Types Of Molecular Data Research Articles

TCGAbiolinks: an R/Bioconductor package for integrative analysis of TCGA data.

The Cancer Genome Atlas (TCGA) research network has made public a large collection of clinical and molecular phenotypes of more than 10 000 tumor patients across 33 different tumor types. Using this cohort, TCGA has published over 20 marker papers detailing the genomic and epigenomic alterations associated with these tumor types. Although many important discoveries have been made by TCGA's research network, opportunities still exist to implement novel methods, thereby elucidating new biological pathways and diagnostic markers. However, mining the TCGA data presents several bioinformatics challenges, such as data retrieval and integration with clinical data and other molecular data types (e.g. RNA and DNA methylation). We developed an R/Bioconductor package called TCGAbiolinks to address these challenges and offer bioinformatics solutions by using a guided workflow to allow users to query, download and perform integrative analyses of TCGA data. We combined methods from computer science and statistics into the pipeline and incorporated methodologies developed in previous TCGA marker studies and in our own group. Using four different TCGA tumor types (Kidney, Brain, Breast and Colon) as examples, we provide case studies to illustrate examples of reproducibility, integrative analysis and utilization of different Bioconductor packages to advance and accelerate novel discoveries.

Network-Based Integration of Disparate Omic Data To Identify "Silent Players" in Cancer.

Development of high-throughput monitoring technologies enables interrogation of cancer samples at various levels of cellular activity. Capitalizing on these developments, various public efforts such as The Cancer Genome Atlas (TCGA) generate disparate omic data for large patient cohorts. As demonstrated by recent studies, these heterogeneous data sources provide the opportunity to gain insights into the molecular changes that drive cancer pathogenesis and progression. However, these insights are limited by the vast search space and as a result low statistical power to make new discoveries. In this paper, we propose methods for integrating disparate omic data using molecular interaction networks, with a view to gaining mechanistic insights into the relationship between molecular changes at different levels of cellular activity. Namely, we hypothesize that genes that play a role in cancer development and progression may be implicated by neither frequent mutation nor differential expression, and that network-based integration of mutation and differential expression data can reveal these “silent players”. For this purpose, we utilize network-propagation algorithms to simulate the information flow in the cell at a sample-specific resolution. We then use the propagated mutation and expression signals to identify genes that are not necessarily mutated or differentially expressed genes, but have an essential role in tumor development and patient outcome. We test the proposed method on breast cancer and glioblastoma multiforme data obtained from TCGA. Our results show that the proposed method can identify important proteins that are not readily revealed by molecular data, providing insights beyond what can be gleaned by analyzing different types of molecular data in isolation.

GEneSTATION 1.0: a synthetic resource of diverse evolutionary and functional genomic data for studying the evolution of pregnancy-associated tissues and phenotypes.

Mammalian gestation and pregnancy are fast evolving processes that involve the interaction of the fetal, maternal and paternal genomes. Version 1.0 of the GEneSTATION database (http://genestation.org) integrates diverse types of omics data across mammals to advance understanding of the genetic basis of gestation and pregnancy-associated phenotypes and to accelerate the translation of discoveries from model organisms to humans. GEneSTATION is built using tools from the Generic Model Organism Database project, including the biology-aware database CHADO, new tools for rapid data integration, and algorithms that streamline synthesis and user access. GEneSTATION contains curated life history information on pregnancy and reproduction from 23 high-quality mammalian genomes. For every human gene, GEneSTATION contains diverse evolutionary (e.g. gene age, population genetic and molecular evolutionary statistics), organismal (e.g. tissue-specific gene and protein expression, differential gene expression, disease phenotype), and molecular data types (e.g. Gene Ontology Annotation, protein interactions), as well as links to many general (e.g. Entrez, PubMed) and pregnancy disease-specific (e.g. PTBgene, dbPTB) databases. By facilitating the synthesis of diverse functional and evolutionary data in pregnancy-associated tissues and phenotypes and enabling their quick, intuitive, accurate and customized meta-analysis, GEneSTATION provides a novel platform for comprehensive investigation of the function and evolution of mammalian pregnancy.

Epigenomic mapping and effect sizes of noncoding variants associated with psychotropic drug response.

To provide insight into potential regulatory mechanisms of gene expression underlying addiction, analgesia, psychotropic drug response and adverse drug events, genome-wide association studies searching for variants associated with these phenotypes has been undertaken with limited success. We undertook analysis of these results with the aim of applying epigenetic knowledge to aid variant discovery and interpretation. We applied conditional imputation to results from 26 genome-wide association studies and three candidate gene-association studies. The analysis workflow included data from chromatin conformation capture, chromatin state annotation, DNase I hypersensitivity, hypomethylation, anatomical localization and biochronicity. We also made use of chromatin state data from the epigenome roadmap, transcription factor-binding data, spatial maps from published Hi-C datasets and 'guilt by association' methods. We identified 31 pharmacoepigenomic SNPs from a total of 2024 variants in linkage disequilibrium with lead SNPs, of which only 6% were coding variants. Interrogation of chromatin state using our workflow and the epigenome roadmap showed agreement on 34 of 35 tissue assignments to regulatory elements including enhancers and promoters. Loop boundary domains were inferred by association with CTCF (CCCTC-binding factor) and cohesin, suggesting proximity to topologically associating domain boundaries and enhancer clusters. Spatial interactions between enhancer-promoter pairs detected both known and previously unknown mechanisms. Addiction and analgesia SNPs were common in relevant populations and exhibited large effect sizes, whereas a SNP located in the promoter of the SLC1A2 gene exhibited a moderate effect size for lithium response in bipolar disorder in patients of European ancestry. SNPs associated with drug-induced organ injury were rare but exhibited the largest effect sizes, consistent with the published literature. This work demonstrates that an in silico bioinformatics-based approach using integrative analysis of a diversity of molecular and morphological data types can discover pharmacoepigenomic variants that are suitable candidates for further validation in cell lines, animal models and human clinical trials.

Serum N-glycan analysis in breast cancer patients – Relation to tumour biology and clinical outcome

Glycosylation and related processes play important roles in cancer development and progression, including metastasis. Several studies have shown that N-glycans have potential diagnostic value as cancer serum biomarkers. We have explored the significance of the abundance of particular serum N-glycan structures as important features of breast tumour biology by studying the serum glycome and tumour transcriptome (mRNA and miRNA) of 104 breast cancer patients. Integration of these types of molecular data allows us to study the relationship between serum glycans and transcripts representing functional pathways, such as metabolic pathways or DNA damage response. We identified tri antennary trigalactosylated trisialylated glycans in serum as being associated with lower levels of tumour transcripts involved in focal adhesion and integrin-mediated cell adhesion. These glycan structures were also linked to poor prognosis in patients with ER negative tumours. High abundance of simple monoantennary glycan structures were associated with increased survival, particularly in the basal-like subgroup. The presence of circulating tumour cells was found to be significantly associated with several serum glycome structures like bi and triantennary, di- and trigalactosylated, di- and trisialylated. The link between tumour miRNA expression levels and N-glycan production is also examined.

Development of the radial dose distribution function relevant to the treatment planning system for heavy particle cancer therapy

This paper presents a study of the radial dose due to the irradiation of a heavy ion, through simulations using a selection of types of atomic and molecular (AM) data. In order to widely spread our radial dose simulation results, a simple radial dose distribution function is proposed. This required a comparison of our results with the available conventional radial dose distributions. It is also shown that the treatment planning system for heavy particle cancer therapy is expected to become one of the most important applications of AM data to biological and medical science.

Quantitative and logic modelling of molecular and gene networks.

Behaviours of complex biomolecular systems are often irreducible to the elementary properties of their individual components. Explanatory and predictive mathematical models are therefore useful for fully understanding and precisely engineering cellular functions. The development and analyses of these models require their adaptation to the problems that need to be solved and the type and amount of available genetic or molecular data. Quantitative and logic modelling are among the main methods currently used to model molecular and gene networks. Each approach comes with inherent advantages and weaknesses. Recent developments show that hybrid approaches will become essential for further progress in synthetic biology and in the development of virtual organisms.

Sequencing platform modeling and analysis.

As the cost rapidly declines, the next-generation sequencing (NGS) technologies are becoming the main work horse of modern molecular biology to measure the abundance of genomic markers. NGS offers a number of technical advantages such as a greater dynamic range for measuring abundance and a higher resolution for low abundance molecules; moreover, it allows studying innovative biological problems such as the detection of novel molecular features and performing a more seamless integrative analysis of multiple types of molecular data for the same set of samples. Their utilization in cancer genomic studies such as The Cancer Genome Atlas (TCGA) presents unprecedented opportunities to advance our understanding of cancer. While certain aspects of NGS data analysis fall under the general framework of high-throughput data analysis and can be addressed by existing statistical methodologies, it also presents unique analytic challenges that require development of new statistical approaches. We take the opportunity of this special issue to highlight several recent methodological develop ments on NGS data analysis by leading researchers in the field. Some of the articles in this special issue are summarized below. • RNA Expression Level: Identification of molecular signatures is an important aspect of the analysis of RNA expression data. Ha et al developed a novel method in a causal structure learning framework to discover prognostic gene signatures. Their method represented the causal structure by directed acyclic graphs and constructed genespecific network modules to constitute a gene and its corresponding regulators; the method then correlated each module with a survival outcome to allow for a network oriented approach to select prognostic genes. They applied the new method to a clear cell renal cell carcinoma study from TCGA and found several novel prognostic genes. • RNA Splicing Variant: Alternative splicing is a posttranscriptional process that allows a single gene to produce multiple mRNA isoforms (namely, mRNA slicing variants). This process can be regulated by genetic elements such as single nucleotide polymorphisms (SNPs), which are called splicing quantitative trait loci (sQTL). The main analytic challenge of identifying sQTL is the estimation of isoform-specific mRNA expression based on RNA sequencing data. Jia et al evaluated three statistical Supplement Aims and Scope

Molecular Analyses of Clonality Via in Vitro Models: Approaches for Describing Clinical Clonal Evolution

While the role of clonal evolution during leukemia development and therapy has been a focus for a number of avenues of research, our abilities to deduce the clonal composition of individual samples have been limited by the use of bulk samples. Leukemia populations of unknown heterogeneity are typically described by extracting nucleic acids from the entire sample, including cells of unrelated lineages, any residual normal cells, and those cells constituting the leukemic population. Then bioinformatic approaches are employed to reconstruct the identities of the clones involved using feature frequencies in sequence data from the large scale sequencing done (e.g., whole exome or methylation). By definition, this type of approach, while realistic to perform on a large number of clinical samples, must rely on a basic set of assumptions regarding the limits of the type of genomic features that can exist in a sample. For whole exome sequencing data, assumptions include that mutations occur in cells heterozygously and that mutations which cluster together based on observed allele frequencies in the entire DNA sample occur concurrently in the same cells. To describe sub-clones within a population, a typical assumption is that mutations that occur at lower frequencies are sub-clones of the more abundant clone rather than a unique population. It is entirely possible that these assumptions are biologically appropriate for certain loci. However, for those loci for which the evolutionary patterns do not match this model (mutations occur sequentially and are retained throughout the subsequent lineage), we will inevitably come to an incorrect conclusion if this type of model is applied.In order to best begin testing bioinformatic methods to describe clonal structure and identify the presence of evolution, we have begun experiments mixing AML cell lines with distinct immunologic and mutational characteristics. Initial flow cytometric validation of perspective cell lines along with the molecular characterization of each cell type and verification of comparable growth rates in a normalized medium in vitro has been performed. Subsequent mixing of these cell lines and application of drugs specifically targeting one line or another provides a valuable opportunity to create controlled in vitro clonal evolution scenarios in which we can validate bulk, sorted fractions, and single cell methods and the required computational frameworks for each.As an example clonal framework, the cell lines were mixed in equal concentrations and allowed to compete in culture for 2 days with or without the presence of crenolanib, to which only one cell line is sensitive. The results of analyzing bulk and sorted fractions showed that we can effectively remove one “clone” in this in vitro poly-clonal leukemia model with crenolanib and that the observed flow cytometric frequency data reflect that seen in the genotyping data knowing the mutational distribution in the sample. However, if the allele frequency data is analyzed via the typical bulk sequencing assumptions, the reconstructed clonal structure is actually incorrect. If this were a clinical sample, we would conclude that initially the leukemia had one predominant clone with one mutation, and a subclone which had acquired 3 additional mutations. The true clonal structure was two clones with two known mutations each, (one mutation can be carried as a homozygous mutant), and one clone with no known mutations. With the addition of therapy, one would conclude that the subclone was reduced in frequency when in reality one clone was nearly completely removed, but two remained untouched.We have created a simple system to which we can apply different conditions to the in vitro culture, generate different types of molecular data, and test methods of analyzing those data to better understand how to approach bulk sample analyses for clonal evolution. DisclosuresNo relevant conflicts of interest to declare.

Abstract 4168: Dissect the clinical utility of TCGA genomic data across tumor types

Abstract A central question for the cancer research community is how to use genomic and proteomic data to guide cancer care. We investigated two potential clinical applications of The Cancer Genome Atlas (TCGA) data: predicting patient prognosis and identifying clinically actionable alterations. For four TCGA cohorts, we systematically evaluated the power of molecular data with or without clinical variables in predicting patient survival; and to facilitate further community efforts, we established an open-access model evaluation platform. We demonstrate that incorporating molecular data with clinical variables can improve predictive power. Interestingly, one model derived from ovarian cancer can predict outcomes in kidney cancer, suggesting the potential utility of cross-tumor analysis. Across 12 cancer types, we identified 10,281 somatic alterations in clinically relevant genes in 2,928 out of 3,277 patients (89.4%), revealing recurrent and potentially targetable alterations not revealed in single-tumor datasets. Our study represents the first systematic and comprehensive analysis evaluating the prognostic power of different types of molecular data across multiple cancer types. In addition, the clinically actionable alterations we identified may inform novel clinical trial design and treatment choice. Citation Format: Han Liang, TCGA Pan-Cancer Clinical/Predictor Group. Dissect the clinical utility of TCGA genomic data across tumor types. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 4168. doi:10.1158/1538-7445.AM2014-4168

KDDN: an open-source Cytoscape app for constructing differential dependency networks with significant rewiring.

We have developed an integrated molecular network learning method, within a well-grounded mathematical framework, to construct differential dependency networks with significant rewiring. This knowledge-fused differential dependency networks (KDDN) method, implemented as a Java Cytoscape app, can be used to optimally integrate prior biological knowledge with measured data to simultaneously construct both common and differential networks, to quantitatively assign model parameters and significant rewiring p-values and to provide user-friendly graphical results. The KDDN algorithm is computationally efficient and provides users with parallel computing capability using ubiquitous multi-core machines. We demonstrate the performance of KDDN on various simulations and real gene expression datasets, and further compare the results with those obtained by the most relevant peer methods. The acquired biologically plausible results provide new insights into network rewiring as a mechanistic principle and illustrate KDDN's ability to detect them efficiently and correctly. Although the principal application here involves microarray gene expressions, our methodology can be readily applied to other types of quantitative molecular profiling data. Source code and compiled package are freely available for download at http://apps.cytoscape.org/apps/kddn. Supplementary data are available at Bioinformatics online.

UNDO: a Bioconductor R package for unsupervised deconvolution of mixed gene expressions in tumor samples

We develop a novel unsupervised deconvolution method, within a well-grounded mathematical framework, to dissect mixed gene expressions in heterogeneous tumor samples. We implement an R package, UNsupervised DecOnvolution (UNDO), that can be used to automatically detect cell-specific marker genes (MGs) located on the scatter radii of mixed gene expressions, estimate cellular proportions in each sample and deconvolute mixed expressions into cell-specific expression profiles. We demonstrate the performance of UNDO over a wide range of tumor-stroma mixing proportions, validate UNDO on various biologically mixed benchmark gene expression datasets and further estimate tumor purity in TCGA/CPTAC datasets. The highly accurate deconvolution results obtained suggest not only the existence of cell-specific MGs but also UNDO's ability to detect them blindly and correctly. Although the principal application here involves microarray gene expressions, our methodology can be readily applied to other types of quantitative molecular profiling data. UNDO is available at http://bioconductor.org/packages.

Integration of molecular network data reconstructs Gene Ontology.

Motivation: Recently, a shift was made from using Gene Ontology (GO) to evaluate molecular network data to using these data to construct and evaluate GO. Dutkowski et al. provide the first evidence that a large part of GO can be reconstructed solely from topologies of molecular networks. Motivated by this work, we develop a novel data integration framework that integrates multiple types of molecular network data to reconstruct and update GO. We ask how much of GO can be recovered by integrating various molecular interaction data.Results: We introduce a computational framework for integration of various biological networks using penalized non-negative matrix tri-factorization (PNMTF). It takes all network data in a matrix form and performs simultaneous clustering of genes and GO terms, inducing new relations between genes and GO terms (annotations) and between GO terms themselves. To improve the accuracy of our predicted relations, we extend the integration methodology to include additional topological information represented as the similarity in wiring around non-interacting genes. Surprisingly, by integrating topologies of bakers’ yeasts protein–protein interaction, genetic interaction (GI) and co-expression networks, our method reports as related 96% of GO terms that are directly related in GO. The inclusion of the wiring similarity of non-interacting genes contributes 6% to this large GO term association capture. Furthermore, we use our method to infer new relationships between GO terms solely from the topologies of these networks and validate 44% of our predictions in the literature. In addition, our integration method reproduces 48% of cellular component, 41% of molecular function and 41% of biological process GO terms, outperforming the previous method in the former two domains of GO. Finally, we predict new GO annotations of yeast genes and validate our predictions through GIs profiling.Availability and implementation: Supplementary Tables of new GO term associations and predicted gene annotations are available at http://bio-nets.doc.ic.ac.uk/GO-Reconstruction/.Contact: natasha@imperial.ac.ukSupplementary information: Supplementary data are available at Bioinformatics online.

MODMatcher: multi-omics data matcher for integrative genomic analysis.

Errors in sample annotation or labeling often occur in large-scale genetic or genomic studies and are difficult to avoid completely during data generation and management. For integrative genomic studies, it is critical to identify and correct these errors. Different types of genetic and genomic data are inter-connected by cis-regulations. On that basis, we developed a computational approach, Multi-Omics Data Matcher (MODMatcher), to identify and correct sample labeling errors in multiple types of molecular data, which can be used in further integrative analysis. Our results indicate that inspection of sample annotation and labeling error is an indispensable data quality assurance step. Applied to a large lung genomic study, MODMatcher increased statistically significant genetic associations and genomic correlations by more than two-fold. In a simulation study, MODMatcher provided more robust results by using three types of omics data than two types of omics data. We further demonstrate that MODMatcher can be broadly applied to large genomic data sets containing multiple types of omics data, such as The Cancer Genome Atlas (TCGA) data sets.

Molecular features of the basal-like breast cancer subtype based on BRCA1 mutation status.

BRCA1-mutated breast cancer is associated with basal-like disease; however, it is currently unclear if the presence of a BRCA1 mutation depicts a different entity within this subgroup. In this study, we compared the molecular features among basal-like tumors with and without BRCA1 mutations. Fourteen patients with BRCA1-mutated (nine germline and five somatic) tumors and basal-like disease, and 79 patients with BRCA1 non-mutated tumors and basal-like disease, were identified from the cancer genome atlas dataset. The following molecular data types were evaluated: global gene expression, selected protein and phospho-protein expression, global miRNA expression, global DNA methylation, total number of somatic mutations, TP53 and PIK3CA somatic mutations, and global DNA copy-number aberrations. For intrinsic subtype identification, we used the PAM50 subtype predictor. Within the basal-like disease, we observed minor molecular differences in terms of gene, protein, and miRNA expression, and DNA methylation variation, according to BRCA1 status (either germinal or somatic). However, there were significant differences according to average number of mutations and DNA copy-number aberrations, and four amplified regions (2q32.2, 3q29, 6p22.3, and 22q12.2), which are characteristic in high-grade serous ovarian carcinomas, were observed in both germline and somatic BRCA1-mutated breast tumors. These results suggest that minor, but potentially relevant, baseline molecular features exist among basal-like tumors according to BRCA1 status. Additional studies are needed to better clarify if BRCA1 genetic status is an independent prognostic feature, and more importantly, if BRCA1 mutation status is a predictive biomarker of benefit from DNA-damaging agents among basal-like disease.Electronic supplementary materialThe online version of this article (doi:10.1007/s10549-014-3056-x) contains supplementary material, which is available to authorized users.

Outlier Analysis and Top Scoring Pair for Integrated Data Analysis and Biomarker Discovery.

Pathway deregulation has been identified as a key driver of carcinogenesis, with proteins in signaling pathways serving as primary targets for drug development. Deregulation can be driven by a number of molecular events, including gene mutation, epigenetic changes in gene promoters, overexpression, and gene amplifications or deletions. We demonstrate a novel approach that identifies pathways of interest by integrating outlier analysis within and across molecular data types with gene set analysis. We use the results to seed the top-scoring pair algorithm to identify robust biomarkers associated with pathway deregulation. We demonstrate this methodology on pediatric acute myeloid leukemia (AML) data. We develop a biomarker in primary AML tumors, demonstrate robustness with an independent primary tumor data set, and show that the identified biomarkers also function well in relapsed pediatric AML tumors.

Combined analysis of gene expression, DNA copy number, and mutation profiling data to display biological process anomalies in individual breast cancers

The goal of this analysis was to develop a computational tool that integrates the totality of gene expression, DNA copy number, and sequence abnormalities in individual cancers in the framework of biological processes. We used the hierarchical structure of the gene ontology (GO) database to create a reference network and projected mRNA expression, DNA copy number and mutation anomalies detected in single samples into this space. We applied our method to 59 breast cancers where all three types of molecular data were available. Each cancer had a large number of disturbed biological processes. Locomotion, multicellular organismal process, and signal transduction pathways were the most commonly affected GO terms, but the individual molecular events were different from case-to-case. Estrogen receptor-positive and -negative cancers had different repertoire of anomalies. We tested the functional impact of 27 mRNAs that had overexpression in cancer with variable frequency (<2-42 %) using an siRNA screen. Each of these genes inhibited cell growth in at least some of 18 breast cancer cell lines. We developed a free, on-line software tool ( http://netgoplot.org ) to display the complex genomic abnormalities in individual cancers in the biological framework of the GO biological processes. Each cancer harbored a variable number of pathway anomalies and the individual molecular events that caused an anomaly varied from case-to-case. Our in vitro experiments indicate that rare case-specific molecular abnormalities can play a functional role and driver events may vary from case-to-case depending on the constellation of other molecular anomalies.

Abstract PD4-6: Combined analysis of gene expression, DNA copy number and mutation profiling data to display biological process anomalies in individual cancers

Abstract Background: The goal of this analysis was to develop a computational tool that integrates gene expression, DNA copy number and sequence abnormalities in individual cancers in the framework of biological processes. Methods and Findings: We used the hierarchical structure of the Gene Ontology (GO) database to create a reference network and projected mRNA expression, DNA copy number and mutation anomalies detected in single samples into this space. We applied our method to 59 breast cancers where all three types of molecular data were available. Each cancer had a large number of disturbed biological processes. Locomotion, multicellular organismal process and signal transduction pathways were the most commonly affected GO terms but the individual molecular events were different from case-to-case. Estrogen receptor-positive and -negative cancers had different repertoire of anomalies. We tested the functional impact of 27 mRNAs that had overexpression in cancer with variable frequency (&amp;lt;2-42%) using an siRNA screen. Each of these genes inhibited cell growth in at least some of 18 breast cancer cell lines. Conclusions We developed a free, on-line software tool (http://netgoplot.org) to display the complex genomic abnormalities in individual cancers in the biological framework of the GO biological processes. We show that each cancer harbors a large number and unique combination of pathway anomalies, the individual molecular events that underlie the pathway level disturbances vary from case-to-case. Our in vitro experiments indicate that even rare case-specific molecular abnormalities can play a functional role and driver events may vary from case to case depending on the constellation of other molecular anomalies. Citation Information: Cancer Res 2013;73(24 Suppl): Abstract nr PD4-6.

The Long and Winding Road of Molecular Data in Phylogenetic Analysis

The use of molecules and reactions as evidence, markers and/or traits for evolutionary processes has a history more than a century long. Molecules have been used in studies of intra-specific variation and studies of similarity among species that do not necessarily result in the analysis of phylogenetic relations. Promoters of the use of molecular data have sustained the need for quantification as the main argument to make use of them. Moreover, quantification has allowed intensive statistical analysis, as a condition and a product of increasing automation. All of these analyses are subject to the methodological anxiety characteristic of a community in search of objectivity (Suárez-Díaz and Anaya-Muñoz, Stud Hist Philos Biol Biomed Sci 39:451-458, 2008). It is in this context that scientists compared and evaluated protein and nucleic acid sequence data with other types of molecular data - including immunological, electrophoretic and hybridization data. This paper argues that by looking at long-term historical processes, such as the use of molecular evidence in evolutionary biology, we gain valuable insights into the history of science. In that sense, it accompanies a growing concern among historians for big-pictures of science that incorporate the fruitful historical research on local cases of the last decades.

InTB - a data integration platform for molecular and clinical epidemiological analysis of tuberculosis

BackgroundTuberculosis is currently the second highest cause of death from infectious diseases worldwide. The emergence of multi and extensive drug resistance is threatening to make tuberculosis incurable. There is growing evidence that the genetic diversity of Mycobacterium tuberculosis may have important clinical consequences. Therefore, combining genetic, clinical and socio-demographic data is critical to understand the epidemiology of this infectious disease, and how virulence and other phenotypic traits evolve over time. This requires dedicated bioinformatics platforms, capable of integrating and enabling analyses of this heterogeneous data.ResultsWe developed inTB, a web-based system for integrated warehousing and analysis of clinical, socio-demographic and molecular data for Mycobacterium sp. isolates. As a database it can organize and display data from any of the standard genotyping methods (SNP, MIRU-VNTR, RFLP and spoligotype), as well as an extensive array of clinical and socio-demographic variables that are used in multiple countries to characterize the disease. Through the inTB interface it is possible to insert and download data, browse the database and search specific parameters. New isolates are automatically classified into strains according to an internal reference, and data uploaded or typed in is checked for internal consistency. As an analysis framework, the system provides simple, point and click analysis tools that allow multiple types of data plotting, as well as simple ways to download data for external analysis. Individual trees for each genotyping method are available, as well as a super tree combining all of them. The integrative nature of inTB grants the user the ability to generate trees for filtered subsets of data crossing molecular and clinical/socio-demografic information. inTB is built on open source software, can be easily installed locally and easily adapted to other diseases. Its design allows for use by research laboratories, hospitals or public health authorities. The full source code as well as ready to use packages is available at http://www.evocell.org/inTB.ConclusionsTo the best of our knowledge, this is the only system capable of integrating different types of molecular data with clinical and socio-demographic data, empowering researchers and clinicians with easy to use analysis tools that were not possible before.