A completeness-independent method for pre-selection of closely related genomes for species delineation in prokaryotes.

BACKGROUND: Whole-genome approaches are widely preferred for species delineation in prokaryotes. However, these methods require pairwise alignments and calculations at the whole-genome level and thus are computationally intensive. To address this problem, a strategy consisting of sieving (pre-selecting closely related genomes) followed by alignment and calculation has been proposed. RESULTS: Here, we initially test a published approach called "genome-wide tetranucleotide frequency correlation coefficient" (TETRA), which is specially tailored for sieving. Our results show that sieving by TETRA requires > 40% completeness for both genomes of a pair to yield > 95% sensitivity, indicating that TETRA is completeness-dependent. Accordingly, we develop a novel algorithm called "fragment tetranucleotide frequency correlation coefficient" (FRAGTE), which uses fragments rather than whole genomes for sieving. Our results show that FRAGTE achieves ~ 100% sensitivity and high specificity on simulated genomes, real genomes and metagenome-assembled genomes, demonstrating that FRAGTE is completeness-independent. Additionally, FRAGTE sieved a reduced number of total genomes for subsequent alignment and calculation to greatly improve computational efficiency for the process after sieving. Aside from this computational improvement, FRAGTE also reduces the computational cost for the sieving process. Consequently, FRAGTE extremely improves run efficiency for both the processes of sieving and after sieving (subsequent alignment and calculation) to together accelerate genome-wide species delineation. CONCLUSIONS: FRAGTE is a completeness-independent algorithm for sieving. Due to its high sensitivity, high specificity, highly reduced number of sieved genomes and highly improved runtime, FRAGTE will be helpful for whole-genome approaches to facilitate taxonomic studies in prokaryotes.

Integrated analysis of gene expression and methylation profiles of 48 candidate genes in breast cancer patients.

PURPOSE: Gene-specific methylation and expression have shown biological and clinical importance for breast cancer diagnosis and prognosis. Integrated analysis of gene methylation and gene expression may identify genes associated with biology mechanism and clinical outcome of breast cancer and aid in clinical management. METHODS: Using high-throughput microfluidic quantitative PCR, we analyzed the expression profiles of 48 candidate genes in 96 Chinese breast cancer patients and investigated their correlation with gene methylation and associations with breast cancer clinical parameters. RESULTS: Breast cancer-specific gene expression alternation was found in 25 genes with significant expression difference between paired tumor and normal tissues. A total of 9 genes (CCND2, EGFR, GSTP1, PGR, PTGS2, RECK, SOX17, TNFRSF10D, and WIF1) showed significant negative correlation between methylation and gene expression, which were validated in the TCGA database. Total 23 genes (ACADL, APC, BRCA2, CADM1, CAV1, CCND2, CST6, EGFR, ESR2, GSTP1, ICAM5, NPY, PGR, PTGS2, RECK, RUNX3, SFRP1, SOX17, SYK, TGFBR2, TNFRSF10D, WIF1, and WRN) annotated with potential TFBSs in the promoter regions showed negative correlation between methylation and expression. In logistics regression analysis, 31 of the 48 genes showed improved performance in disease prediction with combination of methylation and expression coefficient. CONCLUSIONS: Our results demonstrated the complex correlation and the possible regulatory mechanisms between DNA methylation and gene expression. Integration analysis of methylation and expression of candidate genes could improve performance in breast cancer prediction. These findings would contribute to molecular characterization and identification of biomarkers for potential clinical applications.

Methylation profiling of 48 candidate genes in tumor and matched normal tissues from breast cancer patients.

Gene-specific methylation alterations in breast cancer have been suggested to occur early in tumorigenesis and have the potential to be used for early detection and prevention. The continuous increase in worldwide breast cancer incidences emphasizes the urgent need for identification of methylation biomarkers for early cancer detection and patient stratification. Using microfluidic PCR-based target enrichment and next-generation bisulfite sequencing technology, we analyzed methylation status of 48 candidate genes in paired tumor and normal tissues from 180 Chinese breast cancer patients. Analysis of the sequencing results showed 37 genes differentially methylated between tumor and matched normal tissues. Breast cancer samples with different clinicopathologic characteristics demonstrated distinct profiles of gene methylation. The methylation levels were significantly different between breast cancer subtypes, with basal-like and luminal B tumors having the lowest and the highest methylation levels, respectively. Six genes (ACADL, ADAMTSL1, CAV1, NPY, PTGS2, and RUNX3) showed significant differential methylation among the 4 breast cancer subtypes and also between the ER +/ER- tumors. Using unsupervised hierarchical clustering analysis, we identified a panel of 13 hypermethylated genes as candidate biomarkers that performed a high level of efficiency for cancer prediction. These 13 genes included CST6, DBC1, EGFR, GREM1, GSTP1, IGFBP3, PDGFRB, PPM1E, SFRP1, SFRP2, SOX17, TNFRSF10D, and WRN. Our results provide evidence that well-defined DNA methylation profiles enable breast cancer prediction and patient stratification. The novel gene panel might be a valuable biomarker for early detection of breast cancer.