A RecA-mediated exon profiling method

We have developed a RecA-mediated simple, rapid and scalable method for identifying novel alternatively spliced full-length cDNA candidates. This method is based on the principle that RecA proteins allow to carry radioisotope-labeled probe DNAs to their homologous sequences, resulting in forming triplexes. The resulting complex is easily detected by mobility difference on electrophoresis. We applied this exon profiling method to four selected mouse genes as a feasibility study. To design probes for detection, the information on known exonic regions was extracted from public database, RefSeq. Concerning the potentially transcribed novel exonic regions, RNA mapping experiment using Affymetrix tiling array was performed. As a result, we were able to identify alternative splice variants of Thioredoxin domain containing 5, Interleukin1β, Interleukin 1 family 6 and glutamine-rich hypothetical protein. In addition, full-length sequencing demonstrated that our method could profile exon structures with >90% accuracy. This reliable method can allow us to screen novel splice variants from a huge number of cDNA clone set effectively

iCOD : an integrated clinical omics database based on the systems-pathology view of disease

<p>Abstract</p> <p>Background</p> <p>Variety of information relating between genome and the pathological findings in disease will yield a wealth of clues to discover new function, the role of genes and pathways, and future medicine. In addition to molecular information such as gene expression and genome copy number, detailed clinical information is essential for such systematic omics analysis.</p> <p>Results</p> <p>In order to provide a basic platform to realize a future medicine based on the integration of molecular and clinico-pathological information of disease, we have developed an integrated clinical omics database (iCOD) in which comprehensive disease information of the patients is collected, including not only molecular omics data such as CGH (Comparative Genomic Hybridization) and gene expression profiles but also comprehensive clinical information such as clinical manifestations, medical images (CT, X-ray, ultrasounds, etc), laboratory tests, drug histories, pathological findings and even life-style/environmental information. The iCOD is developed to combine the molecular and clinico-pathological information of the patients to provide the holistic understanding of the disease. Furthermore, we developed several kinds of integrated view maps of disease in the iCOD, which summarize the comprehensive patient data to provide the information for the interrelation between the molecular omics data and clinico-pathological findings as well as estimation for the disease pathways, such as three layer-linked disease map, disease pathway map, and pathome-genome map.</p> <p>Conclusions</p> <p>With these utilities, our iCOD aims to contribute to provide the omics basis of the disease as well as to promote the pathway-directed disease view. The iCOD database is available online, containing 140 patient cases of hepatocellular carcinoma, with raw data of each case as supplemental data set to download. The iCOD and supplemental data can be accessed at</p> <p><url>http://omics.tmd.ac.jp/icod_pub_eng</url></p

Large-scale clustering of CAGE tag expression data

Background: Recent analyses have suggested that many genes possess multiple transcription start sites (TSSs) that are differentially utilized in different tissues and cell lines. We have identified a huge number of TSSs mapped onto the mouse genome using the cap analysis of gene expression (CAGE) method. The standard hierarchical clustering algorithm, which gives us easily understandable graphical tree images, has difficulties in processing such huge amounts of TSS data and a better method to calculate and display the results is needed. Results: We use a combination of hierarchical and non-hierarchical clustering to cluster expression profiles of TSSs based on a large amount of CAGE data to profit from the best of both methods. We processed the genome-wide expression data, including 159,075 TSSs derived from 127 RNA samples of various organs of mouse, and succeeded in categorizing them into 70-100 clusters. The clusters exhibited intriguing biological features: a cluster supergroup with a ubiquitous expression profile, tissue-specific patterns, a distinct distribution of non-coding RNA and functional TSS groups. Conclusion: Our approach succeeded in greatly reducing the calculation cost, and is an appropriate solution for analyzing large-scale TSS usage data

Next-Generation Visual Supercomputing using PC Clusters with Volume Graphics Hardware Devices

To seek a low-cost, extensible solution for the large-scale data visualization problem, a visual computing system is designed as a result of a collaboration between industry and government research laboratories in Japan, also with participation by researchers in U.S. This scalable system is a commodity PC cluster equipped with the VolumePro 500 volume graphics cards and a specially designed image compositing hardware. Our performance study shows such a system is capable of interactive rendering � and � volume data and highly scalable. In particular, with such a system, simulation and visualization can be performed concurrently which allows scientists to monitor and tune their simulations on the fly. In this paper, both the system and hardware designs are presented

Association of genetic variants influencing lipid levels with coronary artery disease in Japanese individuals.

BACKGROUND/OBJECTIVE: In Japanese populations, we performed a replication study of genetic loci previously identified in European-descent populations as being associated with lipid levels and risk of coronary artery disease (CAD). METHODS: We genotyped 48 single nucleotide polymorphisms (SNPs) from 22 candidate loci that had previously been identified by genome-wide association (GWA) meta-analyses for low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), and/or triglycerides in Europeans. We selected 22 loci with 2 parallel tracks from 95 reported loci: 16 significant loci (p<1 × 10(-30) in Europeans) and 6 other loci including those with suggestive evidence of lipid associations in 1292 GWA-scanned Japanese samples. Genotyping was done in 4990 general population samples, and 1347 CAD cases and 1337 controls. For 9 SNPs, we further examined CAD associations in an additional panel of 3052 CAD cases and 6335 controls. PRINCIPAL FINDINGS: Significant lipid associations (one-tailed p<0.05) were replicated for 18 of 22 loci in Japanese samples, with significant inter-ethnic heterogeneity at 4 loci-APOB, APOE-C1, CETP, and APOA5-and allelic heterogeneity. The strongest association was detected at APOE rs7412 for LDL-C (p=1.3 × 10(-41)), CETP rs3764261 for HDL-C (p=5.2 × 10(-24)), and APOA5 rs662799 for triglycerides (p=5.8 × 10(-54)). CAD association was replicated and/or verified for 4 loci: SORT1 rs611917 (p=1.7 × 10(-8)), APOA5 rs662799 (p=0.0014), LDLR rs1433099 (p=2.1 × 10(-7)), and APOE rs7412 (p=6.1 × 10(-13)). CONCLUSIONS: Our results confirm that most of the tested lipid loci are associated with lipid traits in the Japanese, further indicating that in genetic susceptibility to lipid levels and CAD, the related metabolic pathways are largely common across the populations, while causal variants at individual loci can be population-specific