Bioinformatics and systems biology.
Ontology highlight
ABSTRACT:
SUBMITTER: Ram PT
PROVIDER: S-EPMC3345061 | biostudies-other | 2012 Apr
REPOSITORIES: biostudies-other
Similar Datasets
Project description:Although neuropsychiatric (NP) disorders are among the top causes of disability worldwide with enormous financial costs, they can still be viewed as part of the most complex disorders that are of unknown etiology and incomprehensible pathophysiology. The complexity of NP disorders arises from their etiologic heterogeneity and the concurrent influence of environmental and genetic factors. In addition, the absence of rigid boundaries between the normal and diseased state, the remarkable overlap of symptoms among conditions, the high inter-individual and inter-population variations, and the absence of discriminative molecular and/or imaging biomarkers for these diseases makes difficult an accurate diagnosis. Along with the complexity of NP disorders, the practice of psychiatry suffers from a "top-down" method that relied on symptom checklists. Although checklist diagnoses cost less in terms of time and money, they are less accurate than a comprehensive assessment. Thus, reliable and objective diagnostic tools such as biomarkers are needed that can detect and discriminate among NP disorders. The real promise in understanding the pathophysiology of NP disorders lies in bringing back psychiatry to its biological basis in a systemic approach which is needed given the NP disorders' complexity to understand their normal functioning and response to perturbation. This approach is implemented in the systems biology discipline that enables the discovery of disease-specific NP biomarkers for diagnosis and therapeutics. Systems biology involves the use of sophisticated computer software "omics"-based discovery tools and advanced performance computational techniques in order to understand the behavior of biological systems and identify diagnostic and prognostic biomarkers specific for NP disorders together with new targets of therapeutics. In this review, we try to shed light on the need of systems biology, bioinformatics, and biomarkers in neuropsychiatry, and illustrate how the knowledge gained through these methodologies can be translated into clinical use providing clinicians with improved ability to diagnose, manage, and treat NP patients.
| S-EPMC3529307 | biostudies-other
Project description:Systems biology aims at achieving a system-level understanding of living organisms and applying this knowledge to various fields such as synthetic biology, metabolic engineering, and medicine. System-level understanding of living organisms can be derived from insight into: (i) system structure and the mechanism of biological networks such as gene regulation, protein interactions, signaling, and metabolic pathways; (ii) system dynamics of biological networks, which provides an understanding of stability, robustness, and transduction ability through system identification, and through system analysis methods; (iii) system control methods at different levels of biological networks, which provide an understanding of systematic mechanisms to robustly control system states, minimize malfunctions, and provide potential therapeutic targets in disease treatment; (iv) systematic design methods for the modification and construction of biological networks with desired behaviors, which provide system design principles and system simulations for synthetic biology designs and systems metabolic engineering. This review describes current developments in systems biology, systems synthetic biology, and systems metabolic engineering for engineering and biology researchers. We also discuss challenges and future prospects for systems biology and the concept of systems biology as an integrated platform for bioinformatics, systems synthetic biology, and systems metabolic engineering.
| S-EPMC3972654 | biostudies-literature
Project description:Several studies in Bioinformatics, Computational Biology and Systems Biology rely on the definition of physico-chemical or mathematical models of biological systems at different scales and levels of complexity, ranging from the interaction of atoms in single molecules up to genome-wide interaction networks. Traditional computational methods and software tools developed in these research fields share a common trait: they can be computationally demanding on Central Processing Units (CPUs), therefore limiting their applicability in many circumstances. To overcome this issue, general-purpose Graphics Processing Units (GPUs) are gaining an increasing attention by the scientific community, as they can considerably reduce the running time required by standard CPU-based software, and allow more intensive investigations of biological systems. In this review, we present a collection of GPU tools recently developed to perform computational analyses in life science disciplines, emphasizing the advantages and the drawbacks in the use of these parallel architectures. The complete list of GPU-powered tools here reviewed is available at http://bit.ly/gputools.
| S-EPMC5862309 | biostudies-literature
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| S-EPMC7399369 | biostudies-literature
Project description:Obstetric diseases remain underserved and understudied. Drug repurposing-utilization of a drug whose use is accepted in one condition for a different condition-could represent a rapid and low-cost way to identify new therapies that are known to be safe. In diseases of pregnancy, the known safety profile is a strong additional incentive. We describe the techniques and steps used in the use of 'omics data for drug repurposing. We illustrate these techniques using case studies of published drug repurposing projects. We provide a set of available databases with low barriers to entry which investigators can use to perform their own projects. The promise of 'omics techniques is unbiased screening, either of all drug targets or of all patients using particular drugs to find which are likely to alter disease risk or progression. However, we caution that reproducibility across the underlying studies, and thus the drugs suggested for repurposing, can be poor. We suggest that improved nosology, for example correlating patient clinical conditions with placental pathology, could yield more robust associations. We conclude that 'omics-driven drug repurposing represents a potential fruitful path to discover new, safe treatments of obstetric diseases.
| S-EPMC6105384 | biostudies-literature
Project description:Bioinformatics analysis has become an integral part of research in biology. However, installation and use of scientific software can be difficult and often requires technical expert knowledge. Reasons are dependencies on certain operating systems or required third-party libraries, missing graphical user interfaces and documentation, or nonstandard input and output formats. In order to make bioinformatics software easily accessible to researchers, we here present a web-based platform. The Center for Bioinformatics Tuebingen (ZBIT) Bioinformatics Toolbox provides web-based access to a collection of bioinformatics tools developed for systems biology, protein sequence annotation, and expression data analysis. Currently, the collection encompasses software for conversion and processing of community standards SBML and BioPAX, transcription factor analysis, and analysis of microarray data from transcriptomics and proteomics studies. All tools are hosted on a customized Galaxy instance and run on a dedicated computation cluster. Users only need a web browser and an active internet connection in order to benefit from this service. The web platform is designed to facilitate the usage of the bioinformatics tools for researchers without advanced technical background. Users can combine tools for complex analyses or use predefined, customizable workflows. All results are stored persistently and reproducible. For each tool, we provide documentation, tutorials, and example data to maximize usability. The ZBIT Bioinformatics Toolbox is freely available at https://webservices.cs.uni-tuebingen.de/.
| S-EPMC4801062 | biostudies-literature
Project description:BackgroundThe Bioinformatics Resource Manager (BRM) is a web-based tool developed to facilitate identifier conversion and data integration for Homo sapiens (human), Mus musculus (mouse), Rattus norvegicus (rat), Danio rerio (zebrafish), and Macaca mulatta (macaque), as well as perform orthologous conversions among the supported species. In addition to providing a robust means of identifier conversion, BRM also incorporates a suite of microRNA (miRNA)-target databases upon which to query target genes or to perform reverse target lookups using gene identifiers.ResultsBRM has the capability to perform cross-species identifier lookups across common identifier types, directly integrate datasets across platform or species by performing identifier retrievals in the background, and retrieve miRNA targets from multiple databases simultaneously and integrate the resulting gene targets with experimental mRNA data. Here we use workflows provided in BRM to integrate RNA sequencing data across species to identify common biomarkers of exposure after treatment of human lung cells and zebrafish to benzo[a]pyrene (BAP). We further use the miRNA Target workflow to experimentally determine the role of miRNAs as regulators of BAP toxicity and identify the predicted functional consequences of miRNA-target regulation in our system. The output from BRM can easily and directly be uploaded to freely available visualization tools for further analysis. From these examples, we were able to identify an important role for several miRNAs as potential regulators of BAP toxicity in human lung cells associated with cell migration, cell communication, cell junction assembly and regulation of cell death.ConclusionsOverall, BRM provides bioinformatics tools to assist biologists having minimal programming skills with analysis and integration of high-content omics' data from various transcriptomic and proteomic platforms. BRM workflows were developed in Java and other open-source technologies and are served publicly using Apache Tomcat at https://cbb.pnnl.gov/brm/ .
| S-EPMC6525352 | biostudies-literature
Project description:Background: Clostridium thermocellum is a Gram-positive, anaerobic, thermophilic bacterium with a great potential to be a consolidated bioprocessing biocatalyst that can ferment cellulose to ethanol. To understand its physiology and genetic targets for future strain development, we have carried out several studies including ethanol-tolerant mutant resequencing and ethanol shock experiments. Methods: In this study, several approaches were applied to enrich mRNA for next-generation sequencing based RNA-Seq and the transcriptomic profiling of C. thermocellum ethanol shock responses was investigated using high-density tiling array and RNA-Seq. Correlations among different transcriptomic platforms of expression array, tiling array, and RNA-Seq were then compared, and data generated from systems biology studies were used for transcriptional architecture improvement. Results: Our results indicate that cloning-based Sanger sequencing can be used for mRNA enrichment ratio determination, and low-copy number plasmid performed better than high-copy one for cDNA library construction. In addition, high correlations were observed among same array platform using different analyses as well as those between two different array platforms of tiling and expression array when probe intensity was compared. Correlations between RNA-Seq and array results especially the one between RNA-Seq and tiling array are also high. Moreover, combining both RNA-Seq and microarray data the transcriptional architecture of C. thermocellum including the prediction and verification of novel transcript (gene), transcriptional start site (TSS), CAZyme genes, ncRNA, and operon were systematically updated and improved. Conclusions: RNA-seq has high correlation with array-based transcriptomic platforms and can provide additional information for transcriptional architecture and genome annotation improvement, which will facilitate future omics-based studies. A twelve array study using total RNA recovered from wild-type cultures of Clostridium thermocellum at different time points of 30, 60, and 120 min post-inoculation with 3.95 g/L [0.5% (v/v)] treatment compred to that of control without ethanol supplementation. Two biological replicates for treatment and control condition.
2015-11-08 | E-GEOD-26889 | biostudies-arrayexpress
Project description:Background: Clostridium thermocellum is a Gram-positive, anaerobic, thermophilic bacterium with a great potential to be a consolidated bioprocessing biocatalyst that can ferment cellulose to ethanol. To understand its physiology and genetic targets for future strain development, we have carried out several studies including ethanol-tolerant mutant resequencing and ethanol shock experiments. Methods: In this study, several approaches were applied to enrich mRNA for next-generation sequencing based RNA-Seq and the transcriptomic profiling of C. thermocellum ethanol shock responses was investigated using high-density tiling array and RNA-Seq. Correlations among different transcriptomic platforms of expression array, tiling array, and RNA-Seq were then compared, and data generated from systems biology studies were used for transcriptional architecture improvement. Results: Our results indicate that cloning-based Sanger sequencing can be used for mRNA enrichment ratio determination, and low-copy number plasmid performed better than high-copy one for cDNA library construction. In addition, high correlations were observed among same array platform using different analyses as well as those between two different array platforms of tiling and expression array when probe intensity was compared. Correlations between RNA-Seq and array results especially the one between RNA-Seq and tiling array are also high. Moreover, combining both RNA-Seq and microarray data the transcriptional architecture of C. thermocellum including the prediction and verification of novel transcript (gene), transcriptional start site (TSS), CAZyme genes, ncRNA, and operon were systematically updated and improved. Conclusions: RNA-seq has high correlation with array-based transcriptomic platforms and can provide additional information for transcriptional architecture and genome annotation improvement, which will facilitate future omics-based studies.
2015-11-08 | GSE26889 | GEO