Tomato Functional Genomics Database: a comprehensive resource and analysis package for tomato functional genomics

BackgroundSingular value decomposition (SVD) is a powerful technique for information retrieval; it helps uncover relationships between elements that are not prima facie related. SVD was initially developed to reduce the time needed for information retrieval and analysis of very large data sets in the complex internet environment. Since information retrieval from large-scale genome and proteome data sets has a similar level of complexity, SVD-based methods could also facilitate data analysis in this research area.ResultsWe found that SVD applied to amino acid sequences demonstrates relationships and provides a basis for producing clusters and cladograms, demonstrating evolutionary relatedness of species that correlates well with Linnaean taxonomy. The choice of a reasonable number of singular values is crucial for SVD-based studies. We found that fewer singular values are needed to produce biologically significant clusters when SVD is employed. Subsequently, we developed a method to determine the lowest number of singular values and fewest clusters needed to guarantee biological significance; this system was developed and validated by comparison with Linnaean taxonomic classification.ConclusionsBy using SVD, we can reduce uncertainty concerning the appropriate rank value necessary to perform accurate information retrieval analyses. In tests, clusters that we developed with SVD perfectly matched what was expected based on Linnaean taxonomy.

This chapter contains sections titled: Introduction The ORFeome: the First Step toward the interactome of C. elegans Large-Scale High-Throughput Yeast Two-Hybrid Screens to Map the C. elegans Protein–Protein Interaction (Interactome) Network: Technical Aspects Visualization and Topology of Protein–Protein Interaction Networks Cross-Talk between the C. elegans Interactome and other Large-Scale Genomics and Post-Genomics Data Sets Conclusion: From Interactions to Therapies References

Summary: We present a tool designed for visualization of large-scale genetic and genomic data exemplified by results from genome-wide association studies. This software provides an integrated framework to facilitate the interpretation of SNP association studies in genomic context. Gene annotations can be retrieved from Ensembl, linkage disequilibrium data downloaded from HapMap and custom data imported in BED or WIG format. AssociationViewer integrates functionalities that enable the aggregation or intersection of data tracks. It implements an efficient cache system and allows the display of several, very large-scale genomic datasets.Availability: The Java code for AssociationViewer is distributed under the GNU General Public Licence and has been tested on Microsoft Windows XP, MacOSX and GNU/Linux operating systems. It is available from the SourceForge repository. This also includes Java webstart, documentation and example datafiles.Contact: brian.stevenson@licr.orgSupplementary information: Supplementary data are available at http://sourceforge.net/projects/associationview/ online.

BackgroundGene set enrichment analysis (GSEA) is an important approach to the analysis of coordinate expression changes at a pathway level. Although many statistical and computational methods have been proposed for GSEA, the issue of a concordant integrative GSEA of multiple expression data sets has not been well addressed. Among different related data sets collected for the same or similar study purposes, it is important to identify pathways or gene sets with concordant enrichment.MethodsWe categorize the underlying true states of differential expression into three representative categories: no change, positive change and negative change. Due to data noise, what we observe from experiments may not indicate the underlying truth. Although these categories are not observed in practice, they can be considered in a mixture model framework. Then, we define the mathematical concept of concordant gene set enrichment and calculate its related probability based on a three-component multivariate normal mixture model. The related false discovery rate can be calculated and used to rank different gene sets.ResultsWe used three published lung cancer microarray gene expression data sets to illustrate our proposed method. One analysis based on the first two data sets was conducted to compare our result with a previous published result based on a GSEA conducted separately for each individual data set. This comparison illustrates the advantage of our proposed concordant integrative gene set enrichment analysis. Then, with a relatively new and larger pathway collection, we used our method to conduct an integrative analysis of the first two data sets and also all three data sets. Both results showed that many gene sets could be identified with low false discovery rates. A consistency between both results was also observed. A further exploration based on the KEGG cancer pathway collection showed that a majority of these pathways could be identified by our proposed method.ConclusionsThis study illustrates that we can improve detection power and discovery consistency through a concordant integrative analysis of multiple large-scale two-sample gene expression data sets.

Scientists from around the world gathered in San Francisco, California, for the Second American Association for Cancer Research MACR) International Conference on Frontiers in Basic Cancer Research, held September 14 to 18, 2011. Among featured studies were: Researchers are working on understanding how genes interact to trigger cancer progression, anticipating that their findings may lead to new targeted therapies for breast cancer. The investigators found that a diuretic used to treat hypertension and edema also stops breast cancer cells from progressing and, in culture, appeared to be as effective as several cancer drugs on the market. Bin Zhang, PhD, principal scientist and group leader at Sage Bionetworks in Seattle, Washington, says the treatment may eventually be tested in human clinical trials but still has a long way to go. His group predicted and validated the drug's effectiveness in preferentially killing cancer cells by inhibiting cell cycle pathways responsible for uncontrolled cell proliferation. The researchers analyzed multiple, large-scale cancer genomic data sets to find novel pathways and driver genes that help breast cancer progress. They then tested them against various compounds anti drugs to determine how they would react. The research indicated that many genes work as groups that correspond to different pathways. The researchers concluded that future targeted therapies will be more effective they target more than 1 pathway and that combination therapy may work better against different pathways. The researchers developed complex and advanced algorithms to reconstruct multiscale gene regulatory networks that reveal global patterns of gene interactions in cancer and also detail regulatory maps, says Dr. Zhang. He adds that the networks will serve as blueprints for understanding cancer progression and developing novel therapeutics. How the virus that causes most forms of Merkel cell carcinoma operates is a research focus for scientists at the University of Pittsburgh Cancer Institute in Pittsburgh, Pennsylvania. The group hopes that better understanding of this virus and its effect on this rare and aggressive skin cancer may lead to effective chemotherapeutic targets. Merkel cell carcinoma incidence has increased 4-fold during the last 20 years, particularly in immunosuppressed populations, says Patrick Moore, MD, MPH, a member of the research team. Not only is the cancer difficult to treat, but most clinical trials of drugs targeting it have been disappointing, says Dr. Moore. In 2008, he and colleagues discovered Merkel cell polyomavirus (MCV), which is believed to cause 80% of Merkel cell carcinomas. Their laboratory also discovered the herpes virus that causes Kaposi's sarcoma, which is common in patients with acquired immune deficiency syndrome. MCV small T antigen protein is a new oncogene that can contribute to abnormal cell growth in both rodent and human cells. MCV does not act the same as other polyomaviruses that have served as classic models of cancer. Others depend on viral interaction with the enzyme protein phosphatase 2A (PP2A and heat shock proteins. MCV interacts with, but does not depend on, these enzymes and proteins. It can still cause abnormal cell growth, even after abolishing PP2A and heat shock protein binding sites. The team hopes to develop treatments that will target the pathways affected by MCV. They have tested more than 1350 drugs to identify ways to better target this cancer. The vaccine virus construct GLV-1h53,which is engineered to encode for the human sodium iodide symporter gene (hNIS), may provide effective virotherapy for cancer as well as a means of monitoring therapeutic response through imaging methods such as positron emission tomography. In addition, it can be used for targeted radiotherapy, according to researchers at Memorial Sloan-Kettering Cancer Center in NewYork, NewYork, and Genelux Corporation. In their study, more than 50 mice were treated and imaged with GLV-1h53 to gain insight into tumor therapeutic response, according to Dana Haddad, MD, PhD, who previously was with Memorial Sloan-Kettering Cancer Center and is now at the Mayo Clinic in Phoenix/Scottsdale, Mizona. The virus construct was also combined with radioiodine for targeted radiotherapy of pancreatic tumors. Dr. Haddad says she and colleagues initially were discouraged when the positron emission tomography signal pancreatic tumors began to disappear after 2 weeks of treatment with the virus, but they soon learned that its disappearance likely was the result of tumor killing activity and necrosis. They also discovered that by using lower doses the virus along with radiotherapy, they could more effectively kill the tumor while at the same time minimizing toxicity and side effects associated with both therapies.

Genome-wide analyses and high-throughput screening was long reserved for biomedical applications and genetic model organisms. With the rapid development of massively parallel sequencing nanotechnology (or next-generation sequencing) and simultaneous maturation of bioinformatic tools, this situation has dramatically changed. Genome-wide thinking is forging its way into disciplines like evolutionary biology or molecular ecology that were historically confined to small-scale genetic approaches. Accessibility to genome-scale information is transforming these fields, as it allows us to answer long-standing questions like the genetic basis of local adaptation and speciation or the evolution of gene expression profiles that until recently were out of reach. Many in the eco-evolutionary sciences will be working with large-scale genomic data sets, and a basic understanding of the concepts and underlying methods is necessary to judge the work of others. Here, I briefly introduce next-generation sequencing and then focus on transcriptome shotgun sequencing (RNA-seq). This article gives a broad overview and provides practical guidance for the many steps involved in a typical RNA-seq work flow from sampling, to RNA extraction, library preparation and data analysis. I focus on principles, present useful tools where appropriate and point out where caution is needed or progress to be expected. This tutorial is mostly targeted at beginners, but also contains potentially useful reflections for the more experienced.

Anticoagulants are routinely used by millions of patients worldwide to prevent blood clots. Yet, problems with anticoagulant therapy remain, including a persistent and cumulative bleeding risk in patients undergoing prolonged anticoagulation. New safer anticoagulant targets are needed. To prioritize anticoagulant targets with the strongest efficacy [venous thromboembolism (VTE) prevention] and safety (low bleeding risk) profiles, we performed two-sample Mendelian randomization and genetic colocalization. We leveraged three large-scale plasma protein data sets (deCODE as discovery data set and Fenland and Atherosclerosis Risk in Communities as replication data sets] and one liver gene expression data set (Institut Universitaire de Cardiologie et de Pneumologie de Québec bariatric biobank) to evaluate evidence for a causal effect of 26 coagulation cascade proteins on VTE from a new genome-wide association meta-analysis of 44 232 VTE cases and 847 152 controls, stroke subtypes, bleeding outcomes, and parental lifespan as an overall measure of efficacy/safety ratio. A 1 SD genetically predicted reduction in F2 blood levels was associated with lower risk of VTE [odds ratio (OR) = 0.44, 95% confidence interval (CI) = 0.38-0.51, P = 2.6e-28] and cardioembolic stroke risk (OR = 0.55, 95% CI = 0.39-0.76, P = 4.2e-04) but not with bleeding (OR = 1.13, 95% CI = 0.93-1.36, P = 2.2e-01). Genetically predicted F11 reduction was associated with lower risk of VTE (OR = 0.61, 95% CI = 0.58-0.64, P = 4.1e-85) and cardioembolic stroke (OR = 0.77, 95% CI = 0.69-0.86, P = 4.1e-06) but not with bleeding (OR = 1.01, 95% CI = 0.95-1.08, P = 7.5e-01). These Mendelian randomization associations were concordant across the three blood protein data sets and the hepatic gene expression data set as well as colocalization analyses. These results provide strong genetic evidence that F2 and F11 may represent safe and efficacious therapeutic targets to prevent VTE and cardioembolic strokes without substantially increasing bleeding risk.

The genus Camellia consists of about 200 species, which include many economically important species widely used for making tea, ornamental flowers and edible oil. Here, we present an updated tea plant information archive for Camellia genomics (TPIA2; http://tpia.teaplants.cn) by integrating more novel large-scale genomic, transcriptomic, metabolic and genetic variation datasets as well as a variety of useful tools. Specifically, TPIA2 hosts all currently available and well assembled 10 Camellia genomes and their comprehensive annotations from three major sections of Camellia. A collection of 15 million SNPs and 950 950 small indels from large-scale genome resequencing of 350 diverse tea accessions were newly incorporated, followed by the implementation of a novel ‘Variation’ module to facilitate data retrieval and analysis of the functionally annotated variome. Moreover, 116 Camellia transcriptomes were newly assembled and added, leading to a significant extension of expression profiles of Camellia genes to 13 developmental stages and eight abiotic/biotic treatments. An updated ‘Expression’ function has also been implemented to provide a comprehensive gene expression atlas for Camellia. Two novel analytic tools (e.g. Gene ID Convert and Population Genetic Analysis) were specifically designed to facilitate the data exchange and population genomics in Camellia. Collectively, TPIA2 provides diverse updated valuable genomic resources and powerful functions, and will continue to be an important gateway for functional genomics and population genetic studies in Camellia.

Advanced medical imaging technologies have enabled biologists and other researchers in biomedicine, biochemistry and bio-informatics to gain better insight in complex, large-scale data sets. These datasets, which occupy large amounts of space, can no longer be stored on local hard drives. San Diego Supercomputer Center (SDSC) maintains a large data repository, called High Performance Storage System (HPSS), where large-scale biomedical data sets can be stored. These data sets must be transmitted over an open or closed network (Internet or Intranet) within a reasonable amount of time to make them accessible in an interactive fashion to the researchers all over the world. Our approach deals with extracting, compressing and transmitting these data sets using the Haar wavelets, over a low- to medium-bandwidth network. These compressed data sets are then transformed and reconstructed into a 3-D volume on the client side using texture mapping in Java3D. These data sets are handled using the Scalable Visualization Toolkits provided by the NPACI (National Partnership for Advanced Computational Infrastructure). Sub-volumes of the data sets are extracted to provide a detailed view of a particular region of interest (ROI). This application is being ported to C++ platform to obtain higher rendering speed and better performance but lacks platform independency.

The mouse intestinal epithelium undergoes continuous renewal throughout life. Intraepithelial lymphocytes (IELs) represent a significant fraction of this epithelium and play an important role in intestinal mucosal barrier function. We have generated a germ-free transgenic mouse model to examine the effects of a genetically engineered proliferative abnormality in the principal epithelial cell lineage (enterocytes) on IEL census and on IEL-enterocytic cross-talk. SV40 large T antigen (TAg(Wt)) or a mutant derivative (TAg(K107/8)) that does not bind pRB was expressed in small intestinal villus enterocytes under the control of elements from the intestinal fatty acid binding protein gene (Fabpi). Quantitative immunohistochemical and flow cytometric analyses of conventionally raised and germ-free FVB/N Fabpi-TAg(Wt), Fabpi-TAg(K107/8), and nontransgenic mice disclosed that forced reentry of enterocytes into the cell cycle is accompanied by an influx of thymically educated alphabeta T cell receptor (TCR)(+) CD4(+) and alphabeta TCR(+) CD8alphabeta(+) IELs and a decrease in intestinally derived gammadelta TCR(+) CD8alphaalpha IELs. Real time quantitative reverse transcriptase-PCR studies of jejunal villus epithelium recovered from germ-free transgenic and normal mice by laser capture microdissection and gammadelta TCR(+) jejunal IELs purified by flow cytometry disclosed that the proliferative abnormality is accompanied by decreased expression of enterocytic interleukin-7 as well as IEL interleukin-7Ralpha and transforming growth factor beta3. The analysis also revealed that normal villus epithelium expresses Fms-like tyrosine kinase 3 (Flt3), a known regulator of hematopoietic stem cell proliferation and neuronal cell survival, and its ligand (Flt3L). Epithelial expression of this receptor and its ligand is reduced by the proliferative abnormality, whereas IEL expression of Flt3L remains constant. Together, these findings demonstrate that changes in the proliferative status of the intestinal epithelium affects maturation of gammadelta TCR(+) IELs and produces an influx of alphabeta TCR(+) IELs even in the absence of a microflora.

Public repositories of large-scale biological data currently contain hundreds of thousands of experiments, including high-throughput sequencing and microarray data. The potential of using these resources to assemble data sets combining samples previously not associated is vastly unexplored. This requires the ability to associate samples with clear annotations and to relate experiments matched with different annotation terms. In this study, we illustrate the semantic annotation of Gene Expression Omnibus samples metadata using concepts from biomedical ontologies, focusing on the association of thousands of chromatin immunoprecipitation followed by high-throughput sequencing (ChIP-seq) samples with a given target, tissue and disease state. Next, we demonstrate the feasibility of quantitatively measuring the semantic similarity between different samples, with the aim of combining experiments associated with the same or similar semantic annotations, thus allowing the generation of large data sets without the need of additional experiments. We compared tools based on Unified Medical Language System with tools that use topic-specific ontologies, showing that the second approach outperforms the first both in the annotation process and in the computation of semantic similarity measures. Finally, we demonstrated the potential of this approach by identifying semantically homogeneous groups of ChIP-seq samples targeting the Myc transcription factor, and expanding this data set with semantically coherent epigenetic samples. The semantic information of these data sets proved to be coherent with the ChIP-seq signal and with the current knowledge about this transcription factor.

Currently, the population dynamics of preclonal cancer cells before clonal expansion of tumors has not been sufficiently addressed thus far. By focusing on preclonal cancer cell population as a Darwinian evolutionary system, we formulated and analyzed the observed mutation frequency among tumors (MFaT) as a proxy for the hypothesized sequence read frequency and beneficial fitness effect of a cancer driver mutation. Analogous to intestinal crypts, we assumed that sample donor patients are separate culture tanks where proliferating cells follow certain population dynamics described by extreme value theory (EVT). To validate this, we analyzed three large-scale cancer genome datasets, each harboring > 10000 tumor samples and in total involving > 177898 observed mutation sites. We clarified the necessary premises for the application of EVT in the strong selection and weak mutation (SSWM) regime in relation to cancer genome sequences at scale. We also confirmed that the stochastic distribution of MFaT is likely of the Fréchet type, which challenges the well-known Gumbel hypothesis of beneficial fitness effects. Based on statistical data analysis, we demonstrated the potential of EVT as a population genetics framework to understand and explain the stochastic behavior of driver-mutation frequency in cancer genomes as well as its applicability in real cancer genome sequence data.

The retinal pigment epithelium (RPE) serves vital roles in ocular development and retinal homeostasis but has limited representation in large-scale functional genomics datasets. Understanding how common human genetic variants affect RPE gene expression could elucidate the sources of phenotypic variability in selected monogenic ocular diseases and pinpoint causal genes at genome-wide association study (GWAS) loci. We interrogated the genetics of gene expression of cultured human fetal RPE (fRPE) cells under two metabolic conditions and discovered hundreds of shared or condition-specific expression or splice quantitative trait loci (e/sQTLs). Co-localizations of fRPE e/sQTLs with age-related macular degeneration (AMD) and myopia GWAS data suggest new candidate genes, and mechanisms by which a common RDH5 allele contributes to both increased AMD risk and decreased myopia risk. Our study highlights the unique transcriptomic characteristics of fRPE and provides a resource to connect e/sQTLs in a critical ocular cell type to monogenic and complex eye disorders.

BackgroundMUC4 is a membrane-bound mucin that promotes carcinogenetic progression and is often proposed as a promising biomarker for various carcinomas. In this manuscript, we analyzed large scale genomic datasets in order to evaluate MUC4 expression, identify genes that are correlated with MUC4 and propose new signatures as a prognostic marker of epithelial cancers.MethodsUsing cBioportal or SurvExpress tools, we studied MUC4 expression in large-scale genomic public datasets of human cancer (the cancer genome atlas, TCGA) and cancer cell line encyclopedia (CCLE).ResultsWe identified 187 co-expressed genes for which the expression is correlated with MUC4 expression. Gene ontology analysis showed they are notably involved in cell adhesion, cell–cell junctions, glycosylation and cell signaling. In addition, we showed that MUC4 expression is correlated with MUC16 and MUC20, two other membrane-bound mucins. We showed that MUC4 expression is associated with a poorer overall survival in TCGA cancers with different localizations including pancreatic cancer, bladder cancer, colon cancer, lung adenocarcinoma, lung squamous adenocarcinoma, skin cancer and stomach cancer. We showed that the combination of MUC4, MUC16 and MUC20 signature is associated with statistically significant reduced overall survival and increased hazard ratio in pancreatic, colon and stomach cancer.ConclusionsAltogether, this study provides the link between (i) MUC4 expression and clinical outcome in cancer and (ii) MUC4 expression and correlated genes involved in cell adhesion, cell–cell junctions, glycosylation and cell signaling. We propose the MUC4/MUC16/MUC20high signature as a marker of poor prognostic for pancreatic, colon and stomach cancers.

To solve the problem that traditional multi-label support vector machine (SVM) classification algorithm adopting nonlinear kernel has been severely restricted from being used on large-scale data sets, we propose fast multi-label low-rank-linearized SVM classification algorithm based on approximate extreme points (AEML-LLSVM). First, it adopts the approximate extreme points' method to obtain representative sets from the training data set. Then, the approximate extreme points' low-rank-linearized SVM (AELLSVM) is trained on the representative sets. The AELLSVM integrates the advantages of approximate extreme points' method and LLSVM. Experimental results on three large-scale multi-label data sets have proven that the training and the testing speed of AEML-LLSVM classification algorithm are greatly improved under the premise that its classification performance is similar to that of ML-LIBSVM classification algorithm and superior to that of other fast multi-label SVM classification algorithms.