Ik-Young Choi

Uncovering the novel characteristics of Asian honey bee, Apis cerana , by whole genome sequencing

BackgroundThe honey bee is an important model system for increasing understanding of molecular and neural mechanisms underlying social behaviors relevant to the agricultural industry and basic science. The western honey bee, Apis mellifera, has served as a model species, and its genome sequence has been published. In contrast, the genome of the Asian honey bee, Apis cerana, has not yet been sequenced. A. cerana has been raised in Asian countries for thousands of years and has brought considerable economic benefits to the apicultural industry. A cerana has divergent biological traits compared to A. mellifera and it has played a key role in maintaining biodiversity in eastern and southern Asia. Here we report the first whole genome sequence of A. cerana.ResultsUsing de novo assembly methods, we produced a 238 Mbp draft of the A. cerana genome and generated 10,651 genes. A.cerana-specific genes were analyzed to better understand the novel characteristics of this honey bee species. Seventy-two percent of the A. cerana-specific genes had more than one GO term, and 1,696 enzymes were categorized into 125 pathways. Genes involved in chemoreception and immunity were carefully identified and compared to those from other sequenced insect models. These included 10 gustatory receptors, 119 odorant receptors, 10 ionotropic receptors, and 160 immune-related genes.ConclusionsThis first report of the whole genome sequence of A. cerana provides resources for comparative sociogenomics, especially in the field of social insect communication. These important tools will contribute to a better understanding of the complex behaviors and natural biology of the Asian honey bee and to anticipate its future evolutionary trajectory.

Precision genome engineering with programmable DNA-nicking enzymes

Zinc finger nucleases (ZFNs) are powerful tools of genome engineering but are limited by their inevitable reliance on error-prone nonhomologous end-joining (NHEJ) repair of DNA double-strand breaks (DSBs), which gives rise to randomly generated, unwanted small insertions or deletions (indels) at both on-target and off-target sites. Here, we present programmable DNA-nicking enzymes (nickases) that produce single-strand breaks (SSBs) or nicks, instead of DSBs, which are repaired by error-free homologous recombination (HR) rather than mutagenic NHEJ. Unlike their corresponding nucleases, zinc finger nickases allow site-specific genome modifications only at the on-target site, without the induction of unwanted indels. We propose that programmable nickases will be of broad utility in research, medicine, and biotechnology, enabling precision genome engineering in any cell or organism.

High-throughput SNP discovery and assay development in common bean

BackgroundNext generation sequencing has significantly increased the speed at which single nucleotide polymorphisms (SNPs) can be discovered and subsequently used as molecular markers for research. Unfortunately, for species such as common bean (Phaseolus vulgaris L.) which do not have a whole genome sequence available, the use of next generation sequencing for SNP discovery is much more difficult and costly. To this end we developed a method which couples sequences obtained from the Roche 454-FLX system (454) with the Illumina Genome Analyzer (GA) for high-throughput SNP discovery.ResultsUsing a multi-tier reduced representation library we discovered a total of 3,487 SNPs of which 2,795 contained sufficient flanking genomic sequence for SNP assay development. Using Sanger sequencing to determine the validation rate of these SNPs, we found that 86% are likely to be true SNPs. Furthermore, we designed a GoldenGate assay which contained 1,050 of the 3,487 predicted SNPs. A total of 827 of the 1,050 SNPs produced a working GoldenGate assay (79%).ConclusionsThrough combining two next generation sequencing techniques we have developed a method that allows high-throughput SNP discovery in any diploid organism without the need of a whole genome sequence or the creation of normalized cDNA libraries. The need to only perform one 454 run and one GA sequencer run allows high-throughput SNP discovery with sufficient sequence for assay development to be performed in organisms, such as common bean, which have limited genomic resources.

Population Structure and Domestication Revealed by High-Depth Resequencing of Korean Cultivated and Wild Soybean Genomes

Despite the importance of soybean as a major crop, genome-wide variation and evolution of cultivated soybeans are largely unknown. Here, we catalogued genome variation in an annual soybean population by high-depth resequencing of 10 cultivated and 6 wild accessions and obtained 3.87 million high-quality single-nucleotide polymorphisms (SNPs) after excluding the sites with missing data in any accession. Nuclear genome phylogeny supported a single origin for the cultivated soybeans. We identified 10-fold longer linkage disequilibrium (LD) in the wild soybean relative to wild maize and rice. Despite the small population size, the long LD and large SNP data allowed us to identify 206 candidate domestication regions with significantly lower diversity in the cultivated, but not in the wild, soybeans. Some of the genes in these candidate regions were associated with soybean homologues of canonical domestication genes. However, several examples, which are likely specific to soybean or eudicot crop plants, were also observed. Consequently, the variation data identified in this study should be valuable for breeding and for identifying agronomically important genes in soybeans. However, the long LD of wild soybeans may hinder pinpointing causal gene(s) in the candidate regions.

Application of machine learning in SNP discovery

BackgroundSingle nucleotide polymorphisms (SNP) constitute more than 90% of the genetic variation, and hence can account for most trait differences among individuals in a given species. Polymorphism detection software PolyBayes and PolyPhred give high false positive SNP predictions even with stringent parameter values. We developed a machine learning (ML) method to augment PolyBayes to improve its prediction accuracy. ML methods have also been successfully applied to other bioinformatics problems in predicting genes, promoters, transcription factor binding sites and protein structures.ResultsThe ML program C4.5 was applied to a set of features in order to build a SNP classifier from training data based on human expert decisions (True/False). The training data were 27,275 candidate SNP generated by sequencing 1973 STS (sequence tag sites) (12 Mb) in both directions from 6 diverse homozygous soybean cultivars and PolyBayes analysis. Test data of 18,390 candidate SNP were generated similarly from 1359 additional STS (8 Mb). SNP from both sets were classified by experts. After training the ML classifier, it agreed with the experts on 97.3% of test data compared with 7.8% agreement between PolyBayes and experts. The PolyBayes positive predictive values (PPV) (i.e., fraction of candidate SNP being real) were 7.8% for all predictions and 16.7% for those with 100% posterior probability of being real. Using ML improved the PPV to 84.8%, a 5- to 10-fold increase. While both ML and PolyBayes produced a similar number of true positives, the ML program generated only 249 false positives as compared to 16,955 for PolyBayes. The complexity of the soybean genome may have contributed to high false SNP predictions by PolyBayes and hence results may differ for other genomes.ConclusionA machine learning (ML) method was developed as a supplementary feature to the polymorphism detection software for improving prediction accuracies. The results from this study indicate that a trained ML classifier can significantly reduce human intervention and in this case achieved a 5–10 fold enhanced productivity. The optimized feature set and ML framework can also be applied to all polymorphism discovery software. ML support software is written in Perl and can be easily integrated into an existing SNP discovery pipeline.

SNP-PHAGE--High throughput SNP discovery pipeline.

BackgroundSingle nucleotide polymorphisms (SNPs) as defined here are single base sequence changes or short insertion/deletions between or within individuals of a given species. As a result of their abundance and the availability of high throughput analysis technologies SNP markers have begun to replace other traditional markers such as restriction fragment length polymorphisms (RFLPs), amplified fragment length polymorphisms (AFLPs) and simple sequence repeats (SSRs or microsatellite) markers for fine mapping and association studies in several species. For SNP discovery from chromatogram data, several bioinformatics programs have to be combined to generate an analysis pipeline. Results have to be stored in a relational database to facilitate interrogation through queries or to generate data for further analyses such as determination of linkage disequilibrium and identification of common haplotypes. Although these tasks are routinely performed by several groups, an integrated open source SNP discovery pipeline that can be easily adapted by new groups interested in SNP marker development is currently unavailable.ResultsWe developed SNP-PHAGE (SNP discovery P ipeline with additional features for identification of common haplotypes within a sequence tagged site (H aplotype A nalysis) and Ge nBank (-dbSNP) submissions. This tool was applied for analyzing sequence traces from diverse soybean genotypes to discover over 10,000 SNPs. This package was developed on UNIX/Linux platform, written in Perl and uses a MySQL database. Scripts to generate a user-friendly web interface are also provided with common queries for preliminary data analysis. A machine learning tool developed by this group for increasing the efficiency of SNP discovery is integrated as a part of this package as an optional feature. The SNP-PHAGE package is being made available open source at http://bfgl.anri.barc.usda.gov/ML/snp-phage/.ConclusionSNP-PHAGE provides a bioinformatics solution for high throughput SNP discovery, identification of common haplotypes within an amplicon, and GenBank (dbSNP) submissions. SNP selection and visualization are aided through a user-friendly web interface. This tool is useful for analyzing sequence tagged sites (STSs) of genomic sequences, and this software can serve as a starting point for groups interested in developing SNP markers.

Complete Genome Sequence of Burkholderia gladioli BSR3

We report the complete genome sequence of Burkholderia gladioli BSR3, isolated from a diseased rice sheath in South Korea.

The Hot Pepper (Capsicum annuum) MicroRNA Transcriptome Reveals Novel and Conserved Targets: A Foundation for Understanding MicroRNA Functional Roles in Hot Pepper

MicroRNAs (miRNAs) are a class of non-coding RNAs approximately 21 nt in length which play important roles in regulating gene expression in plants. Although many miRNA studies have focused on a few model plants, miRNAs and their target genes remain largely unknown in hot pepper (Capsicum annuum), one of the most important crops cultivated worldwide. Here, we employed high-throughput sequencing technology to identify miRNAs in pepper extensively from 10 different libraries, including leaf, stem, root, flower, and six developmental stage fruits. Based on a bioinformatics pipeline, we successfully identified 29 and 35 families of conserved and novel miRNAs, respectively. Northern blot analysis was used to validate further the expression of representative miRNAs and to analyze their tissue-specific or developmental stage-specific expression patterns. Moreover, we computationally predicted miRNA targets, many of which were experimentally confirmed using 5′ rapid amplification of cDNA ends analysis. One of the validated novel targets of miR-396 was a domain rearranged methyltransferase, the major de novo methylation enzyme, involved in RNA-directed DNA methylation in plants. This work provides the first reliable draft of the pepper miRNA transcriptome. It offers an expanded picture of pepper miRNAs in relation to other plants, providing a basis for understanding the functional roles of miRNAs in pepper.

Complete Genome Sequence of Japanese Erwinia Strain Ejp617, a Bacterial Shoot Blight Pathogen of Pear

The Japanese Erwinia strain Ejp617 is a plant pathogen that causes bacterial shoot blight of pear in Japan. Here, we report the complete genome sequence of strain Ejp617 isolated from Nashi pears in Japan to provide further valuable insight among related Erwinia species.

Effects of benzo[a]pyrene on whole cytochrome P450-involved molecular responses in the marine medaka Oryzias melastigma.

Despite being a strong toxicant for aquatic ecosystems, the effect of benzo[a]pyrene (B[a]P) on whole cytochrome P450 (CYP) biotransformation mechanisms has not been deeply investigated in aquatic organisms. To understand the mode of action of B[a]P on CYP molecular responses in fish, we analyzed the full spectrum of cyp genes and the activities of enzymes that are involved in detoxification and antioxidant defense systems after exposure to different concentrations of B[a]P over different time courses in the marine medaka, Oryzias melastigma. Upon B[a]P exposure, we found significant downregulation of cyp genes associated with steroidogenesis with decreased concentrations of actual hormones including estradiol (E2) and testosterone (11-KT), indicating that B[a]P-treated groups were closely associated with the dysfunction of hormone synthesis in a dose-dependent manner. In addition, B[a]P exposure strongly influenced transcriptional levels of antioxidant-related genes and their enzyme activities. Based on these results, we suggest that B[a]P induced the CYPs-involved systematic biotransformation mechanism with oxidative stress in the juvenile marine medaka, resulting in changes of endogenous hormonal levels and transcriptional levels of several steroidogenic metabolism-related CYPs.