Quantification of aneuploidy in targeted sequencing data using ASCETS.
Ontology highlight
ABSTRACT: Summary
The expansion of targeted panel sequencing efforts has created opportunities for large-scale genomic analysis, but tools for copy-number quantification on panel data are lacking. We introduce ASCETS, a method for the efficient quantitation of arm and chromosome-level copy-number changes from targeted sequencing data.Availability and implementation
ASCETS is implemented in R and is freely available to non-commercial users on GitHub: https://github.com/beroukhim-lab/ascets, along with detailed documentation.Supplementary information
Supplementary data are available at Bioinformatics online.
SUBMITTER: Spurr LF
PROVIDER: S-EPMC8388019 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:Tumors are characterized by properties of genetic instability, heterogeneity, and significant oligoclonality. Elucidating this intratumoral heterogeneity is challenging but important. In this study, we propose a framework, BubbleTree, to characterize the tumor clonality using next generation sequencing (NGS) data. BubbleTree simultaneously elucidates the complexity of a tumor biopsy, estimating cancerous cell purity, tumor ploidy, allele-specific copy number, and clonality and represents this in an intuitive graph. We further developed a three-step heuristic method to automate the interpretation of the BubbleTree graph, using a divide-and-conquer strategy. In this study, we demonstrated the performance of BubbleTree with comparisons to similar commonly used tools such as THetA2, ABSOLUTE, AbsCN-seq and ASCAT, using both simulated and patient-derived data. BubbleTree outperformed these tools, particularly in identifying tumor subclonal populations and polyploidy. We further demonstrated BubbleTree's utility in tracking clonality changes from patients' primary to metastatic tumor and dating somatic single nucleotide and copy number variants along the tumor clonal evolution. Overall, the BubbleTree graph and corresponding model is a powerful approach to provide a comprehensive spectrum of the heterogeneous tumor karyotype in human tumors. BubbleTree is R-based and freely available to the research community (https://www.bioconductor.org/packages/release/bioc/html/BubbleTree.html).
| S-EPMC4770205 | biostudies-literature
Project description:Metastasis is a major cause of cancer-related mortality, and it is essential to understand how metastasis occurs in order to overcome it. One relevant question is the origin of a metastatic tumor cell population. Although the hypothesis of a single-cell origin for metastasis from a primary tumor has long been prevalent, several recent studies using mouse models have supported a multicellular origin of metastasis. Human bulk whole-exome sequencing (WES) studies also have demonstrated a multiple "clonal" origin of metastasis, with different mutational compositions. Specifically, there has not yet been strong research to determine how many founder cells colonize a metastatic tumor. To address this question, under the metastatic model of "single bottleneck followed by rapid growth," we developed a method to quantify the "founder cell population size" in a metastasis using paired WES data from primary and metachronous metastatic tumors. Simulation studies demonstrated the proposed method gives unbiased results with sufficient accuracy in the range of realistic settings. Applying the proposed method to real WES data from four colorectal cancer patients, all samples supported a multicellular origin of metastasis and the founder size was quantified, ranging from 3 to 17 cells. Such a wide-range of founder sizes estimated by the proposed method suggests that there are large variations in genetic similarity between primary and metastatic tumors in the same subjects, which may explain the observed (dis)similarity of drug responses between tumors.
| S-EPMC7079087 | biostudies-literature
Project description:ObjectiveThis study aims to develop a noninvasive prenatal test on the basis of the analysis of cell-free DNA in maternal blood to detect fetal aneuploidy at chromosomes 13, 18, 21, X, and Y.MethodsA total of 166 samples from pregnant women, including 11 trisomy 21, three trisomy 18, two trisomy 13, two 45,X, and two 47,XXY samples, were analyzed using an informatics-based method. Cell-free DNA from maternal blood was isolated, amplified using a multiplex polymerase chain reaction (PCR) assay targeting 11,000 single nucleotide polymorphisms on chromosomes 13, 18, 21, X, and Y in a single reaction, and sequenced. A Bayesian-based maximum likelihood statistical method was applied to determine the chromosomal count of the five chromosomes interrogated in each sample, along with a sample-specific calculated accuracy for each test result.ResultsThe algorithm correctly reported the chromosome copy number at all five chromosomes in 145 samples that passed a DNA quality test, for a total of 725/725 correct calls. The average calculated accuracy for these samples was 99.92%. Twenty-one samples did not pass the DNA quality test.ConclusionsThis informatics-based method noninvasively detected fetuses with trisomy 13, 18, and 21, 45,X, and 47,XXY with high sample-specific calculated accuracies for each individual chromosome and across all five chromosomes.
| S-EPMC3548605 | biostudies-literature
Project description:Due to the large size, complex splicing and wide dynamic range of eukaryotic transcriptomes, RNA sequencing samples the majority of expressed genes infrequently, resulting in sparse sequencing coverage that can hinder robust isoform assembly and quantification. Targeted RNA sequencing addresses this challenge by using oligonucleotide probes to capture selected genes or regions of interest for focused sequencing. This enhanced sequencing coverage confers sensitive gene discovery, robust transcript assembly and accurate gene quantification. Here we describe a detailed protocol for all stages of targeted RNA sequencing, from initial probe design considerations, capture of targeted genes, to final assembly and quantification of captured transcripts. Initial probe design and final analysis can take less than a day, while the central experimental capture stage requires ~7 days. Targetted RNA sequencing of long noncoding RNAs
2014-03-01 | E-GEOD-52503 | biostudies-arrayexpress
Project description:The most important information about microorganisms might be their accurate genome sequence. Using current Next Generation Sequencing methods, sequencing data can be generated at an unprecedented pace. However, we still lack tools for the automated and accurate reference-based genotyping of viral sequencing reads. This paper presents our pipeline designed to reconstruct the dominant consensus genome of viral samples and analyze their within-host variability. We benchmarked our approach on numerous datasets and showed that the consensus genome of samples could be obtained reliably without further manual data curation. Our pipeline can be a valuable tool for fast identifying viral samples. The pipeline is publicly available on the project's GitHub page (https://github.com/laczkol/QVG).
| S-EPMC9481040 | biostudies-literature
Project description:Due to the large size, complex splicing and wide dynamic range of eukaryotic transcriptomes, RNA sequencing samples the majority of expressed genes infrequently, resulting in sparse sequencing coverage that can hinder robust isoform assembly and quantification. Targeted RNA sequencing addresses this challenge by using oligonucleotide probes to capture selected genes or regions of interest for focused sequencing. This enhanced sequencing coverage confers sensitive gene discovery, robust transcript assembly and accurate gene quantification. Here we describe a detailed protocol for all stages of targeted RNA sequencing, from initial probe design considerations, capture of targeted genes, to final assembly and quantification of captured transcripts. Initial probe design and final analysis can take less than a day, while the central experimental capture stage requires ~7 days.
2014-03-01 | GSE52503 | GEO
Project description:MinION is a memory stick-sized nanopore-based sequencer designed primarily for single-molecule sequencing of long DNA fragments (>6 kb). We developed a library preparation and data-analysis method to enable rapid real-time sequencing of short DNA fragments (<1 kb) that resulted in the sequencing of 500 reads in 3 min and 40,000-80,000 reads in 2-4 hr at a rate of 30 nt/sec. We then demonstrated the clinical applicability of this approach by performing successful aneuploidy detection in prenatal and miscarriage samples with sequencing in <4 hr. This method broadens the application of nanopore-based single-molecule sequencing and makes it a promising and versatile tool for rapid clinical and research applications.
| S-EPMC4701100 | biostudies-literature
Project description:In our previous study, we detected the effects of centrifugal forces on plasma RNA quantification by quantitative reverse transcription PCR. The aims of this study were to perform targeted mRNA sequencing and data analysis in healthy donors' plasma prepared by two centrifugation protocols and to investigate the effects of centrifugal forces on plasma mRNA quality and quantity. Targeted mRNA sequencing was performed using a custom panel with 108 colorectal cancer-related genes in 18 healthy donors' plasma that prepared by (1) 3,500 g for 10 min at 4°C and (2) 1,600 g for 10 min at 4°C followed by 16,000 g for 10 min at 4°C. Results showed that plasma ribosomal RNA was detected in 16/18 (88.9%) 3,500 g and 6/18 (33.3%) 1,600 g followed by 16,000 g centrifuged plasma. For targeted sequencing, 75/108 (69.4%) and 86/108 (79.6%) genes were detected in 3,500 and 1,600 g followed by 16,000 g, respectively, while 16/108 (14.8%) genes were not detected in both centrifugations. Detailed analysis showed that 2 of 108 (1.85%) genes showed lower expressions in 3,500 g than in 1,600 g followed by 16,000 g. The median expressions of genes in 3,500 g were positively correlated with the expressions in 1,600 g followed by 16,000 g (R2 = 0.9471, P < 0.0001, Spearman rank correlation). Meanwhile, plasma samples were not distinctively clustered based on centrifugal forces according to hierarchical clustering. Targeted mRNA sequencing and subsequent data analysis were performed in this study to investigate the effects of two different centrifugal forces that are commonly used in plasma collection. Our targeted sequencing results help to understand the centrifugal force effects on plasma mRNA, and these findings show that the centrifugation protocol for plasma mRNA research using targeted sequencing can be standardized which facilitates multicenter studies for comparison and quality assurance in the future.
| S-EPMC5963087 | biostudies-literature
Project description:Genomic structural variation (SV), a common hallmark of cancer, has important predictive and therapeutic implications. However, accurately detecting SV using high-throughput sequencing data remains challenging, especially for 'targeted' resequencing efforts. This is critically important in the clinical setting where targeted resequencing is frequently being applied to rapidly assess clinically actionable mutations in tumor biopsies in a cost-effective manner. We present BreaKmer, a novel approach that uses a 'kmer' strategy to assemble misaligned sequence reads for predicting insertions, deletions, inversions, tandem duplications and translocations at base-pair resolution in targeted resequencing data. Variants are predicted by realigning an assembled consensus sequence created from sequence reads that were abnormally aligned to the reference genome. Using targeted resequencing data from tumor specimens with orthogonally validated SV, non-tumor samples and whole-genome sequencing data, BreaKmer had a 97.4% overall sensitivity for known events and predicted 17 positively validated, novel variants. Relative to four publically available algorithms, BreaKmer detected SV with increased sensitivity and limited calls in non-tumor samples, key features for variant analysis of tumor specimens in both the clinical and research settings.
| S-EPMC4330340 | biostudies-literature
Project description:Tumor type guides clinical treatment decisions in cancer, but histology-based diagnosis remains challenging. Genomic alterations are highly diagnostic of tumor type, and tumor type classifiers trained on genomic features have been explored, but the most accurate methods are not clinically feasible, relying on features derived from whole genome sequencing (WGS), or predicting across limited cancer types. We use genomic features from a dataset of 39,787 solid tumors sequenced using a clinical targeted cancer gene panel to develop Genome-Derived-Diagnosis Ensemble (GDD-ENS): a hyperparameter ensemble for classifying tumor type using deep neural networks. GDD-ENS achieves 93% accuracy for high-confidence predictions across 38 cancer types, rivalling performance of WGS-based methods. GDD-ENS can also guide diagnoses on rare type and cancers of unknown primary, and incorporate patient-specific clinical information for improved predictions. Overall, integrating GDD-ENS into prospective clinical sequencing workflows has enabled clinically-relevant tumor type predictions to guide treatment decisions in real time.
| S-EPMC10508812 | biostudies-literature