Conservation unit status inferred for plants by combining interspecific crosses and AFLP

Hybridization and introgression are common in plants and lead to morphological similarity between species and taxonomic confusion. This gene flow with closely related species can complicate efforts to determine whether an endangered taxon is evolutionarily distinctive and should be identified as a separate conservation unit. Potentilla delphinensis is a rare and threatened endemic species of the Southern French Alps. Two common related taxa (P. grandiflora and P. thuringiaca) are morphologically similar and occur in the same geographical locations. Thus, whether P. delphinensis represents a reliable conservation unit remained unclear. Our evaluation procedure based on a combination of molecular biology and interspecific crosses was used to define taxa within these plants. Plants were sampled from a total of 23 single and mixed localities for the three supposed taxa and were genotyped with 68 polymorphic Amplified Fragment Length Polymorphism (AFLP) loci. Fourty-one seedlings from interspecific crosses were obtained and genotyped. Amplified Fragment Length Polymorphism markers identified four genetically distinct units (P. delphinensis, P. grandiflora and two distinct groups of P. thuringiaca). All individuals of P. delphinensis formed a homogeneous and distinct taxon. This taxon was most probably an old allopolyploid from P. grandiflora and the related group of P. thuringiaca. Interspecific crosses gave low seed set and low germination rate. Furthermore, assignment test indicated that seedlings obtained from interspecific crosses were essentially apomictic rather than hybrids. These results suggest that a reproductive barrier exists between the different taxa. In conclusion, all results supported P. delphinensis as a true biological species and justified its conservation unit status. A surprising outcome of this work was the evidence of a potential new cryptic species. This study demonstrated the need to combine a molecular marker-based approach and pollination experiments for an accurate evaluation of plant taxa.     

SNPTools: a software tool for visualization and analysis of microarray data

SUMMARY: We have created a software tool, SNPTools, for analysis and visualization of microarray data, mainly SNP array data. The software can analyse and find differences in intensity levels between groups of arrays and identify segments of SNPs (genes, clones), where the intensity levels differ significantly between the groups. In addition, SNPTools can show jointly loss-of-heterozygosity (LOH) data (derived from genotypes) and intensity data for paired samples of tumour and normal arrays. The output graphs can be manipulated in various ways to modify and adjust the layout. A wizard allows options and parameters to be changed easily and graphs replotted. All output can be saved in various formats, and also re-opened in SNPTools for further analysis. For explorative use, SNPTools allows various genome information to be loaded onto the graphs. AVAILABILITY: The software, example data sets and tutorials are freely available from http://www.birc.au.dk/snptools     

Mfuzz: a software package for soft clustering of microarray data

For the analysis of microarray data, clustering techniques are frequently used. Most of such methods are based on hard clustering of data wherein one gene (or sample) is assigned to exactly one cluster. Hard clustering, however, suffers from several drawbacks such as sensitivity to noise and information loss. In contrast, soft clustering methods can assign a gene to several clusters. They can overcome shortcomings of conventional hard clustering techniques and offer further advantages. Thus, we constructed an R package termed Mfuzz implementing soft clustering tools for microarray data analysis. The additional package Mfuzzgui provides a convenient TclTk based graphical user interface. AVAILABILITY: The R package Mfuzz and Mfuzzgui are available at http://itb1.biologie.hu-berlin.de/~futschik/software/R/Mfuzz/index.html. Their distribution is subject to GPL version 2 license.     

GGT 2.0: versatile software for visualization and analysis of genetic data

Ever since its first release in 1999, the free software package for visualization of molecular marker data, graphical genotype (GGT), has been constantly adapted and improved. The GGT package was developed in a plant-breeding context and thus focuses on plant genetic data but was not intended to be limited to plants only. The current version has many options for genetic analysis of populations including diversity analyses and simple association studies. A second release of the GGT package, GGT 2.0 (available through http://www.plantbreeding.wur.nl), is therefore presented in this paper. An overview of existing and new features that are available within GGT 2.0, and a case study in which GGT 2.0 is applied to analyze an existing set of plant genetic data, are presented and discussed.     

MarVis: a tool for clustering and visualization of metabolic biomarkers

Background

Gene set analysis based on Gene Ontology (GO) can be a promising method for the analysis of differential expression patterns. However, current studies that focus on individual GO terms have limited analytical power, because the complex structure of GO introduces strong dependencies among the terms, and some genes that are annotated to a GO term cannot be found by statistically significant enrichment.

Results

We proposed a method for enriching clustered GO terms based on semantic similarity, namely cluster enrichment analysis based on GO (CeaGO), to extend the individual term analysis method. Using an Affymetrix HGU95aV2 chip dataset with simulated gene sets, we illustrated that CeaGO was sensitive enough to detect moderate expression changes. When compared to parent-based individual term analysis methods, the results showed that CeaGO may provide more accurate differentiation of gene expression results. When used with two acute leukemia (ALL and ALL/AML) microarray expression datasets, CeaGO correctly identified specifically enriched GO groups that were overlooked by other individual test methods.

Conclusion

By applying CeaGO to both simulated and real microarray data, we showed that this approach could enhance the interpretation of microarray experiments. CeaGO is currently available at http://chgc.sh.cn/en/software/CeaGO/.     

MarVis: a tool for clustering and visualization of metabolic biomarkers

Background  

A central goal of experimental studies in systems biology is to identify meaningful markers that are hidden within a diffuse background of data originating from large-scale analytical intensity measurements as obtained from metabolomic experiments. Intensity-based clustering is an unsupervised approach to the identification of metabolic markers based on the grouping of similar intensity profiles. A major problem of this basic approach is that in general there is no prior information about an adequate number of biologically relevant clusters.     

ACES: a machine learning toolbox for clustering analysis and visualization

Background

Studies that aim at explaining phenotypes or disease susceptibility by genetic or epigenetic variants often rely on clustering methods to stratify individuals or samples. While statistical associations may point at increased risk for certain parts of the population, the ultimate goal is to make precise predictions for each individual. This necessitates tools that allow for the rapid inspection of each data point, in particular to find explanations for outliers.

Results

ACES is an integrative cluster- and phenotype-browser, which implements standard clustering methods, as well as multiple visualization methods in which all sample information can be displayed quickly. In addition, ACES can automatically mine a list of phenotypes for cluster enrichment, whereby the number of clusters and their boundaries are estimated by a novel method. For visual data browsing, ACES provides a 2D or 3D PCA or Heat Map view. ACES is implemented in Java, with a focus on a user-friendly, interactive, graphical interface.

Conclusions

ACES has been proven an invaluable tool for analyzing large, pre-filtered DNA methylation data sets and RNA-Sequencing data, due to its ease to link molecular markers to complex phenotypes. The source code is available from https://github.com/GrabherrGroup/ACES.

     

GAAS: gene array analyzer software for management,analysis and visualization of gene expression data

SUMMARY: GAAS, Gene Array Analyzer Software supports multi-user efficient management and suitable analyses of large amounts of gene expression data across replicated experiments. Its management framework handles input data generated by different technologies. A multi-user environment allows each user to store his/her own data visualization scheme, analysis parameters used, values and formats of the output data. The analysis engine performs: background and spot quality evaluation, data normalization, differential gene expression analyses in single and multiple replica experiments. Results of expression profiles can be interactively navigated through graphical interfaces and stored into output databases.     

BiVisu: software tool for bicluster detection and visualization

BiVisu is an open-source software tool for detecting and visualizing biclusters embedded in a gene expression matrix. Through the use of appropriate coherence relations, BiVisu can detect constant, constant-row, constant-column, additive-related as well as multiplicative-related biclusters. The biclustering results are then visualized under a 2D setting for easy inspection. In particular, parallel coordinate (PC) plots for each bicluster are displayed, from which objective and subjective cluster quality evaluation can be performed. Availability: BiVisu has been developed in Matlab and is available at http://www.eie.polyu.edu.hk/~nflaw/Biclustering/.     

ANDVisio: a new tool for graphic visualization and analysis of literature mined associative gene networks in the ANDSystem

The ANDVisio tool is designed to reconstruct and analyze associative gene networks in the earlier developed Associative Network Discovery System (ANDSystem) software package. The ANDSystem incorporates utilities for automated extraction of knowledge from Pubmed published scientific texts, analysis of factographic databases, also the ANDCell database containing information on molecular-genetic events retrieved from texts and databases. ANDVisio is a new user's interface to the ANDCell database stored in a remote server. ANDVisio provides graphic visualization, editing, search, also saving of associative gene networks in different formats resulting from user's request. The associative gene networks describe semantic relationships between molecular-genetic objects (proteins, genes, metabolites and others), biological processes, and diseases. ANDVisio is provided with various tools to support filtering by object types, relationships between objects and information sources; graph layout; search of the shortest pathway; cycles in graphs.     

Metabolite-based clustering and visualization of mass spectrometry data using one-dimensional self-organizing maps

Background  

One of the goals of global metabolomic analysis is to identify metabolic markers that are hidden within a large background of data originating from high-throughput analytical measurements. Metabolite-based clustering is an unsupervised approach for marker identification based on grouping similar concentration profiles of putative metabolites. A major problem of this approach is that in general there is no prior information about an adequate number of clusters.     

JColorGrid: software for the visualization of biological measurements

Background  

Two-dimensional data colourings are an effective medium by which to represent three-dimensional data in two dimensions. Such "color-grid" representations have found increasing use in the biological sciences (e.g. microarray 'heat maps' and bioactivity data) as they are particularly suited to complex data sets and offer an alternative to the graphical representations included in traditional statistical software packages. The effectiveness of color-grids lies in their graphical design, which introduces a standard for customizable data representation. Currently, software applications capable of generating limited color-grid representations can be found only in advanced statistical packages or custom programs (e.g. micro-array analysis tools), often associated with steep learning curves and requiring expert knowledge.     

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.     

Gene mapping on mouse chromosome 8 by interspecific crosses: new data on a linkage group conserved on human chromosome 16q

A large conserved linkage group exists on mouse chromosome 8 and human chromosome 16q, including the loci for chymotrypsinogen B (Ctrb), haptoglobin (Hp), lecithin:cholesterol acyltransferase (Lcat), metallothionein-1,-2 (Mt-1,-2), tyrosine aminotransferase (Tat), and uvomorulin (Um). Using cloned gene probes, these six loci were mapped in M. m. domesticus X M. spretus interspecific crosses relative to a number of chromosome 8 anchor loci resulting in the gene order Es-1,Es-9-Mt-1,-2-Got-2-Es-2,Es-7,Lcat,Um-Hp,Tat,Ctrb-e. These results complement earlier studies and redefine the conserved segment on mouse chromosome 8, previously defined by the Hp-Tat interval, by the 24-cM interval between Mt-1,-2 and the conserved locus for adenine phosphoribosyltransferase, Aprt, mapped at 25 cM from Es-1 by T. B. Nesterova, P. M. Borodin, S. M. Zakian, and O. L. Serov (1987, Biochem. Genet. 25: 563-568). Within this segment, the gene order appears the same in man and mouse. While map distances between HP-TAT,HP-CTRB, and TAT-CTRB of respectively 7, 11, and 9 cM have previously been measured in man, no crossovers between Hp, Tat, and Ctrb were observed in over 100 meioses in the mouse.     

create: a software to create input files from diploid genotypic data for 52 genetic software programs

create is a Windows program for the creation of new and conversion of existing data input files for 52 genetic data analysis software programs. Programs are grouped into areas of sibship reconstruction, parentage assignment, genetic data analysis, and specialized applications. create is able to read in data from text, Microsoft Excel and Access sources and allows the user to specify columns containing individual and population identifiers, birth and death data, sex data, relationship information, and spatial location data. create's only constraints on source data are that one individual is contained in one row, and the genotypic data is contiguous. create is available for download at http://www.lsc.usgs.gov/CAFL/Ecology/Software.html.