Project description:We report the human homologous microRNA profiles in food-grade, bovine-sourced sirloin, heart and adrenal tissue (raw, cooked, and laboratory-prepared pasteurized, freeze-dried extracts) Deep miRNA sequencing of sirloin (raw and cooked), heart tissue (raw, cooked, and pastuerized, freeze-dried extracts) and adrenal tissue (raw, cooked, and laboratory-prepared pasteurized, freeze-dried extracts), 3 replicates each process group
Project description:The ProMetIS dataset corresponds to the proteomic and metabolomic analysis of mouse lines lacking the LAT (linker for activation of T cells; OMIM: 602354) or MX2 gene (MX dynamin-like GTPase 2; OMIM: 147890), as well as the control (WT) line. Besides its role in T-cell receptor (TCR) signaling (Roncagalli et al., 2010), LAT has been shown to be involved in neurodevelopmental diseases (Loviglio et al., 2017). The systematic generation of mouse models is part of the functional characterization of the genome by the IMPC consortium (Brown et al., 2018). This characterization is currently based on a battery of animal phenotypic tests (anatomy, behaviour, histology, haematology, physiology), the results of which feed the IMPC database (https://www.mousephenotype.org). In particular, gene knockouts associated to metabolic diseases have been identified (Rozman et al., 2018). To further characterize these models, global molecular approaches are required, such as the metabolomics analysis of plasma samples which has been described recently (Barupal et al., 2019). The originality of ProMetIS is to provide, in addition to the clinical data, the proteomics and metabolomics analysis of the LAT, MX2 and WT mouse models in liver and plasma. ProMetIS provides access to unique molecular functional information on the LAT and MX2 genes. In addition, the dataset has been generated by the four national infrastructures for mouse phenogenomics (PHENOMIN-ICS), proteomics (ProFI), metabolomics (MetaboHUB) and bioinformatics (IFB) for optimal quality, reproducibility and accessibility. The protocols and computational workflows provided here can be easily extended to a larger number of individuals and tissues. Finally, ProMetIS will be of great interest to develop and evaluate bioinformatics and biostatistical methods for the processing and integration of proteomic and metabolomic data.
Project description:Chromatin immunoprecipitation followed by ultra-high throughput (UHTP) sequencing (ChIP-seq) is a powerful tool to establish protein-DNA interactions genome-wide, and the primary limitation of its broad application at present is the often-limited access to sequencers. Here, we report a protocol, Mab-Seq, to generate preliminary quality evaluations and single-chromosome data for deep-sequencing libraries. We show that commercially available genomic microarrays can be used to maximize the efficiency of library creation, quickly generate preliminary data on a chromosomal scale, and help establish the depth to which novel libraries will require deep sequencing.
Project description:Deep sequencing of tRNAs have historically been nutoriously difficult. Here, we benchmark a newly developed library prep protocol termed OTTR agains intact tRNAs as well as tRNA fragments and show that OTTR outperforms any other commercial cloning protocol.
