Project description:Recovery of biological information (i.e. Breast tumor versus non-tumor part of breast) from microarray data under heterogeneous experimental conditions (Buffer1 versus Buffer2) using subgroup standardization. Microarray is a useful tool for gene expression analysis and prediction. For a given disease, a microarray database may come from various sources with different experimental setups, collected over time. This heterogeneity provides unnecessary complication in data analysis and, even worse, given false classification in clustering. Therefore, it is practically important to provide a standard data treatment for microarray data from heterogeneous experimental conditions. In this work, “subgroup standardization” is proposed to compensate technical heterogeneities (e.g., buffers, time, machines etc.) in microarray experimental conditions. Provided with repetitive microarray experiments, over time and buffers, the results indicate that the proposed approach can extract correct biological information in the presence of technical irregularities. Hierarchical clustering is used to validate the effectiveness of the proposed approach. Keywords: disease state study
Project description:Here we report 16S rRNA data in gut microbiota of autism spectrum disorders compared with healthy volunteers. A total of 1322 operational taxonomic units (OTUs) were identified in the sequence data. The Bacteroidetes and Firmicutes were both dominated phylum in ausitic subjects and healthy controls. Phylum level analysis showed a clear alteration of the bacterial gut community in ASD characterized by a higher Firmicutes (P < 0.05), Proteobacteria (P < 0.001), and Actinobacteria (P < 0.001) than that in healthy controls. However, Bacteroidetes were significantly decreased in ASD patients (P < 0.001).
Project description:Hypervariable regions V3-V5 of bacterial 16S rRNA genes. This data is part of a pre-publication release. For information on the proper use of pre-publication data shared by the Wellcome Trust Sanger Institute (including details of any publication moratoria), please see http://www.sanger.ac.uk/datasharing/
Project description:In previous study, patients with Diabetes Mellitus (DM) have high risk of active TB and LTBI. Here we report and compare 16S rRNA data of DM-LTBI and DM-nonLTBI in gut microbiota to identify differential candidates between the two groups. The results showed the differential genera have potential to predict the LTBI status in patients.
Project description:To examine the microbiota abundance difference, we performed fecal 16s sequencing of wild type, TCRb-/-, TCRb-/- co-housed with WT and TCRb-/- receiving WT T cells.
Project description:Mass spectrometry remains an important method for analysis of modified nucleosides ubiquitously present in cellular RNAs, in particular for ribosomal and transfer RNAs that play crucial roles in mRNA translation and decoding. Furthermore, modifications have effect on the lifetimes of nucleic acids in plasma and cells and are consequently incorporated into RNA therapeutics. To provide an analytical tool for sequence characterization of modified RNAs, we developed Pytheas, an open-source software package for automated analysis of tandem MS data for RNA. This dataset contains the analysis of 14N and 15N-labeled 16S RNA from E. coli, including all the known RNA modifications (excluding pseudouridines). The analysis has been performed using three different protocols and instruments: Agilent Q-TOF, Waters Synapt G2-S, and Thermo Scientific Orbitrap Fusion Lumos.
Project description:Recovery of biological information (i.e. Breast tumor versus non-tumor part of breast) from microarray data under heterogeneous experimental conditions (Buffer1 versus Buffer2) using subgroup standardization. Microarray is a useful tool for gene expression analysis and prediction. For a given disease, a microarray database may come from various sources with different experimental setups, collected over time. This heterogeneity provides unnecessary complication in data analysis and, even worse, given false classification in clustering. Therefore, it is practically important to provide a standard data treatment for microarray data from heterogeneous experimental conditions. In this work, âsubgroup standardizationâ is proposed to compensate technical heterogeneities (e.g., buffers, time, machines etc.) in microarray experimental conditions. Provided with repetitive microarray experiments, over time and buffers, the results indicate that the proposed approach can extract correct biological information in the presence of technical irregularities. Hierarchical clustering is used to validate the effectiveness of the proposed approach. Experiment Overall Design: 98 of breast cancer specimens and 8 of non-tumor part of breast specimens were applied in the study. All the signals from the mRNA profile of each sample in the microarrays were normalized using the internal control RNA- Stratagene's human common reference RNA.
Project description:To address the role of gut microbiota in the development of paclitaxel-induced peripheral neuropathy (PIPN), we performed 16S rRNA sequencing analysis of feces samples at 14 days and 28 days after the initiation of paclitaxel or vehicle injections.