Anomaly detection in genomic catalogues using unsupervised multi-view autoencoders.

ABSTRACT:

Background

Accurate identification of Transcriptional Regulator binding locations is essential for analysis of genomic regions, including Cis Regulatory Elements. The customary NGS approaches, predominantly ChIP-Seq, can be obscured by data anomalies and biases which are difficult to detect without supervision.

Results

Here, we develop a method to leverage the usual combinations between many experimental series to mark such atypical peaks. We use deep learning to perform a lossy compression of the genomic regions' representations with multiview convolutions. Using artificial data, we show that our method correctly identifies groups of correlating series and evaluates CRE according to group completeness. It is then applied to the ReMap database's large volume of curated ChIP-

SUBMITTER: Ferre Q 

PROVIDER: S-EPMC8467021 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature