Clinical Whole Genome and Exome Sequencing Pipeline

CADD-SV – a framework to score the effect of structural variants

Genomic Pre-trained Network

Master's Thesis Project

An explorative tool to identify functional hotspots

Pipeline for variant annotation using Variant Effect Predictor (VEP)

Deep learning framework to predict functional effects of missense variants in human

Fully convolutional deep learning variant effect predictor architecture

Experimenting with protein language model predictions

Transcriptional variant verification to validate predicted variants from genomic data in expressed transcripts

Deep learning model for non-coding regulatory variants

Tissue-specific variant effect predictions on splicing

Implementation of evolutionary model of variant effect (EVE), a deep generative model of evolutionary data, in PyTorch.

Implementation of SpliceAI, Illumina's deep neural network to predict variant effects on splicing, in PyTorch.

Preliminary analysis of a combined DMS dataset, including clustering and VEP benchmark

interactive variant tables for easy filtering

Predicting the effect of mutations on protein stability and protein binding affinity using pretrained neural networks and a ranking objective function.

A Haskell script that performs basic parsing on the default output of ensembl-vep and variant-calling format (vcf) files.

We present Envision, an accurate predictor of protein variant molecular effect, trained using large-scale experimental mutagenesis data. All data and software in this study are freely available. The training data set and all code used to train the models and generate the figures presented in this manuscript are available here. Envision predictio…