PhenoApt leverages clinical expertise to prioritize candidate genes via machine learning

In recent years, exome sequencing (ES) has shown great utility in the diagnoses of Mendelian disorders. However, after rigorous filtering, a typical ES analysis still involves the interpretation of hundreds of variants, which greatly hinders the rapid identification of causative genes. Since the interpretations of ES data require comprehensive clinical analyses, taking clinical expertise into consideration can speed the molecular diagnoses of Mendelian disorders. To leverage clinical expertise to prioritize candidate genes, we developed PhenoApt, a phenotype-driven gene prioritization tool that allows users to assign a customized weight to each phenotype, via a machine-learning algorithm. Using the ability to rank causative genes in top-10 lists as an evaluation metric, baseline analysis demonstrated that PhenoApt outperformed previous phenotype-driven gene prioritization tools by a relative increase of 22.7%–140.0% in three independent, real-world, multi-center cohorts (cohort 1, n = 185; cohort 2, n = 784; and cohort 3, n = 208). Additional trials showed that, by adding weights to clinical indications, which should be explained by the causative gene, PhenoApt performance was improved by a relative increase of 37.3% in cohort 2 (n = 471) and 21.4% in cohort 3 (n = 208). Moreover, PhenoApt could assign an intrinsic weight to each phenotype based on the likelihood of its being a Mendelian trait using term frequency-inverse document frequency techniques. When clinical indications were assigned with intrinsic weights, PhenoApt performance was improved by a relative increase of 23.7% in cohort 2 and 15.5% in cohort 3. For the integration of PhenoApt into clinical practice, we developed a user-friendly website and a command-line tool.     

Integration of QTL and bioinformatic tools to identify candidate genes for triglycerides in mice

To identify genetic loci influencing lipid levels, we performed quantitative trait loci (QTL) analysis between inbred mouse strains MRL/MpJ and SM/J, measuring triglyceride levels at 8 weeks of age in F2 mice fed a chow diet. We identified one significant QTL on chromosome (Chr) 15 and three suggestive QTL on Chrs 2, 7, and 17. We also carried out microarray analysis on the livers of parental strains of 282 F2 mice and used these data to find cis-regulated expression QTL. We then narrowed the list of candidate genes under significant QTL using a "toolbox" of bioinformatic resources, including haplotype analysis; parental strain comparison for gene expression differences and nonsynonymous coding single nucleotide polymorphisms (SNP); cis-regulated eQTL in livers of F2 mice; correlation between gene expression and phenotype; and conditioning of expression on the phenotype. We suggest Slc25a7 as a candidate gene for the Chr 7 QTL and, based on expression differences, five genes (Polr3 h, Cyp2d22, Cyp2d26, Tspo, and Ttll12) as candidate genes for Chr 15 QTL. This study shows how bioinformatics can be used effectively to reduce candidate gene lists for QTL related to complex traits.     

Computational tools for prioritizing candidate genes: boosting disease gene discovery

At different stages of any research project, molecular biologists need to choose - often somewhat arbitrarily, even after careful statistical data analysis - which genes or proteins to investigate further experimentally and which to leave out because of limited resources. Computational methods that integrate complex, heterogeneous data sets - such as expression data, sequence information, functional annotation and the biomedical literature - allow prioritizing genes for future study in a more informed way. Such methods can substantially increase the yield of downstream studies and are becoming invaluable to researchers.     

Literature-based knowledgebase of pancreatic cancer gene to prioritize the key genes and pathways

正Pancreatic cancer(PC)occurs when malignant cells develop in the part of the pancreas,a glandular organ behind the stomach.For 2015,there are about 40,560 people dead of pancreatic cancer(20,710 men and 19,850 women)in the US(Siegel et al.,2015).Though PC accounts for about 3%of all cancers in the US,it can cause about 7%of cancer deaths.This is mainly because that the     

webFOG: A web tool to map genomic features onto genes

A large number of new genomic features are being discovered using high throughput techniques. The next challenge is to automatically map them to the reference genome for further analysis and functional annotation. We have developed a tool that can be used to map important genomic features to the latest version of the human genome and also to annotate new features. These genomic features could be of many different source types, including miRNAs, microarray primers or probes, Chip-on-Chip data, CpG islands and SNPs to name a few. A standalone version and web interface for the tool can be accessed through: http://populationhealth.qimr.edu.au/cgi-bin/webFOG/index.cgi. The project details and source code is also available at http://www.bioinformatics.org/webfog.     

The hitchhiker's guide to malaria parasite genes

There have been many recent developments in malaria genetics, with much information coming from the field of drug resistance. The findings of classical genetic crossing experiments, together with data from sequencing Plasmodium genomes and the powerful new tools of population genetics, are beginning to explain the ingenuity of Plasmodium at evading the control measures used against it so far.     

A guide to the Lhc genes and their relatives in Arabidopsis

The Lhc super-gene family encodes the light-harvesting chlorophyll a/b-binding (LHC) proteins that constitute the antenna system of the photosynthetic apparatus, and also includes some relatives whose functions are more or less unknown. The Lhc super-gene family of Arabidopsis contains >30 members and the databases contain >1000 EST clones originating from these genes. This article presents an overview of these genes and provides some tools for researchers who want to use them in their studies.     

Integrating multiple protein-protein interaction networks to prioritize disease genes: a Bayesian regression approach

Background

The identification of genes responsible for human inherited diseases is one of the most challenging tasks in human genetics. Recent studies based on phenotype similarity and gene proximity have demonstrated great success in prioritizing candidate genes for human diseases. However, most of these methods rely on a single protein-protein interaction (PPI) network to calculate similarities between genes, and thus greatly restrict the scope of application of such methods. Meanwhile, independently constructed and maintained PPI networks are usually quite diverse in coverage and quality, making the selection of a suitable PPI network inevitable but difficult.

Methods

We adopt a linear model to explain similarities between disease phenotypes using gene proximities that are quantified by diffusion kernels of one or more PPI networks. We solve this model via a Bayesian approach, and we derive an analytic form for Bayes factor that naturally measures the strength of association between a query disease and a candidate gene and thus can be used as a score to prioritize candidate genes. This method is intrinsically capable of integrating multiple PPI networks.

Results

We show that gene proximities calculated from PPI networks imply phenotype similarities. We demonstrate the effectiveness of the Bayesian regression approach on five PPI networks via large scale leave-one-out cross-validation experiments and summarize the results in terms of the mean rank ratio of known disease genes and the area under the receiver operating characteristic curve (AUC). We further show the capability of our approach in integrating multiple PPI networks.

Conclusions

The Bayesian regression approach can achieve much higher performance than the existing CIPHER approach and the ordinary linear regression method. The integration of multiple PPI networks can greatly improve the scope of application of the proposed method in the inference of disease genes.

     

GTC: A web server for integrating systems biology data with web tools and desktop applications

Gaggle Tool Creator (GTC) is a web application which provides access to public annotation, interaction, orthology, and genomic data for hundreds of organisms, and enables instant analysis of the data using many popular web-based and desktop applications.     

Xenopus to the rescue: A model to validate and characterize candidate ciliopathy genes

Cilia are present on most vertebrate cells and play a central role in development, growth, and homeostasis. Thus, cilia dysfunction can manifest into an array of diseases, collectively termed ciliopathies, affecting millions of lives worldwide. Yet, our understanding of the gene regulatory networks that control cilia assembly and functions remain incomplete. With the advances in next‐generation sequencing technologies, we can now rapidly predict pathogenic variants from hundreds of ciliopathy patients. While the pace of candidate gene discovery is exciting, most of these genes have never been previously implicated in cilia assembly or function. This makes assigning the disease causality difficult. This review discusses how Xenopus, a genetically tractable and high‐throughput vertebrate model, has played a central role in identifying, validating, and characterizing candidate ciliopathy genes. The review is focused on multiciliated cells (MCCs) and diseases associated with MCC dysfunction. MCCs harbor multiple motile cilia on their apical surface to generate extracellular fluid flow inside the airway, the brain ventricles, and the oviduct. In Xenopus, these cells are external and present on the embryonic epidermal epithelia, facilitating candidate genes analysis in MCC development in vivo. The ability to introduce patient variants to study their effects on disease progression makes Xenopus a powerful model to improve our understanding of the underlying disease mechanisms and explain the patient phenotype.     

A guide to metabolic flux analysis in metabolic engineering: Methods,tools and applications

The field of metabolic engineering is primarily concerned with improving the biological production of value-added chemicals, fuels and pharmaceuticals through the design, construction and optimization of metabolic pathways, redirection of intracellular fluxes, and refinement of cellular properties relevant for industrial bioprocess implementation. Metabolic network models and metabolic fluxes are central concepts in metabolic engineering, as was emphasized in the first paper published in this journal, “Metabolic fluxes and metabolic engineering” (Metabolic Engineering, 1: 1–11, 1999). In the past two decades, a wide range of computational, analytical and experimental approaches have been developed to interrogate the capabilities of biological systems through analysis of metabolic network models using techniques such as flux balance analysis (FBA), and quantify metabolic fluxes using constrained-based modeling approaches such as metabolic flux analysis (MFA) and more advanced experimental techniques based on the use of stable-isotope tracers, i.e. ¹³C-metabolic flux analysis (¹³C-MFA). In this review, we describe the basic principles of metabolic flux analysis, discuss current best practices in flux quantification, highlight potential pitfalls and alternative approaches in the application of these tools, and give a broad overview of pragmatic applications of flux analysis in metabolic engineering practice.     

几种可能影响耐力素质的潜在基因

个体间运动能力的差异受多种因素影响,其中环境(运动训练、营养与技术方法)和遗传因素在很大程度上起决定作用。迄今已进行了大量与运动能力有关的基因多态研究,但近年来有关新基因、新位点的探索却渐入瓶颈,限制了优秀运动员分子选材研究的发展。文章着重叙述了几个可能影响耐力素质的潜在基因的运动相关功能和多态研究的结果,以期为研究者提供新的思路和研究的可行性依据。