Enhancing protein-vitamin binding residues prediction by multiple heterogeneous subspace SVMs ensemble

Background

Vitamins are typical ligands that play critical roles in various metabolic processes. The accurate identification of the vitamin-binding residues solely based on a protein sequence is of significant importance for the functional annotation of proteins, especially in the post-genomic era, when large volumes of protein sequences are accumulating quickly without being functionally annotated.

Results

In this paper, a new predictor called TargetVita is designed and implemented for predicting protein-vitamin binding residues using protein sequences. In TargetVita, features derived from the position-specific scoring matrix (PSSM), predicted protein secondary structure, and vitamin binding propensity are combined to form the original feature space; then, several feature subspaces are selected by performing different feature selection methods. Finally, based on the selected feature subspaces, heterogeneous SVMs are trained and then ensembled for performing prediction.

Conclusions

The experimental results obtained with four separate vitamin-binding benchmark datasets demonstrate that the proposed TargetVita is superior to the state-of-the-art vitamin-specific predictor, and an average improvement of 10% in terms of the Matthews correlation coefficient (MCC) was achieved over independent validation tests. The TargetVita web server and the datasets used are freely available for academic use at http://csbio.njust.edu.cn/bioinf/TargetVita or http://www.csbio.sjtu.edu.cn/bioinf/TargetVita.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2105-15-297) contains supplementary material, which is available to authorized users.     

CompareSVM: supervised,Support Vector Machine (SVM) inference of gene regularity networks

Background

Predication of gene regularity network (GRN) from expression data is a challenging task. There are many methods that have been developed to address this challenge ranging from supervised to unsupervised methods. Most promising methods are based on support vector machine (SVM). There is a need for comprehensive analysis on prediction accuracy of supervised method SVM using different kernels on different biological experimental conditions and network size.

Results

We developed a tool (CompareSVM) based on SVM to compare different kernel methods for inference of GRN. Using CompareSVM, we investigated and evaluated different SVM kernel methods on simulated datasets of microarray of different sizes in detail. The results obtained from CompareSVM showed that accuracy of inference method depends upon the nature of experimental condition and size of the network.

Conclusions

For network with nodes (<200) and average (over all sizes of networks), SVM Gaussian kernel outperform on knockout, knockdown, and multifactorial datasets compared to all the other inference methods. For network with large number of nodes (~500), choice of inference method depend upon nature of experimental condition. CompareSVM is available at http://bis.zju.edu.cn/CompareSVM/.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-014-0395-x) contains supplementary material, which is available to authorized users.     

SHEAR: sample heterogeneity estimation and assembly by reference

Background

Personal genome assembly is a critical process when studying tumor genomes and other highly divergent sequences. The accuracy of downstream analyses, such as RNA-seq and ChIP-seq, can be greatly enhanced by using personal genomic sequences rather than standard references. Unfortunately, reads sequenced from these types of samples often have a heterogeneous mix of various subpopulations with different variants, making assembly extremely difficult using existing assembly tools. To address these challenges, we developed SHEAR (Sample Heterogeneity Estimation and Assembly by Reference; http://vk.cs.umn.edu/SHEAR), a tool that predicts SVs, accounts for heterogeneous variants by estimating their representative percentages, and generates personal genomic sequences to be used for downstream analysis.

Results

By making use of structural variant detection algorithms, SHEAR offers improved performance in the form of a stronger ability to handle difficult structural variant types and better computational efficiency. We compare against the lead competing approach using a variety of simulated scenarios as well as real tumor cell line data with known heterogeneous variants. SHEAR is shown to successfully estimate heterogeneity percentages in both cases, and demonstrates an improved efficiency and better ability to handle tandem duplications.

Conclusion

SHEAR allows for accurate and efficient SV detection and personal genomic sequence generation. It is also able to account for heterogeneous sequencing samples, such as from tumor tissue, by estimating the subpopulation percentage for each heterogeneous variant.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-84) contains supplementary material, which is available to authorized users.     

Identification of indels in next-generation sequencing data

Background

The discovery and mapping of genomic variants is an essential step in most analysis done using sequencing reads. There are a number of mature software packages and associated pipelines that can identify single nucleotide polymorphisms (SNPs) with a high degree of concordance. However, the same cannot be said for tools that are used to identify the other types of variants. Indels represent the second most frequent class of variants in the human genome, after single nucleotide polymorphisms. The reliable detection of indels is still a challenging problem, especially for variants that are longer than a few bases.

Results

We have developed a set of algorithms and heuristics collectively called indelMINER to identify indels from whole genome resequencing datasets using paired-end reads. indelMINER uses a split-read approach to identify the precise breakpoints for indels of size less than a user specified threshold, and supplements that with a paired-end approach to identify larger variants that are frequently missed with the split-read approach. We use simulated and real datasets to show that an implementation of the algorithm performs favorably when compared to several existing tools.

Conclusions

indelMINER can be used effectively to identify indels in whole-genome resequencing projects. The output is provided in the VCF format along with additional information about the variant, including information about its presence or absence in another sample. The source code and documentation for indelMINER can be freely downloaded from www.bx.psu.edu/miller_lab/indelMINER.tar.gz.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-015-0483-6) contains supplementary material, which is available to authorized users.     

ChIPpeakAnno: a Bioconductor package to annotate ChIP-seq and ChIP-chip data

Background  

Chromatin immunoprecipitation (ChIP) followed by high-throughput sequencing (ChIP-seq) or ChIP followed by genome tiling array analysis (ChIP-chip) have become standard technologies for genome-wide identification of DNA-binding protein target sites. A number of algorithms have been developed in parallel that allow identification of binding sites from ChIP-seq or ChIP-chip datasets and subsequent visualization in the University of California Santa Cruz (UCSC) Genome Browser as custom annotation tracks. However, summarizing these tracks can be a daunting task, particularly if there are a large number of binding sites or the binding sites are distributed widely across the genome.     

OTO: Ontology Term Organizer

Background

The need to create controlled vocabularies such as ontologies for knowledge organization and access has been widely recognized in various domains. Despite the indispensable need of thorough domain knowledge in ontology construction, most software tools for ontology construction are designed for knowledge engineers and not for domain experts to use. The differences in the opinions of different domain experts and in the terminology usages in source literature are rarely addressed by existing software.

Methods

OTO software was developed based on the Agile principles. Through iterations of software release and user feedback, new features are added and existing features modified to make the tool more intuitive and efficient to use for small and large data sets. The software is open source and built in Java.

Results

Ontology Term Organizer (OTO; http://biosemantics.arizona.edu/OTO/) is a user-friendly, web-based, consensus-promoting, open source application for organizing domain terms by dragging and dropping terms to appropriate locations. The application is designed for users with specific domain knowledge such as biology but not in-depth ontology construction skills. Specifically OTO can be used to establish is_a, part_of, synonym, and order relationships among terms in any domain that reflects the terminology usage in source literature and based on multiple experts’ opinions. The organized terms may be fed into formal ontologies to boost their coverage. All datasets organized on OTO are publicly available.

Conclusion

OTO has been used to organize the terms extracted from thirty volumes of Flora of North America and Flora of China combined, in addition to some smaller datasets of different taxon groups. User feedback indicates that the tool is efficient and user friendly. Being open source software, the application can be modified to fit varied term organization needs for different domains.     

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.     

CMS: A Web-Based System for Visualization and Analysis of Genome-Wide Methylation Data of Human Cancers

Background

DNA methylation of promoter CpG islands is associated with gene suppression, and its unique genome-wide profiles have been linked to tumor progression. Coupled with high-throughput sequencing technologies, it can now efficiently determine genome-wide methylation profiles in cancer cells. Also, experimental and computational technologies make it possible to find the functional relationship between cancer-specific methylation patterns and their clinicopathological parameters.

Methodology/Principal Findings

Cancer methylome system (CMS) is a web-based database application designed for the visualization, comparison and statistical analysis of human cancer-specific DNA methylation. Methylation intensities were obtained from MBDCap-sequencing, pre-processed and stored in the database. 191 patient samples (169 tumor and 22 normal specimen) and 41 breast cancer cell-lines are deposited in the database, comprising about 6.6 billion uniquely mapped sequence reads. This provides comprehensive and genome-wide epigenetic portraits of human breast cancer and endometrial cancer to date. Two views are proposed for users to better understand methylation structure at the genomic level or systemic methylation alteration at the gene level. In addition, a variety of annotation tracks are provided to cover genomic information. CMS includes important analytic functions for interpretation of methylation data, such as the detection of differentially methylated regions, statistical calculation of global methylation intensities, multiple gene sets of biologically significant categories, interactivity with UCSC via custom-track data. We also present examples of discoveries utilizing the framework.

Conclusions/Significance

CMS provides visualization and analytic functions for cancer methylome datasets. A comprehensive collection of datasets, a variety of embedded analytic functions and extensive applications with biological and translational significance make this system powerful and unique in cancer methylation research. CMS is freely accessible at: http://cbbiweb.uthscsa.edu/KMethylomes/.     

An efficiency analysis of high-order combinations of gene–gene interactions using multifactor-dimensionality reduction

Background

Multifactor dimensionality reduction (MDR) is widely used to analyze interactions of genes to determine the complex relationship between diseases and polymorphisms in humans. However, the astronomical number of high-order combinations makes MDR a highly time-consuming process which can be difficult to implement for multiple tests to identify more complex interactions between genes. This study proposes a new framework, named fast MDR (FMDR), which is a greedy search strategy based on the joint effect property.

Results

Six models with different minor allele frequencies (MAFs) and different sample sizes were used to generate the six simulation data sets. A real data set was obtained from the mitochondrial D-loop of chronic dialysis patients. Comparison of results from the simulation data and real data sets showed that FMDR identified significant gene–gene interaction with less computational complexity than the MDR in high-order interaction analysis.

Conclusion

FMDR improves the MDR difficulties associated with the computational loading of high-order SNPs and can be used to evaluate the relative effects of each individual SNP on disease susceptibility. FMDR is freely available at http://bioinfo.kmu.edu.tw/FMDR.rar.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-1717-8) contains supplementary material, which is available to authorized users.     

MCMC implementation of the optimal Bayesian classifier for non-Gaussian models: model-based RNA-Seq classification

Background

Sequencing datasets consist of a finite number of reads which map to specific regions of a reference genome. Most effort in modeling these datasets focuses on the detection of univariate differentially expressed genes. However, for classification, we must consider multiple genes and their interactions.

Results

Thus, we introduce a hierarchical multivariate Poisson model (MP) and the associated optimal Bayesian classifier (OBC) for classifying samples using sequencing data. Lacking closed-form solutions, we employ a Monte Carlo Markov Chain (MCMC) approach to perform classification. We demonstrate superior or equivalent classification performance compared to typical classifiers for two synthetic datasets and over a range of classification problem difficulties. We also introduce the Bayesian minimum mean squared error (MMSE) conditional error estimator and demonstrate its computation over the feature space. In addition, we demonstrate superior or leading class performance over an RNA-Seq dataset containing two lung cancer tumor types from The Cancer Genome Atlas (TCGA).

Conclusions

Through model-based, optimal Bayesian classification, we demonstrate superior classification performance for both synthetic and real RNA-Seq datasets. A tutorial video and Python source code is available under an open source license at http://bit.ly/1gimnss.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-014-0401-3) contains supplementary material, which is available to authorized users.     

dbVOR: a database system for importing pedigree,phenotype and genotype data and exporting selected subsets

Background

When studying the genetics of a human trait, we typically have to manage both genome-wide and targeted genotype data. There can be overlap of both people and markers from different genotyping experiments; the overlap can introduce several kinds of problems. Most times the overlapping genotypes are the same, but sometimes they are different. Occasionally, the lab will return genotypes using a different allele labeling scheme (for example 1/2 vs A/C). Sometimes, the genotype for a person/marker index is unreliable or missing. Further, over time some markers are merged and bad samples are re-run under a different sample name. We need a consistent picture of the subset of data we have chosen to work with even though there might possibly be conflicting measurements from multiple data sources.

Results

We have developed the dbVOR database, which is designed to hold data efficiently for both genome-wide and targeted experiments. The data are indexed for fast retrieval by person and marker. In addition, we store pedigree and phenotype data for our subjects. The dbVOR database allows us to select subsets of the data by several different criteria and to merge their results into a coherent and consistent whole. Data may be filtered by: family, person, trait value, markers, chromosomes, and chromosome ranges. The results can be presented in columnar, Mega2, or PLINK format.

Conclusions

dbVOR serves our needs well. It is freely available from https://watson.hgen.pitt.edu/register. Documentation for dbVOR can be found at https://watson.hgen.pitt.edu/register/docs/dbvor.html.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-015-0505-4) contains supplementary material, which is available to authorized users.     

A Consistency-Based Feature Selection Method Allied with Linear SVMs for HIV-1 Protease Cleavage Site Prediction

Background

Predicting type-1 Human Immunodeficiency Virus (HIV-1) protease cleavage site in protein molecules and determining its specificity is an important task which has attracted considerable attention in the research community. Achievements in this area are expected to result in effective drug design (especially for HIV-1 protease inhibitors) against this life-threatening virus. However, some drawbacks (like the shortage of the available training data and the high dimensionality of the feature space) turn this task into a difficult classification problem. Thus, various machine learning techniques, and specifically several classification methods have been proposed in order to increase the accuracy of the classification model. In addition, for several classification problems, which are characterized by having few samples and many features, selecting the most relevant features is a major factor for increasing classification accuracy.

Results

We propose for HIV-1 data a consistency-based feature selection approach in conjunction with recursive feature elimination of support vector machines (SVMs). We used various classifiers for evaluating the results obtained from the feature selection process. We further demonstrated the effectiveness of our proposed method by comparing it with a state-of-the-art feature selection method applied on HIV-1 data, and we evaluated the reported results based on attributes which have been selected from different combinations.

Conclusion

Applying feature selection on training data before realizing the classification task seems to be a reasonable data-mining process when working with types of data similar to HIV-1. On HIV-1 data, some feature selection or extraction operations in conjunction with different classifiers have been tested and noteworthy outcomes have been reported. These facts motivate for the work presented in this paper.

Software availability

The software is available at http://ozyer.etu.edu.tr/c-fs-svm.rar.The software can be downloaded at esnag.etu.edu.tr/software/hiv_cleavage_site_prediction.rar; you will find a readme file which explains how to set the software in order to work.