8,884 results match your criteria BMC Bioinformatics [Journal]


SEQprocess: a modularized and customizable pipeline framework for NGS processing in R package.

BMC Bioinformatics 2019 Feb 20;20(1):90. Epub 2019 Feb 20.

Department of Physiology, Ajou University School of Medicine, 164 Worldcup-ro, Yeongtong-gu, Suwon, 16499, Republic of Korea.

Backgrounds: Next-Generation Sequencing (NGS) is now widely used in biomedical research for various applications. Processing of NGS data requires multiple programs and customization of the processing pipelines according to the data platforms. However, rapid progress of the NGS applications and processing methods urgently require prompt update of the pipelines. Read More

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http://dx.doi.org/10.1186/s12859-019-2676-xDOI Listing
February 2019

ElemCor: accurate data analysis and enrichment calculation for high-resolution LC-MS stable isotope labeling experiments.

BMC Bioinformatics 2019 Feb 19;20(1):89. Epub 2019 Feb 19.

Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.

Background: The investigation of intracellular metabolism is the mainstay in the biotechnology and physiology settings. Intracellular metabolic rates are commonly evaluated using labeling pattern of the identified metabolites obtained from stable isotope labeling experiments. The labeling pattern or mass distribution vector describes the fractional abundances of all isotopologs with different masses as a result of isotopic labeling, which are typically resolved using mass spectrometry. Read More

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http://dx.doi.org/10.1186/s12859-019-2669-9DOI Listing
February 2019

Prediction of lncRNA-disease associations by integrating diverse heterogeneous information sources with RWR algorithm and positive pointwise mutual information.

BMC Bioinformatics 2019 Feb 19;20(1):87. Epub 2019 Feb 19.

Department of Biomedical Informatics, University of Pittsburgh, 5607 Baum Blvd, Pittsburgh, PA, 15206, USA.

Background: Long non-coding RNAs play an important role in human complex diseases. Identification of lncRNA-disease associations will gain insight into disease-related lncRNAs and benefit disease diagnoses and treatment. However, using experiments to explore the lncRNA-disease associations is expensive and time consuming. Read More

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http://dx.doi.org/10.1186/s12859-019-2675-yDOI Listing
February 2019

SLIM: a flexible web application for the reproducible processing of environmental DNA metabarcoding data.

BMC Bioinformatics 2019 Feb 19;20(1):88. Epub 2019 Feb 19.

Department of Genetics and Evolution, University of Geneva, Science III, 4 Boulevard d'Yvoy, 1205, Geneva, Switzerland.

Background: High-throughput amplicon sequencing of environmental DNA (eDNA metabarcoding) has become a routine tool for biodiversity survey and ecological studies. By including sample-specific tags in the primers prior PCR amplification, it is possible to multiplex hundreds of samples in a single sequencing run. The analysis of millions of sequences spread into hundreds to thousands of samples prompts for efficient, automated yet flexible analysis pipelines. Read More

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http://dx.doi.org/10.1186/s12859-019-2663-2DOI Listing
February 2019

Identifying cancer prognostic modules by module network analysis.

BMC Bioinformatics 2019 Feb 18;20(1):85. Epub 2019 Feb 18.

Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan, 430070, People's Republic of China.

Background: The identification of prognostic genes that can distinguish the prognostic risks of cancer patients remains a significant challenge. Previous works have proven that functional gene sets were more reliable for this task than the gene signature. However, few works have considered the cross-talk among functional gene sets, which may result in neglecting important prognostic gene sets for cancer. Read More

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https://bmcbioinformatics.biomedcentral.com/articles/10.1186
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http://dx.doi.org/10.1186/s12859-019-2674-zDOI Listing
February 2019
2 Reads

DeepUbi: a deep learning framework for prediction of ubiquitination sites in proteins.

BMC Bioinformatics 2019 Feb 18;20(1):86. Epub 2019 Feb 18.

Department of Information and Computing Science, University of Science and Technology Beijing, Beijing, 100083, China.

Background: Protein ubiquitination occurs when the ubiquitin protein binds to a target protein residue of lysine (K), and it is an important regulator of many cellular functions, such as signal transduction, cell division, and immune reactions, in eukaryotes. Experimental and clinical studies have shown that ubiquitination plays a key role in several human diseases, and recent advances in proteomic technology have spurred interest in identifying ubiquitination sites. However, most current computing tools for predicting target sites are based on small-scale data and shallow machine learning algorithms. Read More

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https://bmcbioinformatics.biomedcentral.com/articles/10.1186
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http://dx.doi.org/10.1186/s12859-019-2677-9DOI Listing
February 2019
1 Read
2.576 Impact Factor

CirGO: an alternative circular way of visualising gene ontology terms.

BMC Bioinformatics 2019 Feb 18;20(1):84. Epub 2019 Feb 18.

Harry Perkins Institute of Medical Research, Nedlands, Western Australia, 6009, Australia.

Background: Prioritisation of gene ontology terms from differential gene expression analyses in a two-dimensional format remains a challenge with exponentially growing data volumes. Typically, gene ontology terms are represented as tree-maps that enclose all data into defined space. However, large datasets make this type of visualisation appear cluttered and busy, and often not informative as some labels are omitted due space limits, especially when published in two-dimensional (2D) figures. Read More

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http://dx.doi.org/10.1186/s12859-019-2671-2DOI Listing
February 2019

Statistical assessment and visualization of synergies for large-scale sparse drug combination datasets.

BMC Bioinformatics 2019 Feb 18;20(1):83. Epub 2019 Feb 18.

The Center of Cancer Research, Massachusetts General Hospital, 149 13th Street, Charlestown, MA, 02129, USA.

Background: Drug combinations have the potential to improve efficacy while limiting toxicity. To robustly identify synergistic combinations, high-throughput screens using full dose-response surface are desirable but require an impractical number of data points. Screening of a sparse number of doses per drug allows to screen large numbers of drug pairs, but complicates statistical assessment of synergy. Read More

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http://dx.doi.org/10.1186/s12859-019-2642-7DOI Listing
February 2019

Parameter estimation in models of biological oscillators: an automated regularised estimation approach.

BMC Bioinformatics 2019 Feb 15;20(1):82. Epub 2019 Feb 15.

(Bio)Process Engineering Group, IIM-CSIC, Eduardo Cabello 6, Vigo, 36208, Spain.

Background: Dynamic modelling is a core element in the systems biology approach to understanding complex biosystems. Here, we consider the problem of parameter estimation in models of biological oscillators described by deterministic nonlinear differential equations. These problems can be extremely challenging due to several common pitfalls: (i) a lack of prior knowledge about parameters (i. Read More

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http://dx.doi.org/10.1186/s12859-019-2630-yDOI Listing
February 2019

Functional Heatmap: an automated and interactive pattern recognition tool to integrate time with multi-omics assays.

BMC Bioinformatics 2019 Feb 15;20(1):81. Epub 2019 Feb 15.

Integrative Systems Biology Program, US Army Center for Environmental Health Research, Fort Detrick, Frederick, MD, 21702-5010, USA.

Background: Life science research is moving quickly towards large-scale experimental designs that are comprised of multiple tissues, time points, and samples. Omic time-series experiments offer answers to three big questions: what collective patterns do most analytes follow, which analytes follow an identical pattern or synchronize across multiple cohorts, and how do biological functions evolve over time. Existing tools fall short of robustly answering and visualizing all three questions in a unified interface. Read More

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http://dx.doi.org/10.1186/s12859-019-2657-0DOI Listing
February 2019

Iterative unsupervised domain adaptation for generalized cell detection from brightfield z-stacks.

BMC Bioinformatics 2019 Feb 15;20(1):80. Epub 2019 Feb 15.

Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland.

Background: Cell counting from cell cultures is required in multiple biological and biomedical research applications. Especially, accurate brightfield-based cell counting methods are needed for cell growth analysis. With deep learning, cells can be detected with high accuracy, but manually annotated training data is required. Read More

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http://dx.doi.org/10.1186/s12859-019-2605-zDOI Listing
February 2019

BioVR: a platform for virtual reality assisted biological data integration and visualization.

BMC Bioinformatics 2019 Feb 15;20(1):78. Epub 2019 Feb 15.

Thomas H. Gosnell School of Life Sciences, Rochester Institute of Technology, One Lomb Memorial Drive, Rochester, NY, 14623, USA.

Background: Functional characterization of single nucleotide variants (SNVs) involves two steps, the first step is to convert DNA to protein and the second step is to visualize protein sequences with their structures. As massively parallel sequencing has emerged as a leading technology in genomics, resulting in a significant increase in data volume, direct visualization of SNVs together with associated protein sequences/structures in a new user interface (UI) would be a more effective way to assess their potential effects on protein function.

Results: We have developed BioVR, an easy-to-use interactive, virtual reality (VR)-assisted platform for integrated visual analysis of DNA/RNA/protein sequences and protein structures using Unity3D and the C# programming language. Read More

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http://dx.doi.org/10.1186/s12859-019-2666-zDOI Listing
February 2019

FunMappOne: a tool to hierarchically organize and visually navigate functional gene annotations in multiple experiments.

BMC Bioinformatics 2019 Feb 15;20(1):79. Epub 2019 Feb 15.

Faculty of Medicine and Life Sciences, University of Tampere, Arvo Ylpön katu 34 - Arvo building, Tampere, FI-33014, Finland.

Background: Functional annotation of genes is an essential step in omics data analysis. Multiple databases and methods are currently available to summarize the functions of sets of genes into higher level representations, such as ontologies and molecular pathways. Annotating results from omics experiments into functional categories is essential not only to understand the underlying regulatory dynamics but also to compare multiple experimental conditions at a higher level of abstraction. Read More

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http://dx.doi.org/10.1186/s12859-019-2639-2DOI Listing
February 2019

MultiDomainBenchmark: a multi-domain query and subject database suite.

BMC Bioinformatics 2019 Feb 14;20(1):77. Epub 2019 Feb 14.

National Center for Biotechnology Information, Bethesda, National Institutes of Health, 8600 Rockville Pike, Bethesda, 20894, MD, USA.

Background: Genetic sequence database retrieval benchmarks play an essential role in evaluating the performance of sequence searching tools. To date, all phylogenetically diverse benchmarks known to the authors include only query sequences with single protein domains. Domains are the primary building blocks of protein structure and function. Read More

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http://dx.doi.org/10.1186/s12859-019-2660-5DOI Listing
February 2019

ADS-HCSpark: A scalable HaplotypeCaller leveraging adaptive data segmentation to accelerate variant calling on Spark.

BMC Bioinformatics 2019 Feb 14;20(1):76. Epub 2019 Feb 14.

Communication & Computer Network Lab of Guangdong, School of Computer Science & Engineering, South China University of Technology, Wushan Road, Guangzhou, 510641, China.

Background: The advance of next generation sequencing enables higher throughput with lower price, and as the basic of high-throughput sequencing data analysis, variant calling is widely used in disease research, clinical treatment and medicine research. However, current mainstream variant caller tools have a serious problem of computation bottlenecks, resulting in some long tail tasks when performing on large datasets. This prevents high scalability on clusters of multi-node and multi-core, and leads to long runtime and inefficient usage of computing resources. Read More

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http://dx.doi.org/10.1186/s12859-019-2665-0DOI Listing
February 2019

WGDdetector: a pipeline for detecting whole genome duplication events using the genome or transcriptome annotations.

BMC Bioinformatics 2019 Feb 13;20(1):75. Epub 2019 Feb 13.

CAS Key Laboratory of Tropical Forest Ecology, Xishuangbanna Tropical Botanical Garden, Chinese Academy of Sciences, Mengla, 666303, Yunnan, China.

Background: With the availability of well-assembled genomes of a growing number of organisms, identifying the bioinformatic basis of whole genome duplication (WGD) is a growing field of genomics. The most extant software for detecting footprints of WGDs has been restricted to a well-assembled genome. However, the massive poor quality genomes and the more accessible transcriptomes have been largely ignored, and in theoretically they are also likely to contribute to detect WGD using dS based method. Read More

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http://dx.doi.org/10.1186/s12859-019-2670-3DOI Listing
February 2019

Disease Pathway Cut for Multi-Target drugs.

BMC Bioinformatics 2019 Feb 13;20(1):74. Epub 2019 Feb 13.

Department of Industrial Engineering, Ajou University, 206, World cup-ro, Yeongtong-gu, Suwon-si, Gyeonggi-do, 16499, Republic of Korea.

Background: Biomarker discovery studies have been moving the focus from a single target gene to a set of target genes. However, the number of target genes in a drug should be minimum to avoid drug side-effect or toxicity. But still, the set of target genes should effectively block all possible paths of disease progression. Read More

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http://dx.doi.org/10.1186/s12859-019-2638-3DOI Listing
February 2019
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From POS tagging to dependency parsing for biomedical event extraction.

BMC Bioinformatics 2019 Feb 12;20(1):72. Epub 2019 Feb 12.

School of Computing and Information Systems, The University of Melbourne, Melbourne, Australia.

Background: Given the importance of relation or event extraction from biomedical research publications to support knowledge capture and synthesis, and the strong dependency of approaches to this information extraction task on syntactic information, it is valuable to understand which approaches to syntactic processing of biomedical text have the highest performance.

Results: We perform an empirical study comparing state-of-the-art traditional feature-based and neural network-based models for two core natural language processing tasks of part-of-speech (POS) tagging and dependency parsing on two benchmark biomedical corpora, GENIA and CRAFT. To the best of our knowledge, there is no recent work making such comparisons in the biomedical context; specifically no detailed analysis of neural models on this data is available. Read More

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http://dx.doi.org/10.1186/s12859-019-2604-0DOI Listing
February 2019

IMMAN: an R/Bioconductor package for Interolog protein network reconstruction, mapping and mining analysis.

BMC Bioinformatics 2019 Feb 12;20(1):73. Epub 2019 Feb 12.

School of Biological Science, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran.

Background: Reconstruction of protein-protein interaction networks (PPIN) has been riddled with controversy for decades. Particularly, false-negative and -positive interactions make this progress even more complicated. Also, lack of a standard PPIN limits us in the comparison studies and results in the incompatible outcomes. Read More

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http://dx.doi.org/10.1186/s12859-019-2659-yDOI Listing
February 2019

Analyzing a co-occurrence gene-interaction network to identify disease-gene association.

BMC Bioinformatics 2019 Feb 8;20(1):70. Epub 2019 Feb 8.

Department of Physics, Khalifa University of Science and Technology, Abu Dhabi, P.O. Box 127788,, United Arab Emirates.

Background: Understanding the genetic networks and their role in chronic diseases (e.g., cancer) is one of the important objectives of biological researchers. Read More

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http://dx.doi.org/10.1186/s12859-019-2634-7DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6368766PMC
February 2019

Genomic prediction of tuberculosis drug-resistance: benchmarking existing databases and prediction algorithms.

BMC Bioinformatics 2019 Feb 8;20(1):68. Epub 2019 Feb 8.

NUS Graduate School for Integrative Science and Engineering, National University of Singapore, Singapore, 119077, Singapore.

Background: It is possible to predict whether a tuberculosis (TB) patient will fail to respond to specific antibiotics by sequencing the genome of the infecting Mycobacterium tuberculosis (Mtb) and observing whether the pathogen carries specific mutations at drug-resistance sites. This advancement has led to the collation of TB databases such as PATRIC and ReSeqTB that possess both whole genome sequences and drug resistance phenotypes of infecting Mtb isolates. Bioinformatics tools have also been developed to predict drug resistance from whole genome sequencing (WGS) data. Read More

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http://dx.doi.org/10.1186/s12859-019-2658-zDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6368788PMC
February 2019

Predicting clinically promising therapeutic hypotheses using tensor factorization.

BMC Bioinformatics 2019 Feb 8;20(1):69. Epub 2019 Feb 8.

Computational Biology, GSK R&D, 1250 S. Collegeville Road, UP12-200, Collegeville, PA, USA.

Background: Determining which target to pursue is a challenging and error-prone first step in developing a therapeutic treatment for a disease, where missteps are potentially very costly given the long-time frames and high expenses of drug development. With current informatics technology and machine learning algorithms, it is now possible to computationally discover therapeutic hypotheses by predicting clinically promising drug targets based on the evidence associating drug targets with disease indications. We have collected this evidence from Open Targets and additional databases that covers 17 sources of evidence for target-indication association and represented the data as a tensor of 21,437 × 2211 × 17. Read More

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http://dx.doi.org/10.1186/s12859-019-2664-1DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6368709PMC
February 2019

Predicting protein functions by applying predicate logic to biomedical literature.

BMC Bioinformatics 2019 Feb 8;20(1):71. Epub 2019 Feb 8.

Department of Electrical and Computer Engineering, Khalifa University, Abu Dhabi, United Arab Emirates.

Background: A large number of computational methods have been proposed for predicting protein functions. The underlying techniques adopted by most of these methods revolve around predicting the functions of an unannotated protein p from already annotated proteins that have similar characteristics as p. Recent Information Extraction methods take advantage of the huge growth of biomedical literature to predict protein functions. Read More

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http://dx.doi.org/10.1186/s12859-019-2594-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6368809PMC
February 2019

CeModule: an integrative framework for discovering regulatory patterns from genomic data in cancer.

BMC Bioinformatics 2019 Feb 7;20(1):67. Epub 2019 Feb 7.

College of Computer Science and Electronic Engineering, Hunan University, Changsha, 410082, China.

Background: Non-coding RNAs (ncRNAs) are emerging as key regulators and play critical roles in a wide range of tumorigenesis. Recent studies have suggested that long non-coding RNAs (lncRNAs) could interact with microRNAs (miRNAs) and indirectly regulate miRNA targets through competing interactions. Therefore, uncovering the competing endogenous RNA (ceRNA) regulatory mechanism of lncRNAs, miRNAs and mRNAs in post-transcriptional level will aid in deciphering the underlying pathogenesis of human polygenic diseases and may unveil new diagnostic and therapeutic opportunities. Read More

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http://dx.doi.org/10.1186/s12859-019-2654-3DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6367773PMC
February 2019
3 Reads

CERENKOV2: improved detection of functional noncoding SNPs using data-space geometric features.

BMC Bioinformatics 2019 Feb 6;20(1):63. Epub 2019 Feb 6.

School of Electrical Engineering and Computer Science, Oregon State University, Corvallis, 97330, OR, USA.

Background: We previously reported on CERENKOV, an approach for identifying regulatory single nucleotide polymorphisms (rSNPs) that is based on 246 annotation features. CERENKOV uses the xgboost classifier and is designed to be used to find causal noncoding SNPs in loci identified by genome-wide association studies (GWAS). We reported that CERENKOV has state-of-the-art performance (by two traditional measures and a novel GWAS-oriented measure, AVGRANK) in a comparison to nine other tools for identifying functional noncoding SNPs, using a comprehensive reference SNP set (OSU17, 15,331 SNPs). Read More

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http://dx.doi.org/10.1186/s12859-019-2637-4DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6364436PMC
February 2019

Estimation of duplication history under a stochastic model for tandem repeats.

BMC Bioinformatics 2019 Feb 6;20(1):64. Epub 2019 Feb 6.

Department of Electrical Engineering, California Institute of Technology, Pasadena, USA.

Background: Tandem repeat sequences are common in the genomes of many organisms and are known to cause important phenomena such as gene silencing and rapid morphological changes. Due to the presence of multiple copies of the same pattern in tandem repeats and their high variability, they contain a wealth of information about the mutations that have led to their formation. The ability to extract this information can enhance our understanding of evolutionary mechanisms. Read More

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http://dx.doi.org/10.1186/s12859-019-2603-1DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6364452PMC
February 2019

Shambhala: a platform-agnostic data harmonizer for gene expression data.

BMC Bioinformatics 2019 Feb 6;20(1):66. Epub 2019 Feb 6.

I.M. Sechenov First Moscow State Medical University, Sechenov University, Moscow, 119991, Russia.

Background: Harmonization techniques make different gene expression profiles and their sets compatible and ready for comparisons. Here we present a new bioinformatic tool termed Shambhala for harmonization of multiple human gene expression datasets obtained using different experimental methods and platforms of microarray hybridization and RNA sequencing.

Results: Unlike previously published methods enabling good quality data harmonization for only two datasets, Shambhala allows conversion of multiple datasets into the universal form suitable for further comparisons. Read More

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http://dx.doi.org/10.1186/s12859-019-2641-8DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6366102PMC
February 2019

DeepPVP: phenotype-based prioritization of causative variants using deep learning.

BMC Bioinformatics 2019 Feb 6;20(1):65. Epub 2019 Feb 6.

Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology, 4700 KAUST, Thuwal, 23955-6900, Kingdom of Saudi Arabia.

Background: Prioritization of variants in personal genomic data is a major challenge. Recently, computational methods that rely on comparing phenotype similarity have shown to be useful to identify causative variants. In these methods, pathogenicity prediction is combined with a semantic similarity measure to prioritize not only variants that are likely to be dysfunctional but those that are likely involved in the pathogenesis of a patient's phenotype. Read More

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http://dx.doi.org/10.1186/s12859-019-2633-8DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6364462PMC
February 2019
1 Read

Application of the geographic population structure (GPS) algorithm for biogeographical analyses of wild and captive gorillas.

BMC Bioinformatics 2019 Feb 5;20(Suppl 1):35. Epub 2019 Feb 5.

Department of Medical Genetics, Kasturba Medical College, Manipal Academy of Higher Education, Manipal, Karnataka, India.

Background: The utilization of high resolution genome data has important implications for the phylogeographical evaluation of non-human species. Biogeographical analyses can yield detailed understanding of their population biology and facilitate the geo-localization of individuals to promote their efficacious management, particularly when bred in captivity. The Geographic Population Structure (GPS) algorithm is an admixture based tool for inference of biogeographical affinities and has been employed for the geo-localization of various human populations worldwide. Read More

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http://dx.doi.org/10.1186/s12859-018-2568-5DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6362561PMC
February 2019

A new version of the ANDSystem tool for automatic extraction of knowledge from scientific publications with expanded functionality for reconstruction of associative gene networks by considering tissue-specific gene expression.

BMC Bioinformatics 2019 Feb 5;20(Suppl 1):34. Epub 2019 Feb 5.

Laboratory of Computer-Assisted Proteomics, Institute of Cytology and Genetics, Siberian Branch, Russian Academy of Sciences, Prospekt Lavrentyeva 10, Novosibirsk, 630090, Russia.

Background: Consideration of tissue-specific gene expression in reconstruction and analysis of molecular genetic networks is necessary for a proper description of the processes occurring in a specified tissue. Currently, there are a number of computer systems that allow the user to reconstruct molecular-genetic networks using the data automatically extracted from the texts of scientific publications. Examples of such systems are STRING, Pathway Commons, MetaCore and Ingenuity. Read More

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http://dx.doi.org/10.1186/s12859-018-2567-6DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6362586PMC
February 2019
1 Read

Development of electronic medical records for clinical and research purposes: the breast cancer module using an implementation framework in a middle income country- Malaysia.

BMC Bioinformatics 2019 Feb 4;19(Suppl 13):402. Epub 2019 Feb 4.

Data Science and Bioinformatics Laboratory, Institute of Biological Sciences, Faculty of Science, University of Malaya, 50603, Kuala Lumpur, Malaysia.

Background: Advances in medical domain has led to an increase of clinical data production which offers enhancement opportunities for clinical research sector. In this paper, we propose to expand the scope of Electronic Medical Records in the University Malaya Medical Center (UMMC) using different techniques in establishing interoperability functions between multiple clinical departments involving diagnosis, screening and treatment of breast cancer and building automatic systems for clinical audits as well as for potential data mining to enhance clinical breast cancer research in the future.

Results: Quality Implementation Framework (QIF) was adopted to develop the breast cancer module as part of the in-house EMR system used at UMMC, called i-Pesakit©. Read More

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http://dx.doi.org/10.1186/s12859-018-2406-9DOI Listing
February 2019

Siberian larch (Larix sibirica Ledeb.) chloroplast genome and development of polymorphic chloroplast markers.

BMC Bioinformatics 2019 Feb 5;20(Suppl 1):38. Epub 2019 Feb 5.

Laboratory of Forest Genomics, Genome Research and Education Center, Siberian Federal University, 660036, Krasnoyarsk, Russian Federation.

Background: The main objectives of this study were sequencing, assembling, and annotation of chloroplast genome of one of the main Siberian boreal forest tree conifer species Siberian larch (Larix sibirica Ledeb.) and detection of polymorphic genetic markers - microsatellite loci or simple sequence repeats (SSRs) and single nucleotide polymorphisms (SNPs).

Results: We used the data of the whole genome sequencing of three Siberian larch trees from different regions - the Urals, Krasnoyarsk, and Khakassia, respectively. Read More

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http://dx.doi.org/10.1186/s12859-018-2571-xDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6362560PMC
February 2019

Bioinformatics research at BGRS-2018.

BMC Bioinformatics 2019 Feb 5;20(Suppl 1):33. Epub 2019 Feb 5.

Institute of Cytology and Genetics SB RAS, 630090, Novosibirsk, Russia.

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http://dx.doi.org/10.1186/s12859-018-2566-7DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6362573PMC
February 2019
1 Read

GCAC: galaxy workflow system for predictive model building for virtual screening.

BMC Bioinformatics 2019 Feb 4;19(Suppl 13):550. Epub 2019 Feb 4.

School of Computational and Integrative Sciences, Jawaharlal Nehru University, New Delhi, 110067, India.

Background: Traditional drug discovery approaches are time-consuming, tedious and expensive. Identifying a potential drug-like molecule using high throughput screening (HTS) with high confidence is always a challenging task in drug discovery and cheminformatics. A small percentage of molecules that pass the clinical trial phases receives FDA approval. Read More

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http://dx.doi.org/10.1186/s12859-018-2492-8DOI Listing
February 2019
2 Reads

Functional homogeneity and specificity of topological modules in human proteome.

BMC Bioinformatics 2019 Feb 4;19(Suppl 13):553. Epub 2019 Feb 4.

School of Computer Science and Engineering, Nanyang Technological University, Singapore, Singapore.

Background: Functional modules in protein-protein interaction networks (PPIN) are defined by maximal sets of functionally associated proteins and are vital to understanding cellular mechanisms and identifying disease associated proteins. Topological modules of the human proteome have been shown to be related to functional modules of PPIN. However, the effects of the weights of interactions between protein pairs and the integration of physical (direct) interactions with functional (indirect expression-based) interactions have not been investigated in the detection of functional modules of the human proteome. Read More

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http://dx.doi.org/10.1186/s12859-018-2549-8DOI Listing
February 2019

Inverse similarity and reliable negative samples for drug side-effect prediction.

BMC Bioinformatics 2019 Feb 4;19(Suppl 13):554. Epub 2019 Feb 4.

Advanced Analytics Institute, FEIT, University of Technology Sydney, 15 Broadway, Ultimo, NSW 2007, Australia.

Background: In silico prediction of potential drug side-effects is of crucial importance for drug development, since wet experimental identification of drug side-effects is expensive and time-consuming. Existing computational methods mainly focus on leveraging validated drug side-effect relations for the prediction. The performance is severely impeded by the lack of reliable negative training data. Read More

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https://bmcbioinformatics.biomedcentral.com/articles/10.1186
Publisher Site
http://dx.doi.org/10.1186/s12859-018-2563-xDOI Listing
February 2019
1 Read

ENVirT: inference of ecological characteristics of viruses from metagenomic data.

BMC Bioinformatics 2019 Feb 4;19(Suppl 13):377. Epub 2019 Feb 4.

Optimisation and Pattern Recognition Research Group, Department of Mechanical Engineering, Melbourne School of Engineering, The University of Melbourne, Parkville, VIC 3010, Australia.

Background: Estimating the parameters that describe the ecology of viruses,particularly those that are novel, can be made possible using metagenomic approaches. However, the best-performing existing methods require databases to first estimate an average genome length of a viral community before being able to estimate other parameters, such as viral richness. Although this approach has been widely used, it can adversely skew results since the majority of viruses are yet to be catalogued in databases. Read More

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http://dx.doi.org/10.1186/s12859-018-2398-5DOI Listing
February 2019

Latent network-based representations for large-scale gene expression data analysis.

BMC Bioinformatics 2019 Feb 4;19(Suppl 13):466. Epub 2019 Feb 4.

University of Lille, 42, rue Paul Duez, Lille, 59000, France.

Background: With the recent advancements in high-throughput experimental procedures, biologists are gathering huge quantities of data. A main priority in bioinformatics and computational biology is to provide system level analytical tools capable of meeting an ever-growing production of high-throughput biological data while taking into account its biological context. In gene expression data analysis, genes have widely been considered as independent components. Read More

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http://dx.doi.org/10.1186/s12859-018-2481-yDOI Listing
February 2019

Computational discovery and annotation of conserved small open reading frames in fungal genomes.

BMC Bioinformatics 2019 Feb 4;19(Suppl 13):551. Epub 2019 Feb 4.

Centre for Frontier Sciences, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, UKM, 43600, Bangi, Selangor, Malaysia.

Background: Small open reading frames (smORF/sORFs) that encode short protein sequences are often overlooked during the standard gene prediction process thus leading to many sORFs being left undiscovered and/or misannotated. For many genomes, a second round of sORF targeted gene prediction can complement the existing annotation. In this study, we specifically targeted the identification of ORFs encoding for 80 amino acid residues or less from 31 fungal genomes. Read More

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http://dx.doi.org/10.1186/s12859-018-2550-2DOI Listing
February 2019

Stepwise large genome assembly approach: a case of Siberian larch (Larix sibirica Ledeb).

BMC Bioinformatics 2019 Feb 5;20(Suppl 1):37. Epub 2019 Feb 5.

Laboratory of Forest Genomics, Genome Research and Education Center, Siberian Federal University, 660036, Krasnoyarsk, Russia.

Background: De novo assembling of large genomes, such as in conifers (~ 12-30 Gbp), which also consist of ~ 80% of repetitive DNA, is a very complex and computationally intense endeavor. One of the main problems in assembling such genomes lays in computing limitations of nucleotide sequence assembly programs (DNA assemblers). As a rule, modern assemblers are usually designed to assemble genomes with a length not exceeding the length of the human genome (3. Read More

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http://dx.doi.org/10.1186/s12859-018-2570-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6362582PMC
February 2019

BioReader: a text mining tool for performing classification of biomedical literature.

BMC Bioinformatics 2019 Feb 4;19(Suppl 13):57. Epub 2019 Feb 4.

Department of Health Technology, Technical University of Denmark, 2800, Lyngby, Denmark.

Background: Scientific data and research results are being published at an unprecedented rate. Many database curators and researchers utilize data and information from the primary literature to populate databases, form hypotheses, or as the basis for analyses or validation of results. These efforts largely rely on manual literature surveys for collection of these data, and while querying the vast amounts of literature using keywords is enabled by repositories such as PubMed, filtering relevant articles from such query results can be a non-trivial and highly time consuming task. Read More

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https://bmcbioinformatics.biomedcentral.com/articles/10.1186
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http://dx.doi.org/10.1186/s12859-019-2607-xDOI Listing
February 2019
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Automated craniofacial landmarks detection on 3D image using geometry characteristics information.

BMC Bioinformatics 2019 Feb 4;19(Suppl 13):548. Epub 2019 Feb 4.

Department of Paediatric Dentistry and Orthodontics / Clinical Craniofacial Dentistry Research Group, Faculty of Dentistry, University of Malaya, 50603, Kuala Lumpur, Malaysia.

Background: Indirect anthropometry (IA) is one of the craniofacial anthropometry methods to perform the measurements on the digital facial images. In order to get the linear measurements, a few definable points on the structures of individual facial images have to be plotted as landmark points. Currently, most anthropometric studies use landmark points that are manually plotted on a 3D facial image by the examiner. Read More

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http://dx.doi.org/10.1186/s12859-018-2548-9DOI Listing
February 2019

Exploitation of reverse vaccinology and immunoinformatics as promising platform for genome-wide screening of new effective vaccine candidates against Plasmodium falciparum.

BMC Bioinformatics 2019 Feb 4;19(Suppl 13):468. Epub 2019 Feb 4.

Amity Institute of Biotechnology, Amity University Uttar Pradesh, Lucknow Campus, Lucknow, 226028, India.

Background: In the current scenario, designing of world-wide effective malaria vaccine against Plasmodium falciparum remain challenging despite the significant progress has been made in last few decades. Conventional vaccinology (isolate, inactivate and inject) approaches are time consuming, laborious and expensive; therefore, the use of computational vaccinology tools are imperative, which can facilitate the design of new and promising vaccine candidates.

Results: In current investigation, initially 5548 proteins of P. Read More

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https://bmcbioinformatics.biomedcentral.com/articles/10.1186
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http://dx.doi.org/10.1186/s12859-018-2482-xDOI Listing
February 2019
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Identification and analysis of structurally critical fragments in HopS2.

BMC Bioinformatics 2019 Feb 4;19(Suppl 13):552. Epub 2019 Feb 4.

Department of Molecular Biology and Biotechnology, Tezpur University, Tezpur, Assam, 784028, India.

Background: Among the diverse roles of the Type III secretion-system (T3SS), one of the notable functions is that it serves as unique nano machineries in gram-negative bacteria that facilitate the translocation of effector proteins from bacteria into their host. These effector proteins serve as potential targets to control the pathogenicity conferred to the bacteria. Despite being ideal choices to disrupt bacterial systems, it has been quite an ordeal in the recent times to experimentally reveal and establish a concrete sequence-structure-function relationship for these effector proteins. Read More

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http://dx.doi.org/10.1186/s12859-018-2551-1DOI Listing
February 2019

Benchmarking of different molecular docking methods for protein-peptide docking.

BMC Bioinformatics 2019 Feb 4;19(Suppl 13):426. Epub 2019 Feb 4.

Center for Computation Biology, Indraprastha Institute of Information Technology, Okhla Phase III, New Delhi, 110020, India.

Background: Molecular docking studies on protein-peptide interactions are a challenging and time-consuming task because peptides are generally more flexible than proteins and tend to adopt numerous conformations. There are several benchmarking studies on protein-protein, protein-ligand and nucleic acid-ligand docking interactions. However, a series of docking methods is not rigorously validated for protein-peptide complexes in the literature. Read More

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http://dx.doi.org/10.1186/s12859-018-2449-yDOI Listing
February 2019
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MIGREW: database on molecular identification of genes for resistance in wheat.

BMC Bioinformatics 2019 Feb 5;20(Suppl 1):36. Epub 2019 Feb 5.

Institute of Cytology and Genetics SB RAS, Novosibirsk, Russia.

Population structure of fungal infections in wheat differs between wheat varieties and environments. Taking into account evolution of host-pathogen interactions, genetic diversity of both wheat and fungus must be a monitored. In order to catalogue information to support need of wheat pathologists and breeders, who use conventional methods and Molecular Assisted Selection (MAS) techniques, we have developed the Molecular Identification of Genes for Resistance in Wheat (MIGREW) database. Read More

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http://dx.doi.org/10.1186/s12859-018-2569-4DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6362583PMC
February 2019

Discovering MoRFs by trisecting intrinsically disordered protein sequence into terminals and middle regions.

BMC Bioinformatics 2019 Feb 4;19(Suppl 13):378. Epub 2019 Feb 4.

Laboratory of Medical Science Mathematics, RIKEN Center for Integrative Medical Sciences, Yokohama, 230-0045, Japan.

Background: Molecular Recognition Features (MoRFs) are short protein regions present in intrinsically disordered protein (IDPs) sequences. MoRFs interact with structured partner protein and upon interaction, they undergo a disorder-to-order transition to perform various biological functions. Analyses of MoRFs are important towards understanding their function. Read More

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http://dx.doi.org/10.1186/s12859-018-2396-7DOI Listing
February 2019
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Understanding the evolutionary trend of intrinsically structural disorders in cancer relevant proteins as probed by Shannon entropy scoring and structure network analysis.

BMC Bioinformatics 2019 Feb 4;19(Suppl 13):549. Epub 2019 Feb 4.

Chemistry and Chemical Biology, Harvard University, 12 Oxford Street, Cambridge, Massachusetts, 02138, USA.

Background: Malignant diseases have become a threat for health care system. A panoply of biological processes is involved as the cause of these diseases. In order to unveil the mechanistic details of these diseased states, we analyzed protein families relevant to these diseases. Read More

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http://dx.doi.org/10.1186/s12859-018-2552-0DOI Listing
February 2019
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2.576 Impact Factor

GlyStruct: glycation prediction using structural properties of amino acid residues.

BMC Bioinformatics 2019 Feb 4;19(Suppl 13):547. Epub 2019 Feb 4.

Laboratory for Medical Science Mathematics, RIKEN Center for Integrative Medical Sciences, Tokyo, Japan.

Background: Glycation is a one of the post-translational modifications (PTM) where sugar molecules and residues in protein sequences are covalently bonded. It has become one of the clinically important PTM in recent times attributed to many chronic and age related complications. Being a non-enzymatic reaction, it is a great challenge when it comes to its prediction due to the lack of significant bias in the sequence motifs. Read More

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https://bmcbioinformatics.biomedcentral.com/articles/10.1186
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http://dx.doi.org/10.1186/s12859-018-2547-xDOI Listing
February 2019
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EpiMethEx: a tool for large-scale integrated analysis in methylation hotspots linked to genetic regulation.

BMC Bioinformatics 2019 Feb 4;19(Suppl 13):385. Epub 2019 Feb 4.

Department of Drug Sciences, University of Catania, Viale A. Doria, 6, Catania, 95125, Italy.

Background: DNA methylation is an epigenetic mechanism of genomic regulation involved in the maintenance of homeostatic balance. Dysregulation of DNA methylation status is one of the driver alterations occurring in neoplastic transformation and cancer progression. The identification of methylation hotspots associated to gene dysregulation may contribute to discover new prognostic and diagnostic biomarkers, as well as, new therapeutic targets. Read More

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http://dx.doi.org/10.1186/s12859-018-2397-6DOI Listing
February 2019