Project description:Subsequently, using a combination of BSA-seq, transcriptomic sequencing (RNA-seq), and proteomic sequencing approaches, we identified the candidate gene Nitab4.5_0008674g0010 that encodes dihydroneopterin aldolase as a factor associated with tobacco leaf yellowing.
Project description:We examined the patterns of gene expression of mouse thymic leukemias extracted from Mb1-CreDPB mice by RNA sequencing (RNA-seq). Our goal was to integrate RNA-seq data with whole-exome sequencing (WES) to determined secondary driver mutations of leukemogenesis in the absence of Spi-B and PU.1,
Project description:Background: Whole exome sequencing (WES) has been proven to serve as a valuable basis for various applications such as variant calling and copy number variation (CNV) analyses. For those analyses the read coverage should be optimally balanced throughout protein coding regions at sufficient read depth. Unfortunately, WES is known for its uneven coverage within coding regions due to GC-rich regions or off-target enrichment. Results: In order to examine the irregularities of WES within genes, we applied Agilent SureSelectXT exome capture on human samples and sequenced these via Illumina in 2x101 paired-end mode. As we suspected the sequenced insert length to be crucial in the uneven coverage of exome captured samples, we sheared 12 genomic DNA samples to two different DNA insert size lengths, namely 130 and 170 bp. Interestingly, although mean coverages of target regions were clearly higher in samples of 130 bp insert length, the level of evenness was more pronounced in 170 bp samples. Moreover, merging overlapping paired-end reads revealed a positive effect on evenness indicating overlapping reads as another reason for the unevenness. In addition, mutation analysis on a subset of the samples was performed. In these isogenic subclones almost twofold mutations were failed in the 130 bp samples when compared to the 170 bp samples. Visual inspection of the discarded mutation sites exposed low coverages at the sites embedded in high amplitudes of coverage depth in the affected region. Conclusions: Producing longer insert reads could be a good strategy to achieve better uniform read coverage in coding regions and hereby enhancing the effective sequencing yield to provide an improved basis for further variant calling and CNV analyses.
Project description:We produced an extensive transcript catalog for LCLs of 5 primate species by leveraging isoform sequencing and short-read RNA-seq. The curated transcriptomes were used to assist mass spectrometry protein identifications.
Project description:Triple Negative Breast Cancer (TNBC) is an aggressive subtype of breast cancer with high intra-tumoral heterogeneity, frequently resistant to treatment and no known targeted therapy available to improve patient outcomes. It has been hypothesized that the genomic architecture of a TNBC tumour evolves over time, both before, and during therapy, leading to therapy resistance and a high propensity to relapse. Whether this is an inherent property of the tumour or acquired over time is not well characterized. Despite this important clinical implication, limited studies have been carried out to unravel temporal evolution of TNBC over time. Herein, we report an OMICS based analysis of three TNBC patients who were longitudinally sampled during their treatment at different times of relapse. We recruited three TNBC patients at the time of their first relapse who were then followed-up through the course of their treatment. We obtained retrospective samples (tumour samples) from patient tumours at diagnosis (before neo-adjuvant chemotherapy - NACT) at surgery (post NACT) and prospectively sampled them at each subsequent relapse (tumour, blood plasma, and buffy coat) as determined by RECIST criteria. Tumor and buffy coat DNA were subjected to whole exome sequencing (WES) at 200x, and SNP arrays for copy number variation (CNV) analysis. RNA from tumour samples at relapse was subjected to whole transcriptome sequencing. Pathogenic germline BRCA1 variants identified in WES were validated using Sanger sequencing. 1084 somatic mutations identified in whole exome sequencing of all tumour tissues (n=13) from three patients, were subjected to a custom amplicon ultra-deep sequencing assay at 30,000X in their germline DNA (n=3), tumour DNA (n=10), and cfDNA from plasma samples at relapse (n=8). Copy number corrected allele frequencies, tumour ploidy, tumour purity, and ultra-deep sequencing assay derived variant allele frequencies were used to infer clonal and phylogenetic architecture of each patient as it evolved under selective pressure of therapy over time. Clonality analysis incorporating allele fractions from ultra-deep sequencing identified clones comprising of mutations that are present throughout the course of therapy which we term as founding clones and stem mutations respectively. Such founding clones comprising of stem mutations in all 3 patients were present throughout the course of treatment, irrespective of change in treatment modalities. These stem clones included well characterized cancer related genes like PDGFRB & ARID2 (Patient 02), TP53, BRAF & CSF3R (Patient 04) and ESR1, APC, EZH2 & TP53 (Patient 07). Such branching evolution is seen in all three patients wherein the dominant clone (stem clone) acquires additional mutations to form sub-clones, while persisting over time. These sub-clones may be chemo and radio resistant, while also providing for organ specific metastatic potential. Allele fractions of expressed variants inferred from RNA-Seq data co-related with allele fractions from WES data indicating that all somatic.
Project description:Triple Negative Breast Cancer (TNBC) is an aggressive subtype of breast cancer with high intra-tumoral heterogeneity, frequently resistant to treatment and no known targeted therapy available to improve patient outcomes. It has been hypothesized that the genomic architecture of a TNBC tumour evolves over time, both before, and during therapy, leading to therapy resistance and a high propensity to relapse. Whether this is an inherent property of the tumour or acquired over time is not well characterized. Despite this important clinical implication, limited studies have been carried out to unravel temporal evolution of TNBC over time. Herein, we report an OMICS based analysis of three TNBC patients who were longitudinally sampled during their treatment at different times of relapse. We recruited three TNBC patients at the time of their first relapse who were then followed-up through the course of their treatment. We obtained retrospective samples (tumour samples) from patient tumours at diagnosis (before neo-adjuvant chemotherapy - NACT) at surgery (post NACT) and prospectively sampled them at each subsequent relapse (tumour, blood plasma, and buffy coat) as determined by RECIST criteria. Tumor and buffy coat DNA were subjected to whole exome sequencing (WES) at 200x, and SNP arrays for copy number variation (CNV) analysis. RNA from tumour samples at relapse was subjected to whole transcriptome sequencing. Pathogenic germline BRCA1 variants identified in WES were validated using Sanger sequencing. 1084 somatic mutations identified in whole exome sequencing of all tumour tissues (n=13) from three patients, were subjected to a custom amplicon ultra-deep sequencing assay at 30,000X in their germline DNA (n=3), tumour DNA (n=10), and cfDNA from plasma samples at relapse (n=8). Copy number corrected allele frequencies, tumour ploidy, tumour purity, and ultra-deep sequencing assay derived variant allele frequencies were used to infer clonal and phylogenetic architecture of each patient as it evolved under selective pressure of therapy over time. Clonality analysis incorporating allele fractions from ultra-deep sequencing identified clones comprising of mutations that are present throughout the course of therapy which we term as founding clones and stem mutations respectively. Such founding clones comprising of stem mutations in all 3 patients were present throughout the course of treatment, irrespective of change in treatment modalities. These stem clones included well characterized cancer related genes like PDGFRB & ARID2 (Patient 02), TP53, BRAF & CSF3R (Patient 04) and ESR1, APC, EZH2 & TP53 (Patient 07). Such branching evolution is seen in all three patients wherein the dominant clone (stem clone) acquires additional mutations to form sub-clones, while persisting over time. These sub-clones may be chemo and radio resistant, while also providing for organ specific metastatic potential. Allele fractions of expressed variants inferred from RNA-Seq data co-related with allele fractions from WES data indicating that all somatic.
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: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. DNA copy number profiles of mouse squamous cell lung cancer (SCLC) were generated with ENCODER from whole exome sequencing data and compared to results from the NimbleGen array
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. DNA copy number profiles of melanoma PDX sample were generated with ENCODER from whole exome sequencing data and compared to results from the SNP6 platform.