Genome-wide association studies pipeline (GWASpi): a desktop application for genome-wide SNP analysis and management

MOTIVATION: Genome-wide association studies (GWAS) based on single nucleotide polymorphism (SNP) arrays are the most widely used approach to detect loci associated to human traits. Due to the complexity of the methods and software packages available, each with its particular format requiring intricate management workflows, the analysis of GWAS usually confronts scientists with steep learning curves. Indeed, the wide variety of tools makes the parsing and manipulation of data the most time consuming and error prone part of a study. To help resolve these issues, we present GWASpi, a user-friendly, multiplatform, desktop-able application for the management and analysis of GWAS data, with a novel approach on database technologies to leverage the most out of commonly available desktop hardware. GWASpi aims to be a start-to-finish GWAS management application, from raw data to results, containing the most common analysis tools. As a result, GWASpi is easy to use and reduces in up to two orders of magnitude the time needed to perform the fundamental steps of a GWAS. AVAILABILITY: Freely available on the web at http://www.gwaspi.org. Implemented in Java, Apache-Derby and NetCDF-3, with all major operating systems supported. CONTACT: gwaspi@upf.edu; arcadi.navarro@upf.edu.     

The development of PIPA: an integrated and automated pipeline for genome-wide protein function annotation

Background  

Automated protein function prediction methods are needed to keep pace with high-throughput sequencing. With the existence of many programs and databases for inferring different protein functions, a pipeline that properly integrates these resources will benefit from the advantages of each method. However, integrated systems usually do not provide mechanisms to generate customized databases to predict particular protein functions. Here, we describe a tool termed PIPA (Pipeline for Protein Annotation) that has these capabilities.     

GSEA-SNP: applying gene set enrichment analysis to SNP data from genome-wide association studies

The power of genome-wide SNP association studies is limited, among others, by the large number of false positive test results. To provide a remedy, we combined SNP association analysis with the pathway-driven gene set enrichment analysis (GSEA), recently developed to facilitate handling of genome-wide gene expression data. The resulting GSEA-SNP method rests on the assumption that SNPs underlying a disease phenotype are enriched in genes constituting a signaling pathway or those with a common regulation. Besides improving power for association mapping, GSEA-SNP may facilitate the identification of disease-associated SNPs and pathways, as well as the understanding of the underlying biological mechanisms. GSEA-SNP may also help to identify markers with weak effects, undetectable in association studies without pathway consideration. The program is freely available and can be downloaded from our website.     

ALOHOMORA: a tool for linkage analysis using 10K SNP array data

SUMMARY: ALOHOMORA is a software tool designed to facilitate genome-wide linkage studies performed with high-density single nucleotide polymorphism (SNP) marker panels such as the Affymetrix GeneChip(R) Human Mapping 10K Array. Genotype data are converted into appropriate formats for a number of common linkage programs and subjected to standard quality control routines before linkage runs are started. ALOHOMORA is written in Perl and may be used to perform state-of-the-art linkage scans in small and large families with any genetic model. Options for using different genetic maps or ethnicity-specific allele frequencies are implemented. Graphic outputs of whole-genome multipoint LOD score values are provided for the entire dataset as well as for individual families. AVAILABILITY: ALOHOMORA is available free of charge for non-commercial research institutions. For more details, see http://gmc.mdc-berlin.de/alohomora/     

A web-based platform for rice microarray annotation and data analysis

Rice (Oryza sativa) feeds over half of the global population. A web-based integrated platform for rice microarray annotation and data analysis in various biological contexts is presented, which provides a convenient query for comprehensive annotation compared with similar databases. Coupled with existing rice microarray data, it provides online analysis methods from the perspective of bioinformatics. This comprehensive bioinformatics analysis platform is composed of five modules, including data retrieval, microarray annotation, sequence analysis, results visualization and data analysis. The BioChip module facilitates the retrieval of microarray data information via identifiers of “Probe Set ID”, “Locus ID” and “Analysis Name”. The BioAnno module is used to annotate the gene or probe set based on the gene function, the domain information, the KEGG biochemical and regulatory pathways and the potential microRNA which regulates the genes. The BioSeq module lists all of the related sequence information by a microarray probe set. The BioView module provides various visual results for the microarray data. The BioAnaly module is used to analyze the rice microarray’s data set.     

Application of genome-wide SNP data for uncovering pairwise relationships and quantitative trait loci

The genetic analysis of quantitative traits in humans is changing as a result of the availability of whole-genome SNP data. Heritability analysis can make use of actual genetic sharing between pairs of individuals estimated from the genotype data, rather than the expected genetic sharing implied by their family relationship. This could provide more accurate heritability estimates and help to overcome the equal environment assumption. Quantitative trait locus (QTL) linkage mapping can make use of local genetic sharing inferred from very dense local genotype data from pedigree members or individuals not previously known to be related. This approach may be particularly suited for detecting loci that contain rare variants with major effect on the phenotype. Finally, whole-genome SNP data can be used to measure the genetic similarity between individuals to provide matched sets for association studies, in order to avoid spurious association from population stratification.     

A versatile computational pipeline for bacterial genome annotation improvement and comparative analysis, with Brucella as a use case

We present a bacterial genome computational analysis pipeline, called GenVar. The pipeline, based on the program GeneWise, is designed to analyze an annotated genome and automatically identify missed gene calls and sequence variants such as genes with disrupted reading frames (split genes) and those with insertions and deletions (indels). For a given genome to be analyzed, GenVar relies on a database containing closely related genomes (such as other species or strains) as well as a few additional reference genomes. GenVar also helps identify gene disruptions probably caused by sequencing errors. We exemplify GenVar's capabilities by presenting results from the analysis of four Brucella genomes. Brucella is an important human pathogen and zoonotic agent. The analysis revealed hundreds of missed gene calls, new split genes and indels, several of which are species specific and hence provide valuable clues to the understanding of the genome basis of Brucella pathogenicity and host specificity.     

Proteomic 2DE database for spot selection,automated annotation,and data analysis

We present a software solution that enables faster and more accurate data analysis of 2DE/MALDI TOF MS data. The software supports data analysis through a number of automated data selection functions and advanced graphical tools. Once protein identities are determined using MALDI TOF MS, automated data retrieval from online databases provides biological information. The software, called 2DDB, reduces analysis time to a fraction without losing any quality compared to more manual data analysis. The database contains over 100,000 data entries, and selected parts can be reached at http://2ddb.org.     

EasyGO: Gene Ontology-based annotation and functional enrichment analysis tool for agronomical species

Following the publication of our recent article (Kapp et al., BMC Genomics 2006, 7:231), we (the authors) regrettably found several errors in the published Table 5. This correction article not only describes what makes the published Table 5 incorrect, it also presents the correct Table 5.     

SNP2CAPS: a SNP and INDEL analysis tool for CAPS marker development

With the influx of various SNP genotyping assays in recent years, there has been a need for an assay that is robust, yet cost effective, and could be performed using standard gel-based procedures. In this context, CAPS markers have been shown to meet these criteria. However, converting SNPs to CAPS markers can be a difficult process if done manually. In order to address this problem, we describe a computer program, SNP2CAPS, that facilitates the computational conversion of SNP markers into CAPS markers. 413 multiple aligned sequences derived from barley ESTs were analysed for the presence of polymorphisms in 235 distinct restriction sites. 282 (90%) of 314 alignments that contain sequence variation due to SNPs and InDels revealed at least one polymorphic restriction site. After reducing the number of restriction enzymes from 235 to 10, 31% of the polymorphic sites could still be detected. In order to demonstrate the usefulness of this tool for marker development, we experimentally validated some of the results predicted by SNP2CAPS.     

GI-POP: A combinational annotation and genomic island prediction pipeline for ongoing microbial genome projects

Sequencing of microbial genomes is important because of microbial-carrying antibiotic and pathogenetic activities. However, even with the help of new assembling software, finishing a whole genome is a time-consuming task. In most bacteria, pathogenetic or antibiotic genes are carried in genomic islands. Therefore, a quick genomic island (GI) prediction method is useful for ongoing sequencing genomes. In this work, we built a Web server called GI-POP (http://gipop.life.nthu.edu.tw) which integrates a sequence assembling tool, a functional annotation pipeline, and a high-performance GI predicting module, in a support vector machine (SVM)-based method called genomic island genomic profile scanning (GI-GPS). The draft genomes of the ongoing genome projects in contigs or scaffolds can be submitted to our Web server, and it provides the functional annotation and highly probable GI-predicting results. GI-POP is a comprehensive annotation Web server designed for ongoing genome project analysis. Researchers can perform annotation and obtain pre-analytic information include possible GIs, coding/non-coding sequences and functional analysis from their draft genomes. This pre-analytic system can provide useful information for finishing a genome sequencing project.     

IPGWAS: an integrated pipeline for rational quality control and association analysis of genome-wide genetic studies

Alveolar rhabdomyosarcoma (aRMS) is a very aggressive sarcoma of children and young adults. Our previous studies have shown that small molecule inhibition of Pdgfra is initially very effective in an aRMS mouse model. However, slowly evolving, acquired resistance to a narrow-spectrum kinase inhibitor (imatinib) was common. We identified Src family kinases (SFKs) to be potentiators of Pdgfra in murine aRMS primary cell cultures from mouse tumors with evolved resistance in vivo in comparison to untreated cultures. Treating the resistant primary cell cultures with a combination of Pdgfra and Src inhibitors had a strong additive effect on cell viability. In Pdgfra knockout tumors, however, the Src inhibitor had no effect on tumor cell viability. Sorafenib, whose targets include not only PDGFRA but also the Src downstream target Raf, was effective at inhibiting mouse and human tumor cell growth and halted progression of mouse aRMS tumors in vivo. These results suggest that an adaptive Src-Pdgfra-Raf-Mapk axis is relevant to PDGFRA inhibition in rhabdomyosarcoma.     

Tilescope: online analysis pipeline for high-density tiling microarray data

We developed Tilescope, a fully integrated data processing pipeline for analyzing high-density tiling-array data . In a completely automated fashion, Tilescope will normalize signals between channels and across arrays, combine replicate experiments, score each array element, and identify genomic features. The program is designed with a modular, three-tiered architecture, facilitating parallelism, and a graphic user-friendly interface, presenting results in an organized web page, downloadable for further analysis.     

A fast algorithm for genome-wide analysis of proteins with repeated sequences.

We present a fast algorithm to search for repeating fragments within protein sequences. The technique is based on an extension of the Smith-Waterman algorithm that allows the calculation of sub-optimal alignments of a sequence against itself. We are able to estimate the statistical significance of all sub-optimal alignment scores. We also rapidly determine the length of the repeating fragment and the number of times it is found in a sequence. The technique is applied to sequences in the Swissprot database, and to 16 complete genomes. We find that eukaryotic proteins contain more internal repeats than those of prokaryotic and archael organisms. The finding that 18% of yeast sequences and 28% of the known human sequences contain detectable repeats emphasizes the importance of internal duplication in protein evolution.