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BMC Bioinformatics

Alyssa Baccarella, Claire R Williams, Jay Z Parrish, Charles C Kim
BACKGROUND: RNA-Sequencing analysis methods are rapidly evolving, and the tool choice for each step of one common workflow, differential expression analysis, which includes read alignment, expression modeling, and differentially expressed gene identification, has a dramatic impact on performance characteristics. Although a number of workflows are emerging as high performers that are robust to diverse input types, the relative performance characteristics of these workflows when either read depth or sample number is limited-a common occurrence in real-world practice-remain unexplored...
November 14, 2018: BMC Bioinformatics
Hyoyoung Choo-Wosoba, Paul S Albert, Bin Zhu
BACKGROUND: Somatic copy number alternation (SCNA) is a common feature of the cancer genome and is associated with cancer etiology and prognosis. The allele-specific SCNA analysis of a tumor sample aims to identify the allele-specific copy numbers of both alleles, adjusting for the ploidy and the tumor purity. Next generation sequencing platforms produce abundant read counts at the base-pair resolution across the exome or whole genome which is susceptible to hypersegmentation, a phenomenon where numerous regions with very short length are falsely identified as SCNA...
November 14, 2018: BMC Bioinformatics
Jialu Hu, Yiqun Gao, Junhao He, Yan Zheng, Xuequn Shang
BACKGROUND: The discovery of functionally conserved proteins is a tough and important task in system biology. Global network alignment provides a systematic framework to search for these proteins from multiple protein-protein interaction (PPI) networks. Although there exist many web servers for network alignment, no one allows to perform global multiple network alignment tasks on users' test datasets. RESULTS: Here, we developed a web server WebNetcoffee based on the algorithm of NetCoffee to search for a global network alignment from multiple networks...
November 12, 2018: BMC Bioinformatics
Konstantina Dimitrakopoulou, Elisabeth Wik, Lars A Akslen, Inge Jonassen
BACKGROUND: Towards discovering robust cancer biomarkers, it is imperative to unravel the cellular heterogeneity of patient samples and comprehend the interactions between cancer cells and the various cell types in the tumor microenvironment. The first generation of 'partial' computational deconvolution methods required prior information either on the cell/tissue type proportions or the cell/tissue type-specific expression signatures and the number of involved cell/tissue types. The second generation of 'complete' approaches allowed estimating both of the cell/tissue type proportions and cell/tissue type-specific expression profiles directly from the mixed gene expression data, based on known (or automatically identified) cell/tissue type-specific marker genes...
November 7, 2018: BMC Bioinformatics
Teresa M R Noviello, Antonella Di Liddo, Giovanna M Ventola, Antonietta Spagnuolo, Salvatore D'Aniello, Michele Ceccarelli, Luigi Cerulo
BACKGROUND: Long non-coding RNAs (lncRNAs) represent a novel class of non-coding RNAs having a crucial role in many biological processes. The identification of long non-coding homologs among different species is essential to investigate such roles in model organisms as homologous genes tend to retain similar molecular and biological functions. Alignment-based metrics are able to effectively capture the conservation of transcribed coding sequences and then the homology of protein coding genes...
November 6, 2018: BMC Bioinformatics
Erno Lindfors, Jesse C J van Dam, Carolyn Ming Chi Lam, Niels A Zondervan, Vitor A P Martins Dos Santos, Maria Suarez-Diez
BACKGROUND: Systems biology takes a holistic approach by handling biomolecules and their interactions as big systems. Network based approach has emerged as a natural way to model these systems with the idea of representing biomolecules as nodes and their interactions as edges. Very often the input data come from various sorts of omics analyses. Those resulting networks sometimes describe a wide range of aspects, for example different experiment conditions, species, tissue types, stimulating factors, mutants, or simply distinct interaction features of the same network produced by different algorithms...
November 6, 2018: BMC Bioinformatics
Momeneh Foroutan, Dharmesh D Bhuva, Ruqian Lyu, Kristy Horan, Joseph Cursons, Melissa J Davis
BACKGROUND: Gene set scoring provides a useful approach for quantifying concordance between sample transcriptomes and selected molecular signatures. Most methods use information from all samples to score an individual sample, leading to unstable scores in small data sets and introducing biases from sample composition (e.g. varying numbers of samples for different cancer subtypes). To address these issues, we have developed a truly single sample scoring method, and associated R/Bioconductor package singscore ( https://bioconductor...
November 6, 2018: BMC Bioinformatics
Farzaneh Salari, Fatemeh Zare-Mirakabad, Mehdi Sadeghi, Hassan Rokni-Zadeh
BACKGROUND: Nowadays, according to valuable resources of high-quality genome sequences, reference-based assembly methods with high accuracy and efficiency are strongly required. Many different algorithms have been designed for mapping reads onto a genome sequence which try to enhance the accuracy of reconstructed genomes. In this problem, one of the challenges occurs when some reads are aligned to multiple locations due to repetitive regions in the genomes. RESULTS: In this paper, our goal is to decrease the error rate of rebuilt genomes by resolving multi-mapping reads...
November 6, 2018: BMC Bioinformatics
Andon Tchechmedjiev, Amine Abdaoui, Vincent Emonet, Stella Zevio, Clement Jonquet
BACKGROUND: Despite a wide adoption of English in science, a significant amount of biomedical data are produced in other languages, such as French. Yet a majority of natural language processing or semantic tools as well as domain terminologies or ontologies are only available in English, and cannot be readily applied to other languages, due to fundamental linguistic differences. However, semantic resources are required to design semantic indexes and transform biomedical (text)data into knowledge for better information mining and retrieval...
November 6, 2018: BMC Bioinformatics
Xin-Ping Xie, Yu-Feng Xie, Yi-Tong Liu, Hong-Qiang Wang
BACKGROUND: Identifying cancer biomarkers from transcriptomics data is of importance to cancer research. However, transcriptomics data are often complex and heterogeneous, which complicates the identification of cancer biomarkers in practice. Currently, the heterogeneity still remains a challenge for detecting subtle but consistent changes of gene expression in cancer cells. RESULTS: In this paper, we propose to adaptively capture the heterogeneity of expression across samples in a gene regulation space instead of in a gene expression space...
November 3, 2018: BMC Bioinformatics
Michal R Grzadkowski, Dorota H Sendorek, Christine P'ng, Vincent Huang, Paul C Boutros
BACKGROUND: The development of clinical -omic biomarkers for predicting patient prognosis has mostly focused on multi-gene models. However, several studies have described significant weaknesses of multi-gene biomarkers. Indeed, some high-profile reports have even indicated that multi-gene biomarkers fail to consistently outperform simple single-gene ones. Given the continual improvements in -omics technologies and the availability of larger, better-powered datasets, we revisited this "single-gene hypothesis" using new techniques and datasets...
November 3, 2018: BMC Bioinformatics
Jaesik Kwak, Joonhong Park
BACKGROUND: Since the analysis of a large number of metagenomic sequences costs heavy computing resources and takes long time, we examined a selected small part of metagenomic sequences as "sample"s of the entire full sequences, both for a mock community and for 10 different existing metagenomics case studies. A mock community with 10 bacterial strains was prepared, and their mixed genome were sequenced by Hiseq. The hits of BLAST search for reference genome of each strain were counted...
November 3, 2018: BMC Bioinformatics
Michael A Zeller, Tavis K Anderson, Rasna W Walia, Amy L Vincent, Phillip C Gauger
BACKGROUND: Influenza A Virus (IAV) causes respiratory disease in swine and is a zoonotic pathogen. Uncontrolled IAV in swine herds not only affects animal health, it also impacts production through increased costs associated with treatment and prevention efforts. The Iowa State University Veterinary Diagnostic Laboratory (ISU VDL) diagnoses influenza respiratory disease in swine and provides epidemiological analyses on samples submitted by veterinarians. DESCRIPTION: To assess the incidence of IAV in swine and inform stakeholders, the ISU FLUture website was developed as an interactive visualization tool that allows the exploration of the ISU VDL swine IAV aggregate data in the clinical diagnostic database...
November 1, 2018: BMC Bioinformatics
Guilherme S Pereira, Antonio Augusto F Garcia, Gabriel R A Margarido
BACKGROUND: Genotyping-by-sequencing (GBS) has been used broadly in genetic studies for several species, especially those with agricultural importance. However, its use is still limited in autopolyploid species because genotype calling software generally fails to properly distinguish heterozygous classes based on allele dosage. RESULTS: VCF2SM is a Python script that integrates sequencing depth information of polymorphisms in variant call format (VCF) files and SUPERMASSA software for quantitative genotype calling...
November 1, 2018: BMC Bioinformatics
Peizhuo Wang, Lin Gao, Yuxuan Hu, Feng Li
BACKGROUND: Comprehensive analyzing multi-omics biological data in different conditions is important for understanding biological mechanism in system level. Multiple or multi-layer network model gives us a new insight into simultaneously analyzing these data, for instance, to identify conserved functional modules in multiple biological networks. However, because of the larger scale and more complicated structure of multiple networks than single network, how to accurate and efficient detect conserved functional biological modules remains a significant challenge...
October 29, 2018: BMC Bioinformatics
Shenghui Liu, Chunrui Xu, Yusen Zhang, Jiaguo Liu, Bin Yu, Xiaoping Liu, Matthias Dehmer
BACKGROUND: Using knowledge-based interpretation to analyze omics data can not only obtain essential information regarding various biological processes, but also reflect the current physiological status of cells and tissue. The major challenge to analyze gene expression data, with a large number of genes and small samples, is to extract disease-related information from a massive amount of redundant data and noise. Gene selection, eliminating redundant and irrelevant genes, has been a key step to address this problem...
October 29, 2018: BMC Bioinformatics
Shaun D Jackman, Lauren Coombe, Justin Chu, Rene L Warren, Benjamin P Vandervalk, Sarah Yeo, Zhuyi Xue, Hamid Mohamadi, Joerg Bohlmann, Steven J M Jones, Inanc Birol
BACKGROUND: Genome sequencing yields the sequence of many short snippets of DNA (reads) from a genome. Genome assembly attempts to reconstruct the original genome from which these reads were derived. This task is difficult due to gaps and errors in the sequencing data, repetitive sequence in the underlying genome, and heterozygosity. As a result, assembly errors are common. In the absence of a reference genome, these misassemblies may be identified by comparing the sequencing data to the assembly and looking for discrepancies between the two...
October 26, 2018: BMC Bioinformatics
Shu-Ju Lin, Tzu-Pin Lu, Qi-You Yu, Chuhsing Kate Hsiao
BACKGROUND: Current methods for gene-set or pathway analysis are usually designed to test the enrichment of a single gene-set. Once the analysis is carried out for each of the sets under study, a list of significant sets can be obtained. However, if one wishes to further prioritize the importance or strength of association of these sets, no such quantitative measure is available. Using the magnitude of p-value to rank the pathways may not be appropriate because p-value is not a measure for strength of significance...
October 24, 2018: BMC Bioinformatics
Deisy Morselli Gysi, Andre Voigt, Tiago de Miranda Fragoso, Eivind Almaas, Katja Nowick
BACKGROUND: Network analyses, such as of gene co-expression networks, metabolic networks and ecological networks have become a central approach for the systems-level study of biological data. Several software packages exist for generating and analyzing such networks, either from correlation scores or the absolute value of a transformed score called weighted topological overlap (wTO). However, since gene regulatory processes can up- or down-regulate genes, it is of great interest to explicitly consider both positive and negative correlations when constructing a gene co-expression network...
October 24, 2018: BMC Bioinformatics
Rolf Hühne, Viktor Kessler, Axel Fürstberger, Silke Kühlwein, Matthias Platzer, Jürgen Sühnel, Ludwig Lausser, Hans A Kestler
BACKGROUND: The Ageing Factor Database AgeFactDB contains a large number of lifespan observations for ageing-related factors like genes, chemical compounds, and other factors such as dietary restriction in different organisms. These data provide quantitative information on the effect of ageing factors from genetic interventions or manipulations of lifespan. Analysis strategies beyond common static database queries are highly desirable for the inspection of complex relationships between AgeFactDB data sets...
October 23, 2018: BMC Bioinformatics
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