Cancer Genomics and Diagnostics

Alleloscope analyzes allele-specific copy number and chromatin accessibility in single-cell cancer data, revealing complex copy number events and their role in intratumor heterogeneity.

SHARC rapidly identifies somatic genomic structural variations from nanopore sequencing for blood-based cancer monitoring, detecting ten SVs per sample and enabling treatment response tracking in metastatic prostate cancer.

This study develops NBDriver, a tool that uses sequence neighborhoods to predict pathogenic mutations in cancer genomes, outperforming existing methods in identifying driver mutations.

This study develops a bioinformatics pipeline to generate CNA signatures for 31 cancer subtypes and constructs a classifier to identify cancer type and origin from copy number data.

The paper systematically examines mutation groupings in cancer genes to identify downstream expression programs and reveals functional divergence among mutations within the same gene.

This study validates whole genome sequencing as a reliable single assay for detecting various cancer mutations with high sensitivity and precision.

This study identifies five copy number signatures in prostate cancer using sigminer, revealing distinct etiologies and improved clinical outcome associations compared to single base substitution signatures.

The paper characterizes mutation rate variability in regulatory elements across cancer genomes and identifies mutational signatures and drivers associated with specific genomic features.

The paper identifies short single-stranded DNA fragments in plasma that form non-canonical structures and are enriched in open chromatin and transcription factor-binding sites.

The paper analyzes 3000 whole genome sequencing datasets to uncover links between bacteria, viruses, and cancer, identifying novel associations and phenotypic differences.

pDriver identifies personalized coding and miRNA cancer drivers for individual patients using gene networks and outperforms existing methods on TCGA datasets.

The paper introduces HotPho, a tool that identifies 3D co-clustering of phosphosites and cancer mutations on protein structures, revealing 474 hybrid clusters with potential functional significance.

This paper evaluates three cfDNA extraction kits and a PEG-based cleanup method, finding that DNApure improves yield and purity of cfDNA from urine and plasma.

This study identifies somatic SNVs in non-cancerous human brain tissues and establishes best practices for detecting low-frequency variants.

This paper uses computational modeling to show that metastatic-primary genomic divergence depends on tumor growth dynamics and variant detectability, revealing non-monotonic relationships in some scenarios.

The paper uses clinical-grade whole genome sequencing and 3' transcriptome analysis to identify targetable genomic alterations in colorectal cancer patients.

The paper introduces MEDALT and LSA to reconstruct copy number lineage trees from single-cell data and identify fitness-associated genes, showing improved accuracy over phylogenetics methods in cancer datasets.

The paper describes the pipelines and workflows used to process and harmonize genomic data in the NCI Genomic Data Commons, including data types like somatic mutations and gene expression.

This study characterizes urine cfDNA fragmentation patterns and shows they reflect sub-nucleosomal organization, with potential for cancer diagnostics.

eTumorMetastasis uses whole-exome sequencing data to predict tumor recurrence by analyzing network-based gene signatures from founding clones.

ATOM-Seq is a new NGS library preparation method that improves sensitivity and reduces errors in detecting low-frequency mutations in cfDNA and cfRNA.

This paper introduces SubMARine, a polynomial-time algorithm for reconstructing tumor clone trees that efficiently captures uncertainty and scales to large datasets.

Accucopy infers total and allele-specific copy numbers from low-coverage low-purity tumor data, showing higher accuracy than existing methods.

The paper introduces cfChIP-seq to analyze cell-free nucleosomes and shows it can detect disease-related transcriptional changes and patient-specific features in cancer.

The paper identifies 407 Master-Regulator proteins that canalize cancer genomics into transcriptional subtypes and links genetic alterations to tumor biology through modular regulatory architectures.

AStra is a framework for aneuploidy profiling of cancer data that assesses suitability for copy number analysis, showing high accuracy in identifying aneuploidy spectra and ploidy levels.

PyClone-VI infers cancer clonal structures from whole genome data efficiently and accurately, validated on PCAWG and TRACERx datasets.

The paper develops a method using k-mer sequences to validate somatic mutations in cancer genome sequencing data, showing it effectively identifies true positive mutations including insertions and deletions.

annoFuse filters and prioritizes oncogenic gene fusions from RNA-Seq data, improving identification of clinically relevant fusions in cancer datasets.

This paper introduces survClust, an outcome-weighted clustering algorithm that identifies clinically relevant genomic subtypes in pan-cancer data, outperforming unsupervised methods in detecting subpopulations with aggressive clinical behavior.