Project description:Precision de novo peptide sequencing using mirror proteases of Ac-LysargiNase and trypsin for large-scale proteomicsPrecision de novo peptide sequencing using mirror proteases of Ac-LysargiNase and trypsin for large-scale proteomics
Project description:Deep whole genome sequencing of Drosophila melanogaster inbred lines: DGRP-28, DGRP-307, DGRP-399, DGRP-57, DGRP-639, DGRP-712, DGRP-714, DGRP-852 and Virginizer (VGN). The lines were sequenced deeply giving between 54M and 92M reads to achieve a whole genome coverage that ranged between 74X and 125X. The sequencing was used for de novo genotyping.
Project description:Mutations in components of the subcortical maternal complex (SMC) of the human oocyte are enigmatically associated with DNA methylation abnormalities specifically at imprinted genes in conceptuses, but the developmental timing, genomic extent and mechanistic details of these defects are unknown. Here, we show, by single-cell bisulphite sequencing, that mutation in human KHDC3L that causes recurrent hydatidiform mole results in a genome-wide deficit of de novo methylation in oocytes.
Project description:DNA methylation plays a critical role in development, particularly in repressing retrotransposons. The mammalian methylation landscape is dependent on the combined activities of the canonical maintenance enzyme Dnmt1 and the de novo Dnmts, 3a and 3b. Here we demonstrate that Dnmt1 displays de novo methylation activity in vitro and in vivo with specific retrotransposon targeting. We used whole-genome bisulfite and long-read Nanopore sequencing in genetically engineered methylation depleted embryonic stem cells to provide an in-depth assessment and quantification of this activity. Utilizing additional knockout lines and molecular characterization, we show that Dnmt1's de novo methylation activity depends on Uhrf1 and its genomic recruitment overlaps with targets that enrich for Trim28 and H3K9 trimethylation. Our data demonstrate that Dnmt1 can de novo add and maintain DNA methylation, especially at retrotransposons and that this mechanism may provide additional stability for long-term repression and epigenetic propagation throughout development.
Project description:Dependent on concise, pre-defined protein sequence databases, traditional search algorithms perform poorly when analyzing mass spectra derived from wholly uncharacterized protein products. Conversely, de novo peptide sequencing algorithms can interpret mass spectra without relying on reference databases. However, such algorithms have been difficult to apply to complex protein mixtures, in part due to a lack of methods for automatically validating de novo sequencing results. Here, we present novel metrics for benchmarking de novo sequencing algorithm performance on large scale proteomics datasets, and present a method for accurately calibrating false discovery rates on de novo results. We also present a novel algorithm (LADS) which leverages experimentally disambiguated fragmentation spectra to boost sequencing accuracy and sensitivity. LADS improves sequencing accuracy on longer peptides relative to other algorithms and improves discriminability of correct and incorrect sequences. Using these advancements, we demonstrate accurate de novo identification of peptide sequences not identifiable using database search-based approaches.
Project description:Primary objectives: The primary objective is to investigate circulating tumor DNA (ctDNA) via deep sequencing for mutation detection and by whole genome sequencing for copy number analyses before start (baseline) with regorafenib and at defined time points during administration of regorafenib for treatment efficacy in colorectal cancer patients in terms of overall survival (OS).
Primary endpoints: circulating tumor DNA (ctDNA) via deep sequencing for mutation detection and by whole genome sequencing for copy number analyses before start (baseline) with regorafenib and at defined time points during administration of regorafenib for treatment efficacy in colorectal cancer patients in terms of overall survival (OS).
