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Briefings in Bioinformatics

Chuan Dong, Yan-Ting Jin, Hong-Li Hua, Qing-Feng Wen, Sen Luo, Wen-Xin Zheng, Feng-Biao Guo
Essential genes have attracted increasing attention in recent years due to the important functions of these genes in organisms. Among the methods used to identify the essential genes, accurate and efficient computational methods can make up for the deficiencies of expensive and time-consuming experimental technologies. In this review, we have collected researches on essential gene predictions in prokaryotes and eukaryotes and summarized the five predominant types of features used in these studies. The five types of features include evolutionary conservation, domain information, network topology, sequence component and expression level...
November 29, 2018: Briefings in Bioinformatics
Haitao Yang, Hongyan Cao, Tao He, Tong Wang, Yuehua Cui
High-throughput omics data are generated almost with no limit nowadays. It becomes increasingly important to integrate different omics data types to disentangle the molecular machinery of complex diseases with the hope for better disease prevention and treatment. Since the relationship among different omics data features are typically unknown, a supervised learning model assuming a particular distribution with a specific structure will not serve the purpose to capture the underlying complex relationship between multiple features and a disease phenotype...
November 29, 2018: Briefings in Bioinformatics
Feng-Xu Wu, Fan Wang, Jing-Fang Yang, Wen Jiang, Meng-Yao Wang, Chen-Yang Jia, Ge-Fei Hao, Guang-Fu Yang
Drug resistance is one of the most intractable issues for successful treatment in current clinical practice. Although many mutations contributing to drug resistance have been identified, the relationship between the mutations and the related pharmacological profile of drug candidates has yet to be fully elucidated, which is valuable both for the molecular dissection of drug resistance mechanisms and for suggestion of promising treatment strategies to counter resistant. Hence, effective prediction approach for estimating the sensitivity of mutations to agents is a new opportunity that counters drug resistance and creates a high interest in pharmaceutical research...
November 29, 2018: Briefings in Bioinformatics
Quanhu Sheng, David C Samuels, Hui Yu, Scott Ness, Ying-Yong Zhao, Yan Guo
Expression quantitative trait loci (eQTLs) have been touted as the missing piece that can bridge the gap between genetic variants and phenotypes. Over the past decade, we have witnessed a sharp rise of effort in the identification and application of eQTLs. The successful application of eQTLs relies heavily on their reproducibility. The current eQTL databases such as Genotype-Tissue Expression (GTEx) were populated primarily with eQTLs deriving from germline single nucleotide polymorphisms and normal tissue gene expression...
November 23, 2018: Briefings in Bioinformatics
Maxwell Lewis Neal, Matthias König, David Nickerson, Göksel Misirli, Reza Kalbasi, Andreas Dräger, Koray Atalag, Vijayalakshmi Chelliah, Michael T Cooling, Daniel L Cook, Sharon Crook, Miguel de Alba, Samuel H Friedman, Alan Garny, John H Gennari, Padraig Gleeson, Martin Golebiewski, Michael Hucka, Nick Juty, Chris Myers, Brett G Olivier, Herbert M Sauro, Martin Scharm, Jacky L Snoep, Vasundra Touré, Anil Wipat, Olaf Wolkenhauer, Dagmar Waltemath
Life science researchers use computational models to articulate and test hypotheses about the behavior of biological systems. Semantic annotation is a critical component for enhancing the interoperability and reusability of such models as well as for the integration of the data needed for model parameterization and validation. Encoded as machine-readable links to knowledge resource terms, semantic annotations describe the computational or biological meaning of what models and data represent. These annotations help researchers find and repurpose models, accelerate model composition and enable knowledge integration across model repositories and experimental data stores...
November 21, 2018: Briefings in Bioinformatics
Jamie J Alnasir, Hugh P Shanahan
The paper reviews the use of the Hadoop platform in structural bioinformatics applications. For structural bioinformatics, Hadoop provides a new framework to analyse large fractions of the Protein Data Bank that is key for high-throughput studies of, for example, protein-ligand docking, clustering of protein-ligand complexes and structural alignment. Specifically we review in the literature a number of implementations using Hadoop of high-throughput analyses and their scalability. We find that these deployments for the most part use known executables called from MapReduce rather than rewriting the algorithms...
November 20, 2018: Briefings in Bioinformatics
Sangsoo Lim, Sangseon Lee, Inuk Jung, Sungmin Rhee, Sun Kim
Motivation: : Biological pathways are extensively used for the analysis of transcriptome data to characterize biological mechanisms underlying various phenotypes. There are a number of computational tools that summarize transcriptome data at the pathway level. However, there is no comparative study on how well these tools produce useful information at the cohort level, enabling comparison of many samples or patients. Results: : In this study, we systematically compared and evaluated 13 different pathway activity inference tools based on 5 comparison criteria using pan-cancer data set...
November 20, 2018: Briefings in Bioinformatics
Emile R Chimusa, Joel Defo, Prisca K Thami, Denis Awany, Delesa D Mulisa, Imane Allali, Hassan Ghaza, Ahmed Moussa, Gaston K Mazandu
Advances in human sequencing technologies, coupled with statistical and computational tools, have fostered the development of methods for dating admixture events. These methods have merits and drawbacks in estimating admixture events in multi-way admixed populations. Here, we first provide a comprehensive review and comparison of current methods pertinent to dating admixture events. Second, we assess various admixture dating tools. We do so by performing various simulations. Third, we apply the top two assessed methods to real data of a uniquely admixed population from South Africa...
November 19, 2018: Briefings in Bioinformatics
Tianlei Xu, Xiaoqi Zheng, Ben Li, Peng Jin, Zhaohui Qin, Hao Wu
There are significant correlations among different types of genetic, genomic and epigenomic features within the genome. These correlations make the in silico feature prediction possible through statistical or machine learning models. With the accumulation of a vast amount of high-throughput data, feature prediction has gained significant interest lately, and a plethora of papers have been published in the past few years. Here we provide a comprehensive review on these published works, categorized by the prediction targets, including protein binding site, enhancer, DNA methylation, chromatin structure and gene expression...
November 16, 2018: Briefings in Bioinformatics
Eugenio Del Prete, Angelo Facchiano, Pietro Liò
Coeliac disease (CD) is a complex, multifactorial pathology caused by different factors, such as nutrition, immunological response and genetic factors. Many autoimmune diseases are comorbidities for CD, and a comprehensive and integrated analysis with bioinformatics approaches can help in evaluating the interconnections among all the selected pathologies. We first performed a detailed survey of gene expression data available in public repositories on CD and less commonly considered comorbidities. Then we developed an innovative pipeline that integrates gene expression, cell-type data and online resources (e...
November 16, 2018: Briefings in Bioinformatics
Liu Qin, Yanhong Liu, Menglong Li, Xuemei Pu, Yanzhi Guo
We know that different types of cancers usually have different responses to the same treatment. Therefore, it is important to understand the similarities and differences across subtypes of cancers, so as to provide a basis for the individualized treatments. Until now, no comprehensive investigation on competing endogenous RNAs (ceRNAs) has been reported for the three main subtypes of renal cell carcinoma (RCC), so the regulation characteristics of ceRNAs in three subtypes are not well revealed. This paper firstly describes a comparative analysis of ceRNA-ceRNA interaction networks for all the three subtypes of RCC based on differential microRNAs (miRNAs)...
November 16, 2018: Briefings in Bioinformatics
Qinjie Chu, Panpan Bai, Xintian Zhu, Xingchen Zhang, Lingfeng Mao, Qian-Hao Zhu, Longjiang Fan, Chu-Yu Ye
Circular RNA (circRNA) is a kind of covalently closed single-stranded RNA molecules that have been proved to play important roles in transcriptional regulation of genes in diverse species. With the rapid development of bioinformatics tools, a huge number (95143) of circRNAs have been identified from different plant species, providing an opportunity for uncovering the overall characteristics of plant circRNAs. Here, based on publicly available circRNAs, we comprehensively analyzed characteristics of plant circRNAs with the help of various bioinformatics tools as well as in-house scripts and workflows, including the percentage of coding genes generating circRNAs, the frequency of alternative splicing events of circRNAs, the non-canonical splicing signals of circRNAs and the networks involving circRNAs, miRNAs and mRNAs...
November 15, 2018: Briefings in Bioinformatics
Dan Yao, Hainan Wu, Yuhua Chen, Wenguo Yang, Hua Gao, Chunfa Tong
Restriction site-associated DNA sequencing (RADseq) is a powerful technology that has been extensively applied in population genetics, phylogenetics and genetic mapping. Although many software packages are available for ecological and evolutionary studies, a few effective tools are available for extracting genotype data with RADseq for genetic mapping, a prerequisite for quantitative trait locus mapping, comparative genomics and genome scaffold assembly. Here, we present an integrated pipeline called gmRAD for generating single nucleotide polymorphism (SNP) genotypes from RADseq data, de novo, across a genetic mapping population derived by crossing two parents...
November 14, 2018: Briefings in Bioinformatics
Bin Liu, Shuangyan Jiang, Quan Zou
As one of the most important fundamental problems in protein sequence analysis, protein remote homology detection is critical for both theoretical research (protein structure and function studies) and real world applications (drug design). Although several computational predictors have been proposed, their detection performance is still limited. In this study, we treat protein remote homology detection as a document retrieval task, where the proteins are considered as documents and its aim is to find the highly related documents with the query documents in a database...
November 7, 2018: Briefings in Bioinformatics
Leyi Wei, Jie Hu, Fuyi Li, Jiangning Song, Ran Su, Quan Zou
Quorum-sensing peptides (QSPs) are the signal molecules that are closely associated with diverse cellular processes, such as cell-cell communication, and gene expression regulation in Gram-positive bacteria. It is therefore of great importance to identify QSPs for better understanding and in-depth revealing of their functional mechanisms in physiological processes. Machine learning algorithms have been developed for this purpose, showing the great potential for the reliable prediction of QSPs. In this study, several sequence-based feature descriptors for peptide representation and machine learning algorithms are comprehensively reviewed, evaluated and compared...
October 31, 2018: Briefings in Bioinformatics
Chao Shen, Zhe Wang, Xiaojun Yao, Youyong Li, Tailong Lei, Ercheng Wang, Lei Xu, Feng Zhu, Dan Li, Tingjun Hou
Protein kinases have been regarded as important therapeutic targets for many diseases. Currently, a total of 41 kinase inhibitors have been approved by the Food and Drug Administration, along with a large number of kinase inhibitors being evaluated in clinical and preclinical trials. Among all, allosteric inhibitors, such as type II kinase inhibitors, have attracted extensive attention owing to their potential high selectivity. Nowadays, molecular docking has become a powerful tool to search for novel kinase inhibitors...
October 31, 2018: Briefings in Bioinformatics
Sameer Salunkhe, Naren Chandran, Pratik Chandrani, Amit Dutt, Shilpee Dutt
Cytogenetic-based subjective prognostication of acute myeloid leukemia (AML) patients is a cumbersome process. Top scoring pair (TSP)-based decision tree using a robust analytical algorithm with statistical rigor offers a promising alternative. We describe CytoPred as a 7-gene pair signature based on the analysis of 2547 AML patient sample gene expression data using a modified TSP algorithm to estimate cytogenetic risk. The essential modification in TSP that helped computational encumbrance includes the filtration of gene pairs above random weighted guessers as well as sampling the gene pairs from the original gene pair pool to reduce overfitting issue...
October 30, 2018: Briefings in Bioinformatics
Zhenyu Yue, Le Zhao, Junfeng Xia
While recently emergent driver mutation data sets are available for developing computational methods to predict cancer mutation effects, benchmark sets focusing on passenger mutations are largely missing. Here, we developed a comprehensive literature-based database of Cancer Passenger Mutations (dbCPM), which contains 941 experimentally supported and 978 putative passenger mutations derived from a manual curation of the literature. Using the missense mutation data, the largest group in the dbCPM, we explored patterns of missense passenger mutations by comparing them with the missense driver mutations and assessed the performance of four cancer-focused mutation effect predictors...
October 30, 2018: Briefings in Bioinformatics
Hong Wang, Xiaoyan Lu, Fukun Chen, Yu Ding, Hewei Zheng, Lianzong Wang, Guosi Zhang, Jiaxin Yang, Yu Bai, Jing Li, Jingqi Wu, Meng Zhou, Liangde Xu
An increasing number of functional studies shows that long noncoding RNAs (lncRNAs) are involved in many aspects of cellular physiology and fulfills a wide variety of regulatory roles at almost every stage of gene expression. A major feature of lncRNAs is the highly folded modular domains in transcripts. With improved modeling and definition, it is now feasible to explore and gain novel insights into the structural-functional relationship of lncRNAs and their association with complex human diseases. In this study, we utilized an automatic computational pipeline to scan lncRNA architecture at the genome-wide scale and to obtain a landscape of functional domains...
October 30, 2018: Briefings in Bioinformatics
Adriana Pitea, Ivan Kondofersky, Steffen Sass, Fabian J Theis, Nikola S Mueller, Kristian Unger
Copy number aberrations (CNAs) are known to strongly affect oncogenes and tumour suppressor genes. Given the critical role CNAs play in cancer research, it is essential to accurately identify CNAs from tumour genomes. One particular challenge in finding CNAs is the effect of confounding variables. To address this issue, we assessed how commonly used CNA identification algorithms perform on SNP 6.0 genotyping data in the presence of confounding variables. We simulated realistic synthetic data with varying levels of three confounding variables-the tumour purity, the length of a copy number region and the CNA burden (the percentage of CNAs present in a profiled genome)-and evaluated the performance of OncoSNP, ASCAT, GenoCNA, GISTIC and CGHcall...
October 23, 2018: Briefings in Bioinformatics
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