Junehawk Lee

Somatic Mutations in TSC1 and TSC2 Cause Focal Cortical Dysplasia

Focal cortical dysplasia (FCD) is a major cause of the sporadic form of intractable focal epilepsies that require surgical treatment. It has recently been reported that brain somatic mutations in MTOR account for 15%-25% of FCD type II (FCDII), characterized by cortical dyslamination and dysmorphic neurons. However, the genetic etiologies of FCDII-affected individuals who lack the MTOR mutation remain unclear. Here, we performed deep hybrid capture and amplicon sequencing (read depth of 100×-20,012×) of five important mTOR pathway genes-PIK3CA, PIK3R2, AKT3, TSC1, and TSC2-by using paired brain and saliva samples from 40 FCDII individuals negative for MTOR mutations. We found that 5 of 40 individuals (12.5%) had brain somatic mutations in TSC1 (c.64C>T [p.Arg22Trp] and c.610C>T [p.Arg204Cys]) and TSC2 (c.4639G>A [p.Val1547Ile]), and these results were reproducible on two different sequencing platforms. All identified mutations induced hyperactivation of the mTOR pathway by disrupting the formation or function of the TSC1-TSC2 complex. Furthermore, in utero CRISPR-Cas9-mediated genome editing of Tsc1 or Tsc2 induced the development of spontaneous behavioral seizures, as well as cytomegalic neurons and cortical dyslamination. These results show that brain somatic mutations in TSC1 and TSC2 cause FCD and that in utero application of the CRISPR-Cas9 system is useful for generating neurodevelopmental disease models of somatic mutations in the brain.

Sensitivity Analysis of the Parameters of Korea's Pandemic Influenza Preparedness Plan.

Objectives Our aim was to evaluate Korea’s Pandemic Influenza Preparedness Plan. Methods We conducted a sensitivity analysis on the expected number of outpatients and hospital bed occupancy, with 1,000,000 parameter combinations, in a situation of pandemic influenza, using the mathematical simulation program InfluSim. Results Given the available resources in Korea, antiviral treatment and social distancing must be combined to reduce the number of outpatients and hospitalizations sufficiently; any single intervention is not enough. The antiviral stockpile of 4–6% is sufficient for the expected eligible number of cases to be treated. However, the eligible number assumed (30% for severe cases and 26% for extremely severe cases) is very low compared to the corresponding number in European countries, where up to 90% of the population are assumed to be eligible for antiviral treatment. Conclusions A combination of antiviral treatment and social distancing can mitigate a pandemic, but will only bring it under control for the most optimistic parameter combinations.

Vecuum: identification and filtration of false somatic variants caused by recombinant vector contamination

MOTIVATION Advances in sequencing technologies have remarkably lowered the detection limit of somatic variants to a low frequency. However, calling mutations at this range is still confounded by many factors including environmental contamination. Vector contamination is a continuously occurring issue and is especially problematic since vector inserts are hardly distinguishable from the sample sequences. Such inserts, which may harbor polymorphisms and engineered functional mutations, can result in calling false variants at corresponding sites. Numerous vector-screening methods have been developed, but none could handle contamination from inserts because they are focusing on vector backbone sequences alone. RESULTS We developed a novel method-Vecuum-that identifies vector-originated reads and resultant false variants. Since vector inserts are generally constructed from intron-less cDNAs, Vecuum identifies vector-originated reads by inspecting the clipping patterns at exon junctions. False variant calls are further detected based on the biased distribution of mutant alleles to vector-originated reads. Tests on simulated and spike-in experimental data validated that Vecuum could detect 93% of vector contaminants and could remove up to 87% of variant-like false calls with 100% precision. Application to public sequence datasets demonstrated the utility of Vecuum in detecting false variants resulting from various types of external contamination. AVAILABILITY AND IMPLEMENTATION Java-based implementation of the method is available at http://vecuum.sourceforge.net/ CONTACT: swkim@yuhs.acSupplementary information: Supplementary data are available at Bioinformatics online.

Accelerating Genome Sequence Alignment on Hadoop on Lustre Environment

Genome sequence alignment is one of the basic procedure of genome sequencing analysis pipeline and also one of the most time-consuming parts. Including BigBWA, a number of tools were proposed to accelerate genome sequence alignment by parallelizing computation with Hadoop technologies. However, HDFS incurs considerable I/O overhead. In this research, we propose a new sequence alignment tool adopting Hadoop on Lustre. Based on BigBWA, we removed data transfer overhead caused by HDFS and parallelized whole I/O steps. Experimental result shows that our solution is five times faster than original BigBWA in a ten-node Lustre based Hadoop cluster.

Differential Co-Expression Networks using RNA-seq and microarrays in Alzheimer's disease

Differential Co-Expression Networks (DCENs) are graphical representations of genes showing differential co-expression pattern in response to experimental conditions or genetic changes. They have been successfully applied to identify condition-specific modules and provide a picture of the dynamic changes in gene regulatory networks. DCENs analysis investigates the differences among gene interconnections by calculating the expression correlation change of each gene pair between conditions. In this study, we collected many different datasets from NCBI GEO including 25 RNA-seq and 2,102 microarray samples derived from human brain and blood in Alzheimers disease and performed differential co-expression analyses to identify functional modules responsible for the characterization of Alzheimers disease. The DCENs were generated using Pearson correlation coefficient and meta-analysis was conducted using rank-based method. The preliminary results show that the structural characteristics of DCENs can provide new insights into the underlying gene regulatory dynamics in Alzheimers disease. There is low size overlap between microarray- and RNA-seq-derived DCENs however, DCENs from RNA-seq would complement ones from microarray due to the higher coverage and dynamic range of RNA-seq.

Implementing Biological Network Analysis System through Oriental Medical Literature Analysis

최근 한의학에 대한 과학적 접근이 진행되면서 한약재 성분의 효능을 검증하고자 하는 다양한 분자 생물 학 분야의 연구가 진행되고 있다. 하지만 관련 한약재의 주요 성분과 관련된 생화학적 기작을 손쉽게 검색 할 수 있는 시스템이 갖추어져 있지 못한 실정이다. 본 연구는 국내 한약재에 대한 약효 성분과 생물학적 기작에 대한 정보를 수집 및 텍스트마이닝을 수행하여 한약재 정보 데이터베이스를 구축하고자 하였다. 연구자가 손쉽게 분석된 한약재의 화합물, 유전자 그리고 생물학적 상호작용 정보를 검색할 수 있는 웹사 이트 원형을 개발하였다. 문헌 분석결과 한의학분야 주요 화합물 및 유전자/단백질 정보를 추출할 수 있었 고 현대 한의학 연구 현황의 특징을 보여주었다. 분석된 결과는 웹을 통해 한약재별 PubMed 문헌 정보와 관련된 한약재의 약재 정보 및 생물학적 상호작용 정보를 가시화하여 볼 수 있도록 개발하였다.

Application Architecture of Avian Influenza Research Collaboration Network in Korea e-Science

In the pursuit of globalization of the AI e-Science environment, KISTI is fostering to extend the AI research community to the AI research institutes of neighboring countries and to share the AI e-Science environment with them in the near future. In this paper we introduce the application architecture of AI research collaboration network (AIRCoN). AIRCoN is a global e-Science environment for AI research conducted by KISTI. It consists of AI virus sequence information sharing system for sufficing data requirement of research community, integrated analysis environment for analyzing the mutation pattern of AI viruses and their risks, epidemic modeling and simulation environment for establishing national effective readiness strategy against AI pandemics, and knowledge portal for sharing expertise of epidemic study and unpublished research results with community members.