Project description:We performed proteomics on naturally infested green ash (F. pennsylvanica) trees at low and high levels of emerald ash borer (A. planipennis) infestation.
Our integrative analysis of the RNA-Seq and proteomics data identified 14 proteins and 4 transcripts that contribute most to the difference between highly infested and low infested trees.
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:MIPP-Seq provides an ultra-sensitive, low-cost approach for detecting and validating known and novel mutations in a highly scalable system with broad utility spanning both research and clinical diagnostic testing applications. The scalability of MIPP-Seq allows for multiplexing mutations and samples, which dramatically reduce costs of variant validation when compared to methods like ddPCR. By leveraging the power of individual analyses of multiple unique and independent reactions, MIPP-Seq can validate and precisely quantitate extremely low AAFs across multiple tissues and mutational categories including both indels and SNVs. Furthermore, using Illumina sequencing technology, MIPP-seq provides a robust method for accurate detection of novel mutations at an extremely low AAF.
Project description:Here, we describe the DEMETER-assisted 5-Methylcytosine Nicking sequencing (DMN-seq) utilizing 5mC-specific glycosylase DEMETER to nick DNA at 5mC sites, enabling 5mC detection at the single-base resolution. Leveraging this nicking activity to deplete hypermethylated sites, we adapted DMN-seq to preferentially enrich and investigate hypomethylated regions in colorectal cancer samples. When applied to cell-free DNA as low as 0.1 ng, DMN-seq significantly expands the scope of cancer biomarkers by capturing hypomethylated regions, with high sensitivity and reproducibility even in low-input clinical samples.
Project description:Here, we describe the DEMETER-assisted 5-Methylcytosine Nicking sequencing (DMN-seq) utilizing 5mC-specific glycosylase DEMETER to nick DNA at 5mC sites, enabling 5mC detection at the single-base resolution. Leveraging this nicking activity to deplete hypermethylated sites, we adapted DMN-seq to preferentially enrich and investigate hypomethylated regions in colorectal cancer samples. When applied to cell-free DNA as low as 0.1 ng, DMN-seq significantly expands the scope of cancer biomarkers by capturing hypomethylated regions, with high sensitivity and reproducibility even in low-input clinical samples.
Project description:Here, we describe the DEMETER-assisted 5-Methylcytosine Nicking sequencing (DMN-seq) utilizing 5mC-specific glycosylase DEMETER to nick DNA at 5mC sites, enabling 5mC detection at the single-base resolution. Leveraging this nicking activity to deplete hypermethylated sites, we adapted DMN-seq to preferentially enrich and investigate hypomethylated regions in colorectal cancer samples. When applied to cell-free DNA as low as 0.1 ng, DMN-seq significantly expands the scope of cancer biomarkers by capturing hypomethylated regions, with high sensitivity and reproducibility even in low-input clinical samples.
Project description:Single-cell sequencing has advanced our understanding of cell-type diversity and heterogeneity. However, existing single-cell multi-omics methods lack DNA data resolution and full-length total RNA detection. Here, we introduce single-cell (sc)Repli-RamDA-seq (scRR-seq), a novel multi-omics method that enables high-resolution DNA replication profiling and full-length total RNA sequencing from the same single cell, made possible by the clean separation of DNA and RNA. scRR-seq generates DNA replication and RNA sequencing data comparable to individually obtained scRepli-seq and scRamDA-seq data, respectively. scRR-seq also allows one to tell the cell-cycle stage of a given S-phase cell and detects copy-number variation (CNV) in non-S-phase cells. scRR-seq outperforms other scRNA-seq methods in the number of transcripts identified and full-length total RNA detection. Furthermore, scRR-seq allowed haplotype-specific analysis and the identification of novel S-phase markers. Taken together, scRR-seq is a robust single-cell multi-omics method with promising potential for comprehensive genome and transcriptome analysis.
Project description:Single-cell sequencing has advanced our understanding of cell-type diversity and heterogeneity. However, existing single-cell multi-omics methods lack DNA data resolution and full-length total RNA detection. Here, we introduce single-cell (sc)Repli-RamDA-seq (scRR-seq), a novel multi-omics method that enables high-resolution DNA replication profiling and full-length total RNA sequencing from the same single cell, made possible by the clean separation of DNA and RNA. scRR-seq generates DNA replication and RNA sequencing data comparable to individually obtained scRepli-seq and scRamDA-seq data, respectively. scRR-seq also allows one to tell the cell-cycle stage of a given S-phase cell and detects copy-number variation (CNV) in non-S-phase cells. scRR-seq outperforms other scRNA-seq methods in the number of transcripts identified and full-length total RNA detection. Furthermore, scRR-seq allowed haplotype-specific analysis and the identification of novel S-phase markers. Taken together, scRR-seq is a robust single-cell multi-omics method with promising potential for comprehensive genome and transcriptome analysis.