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Briefings in Bioinformatics

Yu Ding, Hong Wang, Hewei Zheng, Lianzong Wang, Guosi Zhang, Jiaxin Yang, Xiaoyan Lu, Yu Bai, Haotian Zhang, Jing Li, Wenyan Gao, Fukun Chen, Shui Hu, Jingqi Wu, Liangde Xu
The spatial position and interaction of drugs and their targets is the most important characteristics for understanding a drug's pharmacological effect, and it could help both in finding new and more precise treatment targets for diseases and in exploring the targeting effects of the new drugs. In this work, we develop a computational pipeline to confirm the spatial interaction relationship of the drugs and their targets and compare the drugs' efficacies based on the interaction centers. First, we produce a 100-sample set to reconstruct a stable docking model of the confirmed drug-target pairs...
March 13, 2019: Briefings in Bioinformatics
Ran Su, Xinyi Liu, Guobao Xiao, Leyi Wei
Anticancer drug response prediction plays an important role in personalized medicine. In particular, precisely predicting drug response in specific cancer types and patients is still a challenge problem. Here we propose Meta-GDBP, a novel anticancer drug-response model, which involves two levels. At the first level of Meta-GDBP, we build four optimized base models (BMs) using genetic information, chemical properties and biological context with an ensemble optimization strategy, while at the second level, we construct a weighted model to integrate the four BMs...
March 13, 2019: Briefings in Bioinformatics
Regina Brinster, Dominique Scherer, Justo Lorenzo Bermejo
Population stratification is usually corrected relying on principal component analysis (PCA) of genome-wide genotype data, even in populations considered genetically homogeneous, such as Europeans. The need to genotype only a small number of genetic variants that show large differences in allele frequency among subpopulations-so-called ancestry-informative markers (AIMs)-instead of the whole genome for stratification adjustment could represent an advantage for replication studies and candidate gene/pathway studies...
March 13, 2019: Briefings in Bioinformatics
Ran Su, Xinyi Liu, Leyi Wei
Recursive feature elimination (RFE), as one of the most popular feature selection algorithms, has been extensively applied to bioinformatics. During the training, a group of candidate subsets are generated by iteratively eliminating the least important features from the original features. However, how to determine the optimal subset from them still remains ambiguous. Among most current studies, either overall accuracy or subset size (SS) is used to select the most predictive features. Using which one or both and how they affect the prediction performance are still open questions...
March 12, 2019: Briefings in Bioinformatics
Ziye Wang, Ying Wang, Jed A Fuhrman, Fengzhu Sun, Shanfeng Zhu
In metagenomic studies of microbial communities, the short reads come from mixtures of genomes. Read assembly is usually an essential first step for the follow-up studies in metagenomic research. Understanding the power and limitations of various read assembly programs in practice is important for researchers to choose which programs to use in their investigations. Many studies evaluating different assembly programs used either simulated metagenomes or real metagenomes with unknown genome compositions. However, the simulated datasets may not reflect the real complexities of metagenomic samples and the estimated assembly accuracy could be misleading due to the unknown genomes in real metagenomes...
March 11, 2019: Briefings in Bioinformatics
Yunxia An, Nan Wei, Xiangsong Cheng, Ying Li, Haiyang Liu, Jia Wang, Zhiwei Xu, Zhifu Sun, Xiaoju Zhang
MCAM (CD146) is a cell surface adhesion molecule that has been reported to promote cancer development, progression and metastasis and is considered as a potential tumor biomarker and therapeutic target. However, inconsistent reports exist, and its clinical value is yet to be confirmed. Here we took advantage of several large genomic data collections (Genotype-Tissue Expression, The Cancer Genome Atlas and Cancer Cell Line Encyclopedia) and comprehensively analyzed MCAM expression in thousands of normal and cancer samples and cell lines along with their clinical phenotypes and drug response information...
February 28, 2019: Briefings in Bioinformatics
Martin Ayling, Matthew D Clark, Richard M Leggett
In recent years, the use of longer range read data combined with advances in assembly algorithms has stimulated big improvements in the contiguity and quality of genome assemblies. However, these advances have not directly transferred to metagenomic data sets, as assumptions made by the single genome assembly algorithms do not apply when assembling multiple genomes at varying levels of abundance. The development of dedicated assemblers for metagenomic data was a relatively late innovation and for many years, researchers had to make do using tools designed for single genomes...
February 28, 2019: Briefings in Bioinformatics
Siyuan Chen, Chengzhi Ren, Jingjing Zhai, Jiantao Yu, Xuyang Zhao, Zelong Li, Ting Zhang, Wenlong Ma, Zhaoxue Han, Chuang Ma
A widely used approach in transcriptome analysis is the alignment of short reads to a reference genome. However, owing to the deficiencies of specially designed analytical systems, short reads unmapped to the genome sequence are usually ignored, resulting in the loss of significant biological information and insights. To fill this gap, we present Comprehensive Assembly and Functional annotation of Unmapped RNA-Seq data (CAFU), a Galaxy-based framework that can facilitate the large-scale analysis of unmapped RNA sequencing (RNA-Seq) reads from single- and mixed-species samples...
February 28, 2019: Briefings in Bioinformatics
Xingyi Li, Wenkai Li, Min Zeng, Ruiqing Zheng, Min Li
Genes that are thought to be critical for the survival of organisms or cells are called essential genes. The prediction of essential genes and their products (essential proteins) is of great value in exploring the mechanism of complex diseases, the study of the minimal required genome for living cells and the development of new drug targets. As laboratory methods are often complicated, costly and time-consuming, a great many of computational methods have been proposed to identify essential genes/proteins from the perspective of the network level with the in-depth understanding of network biology and the rapid development of biotechnologies...
February 18, 2019: Briefings in Bioinformatics
Azza E Ahmed, Ayah A Awadallah, Mawada Tagelsir, Maram A Suliman, Atheer Eltigani, Hassan Elsafi, Basil D Hamdelnile, Mohamed A Mukhtar, Faisal M Fadlelmola
MOTIVATION: Delivering high-quality distance-based courses in resource-limited settings is a challenging task. Besides the needed infrastructure and expertise, effective delivery of a bioinformatics course could benefit from hands-on sessions, interactivity and problem-based learning approaches. RESULTS: In this article, we discuss the challenges and best practices in delivering bioinformatics training in resource-limited settings taking the example of hosting and running a multiple-delivery online course, Introduction to Bioinformatics, that was developed by the H3ABioNet Education and Training working group and delivered in 27 remote classrooms across Africa in 2017...
February 15, 2019: Briefings in Bioinformatics
Abukari Mohammed Yakubu, Yi-Ping Phoebe Chen
In recent times, the reduced cost of DNA sequencing has resulted in a plethora of genomic data that is being used to advance biomedical research and improve clinical procedures and healthcare delivery. These advances are revolutionizing areas in genome-wide association studies (GWASs), diagnostic testing, personalized medicine and drug discovery. This, however, comes with security and privacy challenges as the human genome is sensitive in nature and uniquely identifies an individual. In this article, we discuss the genome privacy problem and review relevant privacy attacks, classified into identity tracing, attribute disclosure and completion attacks, which have been used to breach the privacy of an individual...
February 12, 2019: Briefings in Bioinformatics
Xiangrong Liu, Zengyan Hong, Juan Liu, Yuan Lin, Alfonso Rodríguez-Patón, Quan Zou, Xiangxiang Zeng
A biological network is complex. A group of critical nodes determines the quality and state of such a network. Increasing studies have shown that diseases and biological networks are closely and mutually related and that certain diseases are often caused by errors occurring in certain nodes in biological networks. Thus, studying biological networks and identifying critical nodes can help determine the key targets in treating diseases. The problem is how to find the critical nodes in a network efficiently and with low cost...
February 12, 2019: Briefings in Bioinformatics
Xu Zhou, Enyu Dai, Qian Song, Xueyan Ma, Qianqian Meng, Yongshuai Jiang, Wei Jiang
Drug repositioning has become a prevailing tactic as this strategy is efficient, economical and low risk for drug discovery. Meanwhile, recent studies have confirmed that small-molecule drugs can modulate the expression of disease-related miRNAs, which indicates that miRNAs are promising therapeutic targets for complex diseases. In this study, we put forward and verified the hypothesis that drugs with similar miRNA profiles may share similar therapeutic properties. Furthermore, a comprehensive drug-drug interaction network was constructed based on curated drug-miRNA associations...
February 11, 2019: Briefings in Bioinformatics
Giulia Simoni, Hong Thanh Vo, Corrado Priami, Luca Marchetti
With the recent rising application of mathematical models in the field of computational systems biology, the interest in sensitivity analysis methods had increased. The stochastic approach, based on chemical master equations, and the deterministic approach, based on ordinary differential equations (ODEs), are the two main approaches for analyzing mathematical models of biochemical systems. In this work, the performance of these approaches to compute sensitivity coefficients is explored in situations where stochastic and deterministic simulation can potentially provide different results (systems with unstable steady states, oscillators with population extinction and bistable systems)...
February 7, 2019: Briefings in Bioinformatics
Helena Cirenajwis, Martin Lauss, Maria Planck, Johan Vallon-Christersson, Johan Staaf
The development of multigene classifiers for cancer prognosis, treatment prediction, molecular subtypes or clinicopathological groups has been a cornerstone in transcriptomic analyses of human malignancies for nearly two decades. However, many reported classifiers are critically limited by different preprocessing needs like normalization and data centering. In response, a new breed of classifiers, single sample predictors (SSPs), has emerged. SSPs classify samples in an N-of-1 fashion, relying on, e.g. gene rules comparing expression values within a sample...
February 4, 2019: Briefings in Bioinformatics
Zhi Ruan, Yunsong Yu, Ye Feng
Whole genome sequencing (WGS) has revolutionized the genotyping of bacterial pathogens and is expected to become the new gold standard for tracing the transmissions of bacterial infectious diseases for public health purposes. Traditional genomic epidemiology often uses WGS as a verification tool, namely, when a common source or epidemiological link is suspected, the collected isolates are sequenced for the determination of clonal relationships. However, increasingly frequent international travel and food transportation, and the associated potential for the cross-border transmission of bacterial pathogens, often lead to an absence of information on bacterial transmission routes...
February 4, 2019: Briefings in Bioinformatics
Manuel Franco, Juana María Vivo, Manuel Quesada-Martínez, Astrid Duque-Ramos, Jesualdo Tomás Fernández-Breis
The development and application of biological ontologies have increased significantly in recent years. These ontologies can be retrieved from different repositories, which do not provide much information about quality aspects of the ontologies. In the past years, some ontology structural metrics have been proposed, but their validity as measurement instrument has not been sufficiently studied to date. In this work, we evaluate a set of reproducible and objective ontology structural metrics. Given the lack of standard methods for this purpose, we have applied an evaluation method based on the stability and goodness of the classifications of ontologies produced by each metric on an ontology corpus...
February 4, 2019: Briefings in Bioinformatics
Xin Qi, Yuxin Lin, Jiajia Chen, Bairong Shen
Crosstalk between competing endogenous RNAs (ceRNAs) is mediated by shared microRNAs (miRNAs) and plays important roles both in normal physiology and tumorigenesis; thus, it is attractive for systems-level decoding of gene regulation. As ceRNA networks link the function of miRNAs with that of transcripts sharing the same miRNA response elements (MREs), e.g. pseudogenes, competing mRNAs, long non-coding RNAs, and circular RNAs, the perturbation of crucial interactions in ceRNA networks may contribute to carcinogenesis by affecting the balance of cellular regulatory system...
January 30, 2019: Briefings in Bioinformatics
Basant K Tiwary
Biological complex systems are composed of numerous components that interact within and across different scales. The ever-increasing generation of high-throughput biomedical data has given us an opportunity to develop a quantitative model of nonlinear biological systems having implications in health and diseases. Multidimensional molecular data can be modeled using various statistical methods at different scales of biological organization, such as genome, transcriptome and proteome. I will discuss recent advances in the application of computational medicine in complex diseases such as network-based studies, genome-scale metabolic modeling, kinetic modeling and support vector machines with specific examples in the field of cancer, psychiatric disorders and type 2 diabetes...
January 30, 2019: Briefings in Bioinformatics
Pablo Mier, Lisanna Paladin, Stella Tamana, Sophia Petrosian, Borbála Hajdu-Soltész, Annika Urbanek, Aleksandra Gruca, Dariusz Plewczynski, Marcin Grynberg, Pau Bernadó, Zoltán Gáspári, Christos A Ouzounis, Vasilis J Promponas, Andrey V Kajava, John M Hancock, Silvio C E Tosatto, Zsuzsanna Dosztanyi, Miguel A Andrade-Navarro
There are multiple definitions for low complexity regions (LCRs) in protein sequences, with all of them broadly considering LCRs as regions with fewer amino acid types compared to an average composition. Following this view, LCRs can also be defined as regions showing composition bias. In this critical review, we focus on the definition of sequence complexity of LCRs and their connection with structure. We present statistics and methodological approaches that measure low complexity (LC) and related sequence properties...
January 30, 2019: Briefings in Bioinformatics
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