Thermodynamically optimal whole-genome tiling microarray design and validation

Additional file 2. Assembled genome sequences for E. coli MG1655 and Agrobacterium tumefaciens C58. Two FASTA files containing the assembled genomic sequences are provided in this compressed ZIP archive

Identification of genes and pathways associated with cytotoxic T lymphocyte infiltration of serous ovarian cancer

BACKGROUND: Tumour-infiltrating lymphocytes (TILs) are predictors of disease-specific survival (DSS) in ovarian cancer. It is largely unknown what factors contribute to lymphocyte recruitment. Our aim was to evaluate genes and pathways contributing to infiltration of cytotoxic T lymphocytes (CTLs) in advanced-stage serous ovarian cancer. METHODS: For this study global gene expression was compared between low TIL (n=25) and high TIL tumours (n=24). The differences in gene expression were evaluated using parametric T-testing. Selectively enriched biological pathways were identified with gene set enrichment analysis. Prognostic influence was validated in 157 late-stage serous ovarian cancer patients. Using immunohistochemistry, association of selected genes from identified pathways with CTL was validated. RESULTS: The presence of CTL was associated with 320 genes and 23 pathways (P<0.05). In addition, 54 genes and 8 pathways were also associated with DSS in our validation cohort. Immunohistochemical evaluation showed strong correlations between MHC class I and II membrane expression, parts of the antigen processing and presentation pathway, and CTL recruitment. CONCLUSION: Gene expression profiling and pathway analyses are valuable tools to obtain more understanding of tumour characteristics influencing lymphocyte recruitment in advanced-stage serous ovarian cancer. Identified genes and pathways need to be further investigated for suitability as therapeutic targets

Statistically designing microarrays and microarray experiments to enhance sensitivity and specificity

*Corresponding authors Gene expression signatures from microarray experiments promise to provide important prognostic tools for predicting disease outcome or response to treatment. A number of microarray studies in various cancers have reported such gene signatures. However, the overlap of gene signatures in the same disease has been limited so far, and some reported signatures have not been reproduced in other populations. Clearly, the methods used for verifying novel gene signatures need improvement. In this paper, we describe an experiment in which microarrays and sample hybridization are designed according to the statistical principles of randomization, replication, and blocking. Our results show that such designs provide unbiased estimation of differential expression levels as well as powerful tests for them. Key words: Microarray experiments, experimental design, familywise error rate, multiple comparisons, sensitivity and specificity. Key points: Statistically designing microarray experiments may improve the reproducibility of gene expression signatures for cancer prognoses. We describe an experiment in which microarrays and sample hybridization are designed according to the statistical principles of randomization, replication, and blocking. Such designs avoids confounding effects, provide unbiased estimation of differential expression levels, as well as increase sensitivity and specificity

Computational prediction, experiment design and statistical validations of non-coding regulatory RNA

Non-coding regulatory RNAs (ncRNAs) regulate a host of gene functions in prokaryotes, e.g., transcription and translation regulations, RNA processing and modification, and mRNA stability. Some ncRNAs have been identified experimentally, but many are yet to be found. ncRNAs can be classified as either cis- or trans-acting. cis-ncRNAs perfectly complement their target genes and are usually encoded on the anti-sense strands of the targets. On the contrary, trans-ncRNAs regulate their target genes through short and often imperfect base-pairings with the targets, and are usually encoded elsewhere on the genome. A whole-genome thermodynamic analysis can be performed to identify all imperfect but stable base-pairings between all annotated genes and some genomic regions encoding ncRNAs from the same species. However, the sizes of these base-paring regions are short and variable, and their melting temperatures vary greatly between perfectly and imperfectly matched targets. It is difficult to predict trans-acting ncRNAs solely based on the thermodynamic analysis. Therefore, we also have to consider known ncRNA structures to improve our predictions. We find that Hfq-binding ncRNAs, which require Hfq protein to function, share three common structural properties. We predict these special ncRNAs in E. coli and Agrobacterium tumefaciens according to a systematic, novel 5-step approach based on thermodynamic analyses as well as known structural properties of this class of ncRNAs. Whole genome tiling microarrays are chosen to validate our predictions. We describe how the microarrays have been designed, created, and validated for E. coli MG1655 and Agrobacterium tumefaciens C58. We match our new ncRNA prediction results with known ncRNAs, calculate correlation coefficient values between each ncRNA candidate and their predicted targets measure by the whole-genome tiling microarrays, and confirm the results with 3 other ncRNA identification software tools. We also perform a gene ontology network analysis to reveal the associations of ncRNA candidates and their predicted targets. Our novel 5-step prediction method is generally applicable to other prokaryote species and may help advance ncRNA research in prokaryotes