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Briefings in Bioinformatics

Felicia S L Ng, David Ruau, Lorenz Wernisch, Berthold Göttgens
Integrated analysis of multiple genome-wide transcription factor (TF)-binding profiles will be vital to advance our understanding of the global impact of TF binding. However, existing methods for measuring similarity in large numbers of chromatin immunoprecipitation assays with sequencing (ChIP-seq), such as correlation, mutual information or enrichment analysis, are limited in their ability to display functionally relevant TF relationships. In this study, we propose the use of graphical models to determine conditional independence between TFs and showed that network visualization provides a promising alternative to distinguish 'direct' versus 'indirect' TF interactions...
October 25, 2016: Briefings in Bioinformatics
Yi An, Jiawei Wang, Chen Li, André Leier, Tatiana Marquez-Lago, Jonathan Wilksch, Yang Zhang, Geoffrey I Webb, Jiangning Song, Trevor Lithgow
Bacterial effector proteins secreted by various protein secretion systems play crucial roles in host-pathogen interactions. In this context, computational tools capable of accurately predicting effector proteins of the various types of bacterial secretion systems are highly desirable. Existing computational approaches use different machine learning (ML) techniques and heterogeneous features derived from protein sequences and/or structural information. These predictors differ not only in terms of the used ML methods but also with respect to the used curated data sets, the features selection and their prediction performance...
October 24, 2016: Briefings in Bioinformatics
Paola G Ferrario, Inke R König
Genome-wide association studies are moving to genome-wide interaction studies, as the genetic background of many diseases appears to be more complex than previously supposed. Thus, many statistical approaches have been proposed to detect gene-gene (GxG) interactions, among them numerous information theory-based methods, inspired by the concept of entropy. These are suggested as particularly powerful and, because of their nonlinearity, as better able to capture nonlinear relationships between genetic variants and/or variables...
October 21, 2016: Briefings in Bioinformatics
Maoqi Xu, Liang Chen
The individual sample heterogeneity is one of the biggest obstacles in biomarker identification for complex diseases such as cancers. Current statistical models to identify differentially expressed genes between disease and control groups often overlook the substantial human sample heterogeneity. Meanwhile, traditional nonparametric tests lose detailed data information and sacrifice the analysis power, although they are distribution free and robust to heterogeneity. Here, we propose an empirical likelihood ratio test with a mean-variance relationship constraint (ELTSeq) for the differential expression analysis of RNA sequencing (RNA-seq)...
October 21, 2016: Briefings in Bioinformatics
(no author information available yet)
Many disciplines, from human genetics and oncology to plant breeding, microbiology and virology, commonly face the challenge of analyzing rapidly increasing numbers of genomes. In case of Homo sapiens, the number of sequenced genomes will approach hundreds of thousands in the next few years. Simply scaling up established bioinformatics pipelines will not be sufficient for leveraging the full potential of such rich genomic data sets. Instead, novel, qualitatively different computational methods and paradigms are needed...
October 21, 2016: Briefings in Bioinformatics
Linna Zhao, Di Liu, Jing Xu, Zhaoyang Wang, Yang Chen, Changgui Lei, Ying Li, Guiyou Liu, Yongshuai Jiang
At present, understanding of DNA methylation at the population level is still limited. Here, we first extended the classical framework of population genetics, such as single nucleotide polymorphism allele frequency, linkage disequilibrium (LD), LD block and haplotype, to epigenetics. Then, as an example, we compared the DNA methylation disequilibrium (MD) maps between HapMap CEU (Caucasian residents of European ancestry from Utah) population and YRI (Yoruba people from Ibadan) population (lymphoblastoid cell lines)...
October 19, 2016: Briefings in Bioinformatics
Yongchang Zheng, Qianqian Huang, Zijian Ding, Tingting Liu, Chenghai Xue, Xinting Sang, Jin Gu
The alteration of DNA methylation landscape is a key epigenetic event in cancer. As the accumulation of large-scale genome-wide DNA methylation data from clinical samples, we are able to characterize the patterns of DNA methylation alterations for identifying candidate epigenetic markers and drivers. In this survey, we take hepatocellular carcinoma (HCC) as an example to show the basic steps of analyzing the DNA methylation patterns in cancer across multiple data sets. We collected three genome-wide DNA methylation data sets with ∼800 clinical samples and the corresponding gene expression data sets...
October 19, 2016: Briefings in Bioinformatics
Ron Henkel, Robert Hoehndorf, Tim Kacprowski, Christian Knüpfer, Wolfram Liebermeister, Dagmar Waltemath
Systems biology models are rapidly increasing in complexity, size and numbers. When building large models, researchers rely on software tools for the retrieval, comparison, combination and merging of models, as well as for version control. These tools need to be able to quantify the differences and similarities between computational models. However, depending on the specific application, the notion of 'similarity' may greatly vary. A general notion of model similarity, applicable to various types of models, is still missing...
October 14, 2016: Briefings in Bioinformatics
James M Brown, Neil R Horner, Thomas N Lawson, Tanja Fiegel, Simon Greenaway, Hugh Morgan, Natalie Ring, Luis Santos, Duncan Sneddon, Lydia Teboul, Jennifer Vibert, Gagarine Yaikhom, Henrik Westerberg, Ann-Marie Mallon
High-throughput phenotyping is a cornerstone of numerous functional genomics projects. In recent years, imaging screens have become increasingly important in understanding gene-phenotype relationships in studies of cells, tissues and whole organisms. Three-dimensional (3D) imaging has risen to prominence in the field of developmental biology for its ability to capture whole embryo morphology and gene expression, as exemplified by the International Mouse Phenotyping Consortium (IMPC). Large volumes of image data are being acquired by multiple institutions around the world that encompass a range of modalities, proprietary software and metadata...
October 14, 2016: Briefings in Bioinformatics
Juan Xu, ZiShan Wang, Shengli Li, Juan Chen, Jinwen Zhang, Chunjie Jiang, Zheng Zhao, Jing Li, Yongsheng Li, Xia Li
Although systematic genomic studies have identified a broad spectrum of non-coding RNAs (ncRNAs) that are involved in breast cancer, our understanding of the epigenetic dysregulation of those ncRNAs remains limited. Here, we systematically analysed the epigenetic alterations of microRNAs (miRNAs) and long non-coding RNAs (lncRNAs) in two breast cancer subtypes (luminal and basal). Widespread epigenetic alterations of miRNAs and lncRNAs were observed in both cancer subtypes. In contrast to protein-coding genes, the majority of epigenetically dysregulated ncRNAs were shared between subtypes, but a subset of transcriptomic and corresponding epigenetic changes occurred in a subtype-specific manner...
October 14, 2016: Briefings in Bioinformatics
Guillem Rigaill, Sandrine Balzergue, Véronique Brunaud, Eddy Blondet, Andrea Rau, Odile Rogier, José Caius, Cathy Maugis-Rabusseau, Ludivine Soubigou-Taconnat, Sébastien Aubourg, Claire Lurin, Marie-Laure Martin-Magniette, Etienne Delannoy
Numerous statistical pipelines are now available for the differential analysis of gene expression measured with RNA-sequencing technology. Most of them are based on similar statistical frameworks after normalization, differing primarily in the choice of data distribution, mean and variance estimation strategy and data filtering. We propose an evaluation of the impact of these choices when few biological replicates are available through the use of synthetic data sets. This framework is based on real data sets and allows the exploration of various scenarios differing in the proportion of non-differentially expressed genes...
October 14, 2016: Briefings in Bioinformatics
Jang-Il Sohn, Jin-Wu Nam
As the advent of next-generation sequencing (NGS) technology, various de novo assembly algorithms based on the de Bruijn graph have been developed to construct chromosome-level sequences. However, numerous technical or computational challenges in de novo assembly still remain, although many bright ideas and heuristics have been suggested to tackle the challenges in both experimental and computational settings. In this review, we categorize de novo assemblers on the basis of the type of de Bruijn graphs (Hamiltonian and Eulerian) and discuss the challenges of de novo assembly for short NGS reads regarding computational complexity and assembly ambiguity...
October 14, 2016: Briefings in Bioinformatics
Abdul Arif Khan, Zakir Khan, Mohd Abul Kalam, Azmat Ali Khan
Microbial pathogenesis involves several aspects of host-pathogen interactions, including microbial proteins targeting host subcellular compartments and subsequent effects on host physiology. Such studies are supported by experimental data, but recent detection of bacterial proteins localization through computational eukaryotic subcellular protein targeting prediction tools has also come into practice. We evaluated inter-kingdom prediction certainty of these tools. The bacterial proteins experimentally known to target host subcellular compartments were predicted with eukaryotic subcellular targeting prediction tools, and prediction certainty was assessed...
October 6, 2016: Briefings in Bioinformatics
Tommi Välikangas, Tomi Suomi, Laura L Elo
To date, mass spectrometry (MS) data remain inherently biased as a result of reasons ranging from sample handling to differences caused by the instrumentation. Normalization is the process that aims to account for the bias and make samples more comparable. The selection of a proper normalization method is a pivotal task for the reliability of the downstream analysis and results. Many normalization methods commonly used in proteomics have been adapted from the DNA microarray techniques. Previous studies comparing normalization methods in proteomics have focused mainly on intragroup variation...
October 2, 2016: Briefings in Bioinformatics
Ning Zhao, Yongjing Liu, Yunzhen Wei, Zichuang Yan, Qiang Zhang, Cheng Wu, Zhiqiang Chang, Yan Xu
Cell lines are widely used as in vitro models of tumorigenesis. However, an increasing number of researchers have found that cell lines differ from their sourced tumour samples after long-term cell culture. The application of unsuitable cell lines in experiments will affect the experimental accuracy and the treatment of patients. Therefore, it is imperative to identify optimal cell lines for each cancer type. Here, we review the methods used to evaluate cell lines since 2005. Furthermore, gene expression, copy number and mutation profiles from The Cancer Genome Atlas and the Cancer Cell Line Encyclopedia are used to calculate similarity between tumours and cell lines...
October 2, 2016: Briefings in Bioinformatics
David John, Tyler Weirick, Stefanie Dimmeler, Shizuka Uchida
RNA editing of adenosine residues to inosine ('A-to-I editing') is the most common RNA modification event detectible with RNA sequencing (RNA-seq). While not directly detectable, inosine is read by next-generation sequencers as guanine. Therefore, mapping RNA-seq reads to their corresponding reference genome can detect potential editing events by identifying 'A-to-G' conversions. However, one must exercise caution when searching for editing sites, as A-to-G conversions also arise from sequencing errors as well as mutations...
September 30, 2016: Briefings in Bioinformatics
Wei Jiang, Jing-Hao Xue, Weichuan Yu
The goal of genome-wide association studies (GWASs) is to discover genetic variants associated with diseases/traits. Replication is a common validation method in GWASs. We regard an association as true finding when it shows significance in both primary and replication studies. A question worth pondering is what is the probability of a primary association (i.e. a statistically significant association in the primary study) being validated in the replication study? This article systematically reviews the answers to this question from different points of view...
September 28, 2016: Briefings in Bioinformatics
David Roy Smith, Matheus Sanitá Lima
Online sequence repositories are teeming with RNA sequencing (RNA-Seq) data from a wide range of eukaryotes. Although most of these data sets contain large numbers of organelle-derived reads, researchers tend to ignore these data, focusing instead on the nuclear-derived transcripts. Consequently, GenBank contains massive amounts of organelle RNA-Seq data that are just waiting to be downloaded and analyzed. Recently, a team of scientists designed an open-source bioinformatics program called ChloroSeq, which systemically analyzes an organelle transcriptome using RNA-Seq...
September 26, 2016: Briefings in Bioinformatics
Davide S Sardina, Salvatore Alaimo, Alfredo Ferro, Alfredo Pulvirenti, Rosalba Giugno
Posttranscriptional cross talk and communication between genes mediated by microRNA response element (MREs) yield large regulatory competing endogenous RNA (ceRNA) networks. Their inference may improve the understanding of pathologies and shed new light on biological mechanisms. A variety of RNA: messenger RNA, transcribed pseudogenes, noncoding RNA, circular RNA and proteins related to RNA-induced silencing complex complex interacting with RNA transfer and ribosomal RNA have been experimentally proved to be ceRNAs...
September 26, 2016: Briefings in Bioinformatics
Xuefeng Wang, Zhenyu Zhang, Nathan Morris, Tianxi Cai, Seunggeun Lee, Chaolong Wang, Timothy W Yu, Christopher A Walsh, Xihong Lin
The objective of this article is to introduce valid and robust methods for the analysis of rare variants for family-based exome chips, whole-exome sequencing or whole-genome sequencing data. Family-based designs provide unique opportunities to detect genetic variants that complement studies of unrelated individuals. Currently, limited methods and software tools have been developed to assist family-based association studies with rare variants, especially for analyzing binary traits. In this article, we address this gap by extending existing burden and kernel-based gene set association tests for population data to related samples, with a particular emphasis on binary phenotypes...
September 26, 2016: Briefings in Bioinformatics
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