March 10, 2017
Users will be able to visualize CHO cell mRNA expression data either from published DNA-microarray or RNA-Seq experiments via JBrowse. The visualized data will enable users to easily obtain gene expression levels and compare gene expression levels among different conditions.
August 19, 2016
CHOmine, the InterMine site for CHO and Chinese hamster, went online on August 11, 2016 at https://chomine.boku.ac.at/. CHOmine collects and integrates publicly available Chinese hamster and CHO data, where different datasets are connected to provide user-friendly access. Currently, users are asked to test the site and to provide feedback (impressions, comments, issues, and suggestions for improvements) to Matthias.Gerstl@acib.at or Nicole.Borth@boku.ac.at.
CHO Epigenomic Data
April 14, 2016
A new database of epigenomic information (cho-epigenome.boku.ac.at) for 6 CHO cell lines has been created by Dr. Nicole Borth's group. A link to this database has been added to CHOgenome.org's Data webpage. The epigenomic database website hosts genome sequences for 6 cell lines, SNPs, structural variants, full genome methylation data, and batch culture histone modification data. The manuscript describing the acquisition and analysis of the data can be found here.
This webpage contains links to the current and past featured articles. Journal access may be limited to subscribed users for some of the articles.
|The authors created a consensus genome-scale model of CHO cell metabolism with 1,766 genes and 6,663 reactions describing metabolism and protein production during cell growth. Cell line-specific models for CHO-K1, CHO-S, and CHO-DG44 cells were also created and the integration of -omics data revealed amino acid auxotrophy bases in various cell lines.||A Consensus Genome-scale Reconstruction of Chinese Hamster Ovary Cell Metabolism||Hefzi H et al. Cell Systems (2016) 3, 434-443|
|The authors evaluated the RNA-seq mapping of four CHO cell reference sequences hosted by the NCBI RefSeq. The reference sequences were the 2012 annotated CHO-K1 genome, the 2014 annotated Chinese hamster genome, and their respective transcriptomes. The 2014 Chinese hamster genome had the best total mapping rate of RNA-seq data (73.5%) and the total mapping rate could be improved by approximately 15% with the addition of the human and mouse genomes.||An evaluation of public genomic references for mapping RNA-Seq data from Chinese hamster ovary cells||Le H et al. Biotechnol. Bioeng. (2015) 112, 2412-2416|
|Compares host cell protein production in three different CHO cell lines each lacking a recombinant protein product, in order to understand cell culture harvest diversity.||More similar than different: Host cell protein production using three null CHO cell lines||Yuk I et al. Biotechnol. Bioeng. (2015) 112, 2068-83|
|The authors identified novel miRNAs in CHO by searching the CHO genome for conserved miRNA sequences identified in other species. Further criteria, such as secondary structure prediction, were used to filter the list of novel predicted miRNAs down to 71 expressed novel miRNAs and 56 pre-miRNAs that were added to the hamster miRNome.||Annotation of additional evolutionary conserved microRNAs in CHO cells from updated genomic data||Diendorfer A et al. Biotechnol. Bioeng. (2015) 112, 1488-93|
|The genome of the DHFR negative CHO DXB11 cell line was sequenced with a depth of 33x. Genome analyses revealed that about 17% of CHO DXB11 genes are single copies and that copy number variations of the currently sequenced CHO cell lines are distinct from each other.||Sequencing the CHO DXB11 genome reveals regional variations in genomic stability and haploidy||Kaas C et al. BMC Genomics. 2015; 16(1): 160|
|The authors use CRISPR Cas9 to disrupt the function of the FUT8 and COSMC genes in CHO cells and introduce a web-based tool called CRISPy to identify sgRNA target sequences in the CHO-K1 genome. The tool has identified about 2 million CRISPR target sites over 27,553 genes.||Accelerating genome editing in CHO cells using CRISPR Cas9 and CRISPy, a web-based target finding tool||Ronda C et al. Biotechnol. Bioeng. (2014) 111, 1604-1616|