Biology subjects

Frey, B. J.

Publications and source records attributed to Frey, B. J..

3 recordsLinked to original sources

bioRxiv2018

Allele-specific transcription factor binding as a benchmark for assessing variant impact predictors

Genetic variation has long been known to alter transcription factor binding sites, resulting in sometimes major phenotypic consequences. While the performance for current binding site predictors is well characterised, little is known on how these models perform at predicting impact of variants. We collected and curated over 132,000 potential allele-specific binding (ASB) ChIP-seq variants across 101 transcription factors (TFs). We then assessed the accuracy of TF binding models from five different methods on these high-confidence measurements, finding that deep learning methods were best performing yet still have room for improvement. Importantly, machine learning methods were consistently better than the venerable position weight matrix (PWM). Finally, predictions for certain TFs were consistently poor, and our investigation supports efforts to use features beyond sequence, such as methylation, DNA shape, and post-translational modifications. We submit that ASB data is a valuable benchmark for variant impact on TF binding.

bioinformatics↗

bioRxiv2018

COSSMO: Predicting Competitive Alternative Splice Site Selection using Deep Learning

MotivationAlternative splice site selection is inherently competitive and the probability of a given splice site to be used also depends strongly on the strength of neighboring sites. Here we present a new model named Competitive Splice Site Model (COSSMO), which explicitly models these competitive effects and predict the PSI distribution over any number of putative splice sites. We model an alternative splicing event as the choice of a 3 acceptor site conditional on a fixed upstream 5 donor site, or the choice of a 5 donor site conditional on a fixed 3 acceptor site. We build four different architectures that use convolutional layers, communication layers, LSTMS, and residual networks, respectively, to learn relevant motifs from sequence alone. We also construct a new dataset from genome annotations and RNA-Seq read data that we use to train our model.\n\nResultsCOSSMO is able to predict the most frequently used splice site with an accuracy of 70% on unseen test data, and achieve an R2 of 60% in modeling the PSI distribution. We visualize the motifs that COSSMO learns from sequence and show that COSSMO recognizes the consensus splice site sequences as well as many known splicing factors with high specificity.\n\nAvailabilityOur dataset is available from http://cossmo.deepgenomics.com.\n\nContactfrey@deepgenomics.com\n\nSupplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗

bioRxiv2017

Inference Of The Human Polyadenylation Code

Processing of transcripts at the 3-end involves cleavage at a polyadenylation site followed by the addition of a poly(A)-tail. By selecting which polyadenylation site is cleaved, alternative polyadenylation enables genes to produce transcript isoforms with different 3-ends. To facilitate the identification and treatment of disease-causing mutations that affect polyadenylation and to understand the underlying regulatory processes, a computational model that can accurately predict polyadenylation patterns based on genomic features is desirable. Previous works have focused on identifying candidate polyadenylation sites and classifying sites which may be tissue-specific. What is lacking is a predictive model of the underlying mechanism of site selection, competition, and processing efficiency in a tissue-specific manner. We develop a deep learning model that trains on 3-end sequencing data and predicts tissue-specific site selection among competing polyadenylation sites in the 3 untranslated region of the human genome.\n\nTwo neural network architectures are evaluated: one built on hand-engineered features, and another that directly learns from the genomic sequence. The hand-engineered features include polyadenylation signals, cis-regulatory elements, n-mer counts, nucleosome occupancy, and RNA-binding protein motifs. The direct-from-sequence model is inferred without prior knowledge on polyadenylation, based on a convolutional neural network trained with genomic sequences surrounding each polyadenylation site as input. Both models are trained using the TensorFlow library.\n\nThe proposed polyadenylation code can predict site selection among competing polyadenylation sites in different tissues. Importantly, it does so without relying on evolutionary conservation. The model can distinguish pathogenic from benign variants that appear near annotated polyadenylation sites in ClinVar and inspect the genome to find candidate polyadenylation sites. We also provide an analysis on how different features affect the models performance.

bioinformatics↗