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BMC Bioinformatics

Jan Schröder, Adrianto Wirawan, Bertil Schmidt, Anthony T Papenfuss
BACKGROUND: A precise understanding of structural variants (SVs) in DNA is important in the study of cancer and population diversity. Many methods have been designed to identify SVs from DNA sequencing data. However, the problem remains challenging because existing approaches suffer from low sensitivity, precision, and positional accuracy. Furthermore, many existing tools only identify breakpoints, and so not collect related breakpoints and classify them as a particular type of SV. Due to the rapidly increasing usage of high throughput sequencing technologies in this area, there is an urgent need for algorithms that can accurately classify complex genomic rearrangements (involving more than one breakpoint or fusion)...
July 20, 2017: BMC Bioinformatics
Sharon M Lutz, Annie Thwing, Sarah Schmiege, Miranda Kroehl, Christopher D Baker, Anne P Starling, John E Hokanson, Debashis Ghosh
BACKGROUND: In mediation analysis if unmeasured confounding is present, the estimates for the direct and mediated effects may be over or under estimated. Most methods for the sensitivity analysis of unmeasured confounding in mediation have focused on the mediator-outcome relationship. RESULTS: The Umediation R package enables the user to simulate unmeasured confounding of the exposure-mediator, exposure-outcome, and mediator-outcome relationships in order to see how the results of the mediation analysis would change in the presence of unmeasured confounding...
July 19, 2017: BMC Bioinformatics
Se-Ran Jun, Intawat Nookaew, Loren Hauser, Andrey Gorin
BACKGROUND: Functional annotation of bacterial genomes is an obligatory and crucially important step of information processing from the genome sequences into cellular mechanisms. However, there is a lack of computational methods to evaluate the quality of functional assignments. RESULTS: We developed a genome-scale model that assigns Bayesian probability to each gene utilizing a known property of functional similarity between neighboring genes in bacteria. CONCLUSIONS: Our model clearly distinguished true annotation from random annotation with Bayesian annotation probability >0...
July 19, 2017: BMC Bioinformatics
Jie Liu, Minyi Su, Zhihai Liu, Jie Li, Yan Li, Renxiao Wang
BACKGROUND: In structure-based drug design, binding affinity prediction remains as a challenging goal for current scoring functions. Development of target-biased scoring functions provides a new possibility for tackling this problem, but this approach is also associated with certain technical difficulties. We previously reported the Knowledge-Guided Scoring (KGS) method as an alternative approach (BMC Bioinformatics, 2010, 11, 193-208). The key idea is to compute the binding affinity of a given protein-ligand complex based on the known binding data of an appropriate reference complex, so the error in binding affinity prediction can be reduced effectively...
July 18, 2017: BMC Bioinformatics
John Alexander, Dimitris Mantzaris, Marianthi Georgitsi, Petros Drineas, Peristera Paschou
BACKGROUND: The increasing volume and complexity of high-throughput genomic data make analysis and prioritization of variants difficult for researchers with limited bioinformatics skills. Variant Ranker allows researchers to rank identified variants and determine the most confident variants for experimental validation. RESULTS: We describe Variant Ranker, a user-friendly simple web-based tool for ranking, filtering and annotation of coding and non-coding variants...
July 17, 2017: BMC Bioinformatics
Bogumił M Konopka, Marta Marciniak, Witold Dyrka
BACKGROUND: The field of protein sequence analysis is dominated by tools rooted in substitution matrices and alignments. A complementary approach is provided by methods of quantitative characterization. A major advantage of the approach is that quantitative properties defines a multidimensional solution space, where sequences can be related to each other and differences can be meaningfully interpreted. RESULTS: Quantiprot is a software package in Python, which provides a simple and consistent interface to multiple methods for quantitative characterization of protein sequences...
July 17, 2017: BMC Bioinformatics
Sara González-Pérez, Florencio Pazos, Mónica Chagoyen
BACKGROUND: Clinical signs are a fundamental aspect of human pathologies. While disease diagnosis is problematic or impossible in many cases, signs are easier to perceive and categorize. Clinical signs are increasingly used, together with molecular networks, to prioritize detected variants in clinical genomics pipelines, even if the patient is still undiagnosed. Here we analyze the ability of these network-based methods to predict genes that underlie clinical signs from the human interactome...
July 17, 2017: BMC Bioinformatics
Alvin Farrel, Jun-Tao Guo
BACKGROUND: Gene expression is regulated by transcription factors binding to specific target DNA sites. Understanding how and where transcription factors bind at genome scale represents an essential step toward our understanding of gene regulation networks. Previously we developed a structure-based method for prediction of transcription factor binding sites using an integrative energy function that combines a knowledge-based multibody potential and two atomic energy terms. While the method performs well, it is not computationally efficient due to the exponential increase in the number of binding sequences to be evaluated for longer binding sites...
July 17, 2017: BMC Bioinformatics
Sehrish Kanwal, Farah Zaib Khan, Andrew Lonie, Richard O Sinnott
BACKGROUND: Computational bioinformatics workflows are extensively used to analyse genomics data, with different approaches available to support implementation and execution of these workflows. Reproducibility is one of the core principles for any scientific workflow and remains a challenge, which is not fully addressed. This is due to incomplete understanding of reproducibility requirements and assumptions of workflow definition approaches. Provenance information should be tracked and used to capture all these requirements supporting reusability of existing workflows...
July 12, 2017: BMC Bioinformatics
Ksenia Khelik, Karin Lagesen, Geir Kjetil Sandve, Torbjørn Rognes, Alexander Johan Nederbragt
BACKGROUND: Comparing sets of sequences is a situation frequently encountered in bioinformatics, examples being comparing an assembly to a reference genome, or two genomes to each other. The purpose of the comparison is usually to find where the two sets differ, e.g. to find where a subsequence is repeated or deleted, or where insertions have been introduced. Such comparisons can be done using whole-genome alignments. Several tools for making such alignments exist, but none of them 1) provides detailed information about the types and locations of all differences between the two sets of sequences, 2) enables visualisation of alignment results at different levels of detail, and 3) carefully takes genomic repeats into consideration...
July 12, 2017: BMC Bioinformatics
Eleni Ioanna Delatola, Emilie Lebarbier, Tristan Mary-Huard, François Radvanyi, Stéphane Robin, Jennifer Wong
BACKGROUND: Detecting local correlations in expression between neighboring genes along the genome has proved to be an effective strategy to identify possible causes of transcriptional deregulation in cancer. It has been successfully used to illustrate the role of mechanisms such as copy number variation (CNV) or epigenetic alterations as factors that may significantly alter expression in large chromosomal regions (gene silencing or gene activation). RESULTS: The identification of correlated regions requires segmenting the gene expression correlation matrix into regions of homogeneously correlated genes and assessing whether the observed local correlation is significantly higher than the background chromosomal correlation...
July 11, 2017: BMC Bioinformatics
Marc Jeanmougin, Josselin Noirel, Cédric Coulonges, Jean-François Zagury
BACKGROUND: The major histocompatibility complex (MHC) region of the human genome, and specifically the human leukocyte antigen (HLA) genes, play a major role in numerous human diseases. With the recent progress of sequencing methods (eg, Next-Generation Sequencing, NGS), the accurate genotyping of this region has become possible but remains relatively costly. In order to obtain the HLA information for the millions of samples already genotyped by chips in the past ten years, efficient bioinformatics tools, such as SNP2HLA or HIBAG, have been developed that infer HLA information from the linkage disequilibrium existing between HLA alleles and SNP markers in the MHC region...
July 11, 2017: BMC Bioinformatics
Sheng Zhang, Bo Wang, Lin Wan, Lei M Li
BACKGROUND: Phred quality scores are essential for downstream DNA analysis such as SNP detection and DNA assembly. Thus a valid model to define them is indispensable for any base-calling software. Recently, we developed the base-caller 3Dec for Illumina sequencing platforms, which reduces base-calling errors by 44-69% compared to the existing ones. However, the model to predict its quality scores has not been fully investigated yet. RESULTS: In this study, we used logistic regression models to evaluate quality scores from predictive features, which include different aspects of the sequencing signals as well as local DNA contents...
July 11, 2017: BMC Bioinformatics
Su Hee Chu, Yen-Tsung Huang
BACKGROUND: Burgeoning interest in integrative analyses has produced a rise in studies which incorporate data from multiple genomic platforms. Literature for conducting formal hypothesis testing on an integrative gene set level is considerably sparse. This paper is biologically motivated by our interest in the joint effects of epigenetic methylation loci and their associated mRNA gene expressions on lung cancer survival status. RESULTS: We provide an efficient screening approach across multiplatform genomic data on the level of biologically related sets of genes, and our methods are applicable to various disease models regardless whether the underlying true model is known (iTEGS) or unknown (iNOTE)...
July 11, 2017: BMC Bioinformatics
Ziyi Li, Sandra E Safo, Qi Long
BACKGROUND: Sparse principal component analysis (PCA) is a popular tool for dimensionality reduction, pattern recognition, and visualization of high dimensional data. It has been recognized that complex biological mechanisms occur through concerted relationships of multiple genes working in networks that are often represented by graphs. Recent work has shown that incorporating such biological information improves feature selection and prediction performance in regression analysis, but there has been limited work on extending this approach to PCA...
July 11, 2017: BMC Bioinformatics
Mustafa Alshawaqfeh, Ahmad Bashaireh, Erchin Serpedin, Jan Suchodolski
BACKGROUND: Biomarker detection presents itself as a major means of translating biological data into clinical applications. Due to the recent advances in high throughput sequencing technologies, an increased number of metagenomics studies have suggested the dysbiosis in microbial communities as potential biomarker for certain diseases. The reproducibility of the results drawn from metagenomic data is crucial for clinical applications and to prevent incorrect biological conclusions. The variability in the sample size and the subjects participating in the experiments induce diversity, which may drastically change the outcome of biomarker detection algorithms...
July 10, 2017: BMC Bioinformatics
Kuleesha, Lin Feng, Martin Wasser
BACKGROUND: Many human muscle wasting diseases are associated with abnormal nuclear localization. During metamorphosis in Drosophila melanogaster, multi-nucleated larval dorsal abdominal muscles either undergo cell death or are remodeled to temporary adult muscles. Muscle remodeling is associated with anti-polar nuclear migration and atrophy during early pupation followed by polar migration and muscle growth during late pupation. Muscle remodeling is a useful model to study genes involved in myonuclear migration...
July 10, 2017: BMC Bioinformatics
Hongrui Wang, Hongwei Liu, Leixin Cai, Caixia Wang, Qiang Lv
BACKGROUND: In this study, we extended the replica exchange Monte Carlo (REMC) sampling method to protein-small molecule docking conformational prediction using RosettaLigand. In contrast to the traditional Monte Carlo (MC) and REMC sampling methods, these methods use multi-objective optimization Pareto front information to facilitate the selection of replicas for exchange. RESULTS: The Pareto front information generated to select lower energy conformations as representative conformation structure replicas can facilitate the convergence of the available conformational space, including available near-native structures...
July 10, 2017: BMC Bioinformatics
Jeremy P Koelmel, Nicholas M Kroeger, Candice Z Ulmer, John A Bowden, Rainey E Patterson, Jason A Cochran, Christopher W W Beecher, Timothy J Garrett, Richard A Yost
BACKGROUND: Lipids are ubiquitous and serve numerous biological functions; thus lipids have been shown to have great potential as candidates for elucidating biomarkers and pathway perturbations associated with disease. Methods expanding coverage of the lipidome increase the likelihood of biomarker discovery and could lead to more comprehensive understanding of disease etiology. RESULTS: We introduce LipidMatch, an R-based tool for lipid identification for liquid chromatography tandem mass spectrometry workflows...
July 10, 2017: BMC Bioinformatics
Dianhao Guo, Jiapeng Luo, Yuenan Zhou, Huamei Xiao, Kang He, Chuanlin Yin, Jianhua Xu, Fei Li
BACKGROUND: Insecticide resistance is a substantial problem in controlling agricultural and medical pests. Detecting target site mutations is crucial to manage insecticide resistance. Though PCR-based methods have been widely used in this field, they are time-consuming and inefficient, and typically have a high false positive rate. Acetylcholinesterases (Ace) is the neural target of the widely used organophosphate (OP) and carbamate insecticides. However, there is not any software available to detect insecticide resistance associated mutations in RNA-Seq data at present...
July 10, 2017: BMC Bioinformatics
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