GO Biological Process sub-graph with probabilities and minimum subsumer

<p><b>Copyright information:</b></p><p>Taken from "Quantitative assessment of relationship between sequence similarity and function similarity"</p><p>http://www.biomedcentral.com/1471-2164/8/222</p><p>BMC Genomics 2007;8():222-222.</p><p>Published online 9 Jul 2007</p><p>PMCID:PMC1949826.</p><p></p> The numbers in parentheses denote the occurrence of the GO term and any of its descendants in the GO

Haplotype analysis of the <i>E4</i> gene region.

<p>The SNPViz clustering pictorial displayed the SNPs in a 12.6-kb region on chromosome 20, which included Glyma20g22160. Nucleotide polymorphisms were examined in A) the wild (black) and cultivated (red) lines from the Chinese collection and B) the NAM parents (blue). Details about the SNPViz clustering pictorial were described in the <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0094150#pone-0094150-g001" target="_blank">Figure 1</a> legend.</p

Haplotype analysis of the <i>E3</i> gene region.

<p>The SNPViz clustering pictorial displayed the SNPs in a 14.4-kb region on chromosome 19, which included Glyma19g41210. Nucleotide polymorphisms were examined in A) the wild (black) and cultivated (red) lines from the Chinese collection and B) the NAM parents (blue). Details about the SNPViz clustering pictorial were described in the <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0094150#pone-0094150-g001" target="_blank">Figure 1</a> legend.</p

Haplotype analysis of the <i>E2</i> gene region for the Chinese collection.

<p>The SNPViz clustering pictorial displayed the SNPs in a 28.3-kb region on chromosome ten, which included Glyma10g36600. Nucleotide polymorphisms were examined in the wild (black) and cultivated (red) lines from the Chinese collection.</p

Haplotype analysis of the <i>E2</i> gene region for the NAM parents.

<p>The SNPViz clustering pictorial displayed the SNPs in a 28.3-kb region on chromosome ten, which included Glyma10g36600. Nucleotide polymorphisms were examined in the NAM parents (blue). Details about the SNPViz clustering pictorial were described in the <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0094150#pone-0094150-g001" target="_blank">Figure 1</a> legend.</p

Maturity and growth determinate genotypes for cultivated soybean.

1<p><i>E3</i> and <i>E4</i> genotypes are not shown because the causative SNP was not identified in the data.</p

Major flowering time/maturity genes present in the Williams 82 reference sequence and positions used around those genes for haplotype analysis.

1<p>Gene location based on the Williams 82 reference sequence Glyma1.1.</p>2<p>Gene location based on the Williams 82 reference sequence Glyma1.0.</p

Table_1_Phenotype Prediction and Genome-Wide Association Study Using Deep Convolutional Neural Network of Soybean.xlsx

Genomic selection uses single-nucleotide polymorphisms (SNPs) to predict quantitative phenotypes for enhancing traits in breeding populations and has been widely used to increase breeding efficiency for plants and animals. Existing statistical methods rely on a prior distribution assumption of imputed genotype effects, which may not fit experimental datasets. Emerging deep learning technology could serve as a powerful machine learning tool to predict quantitative phenotypes without imputation and also to discover potential associated genotype markers efficiently. We propose a deep-learning framework using convolutional neural networks (CNNs) to predict the quantitative traits from SNPs and also to investigate genotype contributions to the trait using saliency maps. The missing values of SNPs are treated as a new genotype for the input of the deep learning model. We tested our framework on both simulation data and experimental datasets of soybean. The results show that the deep learning model can bypass the imputation of missing values and achieve more accurate results for predicting quantitative phenotypes than currently available other well-known statistical methods. It can also effectively and efficiently identify significant markers of SNPs and SNP combinations associated in genome-wide association study.</p

Major Soybean Maturity Gene Haplotypes Revealed by SNPViz Analysis of 72 Sequenced Soybean Genomes

<div><p>In this Genomics Era, vast amounts of next-generation sequencing data have become publicly available for multiple genomes across hundreds of species. Analyses of these large-scale datasets can become cumbersome, especially when comparing nucleotide polymorphisms across many samples within a dataset and among different datasets or organisms. To facilitate the exploration of allelic variation and diversity, we have developed and deployed an in-house computer software to categorize and visualize these haplotypes. The SNPViz software enables users to analyze region-specific haplotypes from single nucleotide polymorphism (SNP) datasets for different sequenced genomes. The examination of allelic variation and diversity of important soybean [<i>Glycine max</i> (L.) Merr.] flowering time and maturity genes may provide additional insight into flowering time regulation and enhance researchers' ability to target soybean breeding for particular environments. For this study, we utilized two available soybean genomic datasets for a total of 72 soybean genotypes encompassing cultivars, landraces, and the wild species <i>Glycine soja</i>. The major soybean maturity genes <i>E1</i>, <i>E2</i>, <i>E3</i>, and <i>E4</i> along with the <i>Dt1</i> gene for plant growth architecture were analyzed in an effort to determine the number of major haplotypes for each gene, to evaluate the consistency of the haplotypes with characterized variant alleles, and to identify evidence of artificial selection. The results indicated classification of a small number of predominant haplogroups for each gene and important insights into possible allelic diversity for each gene within the context of known causative mutations. The software has both a stand-alone and web-based version and can be used to analyze other genes, examine additional soybean datasets, and view similar genome sequence and SNP datasets from other species.</p></div

Reported polymorphic alleles of major maturity genes with reference to the Williams 82 sequence.

1<p>Uppercase allele designations indicate the dominant functional versions of the gene. In each case, the recessive mutant version of the gene is earlier flowering and maturing than the functional dominant version of the gene. The Williams 82 genome contains an earlier maturing missense version of <i>E1</i> (<i>e1-as</i>; T15R compared to the wild-type functional <i>E1</i>) <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0094150#pone.0094150-Xia1" target="_blank">[5]</a>. Allele names are taken or modified from the published descriptions for clarity.</p>2<p>The underlined alleles were identified and described in the literature but were not present in the two datasets used for this analysis.</p>3<p>Although the Williams 82 <i>E3</i> allele is considered functional, it was shown to contain an insertion in intron three consisting of transposable element-like sequences when compared to other functional <i>E3</i> alleles without the insertion in intron 3 <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0094150#pone.0094150-Watanabe2" target="_blank">[7]</a>. We herein denote the <i>E3</i> from Williams 82 as <i>E3</i> and the equivalently functional shorter <i>E3</i> allele as <i>E3 (short)</i>.</p