Efficient sorting of genomic permutations by translocation, inversion and block interchange

MOTIVATION: Finding genomic distance based on gene order is a classic problem in genome rearrangements. Efficient exact algorithms for genomic distances based on inversions and/or translocations have been found but are complicated by special cases, rare in simulations and empirical data. We seek a universal operation underlying a more inclusive set of evolutionary operations and yielding a tractable genomic distance with simple mathematical form. RESULTS: We study a universal double-cut-and-join operation that accounts for inversions, translocations, fissions and fusions, but also produces circular intermediates which can be reabsorbed. The genomic distance, computable in linear time, is given by the number of breakpoints minus the number of cycles (b-c) in the comparison graph of the two genomes; the number of hurdles does not enter into it. Without changing the formula, we can replace generation and re-absorption of a circular intermediate by a generalized transposition, equivalent to a block interchange, with weight two. Our simple algorithm converts one multi-linear chromosome genome to another in the minimum distance.     

GS-Aligner: a novel tool for aligning genomic sequences using bit-level operations

A novel algorithm, GS-Aligner, that uses bit-level operations was developed for aligning genomic sequences. GS-Aligner is efficient in terms of both time and space for aligning two very long genomic sequences and for identifying genomic rearrangements such as translocations and inversions. It is suitable for aligning fairly divergent sequences such as human and mouse genomic sequences. It consists of several efficient components: bit-level coding, search for matching segments between the two sequences as alignment anchors, longest increasing subsequence (LIS), and optimal local alignment. Efforts have been made to reduce the execution time of the program to make it truly practical for aligning very long sequences. Empirical tests suggest that for relatively divergent sequences such as sequences from different mammalian orders or from a mammal and a nonmammalian vertebrate GS-Aligner performs better than existing methods. The program and data can be downloaded from http://pondside.uchicago.edu/~lilab/ and http://webcollab.iis.sinica.edu.tw/~biocom.     

Extracting three-way gene interactions from microarray data

MOTIVATION: It is an important and difficult task to extract gene network information from high-throughput genomic data. A common approach is to cluster genes using pairwise correlation as a distance metric. However, pairwise correlation is clearly too simplistic to describe the complex relationships among real genes since co-expression relationships are often restricted to a specific set of biological conditions/processes. In this study, we described a three-way gene interaction model that captures the dynamic nature of co-expression relationship between a gene pair through the introduction of a controller gene. RESULTS: We surveyed 0.4 billion possible three-way interactions among 1000 genes in a microarray dataset containing 678 human cancer samples. To test the reproducibility and statistical significance of our results, we randomly split the samples into a training set and a testing set. We found that the gene triplets with the strongest interactions (i.e. with the smallest P-values from appropriate statistical tests) in the training set also had the strongest interactions in the testing set. A distinctive pattern of three-way interaction emerged from these gene triplets: depending on the third gene being expressed or not, the remaining two genes can be either co-expressed or mutually exclusive (i.e. expression of either one of them would repress the other). Such three-way interactions can exist without apparent pairwise correlations. The identified three-way interactions may constitute candidates for further experimentation using techniques such as RNA interference, so that novel gene network or pathways could be identified.     

CartograTree: connecting tree genomes,phenotypes and environment

Today, researchers spend a tremendous amount of time gathering, formatting, filtering and visualizing data collected from disparate sources. Under the umbrella of forest tree biology, we seek to provide a platform and leverage modern technologies to connect biotic and abiotic data. Our goal is to provide an integrated web‐based workspace that connects environmental, genomic and phenotypic data via geo‐referenced coordinates. Here, we connect the genomic query web‐based workspace, DiversiTree and a novel geographical interface called CartograTree to data housed on the TreeGenes database. To accomplish this goal, we implemented Simple Semantic Web Architecture and Protocol to enable the primary genomics database, TreeGenes, to communicate with semantic web services regardless of platform or back‐end technologies. The novelty of CartograTree lies in the interactive workspace that allows for geographical visualization and engagement of high performance computing (HPC) resources. The application provides a unique tool set to facilitate research on the ecology, physiology and evolution of forest tree species. CartograTree can be accessed at: http://dendrome.ucdavis.edu/cartogratree .     

Insyght: navigating amongst abundant homologues,syntenies and gene functional annotations in bacteria,it's that symbol!

High-throughput techniques have considerably increased the potential of comparative genomics whilst simultaneously posing many new challenges. One of those challenges involves efficiently mining the large amount of data produced and exploring the landscape of both conserved and idiosyncratic genomic regions across multiple genomes. Domains of application of these analyses are diverse: identification of evolutionary events, inference of gene functions, detection of niche-specific genes or phylogenetic profiling. Insyght is a comparative genomic visualization tool that combines three complementary displays: (i) a table for thoroughly browsing amongst homologues, (ii) a comparator of orthologue functional annotations and (iii) a genomic organization view designed to improve the legibility of rearrangements and distinctive loci. The latter display combines symbolic and proportional graphical paradigms. Synchronized navigation across multiple species and interoperability between the views are core features of Insyght. A gene filter mechanism is provided that helps the user to build a biologically relevant gene set according to multiple criteria such as presence/absence of homologues and/or various annotations. We illustrate the use of Insyght with scenarios. Currently, only Bacteria and Archaea are supported. A public instance is available at http://genome.jouy.inra.fr/Insyght. The tool is freely downloadable for private data set analysis.     

一个园艺植物基因组序列和注释的在线批量实时提取平台

目前, 大量园艺植物基因组测序已经完成或接近尾声, 它们的基因组序列和注释数据极大地促进了功能基因组学研究。为给科研人员提供批量下载特定的基因组区段序列和注释平台, 笔者开发了一个称为OBRRP的生物信息学工具。OBRRP具有提取葡萄(Vitis vinifera)、桃(Prunus persica)、草莓(Fragaria vesca)、黄瓜(Cucumis sativus)、西瓜(Citrullus lanatus)、番茄(Solanum lycopersicum)、甜橙(Citrus sinensis)、苹果(Malus x domestica)、猕猴桃(Actinidia chinensis)、马铃薯(Solanum tuberosum)、香蕉(Musa acuminata)和拟南芥(Arabidopsis thaliana) 12种植物基因组序列及注释数据的功能; 同时, 也具有扩展到其它Gbrowser浏览器架构的数据库功能。测试结果表明, OBRRP是一个快捷简便的在线、批量和实时提取工具, 其登录地址为http://bioinfo.jit.edu.cn/OBRRP/。     

Robust genomic control for association studies

Population-based case-control studies are a useful method to test for a genetic association between a trait and a marker. However, the analysis of the resulting data can be affected by population stratification or cryptic relatedness, which may inflate the variance of the usual statistics, resulting in a higher-than-nominal rate of false-positive results. One approach to preserving the nominal type I error is to apply genomic control, which adjusts the variance of the Cochran-Armitage trend test by calculating the statistic on data from null loci. This enables one to estimate any additional variance in the null distribution of statistics. When the underlying genetic model (e.g., recessive, additive, or dominant) is known, genomic control can be applied to the corresponding optimal trend tests. In practice, however, the mode of inheritance is unknown. The genotype-based chi (2) test for a general association between the trait and the marker does not depend on the underlying genetic model. Since this general association test has 2 degrees of freedom (df), the existing formulas for estimating the variance factor by use of genomic control are not directly applicable. By expressing the general association test in terms of two Cochran-Armitage trend tests, one can apply genomic control to each of the two trend tests separately, thereby adjusting the chi (2) statistic. The properties of this robust genomic control test with 2 df are examined by simulation. This genomic control-adjusted 2-df test has control of type I error and achieves reasonable power, relative to the optimal tests for each model.     

Increasing calling accuracy,coverage, and read-depth in sequence data by the use of haplotype blocks

High-throughput genotyping of large numbers of lines remains a key challenge in plant genetics, requiring geneticists and breeders to find a balance between data quality and the number of genotyped lines under a variety of different existing genotyping technologies when resources are limited. In this work, we are proposing a new imputation pipeline (“HBimpute”) that can be used to generate high-quality genomic data from low read-depth whole-genome-sequence data. The key idea of the pipeline is the use of haplotype blocks from the software HaploBlocker to identify locally similar lines and subsequently use the reads of all locally similar lines in the variant calling for a specific line. The effectiveness of the pipeline is showcased on a dataset of 321 doubled haploid lines of a European maize landrace, which were sequenced at 0.5X read-depth. The overall imputing error rates are cut in half compared to state-of-the-art software like BEAGLE and STITCH, while the average read-depth is increased to 83X, thus enabling the calling of copy number variation. The usefulness of the obtained imputed data panel is further evaluated by comparing the performance of sequence data in common breeding applications to that of genomic data generated with a genotyping array. For both genome-wide association studies and genomic prediction, results are on par or even slightly better than results obtained with high-density array data (600k). In particular for genomic prediction, we observe slightly higher data quality for the sequence data compared to the 600k array in the form of higher prediction accuracies. This occurred specifically when reducing the data panel to the set of overlapping markers between sequence and array, indicating that sequencing data can benefit from the same marker ascertainment as used in the array process to increase the quality and usability of genomic data.     

introgress: a software package for mapping components of isolation in hybrids

A new software package (introgress) provides functions for analysing introgression of genotypes between divergent, hybridizing lineages, including estimating genomic clines from multi-locus genotype data and testing for deviations from neutral expectations. The software works with co-dominant, dominant and haploid marker data, and does not require fixed allelic differences between parental populations for the sampled genetic markers. Permutation and parametric procedures generate neutral expectations for introgression and provide a basis for significance tests of observed genomic clines. The software also implements maximum likelihood estimates of hybrid index from genotypic data and a number of graphical analyses. The package is an extension of the R statistical software, is written in the R language and is freely available through the Comprehensive R Archive Network (CRAN; http://cran.r-project.org/). In this study, we describe introgress and demonstrate its use with a sample data set.     

Kernel-based association test

Association mapping (i.e., linkage disequilibrium mapping) is a powerful tool for positional cloning of disease genes. We propose a kernel-based association test (KBAT), which is a composite function of "P-values of single-locus association tests" and "kernel weights related to intermarker distances and/or linkage disequilibria." The KBAT is a general form of some current test statistics. This method can be applied to the study of candidate genes and can scan each chromosome using a moving average procedure. We evaluated the performance of the KBAT through simulation studies that considered evolutionary parameters, disease models, sample sizes, kernel functions, test statistics, window attributes, empirical P-value estimations, and genetic/physical maps. The results showed that the KBAT had a well-controlled false positive rate and high power compared to existing methods. In addition, the KBAT was also applied to analyze a genomewide data set from the Collaborative Study on the Genetics of Alcoholism. Important genes associated with alcoholism dependence were identified. In summary, the merits of the KBAT are multifold: the KBAT is robust against the inclusion of nuisance markers, is invariant to the map scale, and accommodates different types of genomic data, study designs, and study purposes. The proposed methods are packaged in the user-friendly software, KBAT, available at http://www.stat.sinica.edu.tw/hsinchou/genetics/association/KBAT.htm.     

Null model tests of clustering of species, negative co-occurrence patterns and nestedness in meta-communities

We propose tests for patterns in meta-community structure. The tests for clustering and nestedness of the occurrences of species and negative co-occurrence patterns provide four important innovations. Firstly, they are not restricted to the analysis of communities along one-dimensional gradients or to the main axis of variation. Secondly, abundance data can also be considered in the null model whereas most previous approaches could consider only presence/absence data. And thirdly, habitat suitability and spatial autocorrelation can be incorporated in the null model so that patterns that might be caused by biotic interactions can be distinguished from patterns which are the result of differences in the suitability or accessibility of sites for the examined organisms. Finally, the test for nestedness is also appropriate if there is more than one set of nested subsets. A re-analysis of 35 data sets with these tests showed the importance of considering the autocorrelation of the occurrences of species in analyses of meta-community structure and demonstrated the advantage of abundance data for tests of clustering of species. With abundance data it could be shown that there is a significant clustering of species, i.e. there are positive associations of species in most meta-communities, even if an environmentally or spatially constrained null model is used for the test. Co-occurrence patterns that might indicate interspecific competition were found in many of the analysed presence/absence data sets. Surprisingly the analysis of abundance data sets provides less evidence for interspecific competition. A hierarchical organization of communities, i.e. nestedness, turned out to be a rare pattern, if the autocorrelation of the occurrences of species is considered.     

Multi-generation genomic prediction of maize yield using parametric and non-parametric sparse selection indices

Genomic prediction models are often calibrated using multi-generation data. Over time, as data accumulates, training data sets become increasingly heterogeneous. Differences in allele frequency and linkage disequilibrium patterns between the training and prediction genotypes may limit prediction accuracy. This leads to the question of whether all available data or a subset of it should be used to calibrate genomic prediction models. Previous research on training set optimization has focused on identifying a subset of the available data that is optimal for a given prediction set. However, this approach does not contemplate the possibility that different training sets may be optimal for different prediction genotypes. To address this problem, we recently introduced a sparse selection index (SSI) that identifies an optimal training set for each individual in a prediction set. Using additive genomic relationships, the SSI can provide increased accuracy relative to genomic-BLUP (GBLUP). Non-parametric genomic models using Gaussian kernels (KBLUP) have, in some cases, yielded higher prediction accuracies than standard additive models. Therefore, here we studied whether combining SSIs and kernel methods could further improve prediction accuracy when training genomic models using multi-generation data. Using four years of doubled haploid maize data from the International Maize and Wheat Improvement Center (CIMMYT), we found that when predicting grain yield the KBLUP outperformed the GBLUP, and that using SSI with additive relationships (GSSI) lead to 5–17% increases in accuracy, relative to the GBLUP. However, differences in prediction accuracy between the KBLUP and the kernel-based SSI were smaller and not always significant.Subject terms: Quantitative trait, Genetic models     

ArrayPlex: distributed,interactive and programmatic access to genome sequence,annotation, ontology,and analytical toolsets

ArrayPlex is a software package that centrally provides a large number of flexible toolsets useful for functional genomics, including microarray data storage, quality assessments, data visualization, gene annotation retrieval, statistical tests, genomic sequence retrieval and motif analysis. It uses a client-server architecture based on open source components, provides graphical, command-line, and programmatic access to all needed resources, and is extensible by virtue of a documented application programming interface. ArrayPlex is available at http://sourceforge.net/projects/arrayplex/.     

A Bayesian method for comparing and combining binary classifiers in the absence of a gold standard

ABSTRACT: BACKGROUND: Many problems in bioinformatics involve classification based on features such as sequence, structure or morphology. Given multiple classifiers, two crucial questions arise: how does their performance compare, and how can they best be combined to produce a better classifier? A classifier can be evaluated in terms of sensitivity and specificity using benchmark, or gold standard, data, that is, data for which the true classification is known. However, a gold standard is not always available. Here we demonstrate that a Bayesian model for comparing medical diagnostics without a gold standard can be successfully applied in the bioinformatics domain, to genomic scale data sets. We present a new implementation, which unlike previous implementations is applicable to any number of classifiers. We apply this model, for the first time, to the problem of finding the globally optimal logical combination of classifiers. RESULTS: We compared three classifiers of protein subcellular localisation, and evaluated our estimates of sensitivity and specificity against estimates obtained using a gold standard. The method overestimated sensitivity and specificity with only a small discrepancy, and correctly ranked the classifiers. Diagnostic tests for swine flu were then compared on a small data set. Lastly, classifiers for a genome-wide association study of macular degeneration with 541094 SNPs were analysed. In all cases, run times were feasible, and results precise. The optimal logical combination of classifiers was also determined for all three data sets. Code and data are available from http://bioinformatics.monash.edu.au/downloads/. CONCLUSIONS: The examples demonstrate the methods are suitable for both small and large data sets, applicable to the wide range of bioinformatics classification problems, and robust to dependence between classifiers. In all three test cases, the globally optimal logical combination of the classifiers was found to be their union, according to three out of four ranking criteria. We propose as a general rule of thumb that the union of classifiers will be close to optimal.     

myKaryoView: a light-weight client for visualization of genomic data

The Distributed Annotation System (DAS) is a protocol for easy sharing and integration of biological annotations. In order to visualize feature annotations in a genomic context a client is required. Here we present myKaryoView, a simple light-weight DAS tool for visualization of genomic annotation. myKaryoView has been specifically configured to help analyse data derived from personal genomics, although it can also be used as a generic genome browser visualization. Several well-known data sources are provided to facilitate comparison of known genes and normal variation regions. The navigation experience is enhanced by simultaneous rendering of different levels of detail across chromosomes. A simple interface is provided to allow searches for any SNP, gene or chromosomal region. User-defined DAS data sources may also be added when querying the system. We demonstrate myKaryoView capabilities for adding user-defined sources with a set of genetic profiles of family-related individuals downloaded directly from 23andMe. myKaryoView is a web tool for visualization of genomic data specifically designed for direct-to-consumer genomic data that uses publicly available data distributed throughout the Internet. It does not require data to be held locally and it is capable of rendering any feature as long as it conforms to DAS specifications. Configuration and addition of sources to myKaryoView can be done through the interface. Here we show a proof of principle of myKaryoView's ability to display personal genomics data with 23andMe genome data sources. The tool is available at: http://mykaryoview.com.