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Increasing metadata coverage of SRA BioSample entries using deep learning-based named entity recognition.

Adam Klie^1,2, Brian Y Tsui^1,2, Shamim Mollah^2,3,4

¹Department of Medicine, Division of Medical Genetics, University of California San Diego, La Jolla, CA 92093, USA.

Database : the Journal of Biological Databases and Curation

|April 29, 2021

Summary

This summary is machine-generated.

This study developed a neural network to automatically predict missing metadata for public sequencing data, improving research reproducibility and meta-analysis efficiency. The model enhances data discoverability in repositories like the Sequence Read Archive.

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Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

High-quality metadata is crucial for research reproducibility and meta-analyses.
Public repositories like the Sequence Read Archive (SRA) often lack sufficient metadata for sequencing samples.
Manual metadata curation is time-consuming and limits scalability.

Purpose of the Study:

To develop and evaluate a scalable recurrent neural network for predicting missing metadata in SRA samples.
To improve metadata coverage in public repositories through automated annotation.
To leverage named entity recognition (NER) for efficient metadata extraction.

Main Methods:

Trained a recurrent neural network using nearly 44 million attribute-value pairs from SRA BioSample.
Utilized named entity recognition (NER) for metadata prediction.
Applied the trained model to predict 11 metadata categories from sample TITLE attributes.

Main Results:

The neural network achieved high accuracy in classifying metadata categories (85.2% overall accuracy, 0.977 AUC).
Accurate predictions were obtained for Genus/Species (94.85%), Condition/Disease (95.65%), and Strain (82.03%) from sample titles.
Lower accuracies for other categories indicated existing issues with BioSample metadata quality.

Conclusions:

Recurrent neural networks are effective for NER-based metadata prediction.
Automated metadata prediction can significantly increase coverage in public repositories.
This approach minimizes the need for manual curation, enhancing data accessibility and research efficiency.