternative, complementary information about a specific biological phenomenon, similar to multiple photos of the same subject taken from different angles. In this context, the integration of bioinformatics and high-throughput biological data gets a pivotal role in running a successful bioinformatics study. In the last decades, data originating from proteomics, metabolomics, metagenomics, phenomics, transcriptomics, and epigenomics have been labelled -omics data, as a unique name to refer to them, and the integration of these omics data has gained importance in all biological areas. Even if this omics data integration is useful and relevant, due to its heterogeneity, it is not uncommon to make mistakes during the integration phases. We therefore decided to present these ten quick tips to perform an omics data integration correctly, avoiding common mistakes we experienced or noticed in published studies in the past. Even if we designed our ten guidelines for beginners, by using a simple language that (we hope) can be understood by anyone, we believe our ten recommendations should be taken into account by all the bioinformaticians performing omics data integration, including experts.

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ArcGIS-Based Landscaping Management System

Tan

2023

Lecture Notes on Data Engineering and Communications Technologies

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medna-metadata: an open-source data management system for tracking environmental DNA samples and metadata

Cited by 6 publications

References 51 publications

Three steps towards comparability and standardization among molecular methods for characterizing insect communities

Three steps towards comparability and standardization among molecular methods for characterizing insect communities

Ten quick tips for avoiding pitfalls in multi-omics data integration analyses

ArcGIS-Based Landscaping Management System

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