Advancement in Next Generation Sequencing (NGS) technology are associated with ever-increasing volume of genomic data every year. These genomic data are efficiently processed by empirical parallelism using High Performance Computing (HPC). The processed data can be used for genome-wide association studies, genetics, personalized medicine and many other areas. There are different kind of algorithms and implementations used in different phases of genome processing. In this paper, we used BWAKIT and GATK based software for processing larger volume of genomic data that are referred as "NGS workflow at SIDRA". We used BWAKIT for genome alignment and GATK for variant discovery in the NGS workflow that required larger computation and huge memory requirement respectively. We observed, the CPU utilization is not more than 45% during variant discovery and hence, it is necessary to understand the optimal selection (in terms of number of threads or cores) of the resources during the NGS workflow automation. We analyzed the performance bottleneck and application optimization in terms of "scalability" (use maximum available CPUs and memory) and "multiple instances of NGS workflow with different genome data within a node" (process more volume of genome data concurrently with limited set of CPUs and memory). We observed that, 40%, 65%, 71% and 76% improvement in performance while processing 2, 4, 8 and 16 samples concurrently using our own scheduling heuristics. As a result, our proposed NGS workflow automation will improve the performance upto 76% compared to application scalability based workflows.

BACKGROUND: Early evolution of animals led to profound changes in body plan organization, symmetry and the rise of tissue complexity including formation of muscular and nervous systems. This process was associated with massive restructuring of animal genomes as well as deletion, acquisition and rapid differentiation of genes from a common metazoan ancestor. Here, we present a simple but efficient workflow for elucidation of gene gain and gene loss within major branches of the animal kingdom. METHODS: We have designed a pipeline of sequence comparison, clustering and functional annotation using 12 major phyla as illustrative examples. Specifically, for the input we used sets of ab initio predicted gene models from the genomes of six bilaterians, three basal metazoans (Cnidaria, Placozoa, Porifera), two unicellular eukaryotes (Monosiga and Capsospora) and the green plant Arabidopsis as an out-group. Due to the large amounts of data the software required a high-performance Linux cluster. The final results can be imported into standard spreadsheet analysis software and queried for the numbers and specific sets of genes absent in specific genomes, uniquely present or shared among different taxons. RESULTS AND CONCLUSIONS: The developed software is open source and available free of charge on Open Source principles. It allows the user to address a number of specific questions regarding gene gain and gene loss in particular genomes, and user-defined groups of genomes can be formulated in a type of logical expression. For example, our analysis of 12 sequenced genomes indicated that these genomes possess at least 90,000 unique genes and gene families, suggesting enormous diversity of the genome repertoire in the animal kingdom. Approximately 9% of these gene families are shared universally (homologous) among all genomes, 53% are unique to specific taxa, and the rest are shared between two or more distantly related genomes.

BACKGROUND: Prostate carcinoma is among the most common types of cancer affecting hundreds of thousands people every year. Once the metastatic form of prostate carcinoma is documented, the majority of patients die from their tumors as opposed to other causes. The key to successful treatment is in the earliest possible diagnosis, as well as understanding the molecular mechanisms of metastatic progression. A number of recent studies have identified multiple biomarkers for metastatic progression. However, most of the studies consider only direct comparison between metastatic and non-metastatic classes of samples. RESULTS: We propose an alternative concept of analysis that considers the entire multidimensional space of gene expression and identifies the partition of this space in which metastatic development is possible. To apply this concept in cancer gene expression studies we utilize a modification of high-dimension natural taxonomy algorithm FOREL. Our analysis of microarray data containing primary and metastatic cancer samples has revealed not only differentially expressed genes, but also relations between different groups of primary and metastatic cancer. Metastatic samples tend to occupy a distinct partition of gene expression space. Further pathway analysis suggests that this partition is delineated by a specific pattern of gene expression in cytoskeleton remodeling, cell adhesion and apoptosis/cell survival pathways. We compare our findings with both report of original analysis and recent studies in molecular mechanism of metastasis. CONCLUSION: Our analysis indicates a single molecular mechanism of metastasis. The new approach does not contradict previously reported findings, but reveals important details unattainable with traditional methodology.

BACKGROUND: Circadian rhythm is a crucial factor in orchestration of plant physiology, keeping it in synchrony with the daylight cycle. Previous studies have reported that up to 16% of plant transcriptome are circadially expressed. RESULTS: Our studies of mammalian gene expression revealed circadian baseline oscillation in nearly 100% of genes. Here we present a comprehensive analysis of periodicity in two independent data sets. Application of the advanced algorithms and analytic approached already tested on animal data reveals oscillation in almost every gene of Arabidopsis thaliana. CONCLUSION: This study indicates an even more pervasive role of oscillation in molecular physiology of plants than previously believed. Earlier studies have dramatically underestimated the prevalence of circadian oscillation in plant gene expression.

BACKGROUND: Metastases are responsible for the majority of cancer fatalities. The molecular mechanisms governing metastasis are poorly understood, hindering early diagnosis and treatment. Previous studies of gene expression patterns in metastasis have concentrated on selection of a small number of "signature" biomarkers. RESULTS: We propose an alternative approach that puts into focus gene interaction networks and molecular pathways rather than separate genes. We have reanalyzed expression data from a large set of primary solid and metastatic tumors originating from different tissues using the latest available tools for normalization, identification of differentially expressed genes and pathway analysis. Our studies indicate that regardless of the tissue of origin, all metastatic tumors share a number of common features related to changes in basic energy metabolism, cell adhesion/cytoskeleton remodeling, antigen presentation and cell cycle regulation. Analysis of multiple independent datasets indicates significantly reduced oxidative phosphorylation in metastases compared to primary solid tumors. CONCLUSION: Our methods allow identification of robust, although not necessarily highly expressed biomarkers. A systems approach relying on groups of interacting genes rather than single markers is also essential for understanding the cellular processes leading to metastatic progression. We have identified metabolic pathways associated with metastasis that may serve as novel targets for therapeutic intervention.

BACKGROUND: It has been previously reported that most mammalian genes display a circadian oscillation in their baseline expression. Consequently, the phase and amplitude of each component of a signal transduction cascade has downstream consequences. RESULTS: Here, we report our analysis of alternative transcripts in the leptin signaling pathway which is responsible for the systemic regulation of macronutrient storage and energy balance. We focused on the circadian expression pattern of a critical component of the leptin signaling system, suppressor of cytokine signaling 3 (SOCS3). On an Affymetrix GeneChip 430A2 microarray, this gene is represented by three probe sets targeting different regions within the 3' end of the last exon. We demonstrate that in murine brown adipose tissue two downstream 3' probe sets experience circadian baseline oscillation in counter-phase to the upstream probe set. Such differences in expression patterns are a telltale sign of alternative splicing within the last exon of SOCS3. In contrast, all three probe sets oscillated in a common phase in murine liver and white adipose tissue. This suggests that the regulation of SOCS3 expression in brown fat is tissue specific. Another component of the signaling pathway, Janus kinase (JAK), is directly regulated by SOCS and has alternative transcript probe sets oscillating in counter-phase in a white adipose tissue specific manner. CONCLUSION: We hypothesize that differential oscillation of alternative transcripts may provide a mechanism to maintain steady levels of expression in spite of circadian baseline variation.

In mammals, circadian periodicity has been described for gene expression in the hypothalamus and multiple peripheral tissues. It is accepted that 10%-15% of all genes oscillate in a daily rhythm, regulated by an intrinsic molecular clock. Statistical analyses of periodicity are limited by the small size of datasets and high levels of stochastic noise. Here, we propose a new approach applying digital signal processing algorithms separately to each group of genes oscillating in the same phase. Combined with the statistical tests for periodicity, this method identifies circadian baseline oscillation in almost 100% of all expressed genes. Consequently, circadian oscillation in gene expression should be evaluated in any study related to biological pathways. Changes in gene expression caused by mutations or regulation of environmental factors (such as photic stimuli or feeding) should be considered in the context of changes in the amplitude and phase of genetic oscillations.

BACKGROUND: Periodic processes, such as the circadian rhythm, are important factors modulating and coordinating transcription of genes governing key metabolic pathways. Theoretically, even small fluctuations in the orchestration of circadian gene expression patterns among different tissues may result in functional asynchrony, at the organism level, and may contribute to a wide range of pathologic disorders. Identification of circadian expression pattern in time series data is important, but equally challenging. Microarray technology allows estimation of relative expression of thousands of genes at each time point. However, this estimation often lacks precision and microarray experiments are prohibitively expensive, limiting the number of data points in a time series expression profile. The data produced in these experiments carries a high degree of stochastic variation, obscuring the periodic pattern and a limited number of replicates, typically covering not more than two complete periods of oscillation. RESULTS: To address this issue, we have developed a simple, but effective, computational technique for the identification of a periodic pattern in relatively short time series, typical for microarray studies of circadian expression. This test is based on a random permutation of time points in order to estimate non-randomness of a periodogram. The Permutated time, or Pt-test, is able to detect oscillations within a given period in expression profiles dominated by a high degree of stochastic fluctuations or oscillations of different irrelevant frequencies. We have conducted a comprehensive study of circadian expression on a large data set produced at PBRC, representing three different peripheral murine tissues. We have also re-analyzed a number of similar time series data sets produced and published independently by other research groups over the past few years. CONCLUSION: The Permutated time test (Pt-test) is demonstrated to be effective for detection of periodicity in short time series typical for high-density microarray experiments. The software is a set of C++ programs available from the authors on the open source basis.

Circadian rhythms are prevalent in most organisms. Even the smallest disturbances in the orchestration of circadian gene expression patterns among different tissues can result in functional asynchrony, at the organism level, and may to contribute to a wide range of physiologic disorders. It has been reported that as many as 5%-10% of transcribed genes in peripheral tissues follow a circadian expression pattern. We have conducted a comprehensive study of circadian gene expression on a large dataset representing three different peripheral tissues. The data have been produced in a large-scale microarray experiment covering replicate daily cycles in murine white and brown adipose tissues as well as in liver. We have applied three alternative algorithmic approaches to identify circadian oscillation in time series expression profiles. Analyses of our own data indicate that the expression of at least 7% to 21% of active genes in mouse liver, and in white and brown adipose tissues follow a daily oscillatory pattern. Indeed, analysis of data from other laboratories suggests that the percentage of genes with an oscillatory pattern may approach 50% in the liver. For the rest of the genes, oscillation appears to be obscured by stochastic noise. Our phase classification and computer simulation studies based on multiple datasets indicate no detectable boundary between oscillating and non-oscillating fractions of genes. We conclude that greater attention should be given to the potential influence of circadian mechanisms on any biological pathway related to metabolism and obesity.

BACKGROUND: The continuous flow of EST data remains one of the richest sources for discoveries in modern biology. The first step in EST data mining is usually associated with EST clustering, the process of grouping of original fragments according to their annotation, similarity to known genomic DNA or each other. Clustered EST data, accumulated in databases such as UniGene, STACK and TIGR Gene Indices have proven to be crucial in research areas from gene discovery to regulation of gene expression. RESULTS: We have developed a new nucleotide sequence matching algorithm and its implementation for clustering EST sequences. The program is based on the original CLU match detection algorithm, which has improved performance over the widely used d2_cluster. The CLU algorithm automatically ignores low-complexity regions like poly-tracts and short tandem repeats. CONCLUSION: CLU represents a new generation of EST clustering algorithm with improved performance over current approaches. An early implementation can be applied in small and medium-size projects. The CLU program is available on an open source basis free of charge. It can be downloaded from http://compbio.pbrc.edu/pti.

MOTIVATION: Massive oligonucleotide hybridization is one of the most promising technologies of functional genome analysis. The critical point is to design appropriate sets of oligonucleotides that can be used effectively in identification by hybridization. RESULTS: Using a genetic algorithm approach, we have attempted to design sets of oligo probes capable of identifying new genes belonging to a defined gene family within a cDNA or genomic library. It is not limited by oligonucleotide length and admits the letter 'N' in the structure of the oligonucleotides selected. One of the major advantages of this approach is the low homology required to identify functional families of sequences with little homology. We have designed the oligonucleotide sets that are most selective for the cDNA clones of transmembrane G protein-coupled receptors (GPCRs), a large family of proteins that form part of a modular system of extracellular signal transduction to the intracellular second messenger pathways. The accuracy of identification has been checked on the EST library containing 713 870 cDNA sequences. A set of 15 oligos between 7 and 14 bases in length has correctly identified 70% of the GPCR cDNA collection sequences with 0.02% false positives. AVAILABILITY: The developed software is available by ftp://ftp.bionet.nsc. ru/pub/biology/ and on the Web page http://www.bionet.nsc. ru/SRCG/Oligoselector/. CONTACT: [email protected]; sebastian. [email protected]

We report an object-oriented data handler and supplementary tools for the development of molecular genetics application software for various sequence analyses. Our data handler has a flexible and expandable format that supports the most common data types for molecular genetic software. New data types can be constructed in an object-oriented manner from the basic units. The data handler includes an object library, a format-converting program and a viewer that can visualize simultaneously the data contained in several files to construct a general picture from separate data. This software has been implemented on an IBM PC-compatible personal computer.

A new algorithm for data bank homology search is proposed. The principal advantages of the new algorithm are: (i) linear computation complexity; (ii) low memory requirements; and (iii) high sensitivity to the presence of local region homology. The algorithm first calculates indicative matrices of k-tuple 'realization' in the query sequence and then searches for an appropriate number of matching k-tuples within a narrow range in database sequences. It does not require k-tuple coordinates tabulation and in-memory placement for database sequences. The algorithm is implemented in a program for execution on PC-compatible computers and tested on PIR and GenBank databases with good results. A few modifications designed to improve the selectivity are also discussed. As an application example, the search for homology of the mouse homeotic protein HOX 3.1 is given.