통합 검색 | Korea Science

Kim, Kyung;Seong, Moon-Woo;Chung, Won-Hyong;Park, Sung Sup;Leem, Sangseob;Park, Won;Kim, Jihyun;Lee, KiYoung;Park, Rae Woong;Kim, Namshin
- Genomics & Informatics
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- 제13권2호
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- pp.31-39
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- 2015
Sequencing depth, which is directly related to the cost and time required for the generation, processing, and maintenance of next-generation sequencing data, is an important factor in the practical utilization of such data in clinical fields. Unfortunately, identifying an exome sequencing depth adequate for clinical use is a challenge that has not been addressed extensively. Here, we investigate the effect of exome sequencing depth on the discovery of sequence variants for clinical use. Toward this, we sequenced ten germ-line blood samples from breast cancer patients on the Illumina platform GAII(x) at a high depth of ${\sim}200{\times}$. We observed that most function-related diverse variants in the human exonic regions could be detected at a sequencing depth of $120{\times}$. Furthermore, investigation using a diagnostic gene set showed that the number of clinical variants identified using exome sequencing reached a plateau at an average sequencing depth of about $120{\times}$. Moreover, the phenomena were consistent across the breast cancer samples.
https://doi.org/10.5808/GI.2015.13.2.31 인용 PDF KSCI

Sohn, Chang-Ho;Jung, Jin-Woo;Kang, Gum-Yong;Kim, Kwang-Pyo
- Bioinformatics and Biosystems
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- 제1권2호
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- pp.89-94
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- 2006
Mass spectrometry (MS) is widely applied for high throughput proteomics analysis. When large-scale proteome analysis experiments are performed, it generates massive amount of data. To search these proteomics data against protein databases, fully automated database search algorithms, such as Mascot and SEQUEST are routinely employed. At present, it is critical to reduce false positives and false negatives during such analysis. In this review we have focused on aspects of automated protein identification using tandem mass spectrometry (MS/MS) spectra and validation of the protein identifications of two most common automated protein identification algorithms Mascot and SEQUEST.
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Chung, Tae-Su;Chung, Hee-Joon;Kim, Ju-Han
- Genomics & Informatics
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- 제5권3호
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- pp.124-128
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- 2007
Microarray technology enables us to measure the expression of tens of thousands of genes simultaneously under various experimental conditions. Clustering analysis is one of the most successful methods for analyzing microarray data using the assumption that co-expressed genes may be co-regulated. It is important to extract meaningful clusters from a long unordered list of clusters and to evaluate the functional homogeneity and heterogeneity of clusters. Many quality measures for clustering results have been suggested in different conditions. In the present study, we consider biological pathways as a collection of biological knowledge and used them as a reference for measuring the quality of clustering results and functional homogeneities. PathTalk visualizes and evaluates functional relationships between gene clusters and biological pathways.
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Oh, So-Yeon;Kim, Ji-Hyeon;Kim, Seo-Jin;Nam, Hee-Jo;Park, Hyun-Seok
- Genomics & Informatics
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- 제16권3호
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- pp.75-77
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- 2018
Genomics & Informatics (NLM title abbreviation: Genomics Inform) is the official journal of the Korea Genome Organization. Text corpus for this journal annotated with various levels of linguistic information would be a valuable resource as the process of information extraction requires syntactic, semantic, and higher levels of natural language processing. In this study, we publish our new corpus called GNI Corpus version 1.0, extracted and annotated from full texts of Genomics & Informatics, with NLTK (Natural Language ToolKit)-based text mining script. The preliminary version of the corpus could be used as a training and testing set of a system that serves a variety of functions for future biomedical text mining.
https://doi.org/10.5808/GI.2018.16.3.75 인용 PDF KSCI