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1.  SLAF-seq: An Efficient Method of Large-Scale De Novo SNP Discovery and Genotyping Using High-Throughput Sequencing 
PLoS ONE  2013;8(3):e58700.
Large-scale genotyping plays an important role in genetic association studies. It has provided new opportunities for gene discovery, especially when combined with high-throughput sequencing technologies. Here, we report an efficient solution for large-scale genotyping. We call it specific-locus amplified fragment sequencing (SLAF-seq). SLAF-seq technology has several distinguishing characteristics: i) deep sequencing to ensure genotyping accuracy; ii) reduced representation strategy to reduce sequencing costs; iii) pre-designed reduced representation scheme to optimize marker efficiency; and iv) double barcode system for large populations. In this study, we tested the efficiency of SLAF-seq on rice and soybean data. Both sets of results showed strong consistency between predicted and practical SLAFs and considerable genotyping accuracy. We also report the highest density genetic map yet created for any organism without a reference genome sequence, common carp in this case, using SLAF-seq data. We detected 50,530 high-quality SLAFs with 13,291 SNPs genotyped in 211 individual carp. The genetic map contained 5,885 markers with 0.68 cM intervals on average. A comparative genomics study between common carp genetic map and zebrafish genome sequence map showed high-quality SLAF-seq genotyping results. SLAF-seq provides a high-resolution strategy for large-scale genotyping and can be generally applicable to various species and populations.
doi:10.1371/journal.pone.0058700
PMCID: PMC3602454  PMID: 23527008
2.  Gene conversion in the rice genome 
BMC Genomics  2008;9:93.
Background
Gene conversion causes a non-reciprocal transfer of genetic information between similar sequences. Gene conversion can both homogenize genes and recruit point mutations thereby shaping the evolution of multigene families. In the rice genome, the large number of duplicated genes increases opportunities for gene conversion.
Results
To characterize gene conversion in rice, we have defined 626 multigene families in which 377 gene conversions were detected using the GENECONV program. Over 60% of the conversions we detected were between chromosomes. We found that the inter-chromosomal conversions distributed between chromosome 1 and 5, 2 and 6, and 3 and 5 are more frequent than genome average (Z-test, P < 0.05). The frequencies of gene conversion on the same chromosome decreased with the physical distance between gene conversion partners. Ka/Ks analysis indicates that gene conversion is not tightly linked to natural selection in the rice genome. To assess the contribution of segmental duplication on gene conversion statistics, we determined locations of conversion partners with respect to inter-chromosomal segment duplication. The number of conversions associated with segmentation is less than ten percent. Pseudogenes in the rice genome with low similarity to Arabidopsis genes showed greater likelihood for gene conversion than those with high similarity to Arabidopsis genes. Functional annotations suggest that at least 14 multigene families related to disease or bacteria resistance were involved in conversion events.
Conclusion
The evolution of gene families in the rice genome may have been accelerated by conversion with pseudogenes. Our analysis suggests a possible role for gene conversion in the evolution of pathogen-response genes.
doi:10.1186/1471-2164-9-93
PMCID: PMC2277409  PMID: 18298833
3.  FGF: A web tool for Fishing Gene Family in a whole genome database 
Nucleic Acids Research  2007;35(Web Server issue):W121-W125.
Gene duplication is an important process in evolution. The availability of genome sequences of a number of organisms has made it possible to conduct comprehensive searches for duplicated genes enabling informative studies of their evolution. We have established the FGF (Fishing Gene Family) program to efficiently search for and identify gene families. The FGF output displays the results as visual phylogenetic trees including information on gene structure, chromosome position, duplication fate and selective pressure. It is particularly useful to identify pseudogenes and detect changes in gene structure. FGF is freely available on a web server at http://fgf.genomics.org.cn/
doi:10.1093/nar/gkm426
PMCID: PMC1933194  PMID: 17584790
4.  Identification and characterization of insect-specific proteins by genome data analysis 
BMC Genomics  2007;8:93.
Background
Insects constitute the vast majority of known species with their importance including biodiversity, agricultural, and human health concerns. It is likely that the successful adaptation of the Insecta clade depends on specific components in its proteome that give rise to specialized features. However, proteome determination is an intensive undertaking. Here we present results from a computational method that uses genome analysis to characterize insect and eukaryote proteomes as an approximation complementary to experimental approaches.
Results
Homologs in common to Drosophila melanogaster, Anopheles gambiae, Bombyx mori, Tribolium castaneum, and Apis mellifera were compared to the complete genomes of three non-insect eukaryotes (opisthokonts) Homo sapiens, Caenorhabditis elegans and Saccharomyces cerevisiae. This operation yielded 154 groups of orthologous proteins in Drosophila to be insect-specific homologs; 466 groups were determined to be common to eukaryotes (represented by three opisthokonts). ESTs from the hemimetabolous insect Locust migratoria were also considered in order to approximate their corresponding genes in the insect-specific homologs. Stress and stimulus response proteins were found to constitute a higher fraction in the insect-specific homologs than in the homologs common to eukaryotes.
Conclusion
The significant representation of stress response and stimulus response proteins in proteins determined to be insect-specific, along with specific cuticle and pheromone/odorant binding proteins, suggest that communication and adaptation to environments may distinguish insect evolution relative to other eukaryotes. The tendency for low Ka/Ks ratios in the insect-specific protein set suggests purifying selection pressure. The generally larger number of paralogs in the insect-specific proteins may indicate adaptation to environment changes. Instances in our insect-specific protein set have been arrived at through experiments reported in the literature, supporting the accuracy of our approach.
doi:10.1186/1471-2164-8-93
PMCID: PMC1852559  PMID: 17407609
5.  WEGO: a web tool for plotting GO annotations 
Nucleic Acids Research  2006;34(Web Server issue):W293-W297.
Unified, structured vocabularies and classifications freely provided by the Gene Ontology (GO) Consortium are widely accepted in most of the large scale gene annotation projects. Consequently, many tools have been created for use with the GO ontologies. WEGO (Web Gene Ontology Annotation Plot) is a simple but useful tool for visualizing, comparing and plotting GO annotation results. Different from other commercial software for creating chart, WEGO is designed to deal with the directed acyclic graph structure of GO to facilitate histogram creation of GO annotation results. WEGO has been used widely in many important biological research projects, such as the rice genome project and the silkworm genome project. It has become one of the daily tools for downstream gene annotation analysis, especially when performing comparative genomics tasks. WEGO, along with the two other tools, namely External to GO Query and GO Archive Query, are freely available for all users at . There are two available mirror sites at and . Any suggestions are welcome at wego@genomics.org.cn.
doi:10.1093/nar/gkl031
PMCID: PMC1538768  PMID: 16845012
6.  The Genomes of Oryza sativa: A History of Duplications 
Yu, Jun | Wang, Jun | Lin, Wei | Li, Songgang | Li, Heng | Zhou, Jun | Ni, Peixiang | Dong, Wei | Hu, Songnian | Zeng, Changqing | Zhang, Jianguo | Zhang, Yong | Li, Ruiqiang | Xu, Zuyuan | Li, Shengting | Li, Xianran | Zheng, Hongkun | Cong, Lijuan | Lin, Liang | Yin, Jianning | Geng, Jianing | Li, Guangyuan | Shi, Jianping | Liu, Juan | Lv, Hong | Li, Jun | Wang, Jing | Deng, Yajun | Ran, Longhua | Shi, Xiaoli | Wang, Xiyin | Wu, Qingfa | Li, Changfeng | Ren, Xiaoyu | Wang, Jingqiang | Wang, Xiaoling | Li, Dawei | Liu, Dongyuan | Zhang, Xiaowei | Ji, Zhendong | Zhao, Wenming | Sun, Yongqiao | Zhang, Zhenpeng | Bao, Jingyue | Han, Yujun | Dong, Lingli | Ji, Jia | Chen, Peng | Wu, Shuming | Liu, Jinsong | Xiao, Ying | Bu, Dongbo | Tan, Jianlong | Yang, Li | Ye, Chen | Zhang, Jingfen | Xu, Jingyi | Zhou, Yan | Yu, Yingpu | Zhang, Bing | Zhuang, Shulin | Wei, Haibin | Liu, Bin | Lei, Meng | Yu, Hong | Li, Yuanzhe | Xu, Hao | Wei, Shulin | He, Ximiao | Fang, Lijun | Zhang, Zengjin | Zhang, Yunze | Huang, Xiangang | Su, Zhixi | Tong, Wei | Li, Jinhong | Tong, Zongzhong | Li, Shuangli | Ye, Jia | Wang, Lishun | Fang, Lin | Lei, Tingting | Chen, Chen | Chen, Huan | Xu, Zhao | Li, Haihong | Huang, Haiyan | Zhang, Feng | Xu, Huayong | Li, Na | Zhao, Caifeng | Li, Shuting | Dong, Lijun | Huang, Yanqing | Li, Long | Xi, Yan | Qi, Qiuhui | Li, Wenjie | Zhang, Bo | Hu, Wei | Zhang, Yanling | Tian, Xiangjun | Jiao, Yongzhi | Liang, Xiaohu | Jin, Jiao | Gao, Lei | Zheng, Weimou | Hao, Bailin | Liu, Siqi | Wang, Wen | Yuan, Longping | Cao, Mengliang | McDermott, Jason | Samudrala, Ram | Wang, Jian | Wong, Gane Ka-Shu | Yang, Huanming | Bennetzen, Jeff
PLoS Biology  2005;3(2):e38.
We report improved whole-genome shotgun sequences for the genomes of indica and japonica rice, both with multimegabase contiguity, or almost 1,000-fold improvement over the drafts of 2002. Tested against a nonredundant collection of 19,079 full-length cDNAs, 97.7% of the genes are aligned, without fragmentation, to the mapped super-scaffolds of one or the other genome. We introduce a gene identification procedure for plants that does not rely on similarity to known genes to remove erroneous predictions resulting from transposable elements. Using the available EST data to adjust for residual errors in the predictions, the estimated gene count is at least 38,000–40,000. Only 2%–3% of the genes are unique to any one subspecies, comparable to the amount of sequence that might still be missing. Despite this lack of variation in gene content, there is enormous variation in the intergenic regions. At least a quarter of the two sequences could not be aligned, and where they could be aligned, single nucleotide polymorphism (SNP) rates varied from as little as 3.0 SNP/kb in the coding regions to 27.6 SNP/kb in the transposable elements. A more inclusive new approach for analyzing duplication history is introduced here. It reveals an ancient whole-genome duplication, a recent segmental duplication on Chromosomes 11 and 12, and massive ongoing individual gene duplications. We find 18 distinct pairs of duplicated segments that cover 65.7% of the genome; 17 of these pairs date back to a common time before the divergence of the grasses. More important, ongoing individual gene duplications provide a never-ending source of raw material for gene genesis and are major contributors to the differences between members of the grass family.
Comparative genome sequencing of indica and japonica rice reveals that duplication of genes and genomic regions has played a major part in the evolution of grass genomes
doi:10.1371/journal.pbio.0030038
PMCID: PMC546038  PMID: 15685292

Results 1-6 (6)