Igor Mandric

This study shows that low-coverage single-cell RNA sequencing of more samples increases statistical power for cell-type-specific eQTL analysis compared to high-coverage sequencing of fewer samples.

This study uses single-cell transcriptomics to show that ATOH7 and Neurog2 regulate human retinal progenitor states and neuroblast fate by modulating bHLH factor expression.

BATMAN integrates single-cell RNA-Seq datasets efficiently, outperforming existing methods by up to 80% in batch effect correction while preserving cell relationships.

This study benchmarks error-correction methods for next-generation sequencing data, finding significant variation in performance across datasets with no single method being optimal overall.

MiCoP improves microbial community profiling by accurately detecting viral and fungal organisms in metagenomic samples using a comprehensive reference database.

The paper develops ImReP, a computational method to profile TCR and BCR repertoires from RNA-seq data, creating a large atlas of immunological sequences across 53 tissues.

This paper introduces BATISCAF, a repeat-aware scaffolder that outperforms existing tools on benchmark datasets.

This paper evaluates scaffolding tools by accounting for repeats in genomic sequences, proposing a repeat-aware evaluation framework and applying it to assess state-of-the-art tools on GAGE datasets.