• Title/Summary/Keyword: genome reconstruction

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Creation of an Ethanol-Tolerant Yeast Strain by Genome Reconstruction Based on Chromosome Splitting Technology

  • Park, A-Hwang;Sugiyama, Minetaka;Harashima, Satoshi;Kim, Yeon-Hee
    • Journal of Microbiology and Biotechnology
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    • v.22 no.2
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    • pp.184-189
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    • 2012
  • We sought to breed an industrially useful yeast strain, specifically an ethanol-tolerant yeast strain that would be optimal for ethanol production, using a novel breeding method, called genome reconstruction, based on chromosome splitting technology. To induce genome reconstruction, Saccharomyces cerevisiae strain SH6310, which contains 31 chromosomes including 12 artificial mini-chromosomes, was continuously cultivated in YPD medium containing 6% to 10% ethanol for 33 days. The 12 mini-chromosomes can be randomly or specifically lost because they do not contain any genes that are essential under high-level ethanol conditions. The strains selected by inducing genome reconstruction grew about ten times more than SH6310 in 8% ethanol. To determine the effect of mini-chromosome loss on the ethanol tolerance phenotype, PCR and Southern hybridization were performed to detect the remaining mini-chromosomes. These analyses revealed the loss of mini-chromosomes no. 11 and no. 12. Mini-chromosome no. 11 contains ten genes (YKL225W, PAU16, YKL223W, YKL222C, MCH2, FRE2, COS9, SRY1, JEN1, URA1) and no. 12 contains fifteen genes (YHL050C, YKL050W-A, YHL049C, YHL048C-A, COS8, YHLComega1, ARN2, YHL046W-A, PAU13, YHL045W, YHL044W, ECM34, YHL042W, YHL041W, ARN1). We assumed that the loss of these genes resulted in the ethanol-tolerant phenotype and expect that this genome reconstruction method will be a feasible new alternative for strain improvement.

Gene annotation by the "interactome"analysis in KEGG

  • Kanehisa, Minoru
    • Proceedings of the Korean Society for Bioinformatics Conference
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    • 2000.11a
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    • pp.56-58
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    • 2000
  • Post-genomics may be defined in different ways depending on how one views the challenges after the genome. A popular view is to follow the concept of the central dogma in molecular biology, namely from genome to transcriptome to proteome. Projects are going on to analyze gene expression profiles both at the mRNA and protein levels and to catalog protein 3D structure families, which will no doubt help the understanding of information in the genome. However complete, such catalogs of genes, RNAs, and proteins only tell us about the building blocks of life. They do not tell us much about the wiring (interaction) of building blocks, which is essential for uncovering systemic functional behaviors of the cell or the organism. Thus, an alternative view of post-genomics is to go up from the molecular level to the cellular level, and to understand, what I call, the "interactome"or a complete picture of molecular interactions in the cell. KEGG (http://www.genome.ad.jp/kegg/) is our attempt to computerize current knowledge on various cellular processes as a collection of "generalized"protein-protein interaction networks, to develop new graph-based algorithms for predicting such networks from the genome information, and to actually reconstruct the interactomes for all the completely sequenced genomes and some partial genomes. During the reconstruction process, it becomes readily apparent that certain pathways and molecular complexes are present or absent in each organism, indicating modular structures of the interactome. In addition, the reconstruction uncovers missing components in an otherwise complete pathway or complex, which may result from misannotation of the genome or misrepresentation of the KEGG pathway. When combined with additional experimental data on protein-protein interactions, such as by yeast two-hybrid systems, the reconstruction possibly uncovers unknown partners for a particular pathway or complex. Thus, the reconstruction is tightly coupled with the annotation of individual genes, which is maintained in the GENES database in KEGG. We are also trying to expand our literature surrey to include in the GENES database most up-to-date information about gene functions.

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HapAnalyzer: Minimum Haplotype Analysis System for Association Studies

  • Jung, Ho-Youl;Park, Jung-Sun;Park, Yun-Ju;Kim, Young-Jin;Kimm, Kuchan;Koh, InSong
    • Genomics & Informatics
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    • v.2 no.2
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    • pp.107-109
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    • 2004
  • HapAnalyzer is an analysis system that provides minimum analysis methods for the SNP-based association studies. It consists of Hardy-Weinberg equilibrium (HWE) test, linkage disequilibrium (LD) computation, haplotype reconstruction, and SNP (or haplotype)-phenotype association assessment. It is well suited to a case-control association study for the unrelated population.

iHaplor: A Hybrid Method for Haplotype Reconstruction

  • Jung, Ho-Youl;Heo, Jee-Yeon;Cho, Hye-Yeung;Ryu, Gil-Mi;Lee, Ju-Young;Koh, In-Song;Kimm, Ku-Chan;Oh, Berm-Seok
    • Proceedings of the Korean Society for Bioinformatics Conference
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    • 2003.10a
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    • pp.221-228
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    • 2003
  • This paper presents a novel method that can identify the individual's haplotype from the given genotypes. Because of the limitation of the conventional single-locus analysis, haplotypes have gained increasing attention in the mapping of complex-disease genes. Conventionally there are two approaches which resolve the individual's haplotypes. One is the molecular haplotypings which have many potential limitations in cost and convenience. The other is the in-silico haplotypings which phase the haplotypes from the diploid genotyped populations, and are cost effective and high-throughput method. In-silico haplotyping is divided into two sub-categories - statistical and computational method. The former computes the frequencies of the common haplotypes, and then resolves the individual's haplotypes. The latter directly resolves the individual's haplotypes using the perfect phylogeny model first proposed by Dan Gusfield [7]. Our method combines two approaches in order to increase the accuracy and the running time. The individuals' haplotypes are resolved by considering the MLE (Maximum Likelihood Estimation) in the process of computing the frequencies of the common haplotypes.

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AGB (Ancestral Genome Browser): A Web Interface for Browsing Reconstructed Ancestral Genomes (AGB (Ancestral Genome Browser): 조상유전체 데이터의 시각적 열람을 위한 웹 인터페이스)

  • Lee, Daehwan;Lee, Jongin;Hong, Woon-Young;Jang, Eunji;Kim, Jaebum
    • Journal of KIISE
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    • v.42 no.12
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    • pp.1584-1589
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    • 2015
  • With the advancement of next-generation sequencing (NGS) technologies, various genome browsers have been introduced. Because existing browsers focus on comparison of the genomic data of extant species, however, there is a need for a genome browser for ancestral genomes and their evolution. In this paper, we introduce a genome browser, AGB (Ancestral Genome Browser), that displays ancestral genome data reconstructed from existing species. With AGB, it is possible to trace genomic variations that occurred during evolution in a simple and intuitive way. We explain the capability of AGB in terms of visualizing ancestral genomic information and evolutionary genomic variations. AGB is now available at http://bioinfo.konkuk.ac.kr/genomebrowser/.

Phylogenomics and its Growing Impact on Algal Phylogeny and Evolution

  • Adrian , Reyes-Prieto;Yoon, Hwan-Su;Bhattacharya, Debashish
    • ALGAE
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    • v.21 no.1
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    • pp.1-10
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    • 2006
  • Genomic data is accumulating in public database at an unprecedented rate. Although presently dominated by the sequences of metazoan, plant, parasitic, and picoeukaryotic taxa, both expressed sequence tag (EST) and complete genomes of free-living algae are also slowly appearing. This wealth of information offers the opportunity to clarify many long-standing issues in algal and plant evolution such as the contribution of the plastid endosymbiont to nuclear genome evolution using the tools of comparative genomics and multi-gene phylogenetics. A particularly powerful approach for the automated analysis of genome data from multiple taxa is termed phylogenomics. Phylogenomics is the convergence of genomics science (the study of the function and structure of genes and genomes) and molecular phylogenetics (the study of the hierarchical evolutionary relationships among organisms, their genes and genomes). The use of phylogenetics to drive comparative genome analyses has facilitated the reconstruction of the evolutionary history of genes, gene families, and organisms. Here we survey the available genome data, introduce phylogenomic pipelines, and review some initial results of phylogenomic analyses of algal genome data.

Characterization of a Chalcosyltransferase (gerGTII) in Dihydrochalcomycin Biosynthesis

  • Pageni, Binod Babu;Oh, Tae-Jin;Thuy, Ta Thi Thu;Sohng, Jae Kyung
    • Molecules and Cells
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    • v.26 no.3
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    • pp.278-284
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    • 2008
  • An open reading frame, designated GerGTII and located downstream of the polyketide synthase genes, has been identified as a chalcosyltransferase by sequence analysis in the dihydrochalcomycin biosynthetic gene cluster of Streptomyces sp. KCTC 0041BP. The deduced product of gerGTII is similar to several glycosyltransferases, authentic and putative, and it displays a consensus sequence motif that appears to be characteristic of a sub-group of these enzymes. Specific disruption of gerGTII within the S. sp. KCTC 0041BP genome by insertional in-frame deletion method, resulted complete abolishment of dihydrochalcomycin and got the 20-O-mycinosyl-dihydrochalconolide as intermediate product in dihydrochalcomycin biosynthesis which was confirmed by electron spray ionization-mass spectrometry and liquid chromatography-mass spectrometry. Dihydrochalcomycin also was recovered after complementation of gerGTII.

Review of Biological Network Data and Its Applications

  • Yu, Donghyeon;Kim, MinSoo;Xiao, Guanghua;Hwang, Tae Hyun
    • Genomics & Informatics
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    • v.11 no.4
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    • pp.200-210
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    • 2013
  • Studying biological networks, such as protein-protein interactions, is key to understanding complex biological activities. Various types of large-scale biological datasets have been collected and analyzed with high-throughput technologies, including DNA microarray, next-generation sequencing, and the two-hybrid screening system, for this purpose. In this review, we focus on network-based approaches that help in understanding biological systems and identifying biological functions. Accordingly, this paper covers two major topics in network biology: reconstruction of gene regulatory networks and network-based applications, including protein function prediction, disease gene prioritization, and network-based genome-wide association study.