BMC Bioinformatics

【BMC Bioinformatics】介绍
英文名称:BMC Bioinformatics

BMC Bioinformatics is an open access journal publishing original peer-reviewed research articles in all aspects of the development, testing and novel application of computational and statistical methods for the modeling and analysis of all kinds of biological data, as well as other areas of computational biology. BMC Bioinformatics (ISSN 1471-2105) is indexed/tracked/covered by PubMed, MEDLINE, BIOSIS, CAS, EMBASE, Scopus, ACM, CABI, Thomson Reuters (ISI) and Google Scholar.

  1. Evaluation of gene expression data generated from expired Affymetrix GeneChip® microarrays using MAQC reference RNA samples

  2. BMC Bioinformatics,2010, 11(Suppl 6):S10

    Zhining Wen, Charles Wang, Quan Shi, Ying Huang, Zhenqiang Su, Huixiao Hong, Weida Tong, Leming Shi*

    Zhining Wen 


    Background: The Affymetrix GeneChip® system is a commonly used platform for microarray analysis but the technology is inherently expensive. Unfortunately, changes in experimental planning and execution, such as the unavailability of previously anticipated samples or a shift in research focus, may render significant numbers of prepurchased
    GeneChip® microarrays unprocessed before their manufacturer’s expiration dates. Researchers and microarray core facilities wonder whether expired microarrays are still useful for gene expression analysis. In addition, it was not clear whether the two human reference RNA samples established by the MAQC project in 2005 still maintained their transcriptome integrity over a period of four years. Experiments were conducted to answer these questions.

    Results: Microarray data were generated in 2009 in three replicates for each of the two MAQC samples with either expired Affymetrix U133A or unexpired U133Plus2 microarrays. These results were compared with data obtained in 2005 on the U133Plus2 microarray. The percentage of overlap between the lists of differentially expressed genes (DEGs) from U133Plus2 microarray data generated in 2009 and in 2005 was 97.44%. While there was some degree of fold change compression in the expired U133A microarrays, the percentage of overlap between the lists of DEGs from the expired and unexpired microarrays was as high as 96.99%. Moreover, the microarray data generated using the expired U133A microarrays in 2009 were highly concordant with microarray and TaqMan® data generated by the MAQC project in 2005.

    Conclusions: Our results demonstrated that microarray data generated using U133A microarrays, which were more than four years past the manufacturer’s expiration date, were highly specific and consistent with those from unexpired microarrays in identifying DEGs despite some appreciable fold change compression and decrease in sensitivity. Our data also suggested that the MAQC reference RNA samples, stored at -80°C, were stable over a time frame of at least four years.

  3. Microarray platform consistency is revealed by biologically functional analysis of gene expression profiles

  4. BMC Bioinformatics,2009 Oct 8;10 Suppl 11:S12

    Li Z, Su Z, Wen Z, Shi L, Chen T.

    Zhining Wen 


    BACKGROUND: Several different microarray platforms are available for measuring gene expression. There are disagreements within the microarray scientific community for intra- and inter-platform consistency of these platforms. Both high and low consistencies were demonstrated across different platforms in terms of genes with significantly differential expression. Array studies for gene expression are used to explore biological causes and effects. Therefore, consistency should eventually be evaluated in a biological setting to reveal the functional differences between the examined samples, not just a list of differentially expressed genes (DEG). In this study, we investigated whether different platforms had a high consistency from the biologically functional perspective.

    RESULTS: DEG data without filtering the different probes in microarrays from different platforms generated from kidney samples of rats treated with the kidney carcinogen, aristolochic acid, in five test sites using microarrays from Affymetrix, Applied Biosystems, Agilent, and GE health platforms (two sites using Affymetrix for intra-platform comparison) were input into the Ingenuity Pathway Analysis (IPA) system for functional analysis. The functions of the DEG lists determined by IPA were compared across the four different platforms and two test sites for Affymetrix platform. Analysis results showed that there is a very high level of consistency between the two test sites using the same platform or among different platforms. The top functions determined by the different platforms were very similar and reflected carcinogenicity and toxicity of aristolochic acid in the rat kidney.

    CONCLUSION: Our results demonstrate that highly consistent biological information can be generated from different microarray platforms.

  5. In silico method for systematic analysis of feature importance in microRNA-mRNA interactions

  6. BMC Bioinformatics,2009, 10:427

    JiaMin Xiao  , Yizhou Li  , Ke Long Wang  , Zhining Wen  , Menglong Li*  , LiFang Zhang  , guangxuan Min 


    Background: MicroRNA (miRNA), which is short non-coding RNA, plays a pivotal role in the regulation of many biological processes and affects the stability and/or translation of mRNA. Recently, machine learning algorithms were developed to predict potential miRNA targets. Most of these methods are robust but are not sensitive to redundant or irrelevant features. Despite their good performance, the relative importance of each feature is still unclear. With increasing experimental data becoming available, research interest has shifted from higher prediction performance to uncovering the mechanism of microRNA-mRNA interactions.

    Results: Systematic analysis of sequence, structural and positional features was carried out for two different data sets. The dominant functional features were distinguished from uninformative features in single and hybrid feature sets. Models were developed using only statistically significant sequence, structural and positional features, resulting in area under the receiver operating curves (AUC) values of 0.919, 0.927 and 0.969 for one data set and of 0.926, 0.874 and 0.954 for another data set, respectively. Hybrid models were developed by combining various features and achieved AUC of 0.978 and 0.970 for two different data sets. Functional miRNA information is well reflected in these features, which are expected to be valuable in understanding the mechanism of microRNAmRNA interactions and in designing experiments.

    Conclusions: Differing from previous approaches, this study focused on systematic analysis of all types of features. Statistically significant features were identified and used to construct models that yield similar accuracy to previous studies in a shorter computation time.