Dr. Kucukural designs and implements reusable, robust and production grade bioinformatics analysis pipelines and pipeline generation tools for processing next-generation sequencing data. 

He mainly works on NGS data analysis; RNA-Seq, RIP-Seq, Chip-Seq, CLIP-Seq and derivatives. He implemented algorithms to reduce noise by calling the peaks caused by experimental and alignment biases especially for RIP and CLIP-Seq data.

Dr. Kucukural worked on analyzing deep sequencing data to discover key elements of splicing of pre-mRNAs to have better understanding of post-transcriptional regulations of RNAs. Moreover, he has deep knowledge of finding genome wide mRNA targets of RNA binding proteins (RBPs). Analyzing RNA targets of tdp43 RBP with deep sequencing on Rat and human was another focus of his research to understand the mechanisms of neuro-degenerative diseases such as Alzheimer and ALS. 

He also worked for protein structure characterization and prediction. He applied techniques from graph theory on protein structure analysis and implemented the theories from computer sciences to biology to find solutions in drug design and small molecular docking fields. He developed applications using genetic algorithms to discover biomarkers and implemented feature detection methods using various clustering, classification and machine learning algorithms such as hidden markov models and support vector machines.