MAGIC Tool: integrated microarray data analysis

Summary: Several programs are now available for analyzing thelarge datasets arising from cDNA microarray experiments. Mostprograms are expensive commercial packages or require expensivethird party software. Some are freely available to academicresearchers, but are limited to one operating system. MicroArrayGenome Imaging and Clustering Tool (MAGIC Tool) is an open sourceprogram that works on all major platforms, and takes users ‘fromtiff to gif’. Several unique features of MAGIC Tool areparticularly useful for research and teaching. Availability: http://www.bio.davidson.edu/MAGIC Contact: laheyer{at}davidson.edu     

MIPHENO: data normalization for high throughput metabolite analysis

Background  

High throughput methodologies such as microarrays, mass spectrometry and plate-based small molecule screens are increasingly used to facilitate discoveries from gene function to drug candidate identification. These large-scale experiments are typically carried out over the course of months and years, often without the controls needed to compare directly across the dataset. Few methods are available to facilitate comparisons of high throughput metabolic data generated in batches where explicit in-group controls for normalization are lacking.     

Bayesian integrated functional analysis of microarray data

MOTIVATION: The statistical analysis of microarray data usually proceeds in a sequential manner, with the output of the previous step always serving as the input of the next one. However, the methods currently used in such analyses do not properly account for the fact that the intermediate results may not always be correct, then leading to cumulating error in the inferences drawn based on such steps. RESULTS: Here we show that, by an application of hierarchical Bayesian methodology, this sequential procedure can be replaced by a single joint analysis, while systematically accounting for the uncertainties in this process. Moreover, we can also integrate relevant functional information available from databases into such an analysis, thereby increasing the reliability of the biological conclusions that are drawn. We illustrate these points by analysing real data and by showing that the genes can be divided into categories of interest, with the defining characteristic depending on the biological question that is considered. We contend that the proposed method has advantages at two levels. First, there are gains in the statistical and biological results from the analysis of this particular dataset. Second, it opens up new possibilities in analysing microarray data in general.     

Genome information management and integrated data analysis with HaloLex

HaloLex is a software system for the central management, integration, curation, and web-based visualization of genomic and other -omics data for any given microorganism. The system has been employed for the manual curation of three haloarchaeal genomes, namely Halobacterium salinarum (strain R1), Natronomonas pharaonis, and Haloquadratum walsbyi. HaloLex, in particular, enables the integrated analysis of genome-wide proteomic results with the underlying genomic data. This has proven indispensable to generate reliable gene predictions for GC-rich genomes, which, due to their characteristically low abundance of stop codons, are known to be hard targets for standard gene finders, especially concerning start codon assignment. The proteomic identification of more than 600 N-terminal peptides has greatly increased the reliability of the start codon assignment for Halobacterium salinarum. Application of homology-based methods to the published genome of Haloarcula marismortui allowed to detect 47 previously unidentified genes (a problem that is particularly serious for short protein sequences) and to correct more than 300 start codon misassignments.     

An integrated Bayesian analysis of LOH and copy number data

Background  

Cancer and other disorders are due to genomic lesions. SNP-microarrays are able to measure simultaneously both genotype and copy number (CN) at several Single Nucleotide Polymorphisms (SNPs) along the genome. CN is defined as the number of DNA copies, and the normal is two, since we have two copies of each chromosome. The genotype of a SNP is the status given by the nucleotides (alleles) which are present on the two copies of DNA. It is defined homozygous or heterozygous if the two alleles are the same or if they differ, respectively. Loss of heterozygosity (LOH) is the loss of the heterozygous status due to genomic events.     

Putting microarrays in a context: integrated analysis of diverse biological data

In recent years, multiple types of high-throughput functional genomic data that facilitate rapid functional annotation of sequenced genomes have become available. Gene expression microarrays are the most commonly available source of such data. However, genomic data often sacrifice specificity for scale, yielding very large quantities of relatively lower-quality data than traditional experimental methods. Thus sophisticated analysis methods are necessary to make accurate functional interpretation of these large-scale data sets. This review presents an overview of recently developed methods that integrate the analysis of microarray data with sequence, interaction, localisation and literature data, and further outlines current challenges in the field. The focus of this review is on the use of such methods for gene function prediction, understanding of protein regulation and modelling of biological networks.     

Automics: an integrated platform for NMR-based metabonomics spectral processing and data analysis

Background  

Spectral processing and post-experimental data analysis are the major tasks in NMR-based metabonomics studies. While there are commercial and free licensed software tools available to assist these tasks, researchers usually have to use multiple software packages for their studies because software packages generally focus on specific tasks. It would be beneficial to have a highly integrated platform, in which these tasks can be completed within one package. Moreover, with open source architecture, newly proposed algorithms or methods for spectral processing and data analysis can be implemented much more easily and accessed freely by the public.     

XplorSeq: A software environment for integrated management and phylogenetic analysis of metagenomic sequence data

Background  

Advances in automated DNA sequencing technology have accelerated the generation of metagenomic DNA sequences, especially environmental ribosomal RNA gene (rDNA) sequences. As the scale of rDNA-based studies of microbial ecology has expanded, need has arisen for software that is capable of managing, annotating, and analyzing the plethora of diverse data accumulated in these projects.     

Protein interaction verification and functional annotation by integrated analysis of genome-scale data

Assays capable of determining the properties of thousands of genes in parallel present challenges with regard to accurate data processing and functional annotation. Collections of microarray expression data are applied here to assess the quality of different high-throughput protein interaction data sets. Significant differences are found. Confidence in 973 out of 5342 putative two-hybrid interactions from S. cerevisiae is increased. Besides verification, integration of expression and interaction data is employed to provide functional annotation for over 300 previously uncharacterized genes. The robustness of these approaches is demonstrated by experiments that test the in silico predictions made. This study shows how integration improves the utility of different types of functional genomic data and how well this contributes to functional annotation.     

SSHSuite: an integrated software package for analysis of large-scale suppression subtractive hybridization data

Suppression subtractive hybridization (SSH) is a widely used technique for the identification of differentially expressed genes. SSH as well as other types of sequencing projects generate large amounts of anonymous sequences. SSHSuite automates the handling and storage of these sequences and enables identification through similarity searches. SSHSuite also offers analysis tools for the retrieval and comparison of the resulting similarity data. SSHSuite consists of four programs: SSHHandler, SSHOverview, SSHAnalysis, and SSHCompare.