Project description:In order to evaluate the performance of CNV detection in next-generation sequencing platform in varied sample types, we employed chromosomal microarray analysis (CMA) for validation of the samples with NGS-based detection results (NCBI Sequence Read Archive with accession number SRA296708). Besides array Comparative Genomics Hybridization (aCGH, Agilent) , we used a commerical SNP-array (Illumina) including early abortus, induced termination, prenatal samples and postnatal samples. CMA results were compared with NGS-based detection results. 100% consistency was obtained between NGS-based approach and CMA in pathogenic or likely pathogenic CNVs detection.
Project description:In order to evaluate the performance of CNV detection in next-generation sequencing platform in varied sample types, we employed chromosomal microarray analysis (CMA) for validation of the samples with NGS-based detection results (NCBI Sequence Read Archive with accession number SRA296708). Besides snp-array, we used a customized array Comparative Genomics Hybridization (aCGH, Agilent) approach for a cohort of clinical samples including early abortus, induced termination, prenatal samples and postnatal samples. CMA results were compared with NGS-based detection results. 100% consistency was obtained between NGS-based approach and CMA in pathogenic or likely pathogenic CNVs detection.
Project description:We developed an enrichment-free, metabolic-based assay for rapid detection of tumor cells in the pleural effusion and peripheral blood samples. All nucleated cells are plated on microwell chips that contain 200,000 addressable microwells and then screened the chips. After candidate tumor cells were identified, retrieved single tumor cells with micromanipultor. To detection and analysis molecular characterization of these circulating tumor cells, we performed single cell whole genome amplification with multiple displacement amplification (MDA) technology and whole exome sequencing.
Project description:Current methods for detection of copy number aberrations (CNA) from whole-exome sequencing (WES) data are based on the read counts of the captured exons only. However, accurate CNA determination is complicated by the non-uniform read depth and uneven distribution of exons. Therefore, we developed ENCODER (ENhanced COpy number Detection from Exome Reads), which eludes these problems. By exploiting the ‘off-target’ sequence reads, it allows for creation of robust copy number profiles from WES. The accuracy of ENCODER compares to approaches specifically designed for copy number detection, and outperforms current exon-based WES methods, particularly in samples of low quality. Current methods for detection of copy number aberrations (CNA) from whole-exome sequencing (WES) data are based on the read counts of the captured exons only. However, accurate CNA determination is complicated by the non-uniform read depth and uneven distribution of exons. Therefore, we developed ENCODER (ENhanced COpy number Detection from Exome Reads), which eludes these problems. By exploiting the ‘off-target’ sequence reads, it allows for creation of robust copy number profiles from WES. The accuracy of ENCODER compares to approaches specifically designed for copy number detection, and outperforms current exon-based WES methods, particularly in samples of low quality. Current methods for detection of copy number aberrations (CNA) from whole-exome sequencing (WES) data are based on the read counts of the captured exons only. However, accurate CNA determination is complicated by the non-uniform read depth and uneven distribution of exons. Therefore, we developed ENCODER (ENhanced COpy number Detection from Exome Reads), which eludes these problems. By exploiting the ‘off-target’ sequence reads, it allows for creation of robust copy number profiles from WES. The accuracy of ENCODER compares to approaches specifically designed for copy number detection, and outperforms current exon-based WES methods, particularly in samples of low quality. DNA copy number profiles generated with a new tool, ENCODER, were compared to DNA copy number profiles from SNP6, NimbleGen and low-coverage Whole Genome Sequencing.
Project description:Current methods for detection of copy number aberrations (CNA) from whole-exome sequencing (WES) data are based on the read counts of the captured exons only. However, accurate CNA determination is complicated by the non-uniform read depth and uneven distribution of exons. Therefore, we developed ENCODER (ENhanced COpy number Detection from Exome Reads), which eludes these problems. By exploiting the ‘off-target’ sequence reads, it allows for creation of robust copy number profiles from WES. The accuracy of ENCODER compares to approaches specifically designed for copy number detection, and outperforms current exon-based WES methods, particularly in samples of low quality. Current methods for detection of copy number aberrations (CNA) from whole-exome sequencing (WES) data are based on the read counts of the captured exons only. However, accurate CNA determination is complicated by the non-uniform read depth and uneven distribution of exons. Therefore, we developed ENCODER (ENhanced COpy number Detection from Exome Reads), which eludes these problems. By exploiting the ‘off-target’ sequence reads, it allows for creation of robust copy number profiles from WES. The accuracy of ENCODER compares to approaches specifically designed for copy number detection, and outperforms current exon-based WES methods, particularly in samples of low quality. Current methods for detection of copy number aberrations (CNA) from whole-exome sequencing (WES) data are based on the read counts of the captured exons only. However, accurate CNA determination is complicated by the non-uniform read depth and uneven distribution of exons. Therefore, we developed ENCODER (ENhanced COpy number Detection from Exome Reads), which eludes these problems. By exploiting the ‘off-target’ sequence reads, it allows for creation of robust copy number profiles from WES. The accuracy of ENCODER compares to approaches specifically designed for copy number detection, and outperforms current exon-based WES methods, particularly in samples of low quality. DNA copy number profiles generated with a new tool, ENCODER, were compared to DNA copy number profiles from SNP6, NimbleGen and low-coverage Whole Genome Sequencing.
Project description:Background: MicroRNAs (miRNAs) are small regulatory RNAs that are implicated in cancer pathogenesis and have recently shown promise as blood-based biomarkers for cancer detection. Epithelial ovarian cancer is a deadly disease for which improved outcomes could be achieved by successful early detection and enhanced understanding of molecular pathogenesis that leads to improved therapies. A critical step toward these goals is to establish a comprehensive view of miRNAs expressed in epithelial ovarian cancer tissues as well as in normal ovarian surface epithelial cells.
Project description:The ideal genome sequence for medical interpretation is complete and diploid, capturing the full spectrum of genetic variation. Toward this end, there has been progress in discovery of single nucleotide polymorphism (SNP) and small (<10bp) insertion/deletions (indels), but annotation of larger structural variation (SV) including copy number variation (CNV) has been less comprehensive, even with available diploid sequence assemblies. We applied a multi-step sequence and microarray-based analysis to identify numerous previously unknown SVs within the first genome sequence reported from an individual.
Project description:The ideal genome sequence for medical interpretation is complete and diploid, capturing the full spectrum of genetic variation. Toward this end, there has been progress in discovery of single nucleotide polymorphism (SNP) and small (<10bp) insertion/deletions (indels), but annotation of larger structural variation (SV) including copy number variation (CNV) has been less comprehensive, even with available diploid sequence assemblies. We applied a multi-step sequence and microarray-based analysis to identify numerous previously unknown SVs within the first genome sequence reported from an individual.
Project description:The ideal genome sequence for medical interpretation is complete and diploid, capturing the full spectrum of genetic variation. Toward this end, there has been progress in discovery of single nucleotide polymorphism (SNP) and small (<10bp) insertion/deletions (indels), but annotation of larger structural variation (SV) including copy number variation (CNV) has been less comprehensive, even with available diploid sequence assemblies. We applied a multi-step sequence and microarray-based analysis to identify numerous previously unknown SVs within the first genome sequence reported from an individual.
Project description:The ideal genome sequence for medical interpretation is complete and diploid, capturing the full spectrum of genetic variation. Toward this end, there has been progress in discovery of single nucleotide polymorphism (SNP) and small (<10bp) insertion/deletions (indels), but annotation of larger structural variation (SV) including copy number variation (CNV) has been less comprehensive, even with available diploid sequence assemblies. We applied a multi-step sequence and microarray-based analysis to identify numerous previously unknown SVs within the first genome sequence reported from an individual.