An overview of recent developments in genomics and associated statistical methods

Molecular diagnostics in oral cancer and oral potentially malignant disorders-A clinician's guide

Current risk stratification of individuals for the development of oral squamous cell carcinoma (OSCC), including those with oral potentially malignant disorders (OPMD), remains based on clinical detection of visibly abnormal mucosa and tissue biopsy with histological assessment for the presence of OSCC or oral epithelial dysplasia (OED). In OPMD, the presence of OED remains the only prognostic tool used in standard care for the development of future OSCC, despite its ample limitations. There is assured potential that the analysis of the genome, transcripts and proteome can provide insight into what is occurring at a cellular level preceding the appearance of clinically observable change. The landscape of the role of the genome and its transcriptome on the development of OSCC and relationships with OPMDs are immense with exploration occurring on several fronts. For clinicians involved in the diagnosis and care of patients with OSCC and OPMD, understanding of commonly used molecular diagnostic techniques is imperative to gain useful insight from the expanding literature investigating the development of OSCC and the relationship with the clinical presentations which encompass OPMDs. Here we present an introduction to molecular diagnostic methods used in the study of OSCC and OPMD.

A review of metabolomics approaches and their application in identifying causal pathways of childhood asthma

Because asthma is a disease that results from host-environment interactions, an approach that allows assessment of the effect of the environment on the host is needed to understand the disease. Metabolomics has appealing potential as an application to study pathways to childhood asthma development. The objective of this review is to provide an overview of metabolomics methods and their application to understanding host-environment pathways in asthma development. We reviewed recent literature on advances in metabolomics and their application to study pathways to childhood asthma development. We highlight the (1) potential of metabolomics in understanding the pathogenesis of disease and the discovery of biomarkers; (2) choice of metabolomics techniques, biospecimen handling, and data analysis; (3) application to studying the role of the environment on asthma development; (4) review of metabolomics applied to the outcome of asthma; (5) recommendations for application of metabolomics-based -omics data integration in understanding disease pathogenesis; and (6) limitations. In conclusion, metabolomics allows use of biospecimens to identify useful biomarkers and pathways involved in disease development and subsequently to inform a greater understanding of disease pathogenesis and endotypes and prediction of the clinical course of childhood asthma phenotypes.

VariFunNet, an integrated multiscale modeling framework to study the effects of rare non-coding variants in Genome-Wide Association Studies: applied to Alzheimer's Disease

It is a grand challenge to reveal the causal effects of DNA variants in complex phenotypes. Although statistical techniques can establish correlations between genotypes and phenotypes in Genome-Wide Association Studies (GWAS), they often fail when the variant is rare. The emerging Network-based Association Studies aim to address this shortcoming in statistical analysis, but are mainly applied to coding variations. Increasing evidences suggest that non-coding variants play critical roles in the etiology of complex diseases. However, few computational tools are available to study the effect of rare non-coding variants on phenotypes. Here we have developed a multiscale modeling variant-to-function-to-network framework VariFunNet to address these challenges. VariFunNet first predict the functional variations of molecular interactions, which result from the non-coding variants. Then we incorporate the genes associated with the functional variation into a tissue-specific gene network, and identify subnetworks that transmit the functional variation to molecular phenotypes. Finally, we quantify the functional implication of the subnetwork, and prioritize the association of the non-coding variants with the phenotype. We have applied VariFunNet to investigating the causal effect of rare non-coding variants on Alzheimer's disease (AD). Among top 21 ranked causal non-coding variants, 16 of them are directly supported by existing evidences. The remaining 5 novel variants dysregulate multiple downstream biological processes, all of which are associated with the pathology of AD. Furthermore, we propose potential new drug targets that may modulate diverse pathways responsible for AD. These findings may shed new light on discovering new biomarkers and therapies for the prevention, diagnosis, and treatment of AD. Our results suggest that multiscale modeling is a potentially powerful approach to studying causal genotype-phenotype associations.

Identification of 12 new susceptibility loci for different histotypes of epithelial ovarian cancer

To identify common alleles associated with different histotypes of epithelial ovarian cancer (EOC), we pooled data from multiple genome-wide genotyping projects totaling 25,509 EOC cases and 40,941 controls. We identified nine new susceptibility loci for different EOC histotypes: six for serous EOC histotypes (3q28, 4q32.3, 8q21.11, 10q24.33, 18q11.2 and 22q12.1), two for mucinous EOC (3q22.3 and 9q31.1) and one for endometrioid EOC (5q12.3). We then performed meta-analysis on the results for high-grade serous ovarian cancer with the results from analysis of 31,448 BRCA1 and BRCA2 mutation carriers, including 3,887 mutation carriers with EOC. This identified three additional susceptibility loci at 2q13, 8q24.1 and 12q24.31. Integrated analyses of genes and regulatory biofeatures at each locus predicted candidate susceptibility genes, including OBFC1, a new candidate susceptibility gene for low-grade and borderline serous EOC.

Discovery of biomarkers in rare diseases: innovative approaches by predictive and personalized medicine

There are more than 8000 rare diseases (RDs) that affect >5 % of the world’s population. Many of the RDs have no effective treatment and lack of knowledge creates delayed diagnosis making management difficult. The emerging concept of the personalized medicine allows for early screening, diagnosis, and individualized treatment of human diseases. In this context, the discovery of biomarkers in RDs will be of prime importance to enable timely prevention and effective treatment. Since 80 % of RDs are of genetic origin, identification of new genes and causative mutations become valuable biomarkers. Furthermore, dynamic markers such as expressed genes, metabolites, and proteins are also very important to follow prognosis and response the therapy. Recent advances in omics technologies and their use in combination can define pathophysiological pathways that can be drug targets. Biomarker discovery and their use in diagnosis in RDs is a major pillar in RD research.

A prognostic signature based on three-genes expression in triple-negative breast tumours with residual disease

Residual disease after neoadjuvant chemotherapy (NAC) in triple-negative breast cancer (TNBC) is related with poor prognosis; however, the risk of recurrence after 3 years from surgery, becomes similar to other breast cancer subtypes indicating that TNBC is composed of tumours of different prognosis. To evaluate genes related to TNBC aggressiveness in the outcome of TNBC resistant to NAC, we profiled 82 samples of residual tumours whose expression for 449 genes was quantified with NanoString. The validation set (GSE25066) consisted of 113 TNBC cases with residual disease. The stepwise multivariate survival analysis performed by the Cox proportional hazards mode selected CCL5, DDIT4 and POLR1C as independent prognostic factors for distant recurrence-free survival (DRFS). We developed a three-genes signature using the regression coefficients for each gene (−0.393×CCL5+0.443×DDIT4+0.490×POLR1C). The median score in the discovery set (0.1494) identified two subgroups with different DRFS (P<0.001). The median score in the validation set was 0.0024 and was able to discriminate patients with different DRFS (P=0.002). In addition, the three-genes signature was a prognostic factor in TNBC patients regardless their response to NAC (data set GSE58812; P=0.001) and in patients with oestrogen-receptor-negative tumours (data set GSE16446; P=0.041). Here we describe a prognostic signature based on expression levels of CCL5, DDIT4 and POLR1C. The knowledge about the involvement of these genes in chemotherapy resistance could improve the therapeutic strategies in TNBC.

A framework for establishing predictive relationships between specific bacterial 16S rRNA sequence abundances and biotransformation rates

The rates at which wastewater treatment plant (WWTP) microbial communities biotransform specific substrates can differ by orders of magnitude among WWTP communities. Differences in taxonomic compositions among WWTP communities may predict differences in the rates of some types of biotransformations. In this work, we present a novel framework for establishing predictive relationships between specific bacterial 16S rRNA sequence abundances and biotransformation rates. We selected ten WWTPs with substantial variation in their environmental and operational metrics and measured the in situ ammonia biotransformation rate constants in nine of them. We isolated total RNA from samples from each WWTP and analyzed 16S rRNA sequence reads. We then developed multivariate models between the measured abundances of specific bacterial 16S rRNA sequence reads and the ammonia biotransformation rate constants. We constructed model scenarios that systematically explored the effects of model regularization, model linearity and non-linearity, and aggregation of 16S rRNA sequences into operational taxonomic units (OTUs) as a function of sequence dissimilarity threshold (SDT). A large percentage (greater than 80%) of model scenarios resulted in well-performing and significant models at intermediate SDTs of 0.13-0.14 and 0.26. The 16S rRNA sequences consistently selected into the well-performing and significant models at those SDTs were classified as Nitrosomonas and Nitrospira groups. We then extend the framework by applying it to the biotransformation rate constants of ten micropollutants measured in batch reactors seeded with the ten WWTP communities. We identified phylogenetic groups that were robustly selected into all well-performing and significant models constructed with biotransformation rates of isoproturon, propachlor, ranitidine, and venlafaxine. These phylogenetic groups can be used as predictive biomarkers of WWTP microbial community activity towards these specific micropollutants. This work is an important step towards developing tools to predict biotransformation rates in WWTPs based on taxonomic composition.

Genome-wide study of correlations between genomic features and their relationship with the regulation of gene expression

The broad class of tasks in genetics and epigenetics can be reduced to the study of various features that are distributed over the genome (genome tracks). The rapid and efficient processing of the huge amount of data stored in the genome-scale databases cannot be achieved without the software packages based on the analytical criteria. However, strong inhomogeneity of genome tracks hampers the development of relevant statistics. We developed the criteria for the assessment of genome track inhomogeneity and correlations between two genome tracks. We also developed a software package, Genome Track Analyzer, based on this theory. The theory and software were tested on simulated data and were applied to the study of correlations between CpG islands and transcription start sites in the Homo sapiens genome, between profiles of protein-binding sites in chromosomes of Drosophila melanogaster, and between DNA double-strand breaks and histone marks in the H. sapiens genome. Significant correlations between transcription start sites on the forward and the reverse strands were observed in genomes of D. melanogaster, Caenorhabditis elegans, Mus musculus, H. sapiens, and Danio rerio. The observed correlations may be related to the regulation of gene expression in eukaryotes. Genome Track Analyzer is freely available at http://ancorr.eimb.ru/.

Challenges of Big Data Analysis

Big Data bring new opportunities to modern society and challenges to data scientists. On one hand, Big Data hold great promises for discovering subtle population patterns and heterogeneities that are not possible with small-scale data. On the other hand, the massive sample size and high dimensionality of Big Data introduce unique computational and statistical challenges, including scalability and storage bottleneck, noise accumulation, spurious correlation, incidental endogeneity, and measurement errors. These challenges are distinguished and require new computational and statistical paradigm. This article gives overviews on the salient features of Big Data and how these features impact on paradigm change on statistical and computational methods as well as computing architectures. We also provide various new perspectives on the Big Data analysis and computation. In particular, we emphasize on the viability of the sparsest solution in high-confidence set and point out that exogeneous assumptions in most statistical methods for Big Data can not be validated due to incidental endogeneity. They can lead to wrong statistical inferences and consequently wrong scientific conclusions.

Bioinformatics applied to gene transcription regulation

Understanding regulation of gene transcription is central to molecular biology as well as being of great interest in medicine. The molecular syntax of the concerted transcriptional activation/repression of gene networks in mammal cells, which shape the physiological response to the molecular signals, is often unknown or not completely understood. Combining genome-wide experiments with in silico approaches opens the way to a more systematic comprehension of the molecular mechanisms of transcription regulation. Diverse bioinformatics tools have been developed to help unravel these mechanisms, by handling and processing data at different stages: from data collection and storage to the identification of molecular targets and from the detection of DNA motif signatures in the regulatory sequences of functionally related genes to the identification of relevant regulatory networks. Moreover, the large amount of genome-wide scale data recently produced has attracted professionals from diverse backgrounds to this cutting-edge realm of molecular biology. This mini-review is intended as an orientation for multidisciplinary professionals, introducing a streamlined workflow in gene transcription regulation with emphasis on sequence analysis. It provides an outlook on tools and methods, selected from a host of bioinformatics resources available today. It has been designed for the benefit of students, investigators, and professionals who seek a coherent yet quick introduction to in silico approaches to analyzing regulation of gene transcription in the post-genomic era.

Exploring massive, genome scale datasets with the GenometriCorr package

We have created a statistically grounded tool for determining the correlation of genomewide data with other datasets or known biological features, intended to guide biological exploration of high-dimensional datasets, rather than providing immediate answers. The software enables several biologically motivated approaches to these data and here we describe the rationale and implementation for each approach. Our models and statistics are implemented in an R package that efficiently calculates the spatial correlation between two sets of genomic intervals (data and/or annotated features), for use as a metric of functional interaction. The software handles any type of pointwise or interval data and instead of running analyses with predefined metrics, it computes the significance and direction of several types of spatial association; this is intended to suggest potentially relevant relationships between the datasets.

Availability and implementation: The package, GenometriCorr, can be freely downloaded at http://genometricorr.sourceforge.net/. Installation guidelines and examples are available from the sourceforge repository. The package is pending submission to Bioconductor.

Advanced molecular biologic techniques in toxicologic disease

The advancement of molecular biologic techniques and their capabilities to answer questions pertaining to mechanisms of pathophysiologic events have greatly expanded over the past few years. In particular, these opportunities have provided researchers and clinicians alike the framework from with which to answer clinical questions not amenable for elucidation using previous, more antiquated methods. Utilizing extremely small molecules, namely microRNA, DNA, protein, and nanoparticles, we discuss the background and utility of these approaches to the progressive, practicing physician. Finally, we consider the application of these tools employed as future bedside point of care tests, aiding in the ultimate goal of unsurpassed patient care.

tigre: Transcription factor inference through gaussian process reconstruction of expression for bioconductor

tigre is an R/Bioconductor package for inference of transcription factor activity and ranking candidate target genes from gene expression time series. The underlying methodology is based on Gaussian process inference on a differential equation model that allows the use of short, unevenly sampled, time series. The method has been designed with efficient parallel implementation in mind, and the package supports parallel operation even without additional software.

AVAILABILITY

The tigre package is included in Bioconductor since release 2.6 for R 2.11. The package and a user's guide are available at http://www.bioconductor.org.

Statistical challenges of high-dimensional data

Modern applications of statistical theory and methods can involve extremely large datasets, often with huge numbers of measurements on each of a comparatively small number of experimental units. New methodology and accompanying theory have emerged in response: the goal of this Theme Issue is to illustrate a number of these recent developments. This overview article introduces the difficulties that arise with high-dimensional data in the context of the very familiar linear statistical model: we give a taste of what can nevertheless be achieved when the parameter vector of interest is sparse, that is, contains many zero elements. We describe other ways of identifying low-dimensional subspaces of the data space that contain all useful information. The topic of classification is then reviewed along with the problem of identifying, from within a very large set, the variables that help to classify observations. Brief mention is made of the visualization of high-dimensional data and ways to handle computational problems in Bayesian analysis are described. At appropriate points, reference is made to the other papers in the issue.