Cancer Genomics and Diagnostics

This paper develops a method to improve mutational profiling of pre-malignant lesions using degraded FFPE DNA, achieving better coverage and variant calling accuracy.

FinaleDB provides a database and browser for cfDNA fragmentation patterns, enabling integration with other omics data to study gene regulation and disease.

DORGE identifies cancer driver genes by integrating genetic and epigenetic data, finding histone modifications predict TSGs and missense mutations predict OGs.

The paper identifies mis-annotated multi-nucleotide variants (MNVs) in cancer genomics datasets, showing they lead to incorrect amino acid change predictions and recommends merging SNVs into MNVs for accurate variant annotation.

DeepSSV detects somatic small variants in paired tumor and normal sequencing data using a convolutional neural network, achieving better performance than existing methods in F1 score.

The paper introduces mutSignatures, an R package for analyzing cancer mutational signatures, showing it can identify known signatures and reveal new patterns linked to clinical and molecular features.

NIMBus identifies mutational hotspots by integrating mutation data with genomic features to account for heterogeneity, revealing known drivers and regulatory disruptions in cancer.

This study evaluates how blood collection tubes and time delay affect cfDNA methylation, finding robust results across tubes but recommending EDTA tubes processed within 24 hours.

This study evaluates somatic mutation calling accuracy and reproducibility in clinical-type targeted sequencing data using cell line samples and multiple variant callers.

This paper develops a targeted cfDNA sequencing approach for detecting genomic alterations in breast cancer using UMIs and bioinformatics pipelines, showing high concordance with tumor sequencing.

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.

The paper introduces a framework for assessing the quality of whole genome cancer sequences using a star rating system based on five QC measures, providing a tool for researchers to evaluate and exclude low-quality data.

SigProfilerSimulator generates realistic null mutational landscapes for cancer genomics to evaluate bioinformatics tools and statistical analyses.

This study genotypes individual melanocytes from human skin, revealing mutation patterns linked to sun exposure, skin cancer risk, and melanoma origins.

Sigflow is a pipeline for comprehensive cancer genome mutational signature analysis, supporting multiple DNA alteration types and offering reproducible research through Docker.

PhyDOSE optimizes follow-up single-cell sequencing experiments using bulk data to reduce costs while maintaining accurate tumor phylogeny reconstruction.

This study uses whole genome sequencing of cell-free DNA to identify tissue-of-origin patterns, achieving high specificity and sensitivity in cancer patients.

The paper develops a scoring system to prioritize understudied kinases as potential cancer targets using TCGA data.

HATCHet improves CNA and WGD analysis in multi-sample tumor data by jointly inferring clone-specific alterations across samples, outperforming existing methods and revealing new insights into tumor evolution.

The paper introduces CHISEL, a method to infer allele- and haplotype-specific copy numbers in single cells, revealing previously undetected copy-number aberrations in breast cancer genomes.

This paper introduces a model-based subclonal reconstruction method that integrates evolutionary theory with machine learning to improve accuracy in tumor subclonal analysis.

The paper identifies a novel class of structural aberrations in lung cancers using long-read sequencing and shows their occurrence in vivo and in cancer-related genes.

The paper analyzes whole-genome sequencing data from melanoma metastases to show that branched evolution is common and that subclonal heterogeneity is prevalent in metastatic melanoma.

The paper analyzes oncogenic gene fusions in cancer, finding that 30% of detected fusions produce functional proteins, primarily involving kinases and transcription factors, which deregulate cellular signaling.

This study sequenced 41 stage IV breast cancer samples using MammaSeq to identify 59 genomic alterations, including 49 SNVs and 10 CNVs, with 8 having clinical significance.

PCAT is a portal for pediatric cancer PDX models and associated genomic data, providing clinical, molecular, and preclinical testing information for 324 models and 79 therapeutic agents.

The paper introduces SASC, a method for inferring cancer progression from single-cell sequencing data that allows mutation losses, showing high accuracy on simulated and real datasets.

The paper introduces MP3, a similarity measure for tumor phylogenies that handles multiple mutations, and shows it correctly classifies trees on simulated and real data.

This paper introduces a statistical method to track clonal dynamics in cancer using longitudinal sequencing data, showing improved model performance with temporal information.

sigLASSO optimizes cancer mutation signature analysis by jointly modeling sampling likelihood and signature fitting, improving accuracy in low-count scenarios.