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Ebook Plant DNA Fingerprinting and Barcoding: Methods and Protocols | Ebooks free download | Download free ebooks | Free ebooks download | Free ebooks | PDF download

Ebook Plant DNA Fingerprinting and Barcoding: Methods and Protocols | Ebooks free download | Download free ebooks | Free ebooks download | Free ebooks | PDF download | Databases & Softwares | Scoop.it

Molecular cloning and DNA-based analysis have turn out to be component of every single molecular life science laboratory. The quick adoption of DNA-based methods has been facilitated by the introduction of the polymerase chain reaction (PCR), which has manufactured cloning and characterization of DNA swift and comparatively easy. PCR is almost part of each variation of the plethora of strategies employed for DNA fingerprinting right now. Plant DNA Fingerprinting: Approaches and Protocols aims to carry jointly the diverse currently available genome-based strategies into one particular repository. This volume is made up of in depth protocols for the preparation of plant genomic DNA, fingerprinting of plants for the detection of intra-species variations, the use of DNA barcoding, as well as methods for the bioinformatic analysis of facts. Also integrated are numerous discussions on the broader issues of genome-dependent methods in order to offer a tone knowing of the ideas of these strategies. Developed in the productive Strategies in Molecular Biology™ series format, chapters contain introductions to their respective matters, lists of the essential components and reagents, action-by-stage, conveniently reproducible protocols, and notes on troubleshooting and avoiding acknowledged pitfalls. Authoritative and effortlessly obtainable, Plant DNA Fingerprinting: Techniques and Protocols is customized principally for people who look for to increase their current methods of plant analysis and good quality control making use of genome-primarily based methods as well as for experts and scientists in distinct plant sciences.

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Elviz – exploration of metagenome assemblies with an interactive visualization tool

Background Metagenomics, the sequencing of DNA collected from an entire microbial community, enables the study of natural microbial consortia in their native habitats. Metagenomics studies produce huge volumes of data, including both the sequences themselves and metadata describing their abundance, assembly, predicted functional characteristics and environmental parameters. The ability to explore these data visually is critically important to meaningful biological interpretation. Current genomics applications cannot effectively integrate sequence data, assembly metadata, and annotation to support both genome and community-level inquiry. Results Elviz (Environmental Laboratory Visualization) is an interactive web-based tool for the visual exploration of assembled metagenomes and their complex metadata. Elviz allows scientists to navigate metagenome assemblies across multiple dimensions and scales, plotting parameters such as GC content, relative abundance, phylogenetic affiliation and assembled contig length. Furthermore Elviz enables interactive exploration using real-time plot navigation, search, filters, axis selection, and the ability to drill from a whole-community profile down to individual gene annotations. Thus scientists engage in a rapid feedback loop of visual pattern identification, hypothesis generation, and hypothesis testing. Conclusions Compared to the current alternative of generating a succession of static figures, Elviz can greatly accelerate the speed of metagenome analysis. Elviz can be used to explore both user-submitted datasets and numerous metagenome studies publicly available at the Joint Genome Institute (JGI). Elviz is freely available at http://genome.jgi.doe.gov/viz and runs on most current web-browsers.
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Background Metagenomics, the sequencing of DNA collected from an entire microbial community, enables the study of natural microbial consortia in their native habitats. Metagenomics studies produce huge volumes of data, including both the sequences themselves and metadata describing their abundance, assembly, predicted functional characteristics and environmental parameters. The ability to explore these data visually is critically important to meaningful biological interpretation. Current genomics applications cannot effectively integrate sequence data, assembly metadata, and annotation to support both genome and community-level inquiry. Results Elviz (Environmental Laboratory Visualization) is an interactive web-based tool for the visual exploration of assembled metagenomes and their complex metadata. Elviz allows scientists to navigate metagenome assemblies across multiple dimensions and scales, plotting parameters such as GC content, relative abundance, phylogenetic affiliation and assembled contig length. Furthermore Elviz enables interactive exploration using real-time plot navigation, search, filters, axis selection, and the ability to drill from a whole-community profile down to individual gene annotations. Thus scientists engage in a rapid feedback loop of visual pattern identification, hypothesis generation, and hypothesis testing. Conclusions Compared to the current alternative of generating a succession of static figures, Elviz can greatly accelerate the speed of metagenome analysis. Elviz can be used to explore both user-submitted datasets and numerous metagenome studies publicly available at the Joint Genome Institute (JGI). Elviz is freely available at http://genome.jgi.doe.gov/viz and runs on most current web-browsers.

 
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Babelomics 5.0: functional interpretation for new generations of genomic data

Babelomics 5.0: functional interpretation for new generations of genomic data | Databases & Softwares | Scoop.it
Babelomics has been running for more than one decade offering a user-friendly interface for the functional analysis of gene expression and genomic data. Here we present its fifth release, which includes support for Next Generation Sequencing data including gene expression (RNA-seq), exome or genome resequencing. Babelomics has simplified its interface, being now more intuitive. Improved visualization options, such as a genome viewer as well as an interactive network viewer, have been implemented. New technical enhancements at both, client and server sides, makes the user experience faster and more dynamic. Babelomics offers user-friendly access to a full range of methods that cover: (i) primary data analysis, (ii) a variety of tests for different experimental designs and (iii) different enrichment and network analysis algorithms for the interpretation of the results of such tests in the proper functional context. In addition to the public server, local copies of Babelomics can be downloaded and installed. Babelomics is freely available at: http://www.babelomics.org.
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Babelomics has been running for more than one decade offering a user-friendly interface for the functional analysis of gene expression and genomic data. Here we present its fifth release, which includes support for Next Generation Sequencing data including gene expression (RNA-seq), exome or genome resequencing. Babelomics has simplified its interface, being now more intuitive. Improved visualization options, such as a genome viewer as well as an interactive network viewer, have been implemented. New technical enhancements at both, client and server sides, makes the user experience faster and more dynamic. Babelomics offers user-friendly access to a full range of methods that cover: (i) primary data analysis, (ii) a variety of tests for different experimental designs and (iii) different enrichment and network analysis algorithms for the interpretation of the results of such tests in the proper functional context. In addition to the public server, local copies of Babelomics can be downloaded and installed. Babelomics is freely available at:http://www.babelomics.org.

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DOCKSCORE: a webserver for ranking protein-protein docked poses

Background Proteins interact with a variety of other molecules such as nucleic acids, small molecules and other proteins inside the cell. Structure-determination of protein-protein complexes is challenging due to several reasons such as the large molecular weights of these macromolecular complexes, their dynamic nature, difficulty in purification and sample preparation. Computational docking permits an early understanding of the feasibility and mode of protein-protein interactions. However, docking algorithms propose a number of solutions and it is a challenging task to select the native or near native pose(s) from this pool. DockScore is an objective scoring scheme that can be used to rank protein-protein docked poses. It considers several interface parameters, namely, surface area, evolutionary conservation, hydrophobicity, short contacts and spatial clustering at the interface for scoring. Results We have implemented DockScore in form of a webserver for its use by the scientific community. DockScore webserver can be employed, subsequent to docking, to perform scoring of the docked solutions, starting from multiple poses as inputs. The results, on scores and ranks for all the poses, can be downloaded as a csv file and graphical view of the interface of best ranking poses is possible. Conclusions The webserver for DockScore is made freely available for the scientific community at: http://caps.ncbs.res.in/dockscore/.
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Background Proteins interact with a variety of other molecules such as nucleic acids, small molecules and other proteins inside the cell. Structure-determination of protein-protein complexes is challenging due to several reasons such as the large molecular weights of these macromolecular complexes, their dynamic nature, difficulty in purification and sample preparation. Computational docking permits an early understanding of the feasibility and mode of protein-protein interactions. However, docking algorithms propose a number of solutions and it is a challenging task to select the native or near native pose(s) from this pool. DockScore is an objective scoring scheme that can be used to rank protein-protein docked poses. It considers several interface parameters, namely, surface area, evolutionary conservation, hydrophobicity, short contacts and spatial clustering at the interface for scoring. Results We have implemented DockScore in form of a webserver for its use by the scientific community. DockScore webserver can be employed, subsequent to docking, to perform scoring of the docked solutions, starting from multiple poses as inputs. The results, on scores and ranks for all the poses, can be downloaded as a csv file and graphical view of the interface of best ranking poses is possible. Conclusions The webserver for DockScore is made freely available for the scientific community at: http://caps.ncbs.res.in/dockscore/.

 
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Visualization of proteomics data using R and Bioconductor

Visualization of proteomics data using R and Bioconductor | Databases & Softwares | Scoop.it
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Data visualization plays a key role in high-throughput biology. It is an essential tool for data exploration allowing to shed light on data structure and patterns of interest. Visualization is also of paramount importance as a form of communicating data to a broad audience. Here, we provided a short overview of the application of the R software to the visualization of proteomics data. We present a summary ofR's plotting systems and how they are used to visualize and understand raw and processed MS-based proteomics data.

  
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New bioinformatic tool for quick identification of functionally relevant endogenous retroviral inserts in human genome.

New bioinformatic tool for quick identification of functionally relevant endogenous retroviral inserts in human genome. | Databases & Softwares | Scoop.it
Cell Cycle. 2015 Apr 8:0. [Epub ahead of print]
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Endogenous retroviruses (ERVs) and LTR retrotransposons (LRs) occupy ˜8% of human genome. Deep sequencing technologies provide clues to understanding of functional relevance of individual ERVs/LRs by enabling direct identification of transcription factor binding sites (TFBS) and other landmarks of functional genomic elements. Here, we performed the genome-wide identification of human ERVs/LRs containing TFBS according to the ENCODE project. We created the first interactive ERV/LRs database that groups the individual inserts according to their familial nomenclature, number of mapped TFBS and divergence from their consensus sequence. Information on any particular element can be easily extracted by the user. We also created a genome browser tool, which enables quick mapping of any ERV/LR insert according to genomic coordinates, known human genes and TFBS. These tools can be used to easily explore functionally relevant individual ERV/LRs, and for studying their impact on the regulation of human genes. Overall, we identified ˜110,000 ERV/LR genomic elements having TFBS. We propose a hypothesis of "domestication" of ERV/LR TFBS by the genome milieu including subsequent stages of initial epigenetic repression, partial functional release, and further mutation-driven reshaping of TFBS in tight coevolution with the enclosing genomic loci.

  
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Cellular Phenotype Database: a repository for systems microscopy data

Cellular Phenotype Database: a repository for systems microscopy data | Databases & Softwares | Scoop.it
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Motivation: The Cellular Phenotype Database (CPD) is a repository for data derived from high-throughput systems microscopy studies. The aims of this resource are: (i) to provide easy access to cellular phenotype and molecular localization data for the broader research community; (ii) to facilitate integration of independent phenotypic studies by means of data aggregation techniques, including use of an ontology; and (iii) to facilitate development of analytical methods in this field.

Results: In this article we present CPD, its data structure and user interface, propose a minimal set of information describing RNAi experiments, and suggest a generic schema for management and aggregation of outputs from phenotypic or molecular localization experiments. The database has a flexible structure for management of data from heterogeneous sources of systems microscopy experimental outputs generated by a variety of protocols and technologies and can be queried by gene, reagent, gene attribute, study keywords, phenotype or ontology terms.

Availability: CPD is developed as part of the Systems Microscopy Network of Excellence and is accessible at http://www.ebi.ac.uk/fg/sym

Contact: jes@ebi.ac.uk, ugis@ebi.ac.uk

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ReportSites - A Computational Method to Extract Positional and Physico-Chemical Information from Large-Scale Proteomic Post-Translational Modification Datasets

ReportSites - A Computational Method to Extract Positional and Physico-Chemical Information from Large-Scale Proteomic Post-Translational Modification Datasets,Alistair VG Edwards, Gregory J Edwards, Martin R Larsen and Stuart J Cordwell
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Much effort is currently expended in the search for biological meaning in large-scale proteomic datasets. The sheer volume of data, even in the context of quantitative changes associated with a specific experimental question, means it is not trivial to transition from a list of protein identifications to hypothesis generation regarding biological impact. The problem is substantially compounded by the generation of higher order information regarding post-translational modification (PTM) of proteins, where the effect on the predicted function of the identified protein must be considered in the context of the precise location of the modification. Many investigators choose to use databases and web-based applications such as KEGG (www.genome. jp/kegg/) and STRING (string-db.org) to extract interpretations from modificomic data, particularly for signalling studies, by mapping PTM data on to known signalling pathways and interaction networks [1,2]. Such an analysis is heavily influenced by the accuracy of the relevant database and is also biased to the protein identification inferring a function for the identified PTM. A complementary approach is to apply a probabilistic method to describe ways in which the dataset over- or under-represents certain known characteristics (e.g. protein kinase motifs [3]). In a similar manner, it is likely to be informative to describe basic trends in PTM patterns associated with the local amino acid sequence environment of identified modification sites that may aid in determining their role in cellular behaviour under different biological conditions. We have written a program in Perl that is able to eliminate redundancy in site reporting, document the precise site of modification in the context of the whole protein and record the physico-chemical environment of the modification site. For example, one can detect N- to C-terminal distributions of modification and simultaneously describe the local features of the sites detected (including pI, hydrophobicity and motif/sequon) such that any positional trends or trends in any of the interrogated variables can be detected. This tool was then applied to a large-scale dataset (a phosphoproteomic study of rat myocardium) in both a site-oriented and protein-oriented manner. This does not attempt to replace probabilistic methods, rather providing a complementary tool to detect and describe trends in the distribution of various characteristics which may be relevant to a range of PTMs and which may be difficult to capture statistically due to low stoichiometry of modified species.
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Proteome profile of the endomembrane of developing coleoptiles from switchgrass (Panicum virgatum).

Proteome profile of the endomembrane of developing coleoptiles from switchgrass (Panicum virgatum). | Databases & Softwares | Scoop.it
The cost-effective production of biofuels from lignocellulosic material will likely require manipulation of plant biomass, specifically cell walls. The North American native prairie grass Panicum virgatum (switchgrass) is seen as a potential biofuel crop with an array of genetic resources currently being developed. We have characterized the endomembrane proteome of switchgrass coleoptiles to provide additional information to the switchgrass community. In total, we identified 1750 unique proteins from two biological replicates. These data have been deposited in the ProteomeXchange with the identifier PXD001351 (http://proteomecentral.proteomexchange.org/dataset/PXD001351).
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The cost-effective production of biofuels from lignocellulosic material will likely require manipulation of plant biomass, specifically cell walls. The North American native prairie grass Panicum virgatum (switchgrass) is seen as a potential biofuel crop with an array of genetic resources currently being developed. We have characterized the endomembrane proteome of switchgrass coleoptiles to provide additional information to the switchgrass community. In total, we identified 1750 unique proteins from two biological replicates. These data have been deposited in the ProteomeXchange with the identifier PXD001351 (http://proteomecentral.proteomexchange.org/dataset/PXD001351).

 
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PHABULOSA Controls the Quiescent Center-Independent Root Meristem Activities in Arabidopsis thaliana

PHABULOSA Controls the Quiescent Center-Independent Root Meristem Activities in  Arabidopsis thaliana | Databases & Softwares | Scoop.it
Author Summary Plant roots are programmed to grow continuously into the soil, searching for nutrients and water. The iterative process of cell division, elongation, and differentiation contributes to root growth. The quiescent center (QC) is known to maintain the root meristem, and thus ensure root growth. In this study, we report a novel aspect of root growth regulation controlled independently of the QC by PHABULOSA (PHB). In shr mutant plants, PHB, which in the meristem is actively restr
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Plant growth depends on stem cell niches in meristems. In the root apical meristem, the quiescent center (QC) cells form a niche together with the surrounding stem cells. Stem cells produce daughter cells that are displaced into a transit-amplifying (TA) domain of the root meristem. TA cells divide several times to provide cells for growth. SHORTROOT (SHR) and SCARECROW (SCR) are key regulators of the stem cell niche. Cytokinin controls TA cell activities in a dose-dependent manner. Although the regulatory programs in each compartment of the root meristem have been identified, it is still unclear how they coordinate one another. Here, we investigate how PHABULOSA (PHB), under the posttranscriptional control of SHR and SCR, regulates TA cell activities. The root meristem and growth defects in shr or scrmutants were significantly recovered in the shr phb or scr phb double mutant, respectively. This rescue in root growth occurs in the absence of a QC. Conversely, when the modified PHB, which is highly resistant to microRNA, was expressed throughout the stele of the wild-type root meristem, root growth became very similar to that observed in the shr; however, the identity of the QC was unaffected. Interestingly, a moderate increase in PHB resulted in a root meristem phenotype similar to that observed following the application of high levels of cytokinin. Our protoplast assay and transgenic approach using ARR10 suggest that the depletion of TA cells by high PHB in the stele occurs via the repression of B-ARR activities. This regulatory mechanism seems to help to maintain the cytokinin homeostasis in the meristem. Taken together, our study suggests that PHB can dynamically regulate TA cell activities in a QC-independent manner, and that the SHR-PHB pathway enables a robust root growth system by coordinating the stem cell niche and TA domain.

 
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Metabolome searcher: a high throughput tool for metabolite identification and metabolic pathway mapping directly from mass spectrometry and using genome restriction

Mass spectrometric analysis of microbial metabolism provides a long list of possible compounds. Restricting the identification of the possible compounds to those produced by the specific organism would benefit the identification process. Currently, identification of mass spectrometry (MS) data is commonly done using empirically derived compound databases. Unfortunately, most databases contain relatively few compounds, leaving long lists of unidentified molecules. Incorporating genome-encoded metabolism enables MS output identification that may not be included in databases. Using an organism’s genome as a database restricts metabolite identification to only those compounds that the organism can produce.
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AbstractBackground

Mass spectrometric analysis of microbial metabolism provides a long list of possible compounds. Restricting the identification of the possible compounds to those produced by the specific organism would benefit the identification process. Currently, identification of mass spectrometry (MS) data is commonly done using empirically derived compound databases. Unfortunately, most databases contain relatively few compounds, leaving long lists of unidentified molecules. Incorporating genome-encoded metabolism enables MS output identification that may not be included in databases. Using an organism’s genome as a database restricts metabolite identification to only those compounds that the organism can produce.

Results

To address the challenge of metabolomic analysis from MS data, a web-based application to directly search genome-constructed metabolic databases was developed. The user query returns a genome-restricted list of possible compound identifications along with the putative metabolic pathways based on the name, formula, SMILES structure, and the compound mass as defined by the user. Multiple queries can be done simultaneously by submitting a text file created by the user or obtained from the MS analysis software. The user can also provide parameters specific to the experiment’s MS analysis conditions, such as mass deviation, adducts, and detection mode during the query so as to provide additional levels of evidence to produce the tentative identification. The query results are provided as an HTML page and downloadable text file of possible compounds that are restricted to a specific genome. Hyperlinks provided in the HTML file connect the user to the curated metabolic databases housed in ProCyc, a Pathway Tools platform, as well as the KEGG Pathway database for visualization and metabolic pathway analysis.

Conclusions

Metabolome Searcher, a web-based tool, facilitates putative compound identification of MS output based on genome-restricted metabolic capability. This enables researchers to rapidly extend the possible identifications of large data sets for metabolites that are not in compound databases. Putative compound names with their associated metabolic pathways from metabolomics data sets are returned to the user for additional biological interpretation and visualization. This novel approach enables compound identification by restricting the possible masses to those encoded in the genome.

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Genomic data assimilation using a higher moment filtering technique for restoration of gene regulatory networks

Background As a result of recent advances in biotechnology, many findings related to intracellular systems have been published, e.g., transcription factor (TF) information. Although we can reproduce biological systems by incorporating such findings and describing their dynamics as mathematical equations, simulation results can be inconsistent with data from biological observations if there are inaccurate or unknown parts in the constructed system. For the completion of such systems, relationships among genes have been inferred through several computational approaches, which typically apply several abstractions, e.g., linearization, to handle the heavy computational cost in evaluating biological systems. However, since these approximations can generate false regulations, computational methods that can infer regulatory relationships based on less abstract models incorporating existing knowledge have been strongly required. Results We propose a new data assimilation algorithm that utilizes a simple nonlinear regulatory model and a state space representation to infer gene regulatory networks (GRNs) using time-course observation data. For the estimation of the hidden state variables and the parameter values, we developed a novel method termed a higher moment ensemble particle filter (HMEnPF) that can retain first four moments of the conditional distributions through filtering steps. Starting from the original model, e.g., derived from the literature, the proposed algorithm can sequentially evaluate candidate models, which are generated by partially changing the current best model, to find the model that can best predict the data. For the performance evaluation, we generated six synthetic data based on two real biological networks and evaluated effectiveness of the proposed algorithm by improving the networks inferred by previous methods. We then applied time-course observation data of rat skeletal muscle stimulated with corticosteroid. Since a corticosteroid pharmacogenomic pathway, its kinetic/dynamics and TF candidate genes have been partially elucidated, we incorporated these findings and inferred an extended pathway of rat pharmacogenomics. Conclusions Through the simulation study, the proposed algorithm outperformed previous methods and successfully improved the regulatory structure inferred by the previous methods. Furthermore, the proposed algorithm could extend a corticosteroid related pathway, which has been partially elucidated, with incorporating several information sources.
Biswapriya Biswavas Misra's insight:

Background As a result of recent advances in biotechnology, many findings related to intracellular systems have been published, e.g., transcription factor (TF) information. Although we can reproduce biological systems by incorporating such findings and describing their dynamics as mathematical equations, simulation results can be inconsistent with data from biological observations if there are inaccurate or unknown parts in the constructed system. For the completion of such systems, relationships among genes have been inferred through several computational approaches, which typically apply several abstractions, e.g., linearization, to handle the heavy computational cost in evaluating biological systems. However, since these approximations can generate false regulations, computational methods that can infer regulatory relationships based on less abstract models incorporating existing knowledge have been strongly required. Results We propose a new data assimilation algorithm that utilizes a simple nonlinear regulatory model and a state space representation to infer gene regulatory networks (GRNs) using time-course observation data. For the estimation of the hidden state variables and the parameter values, we developed a novel method termed a higher moment ensemble particle filter (HMEnPF) that can retain first four moments of the conditional distributions through filtering steps. Starting from the original model, e.g., derived from the literature, the proposed algorithm can sequentially evaluate candidate models, which are generated by partially changing the current best model, to find the model that can best predict the data. For the performance evaluation, we generated six synthetic data based on two real biological networks and evaluated effectiveness of the proposed algorithm by improving the networks inferred by previous methods. We then applied time-course observation data of rat skeletal muscle stimulated with corticosteroid. Since a corticosteroid pharmacogenomic pathway, its kinetic/dynamics and TF candidate genes have been partially elucidated, we incorporated these findings and inferred an extended pathway of rat pharmacogenomics. Conclusions Through the simulation study, the proposed algorithm outperformed previous methods and successfully improved the regulatory structure inferred by the previous methods. Furthermore, the proposed algorithm could extend a corticosteroid related pathway, which has been partially elucidated, with incorporating several information sources.

 
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Identification of AMP-activated protein kinase targets by a consensus sequence search of the proteom

AMP-activated protein kinase (AMPK) is a heterotrimeric serine/threonine protein kinase that is activated by cellular perturbations associated with ATP depletion or stress. While AMPK modulates the activity of a variety of targets containing a specific phosphorylation consensus sequence, the number of AMPK targets and their influence over cellular processes is currently thought to be limited.
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AbstractBackground

AMP-activated protein kinase (AMPK) is a heterotrimeric serine/threonine protein kinase that is activated by cellular perturbations associated with ATP depletion or stress. While AMPK modulates the activity of a variety of targets containing a specific phosphorylation consensus sequence, the number of AMPK targets and their influence over cellular processes is currently thought to be limited.

Results

We queried the human and the mouse proteomes for proteins containing AMPK phosphorylation consensus sequences. Integration of this database into Gaggle software facilitated the construction of probable AMPK-regulated networks based on known and predicted molecular associations. In vitro kinase assays were conducted for preliminary validation of 12 novel AMPK targets across a variety of cellular functional categories, including transcription, translation, cell migration, protein transport, and energy homeostasis. Following initial validation, pathways that include NAD synthetase 1 (NADSYN1) and protein kinase B (AKT2) were hypothesized and experimentally tested to provide a mechanistic basis for AMPK regulation of cell migration and maintenance of cellular NAD+ concentrations during catabolic processes.

Conclusions

This study delineates an approach that encompasses both in silico procedures and in vitroexperiments to produce testable hypotheses for AMPK regulation of cellular processes.

 
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BioTechniques - SIFTER-T: A scalable and optimized framework for the SIFTER phylogenomic method of probabilistic protein domain annotation

BioTechniques - SIFTER-T: A scalable and optimized framework for the SIFTER phylogenomic method of probabilistic protein domain annotation | Databases & Softwares | Scoop.it
Statistical Inference of Function Through Evolutionary Relationships (SIFTER) is a powerful computational platform for probabilistic protein domain annotation. Nevertheless, SIFTER is not widely used, likely due to usability and scalability issues. Here we present SIFTER-T (SIFTER Throughput-optimized), a substantial improvement over SIFTER's original proof-of-principle implementation. SIFTER-T is optimized for better performance, allowing it to be used at the genome-wide scale. Compared to SIFTER 2.0, SIFTER-T achieved an 87-fold performance improvement using published test data sets for the known annotations recovering module and a 72.3% speed increase for the gene tree generation module in quad-core machines, as well as a major decrease in memory usage during the realignment phase. Memory optimization allowed an expanded set of proteins to be handled by SIFTER's probabilistic method. The improvement in performance and automation that we achieved allowed us to build a web server to bring the power of Bayesian phylogenomic inference to the genomics community. SIFTER-T and its online interface are freely available under GNU license at http://labpib.fmrp.usp.br/methods/SIFTER-t/ and https://github.com/dcasbioinfo/SIFTER-t.
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Statistical Inference of Function Through Evolutionary Relationships (SIFTER) is a powerful computational platform for probabilistic protein domain annotation. Nevertheless, SIFTER is not widely used, likely due to usability and scalability issues. Here we present SIFTER-T (SIFTER Throughput-optimized), a substantial improvement over SIFTER's original proof-of-principle implementation. SIFTER-T is optimized for better performance, allowing it to be used at the genome-wide scale. Compared to SIFTER 2.0, SIFTER-T achieved an 87-fold performance improvement using published test data sets for the known annotations recovering module and a 72.3% speed increase for the gene tree generation module in quad-core machines, as well as a major decrease in memory usage during the realignment phase. Memory optimization allowed an expanded set of proteins to be handled by SIFTER's probabilistic method. The improvement in performance and automation that we achieved allowed us to build a web server to bring the power of Bayesian phylogenomic inference to the genomics community. SIFTER-T and its online interface are freely available under GNU license at http://labpib.fmrp.usp.br/methods/SIFTER-t/ and https://github.com/dcasbioinfo/SIFTER-t.

 
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CDvist: a webserver for identification and visualization of conserved domains in protein sequences

CDvist: a webserver for identification and visualization of conserved domains in protein sequences | Databases & Softwares | Scoop.it
Summary: Identification of domains in protein sequences allows their assigning to biological functions. Several webservers exist for identification of protein domains using similarity searches against various databases of protein domain models. However, none of them provides comprehensive domain coverage while allowing bulk querying and their visualization schemes can be improved. To address these issues, we developed CDvist (a comprehensive domain visualization tool), which combines the best available search algorithms and databases into a user-friendly framework. First, a given protein sequence is matched to domain models using high-specificity tools and only then unmatched segments are subjected to more sensitive algorithms resulting in a best possible comprehensive coverage. Bulk querying and rich visualization and download options provide improved functionality to domain architecture analysis.
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Summary: Identification of domains in protein sequences allows their assigning to biological functions. Several webservers exist for identification of protein domains using similarity searches against various databases of protein domain models. However, none of them provides comprehensive domain coverage while allowing bulk querying and their visualization schemes can be improved. To address these issues, we developed CDvist (a comprehensive domain visualization tool), which combines the best available search algorithms and databases into a user-friendly framework. First, a given protein sequence is matched to domain models using high-specificity tools and only then unmatched segments are subjected to more sensitive algorithms resulting in a best possible comprehensive coverage. Bulk querying and rich visualization and download options provide improved functionality to domain architecture analysis.

 
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Co-expression analysis of high-throughput transcriptome sequencing data with Poisson mixture models

Co-expression analysis of high-throughput transcriptome sequencing data with Poisson mixture models | Databases & Softwares | Scoop.it
Abstract

Motivation: In recent years, gene expression studies have increasingly made use of high-throughput sequencing technology. In turn, research concerning the appropriate statistical methods for the analysis of digital gene expression (DGE) has flourished, primarily in the context of normalization and differential analysis.

Results: In this work, we focus on the question of clustering DGE profiles as a means to discover groups of co-expressed genes. We propose a Poisson mixture model using a rigorous framework for parameter estimation as well as the choice of the appropriate number of clusters. We illustrate co-expression analyses using our approach on two real RNA-seq datasets. A set of simulation studies also compares the performance of the proposed model with that of several related approaches developed to cluster RNA-seq or serial analysis of gene expression data.

Availability and and implementation: The proposed method is implemented in the open-source R package HTSCluster, available on CRAN.

Contact: andrea.rau@jouy.inra.fr

Supplementary information: Supplementary data are available at Bioinformatics online.
Biswapriya Biswavas Misra's insight:
Abstract

Motivation: In recent years, gene expression studies have increasingly made use of high-throughput sequencing technology. In turn, research concerning the appropriate statistical methods for the analysis of digital gene expression (DGE) has flourished, primarily in the context of normalization and differential analysis.

Results: In this work, we focus on the question of clustering DGE profiles as a means to discover groups of co-expressed genes. We propose a Poisson mixture model using a rigorous framework for parameter estimation as well as the choice of the appropriate number of clusters. We illustrate co-expression analyses using our approach on two real RNA-seq datasets. A set of simulation studies also compares the performance of the proposed model with that of several related approaches developed to cluster RNA-seq or serial analysis of gene expression data.

Availability and and implementation: The proposed method is implemented in the open-source R package HTSCluster, available on CRAN.

Contact: andrea.rau@jouy.inra.fr

Supplementary information: Supplementary data are available atBioinformatics online.

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AmphoraNet - Metagenomic and Genomic Analysis tool (AMPHORA2)

AmphoraNet - Metagenomic and Genomic Analysis tool (AMPHORA2) | Databases & Softwares | Scoop.it
AmphoraNet :: DESCRIPTION AmphoraNet webserver implementation of the AMPHORA2 workflow for phylogenetic analysis of metagenomic shotgun sequencing data and genomic data ::DEVELOPER PIT Bioinformati...
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AmphoraNet webserver implementation of the AMPHORA2 workflow for phylogenetic analysis of metagenomic shotgun sequencing data and genomic data

 
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SNPchiMp v.3: integrating and standardizing single nucleotide polymorphism data for livestock species

In recent years, the use of genomic information in livestock species for genetic improvement, association studies and many other fields has become routine. In order to accommodate different market requirements in terms of genotyping cost, manufacturers of single nucleotide polymorphism (SNP) arrays, private companies and international consortia have developed a large number of arrays with different content and different SNP density. The number of currently available SNP arrays differs among species: ranging from one for goats to more than ten for cattle, and the number of arrays available is increasing rapidly. However, there is limited or no effort to standardize and integrate array- specific (e.g. SNP IDs, allele coding) and species-specific (i.e. past and current assemblies) SNP information.
Biswapriya Biswavas Misra's insight:
AbstractBackground

In recent years, the use of genomic information in livestock species for genetic improvement, association studies and many other fields has become routine. In order to accommodate different market requirements in terms of genotyping cost, manufacturers of single nucleotide polymorphism (SNP) arrays, private companies and international consortia have developed a large number of arrays with different content and different SNP density. The number of currently available SNP arrays differs among species: ranging from one for goats to more than ten for cattle, and the number of arrays available is increasing rapidly. However, there is limited or no effort to standardize and integrate array- specific (e.g. SNP IDs, allele coding) and species-specific (i.e. past and current assemblies) SNP information.

Results

Here we present SNPchiMp v.3, a solution to these issues for the six major livestock species (cow, pig, horse, sheep, goat and chicken). Original data was collected directly from SNP array producers and specific international genome consortia, and stored in a MySQL database. The database was then linked to an open-access web tool and to public databases. SNPchiMp v.3 ensures fast access to the database (retrieving within/across SNP array data) and the possibility of annotating SNP array data in a user-friendly fashion.

Conclusions

This platform allows easy integration and standardization, and it is aimed at both industry and research. It also enables users to easily link the information available from the array producer with data in public databases, without the need of additional bioinformatics tools or pipelines. In recognition of the open-access use of Ensembl resources, SNPchiMp v.3 was officially credited as an Ensembl E!mpowered tool. Availability at http://bioinformatics.tecnoparco.org/SNPchimp. webcite

 
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April 1: Dr. Ada Hamosh and Dr. Nara Sobreira, PhenoDB: A Tool for Collection and Analysis of Phenotypic and Genomic Data - CBIIT Speaker Series

April 1: Dr. Ada Hamosh and Dr. Nara Sobreira, PhenoDB: A Tool for Collection and Analysis of Phenotypic and Genomic Data - CBIIT Speaker Series | Databases & Softwares | Scoop.it
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PhenoDB is a web-based tool developed for the collection, storage and analysis of phenotype data, as well as interpretation of exome and genome data in the context of phenotype data. It has it own taxonomy and links to OMIM for disease terms. There is a single center version that allow identifiers and includes only the phenotype and analysis module (http://phenodb.org) and a tool for larger studies that also includes a sample module and an ELSI module for storage and review of consents (http://researchphenodb.net). Both are freely available for download; http://phenodb.org can be used by individual users to try it out (it is toggled to have only deidentified data). PhenoDB has been in use for the Baylor-Hopkins Center for Mendelian Genomics since March 2012 and holds information on over 5000 individuals from ~3000 families. It has proved efficient and effective in novel disease gene discovery. It can also be used for a laboratory or clinic.

 
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Epigenomic annotation of genetic variants using the Roadmap Epigenome Browser

Epigenomic annotation of genetic variants using the Roadmap Epigenome Browser | Databases & Softwares | Scoop.it
Advances in next-generation sequencing platforms have reshaped the landscape of functional genomic and epigenomic research as well as human genetics studies. Annotation of noncoding regions in the genome with genomic and epigenomic data has facilitated the generation of new, testable hypotheses regarding the functional consequences of genetic variants associated with human complex traits1, 2. Large consortia, such as the US National Institutes of Health (NIH) Roadmap Epigenomics Consortium3 and ENCODE4, have generated tens of thousands of sequencing-based genome-wide data sets, creating a useful resource for the scientific community5. The WashU Epigenome Browser6, 7, 8 continues to provide a platform for investigators to effectively engage with this resource in the context of analyzing their own data. Here, we describe the Roadmap Epigenome Browser (http://epigenomegateway.wustl.edu/browser/roadmap/), which is based on the WashU Epigenome Browser and integrates data from both the NIH Roadmap Epigenomics Consortium and ENCODE in a visualization and bioinformatics tool that enables researchers to explore the tissue-specific regulatory roles of genetic variants in the context of disease.
Biswapriya Biswavas Misra's insight:

Advances in next-generation sequencing platforms have reshaped the landscape of functional genomic and epigenomic research as well as human genetics studies. Annotation of noncoding regions in the genome with genomic and epigenomic data has facilitated the generation of new, testable hypotheses regarding the functional consequences of genetic variants associated with human complex traits1, 2. Large consortia, such as the US National Institutes of Health (NIH) Roadmap Epigenomics Consortium3 and ENCODE4, have generated tens of thousands of sequencing-based genome-wide data sets, creating a useful resource for the scientific community5. The WashU Epigenome Browser6, 7, 8 continues to provide a platform for investigators to effectively engage with this resource in the context of analyzing their own data. Here, we describe the Roadmap Epigenome Browser (http://epigenomegateway.wustl.edu/browser/roadmap/), which is based on the WashU Epigenome Browser and integrates data from both the NIH Roadmap Epigenomics Consortium and ENCODE in a visualization and bioinformatics tool that enables researchers to explore the tissue-specific regulatory roles of genetic variants in the context of disease.

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Woods: A fast and accurate functional annotator and classifier of genomic and metagenomic sequences.

Woods: A fast and accurate functional annotator and classifier of genomic and metagenomic sequences. | Databases & Softwares | Scoop.it
Functional annotation of the gigantic metagenomic data is one of the major time-consuming and computationally demanding tasks, which is currently a bottleneck for the efficient analysis. The commonly used homology-based methods to functionally annotate and classify proteins are extremely slow. Therefore, to achieve faster and accurate functional annotation, we have developed an orthology-based functional classifier 'Woods' by using a combination of machine learning and similarity-based approaches. Woods displayed a precision of 98.79% on independent genomic dataset, 96.66% on simulated metagenomic dataset and >97% on two real metagenomic datasets. In addition, it performed >87 times faster than BLAST on the two real metagenomic datasets. Woods can be used as a highly efficient and accurate classifier with high-throughput capability which facilitates its usability on large metagenomic datasets.
Biswapriya Biswavas Misra's insight:

Functional annotation of the gigantic metagenomic data is one of the major time-consuming and computationally demanding tasks, which is currently a bottleneck for the efficient analysis. The commonly used homology-based methods to functionally annotate and classify proteins are extremely slow. Therefore, to achieve faster and accurate functional annotation, we have developed an orthology-based functional classifier 'Woods' by using a combination of machine learning and similarity-based approaches. Woods displayed a precision of 98.79% on independent genomic dataset, 96.66% on simulated metagenomic dataset and >97% on two real metagenomic datasets. In addition, it performed >87 times faster than BLAST on the two real metagenomic datasets. Woods can be used as a highly efficient and accurate classifier with high-throughput capability which facilitates its usability on large metagenomic datasets.

 
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Predicted protein-protein interactions in the moss Physcomitrella patens: a new bioinformatic resource

Physcomitrella patens, a haploid dominant plant, is fast becoming a useful molecular genetics and bioinformatics tool due to its key phylogenetic position as a bryophyte in the post-genomic era. Genome sequences from select reference species were compared bioinformatically to Physcomitrella patens using reciprocal blasts with the InParanoid software package. A reference protein interaction database assembled using MySQL by compiling BioGrid, BIND, DIP, and Intact databases was queried for moss orthologs existing for both interacting partners. This method has been used to successfully predict interactions for a number of angiosperm plants.
Biswapriya Biswavas Misra's insight:
AbstractBackground

Physcomitrella patens, a haploid dominant plant, is fast becoming a useful molecular genetics and bioinformatics tool due to its key phylogenetic position as a bryophyte in the post-genomic era. Genome sequences from select reference species were compared bioinformatically toPhyscomitrella patens using reciprocal blasts with the InParanoid software package. A reference protein interaction database assembled using MySQL by compiling BioGrid, BIND, DIP, and Intact databases was queried for moss orthologs existing for both interacting partners. This method has been used to successfully predict interactions for a number of angiosperm plants.

Results

The first predicted protein-protein interactome for a bryophyte based on the interolog method contains 67,740 unique interactions from 5,695 different Physcomitrella patens proteins. Most conserved interactions among proteins were those associated with metabolic processes. Over-represented Gene Ontology categories are reported here.

Conclusion

Addition of moss, a plant representative 200 million years diverged from angiosperms to interactomic research greatly expands the possibility of conducting comparative analyses giving tremendous insight into network evolution of land plants. This work helps demonstrate the utility of “guilt-by-association” models for predicting protein interactions, providing provisional roadmaps that can be explored using experimental approaches. Included with this dataset is a method for characterizing subnetworks and investigating specific processes, such as the Calvin-Benson-Bassham cycle.

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In-source fragmentation and correlation analysis as tools for metabolite identification exemplified with CE-TOF untargeted metabolomics

In-source fragmentation and correlation analysis as tools for metabolite identification exemplified with CE-TOF untargeted metabolomics | Databases & Softwares | Scoop.it
The role of non-targeted metabolomics with its discovery power is constantly growing in many different fields of science. However, its biggest advantage of uncovering the unexpected is turning into one of its biggest bottlenecks, particularly in metabolite identification. Among different methods for metabolite identification or ID confirmation, tandem MS analysis plays a very important role. However, this method is limited to only certain types of MS analysers, making for example TOF-MS inaccessible for this type of metabolite identification. To overcome this, in-source fragmentation has been used to fragment molecules and obtain product ions. Since the molecule of interest is not isolated prior to its fragmentation, the acquired spectrum contains many different signals arising from the fragmentation of all compounds present in the sample. Therefore, to assign product ions to their precursors, a novel use of correlation analysis was tested with r ≥ 0.9 as an assignation of a product ion belonging to the precursor. This method and chosen cut-off was tested on three different sample complexity levels: conducting the analysis on a single standard, mix of co-eluting standards and on a plasma sample. Obtained results clearly proved the effectiveness of the proposed methodology for metabolite ID confirmation. Moreover, the proposed strategy can be successfully applied for semi-quantification of co-eluting molecules with the same monoisotopic mass but that differ in fragmentation pattern. The proposed methodology can greatly improve the robustness and throughput of identification in metabolomics studies by use of TOF-MS, which is crucial to obtain meaningful and trustful results.
Biswapriya Biswavas Misra's insight:

The role of non-targeted metabolomics with its discovery power is constantly growing in many different fields of science. However, its biggest advantage of uncovering the unexpected is turning into one of its biggest bottlenecks, particularly in metabolite identification. Among different methods for metabolite identification or ID confirmation, tandem MS analysis plays a very important role. However, this method is limited to only certain types of MS analysers, making for example TOF-MS inaccessible for this type of metabolite identification. To overcome this, in-source fragmentation has been used to fragment molecules and obtain product ions. Since the molecule of interest is not isolated prior to its fragmentation, the acquired spectrum contains many different signals arising from the fragmentation of all compounds present in the sample. Therefore, to assign product ions to their precursors, a novel use of correlation analysis was tested with r ≥ 0.9 as an assignation of a product ion belonging to the precursor. This method and chosen cut-off was tested on three different sample complexity levels: conducting the analysis on a single standard, mix of co-eluting standards and on a plasma sample. Obtained results clearly proved the effectiveness of the proposed methodology for metabolite ID confirmation. Moreover, the proposed strategy can be successfully applied for semi-quantification of co-eluting molecules with the same monoisotopic mass but that differ in fragmentation pattern. The proposed methodology can greatly improve the robustness and throughput of identification in metabolomics studies by use of TOF-MS, which is crucial to obtain meaningful and trustful results.

  
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RNASeqBrowser: A genome browser for simultaneous visualization of raw strand specific RNAseq reads and UCSC genome browser custom tracks

Abstract
Background
Strand specific RNAseq data is now more common in RNAseq projects. Visualizing RNAseq data has become an important matter in Analysis of sequencing data. The most widely used visualization tool is the UCSC genome browser that introduced the custom track concept that enabled researchers to simultaneously visualize gene expression at a particular locus from multiple experiments. Our objective of the software tool is to provide friendly interface for visualization of RNAseq datasets.

Results
This paper introduces a visualization tool (RNASeqBrowser) that incorporates and extends the functionality of the UCSC genome browser. For example, RNASeqBrowser simultaneously displays read coverage, SNPs, InDels and raw read tracks with other BED and wiggle tracks -- all being dynamically built from the BAM file. Paired reads are also connected in the browser to enable easier identification of novel exon/intron borders and chimaeric transcripts. Strand specific RNAseq data is also supported by RNASeqBrowser that displays reads above (positive strand transcript) or below (negative strand transcripts) a central line. Finally, RNASeqBrowser was designed for ease of use for users with few bioinformatic skills, and incorporates the features of many genome browsers into one platform.

Conclusions
The features of RNASeqBrowser: (1) RNASeqBrowser integrates UCSC genome browser and NGS visualization tools such as IGV. It extends the functionality of the UCSC genome browser by adding several new types of tracks to show NGS data such as individual raw reads, SNPs and InDels. (2) RNASeqBrowser can dynamically generate RNA secondary structure. It is useful for identifying non-coding RNA such as miRNA. (3) Overlaying NGS wiggle data is helpful in displaying differential expression and is simple to implement in RNASeqBrowser. (4) NGS data accumulates a lot of raw reads. Thus, RNASeqBrowser collapses exact duplicate reads to reduce visualization space. Normal PC’s can show many windows of NGS individual raw reads without much delay. (5) Multiple popup windows of individual raw reads provide users with more viewing space. This avoids existing approaches (such as IGV) which squeeze all raw reads into one window. This will be helpful for visualizing multiple datasets simultaneously.

RNASeqBrowser and its manual are freely available at http://www.australianprostatecentre.org/research/software/rnaseqbrowser webcite or http://sourceforge.net/projects/rnaseqbrowser/ webcite
Biswapriya Biswavas Misra's insight:
AbstractBackground

Strand specific RNAseq data is now more common in RNAseq projects. Visualizing RNAseq data has become an important matter in Analysis of sequencing data. The most widely used visualization tool is the UCSC genome browser that introduced the custom track concept that enabled researchers to simultaneously visualize gene expression at a particular locus from multiple experiments. Our objective of the software tool is to provide friendly interface for visualization of RNAseq datasets.

Results

This paper introduces a visualization tool (RNASeqBrowser) that incorporates and extends the functionality of the UCSC genome browser. For example, RNASeqBrowser simultaneously displays read coverage, SNPs, InDels and raw read tracks with other BED and wiggle tracks -- all being dynamically built from the BAM file. Paired reads are also connected in the browser to enable easier identification of novel exon/intron borders and chimaeric transcripts. Strand specific RNAseq data is also supported by RNASeqBrowser that displays reads above (positive strand transcript) or below (negative strand transcripts) a central line. Finally, RNASeqBrowser was designed for ease of use for users with few bioinformatic skills, and incorporates the features of many genome browsers into one platform.

Conclusions

The features of RNASeqBrowser: (1) RNASeqBrowser integrates UCSC genome browser and NGS visualization tools such as IGV. It extends the functionality of the UCSC genome browser by adding several new types of tracks to show NGS data such as individual raw reads, SNPs and InDels. (2) RNASeqBrowser can dynamically generate RNA secondary structure. It is useful for identifying non-coding RNA such as miRNA. (3) Overlaying NGS wiggle data is helpful in displaying differential expression and is simple to implement in RNASeqBrowser. (4) NGS data accumulates a lot of raw reads. Thus, RNASeqBrowser collapses exact duplicate reads to reduce visualization space. Normal PC’s can show many windows of NGS individual raw reads without much delay. (5) Multiple popup windows of individual raw reads provide users with more viewing space. This avoids existing approaches (such as IGV) which squeeze all raw reads into one window. This will be helpful for visualizing multiple datasets simultaneously.

RNASeqBrowser and its manual are freely available at http://www.australianprostatecentre.org/research/software/rnaseqbrowserwebcite or http://sourceforge.net/projects/rnaseqbrowser/ webcite

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Metabolic Pathway Predictions for Metabolomics: A Molecular Structure Matching Approach

Metabolic Pathway Predictions for Metabolomics: A Molecular Structure Matching Approach | Databases & Softwares | Scoop.it
Metabolic pathways are composed of a series of chemical reactions occurring within a cell. In each pathway, enzymes catalyze the conversion of substrates into structurally similar products. Thus, structural similarity provides a potential means for mapping newly identified biochemical compounds to known metabolic pathways. In this paper, we present TrackSM, a cheminformatics tool designed to associate a chemical compound to a known metabolic pathway based on molecular structure matching techniques. Validation experiments show that TrackSM is capable of associating 93% of tested structures to their correct KEGG pathway class and 88% to their correct individual KEGG pathway. This suggests that TrackSM may be a valuable tool to aid in associating previously unknown small molecules to known biochemical pathways and improve our ability to link metabolomics, proteomic, and genomic data sets. TrackSM is freely available at http://metabolomics.pharm.uconn.edu/?q=Software.html.
Biswapriya Biswavas Misra's insight:

Metabolic pathways are composed of a series of chemical reactions occurring within a cell. In each pathway, enzymes catalyze the conversion of substrates into structurally similar products. Thus, structural similarity provides a potential means for mapping newly identified biochemical compounds to known metabolic pathways. In this paper, we present TrackSM, a cheminformatics tool designed to associate a chemical compound to a known metabolic pathway based on molecular structure matching techniques. Validation experiments show that TrackSM is capable of associating 93% of tested structures to their correct KEGG pathway class and 88% to their correct individual KEGG pathway. This suggests that TrackSM may be a valuable tool to aid in associating previously unknown small molecules to known biochemical pathways and improve our ability to link metabolomics, proteomic, and genomic data sets. TrackSM is freely available at http://metabolomics.pharm.uconn.edu/?q=Software.html.

 
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DoCM - Database of Curated Mutations

DoCM - Database of Curated Mutations | Databases & Softwares | Scoop.it
DoCM, the Database of Curated Mutations, is a highly curated database of known, disease-causing mutations that provides easily explorable variant lists with direct links to source citations for easy verification.
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DoCM, the Database of Curated Mutations, is a highly curated database of known, disease-causing mutations that provides easily explorable variant lists with direct links to source citations for easy verification.

 
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