An integrative variant analysis suite for whole exome next-generation sequencing data

Background  

Whole exome capture sequencing allows researchers to cost-effectively sequence the coding regions of the genome. Although the exome capture sequencing methods have become routine and well established, there is currently a lack of tools specialized for variant calling in this type of data.     

A comparative analysis of exome capture

Background  

Human exome resequencing using commercial target capture kits has been and is being used for sequencing large numbers of individuals to search for variants associated with various human diseases. We rigorously evaluated the capabilities of two solution exome capture kits. These analyses help clarify the strengths and limitations of those data as well as systematically identify variables that should be considered in the use of those data.     

Optimization of laser capture microdissection and RNA amplification for gene expression profiling of prostate cancer

Background  

To discover prostate cancer biomarkers, we profiled gene expression in benign and malignant cells laser capture microdissected (LCM) from prostate tissues and metastatic prostatic adenocarcinomas. Here we present methods developed, optimized, and validated to obtain high quality gene expression data.     

Mitochondrial proteomics on human fibroblasts for identification of metabolic imbalance and cellular stress

Background  

Mitochondrial proteins are central to various metabolic activities and are key regulators of apoptosis. Disturbance of mitochondrial proteins is therefore often associated with disease. Large scale protein data are required to capture the mitochondrial protein levels and mass spectrometry based proteomics is suitable for generating such data. To study the relative quantities of mitochondrial proteins in cells from cultivated human skin fibroblasts we applied a proteomic method based on nanoLC-MS/MS analysis of iTRAQ-labeled peptides.     

BisoGenet: a new tool for gene network building,visualization and analysis

Background  

The increasing availability and diversity of omics data in the post-genomic era offers new perspectives in most areas of biomedical research. Graph-based biological networks models capture the topology of the functional relationships between molecular entities such as gene, protein and small compounds and provide a suitable framework for integrating and analyzing omics-data. The development of software tools capable of integrating data from different sources and to provide flexible methods to reconstruct, represent and analyze topological networks is an active field of research in bioinformatics.     

Analysing the origin of long-range interactions in proteins using lattice models

Background  

Long-range communication is very common in proteins but the physical basis of this phenomenon remains unclear. In order to gain insight into this problem, we decided to explore whether long-range interactions exist in lattice models of proteins. Lattice models of proteins have proven to capture some of the basic properties of real proteins and, thus, can be used for elucidating general principles of protein stability and folding.     

Graph-based clustering and characterization of repetitive sequences in next-generation sequencing data

Background  

The investigation of plant genome structure and evolution requires comprehensive characterization of repetitive sequences that make up the majority of higher plant nuclear DNA. Since genome-wide characterization of repetitive elements is complicated by their high abundance and diversity, novel approaches based on massively-parallel sequencing are being adapted to facilitate the analysis. It has recently been demonstrated that the low-pass genome sequencing provided by a single 454 sequencing reaction is sufficient to capture information about all major repeat families, thus providing the opportunity for efficient repeat investigation in a wide range of species. However, the development of appropriate data mining tools is required in order to fully utilize this sequencing data for repeat characterization.     

Assessment of the dynamics of atrial signals and local atrial period series during atrial fibrillation: effects of isoproterenol administration

Background  

The autonomic nervous system (ANS) plays an important role in the genesis and maintenance of atrial fibrillation (AF), but quantification of its electrophysiologic effects is extremely complex and difficult. Aim of the study was to evaluate the capability of linear and non-linear indexes to capture the fine changing dynamics of atrial signals and local atrial period (LAP) series during adrenergic activation induced by isoproterenol (a sympathomimetic drug) infusion.     

Individualized markers optimize class prediction of microarray data

Background  

Identification of molecular markers for the classification of microarray data is a challenging task. Despite the evident dissimilarity in various characteristics of biological samples belonging to the same category, most of the marker – selection and classification methods do not consider this variability. In general, feature selection methods aim at identifying a common set of genes whose combined expression profiles can accurately predict the category ofallsamples. Here, we argue that this simplified approach is often unable to capture the complexity of a disease phenotype and we propose an alternative method that takes into account the individuality of each patient-sample.     

High quality RNA from multiple brain regions simultaneously acquired by laser capture microdissection

Background  

Laser capture microdissection enables the isolation of single cells or small cell groups from histological sections under direct microscopic observation. Combined with quantitative PCR or microarray, it is a very powerful approach for studying gene expression profiles in discrete cell populations. The major challenge for such studies is to obtain good quality RNA from small amounts of starting material.     

Seven new dolphin mitochondrial genomes and a time-calibrated phylogeny of whales

Background  

The phylogeny of Cetacea (whales) is not fully resolved with substantial support. The ambiguous and conflicting results of multiple phylogenetic studies may be the result of the use of too little data, phylogenetic methods that do not adequately capture the complex nature of DNA evolution, or both. In addition, there is also evidence that the generic taxonomy of Delphinidae (dolphins) underestimates its diversity. To remedy these problems, we sequenced the complete mitochondrial genomes of seven dolphins and analyzed these data with partitioned Bayesian analyses. Moreover, we incorporate a newly-developed "relaxed" molecular clock to model heterogenous rates of evolution among cetacean lineages.     

"Intelligent" descriptions of microbial kinetics in finitely dispersed bioreactors: neural and cybernetic models for PHB biosynthesis by <Emphasis Type="Italic">Ralstonia eutropha</Emphasis>

Background  

For many microbial processes, the complexity of the metabolisms and the responses to transient and realistic conditions are difficult to capture in mechanistic models. The cells seem to have an innate intelligence that enables them to respond optimally to environmental changes. Some "intelligent" models have therefore been proposed and compared with a mechanistic model for fed-batch cultures of Ralstonia eutropha.     

Qualitative and quantitative characteristics of the extracellular DNA delivered to the nucleus of a living cell

Background  

The blood plasma and other intertissue fluids usually contain a certain amount of DNA, getting there due to a natural cell death in the organism. Cells of this organism can capture the extracellular DNA, whereupon it is delivered to various cell compartments. It is hypothesized that the extracellular DNA is involved in the transfer of genetic information and its fixation in the genome of recipient cell.     

Automatic,context-specific generation of Gene Ontology slims

Background  

The use of ontologies to control vocabulary and structure annotation has added value to genome-scale data, and contributed to the capture and re-use of knowledge across research domains. Gene Ontology (GO) is widely used to capture detailed expert knowledge in genomic-scale datasets and as a consequence has grown to contain many terms, making it unwieldy for many applications. To increase its ease of manipulation and efficiency of use, subsets called GO slims are often created by collapsing terms upward into more general, high-level terms relevant to a particular context. Creation of a GO slim currently requires manipulation and editing of GO by an expert (or community) familiar with both the ontology and the biological context. Decisions about which terms to include are necessarily subjective, and the creation process itself and subsequent curation are time-consuming and largely manual.     

Multiple source genes of HAmo SINE actively expanded and ongoing retroposition in cyprinid genomes relying on its partner LINE

Background  

We recently characterized HAmo SINE and its partner LINE in silver carp and bighead carp based on hybridization capture of repetitive elements from digested genomic DNA in solution using a bead-probe [1]. To reveal the distribution and evolutionary history of SINEs and LINEs in cyprinid genomes, we performed a multi-species search for HAmo SINE and its partner LINE using the bead-probe capture and internal-primer-SINE polymerase chain reaction (PCR) techniques.     

Screening the human exome: a comparison of whole genome and whole transcriptome sequencing

Background  

There is considerable interest in the development of methods to efficiently identify all coding variants present in large sample sets of humans. There are three approaches possible: whole-genome sequencing, whole-exome sequencing using exon capture methods, and RNA-Seq. While whole-genome sequencing is the most complete, it remains sufficiently expensive that cost effective alternatives are important.     

Correlation test to assess low-level processing of high-density oligonucleotide microarray data

Background  

There are currently a number of competing techniques for low-level processing of oligonucleotide array data. The choice of technique has a profound effect on subsequent statistical analyses, but there is no method to assess whether a particular technique is appropriate for a specific data set, without reference to external data.     

FLAME,a novel fuzzy clustering method for the analysis of DNA microarray data

Background  

Data clustering analysis has been extensively applied to extract information from gene expression profiles obtained with DNA microarrays. To this aim, existing clustering approaches, mainly developed in computer science, have been adapted to microarray data analysis. However, previous studies revealed that microarray datasets have very diverse structures, some of which may not be correctly captured by current clustering methods. We therefore approached the problem from a new starting point, and developed a clustering algorithm designed to capture dataset-specific structures at the beginning of the process.     

Trees on networks: resolving statistical patterns of phylogenetic similarities among interacting proteins

Background  

Phylogenies capture the evolutionary ancestry linking extant species. Correlations and similarities among a set of species are mediated by and need to be understood in terms of the phylogenic tree. In a similar way it has been argued that biological networks also induce correlations among sets of interacting genes or their protein products.