Genome Generator
The genomeGenerator is a Python tool generating individual-specific genome sequence and annotation files from a reference genome sequence, annotation and the individuals’ genetic variation information. It currently only works for haploid organisms.
Random Forest QTL Mapping
We compare legacy QTL mapping methods with several modern multivariate methods and evaluate their ability to produce eQTL that agree with independent external data in a systematic way. We also provide an R-package to facilitate QTL-mapping with Random Forest.
TF Target Gene Prediction
We have developed and compared different methods for predicting TF target genes using ChIP-seq data. We implemented all the methods that we tested in an unified R-package, thus providing a convenient tool for testing different methods on your own data.
ImAP
ImAP is a method to detect epistatic interactions based on the frequency of allele pairs in a population.The rationale is that incompatible alleles will occur less frequently than expected in a population. ImAP exploits the known family structure (parent-child trios) in order to improve the statistical power.
Network Propagation
Network propagation algorithm aids in inferring the altered sub-networks or signaling pathways in the given condition. We have noticed that standard network propagation suffered from gene-specific biases created by the connections in the network (‘topology bias’). Therefore, we devised an improved network propagation method that corrects for this topology bias.
Adaptive Dropout Imputer (ADImpute)
ADImpute predicts unmeasured gene expression values from single cell RNA-sequencing data (dropout imputation). This R-package combines multiple dropout imputation methods, including a novel gene regulatory network-based method.