Cancer Genomics and Diagnostics

The paper introduces an updated tool for visualizing and exploring large mutational events in cancer genomes using two classification schemas.

This paper introduces COMPASS, a method to jointly reconstruct mutation and copy number phylogenies from single-cell amplicon sequencing data, showing it detects both clonal and subclonal alterations in leukemia patients.

The paper analyzes 2,787 tumors to show that replication timing and mutation rate association is influenced by both cell autonomous and non-autonomous processes, including DNA repair, chromatin structure, and immune cell interactions.

The paper analyzes genome sequences of 20,066 tumors to identify driver mutations in cancer genes and links mutation patterns to survival outcomes.

The paper introduces a scalable Bayesian method for phylogenetic tree inference from single-cell whole genome sequencing data, demonstrating its application on three datasets.

The paper models clonal interference in cancer cell lines using phenotypic data to predict growth rate and carrying capacity differences between karyotypes.

The paper introduces MuAt, a deep learning model that learns representations of somatic mutations to predict tumour types and subtypes with high accuracy.

This paper develops a method to detect tumor-specific gene fusions in pediatric cancer by integrating RNA-seq and WGS, identifying 165 high-confidence fusions and their structural variants.

CNETML infers phylogenies from spatio-temporal copy number profiles of cancer samples, handling total copy numbers and temporal data to estimate mutation rates and tree topology.

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.

This paper introduces mutation-level covariate models to analyze mutational signatures and shows they better capture replication strand specificity than standard models.

The paper develops an integrated model using fragmentomic and 5-hydroxymethylcytosine features from low-pass sequencing data to achieve high sensitivity and specificity in pan-cancer detection.

The paper develops a nanopore sequencing strategy to measure cfDNA methylomes in cancer patients, enabling longitudinal monitoring during treatment.

FusionInspector identifies and characterizes biologically relevant fusion transcripts from RNA-seq data in tumor and normal tissues using clustering and machine learning.

This study tracks ctDNA mutational complexity in melanoma patients on immunotherapy, finding dynamic mutations and clinical correlations.

The paper develops multi-omics ITH measures using information entropy across six molecular levels and shows their correlations with clinical outcomes and molecular regulatory relationships.

This study maps postzygotic mutations across tissues and lifespan, showing their functional variation and selection pressures, suggesting multiple dynamic genomes per individual.

OncoMerge integrates protein-affecting mutations, gene fusions, and copy number alterations to create a unified somatic mutation profile, improving downstream cancer analysis.

This study uses SpliceAI to identify cryptic splice mutations in cancer, finding over 100,000 such mutations across 18,115 tumors and uncovering new candidate driver genes.

The paper identifies DNA repair deficiency genes using mutational patterns and develops predictive models for their loss-of-function variants across cancers.

The paper introduces a network-based method to uncover relationships between mutational signatures and biological processes in cancer data.

SMITH simulates cancer evolution with spatial constraints, showing that confinement mechanisms can reproduce clonal dynamics efficiently.

PipeIT2 is a somatic variant calling workflow for Ion Torrent sequencing data that identifies mutations without germline control, achieving >95% recall for variants with >10% allele fraction.

The paper identifies a specific copy number footprint in tumors from patients exposed to platinum-based therapies using whole genome sequencing data.

This study identifies 14 CNA signatures from pan-cancer WGS and TCGA SNP array data, revealing novel patterns and prognostic implications for cancer patients.

Analyzing the positioning and nucleotide sequence at fragment ends in plasma DNA may enable cancer diagnostics.

mSigHdp improves mutational signature discovery using hierarchical Dirichlet process mixture models, outperforming NMF-based methods on synthetic data.

Katdetectr detects kataegis loci in genomic data using unsupervised changepoint analysis, showing high accuracy and speed compared to existing tools.

COOBoostR uses extreme gradient boosting to predict tumor tissue or cell-of-origin using chromatin marks and mutation density data, outperforming random forest methods in speed and accuracy.

This study analyzes somatic mutations in GPCRs across cancer patients and healthy individuals, identifying conserved mutation hotspots and prioritizing GPCRs for further cancer research.