Project description:The in-gel digestion of proteins for analysis by liquid chromatograph mass spectrometry has been used since the early 1990s. Although several improvements have contributed to increasing the quality of the data obtained, many recent publications still use sub-optimal approaches. We present an updated in-gel digestion protocol. We show that alternative reducing, alkylating agents and tryptic digestion buffers increase peptide and protein identification and reduce incubation times. Our results indicate that a simultaneous and short, high temperature reduction and alkylation reaction using Tris(2-carboxyethyl)phosphine hydrochloride (TCEP) and chloroacetamide (CAA) with a subsequent gel wash improve protein identification and sequence coverage, diminish peptide side reactions. Additionally, use of 4-(2-Hydroxyethyl)piperazine-1-ethanesulfonic acid buffer (HEPES) allows a significant reduction in the digestion time improving trypsin performance and increasing the peptide recovery. The updated in-gel digestion protocol described here is highly efficient and offers flexibility to be incorporated in any proteomic laboratory.
Project description:Development of an updated genome-scale metabolic model of Clostridium thermocellum and its application for integration of multi-omics datasets
Project description:A new genome of Fraxinus excelsior was assembled using a hybrid approach combining Nanopore and Illumina data (BioProject PRJNA865134, SAMN30100368, genome JANJPF000000000 ). Methylation was also assessed in the genome. Manuscript title: Fraxinus excelsior updated long-read genome reveals the importance of MADS-box genes in tolerance mechanisms against ash dieback, G3:Genes|Genomes|Genetics
Project description:<p>MAVEN, an open-source software program for analysis of LC-MS metabolomics data, was originally released in 2010. As mass spectrometry has advanced in the intervening years, MAVEN has been periodically updated to reflect this advancement. This manuscript describes a major update to the program, MAVEN2, which supports LC-MS/MS analysis of metabolomics and lipidomics samples. We have developed algorithms to support MS/MS spectral matching and efficient search of large-scale fragmentation libraries. We explore the ability of our approach to separate authentic from spurious metabolite identifications using a set of standards spiked into water and yeast backgrounds. To support our improved lipid identification workflow, we introduce a novel <em>in-silico</em> lipidomics library covering major lipid classes and compare searches using our novel library to searches with existing <em>in-silico</em> lipidomics libraries. MAVEN2 source code and cross-platform application installers are freely available for download from GitHub under a GNU permissive license [ver 3], as are the in silico lipidomics libraries and corresponding code repository.</p>
