Cancer Genomics and Diagnostics

hsegHMM is a hidden Markov model for allele-specific copy number analysis that addresses hypersegmentation, outperforming FACETS in uneven coverage scenarios.

Cancer3D 2.0 provides an interactive platform to analyze 3D mutation patterns in cancer subsets, enabling identification of novel driver regions in specific patient subgroups.

The paper benchmarks somatic structural variant detection methods using synthetic tumors and crowd-sourced submissions, finding that ensembles do not always outperform individual methods.

This study evaluates the reliability of whole-exome sequencing in detecting intratumor genetic heterogeneity, finding that technical noise accounts for a significant proportion of apparent genetic differences.

TNER improves mutation detection in ctDNA by reducing background errors, outperforming existing methods on simulated and real data.

PANOPLY prioritizes cancer drugs for individual patients using multi-omics data and machine learning, showing effectiveness in a chemotherapy-resistant tumor model.

This paper establishes a standard operating procedure for manual refinement of somatic variants in tumor sequencing data to improve consistency and reproducibility.

Pro-Seq improves DNA sequencing accuracy without increasing costs by merging DNA copies into single reads, achieving high sensitivity and specificity in liquid biopsies.

The paper shows non-B DNA motifs contribute to mutation density in cancer genomes, with secondary structures playing a causal role in mutagenesis.

The paper creates a simulated tumor genome database to benchmark somatic small variant callers by introducing variants into a well-characterized reference genome.

This paper develops an error-corrected cfDNA sequencing method for highly sensitive mutation detection and genome-wide copy number reconstruction in metastatic colorectal cancer.

This paper introduces a machine learning method to detect repeated cancer evolution patterns across patients using multi-region sequencing data, identifying prognostic patient subgroups.

The paper evaluates whether relaxing the infinite sites assumption improves tumor phylogenies using an ILP-based approach, finding that allowing mutation losses provides better interpretations of real and simulated datasets.

The paper evaluates the robustness of mutation calling algorithms on tumor heterogeneity measures and finds limited clinical predictive power for MATH score.

The paper identifies and characterizes two types of postzygotic single-nucleotide mosaicisms in healthy individuals using whole-genome sequencing across multiple organs.

This study identifies and prioritizes damaging mutations in the Jurkat cell line using whole-genome sequencing and public databases, linking them to known expression defects.

The paper evaluates pre-analytical factors affecting plasma DNA analysis by comparing DNA extraction methods and blood collection tubes using a multiplexed ddPCR assay.

This study identifies cancer subtypes using somatic mutation data from The Cancer Genome Atlas, finding associations with prognostic outcomes and treatment options.

MutationalPatterns analyzes mutational processes genome-wide to quantify mutational signatures and their relation to genomic features.

The paper develops SLAPenrich to analyze pathway alterations in cancer hallmarks across 4,415 patients, revealing hallmark predominance and suggesting novel driver genes.

The paper develops a site-specific model to predict mutation rates in cancer genomes, showing it outperforms regional models by incorporating local genomic variables.

This study evaluates how sequencing depth affects single-cell cancer genomics, finding that depths >5x do not significantly improve variant detection or phylogenetic reconstruction for large sample sizes.

This study demonstrates that shallow whole genome sequencing (sWGS) can robustly profile copy number alterations in FFPE breast cancer tissues, even with low-quality DNA.

Bamgineer simulates allele-specific copy number variants in exome and targeted sequencing data to create reference sets for benchmarking CNV calling algorithms.

The paper shows that sample-index misassignment in tumor exome sequencing can create false somatic variant calls and recommends dual indexing to prevent this.

This paper introduces a method to predict patient-specific functional mutations by integrating mutation recurrence with expression profile similarity across cancer types.

The paper introduces a computational pipeline called MARATHON for copy number profiling and tumor phylogeny inference, addressing challenges in cancer genomics.

The paper quantifies germline leakage in somatic SNV detection across 259 datasets and introduces a tool to correct it, finding low leakage rates that can be further reduced with pre-filtering.

This paper introduces a targeted sequencing method for low-quality DNA that reduces PCR bias and improves variant detection in clinical samples.

The paper introduces MAPEX, a BLAST-based algorithm to filter spurious genetic variant calls in xenografts and tumors, achieving comparable sensitivity and specificity to complex methods.