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Briefings in Bioinformatics

Yuwei Zhang, Yang Tao, Qi Liao
Long noncoding RNAs (lncRNAs) had been defined as a novel class of functional RNAs longer than 200 nucleotides around a decade ago. It is widely acknowledged that lncRNAs play a significant role in regulation of gene expression, but the biological and molecular mechanisms are diverse and complex, and remain to be determined. Especially, the regulatory network of lncRNAs associated with other biological molecules is still a controversial matter, thus becoming a new frontier of the studies on transcriptome. Recent advance in high-throughput sequencing technologies and bioinformatics approaches may be an accelerator to lift the mysterious veil...
April 24, 2017: Briefings in Bioinformatics
Andreas Tauch, Arwa Al-Dilaimi
The German Network for Bioinformatics Infrastructure (de.NBI) is a national initiative funded by the German Federal Ministry of Education and Research (BMBF). The mission of de.NBI is (i) to provide high-quality bioinformatics services to users in basic and applied life sciences research from academia, industry and biomedicine; (ii) to offer bioinformatics training to users in Germany and Europe through a wide range of workshops and courses; and (iii) to foster the cooperation of the German bioinformatics community with international network structures such as the European life-sciences Infrastructure for biological Information (ELIXIR)...
April 18, 2017: Briefings in Bioinformatics
Yulan Liang, Arpad Kelemen
Inferring networks and dynamics of genes, proteins, cells and other biological entities from high-throughput biological omics data is a central and challenging issue in computational and systems biology. This is essential for understanding the complexity of human health, disease susceptibility and pathogenesis for Predictive, Preventive, Personalized and Participatory (P4) system and precision medicine. The delineation of the possible interactions of all genes/proteins in a genome/proteome is a task for which conventional experimental techniques are ill suited...
April 18, 2017: Briefings in Bioinformatics
Md Rezaul Karim, Audrey Michel, Achille Zappa, Pavel Baranov, Ratnesh Sahay, Dietrich Rebholz-Schuhmann
Data workflow systems (DWFSs) enable bioinformatics researchers to combine components for data access and data analytics, and to share the final data analytics approach with their collaborators. Increasingly, such systems have to cope with large-scale data, such as full genomes (about 200 GB each), public fact repositories (about 100 TB of data) and 3D imaging data at even larger scales. As moving the data becomes cumbersome, the DWFS needs to embed its processes into a cloud infrastructure, where the data are already hosted...
April 16, 2017: Briefings in Bioinformatics
Shulan Tian, Huihuang Yan, Eric W Klee, Michael Kalmbach, Susan L Slager
Current variant discovery approaches often rely on an initial read mapping to the reference sequence. Their effectiveness is limited by the presence of gaps, potential misassemblies, regions of duplicates with a high-sequence similarity and regions of high-sequence divergence in the reference. Also, mapping-based approaches are less sensitive to large INDELs and complex variations and provide little phase information in personal genomes. A few de novo assemblers have been developed to identify variants through direct variant calling from the assembly graph, micro-assembly and whole-genome assembly, but mainly for whole-genome sequencing (WGS) data...
April 11, 2017: Briefings in Bioinformatics
Arvind K Chavali, Seung Y Rhee
Specialized metabolites (also called natural products or secondary metabolites) derived from bacteria, fungi, marine organisms and plants constitute an important source of antibiotics, anti-cancer agents, insecticides, immunosuppressants and herbicides. Many specialized metabolites in bacteria and fungi are biosynthesized via metabolic pathways whose enzymes are encoded by clustered genes on a chromosome. Metabolic gene clusters comprise a group of physically co-localized genes that together encode enzymes for the biosynthesis of a specific metabolite...
April 7, 2017: Briefings in Bioinformatics
Georgios V Gkoutos, Paul N Schofield, Robert Hoehndorf
The past decade has seen an explosion in the collection of genotype data in domains as diverse as medicine, ecology, livestock and plant breeding. Along with this comes the challenge of dealing with the related phenotype data, which is not only large but also highly multidimensional. Computational analysis of phenotypes has therefore become critical for our ability to understand the biological meaning of genomic data in the biological sciences. At the heart of computational phenotype analysis are the phenotype ontologies...
April 6, 2017: Briefings in Bioinformatics
Wenwu Wu, Jie Zong, Ning Wei, Jian Cheng, Xuexia Zhou, Yuanming Cheng, Dai Chen, Qinghua Guo, Bo Zhang, Ying Feng
RNA-sequencing (RNA-seq) can generate millions of reads to provide clues for analyzing novel or abnormal alternative splicing (AS) events in cells. However, current methods for exploring AS events are still far from being satisfactory. Here, we present Comprehensive AS Hunting (CASH), which constructs comprehensive splice sites including known and novel AS sites in cells, and identifies differentially AS events between cells. We illuminated the versatility of CASH on RNA-seq data from a wide range of species and also on simulated in silico data, validated the advantages of CASH over other AS predictors and exhibited novel differentially AS events...
April 6, 2017: Briefings in Bioinformatics
Diana Tichy, Julia Maria Anna Pickl, Axel Benner, Holger Sültmann
The identification of microRNA (miRNA) target genes is crucial for understanding miRNA function. Many methods for the genome-wide miRNA target identification have been developed in recent years; however, they have several limitations including the dependence on low-confident prediction programs and artificial miRNA manipulations. Ago-RNA immunoprecipitation combined with high-throughput sequencing (Ago-RIP-Seq) is a promising alternative. However, appropriate statistical data analysis algorithms taking into account the experimental design and the inherent noise of such experiments are largely lacking...
March 31, 2017: Briefings in Bioinformatics
Maria Pamela Dobay, Silke Stertz, Mauro Delorenzi
Various techniques have been developed for identifying the most probable interactants of a protein under a given biological context. In this article, we dissect the effects of the choice of the protein-protein interaction network (PPI) and the manipulation of PPI settings on the network neighborhood of the influenza A virus (IAV) network, as well as hits in genome-wide small interfering RNA screen results for IAV host factors. We investigate the potential of context filtering, which uses text mining evidence linked to PPI edges, as a complement to the edge confidence scores typically provided in PPIs for filtering, for obtaining more biologically relevant network neighborhoods...
March 27, 2017: Briefings in Bioinformatics
Jonathon J O'Brien, Harsha P Gunawardena, Bahjat F Qaqish
Biomedical researchers are often interested in computing the correlation between RNA and protein abundance. However, correlations can be computed between rows of a data matrix or between columns, and the results are not the same. The belief that these two types of correlation are estimating the same phenomenon is a special case of a well-known logical error called the ecological fallacy. In this article, we review different uses of correlation found in the literature, explain the differences between row and column correlations and argue that one of them has an undesirable interpretation in most applications...
March 26, 2017: Briefings in Bioinformatics
Michiel P van Ooijen, Victor L Jong, Marinus J C Eijkemans, Albert J R Heck, Arno C Andeweg, Nadine A Binai, Henk-Jan van den Ham
With the advent of high-throughput proteomics, the type and amount of data pose a significant challenge to statistical approaches used to validate current quantitative analysis. Whereas many studies focus on the analysis at the protein level, the analysis of peptide-level data provides insight into changes at the sub-protein level, including splice variants, isoforms and a range of post-translational modifications. Statistical evaluation of liquid chromatography-mass spectrometry/mass spectrometry peptide-based label-free differential data is most commonly performed using a t-test or analysis of variance, often after the application of data imputation to reduce the number of missing values...
March 23, 2017: Briefings in Bioinformatics
Thilo Muth, Bernhard Y Renard
While peptide identifications in mass spectrometry (MS)-based shotgun proteomics are mostly obtained using database search methods, high-resolution spectrum data from modern MS instruments nowadays offer the prospect of improving the performance of computational de novo peptide sequencing. The major benefit of de novo sequencing is that it does not require a reference database to deduce full-length or partial tag-based peptide sequences directly from experimental tandem mass spectrometry spectra. Although various algorithms have been developed for automated de novo sequencing, the prediction accuracy of proposed solutions has been rarely evaluated in independent benchmarking studies...
March 21, 2017: Briefings in Bioinformatics
Sheng-You Huang
Protein-ligand docking has been playing an important role in modern drug discovery. To model drug-target binding in real systems, a number of flexible-ligand docking algorithms with different sampling strategies and scoring methods have been subsequently developed over the past three decades, while rigid-ligand docking is still being used because of its compelling computational efficiency. Here, a comprehensive assessment has been conducted to investigate the effectiveness of flexible-ligand docking versus rigid-ligand docking for three representative docking algorithms (global optimization, incremental construction and multi-conformer docking) in virtual screening and pose prediction on the Directory of Useful Decoys...
March 14, 2017: Briefings in Bioinformatics
Di Liu, Linna Zhao, Yang Chen, Zhaoyang Wang, Jing Xu, Ying Li, Changgui Lei, Simeng Hu, Miaomiao Niu, Yongshuai Jiang
The murine model serves as an important experimental system in biomedical science because of its high degree of similarities at the sequence level with human. Recent studies have compared the transcriptional landscapes between human and mouse, but the general co-expression landscapes have not been characterized. Here, we calculated the general co-expression coefficients and constructed the general co-expression maps for human and mouse. The differences and similarities of the general co-expression maps between the two species were compared in detail...
March 14, 2017: Briefings in Bioinformatics
Yang-Jun Wen, Hanwen Zhang, Yuan-Li Ni, Bo Huang, Jin Zhang, Jian-Ying Feng, Shi-Bo Wang, Jim M Dunwell, Yuan-Ming Zhang, Rongling Wu
No abstract text is available yet for this article.
March 8, 2017: Briefings in Bioinformatics
Qianrui Fan, Feng Zhang, Wenyu Wang, Jiawen Xu, Jingcan Hao, Awen He, Yan Wen, Ping Li, Xiao Liang, Yanan Du, Li Liu, Cuiyan Wu, Sen Wang, Xi Wang, Yujie Ning, Xiong Guo
Genome-wide association study (GWAS)-based pathway association analysis is a powerful approach for the genetic studies of human complex diseases. However, the genetic confounding effects of environment exposure-related genes can decrease the accuracy of GWAS-based pathway association analysis of target diseases. In this study, we developed a pathway association analysis approach, named Mendelian randomization-based pathway enrichment analysis (MRPEA), which was capable of correcting the genetic confounding effects of environmental exposures, using the GWAS summary data of environmental exposures...
March 8, 2017: Briefings in Bioinformatics
Bingqiang Liu, Jinyu Yang, Yang Li, Adam McDermaid, Qin Ma
Transcription factors are proteins that bind to specific DNA sequences and play important roles in controlling the expression levels of their target genes. Hence, prediction of transcription factor binding sites (TFBSs) provides a solid foundation for inferring gene regulatory mechanisms and building regulatory networks for a genome. Chromatin immunoprecipitation sequencing (ChIP-seq) technology can generate large-scale experimental data for such protein-DNA interactions, providing an unprecedented opportunity to identify TFBSs (a...
March 8, 2017: Briefings in Bioinformatics
Adib Shafi, Cristina Mitrea, Tin Nguyen, Sorin Draghici
DNA methylation is an important epigenetic mechanism that plays a crucial role in cellular regulatory systems. Recent advancements in sequencing technologies now enable us to generate high-throughput methylation data and to measure methylation up to single-base resolution. This wealth of data does not come without challenges, and one of the key challenges in DNA methylation studies is to identify the significant differences in the methylation levels of the base pairs across distinct biological conditions. Several computational methods have been developed to identify differential methylation using bisulfite sequencing data; however, there is no clear consensus among existing approaches...
March 8, 2017: Briefings in Bioinformatics
Jian Zhang, Lukasz Kurgan
Understanding of molecular mechanisms that govern protein-protein interactions and accurate modeling of protein-protein docking rely on accurate identification and prediction of protein-binding partners and protein-binding residues. We review over 40 methods that predict protein-protein interactions from protein sequences including methods that predict interacting protein pairs, protein-binding residues for a pair of interacting sequences and protein-binding residues in a single protein chain. We focus on the latter methods that provide residue-level annotations and that can be broadly applied to all protein sequences...
March 1, 2017: Briefings in Bioinformatics
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