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Soffritto: a deep learning model for predicting high-resolution replication timing
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DOI：10.1093/bioinformatics/btaf231.png)
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Motivation Replication timing (RT) refers to the order in which DNA loci are replicated during S phase. RT is cell-type specific and implicated in cellular processes including transcription, differentiation, and disease. RT is typically quantified genome-wide using two-fraction assays (e.g. Repli-Seq) which sort cells into early and late S phase fractions followed by DNA sequencing, yielding a ratio as the RT signal. While two-fraction RT data are widely available in multiple cell lines, it is limited in its ability to capture high-resolution RT features. To address this, high-resolution Repli-Seq, which quantifies RT across 16 fractions, was developed, but it is costly and technically challenging with very limited data generated to date.Results Here, we developed Soffritto, a deep learning model that predicts high-resolution RT data using two-fraction RT data, histone ChIP-seq data, GC content, and gene density as input. Soffritto is composed of a Long Short-Term Memory (LSTM) module and a prediction module. The LSTM module learns long- and short-range interactions between genomic bins, while the prediction module is composed of a fully connected layer that outputs a 16-fraction probability vector for each bin using the LSTM module's embeddings as input. By performing both within cell line and cross-cell line training and testing for five human and mouse cell lines, we show that Soffritto is able to capture experimental 16-fraction RT signals with high accuracy, and the predicted signals allow detection of high-resolution RT patterns.Availability and implementation Soffritto is available at https://github.com/ay-lab/Soffritto.
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分享收藏Genome-wide analysis of replication timing by next-generation sequencing with E/L Repli-seq.png)
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OAAI分享收藏分享收藏High-resolution Repli-Seq defines the temporal choreography of initiation, elongation and termination of replication in mammalian cellsGENOME BIOLOGYOAAI
GENOME BIOLOGYIF9.4
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