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

Journal: Database : the journal of biological databases and curation
Published Date: Apr 29, 2021

Abstract

High-quality metadata annotations for data hosted in large public repositories are essential for research reproducibility and for conducting fast, powerful and scalable meta-analyses. Currently, a majority of sequencing samples in the National Center for Biotechnology Information's Sequence Read Archive (SRA) are missing metadata across several categories. In an effort to improve the metadata coverage of these samples, we leveraged almost 44 million attribute-value pairs from SRA BioSample to train a scalable, recurrent neural network that predicts missing metadata via named entity recognition (NER). The network was first trained to classify short text phrases according to 11 metadata categories and achieved an overall accuracy and area under the receiver operating characteristic curve of 85.2% and 0.977, respectively. We then applied our classifier to predict 11 metadata categories from the longer TITLE attribute of samples, evaluating performance on a set of samples withheld from model training. Prediction accuracies were high when extracting sample Genus/Species (94.85%), Condition/Disease (95.65%) and Strain (82.03%) from TITLEs, with lower accuracies and lack of predictions for other categories highlighting multiple issues with the current metadata annotations in BioSample. These results indicate the utility of recurrent neural networks for NER-based metadata prediction and the potential for models such as the one presented here to increase metadata coverage in BioSample while minimizing the need for manual curation. Database URL: https://github.com/cartercompbio/PredictMEE.

Authors

  • Adam Klie
    Department of Medicine, Division of Medical Genetics, University of California San Diego, La Jolla, CA 92093, USA.
  • Brian Y Tsui
    Institute for Genomic Medicine, Institute of Engineering in Medicine, and Shiley Eye Institute, University of California, San Diego, La Jolla, CA, USA.
  • Shamim Mollah
    Bioinformatics and Systems Biology Program, University of California San Diego, La Jolla, CA 92093, USA.
  • Dylan Skola
    Department of Medicine, Division of Medical Genetics, University of California San Diego, La Jolla, CA 92093, USA.
  • Michelle Dow
    Department of Medicine, Division of Medical Genetics, University of California San Diego, La Jolla, CA 92093, USA.
  • Chun-Nan Hsu
    University of California San Diego, La Jolla, CA.
  • Hannah Carter
    Institute for Genomic Medicine, Institute of Engineering in Medicine, and Shiley Eye Institute, University of California, San Diego, La Jolla, CA, USA.

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