BackgroundShort-read aligners have recently gained a lot of speed by exploiting the massive parallelism of GPU. An uprising alterative to GPU is Intel MIC; supercomputers like Tianhe-2, currently top of TOP500, is built with 48,000 MIC boards to offer ~55 PFLOPS. The CPU-like architecture of MIC allows CPU-based software to be parallelized easily; however, the performance is often inferior to GPU counterparts as an MIC card contains only ~60 cores (while a GPU card typically has over a thousand cores).ResultsTo better utilize MIC-enabled computers for NGS data analysis, we developed a new short-read aligner MICA that is optimized in view of MIC's limitation and the extra parallelism inside each MIC core. By utilizing the 512-bit vector units in the MIC and implementing a new seeding strategy, experiments on aligning 150 bp paired-end reads show that MICA using one MIC card is 4.9 times faster than BWA-MEM (using 6 cores of a top-end CPU), and slightly faster than SOAP3-dp (using a GPU). Furthermore, MICA's simplicity allows very efficient scale-up when multiple MIC cards are used in a node (3 cards give a 14.1-fold speedup over BWA-MEM).SummaryMICA can be readily used by MIC-enabled supercomputers for production purpose. We have tested MICA on Tianhe-2 with 90 WGS samples (17.47 Tera-bases), which can be aligned in an hour using 400 nodes. MICA has impressive performance even though MIC is only in its initial stage of development.Availability and implementationMICA's source code is freely available at http://sourceforge.net/projects/mica-aligner under GPL v3.Supplementary informationSupplementary information is available as "Additional File 1". Datasets are available at www.bio8.cs.hku.hk/dataset/mica.

BackgroundAn enduring challenge in personalized medicine is to select right drug for individual patients. Testing drugs on patients in large clinical trials is one way to assess their efficacy and toxicity, but it is impractical to test hundreds of drugs currently under development. Therefore the preclinical prediction model is highly expected as it enables prediction of drug response to hundreds of cell lines in parallel.MethodsRecently, two large-scale pharmacogenomic studies screened multiple anticancer drugs on over 1000 cell lines in an effort to elucidate the response mechanism of anticancer drugs. To this aim, we here used gene expression features and drug sensitivity data in Cancer Cell Line Encyclopedia (CCLE) to build a predictor based on Support Vector Machine (SVM) and a recursive feature selection tool. Robustness of our model was validated by cross-validation and an independent dataset, the Cancer Genome Project (CGP).ResultsOur model achieved good cross validation performance for most drugs in the Cancer Cell Line Encyclopedia (≥80 % accuracy for 10 drugs, ≥ 75 % accuracy for 19 drugs). Independent tests on eleven common drugs between CCLE and CGP achieved satisfactory performance for three of them, i.e., AZD6244, Erlotinib and PD-0325901, using expression levels of only twelve, six and seven genes, respectively.ConclusionsThese results suggest that drug response could be effectively predicted from genomic features. Our model could be applied to predict drug response for some certain drugs and potentially play a complementary role in personalized medicine.

BackgroundWe aimed to identify the miRNA expression profiles in left atrial appendage, with the intention of identifying miRNAs that were significantly associated with non-valvular paroxysmal AF.MethodsThe RNA samples were isolated from healthy controls (n = 5) and patients with atrial fibrillation (n = 8). To confirm the findings obtained by analyzing the miRNA profile, we measured the expression of selected miRNAs in the entire cohort by quantitative PCR.ResultsTen specific miRNAs were found to be differentially expressed between atrial fibrillation and healthy controls with more than a 2-fold change (P < 0.05). Consistent with the data obtained for the profile, expression levels of miRNA-155, miRNA-146b-5p and miRNA-19b were significantly increased in patients with atrial fibrillation. Interestingly, levels of miRNA-146b-5p and miRNA-155, which are known to be associated with inflammation, were independently and positively associated with left atrium dimension, atrial fibrillation duration and high sensitivity C-reactive protein levels. By using four Databases (TargetScan, miRanda, Starbase Clip-seq and miRDB) to perform target gene prediction, there were four genes were related to the inflammatory response and fibrosis, and three others encoding cardiac ion channel proteins. As a result of TaqMan qPCR and Western analysis, the relative mRNA and protein expression level of three target genes (DIER-1, TIMP-4 and CACNA1C) were significantly lower in the atrial fibrillation group than that in the healthy control group.ConclusionsExpression of inflammation-associated miRNAs is significantly up-regulated in the left atrial appendage of patients with non-valvular paroxysmal atrial fibrillation, which may play a significant role in electrical and structural remodeling.