RESUMO
Tumor DNA sequencing data can be interpreted by computational methods that analyze genomic heterogeneity to infer evolutionary dynamics. A growing number of studies have used these approaches to link cancer evolution with clinical progression and response to therapy. Although the inference of tumor phylogenies is rapidly becoming standard practice in cancer genome analyses, standards for evaluating them are lacking. To address this need, we systematically assess methods for reconstructing tumor subclonality. First, we elucidate the main algorithmic problems in subclonal reconstruction and develop quantitative metrics for evaluating them. Then we simulate realistic tumor genomes that harbor all known clonal and subclonal mutation types and processes. Finally, we benchmark 580 tumor reconstructions, varying tumor read depth, tumor type and somatic variant detection. Our analysis provides a baseline for the establishment of gold-standard methods to analyze tumor heterogeneity.
Assuntos
Algoritmos , Neoplasias/patologia , Células Clonais , Simulação por Computador , Variações do Número de Cópias de DNA/genética , Dosagem de Genes , Genoma , Humanos , Mutação/genética , Neoplasias/genética , Polimorfismo de Nucleotídeo Único/genética , Padrões de ReferênciaRESUMO
Challenges are achieving broad acceptance for addressing many biomedical questions and enabling tool assessment. But ensuring that the methods evaluated are reproducible and reusable is complicated by the diversity of software architectures, input and output file formats, and computing environments. To mitigate these problems, some challenges have leveraged new virtualization and compute methods, requiring participants to submit cloud-ready software packages. We review recent data challenges with innovative approaches to model reproducibility and data sharing, and outline key lessons for improving quantitative biomedical data analysis through crowd-sourced benchmarking challenges.
Assuntos
Algoritmos , Benchmarking , Disseminação de Informação , Modelos Biológicos , Reprodutibilidade dos TestesRESUMO
BACKGROUND: The NCBI BLAST suite has become ubiquitous in modern molecular biology and is used for small tasks such as checking capillary sequencing results of single PCR products, genome annotation or even larger scale pan-genome analyses. For early adopters of the Galaxy web-based biomedical data analysis platform, integrating BLAST into Galaxy was a natural step for sequence comparison workflows. FINDINGS: The command line NCBI BLAST+ tool suite was wrapped for use within Galaxy. Appropriate datatypes were defined as needed. The integration of the BLAST+ tool suite into Galaxy has the goal of making common BLAST tasks easy and advanced tasks possible. CONCLUSIONS: This project is an informal international collaborative effort, and is deployed and used on Galaxy servers worldwide. Several examples of applications are described here.