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'ngs'에 해당되는 글 2건
2011. 7. 4. 09:30

연구소 네이처 논문중에 처음으로 Author에 이름을 올린 논문!! 비록 지금은 중간이지만 언젠가는...


Abstract

Massively parallel sequencing technologies have identified a broad spectrum of human genome diversity. Here we deep sequenced and correlated 8 genomes and 7 transcriptomes of unrelated Korean individuals. This has allowed us to construct a genomewide map of common and rare variants and also identify variants formed during DNA-RNA transcription. We identified 9.56

million genomic variants, 23.2% of which appear to be previously unidentified. From transcriptome sequencing, we discovered 4,44 transcripts not previously annotated. Finally, we revealed ,809 sites of transcriptional base modification, where the transcriptional landscape is different from the corresponding genomic sequences, and 580 sites of allele-specific expression. Our findings suggest that a considerable number of unexplored genomic variants still remain to be identified in the human genome, and that the integrated analysis of genome and transcriptome sequencing is powerful for understanding the diversity and functional aspects of human genomic variants.

 
NG : http://www.nature.com/ng/journal/vaop/ncurrent/full/ng.872.html


2010. 12. 2. 11:48
이전 TIARA 논문을 쓰기 이전에 co-author로 참여했던 논문 비록 주요 알고리즘 부분보다는 시스템 구현쪽에 참여했던 논문이다. 이 논문에 사용된 engine 기술을 바탕으로 TIARA를 만들게 된것이다.
이 논문은 우리끼리 CARA라 명명한 논문이다.

Abstract
Comparative genomic hybridization (CGH) microarrays have been used to determine copy number variations (CNVs) and their effects on complex diseases. Detection of absolute CNVs independent of genomic variants of an arbitrary reference sample has been a critical issue in CGH array experiments. Whole genome analysis using massively parallel sequencing with multiple ultra-high resolution CGH arrays provides an opportunity to catalog highly accurate genomic variants of the reference DNA (NA10851). Using information on variants, we developed a new method, the CGH array reference-free algorithm (CARA), which can determine reference-unbiased absolute CNVs from any CGH array platform. The algorithm enables the removal and rescue of false positive and false negative CNVs, respectively, which appear due to the effects of genomic variants of the reference sample in raw CGH array experiments. We found that the CARA remarkably enhanced the accuracy of CGH array in determining absolute CNVs. Our method thus provides a new approach to interpret CGH array data for personalized medicine.

PMID : 20802225
CARA Web Site : http://cara.gmi.ac.kr


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