Identification and function prediction of novel microRNAs in adenosine monophosphate activated protein kinase-activated Sertoli cells of immature boar.

This study was carried out with the objective to identify function prediction of novel microRNAs (miRNAs) in immature boar Sertoli cells (SCs) treated with 5-aminoimidazole-4-carboxamide-1-ß-D-ribofuranoside (AICAR), which is an agonist of adenosine monophosphate-activated protein kinase (AMPK) for regulating cellular energy homeostasis. Two small RNA libraries (control and AICAR treatment) prepared from immature boar SCs were constructed and sequenced by the Illumina small RNA deep sequencing. We identified 77 novel miRNAs and predicted 177 potential target genes for 26 differential novel miRNAs (four miRNAs up-regulation and 22 miRNAs down-regulation) in AICAR-treated SCs. Gene Ontology enrichment and Kyoto Encyclopedia of Genes and Genomes pathway suggested that target genes of differential novel miRNAs were implicated in many biological processes and metabolic pathways. Our findings provided useful information for the functional regulation of novel miRNAs and target mRNAs on AMPK-activated immature boar SCs.

Systematic alteration of in vitro metabolic environments reveals empirical growth relationships in cancer cell phenotypes.

Cancer cells, like microbes, live in complex metabolic environments. Recent evidence suggests that microbial behavior across metabolic environments is well described by simple empirical growth relationships, or growth laws. Do such empirical growth relationships also exist in cancer cells? To test this question, we develop a high-throughput approach to extract quantitative measurements of cancer cell behaviors in systematically altered metabolic environments. Using this approach, we examine relationships between growth and three frequently studied cancer phenotypes: drug-treatment survival, cell migration, and lactate overflow. Drug-treatment survival follows simple linear growth relationships, which differ quantitatively between chemotherapeutics and EGFR inhibition. Cell migration follows a weak grow-and-go growth relationship, with substantial deviation in some environments. Finally, lactate overflow is mostly decoupled from growth rate and is instead determined by the cells' ability to maintain high sugar uptake rates. Altogether, this work provides a quantitative approach for formulating empirical growth laws of cancer.

Biological Trace Information Extracted from Bioaerosols Using NGS Analysis

Bioaerosols are atmospheric particles with a biological trace, such as viruses, bacteria, fungi, and plant material such as pollen and plant debris. In this study, we analyzed the biological information in bioaerosols using next generation sequencing of the trace DNA. The samples were collected using an Andersen air sampler and separated into two groups according to particulate matter (PM) size: small (PM2.5) and large (PM10). Amplification and sequencing of the bacterial 16S rDNA gene, prokaryotic internal transcribed spacer 1 (ITS1) region and DNA sequence of a plant chloroplast gene (rbcL) were carried out using several sets of specific primers targeting animal and plant sequences. Lots of bacterial information was detected from the bioaerosols. The most abundant bacteria in several samples were of the Actinobacteria (class), Alphaproteobacteria, Bacilli, and Clostridia. For the animal detection using internal transcribed spacer 1, only uncultured fungi were detected in more than half of the hits, with a high number of Cladosporium sp. in the samples. For the plant identification, the ITS1 information only matched fungal species. However, targeting of the rbcL region revealed diverse plant information, such as Medicago papillosa. In conclusion, traces of bacteria, fungi, and plants could be detected in the bioaerosols, but not of animals using our primers.

Identification of a master transcription factor and a regulatory mechanism for desiccation tolerance in the anhydrobiotic cell line Pv11.

Water is essential for living organisms. Terrestrial organisms are incessantly exposed to the stress of losing water, desiccation stress. Avoiding the mortality caused by desiccation stress, many organisms acquired molecular mechanisms to tolerate desiccation. Larvae of the African midge, Polypedilum vanderplanki, and its embryonic cell line Pv11 tolerate desiccation stress by entering an ametabolic state, anhydrobiosis, and return to active life after rehydration. The genes related to desiccation tolerance have been comprehensively analyzed, but transcriptional regulatory mechanisms to induce these genes after desiccation or rehydration remain unclear. Here, we comprehensively analyzed the gene regulatory network in Pv11 cells and compared it with that of Drosophila melanogaster, a desiccation sensitive species. We demonstrated that nuclear transcription factor Y subunit gamma-like, which is important for drought stress tolerance in plants, and its transcriptional regulation of downstream positive feedback loops have a pivotal role in regulating various anhydrobiosis-related genes. This study provides an initial insight into the systemic mechanism of desiccation tolerance.

GeneFishing to reconstruct context specific portraits of biological processes.

Rapid advances in genomic technologies have led to a wealth of diverse data, from which novel discoveries can be gleaned through the application of robust statistical and computational methods. Here, we describe GeneFishing, a semisupervised computational approach to reconstruct context-specific portraits of biological processes by leveraging gene-gene coexpression information. GeneFishing incorporates multiple high-dimensional statistical ideas, including dimensionality reduction, clustering, subsampling, and results aggregation, to produce robust results. To illustrate the power of our method, we applied it using 21 genes involved in cholesterol metabolism as "bait" to "fish out" (or identify) genes not previously identified as being connected to cholesterol metabolism. Using simulation and real datasets, we found that the results obtained through GeneFishing were more interesting for our study than those provided by related gene prioritization methods. In particular, application of GeneFishing to the GTEx liver RNA sequencing (RNAseq) data not only reidentified many known cholesterol-related genes, but also pointed to glyoxalase I (GLO1) as a gene implicated in cholesterol metabolism. In a follow-up experiment, we found that GLO1 knockdown in human hepatoma cell lines increased levels of cellular cholesterol ester, validating a role for GLO1 in cholesterol metabolism. In addition, we performed pantissue analysis by applying GeneFishing on various tissues and identified many potential tissue-specific cholesterol metabolism-related genes. GeneFishing appears to be a powerful tool for identifying related components of complex biological systems and may be used across a wide range of applications.

Transcriptome Profiles of Human Visceral Adipocytes in Obesity and Colorectal Cancer Unravel the Effects of Body Mass Index and Polyunsaturated Fatty Acids on Genes and Biological Processes Related to Tumorigenesis.

Obesity, a low-grade inflammatory condition, represents a major risk factor for the development of several pathologies including colorectal cancer (CRC). Although the adipose tissue inflammatory state is now recognized as a key player in obesity-associated morbidities, the underlying biological processes are complex and not yet precisely defined. To this end, we analyzed transcriptome profiles of human visceral adipocytes from lean and obese subjects affected or not by CRC by RNA sequencing (n = 6 subjects/category), and validated selected modulated genes by real-time qPCR. We report that obesity and CRC, conditions characterized by the common denominator of inflammation, promote changes in the transcriptional program of adipocytes mostly involving pathways and biological processes linked to extracellular matrix remodeling, and metabolism of pyruvate, lipids and glucose. Interestingly, although the transcriptome of adipocytes shows several alterations that are common to both disorders, some modifications are unique under obesity (e.g., pathways associated with inflammation) and CRC (e.g., TGFß signaling and extracellular matrix remodeling) and are influenced by the body mass index (e.g., processes related to cell adhesion, angiogenesis, as well as metabolism). Indeed, cancer-induced transcriptional program is deeply affected by obesity, with adipocytes from obese individuals exhibiting a more complex response to the tumor. We also report that in vitro exposure of adipocytes to ω3 and ω6 polyunsaturated fatty acids (PUFA) endowed with either anti- or pro-inflammatory properties, respectively, modulates the expression of genes involved in processes potentially relevant to carcinogenesis, as assessed by real-time qPCR. All together our results suggest that genes involved in pyruvate, glucose and lipid metabolism, fibrosis and inflammation are central in the transcriptional reprogramming of adipocytes occurring in obese and CRC-affected individuals, as well as in their response to PUFA exposure. Moreover, our results indicate that the transcriptional program of adipocytes is strongly influenced by the BMI status in CRC subjects. The dysregulation of these interrelated processes relevant for adipocyte functions may contribute to create more favorable conditions to tumor establishment or favor tumor progression, thus linking obesity and colorectal cancer.

MicroRNA-22 Suppresses Breast Cancer Cell Growth and Increases Paclitaxel Sensitivity by Targeting NRAS.

In recent study, microRNAs have various important functions in diverse biological processes and progression of cancer. In human breast cancer, microRNA-22 has been reported to be downregulated. However, molecular mechanism of microRNA-22 in breast cancer progression and chemosensitivity has not been well studied. In our study, these results demonstrated that microRNA-22 expression levels were significantly reduced in 40 pairs of human breast cancer tissues when compared to normal tissues. Enforced expression of microRNA-22 inhibited activity of cell proliferation and cell migration in breast cancer cells. Furthermore, microRNA-22 targeted NRAS proto-oncogene, GTPase (NRAS) in breast cancer cells. The expression levels of NRAS in human clinical specimens were higher in breast cancer tissues when compared to normal tissues. Moreover, microRNA-22 sensitized breast cancer cells to paclitaxel by regulation of NRAS. Our results then demonstrated that microRNA-22 functioned as a tumor suppressor microRNA and indicated potential application for the diagnosis and treatment of cancer in the future.

Deciphering cellular biological processes to clinical application: a new perspective for Tα1 treatment targeting multiple diseases.

BACKGROUND: Thymosin alpha 1 (Tα1) is a well-recognized immune response modulator in a wide range of disorders, particularly infections and cancer. The bioinformatic analysis of public databases allows drug repositioning, predicting a new potential area of clinical intervention. We aimed to decipher the cellular network induced by Tα1 treatment to confirm present use and identify new potential clinical applications. RESEARCH DESIGN AND METHODS: We used the transcriptional profile of human peripheral blood mononuclear cells treated in vitro with Tα1 to perform the enrichment network analysis by the Metascape online tools and the disease enrichment analysis by the DAVID online tool. RESULTS: Networked cellular responses reflected Tα1 regulated biological processes including immune and metabolic responses, response to compounds and oxidative stress, ion homeostasis, peroxisome biogenesis and drug metabolic process. Beyond cancer and infections, the analysis evidenced the association with disorders such as kidney chronic failure, diabetes, cardiovascular, chronic respiratory, neuropsychiatric, neurodegenerative and autoimmune diseases. CONCLUSIONS: In addition to the known ability to promote immune response pathways, the network enrichment analysis demonstrated that Tα1 regulates cellular metabolic processes and oxidative stress response. Notable, the analysis highlighted the association with several diseases, suggesting new translational implication of Tα1 treatment in pathological conditions unexpected until now.

Screening key candidate genes and pathways involved in insulinoma by microarray analysis.

Insulinoma is a rare type tumor and its genetic features remain largely unknown. This study aimed to search for potential key genes and relevant enriched pathways of insulinoma.The gene expression data from GSE73338 were downloaded from Gene Expression Omnibus database. Differentially expressed genes (DEGs) were identified between insulinoma tissues and normal pancreas tissues, followed by pathway enrichment analysis, protein-protein interaction (PPI) network construction, and module analysis. The expressions of candidate key genes were validated by quantitative real-time polymerase chain reaction (RT-PCR) in insulinoma tissues.A total of 1632 DEGs were obtained, including 1117 upregulated genes and 514 downregulated genes. Pathway enrichment results showed that upregulated DEGs were significantly implicated in insulin secretion, and downregulated DEGs were mainly enriched in pancreatic secretion. PPI network analysis revealed 7 hub genes with degrees more than 10, including GCG (glucagon), GCGR (glucagon receptor), PLCB1 (phospholipase C, beta 1), CASR (calcium sensing receptor), F2R (coagulation factor II thrombin receptor), GRM1 (glutamate metabotropic receptor 1), and GRM5 (glutamate metabotropic receptor 5). DEGs involved in the significant modules were enriched in calcium signaling pathway, protein ubiquitination, and platelet degranulation. Quantitative RT-PCR data confirmed that the expression trends of these hub genes were similar to the results of bioinformatic analysis.The present study demonstrated that candidate DEGs and enriched pathways were the potential critical molecule events involved in the development of insulinoma, and these findings were useful for better understanding of insulinoma genesis.

Comprehensive profiling of biological processes reveals two major prognostic subtypes in breast cancer.

Heterogeneity is the major obstacle to breast cancer target therapy. Classification of breast cancer with significant biological process may reduce the influence of heterogeneity of intrinsic tumor. We used survival analysis to filter 95 gene sets and classify 638 breast cancer samples into two subtypes based on those gene sets associated with prognosis. Clinical outcome of two subtypes were evaluated with disease-free survival, distant metastasis-free survival, and overall survival levels in three databases and ER+, PR+ HER2+, and TNBC groups. We established a novel classification with 95 prognostic gene sets. In the training and validation cohorts, the subtype 1 was characterized by significant gene sets associated with regulation of metabolic process and enzyme activity and predicted obviously improved clinical outcome than subtype 2, which was enriched by tumor cell division, mitosis, and cell cycle-related gene sets (P < 0.05). When evaluated prognostic impact of subtypes in ER+, PR+ HER2+, and TNBC groups, we found that patients in subtype 1 showed better prognosis in ER+ and PR+ groups (P < 0.05) but had no difference from prognosis of subtype 2 in HER2+ and TNBC groups. These findings may have implications in understanding of breast cancer and filtering effective therapeutic strategies for targeted therapy.

A differential genome-wide transcriptome analysis: impact of cellular copper on complex biological processes like aging and development.

The regulation of cellular copper homeostasis is crucial in biology. Impairments lead to severe dysfunctions and are known to affect aging and development. Previously, a loss-of-function mutation in the gene encoding the copper-sensing and copper-regulated transcription factor GRISEA of the filamentous fungus Podospora anserina was reported to lead to cellular copper depletion and a pleiotropic phenotype with hypopigmentation of the mycelium and the ascospores, affected fertility and increased lifespan by approximately 60% when compared to the wild type. This phenotype is linked to a switch from a copper-dependent standard to an alternative respiration leading to both a reduced generation of reactive oxygen species (ROS) and of adenosine triphosphate (ATP). We performed a genome-wide comparative transcriptome analysis of a wild-type strain and the copper-depleted grisea mutant. We unambiguously assigned 9,700 sequences of the transcriptome in both strains to the more than 10,600 predicted and annotated open reading frames of the P. anserina genome indicating 90% coverage of the transcriptome. 4,752 of the transcripts differed significantly in abundance with 1,156 transcripts differing at least 3-fold. Selected genes were investigated by qRT-PCR analyses. Apart from this general characterization we analyzed the data with special emphasis on molecular pathways related to the grisea mutation taking advantage of the available complete genomic sequence of P. anserina. This analysis verified but also corrected conclusions from earlier data obtained by single gene analysis, identified new candidates of factors as part of the cellular copper homeostasis system including target genes of transcription factor GRISEA, and provides a rich reference source of quantitative data for further in detail investigations. Overall, the present study demonstrates the importance of systems biology approaches also in cases were mutations in single genes are analyzed to explain the underlying mechanisms controlling complex biological processes like aging and development.

An integrated bioinformatics approach identifies elevated cyclin E2 expression and E2F activity as distinct features of tamoxifen resistant breast tumors.

Approximately half of estrogen receptor (ER) positive breast tumors will fail to respond to endocrine therapy. Here we used an integrative bioinformatics approach to analyze three gene expression profiling data sets from breast tumors in an attempt to uncover underlying mechanisms contributing to the development of resistance and potential therapeutic strategies to counteract these mechanisms. Genes that are differentially expressed in tamoxifen resistant vs. sensitive breast tumors were identified from three different publically available microarray datasets. These differentially expressed (DE) genes were analyzed using gene function and gene set enrichment and examined in intrinsic subtypes of breast tumors. The Connectivity Map analysis was utilized to link gene expression profiles of tamoxifen resistant tumors to small molecules and validation studies were carried out in a tamoxifen resistant cell line. Despite little overlap in genes that are differentially expressed in tamoxifen resistant vs. sensitive tumors, a high degree of functional similarity was observed among the three datasets. Tamoxifen resistant tumors displayed enriched expression of genes related to cell cycle and proliferation, as well as elevated activity of E2F transcription factors, and were highly correlated with a Luminal intrinsic subtype. A number of small molecules, including phenothiazines, were found that induced a gene signature in breast cancer cell lines opposite to that found in tamoxifen resistant vs. sensitive tumors and the ability of phenothiazines to down-regulate cyclin E2 and inhibit proliferation of tamoxifen resistant breast cancer cells was validated. Our findings demonstrate that an integrated bioinformatics approach to analyze gene expression profiles from multiple breast tumor datasets can identify important biological pathways and potentially novel therapeutic options for tamoxifen-resistant breast cancers.

Network-based prediction for sources of transcriptional dysregulation using latent pathway identification analysis.

Understanding the systemic biological pathways and the key cellular mechanisms that dictate disease states, drug response, and altered cellular function poses a significant challenge. Although high-throughput measurement techniques, such as transcriptional profiling, give some insight into the altered state of a cell, they fall far short of providing by themselves a complete picture. Some improvement can be made by using enrichment-based methods to, for example, organize biological data of this sort into collections of dysregulated pathways. However, such methods arguably are still limited to primarily a transcriptional view of the cell. Augmenting these methods still further with networks and additional -omics data has been found to yield pathways that play more fundamental roles. We propose a previously undescribed method for identification of such pathways that takes a more direct approach to the problem than any published to date. Our method, called latent pathway identification analysis (LPIA), looks for statistically significant evidence of dysregulation in a network of pathways constructed in a manner that implicitly links pathways through their common function in the cell. We describe the LPIA methodology and illustrate its effectiveness through analysis of data on (i) metastatic cancer progression, (ii) drug treatment in human lung carcinoma cells, and (iii) diagnosis of type 2 diabetes. With these analyses, we show that LPIA can successfully identify pathways whose perturbations have latent influences on the transcriptionally altered genes.

Modeling microRNA-mRNA interactions using PLS regression in human colon cancer.

BACKGROUND: Changes in microRNA (miRNA) expression patterns have been extensively characterized in several cancers, including human colon cancer. However, how these miRNAs and their putative mRNA targets contribute to the etiology of cancer is poorly understood. In this work, a bioinformatics computational approach with miRNA and mRNA expression data was used to identify the putative targets of miRNAs and to construct association networks between miRNAs and mRNAs to gain some insights into the underlined molecular mechanisms of human colon cancer. METHOD: The miRNA and mRNA microarray expression profiles from the same tissues including 7 human colon tumor tissues and 4 normal tissues, collected by the Broad Institute, were used to identify significant associations between miRNA and mRNA. We applied the partial least square (PLS) regression method and bootstrap based statistical tests to the joint expression profiles of differentially expressed miRNAs and mRNAs. From this analysis, we predicted putative miRNA targets and association networks between miRNAs and mRNAs. Pathway analysis was employed to identify biological processes related to these miRNAs and their associated predicted mRNA targets. RESULTS: Most significantly associated up-regulated mRNAs with a down-regulated miRNA identified by the proposed methodology were considered to be the miRNA targets. On average, approximately 16.5% and 11.0% of targets predicted by this approach were also predicted as targets by the common prediction algorithms TargetScan and miRanda, respectively. We demonstrated that our method detects more targets than a simple correlation based association. Integrative mRNA:miRNA predictive networks from our analysis were constructed with the aid of Cytoscape software. Pathway analysis validated the miRNAs through their predicted targets that may be involved in cancer-associated biological networks. CONCLUSION: We have identified an alternative bioinformatics approach for predicting miRNA targets in human colon cancer and for reverse engineering the miRNA:mRNA network using inversely related mRNA and miRNA joint expression profiles. We demonstrated the superiority of our predictive method compared to the correlation based target prediction algorithm through a simulation study. We anticipate that the unique miRNA targets predicted by the proposed method will advance the understanding of the molecular mechanism of colon cancer and will suggest novel therapeutic targets after further experimental validations.

A large scale survey reveals that chromosomal copy-number alterations significantly affect gene modules involved in cancer initiation and progression.

BACKGROUND: Recent observations point towards the existence of a large number of neighborhoods composed of functionally-related gene modules that lie together in the genome. This local component in the distribution of the functionality across chromosomes is probably affecting the own chromosomal architecture by limiting the possibilities in which genes can be arranged and distributed across the genome. As a direct consequence of this fact it is therefore presumable that diseases such as cancer, harboring DNA copy number alterations (CNAs), will have a symptomatology strongly dependent on modules of functionally-related genes rather than on a unique "important" gene. METHODS: We carried out a systematic analysis of more than 140,000 observations of CNAs in cancers and searched by enrichments in gene functional modules associated to high frequencies of loss or gains. RESULTS: The analysis of CNAs in cancers clearly demonstrates the existence of a significant pattern of loss of gene modules functionally related to cancer initiation and progression along with the amplification of modules of genes related to unspecific defense against xenobiotics (probably chemotherapeutical agents). With the extension of this analysis to an Array-CGH dataset (glioblastomas) from The Cancer Genome Atlas we demonstrate the validity of this approach to investigate the functional impact of CNAs. CONCLUSIONS: The presented results indicate promising clinical and therapeutic implications. Our findings also directly point out to the necessity of adopting a function-centric, rather a gene-centric, view in the understanding of phenotypes or diseases harboring CNAs.

Do two machine-learning based prognostic signatures for breast cancer capture the same biological processes?

The fact that there is very little if any overlap between the genes of different prognostic signatures for early-discovery breast cancer is well documented. The reasons for this apparent discrepancy have been explained by the limits of simple machine-learning identification and ranking techniques, and the biological relevance and meaning of the prognostic gene lists was questioned. Subsequently, proponents of the prognostic gene lists claimed that different lists do capture similar underlying biological processes and pathways. The present study places under scrutiny the validity of this claim, for two important gene lists that are at the focus of current large-scale validation efforts. We performed careful enrichment analysis, controlling the effects of multiple testing in a manner which takes into account the nested dependent structure of gene ontologies. In contradiction to several previous publications, we find that the only biological process or pathway for which statistically significant concordance can be claimed is cell proliferation, a process whose relevance and prognostic value was well known long before gene expression profiling. We found that the claims reported by others, of wider concordance between the biological processes captured by the two prognostic signatures studied, were found either to be lacking statistical rigor or were in fact based on addressing some other question.

Functional genomics reveals diverse cellular processes that modulate tumor cell response to oxaliplatin.

Oxaliplatin is widely used to treat colorectal cancer, as both adjuvant therapy for resected disease and palliative treatment of metastatic disease. However, a significant number of patients experience serious side effects, including prolonged neurotoxicity, from oxaliplatin treatment creating an urgent need for biomarkers of oxaliplatin response or resistance to direct therapy to those most likely to benefit. As a first step to improve selection of patients for oxaliplatin-based chemotherapy, we have conducted an in vitro cell-based small interfering RNA (siRNA) screen of 500 genes aimed at identifying genes whose loss of expression alters tumor cell response to oxaliplatin. The siRNA screen identified twenty-seven genes, which when silenced, significantly altered colon tumor cell line sensitivity to oxaliplatin. Silencing of a group of putative resistance genes increased the extent of oxaliplatin-mediated DNA damage and inhibited cell-cycle progression in oxaliplatin-treated cells. The activity of several signaling nodes, including AKT1 and MEK1, was also altered. We used cDNA transfection to overexpress two genes (LTBR and TMEM30A) that were identified in the siRNA screen as mediators of oxaliplatin sensitivity. In both instances, overexpression conferred resistance to oxaliplatin. In summary, this study identified numerous putative predictive biomarkers of response to oxaliplatin that should be studied further in patient specimens for potential clinical application. Diverse gene networks seem to influence tumor survival in response to DNA damage by oxaliplatin. Finally, those genes whose loss of expression (or function) is related to oxaliplatin sensitivity may be promising therapeutic targets to increase patient response to oxaliplatin.

Profiling of regulatory microRNA transcriptomes in various biological processes: a review.

A class of small, non-coding ribonucleic acids, termed microRNA (miRNA), has recently emerged as a new key player in the cellular control of gene expression. By either blocking translation or inducing target mRNA degradation, miRNA not only participates in regular biological processes within cells and tissues but is also involved in pathological processes. Many human malignancies have been linked to specific miRNA expression patterns, raising hopes for new approaches to therapy. While such human disease-related mechanisms have been widely discussed and frequently reviewed, miRNA's general significance in animals has been less in editorial focus, despite its obvious role in basic physiological processes, e.g. neurosensory maturation, development of fertility, and hibernation. Using selected examples, this review highlights our current knowledge of miRNA's potential and its promise as a new tool for gene regulation.

Gene set-based analysis of polymorphisms: finding pathways or biological processes associated to traits in genome-wide association studies.

Genome-wide association studies have become a popular strategy to find associations of genes to traits of interest. Despite the high-resolution available today to carry out genotyping studies, the success of its application in real studies has been limited by the testing strategy used. As an alternative to brute force solutions involving the use of very large cohorts, we propose the use of the Gene Set Analysis (GSA), a different analysis strategy based on testing the association of modules of functionally related genes. We show here how the Gene Set-based Analysis of Polymorphisms (GeSBAP), which is a simple implementation of the GSA strategy for the analysis of genome-wide association studies, provides a significant increase in the power testing for this type of studies. GeSBAP is freely available at http://bioinfo.cipf.es/gesbap/.