Bionumerics生物信息分析软件应用简介.ppt

BioNumerics生物信息分析统一平台,上海一贝科技 许先生One powerful platform for databasing, analyzing, and sharing all your biological data,BioNumerics,BioNumerics7.1模块,Data modules 数据模块,Fingerprint Data. Normalization and analysis of electrophoresis fingerprints from slab gels, automated sequencers, and lab-on-a chip systems. Preprocessing and analysis of spectral data such as MALDI. Character Data. Import and analyze character data from a wide range of sources including phenotype panels, antibiotic resistance profiles, microarrays, etc. Sequence Data. Assemble and analyze Sanger sequence data and NGS sequence reads. Access a wide variety of sequence analysis, search and alignment, and comparison tools. Whole Genome Map Data. Align and cluster whole genome maps for bacterial strain typing and identification. Trend Data. Analyze sequential measurements that express an evolution of one parameter in function of another, e.g., enzymatic activity, growth curves, rt-PCR, etc.,Analysis modules,Tree and Network Inference. Select from an impressive range of clustering algorithms to calculate evolutionary trees and relationship networks. Display confidence levels on clusters and branches. Dimensioning and Matrix Mining. Create non-hierarchical groupings using various ordination techniques such as principal components analysis, multidimensional scaling, discriminant analysis, and identify discriminating features between groups. Perform in-depth analysis of character matrices. Genome Analysis Tools. Align and compare chromosomes side-by-side or calculate multiple chromosome alignments. Calculate SNPs and mutations on multi-chromosome alignments and annotate new chromosomes. Perform microbial metagenomics diversity analysis. Classifiers and Identification. Identify unknown samples against reference data sets using state-of-the-art classifiers such as Naive Bayesian, SVM, Shrunken Centroids, and a range of similarity coefficients. Enhance your identification projects with parameter optimization and comprehensive cross-validation tools. Compare and validate different techniques or procedures. Versioning and Audit Trails. Create audited databases (fully FDA Title 21 CFR Part 11 compliant) by recording all changes and keeping all versions of selected database objects. Compare and restore versions. Log user activity, create digital signing privileges.,我们能存储什么？,我们能做什么？,Using fields Using experiments Using ranges of values Using any combinations,Multiple alignment Neighbor Joining Parsimony Maximun likelihood,MANOVA(多元方差分析) Discriminant annlysis K-means,Jacknife Significance testing,Pricipal Components Analysis Multi-Dim. Scaling Self-Organizing Maps Neural Networks,UPGMA,NN,FN,Ward Standard deviation Cophenetic correlation Bootstrap,Library constrution Statistical confidence Neural Networks,Bionumerics在疾控系统的主要应用,MLST(多位点序列分析) 实验和分析方法已经标准化，有国际数据库供查询和比对 成功应用于全球、全国性暴发调查和菌型分布调查 适合用于长期的、大范围的流行病学调查和监测菌群结构变化,PFGE(脉冲场电泳) 被广泛承认和使用的方法 分辨能力优于MLST，分型结果与MLST有很高的一致性 可以作为MLST实验菌株的初筛工具 可以为暴发和聚集性病例调查提供实验室数据支持,MLVA (多位点可变数目串联重复序列分型) 在肠道菌和食源性疾病的暴发调查中已经被证实具有作用 在呼吸道细菌中分型结果类似于MLST，分辨力高于PFGE和MLST 需要寻找稳定的VNTR位点组合用于流行病学调查和菌群结构分析,2003-2008年371株脑膜炎奈瑟菌MLST分析,应用实例一：脑膜炎奈瑟菌种群结构分析和流行分布,2003-2008年脑膜炎奈瑟菌主要序列群分布,暴发相关菌株,健康携带本底菌株,健康携带本底菌株,应用实例二：流脑暴发调查,流脑菌株总携带率：28.3 暴发相关菌株携带率：15.7%,应用实例三：肺炎克雷伯菌院内感染鉴别,应用实例四：热带念珠菌种群结构分析,Cluster I,Cluster II,猪链球菌 血清二型 种群关系,应用实例五：热带念珠菌种群结构分析,应用实例六：MLVA数据MST分析,发现Cluster 分型数据提交 聚类分析 “Visual Check”完全一致的分子分型特征,应用实例七：发现各种Cluster,应用实例八：分子流病应用,Pattern趋势,长期监测:观察pattern的变化趋势是否一如既往的稳定,Pattern频率,长期监测:观察pattern出现频率是否有较大变化,应用实例九：归因分析,归因分析 引起食源性疾病的各种食品所占的比例（相对贡献）。,Distance and similarity matrices,Customized display o