Predicted Enhanced Human Propensity of Current Avian-Like H1N1 Swine Influenza Virus from China

Influenza A virus (IAV) subtypes against which little or no pre-existing immunity exists in humans represent a serious threat to global public health. Monitoring of IAV in animal hosts is essential for early and rapid detection of potential pandemic IAV strains to prevent their spread. Recently, the increased pandemic potential of the avian-like swine H1N1 IAV A/swine/Guangdong/104/2013 has been suggested. The virus is infectious in humans and the general population seems to lack neutralizing antibodies against this virus. Here we present an in silico analysis that shows a strong human propensity of this swine virus further confirming its pandemic potential. We suggest mutations which would further enhance its human propensity. We also propose conserved antigenic determinants which could serve as a component of a prepandemic vaccine. The bioinformatics tool, which can be used to further monitor the evolution of swine influenza viruses towards a pandemic virus, are described here and are made publically available (http://www.vin.bg.ac.rs/180/tools/iav_mon.php; http://www.biomedprotection.com/iav_mon.php).     

16S Classifier: A Tool for Fast and Accurate Taxonomic Classification of 16S rRNA Hypervariable Regions in Metagenomic Datasets

The diversity of microbial species in a metagenomic study is commonly assessed using 16S rRNA gene sequencing. With the rapid developments in genome sequencing technologies, the focus has shifted towards the sequencing of hypervariable regions of 16S rRNA gene instead of full length gene sequencing. Therefore, 16S Classifier is developed using a machine learning method, Random Forest, for faster and accurate taxonomic classification of short hypervariable regions of 16S rRNA sequence. It displayed precision values of up to 0.91 on training datasets and the precision values of up to 0.98 on the test dataset. On real metagenomic datasets, it showed up to 99.7% accuracy at the phylum level and up to 99.0% accuracy at the genus level. 16S Classifier is available freely at http://metagenomics.iiserb.ac.in/16Sclassifier and http://metabiosys.iiserb.ac.in/16Sclassifier.     

BioTorrents: A File Sharing Service for Scientific Data

The transfer of scientific data has emerged as a significant challenge, as datasets continue to grow in size and demand for open access sharing increases. Current methods for file transfer do not scale well for large files and can cause long transfer times. In this study we present BioTorrents, a website that allows open access sharing of scientific data and uses the popular BitTorrent peer-to-peer file sharing technology. BioTorrents allows files to be transferred rapidly due to the sharing of bandwidth across multiple institutions and provides more reliable file transfers due to the built-in error checking of the file sharing technology. BioTorrents contains multiple features, including keyword searching, category browsing, RSS feeds, torrent comments, and a discussion forum. BioTorrents is available at http://www.biotorrents.net.     

Distinctive Behaviors of Druggable Proteins in Cellular Networks

The interaction environment of a protein in a cellular network is important in defining the role that the protein plays in the system as a whole, and thus its potential suitability as a drug target. Despite the importance of the network environment, it is neglected during target selection for drug discovery. Here, we present the first systematic, comprehensive computational analysis of topological, community and graphical network parameters of the human interactome and identify discriminatory network patterns that strongly distinguish drug targets from the interactome as a whole. Importantly, we identify striking differences in the network behavior of targets of cancer drugs versus targets from other therapeutic areas and explore how they may relate to successful drug combinations to overcome acquired resistance to cancer drugs. We develop, computationally validate and provide the first public domain predictive algorithm for identifying druggable neighborhoods based on network parameters. We also make available full predictions for 13,345 proteins to aid target selection for drug discovery. All target predictions are available through canSAR.icr.ac.uk. Underlying data and tools are available at https://cansar.icr.ac.uk/cansar/publications/druggable_network_neighbourhoods/.     

Integrative Gene Network Construction to Analyze Cancer Recurrence Using Semi-Supervised Learning

Background

The prognosis of cancer recurrence is an important research area in bioinformatics and is challenging due to the small sample sizes compared to the vast number of genes. There have been several attempts to predict cancer recurrence. Most studies employed a supervised approach, which uses only a few labeled samples. Semi-supervised learning can be a great alternative to solve this problem. There have been few attempts based on manifold assumptions to reveal the detailed roles of identified cancer genes in recurrence.

Results

In order to predict cancer recurrence, we proposed a novel semi-supervised learning algorithm based on a graph regularization approach. We transformed the gene expression data into a graph structure for semi-supervised learning and integrated protein interaction data with the gene expression data to select functionally-related gene pairs. Then, we predicted the recurrence of cancer by applying a regularization approach to the constructed graph containing both labeled and unlabeled nodes.

Conclusions

The average improvement rate of accuracy for three different cancer datasets was 24.9% compared to existing supervised and semi-supervised methods. We performed functional enrichment on the gene networks used for learning. We identified that those gene networks are significantly associated with cancer-recurrence-related biological functions. Our algorithm was developed with standard C++ and is available in Linux and MS Windows formats in the STL library. The executable program is freely available at: http://embio.yonsei.ac.kr/~Park/ssl.php.     

NNvPDB: Neural Network based Protein Secondary Structure Prediction with PDB Validation

Availabilityhttp://bit.srmuniv.ac.in/cgi-bin/bit/cfpdb/nnsecstruct.pl     

SpliceNet: recovering splicing isoform-specific differential gene networks from RNA-Seq data of normal and diseased samples

Conventionally, overall gene expressions from microarrays are used to infer gene networks, but it is challenging to account splicing isoforms. High-throughput RNA Sequencing has made splice variant profiling practical. However, its true merit in quantifying splicing isoforms and isoform-specific exon expressions is not well explored in inferring gene networks. This study demonstrates SpliceNet, a method to infer isoform-specific co-expression networks from exon-level RNA-Seq data, using large dimensional trace. It goes beyond differentially expressed genes and infers splicing isoform network changes between normal and diseased samples. It eases the sample size bottleneck; evaluations on simulated data and lung cancer-specific ERBB2 and MAPK signaling pathways, with varying number of samples, evince the merit in handling high exon to sample size ratio datasets. Inferred network rewiring of well established Bcl-x and EGFR centered networks from lung adenocarcinoma expression data is in good agreement with literature. Gene level evaluations demonstrate a substantial performance of SpliceNet over canonical correlation analysis, a method that is currently applied to exon level RNA-Seq data. SpliceNet can also be applied to exon array data. SpliceNet is distributed as an R package available at http://www.jjwanglab.org/SpliceNet.     

Game On,Science - How Video Game Technology May Help Biologists Tackle Visualization Challenges

The video games industry develops ever more advanced technologies to improve rendering, image quality, ergonomics and user experience of their creations providing very simple to use tools to design new games. In the molecular sciences, only a small number of experts with specialized know-how are able to design interactive visualization applications, typically static computer programs that cannot easily be modified. Are there lessons to be learned from video games? Could their technology help us explore new molecular graphics ideas and render graphics developments accessible to non-specialists? This approach points to an extension of open computer programs, not only providing access to the source code, but also delivering an easily modifiable and extensible scientific research tool. In this work, we will explore these questions using the Unity3D game engine to develop and prototype a biological network and molecular visualization application for subsequent use in research or education. We have compared several routines to represent spheres and links between them, using either built-in Unity3D features or our own implementation. These developments resulted in a stand-alone viewer capable of displaying molecular structures, surfaces, animated electrostatic field lines and biological networks with powerful, artistic and illustrative rendering methods. We consider this work as a proof of principle demonstrating that the functionalities of classical viewers and more advanced novel features could be implemented in substantially less time and with less development effort. Our prototype is easily modifiable and extensible and may serve others as starting point and platform for their developments. A webserver example, standalone versions for MacOS X, Linux and Windows, source code, screen shots, videos and documentation are available at the address: http://unitymol.sourceforge.net/.     

Resampling-Based Approaches to Study Variation in Morphological Modularity

Modularity has been suggested to be connected to evolvability because a higher degree of independence among parts allows them to evolve as separate units. Recently, the Escoufier RV coefficient has been proposed as a measure of the degree of integration between modules in multivariate morphometric datasets. However, it has been shown, using randomly simulated datasets, that the value of the RV coefficient depends on sample size. Also, so far there is no statistical test for the difference in the RV coefficient between a priori defined groups of observations. Here, we (1), using a rarefaction analysis, show that the value of the RV coefficient depends on sample size also in real geometric morphometric datasets; (2) propose a permutation procedure to test for the difference in the RV coefficient between a priori defined groups of observations; (3) show, through simulations, that such a permutation procedure has an appropriate Type I error; (4) suggest that a rarefaction procedure could be used to obtain sample-size-corrected values of the RV coefficient; and (5) propose a nearest-neighbor procedure that could be used when studying the variation of modularity in geographic space. The approaches outlined here, readily extendable to non-morphometric datasets, allow study of the variation in the degree of integration between a priori defined modules. A Java application – that will allow performance of the proposed test using a software with graphical user interface – has also been developed and is available at the Morphometrics at Stony Brook Web page (http://life.bio.sunysb.edu/morph/).     

Exploiting SNP Correlations within Random Forest for Genome-Wide Association Studies

The primary goal of genome-wide association studies (GWAS) is to discover variants that could lead, in isolation or in combination, to a particular trait or disease. Standard approaches to GWAS, however, are usually based on univariate hypothesis tests and therefore can account neither for correlations due to linkage disequilibrium nor for combinations of several markers. To discover and leverage such potential multivariate interactions, we propose in this work an extension of the Random Forest algorithm tailored for structured GWAS data. In terms of risk prediction, we show empirically on several GWAS datasets that the proposed T-Trees method significantly outperforms both the original Random Forest algorithm and standard linear models, thereby suggesting the actual existence of multivariate non-linear effects due to the combinations of several SNPs. We also demonstrate that variable importances as derived from our method can help identify relevant loci. Finally, we highlight the strong impact that quality control procedures may have, both in terms of predictive power and loci identification. Variable importance results and T-Trees source code are all available at www.montefiore.ulg.ac.be/~botta/ttrees/ and github.com/0asa/TTree-source respectively.     

A Toolkit for ARB to Integrate Custom Databases and Externally Built Phylogenies

Researchers are perpetually amassing biological sequence data. The computational approaches employed by ecologists for organizing this data (e.g. alignment, phylogeny, etc.) typically scale nonlinearly in execution time with the size of the dataset. This often serves as a bottleneck for processing experimental data since many molecular studies are characterized by massive datasets. To keep up with experimental data demands, ecologists are forced to choose between continually upgrading expensive in-house computer hardware or outsourcing the most demanding computations to the cloud. Outsourcing is attractive since it is the least expensive option, but does not necessarily allow direct user interaction with the data for exploratory analysis. Desktop analytical tools such as ARB are indispensable for this purpose, but they do not necessarily offer a convenient solution for the coordination and integration of datasets between local and outsourced destinations. Therefore, researchers are currently left with an undesirable tradeoff between computational throughput and analytical capability. To mitigate this tradeoff we introduce a software package to leverage the utility of the interactive exploratory tools offered by ARB with the computational throughput of cloud-based resources. Our pipeline serves as middleware between the desktop and the cloud allowing researchers to form local custom databases containing sequences and metadata from multiple resources and a method for linking data outsourced for computation back to the local database. A tutorial implementation of the toolkit is provided in the supporting information, S1 Tutorial. Availability: http://www.ece.drexel.edu/gailr/EESI/tutorial.php.     

Mammalian Mitochondrial ncRNA Database

Mammalian Mitochondrial ncRNA is a web-based database, which provides specific information on non-coding RNA in mammals. This database includes easy searching, comparing with BLAST and retrieving information on predicted structure and its function about mammalian ncRNAs.

Availability

The database is available for free at http://www.iitm.ac.in/bioinfo/mmndb/     

SBOL Visual: A Graphical Language for Genetic Designs

Synthetic Biology Open Language (SBOL) Visual is a graphical standard for genetic engineering. It consists of symbols representing DNA subsequences, including regulatory elements and DNA assembly features. These symbols can be used to draw illustrations for communication and instruction, and as image assets for computer-aided design. SBOL Visual is a community standard, freely available for personal, academic, and commercial use (Creative Commons CC0 license). We provide prototypical symbol images that have been used in scientific publications and software tools. We encourage users to use and modify them freely, and to join the SBOL Visual community: http://www.sbolstandard.org/visual.     

A Model-Based Approach to Identify Binding Sites in CLIP-Seq Data

Cross-linking immunoprecipitation coupled with high-throughput sequencing (CLIP-Seq) has made it possible to identify the targeting sites of RNA-binding proteins in various cell culture systems and tissue types on a genome-wide scale. Here we present a novel model-based approach (MiClip) to identify high-confidence protein-RNA binding sites from CLIP-seq datasets. This approach assigns a probability score for each potential binding site to help prioritize subsequent validation experiments. The MiClip algorithm has been tested in both HITS-CLIP and PAR-CLIP datasets. In the HITS-CLIP dataset, the signal/noise ratios of miRNA seed motif enrichment produced by the MiClip approach are between 17% and 301% higher than those by the ad hoc method for the top 10 most enriched miRNAs. In the PAR-CLIP dataset, the MiClip approach can identify ∼50% more validated binding targets than the original ad hoc method and two recently published methods. To facilitate the application of the algorithm, we have released an R package, MiClip ( http://cran.r-project.org/web/packages/MiClip/index.html ), and a public web-based graphical user interface software (http://galaxy.qbrc.org/tool_runner?tool_id=mi_clip) for customized analysis.     

The FLIGHT Drosophila RNAi database: 2010 update

FLIGHT (http://flight.icr.ac.uk/) is an online resource compiling data from high-throughput Drosophila in vivo and in vitro RNAi screens. FLIGHT includes details of RNAi reagents and their predicted off-target effects, alongside RNAi screen hits, scores and phenotypes, including images from high-content screens. The latest release of FLIGHT is designed to enable users to upload, analyze, integrate and share their own RNAi screens. Users can perform multiple normalizations, view quality control plots, detect and assign screen hits and compare hits from multiple screens using a variety of methods including hierarchical clustering. FLIGHT integrates RNAi screen data with microarray gene expression as well as genomic annotations and genetic/physical interaction datasets to provide a single interface for RNAi screen analysis and datamining in Drosophila.Key words: RNAi, database, integration, bioinformatics, phenotype     

HSC-Explorer: A Curated Database for Hematopoietic Stem Cells

HSC-Explorer (http://mips.helmholtz-muenchen.de/HSC/) is a publicly available, integrative database containing detailed information about the early steps of hematopoiesis. The resource aims at providing fast and easy access to relevant information, in particular to the complex network of interacting cell types and molecules, from the wealth of publications in the field through visualization interfaces. It provides structured information on more than 7000 experimentally validated interactions between molecules, bioprocesses and environmental factors. Information is manually derived by critical reading of the scientific literature from expert annotators. Hematopoiesis-relevant interactions are accompanied with context information such as model organisms and experimental methods for enabling assessment of reliability and relevance of experimental results. Usage of established vocabularies facilitates downstream bioinformatics applications and to convert the results into complex networks. Several predefined datasets (Selected topics) offer insights into stem cell behavior, the stem cell niche and signaling processes supporting hematopoietic stem cell maintenance. HSC-Explorer provides a versatile web-based resource for scientists entering the field of hematopoiesis enabling users to inspect the associated biological processes through interactive graphical presentation.     

The Importance of cGMP Signaling in Sensory Cilia for Body Size Regulation in Caenorhabditis elegans

The body size of Caenorhabditis elegans is thought to be controlled by sensory inputs because many mutants with sensory cilium structure defects exhibit small body size. The EGL-4 cGMP-dependent protein kinase acts in sensory neurons to reduce body size when animals fail to perceive sensory signals. In addition to body size control, EGL-4 regulates various other behavioral and developmental pathways, including those involved in the regulation of egg laying and chemotaxis behavior. Here we have identified gcy-12, which encodes a receptor-type guanylyl cyclase, as a gene involved in the sensory regulation of body size. Analyses with GFP fusion constructs showed that gcy-12 is expressed in several sensory neurons and localizes to sensory cilia. Genetic analyses indicated that GCY-12 acts upstream of EGL-4 in body size control but does not affect other EGL-4 functions. Our studies indicate that the function of the GCY-12 guanylyl cyclase is to provide cGMP to the EGL-4 cGMP-dependent kinase only for limited tasks including body size regulation. We also found that the PDE-2 cyclic nucleotide phosphodiesterase negatively regulates EGL-4 in controlling body size. Thus, the cGMP level is precisely controlled by GCY-12 and PDE-2 to determine body size through EGL-4, and the defects in the sensory cilium structure may disturb the balanced control of the cGMP level. The large number of guanylyl cyclases encoded in the C. elegans genome suggests that EGL-4 exerts pleiotropic effects by partnering with different guanylyl cyclases for different downstream functions.     

CODA: Accurate Detection of Functional Associations between Proteins in Eukaryotic Genomes Using Domain Fusion

Background

In order to understand how biological systems function it is necessary to determine the interactions and associations between proteins. Gene fusion prediction is one approach to detection of such functional relationships. Its use is however known to be problematic in higher eukaryotic genomes due to the presence of large homologous domain families. Here we introduce CODA (Co-Occurrence of Domains Analysis), a method to predict functional associations based on the gene fusion idiom.

Methodology/Principal Findings

We apply a novel scoring scheme which takes account of the genome-specific size of homologous domain families involved in fusion to improve accuracy in predicting functional associations. We show that CODA is able to accurately predict functional similarities in human with comparison to state-of-the-art methods and show that different methods can be complementary. CODA is used to produce evidence that a currently uncharacterised human protein may be involved in pathways related to depression and that another is involved in DNA replication.

Conclusions/Significance

The relative performance of different gene fusion methodologies has not previously been explored. We find that they are largely complementary, with different methods being more or less appropriate in different genomes. Our method is the only one currently available for download and can be run on an arbitrary dataset by the user. The CODA software and datasets are freely available from ftp://ftp.biochem.ucl.ac.uk/pub/gene3d_data/v6.1.0/CODA/. Predictions are also available via web services from http://funcnet.eu/.     

DichroWeb,a website for calculating protein secondary structure from circular dichroism spectroscopic data

Circular dichroism (CD) spectroscopy is a widely‐used method for characterizing the secondary structures of proteins. The well‐established and highly used analysis website, DichroWeb (located at: http://dichroweb.cryst.bbk.ac.uk/html/home.shtml) enables the facile quantitative determination of helix, sheet, and other secondary structure contents of proteins based on their CD spectra. DichroWeb includes a range of reference datasets and algorithms, plus graphical and quantitative methods for determining the quality of the analyses produced. This article describes the current website content, usage and accessibility, as well as the many upgraded features now present in this highly popular tool that was originally created nearly two decades ago.