Project description:This is the data and results associated with the 2012 study organized by the proteome informatics research group of the Association of Biomolecular Resource Facilities, which investigated participants' ability to identify a range of post-translationally modified peptides spiked into a yeast lysate background. Results and analysis of this study were published in Molecular and Cellular Proteomics:
Chalkley RJ, Bandeira N, Chambers MC, Clauser K, Cottrell J, Deutsch EW, Kapp EA, Lam HH, McDonald WH, Neubert TA, Sun RX Mol Cell Proteomics 2013 [Epub Oct 31]
Project description:Species-specific genes play an important role in defining the phenotype of an organism. However, current gene prediction methods can only efficiently find genes that share features such as sequence similarity or general sequence characteristics with previously known genes. Novel sequencing methods and tiling arrays can be used to find genes without prior information and they have demonstrated that novel genes can still be found from extensively studied model organisms. Unfortunately, these methods are expensive and thus are not easily applicable, e.g., to finding genes that are expressed only in very specific conditions. We demonstrate a method for finding novel genes with sparse arrays, applying it on the 33.9 Mb genome of the filamentous fungus Trichoderma reesei. Our computational method does not require normalisations between arrays and it takes into account the multiple-testing problem typical for analysis of microarray data. In contrast to tiling arrays, that use overlapping probes, only one 25mer microarray oligonucleotide probe was used for every 100 b. Thus, only relatively little space on a microarray slide was required to cover the intergenic regions of a genome. The analysis was done as a by-product of a conventional microarray experiment with no additional costs. We found at least 23 good candidates for novel transcripts that could code for proteins and all of which were expressed at high levels. Candidate genes were found to neighbour ire1 and cre1 and many other regulatory genes. Our simple, low-cost method can easily be applied to finding novel species-specific genes without prior knowledge of their sequence properties.
Project description:We generated a map of NSUN6-dependent RNA 5 methyl-cytosine in the human transcriptome by applying RNA bisulfite conversion and sequencing to two sets of cell lines (HUES9 and HEK293) where the RNA cytosine 5 methyl-transfearse NSUN6 has been either knocked out by CRISPR Cas9 or overexpressed using a PiggyBac vector. We apply this and other molecular methods to validate NSUN6 RNA methylation targets with single nucelotide resolution in the human transcriptome.
Project description:Copy number alterations (CNAs) play a fundamental role in cancer development and constitute a potential tool for tailored treatments. The CNAs recognition in formalin fixed paraffin embedded (FFPE) material, to date, relies on fluorescence in situ hybridization, but the introduction of large-scale next-generation sequencing (NGS) has dramatically improved their discovery at genome-wide level. The detection of CNAs by NGS in FFPE material is, nonetheless, a complex issue, which still requires validation studies. Herein, the CNAs detection by a widely used NGS assay (Oncomine Comprehensive Assay plus®, OCA+) were evaluated in 14 FFPE samples mirroring diagnostic daily practice and compared to a whole-genome assay. OCA+, a targeted DNA panel, showed lower CNAs detection sensitivity and equal specificity for gains and losses. According to proprietary software pipeline, OCA+ accurately identify gains characterized by CN ≥5,2. A much less robust threshold (CN ≤1.18) was identified that maximized the difference between true and false positive losses. Orthogonal FISH tests validated seven CNAs characterized by CN gain ≥6 or complete loss. Considering the CNAs growing significance in precision medicine, our findings further prompt towards a robust validation of NGS detection in FFPE materials.
Project description:Species-specific genes play an important role in defining the phenotype of an organism. However, current gene prediction methods can only efficiently find genes that share features such as sequence similarity or general sequence characteristics with previously known genes. Novel sequencing methods and tiling arrays can be used to find genes without prior information and they have demonstrated that novel genes can still be found from extensively studied model organisms. Unfortunately, these methods are expensive and thus are not easily applicable, e.g., to finding genes that are expressed only in very specific conditions. We demonstrate a method for finding novel genes with sparse arrays, applying it on the 33.9 Mb genome of the filamentous fungus Trichoderma reesei. Our computational method does not require normalisations between arrays and it takes into account the multiple-testing problem typical for analysis of microarray data. In contrast to tiling arrays, that use overlapping probes, only one 25mer microarray oligonucleotide probe was used for every 100 b. Thus, only relatively little space on a microarray slide was required to cover the intergenic regions of a genome. The analysis was done as a by-product of a conventional microarray experiment with no additional costs. We found at least 23 good candidates for novel transcripts that could code for proteins and all of which were expressed at high levels. Candidate genes were found to neighbour ire1 and cre1 and many other regulatory genes. Our simple, low-cost method can easily be applied to finding novel species-specific genes without prior knowledge of their sequence properties. Experiment consists of three conditions relevant for study of protein secretion, growth rate D=0.03 h-1, growth rate D=0.06 h-1 and growth rate D=0.03 h-1 with high cell density. Each condition has three biological repeats thus there are nine chemostat cultivations.
Project description:To establish a method for calling digital karyotypes using the Illumina HumanCoreExome BeadChip, we calculated the percent of cells within a population that were required to detect a chromosome alteration. We performed serial dilutions of an iPSC line with Trisomy 12, 13, 14, 17, 20, and XXY and a genetically matched clone. Dilutions were at 0, 6.25, 12.5, 25 and 50% abnormal cells.