Jongkeun Lee

Intron retention is a widespread mechanism of tumor-suppressor inactivation

A substantial fraction of disease-causing mutations are pathogenic through aberrant splicing. Although genome profiling studies have identified somatic single-nucleotide variants (SNVs) in cancer, the extent to which these variants trigger abnormal splicing has not been systematically examined. Here we analyzed RNA sequencing and exome data from 1,812 patients with cancer and identified ∼900 somatic exonic SNVs that disrupt splicing. At least 163 SNVs, including 31 synonymous ones, were shown to cause intron retention or exon skipping in an allele-specific manner, with ∼70% of the SNVs occurring on the last base of exons. Notably, SNVs causing intron retention were enriched in tumor suppressors, and 97% of these SNVs generated a premature termination codon, leading to loss of function through nonsense-mediated decay or truncated protein. We also characterized the genomic features predictive of such splicing defects. Overall, this work demonstrates that intron retention is a common mechanism of tumor-suppressor inactivation.

Long-term performance of anaerobic digestion for crop residues containing heavy metals and response of microbial communities

In order to investigate the long-term stability on the performance of the anaerobic digestion process, a laboratory-scale continuous stirred-tank reactor (CSTR) was operated for 1100 days with sunflower harvested in a heavy metal contaminated site. Changes of microbial communities during digestion were identified using pyrosequencing. According to the results, soluble heavy metal concentrations were lower than the reported inhibitory level and the reactor performance remained stable up to OLR of 2.0g-VS/L/day at HRT of 20days. Microbial communities commonly found in anaerobic digestion for cellulosic biomass were observed and stably established with respect to the substrate. Thus, the balance of microbial metabolism was maintained appropriately and anaerobic digestion seems to be feasible for disposal of heavy metal-containing crop residues from phytoremediation sites.

Systematic investigation of cancer-associated somatic point mutations in SNP databases

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TIARA genome database: update 2013

The Total Integrated Archive of short-Read and Array (TIARA; http://tiara.gmi.ac.kr) database stores and integrates human genome data generated from multiple technologies including next-generation sequencing and high-resolution comparative genomic hybridization array. The TIARA genome browser is a powerful tool for the analysis of personal genomic information by exploring genomic variants such as SNPs, indels and structural variants simultaneously. As of September 2012, the TIARA database provides raw data and variant information for 13 sequenced whole genomes, 16 sequenced transcriptomes and 33 high resolution array assays. Sequencing reads are available at a depth of ∼30× for whole genomes and 50× for transcriptomes. Information on genomic variants includes a total of ∼9.56 million SNPs, 23 025 of which are non-synonymous SNPs, and ∼1.19 million indels. In this update, by adding high coverage sequencing of additional human individuals, the TIARA genome database now provides an extensive record of rare variants in humans. Following TIARA’s fundamentally integrative approach, new transcriptome sequencing data are matched with whole-genome sequencing data in the genome browser. Users can here observe, for example, the expression levels of human genes with allele-specific quantification. Improvements to the TIARA genome browser include the intuitive display of new complex and large-scale data sets.

Mutalisk: a web-based somatic MUTation AnaLyIS toolKit for genomic, transcriptional and epigenomic signatures

Abstract Somatic genome mutations occur due to combinations of various intrinsic/extrinsic mutational processes and DNA repair mechanisms. Different molecular processes frequently generate different signatures of somatic mutations in their own favored contexts. As a result, the regional somatic mutation rate is dependent on the local DNA sequence, the DNA replication/RNA transcription dynamics and epigenomic chromatin organization landscape in the genome. Here, we propose an online computational framework, termed Mutalisk, which correlates somatic mutations with various genomic, transcriptional and epigenomic features in order to understand mutational processes that contribute to the generation of the mutations. This user-friendly tool explores the presence of localized hypermutations (kataegis), dissects the spectrum of mutations into the maximum likelihood combination of known mutational signatures and associates the mutation density with numerous regulatory elements in the genome. As a result, global patterns of somatic mutations in any query sample can be efficiently screened, thus enabling a deeper understanding of various mutagenic factors. This tool will facilitate more effective downstream analyses of cancer genome sequences to elucidate the diversity of mutational processes underlying the development and clonal evolution of cancer cells. Mutalisk is freely available at http://mutalisk.org.

An Efficient Functional Analysis Method for Micro-array Data Using Gene Ontology

Microarray data includes tens of thousands of gene expressions simultaneously, so it can be effectively used in identifying the phenotypes of diseases. However, the retrieval of functional information from a large corpus of gene expression data is still a time-consuming task. In this paper, we propose an efficient method for identifying functional categories of differentially expressed genes from a micro-array experiment by using Gene Ontology (GO). Our method is as follows: (1) The expression data set is first filtered to include only genes with mean expression values that differ by at least 3-fold between the two groups. (2) The genes are then ranked based on the t-statistics. The 100 most highly ranked genes are selected as informative genes. (3) The t-value of each informative gene is imposed as a score on the associated GO terms. High-scoring GO terms are then listed with their associated genes and represent the functional category information of the micro-array experiment. A system called HMDA (Hallym Micro-array Data analysis) is implemented on publicly available micro-array data sets and validated. Our results were also compared with the original analysis.