Project description:Protein reference databases are a critical part of producing efficient proteomic analyses. However, the method for constructing clean, efficient, and comprehensive protein reference databases is lacking. Existing methods either do not have contamination control procedures, or these methods rely on a three-frame and/or six-frame translation that sharply increases the search space and harms MS results. Herein we propose a framework for constructing a customized comprehensive proteomic reference database (CCPRD) from draft genomes and deep sequencing transcriptomes. Its effectiveness is demonstrated by incorporating the proteomes of nematocysts from endoparasitic cnidarian: myxozoans. By applying customized contamination removal procedures, contaminations in omic data were successfully identified and removed. This is an effective method that does not result in over-decontamination. This can be shown by comparing the CCPRD MS results with an artificially-contaminated database and another database with removed contaminations in genomes and transcriptomes added back. CCPRD outperformed traditional frame-based methods by identifying 35.2%-50.7% more peptides and 35.8%-43.8% more proteins, with a maximum 84.6% in size reduction. A BUSCO analysis showed that the CCPRD maintained a relatively high level of completeness compared to traditional methods. These results confirm the superiority of the CCPRD over existing methods in peptide and protein identification numbers, database size, and completeness. By providing a general framework for generating the reference database, the CCPRD, which does not need a high-quality genome, can potentially be applied to any organisms and significantly contribute to proteomic research.
Project description:Reference datasets are often used to compare, interpret or validate experimental data and analytical methods. In the field of gene expression, a dozen reference datasets have been published. Typically, they consist of individual baseline or spike-in experiments carried out in a single laboratory and representing a particular set of conditions. For most organisms, however, few or no such reference datasets are publicly available. Here, we describe a new type of datasets highly representative for the spatial, temporal and response dimensions of gene expression. They result from integrating expression data from a large number of globally normalized and quality controlled public experiments and aggregating results by anatomical parts, stages of development, perturbations, drugs, diseases, neoplasms, and genotypes. The proposed datasets were created for human and several model organisms and are publicly available at www.expressiondata.org.
Project description:We constructed a protein database (DBCGR2) for gut microbiome metaproteomics, which was based on a database of cultivated genomes (Cultivated Genome Reference 2 - CGR2).
Project description:Purpose: To demonstrate that gene expression and splicing analysis varies considerably depending on the mapping reference genome. Methods: We mapped and analyzed submitted RNA reads using different tools and reference genomes to evaluate the influence of genome on DEG and alternative splicing tools. Results: We observed that these differences in transcriptome analysis are, in part, due to the presence of single nucleotide polymorphisms between the sequenced individual and each respective reference genome, as well as annotation differences between the reference genomes that exist even between syntenic orthologs. Conclusion: We conclude that even between two closely related genomes of similar quality, using the reference genome that is most closely related to the species being sampled significantly improves transcriptome.
Project description:These two transcriptome sequencing datasets were generated from two reference RNA samples established by the US FDA-led MicroArray Quality Control project with Illumina next-generation sequencing technology. The reference RNA sample A (UHRR, Catalog #740000) consists of total RNA extracted from 10 human cell lines of various origins: Blymphocyte, brain, breast, cervix, liposarcoma, liver, macrophage, skin, testis and Tlymphocyte. Equal quantities of DNAase-treated total RNA from each cell line were pooled to generate the UHRR. The reference RNA sample B (HBRR, Catalog #6050) consists of total RNA extracted from several regions of the brains from 23 adult donors.
Project description:These two transcriptome sequencing datasets were generated from two reference RNA samples established by the US FDA-led MicroArray Quality Control project with Illumina next-generation sequencing technology. The reference RNA sample A (UHRR, Catalog #740000) consists of total RNA extracted from 10 human cell lines of various origins: Blymphocyte, brain, breast, cervix, liposarcoma, liver, macrophage, skin, testis and Tlymphocyte. Equal quantities of DNAase-treated total RNA from each cell line were pooled to generate the UHRR. The reference RNA sample B (HBRR, Catalog #6050) consists of total RNA extracted from several regions of the brains from 23 adult donors.