Ibrahim Ihsan Taskiran

A unified framework for quantitative splicing and transcript prediction

This paper introduces SpliceAI2, a deep learning model that improves identification and quantification of splice-altering variants and their effects on transcripts and disease.

CREsted: modeling genomic and synthetic cell type-specific enhancers across tissues and species

CREsted is a software package for modeling and designing cell type-specific enhancers using deep learning, validated across tissues and species.

The evolution of gene regulation in mammalian cerebellum development

This study identifies conserved and diverged gene regulatory networks in mammalian cerebellum development using single-nucleus gene expression and chromatin accessibility data from six species.

Cell type directed design of synthetic enhancers

This study uses deep learning to design synthetic enhancers that target specific cell types in fruit flies and humans, revealing the enhancer code as optimal TF activator motifs and absence of repressor motifs.

Enhancer grammar of liver cell types and hepatocyte zonation states

The paper maps enhancer gene regulatory networks (GRNs) in liver cell types and identifies TFs governing hepatocyte zonation states using multiomics and deep learning.

Decoding gene regulation in the fly brain

The paper identifies regulatory regions and gene regulatory networks in Drosophila brain cell types using chromatin accessibility and transcriptome data.

Analysis of long and short enhancers in melanoma cell states

The paper identifies and characterizes enhancer activity in melanoma cell states using MPRA and DeepMEL2, showing AP-1 drives MES enhancers and SOX/MITF influence MEL enhancers.

Prioritization of enhancer mutations by combining allele-specific chromatin accessibility with deep learning

The paper uses a deep learning model on melanoma genomes to prioritize enhancer mutations by analyzing allelic imbalance in chromatin accessibility and gene expression.

Cross-species analysis of melanoma enhancer logic using deep learning

The paper uses deep learning to analyze enhancer logic across species in melanoma, identifying conserved regulatory complexes and their roles.

Identification of genomic enhancers through spatial integration of single-cell transcriptomics and epigenomics

The paper spatially integrates single-cell transcriptomics and epigenomics data to identify enhancers and gene regulatory relationships in Drosophila eye-antennal disc.