Exome sequencing as a tool for Mendelian disease gene discovery

Exome sequencing - the targeted sequencing of the subset of the human genome that is protein coding - is a powerful and cost-effective new tool for dissecting the genetic basis of diseases and traits that have proved to be intractable to conventional gene-discovery strategies. Over the past 2 years, experimental and analytical approaches relating to exome sequencing have established a rich framework for discovering the genes underlying unsolved Mendelian disorders. Additionally, exome sequencing is being adapted to explore the extent to which rare alleles explain the heritability of complex diseases and health-related traits. These advances also set the stage for applying exome and whole-genome sequencing to facilitate clinical diagnosis and personalized disease-risk profiling.     

Second-generation sequencing for gene discovery in the Brassicaceae

The Brassicaceae contains the most diverse collection of agriculturally important crop species of all plant families. Yet, this is one of the few families that do not form functional symbiotic associations with mycorrhizal fungi in the soil for improved nutrient acquisition. The genes involved in this symbiosis were more recently recruited by legumes for symbiotic association with nitrogen-fixing rhizobia bacteria. This study applied second-generation sequencing (SGS) and analysis tools to discover that two such genes, NSP1 (Nodulation Signalling Pathway 1) and NSP2, remain conserved in diverse members of the Brassicaceae despite the absence of these symbioses. We demonstrate the utility of SGS data for the discovery of putative gene homologs and their analysis in complex polyploid crop genomes with little prior sequence information. Furthermore, we show how this data can be applied to enhance downstream reverse genetics analyses. We hypothesize that Brassica NSP genes may function in the root in other plant-microbe interaction pathways that were recruited for mycorrhizal and rhizobial symbioses during evolution.     

GOrilla: a tool for discovery and visualization of enriched GO terms in ranked gene lists

Background

Although expression microarrays have become a standard tool used by biologists, analysis of data produced by microarray experiments may still present challenges. Comparison of data from different platforms, organisms, and labs may involve complicated data processing, and inferring relationships between genes remains difficult.

Results

S TAR N ET 2 is a new web-based tool that allows post hoc visual analysis of correlations that are derived from expression microarray data. S TAR N ET 2 facilitates user discovery of putative gene regulatory networks in a variety of species (human, rat, mouse, chicken, zebrafish, Drosophila, C. elegans, S. cerevisiae, Arabidopsis and rice) by graphing networks of genes that are closely co-expressed across a large heterogeneous set of preselected microarray experiments. For each of the represented organisms, raw microarray data were retrieved from NCBI's Gene Expression Omnibus for a selected Affymetrix platform. All pairwise Pearson correlation coefficients were computed for expression profiles measured on each platform, respectively. These precompiled results were stored in a MySQL database, and supplemented by additional data retrieved from NCBI. A web-based tool allows user-specified queries of the database, centered at a gene of interest. The result of a query includes graphs of correlation networks, graphs of known interactions involving genes and gene products that are present in the correlation networks, and initial statistical analyses. Two analyses may be performed in parallel to compare networks, which is facilitated by the new H EAT S EEKER module.

Conclusion

S TAR N ET 2 is a useful tool for developing new hypotheses about regulatory relationships between genes and gene products, and has coverage for 10 species. Interpretation of the correlation networks is supported with a database of previously documented interactions, a test for enrichment of Gene Ontology terms, and heat maps of correlation distances that may be used to compare two networks. The list of genes in a S TAR N ET network may be useful in developing a list of candidate genes to use for the inference of causal networks. The tool is freely available at http://vanburenlab.medicine.tamhsc.edu/starnet2.html, and does not require user registration.     

Analysis of large gene databases for discovery of novel therapeutic agents

During the 1980s and early 1990s the recombinant DNA revolution provided a vital source of therapeutic targets and agents for pharmaceutical research. However, during the early 1990s, it became apparent that the identification and cloning of novel human cDNAs was a rate limiting step in drug discovery and that new technological approaches were required to address the challenge. There was an increasing realisation that the new science of 'genomics', together with the associated large gene sequence databases, would provide a radically new means of generating targets. SmithKline Beecham has been at the forefront of this breakthrough in pharmaceutical research. The productivity of this strategy is illustrated by reference to our work on novel enzymes, chemokines and receptors and new approaches linking genes to pathological processes.     

The role of phenotype in gene discovery in the whole genome sequencing era

As whole genome sequence becomes a routine component of gene discovery studies in humans, we will have an exhaustive catalog of genetic variation and the challenge becomes understanding the phenotypic consequences of these variants. Statistical genetic methods and analytical approaches that are concerned with optimizing phenotypes for gene discovery for complex traits offer two general categories of advantages. They may increase power to localize genes of interest and also aid in interpreting associations between genetic variants and disease outcomes by suggesting potential mechanisms and pathways through which genes may affect outcomes. Such phenotype optimization approaches include use of allied phenotypes such as symptoms or ages of onset to reduce genetic heterogeneity within a set of cases, study of quantitative risk factors or endophenotypes, joint analyses of related phenotypes, and derivation of new phenotypes designed to extract independent measures underlying the correlations among a set of related phenotypes through approaches such as principal components. New opportunities are also presented by technological advances that permit efficient collection of hundreds or thousands of phenotypes on an individual, including phenotypes more proximal to the level of gene action such as levels of gene expression, microRNAs, or metabolic and proteomic profiles.     

GOrilla: a tool for discovery and visualization of enriched GO terms in ranked gene lists

Background  

Since the inception of the GO annotation project, a variety of tools have been developed that support exploring and searching the GO database. In particular, a variety of tools that perform GO enrichment analysis are currently available. Most of these tools require as input a target set of genes and a background set and seek enrichment in the target set compared to the background set. A few tools also exist that support analyzing ranked lists. The latter typically rely on simulations or on union-bound correction for assigning statistical significance to the results.     

A high-density admixture map for disease gene discovery in african americans

Admixture mapping (also known as "mapping by admixture linkage disequilibrium," or MALD) provides a way of localizing genes that cause disease, in admixed ethnic groups such as African Americans, with approximately 100 times fewer markers than are required for whole-genome haplotype scans. However, it has not been possible to perform powerful scans with admixture mapping because the method requires a dense map of validated markers known to have large frequency differences between Europeans and Africans. To create such a map, we screened through databases containing approximately 450000 single-nucleotide polymorphisms (SNPs) for which frequencies had been estimated in African and European population samples. We experimentally confirmed the frequencies of the most promising SNPs in a multiethnic panel of unrelated samples and identified 3011 as a MALD map (1.2 cM average spacing). We estimate that this map is approximately 70% informative in differentiating African versus European origins of chromosomal segments. This map provides a practical and powerful tool, which is freely available without restriction, for screening for disease genes in African American patient cohorts. The map is especially appropriate for those diseases that differ in incidence between the parental African and European populations.     

Semiautomated preparation of DNA templates for large-scale sequencing projects

The rate limiting step in a large-scale sequencing project is the generation of single-stranded DNA templates. We describe a fast, semiautomated procedure, using 96-well microtitre plates, in which 192 templates can be readily prepared in 1 day. The technique can be carried out manually or can be semiautomated using a robot pipetting device. We also provide evidence for the reliability and applicability of this method to a large-scale sequencing project.     

Optical mapping and its potential for large-scale sequencing projects.

Physical mapping has been rediscovered as an important component of large-scale sequencing projects. Restriction maps provide landmark sequences at defined intervals, and high-resolution restriction maps can be assembled from ensembles of single molecules by optical means. Such optical maps can be constructed from both large-insert clones and genomic DNA, and are used as a scaffold for accurately aligning sequence contigs generated by shotgun sequencing.     

CAAT-Box, Contigs-Assembly and Annotation Tool-Box for genome sequencing projects

MOTIVATION: Contigs-Assembly and Annotation Tool-Box (CAAT-Box) is a software package developed for the computational part of a genome project where the sequence is obtained by a shotgun strategy. CAAT-Box contains new tools to predict links between contigs by using similarity searches with other whole genome sequences. Most importantly, it allows annotation of a genome to commence during the finishing phase using a gene-oriented strategy. For this purpose, CAAT-Box creates an Individual Protein file (IPF) for each ORF of an assembly. The nucleotide sequence reported in an IPF corresponds to the sequence of the ORF with 500 additional bases before the ORF and 200 bases after. For annotation, additional information like Blast results can be added or linked to the IPFs as well as automatic and/or manual annotations. When a new assembly is performed, CAAT-Box creates new IPFs according to the old IPF panel. CAAT-Box recognizes the modified IPFs which are the only ones used for a new automatic analysis after each assembly. Using this strategy, the user works with a group of IPFs independently of the closure phase progression. The IPFs are accessible by a web server and can therefore be modified and commented by different groups. RESULT: CAAT-Box was used to obtain and to annotate several complete genomes like Listeria monocytogenes or Streptococcus agalactiae. AVAILABILITY: The program may be obtained from the authors and is freely available to non-profit organisations.