dCLIP: a computational approach for comparative CLIP-seq analyses

Although comparison of RNA-protein interaction profiles across different conditions has become increasingly important to understanding the function of RNA-binding proteins (RBPs), few computational approaches have been developed for quantitative comparison of CLIP-seq datasets. Here, we present an easy-to-use command line tool, dCLIP, for quantitative CLIP-seq comparative analysis. The two-stage method implemented in dCLIP, including a modified MA normalization method and a hidden Markov model, is shown to be able to effectively identify differential binding regions of RBPs in four CLIP-seq datasets, generated by HITS-CLIP, iCLIP and PAR-CLIP protocols. dCLIP is freely available at http://qbrc.swmed.edu/software/.     

LipidWrapper: An Algorithm for Generating Large-Scale Membrane Models of Arbitrary Geometry

As ever larger and more complex biological systems are modeled in silico, approximating physiological lipid bilayers with simple planar models becomes increasingly unrealistic. In order to build accurate large-scale models of subcellular environments, models of lipid membranes with carefully considered, biologically relevant curvature will be essential. In the current work, we present a multi-scale utility called LipidWrapper capable of creating curved membrane models with geometries derived from various sources, both experimental and theoretical. To demonstrate its utility, we use LipidWrapper to examine an important mechanism of influenza virulence. A copy of the program can be downloaded free of charge under the terms of the open-source FreeBSD License from http://nbcr.ucsd.edu/lipidwrapper. LipidWrapper has been tested on all major computer operating systems.

This is a PLOS Computational Biology Software Article

     

An Online Database for Informing Ecological Network Models: http://kelpforest.ucsc.edu

Ecological network models and analyses are recognized as valuable tools for understanding the dynamics and resiliency of ecosystems, and for informing ecosystem-based approaches to management. However, few databases exist that can provide the life history, demographic and species interaction information necessary to parameterize ecological network models. Faced with the difficulty of synthesizing the information required to construct models for kelp forest ecosystems along the West Coast of North America, we developed an online database (http://kelpforest.ucsc.edu/) to facilitate the collation and dissemination of such information. Many of the database''s attributes are novel yet the structure is applicable and adaptable to other ecosystem modeling efforts. Information for each taxonomic unit includes stage-specific life history, demography, and body-size allometries. Species interactions include trophic, competitive, facilitative, and parasitic forms. Each data entry is temporally and spatially explicit. The online data entry interface allows researchers anywhere to contribute and access information. Quality control is facilitated by attributing each entry to unique contributor identities and source citations. The database has proven useful as an archive of species and ecosystem-specific information in the development of several ecological network models, for informing management actions, and for education purposes (e.g., undergraduate and graduate training). To facilitate adaptation of the database by other researches for other ecosystems, the code and technical details on how to customize this database and apply it to other ecosystems are freely available and located at the following link (https://github.com/kelpforest-cameo/databaseui).     

Prioritizing Candidate Disease Metabolites Based on Global Functional Relationships between Metabolites in the Context of Metabolic Pathways

Identification of key metabolites for complex diseases is a challenging task in today''s medicine and biology. A special disease is usually caused by the alteration of a series of functional related metabolites having a global influence on the metabolic network. Moreover, the metabolites in the same metabolic pathway are often associated with the same or similar disease. Based on these functional relationships between metabolites in the context of metabolic pathways, we here presented a pathway-based random walk method called PROFANCY for prioritization of candidate disease metabolites. Our strategy not only takes advantage of the global functional relationships between metabolites but also sufficiently exploits the functionally modular nature of metabolic networks. Our approach proved successful in prioritizing known metabolites for 71 diseases with an AUC value of 0.895. We also assessed the performance of PROFANCY on 16 disease classes and found that 4 classes achieved an AUC value over 0.95. To investigate the robustness of the PROFANCY, we repeated all the analyses in two metabolic networks and obtained similar results. Then we applied our approach to Alzheimer''s disease (AD) and found that a top ranked candidate was potentially related to AD but had not been reported previously. Furthermore, our method was applicable to prioritize the metabolites from metabolomic profiles of prostate cancer. The PROFANCY could identify prostate cancer related-metabolites that are supported by literatures but not considered to be significantly differential by traditional differential analysis. We also developed a freely accessible web-based and R-based tool at http://bioinfo.hrbmu.edu.cn/PROFANCY.     

VistaClara: an expression browser plug-in for Cytoscape

SUMMARY: VistaClara is a plug-in for Cytoscape which provides a more flexible means to visualize gene and protein expression within a network context. An extended attribute browser is provided in the form of a graphical and interactive permutation matrix that resembles the heat map displays popular in gene-expression analysis. This extended browser permits a variety of display options and interactions not currently available in Cytoscape. AVAILABILITY: http://chianti.ucsd.edu/cyto_web/plugins/index.php.     

Identifying Cancer Specific Functionally Relevant miRNAs from Gene Expression and miRNA-to-Gene Networks Using Regularized Regression

Identifying microRNA signatures for the different types and subtypes of cancer can result in improved detection, characterization and understanding of cancer and move us towards more personalized treatment strategies. However, using microRNA''s differential expression (tumour versus normal) to determine these signatures may lead to inaccurate predictions and low interpretability because of the noisy nature of miRNA expression data. We present a method for the selection of biologically active microRNAs using gene expression data and microRNA-to-gene interaction network. Our method is based on a linear regression with an elastic net regularization. Our simulations show that, with our method, the active miRNAs can be detected with high accuracy and our approach is robust to high levels of noise and missing information. Furthermore, our results on real datasets for glioblastoma and prostate cancer are confirmed by microRNA expression measurements. Our method leads to the selection of potentially functionally important microRNAs. The associations of some of our identified miRNAs with cancer mechanisms are already confirmed in other studies (hypoxia related hsa-mir-210 and apoptosis-related hsa-mir-296-5p). We have also identified additional miRNAs that were not previously studied in the context of cancer but are coherently predicted as active by our method and may warrant further investigation. The code is available in Matlab and R and can be downloaded on http://www.cs.toronto.edu/goldenberg/Anna_Goldenberg/Current_Research.html.     

Comparative GO: A Web Application for Comparative Gene Ontology and Gene Ontology-Based Gene Selection in Bacteria

Availabilityhttp://turing.ersa.edu.au/BacteriaGO.     

CorSig: A General Framework for Estimating Statistical Significance of Correlation and Its Application to Gene Co-Expression Analysis

With the rapid increase of omics data, correlation analysis has become an indispensable tool for inferring meaningful associations from a large number of observations. Pearson correlation coefficient (PCC) and its variants are widely used for such purposes. However, it remains challenging to test whether an observed association is reliable both statistically and biologically. We present here a new method, CorSig, for statistical inference of correlation significance. CorSig is based on a biology-informed null hypothesis, i.e., testing whether the true PCC (ρ) between two variables is statistically larger than a user-specified PCC cutoff (τ), as opposed to the simple null hypothesis of ρ = 0 in existing methods, i.e., testing whether an association can be declared without a threshold. CorSig incorporates Fisher''s Z transformation of the observed PCC (r), which facilitates use of standard techniques for p-value computation and multiple testing corrections. We compared CorSig against two methods: one uses a minimum PCC cutoff while the other (Zhu''s procedure) controls correlation strength and statistical significance in two discrete steps. CorSig consistently outperformed these methods in various simulation data scenarios by balancing between false positives and false negatives. When tested on real-world Populus microarray data, CorSig effectively identified co-expressed genes in the flavonoid pathway, and discriminated between closely related gene family members for their differential association with flavonoid and lignin pathways. The p-values obtained by CorSig can be used as a stand-alone parameter for stratification of co-expressed genes according to their correlation strength in lieu of an arbitrary cutoff. CorSig requires one single tunable parameter, and can be readily extended to other correlation measures. Thus, CorSig should be useful for a wide range of applications, particularly for network analysis of high-dimensional genomic data.

Software Availability

A web server for CorSig is provided at http://202.127.200.1:8080/probeWeb. R code for CorSig is freely available for non-commercial use at http://aspendb.uga.edu/downloads.     

Investigating microRNA-Target Interaction-Supported Tissues in Human Cancer Tissues Based on miRNA and Target Gene Expression Profiling

Recent studies have revealed that a small non-coding RNA, microRNA (miRNA) down-regulates its mRNA targets. This effect is regarded as an important role in various biological processes. Many studies have been devoted to predicting miRNA-target interactions. These studies indicate that the interactions may only be functional in some specific tissues, which depend on the characteristics of an miRNA. No systematic methods have been established in the literature to investigate the correlation between miRNA-target interactions and tissue specificity through microarray data. In this study, we propose a method to investigate miRNA-target interaction-supported tissues, which is based on experimentally validated miRNA-target interactions. The tissue specificity results by our method are in accordance with the experimental results in the literature.

Availability and Implementation

Our analysis results are available at http://tsmti.mbc.nctu.edu.tw/ and http://www.stat.nctu.edu.tw/hwang/tsmti.html.     

Network-based Survival Analysis Reveals Subnetwork Signatures for Predicting Outcomes of Ovarian Cancer Treatment

Cox regression is commonly used to predict the outcome by the time to an event of interest and in addition, identify relevant features for survival analysis in cancer genomics. Due to the high-dimensionality of high-throughput genomic data, existing Cox models trained on any particular dataset usually generalize poorly to other independent datasets. In this paper, we propose a network-based Cox regression model called Net-Cox and applied Net-Cox for a large-scale survival analysis across multiple ovarian cancer datasets. Net-Cox integrates gene network information into the Cox''s proportional hazard model to explore the co-expression or functional relation among high-dimensional gene expression features in the gene network. Net-Cox was applied to analyze three independent gene expression datasets including the TCGA ovarian cancer dataset and two other public ovarian cancer datasets. Net-Cox with the network information from gene co-expression or functional relations identified highly consistent signature genes across the three datasets, and because of the better generalization across the datasets, Net-Cox also consistently improved the accuracy of survival prediction over the Cox models regularized by or . This study focused on analyzing the death and recurrence outcomes in the treatment of ovarian carcinoma to identify signature genes that can more reliably predict the events. The signature genes comprise dense protein-protein interaction subnetworks, enriched by extracellular matrix receptors and modulators or by nuclear signaling components downstream of extracellular signal-regulated kinases. In the laboratory validation of the signature genes, a tumor array experiment by protein staining on an independent patient cohort from Mayo Clinic showed that the protein expression of the signature gene FBN1 is a biomarker significantly associated with the early recurrence after 12 months of the treatment in the ovarian cancer patients who are initially sensitive to chemotherapy. Net-Cox toolbox is available at http://compbio.cs.umn.edu/Net-Cox/.     

Genome-Scale Screening of Drug-Target Associations Relevant to Ki Using a Chemogenomics Approach

The identification of interactions between drugs and target proteins plays a key role in genomic drug discovery. In the present study, the quantitative binding affinities of drug-target pairs are differentiated as a measurement to define whether a drug interacts with a protein or not, and then a chemogenomics framework using an unbiased set of general integrated features and random forest (RF) is employed to construct a predictive model which can accurately classify drug-target pairs. The predictability of the model is further investigated and validated by several independent validation sets. The built model is used to predict drug-target associations, some of which were confirmed by comparing experimental data from public biological resources. A drug-target interaction network with high confidence drug-target pairs was also reconstructed. This network provides further insight for the action of drugs and targets. Finally, a web-based server called PreDPI-K_i was developed to predict drug-target interactions for drug discovery. In addition to providing a high-confidence list of drug-target associations for subsequent experimental investigation guidance, these results also contribute to the understanding of drug-target interactions. We can also see that quantitative information of drug-target associations could greatly promote the development of more accurate models. The PreDPI-K_i server is freely available via: http://sdd.whu.edu.cn/dpiki.     

Science Translator: An Interview with Louisa Stark

The Genetics Society of America’s Elizabeth W. Jones Award for Excellence in Education recognizes significant and sustained impact on genetics education. The 2015 awardee, Louisa Stark, has made a major impact on global access to genetics education through her work as director of the University of Utah Genetic Science Learning Center. The Center’s Learn.Genetics and Teach.Genetics websites are the most widely used online genetic education resources in the world. In 2014, they were visited by 18 million students, educators, scientists, and members of the public. With over 60 million page views annually, Learn.Genetics is among the most used sites on the Web.Open in a separate window     

MendeLIMS: a web-based laboratory information management system for clinical genome sequencing

Background

Large clinical genomics studies using next generation DNA sequencing require the ability to select and track samples from a large population of patients through many experimental steps. With the number of clinical genome sequencing studies increasing, it is critical to maintain adequate laboratory information management systems to manage the thousands of patient samples that are subject to this type of genetic analysis.

Results

To meet the needs of clinical population studies using genome sequencing, we developed a web-based laboratory information management system (LIMS) with a flexible configuration that is adaptable to continuously evolving experimental protocols of next generation DNA sequencing technologies. Our system is referred to as MendeLIMS, is easily implemented with open source tools and is also highly configurable and extensible. MendeLIMS has been invaluable in the management of our clinical genome sequencing studies.

Conclusions

We maintain a publicly available demonstration version of the application for evaluation purposes at http://mendelims.stanford.edu. MendeLIMS is programmed in Ruby on Rails (RoR) and accesses data stored in SQL-compliant relational databases. Software is freely available for non-commercial use at http://dna-discovery.stanford.edu/software/mendelims/.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2105-15-290) contains supplementary material, which is available to authorized users.     

BSmooth: from whole genome bisulfite sequencing reads to differentially methylated regions

DNA methylation is an important epigenetic modification involved in gene regulation, which can now be measured using whole-genome bisulfite sequencing. However, cost, complexity of the data, and lack of comprehensive analytical tools are major challenges that keep this technology from becoming widely applied. Here we present BSmooth, an alignment, quality control and analysis pipeline that provides accurate and precise results even with low coverage data, appropriately handling biological replicates. BSmooth is open source software, and can be downloaded from http://rafalab.jhsph.edu/bsmooth.     

PROCAIN: protein profile comparison with assisting information

Detection of remote sequence homology is essential for the accurate inference of protein structure, function and evolution. The most sensitive detection methods involve the comparison of evolutionary patterns reflected in multiple sequence alignments (MSAs) of protein families. We present PROCAIN, a new method for MSA comparison based on the combination of ‘vertical’ MSA context (substitution constraints at individual sequence positions) and ‘horizontal’ context (patterns of residue content at multiple positions). Based on a simple and tractable profile methodology and primitive measures for the similarity of horizontal MSA patterns, the method achieves the quality of homology detection comparable to a more complex advanced method employing hidden Markov models (HMMs) and secondary structure (SS) prediction. Adding SS information further improves PROCAIN performance beyond the capabilities of current state-of-the-art tools. The potential value of the method for structure/function predictions is illustrated by the detection of subtle homology between evolutionary distant yet structurally similar protein domains. ProCAIn, relevant databases and tools can be downloaded from: http://prodata.swmed.edu/procain/download. The web server can be accessed at http://prodata.swmed.edu/procain/procain.php.     

SAMA: A Method for 3D Morphological Analysis

Three-dimensional (3D) culture models are critical tools for understanding tissue morphogenesis. A key requirement for their analysis is the ability to reconstruct the tissue into computational models that allow quantitative evaluation of the formed structures. Here, we present Software for Automated Morphological Analysis (SAMA), a method by which epithelial structures grown in 3D cultures can be imaged, reconstructed and analyzed with minimum human intervention. SAMA allows quantitative analysis of key features of epithelial morphogenesis such as ductal elongation, branching and lumen formation that distinguish different hormonal treatments. SAMA is a user-friendly set of customized macros operated via FIJI (http://fiji.sc/Fiji), an open-source image analysis platform in combination with a set of functions in R (http://www.r-project.org/), an open-source program for statistical analysis. SAMA enables a rapid, exhaustive and quantitative 3D analysis of the shape of a population of structures in a 3D image. SAMA is cross-platform, licensed under the GPLv3 and available at http://montevil.theobio.org/content/sama.