S. Cenk Şahinalp

LOCALE: Local-Alignment Embeddings for Noise-Robust DNA Search at Scale

LOCALE improves DNA sequence search by learning variation-robust embeddings, outperforming existing methods on benchmark datasets and real biological variance tasks.

Qombucha: Reconstructing unobserved progenitor methylation profiles reveals distinct developmental programs in glioblastoma

Qombucha infers unobserved progenitor methylation profiles in GBM, revealing subtype-specific developmental programs and improved subtype classification.

GenCore: Genomic distance estimation using Locally Consistent Parsing

GenCore uses Locally Consistent Parsing to sketch and estimate genomic distances between closely related large genomes, successfully reconstructing simulated progression trees and recapitulating primate phylogeny.

Identifying Robust Subclonal Structures through Tumor Progression Tree Alignment

This paper introduces omlta, an algorithm for aligning clonal trees to compare tumor progression, and applies it to cancer datasets.

Wakhan: reconstruction of chromosome-scale copy number profiles of tumor genomes with long-read sequencing

Wakhan reconstructs chromosome-scale copy number profiles of tumor genomes using long-read sequencing, showing better consistency with data than existing methods.

TX-Phase: Secure Phasing of Private Genomes in a Trusted Execution Environment

TX-Phase securely phases private genomes using Trusted Execution Environments, achieving high accuracy and efficiency on UK Biobank and Haplotype Reference Consortium datasets.

Long-read sequencing of single cell-derived melanoma subclones reveals divergent and parallel genomic and epigenomic evolutionary trajectories

The paper uses long-read sequencing to analyze genomic and epigenomic changes in melanoma subclones, revealing parallel evolutionary paths and lineage-specific methylation patterns.

Fair molecular feature selection unveils universally tumor lineage-informative methylation sites in colorectal cancer

This paper introduces FALAFL, a fair feature selection algorithm for single-cell methylation data, identifying universally tumor lineage-informative CpG sites in colorectal cancer.

Severus: accurate detection and characterization of somatic structural variation in tumor genomes using long reads

Severus detects and characterizes somatic structural variations in tumor genomes using long reads, outperforming existing methods in accuracy and revealing clinically relevant rearrangements.

Learning-Augmented Sketching Offers Improved Performance for Privacy Preserving and Secure GWAS

The paper improves the SkSES method for privacy-preserving GWAS by integrating a learning-augmented approach, achieving up to 40% higher accuracy under memory constraints.

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.

Single-cell methylation sequencing data reveal succinct metastatic migration histories and tumor progression models

This paper introduces Sgootr, a method to infer tumor lineage trees from single-cell methylation data, revealing metastatic progression models and identifying lineage-informative CpG sites.

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.

Strain Level Microbial Detection and Quantification with Applications to Single Cell Metagenomics

CAMMiQ improves microbial strain-level detection and quantification in single-cell metagenomics by using combinatorial optimization and doubly-unique substrings, outperforming existing methods in accuracy.

ImmunoTyper-SR: A Novel Computational Approach for Genotyping Immunoglobulin Heavy Chain Variable Genes using Short Read Data

ImmunoTyper-SR genotypes IGHV alleles from short read data and identifies associations between IGHV alleles and anti-type I IFN autoantibodies in COVID-19 patients.

Privacy-Preserving Genotype Imputation in a Trusted Execution Environment

This paper introduces a privacy-preserving genotype imputation method using a Trusted Execution Environment to protect genetic data while maintaining accuracy comparable to existing tools.

Studying the history of tumor evolution from single-cell sequencing data by exploring the space of binary matrices

This paper reviews methods for inferring tumor evolutionary trees using binary matrix optimization and discusses a deep learning approach for topological inference.

Profiles of expressed mutations in single cells reveal subclonal expansion patterns and therapeutic impact of intratumor heterogeneity

Trisicell infers subclonal expansion patterns and therapeutic impact of intratumor heterogeneity from scRNAseq data, revealing correlations between mutational composition and tumor aggressiveness.

Tumor Phylogeny Topology Inference via Deep Learning

This paper introduces deep learning methods to infer tumor phylogeny topology and feasibility from single-cell genotype data, demonstrating the potential of data-driven approaches in cancer evolution analysis.

PhISCS-BnB: A Fast Branch and Bound Algorithm for the Perfect Tumor Phylogeny Reconstruction Problem

PhISCS-BnB computes the most likely perfect tumor phylogeny from single-cell sequencing data, offering optimality guarantees and significant speed improvements over existing methods.

Identification of Conserved Evolutionary Trajectories in Tumors

This paper introduces CONETT, a method to identify conserved tumor evolution trajectories across multiple cancers, finding both previously reported and novel trajectories in renal and lung cancer datasets.

Sketching Algorithms for Genomic Data Analysis and Querying in a Secure Enclave

SkSES enables secure and privacy-preserving SNP analysis across encrypted genomic data using sketching algorithms in Intel SGX enclaves, demonstrating efficient SNP querying in a large dataset.

Unique genomic features and deeply-conserved functions of long non-coding RNAs in the Cancer LncRNA Census (CLC)

The paper introduces the Cancer LncRNA Census (CLC), a curated resource of lncRNAs with causative roles in cancer, and shows their genomic and evolutionary features.

Integrative pathway enrichment analysis of multivariate omics data

ActivePathways integrates multivariate omics data to identify enriched pathways, revealing cancer driver genes and prognostic pathways in breast cancer.

PhISCS - A Combinatorial Approach for Sub-perfect Tumor Phylogeny Reconstruction via Integrative use of Single Cell and Bulk Sequencing Data

PhISCS integrates single-cell and bulk sequencing data to reconstruct sub-perfect tumor phylogenies by minimizing false negatives, false positives, and ISA violations.

Integrative inference of subclonal tumour evolution from single-cell and bulk sequencing data

This paper develops a computational method to infer tumour evolution trees from combined single-cell and bulk sequencing data, showing improved accuracy in subclone identification and mutation history reconstruction.

Combinatorial Detection of Conserved Alteration Patterns for Identifying Cancer Subnetworks

The paper introduces cd-CAP, a method to detect conserved alteration patterns in cancer subnetworks across tumor samples.

MechRNA: prediction of lncRNA mechanisms from RNA-RNA and RNA-protein interactions

MechRNA predicts lncRNA mechanisms by integrating RNA-RNA and RNA-protein interaction predictions to identify potential targets and mechanisms for cancer-related lncRNAs.

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.