Ibrahim Numanagić

Neretva: Neural Variational Inference for Allele-level Genotyping of Highly Polymorphic Genes

Neretva improves allele-level genotyping of highly polymorphic genes using a probabilistic model and variational inference, achieving competitive accuracy on CYP and KIR families.

Genomes from 117 vertebrate species reveal rapidly evolving segmental duplication landscapes

The paper analyzes segmental duplications in 117 vertebrate genomes and starfish, revealing species-specific patterns and rapid evolution of duplication landscapes.

A-LAVA: Detecting impact of germline variants on metabolic pathways in cancer genomes

A-LAVA identifies germline variants affecting metabolic pathways in cancer genomes through SNP, gene, and pathway-level analysis, improving accuracy by correcting for overlapping gene sets and reducing false positives.

Geny: A Genotyping Tool for Allelic Decomposition of Killer Cell Immunoglobulin-Like Receptor Genes

Geny improves KIR gene genotyping accuracy by combining expectation-maximization and integer linear programming to resolve ambiguities in sequencing data.

Biologically-informed Killer cell immunoglobulin-like receptor (KIR) gene annotation tool

BAKIR improves KIR gene annotation accuracy by using a biologically-informed pipeline on phased genome assemblies, enhancing variant calling and allele identification.

Aldy 4: An efficient genotyper and star-allele caller for pharmacogenomics

Aldy 4 is a pharmacogenomics genotyper and star-allele caller that improves accuracy across sequencing technologies through combinatorial optimization and novel models.

Design and performance of a long-read sequencing panel for pharmacogenomics

The paper presents a long-read sequencing panel for pharmacogenomics, achieving high accuracy and phasing for SNVs and clinical star(*)-alleles.

A Python-based optimization framework for high-performance genomics

The paper introduces Seq, a Python-based framework that improves performance in genomics by combining Python's usability with C/C++ efficiency, achieving speedups over existing implementations.

Computational proteogenomic identification and functional interpretation of translated fusions and micro structural variations in cancer

The paper introduces a computational framework integrating genomic, transcriptomic, and proteomic data to identify and interpret translated fusions and micro structural variations in cancer, finding many novel peptides and private aberrations in breast cancer.

Latent variable model for aligning barcoded short-reads improves downstream analyses

EMA improves barcoded read alignment for third-generation sequencing, leading to more accurate genotyping and phasing with fewer false positives and better handling of homologous regions.