1,641 results match your criteria Briefings in bioinformatics[Journal]


Investigations of sequencing data and sample type on HLA class Ia typing with different computational tools.

Brief Bioinform 2020 Jul 14. Epub 2020 Jul 14.

Yucebio Cancer Translational Research Institute and Chief Medical Officer for Yucebio Technology Co.

Human leukocyte antigen (HLA) can encode the human major histocompatibility complex (MHC) proteins and play a key role in adaptive and innate immunity. Emerging clinical evidences suggest that the presentation of tumor neoantigens and neoantigen-specific T cell response associated with MHC class I molecules are of key importance to activate the adaptive immune systemin cancer immunotherapy. Therefore, accurate HLA typing is very essential for the clinical application of immunotherapy. Read More

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http://dx.doi.org/10.1093/bib/bbaa143DOI Listing

Metabolic networks of the Nicotiana genus in the spotlight: content, progress and outlook.

Brief Bioinform 2020 Jul 14. Epub 2020 Jul 14.

Boyce Thompson Institute.

Manually curated metabolic databases residing at the Sol Genomics Network comprise two taxon-specific databases for the Solanaceae family, i.e. SolanaCyc and the genus Nicotiana, i. Read More

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http://dx.doi.org/10.1093/bib/bbaa136DOI Listing

Integrative pharmacological mechanism of vitamin C combined with glycyrrhizic acid against COVID-19: findings of bioinformatics analyses.

Brief Bioinform 2020 Jul 14. Epub 2020 Jul 14.

Guilin Medical University.

Objective: Coronavirus disease 2019 (COVID-19) is a fatal and fast-spreading viral infection. To date, the number of COVID-19 patients worldwide has crossed over six million with over three hundred and seventy thousand deaths (according to the data from World Health Organization; updated on 2 June 2020). Although COVID-19 can be rapidly diagnosed, efficient clinical treatment of COVID-19 remains unavailable, resulting in high fatality. Read More

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http://dx.doi.org/10.1093/bib/bbaa141DOI Listing

SCEBE: an efficient and scalable algorithm for genome-wide association studies on longitudinal outcomes with mixed-effects modeling.

Brief Bioinform 2020 Jul 7. Epub 2020 Jul 7.

Janssen Research and Development LLC, Raritan, NJ, USA.

Genome-wide association studies (GWAS) using longitudinal phenotypes collected over time is appealing due to the improvement of power. However, computation burden has been a challenge because of the complex algorithms for modeling the longitudinal data. Approximation methods based on empirical Bayesian estimates (EBEs) from mixed-effects modeling have been developed to expedite the analysis. Read More

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http://dx.doi.org/10.1093/bib/bbaa130DOI Listing

Predicting microRNA-disease associations from lncRNA-microRNA interactions via Multiview Multitask Learning.

Brief Bioinform 2020 Jul 7. Epub 2020 Jul 7.

City University of Hong Kong.

Motivation: Identifying microRNAs that are associated with different diseases as biomarkers is a problem of great medical significance. Existing computational methods for uncovering such microRNA-diseases associations (MDAs) are mostly developed under the assumption that similar microRNAs tend to associate with similar diseases. Since such an assumption is not always valid, these methods may not always be applicable to all kinds of MDAs. Read More

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http://dx.doi.org/10.1093/bib/bbaa133DOI Listing

Normal tissue content impact on the GBM molecular classification.

Brief Bioinform 2020 Jul 7. Epub 2020 Jul 7.

Brain Tumour Laboratory, Scientific Director at Vithas Hospitals, Managing Director at Fundación Vithas and Professor at the Medial School of Francisco de Vitoria University.

Molecular classification of glioblastoma has enabled a deeper understanding of the disease. The four-subtype model (including Proneural, Classical, Mesenchymal and Neural) has been replaced by a model that discards the Neural subtype, found to be associated with samples with a high content of normal tissue. These samples can be misclassified preventing biological and clinical insights into the different tumor subtypes from coming to light. Read More

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http://dx.doi.org/10.1093/bib/bbaa129DOI Listing

Testing cell-type-specific mediation effects in genome-wide epigenetic studies.

Brief Bioinform 2020 Jul 7. Epub 2020 Jul 7.

Department of Statistics and Actuarial Science, University of Hong Kong, Hong Kong SAR, China.

Epigenome-wide mediation analysis aims to identify DNA methylation CpG sites that mediate the causal effects of genetic/environmental exposures on health outcomes. However, DNA methylations in the peripheral blood tissues are usually measured at the bulk level based on a heterogeneous population of white blood cells. Using the bulk level DNA methylation data in mediation analysis might cause confounding bias and reduce study power. Read More

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http://dx.doi.org/10.1093/bib/bbaa131DOI Listing

Using deep neural networks and biological subwords to detect protein S-sulfenylation sites.

Brief Bioinform 2020 Jul 2. Epub 2020 Jul 2.

Professional Master Program in Artificial Intelligence in Medicine, Taipei Medical University.

Protein S-sulfenylation is one kind of crucial post-translational modifications (PTMs) in which the hydroxyl group covalently binds to the thiol of cysteine. Some recent studies have shown that this modification plays an important role in signaling transduction, transcriptional regulation and apoptosis. To date, the dynamic of sulfenic acids in proteins remains unclear because of its fleeting nature. Read More

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http://dx.doi.org/10.1093/bib/bbaa128DOI Listing
July 2020
9.617 Impact Factor

Gene-based mediation analysis in epigenetic studies.

Brief Bioinform 2020 Jul 1. Epub 2020 Jul 1.

Michigan State University.

Mediation analysis has been a useful tool for investigating the effect of mediators that lie in the path from the independent variable to the outcome. With the increasing dimensionality of mediators such as in (epi)genomics studies, high-dimensional mediation model is needed. In this work, we focus on epigenetic studies with the goal to identify important DNA methylations that act as mediators between an exposure disease outcome. Read More

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http://dx.doi.org/10.1093/bib/bbaa113DOI Listing

DeepTorrent: a deep learning-based approach for predicting DNA N4-methylcytosine sites.

Brief Bioinform 2020 Jul 1. Epub 2020 Jul 1.

Monash University.

DNA N4-methylcytosine (4mC) is an important epigenetic modification that plays a vital role in regulating DNA replication and expression. However, it is challenging to detect 4mC sites through experimental methods, which are time-consuming and costly. Thus, computational tools that can identify 4mC sites would be very useful for understanding the mechanism of this important type of DNA modification. Read More

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http://dx.doi.org/10.1093/bib/bbaa124DOI Listing

Prostate cancer early diagnosis: circulating microRNA pairs potentially beyond single microRNAs upon 1231 serum samples.

Brief Bioinform 2020 Jul 1. Epub 2020 Jul 1.

King's College London.

The accuracy of prostate-specific antigen or clinical examination in prostate cancer (PCa) screening is in question, and circulating microRNAs (miRNAs) can be alternatives to PCa diagnosis. However, recent circulating miRNA biomarkers either are identified upon small sample sizes or cannot have robust diagnostic performance in every aspect of performance indicators. These may decrease applicability of potential biomarkers for the early detection of PCa. Read More

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http://dx.doi.org/10.1093/bib/bbaa111DOI Listing

Biomedical named entity recognition and linking datasets: survey and our recent development.

Brief Bioinform 2020 Jun 30. Epub 2020 Jun 30.

Institute of Biomedical Informatics, National Yang Ming University, Taipei, Taiwan.

Natural language processing (NLP) is widely applied in biological domains to retrieve information from publications. Systems to address numerous applications exist, such as biomedical named entity recognition (BNER), named entity normalization (NEN) and protein-protein interaction extraction (PPIE). High-quality datasets can assist the development of robust and reliable systems; however, due to the endless applications and evolving techniques, the annotations of benchmark datasets may become outdated and inappropriate. Read More

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http://dx.doi.org/10.1093/bib/bbaa054DOI Listing

DeepVF: a deep learning-based hybrid framework for identifying virulence factors using the stacking strategy.

Brief Bioinform 2020 Jun 29. Epub 2020 Jun 29.

Leiden Institute of Advanced Computer Science, Leiden University.

Virulence factors (VFs) enable pathogens to infect their hosts. A wealth of individual, disease-focused studies has identified a wide variety of VFs, and the growing mass of bacterial genome sequence data provides an opportunity for computational methods aimed at predicting VFs. Despite their attractive advantages and performance improvements, the existing methods have some limitations and drawbacks. Read More

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http://dx.doi.org/10.1093/bib/bbaa125DOI Listing

A comprehensive integrated drug similarity resource for in-silico drug repositioning and beyond.

Brief Bioinform 2020 Jun 29. Epub 2020 Jun 29.

University of New South Wales (UNSW Sydney).

Drug similarity studies are driven by the hypothesis that similar drugs should display similar therapeutic actions and thus can potentially treat a similar constellation of diseases. Drug-drug similarity has been derived by variety of direct and indirect sources of evidence and frequently shown high predictive power in discovering validated repositioning candidates as well as other in-silico drug development applications. Yet, existing resources either have limited coverage or rely on an individual source of evidence, overlooking the wealth and diversity of drug-related data sources. Read More

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http://dx.doi.org/10.1093/bib/bbaa126DOI Listing
June 2020
9.617 Impact Factor

Structured sparsity regularization for analyzing high-dimensional omics data.

Authors:
Susana Vinga

Brief Bioinform 2020 Jun 29. Epub 2020 Jun 29.

INESC-ID, Instituto Superior Técnico, Universidade de Lisboa, Lisboa, Portugal.

The development of new molecular and cell technologies is having a significant impact on the quantity of data generated nowadays. The growth of omics databases is creating a considerable potential for knowledge discovery and, concomitantly, is bringing new challenges to statistical learning and computational biology for health applications. Indeed, the high dimensionality of these data may hamper the use of traditional regression methods and parameter estimation algorithms due to the intrinsic non-identifiability of the inherent optimization problem. Read More

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http://dx.doi.org/10.1093/bib/bbaa122DOI Listing

Computationally predicting binding affinity in protein-ligand complexes: free energy-based simulations and machine learning-based scoring functions.

Brief Bioinform 2020 Jun 26. Epub 2020 Jun 26.

College of Science and Engineering, City University of Hong Kong.

Accurately predicting protein-ligand binding affinities can substantially facilitate the drug discovery process, but it remains as a difficult problem. To tackle the challenge, many computational methods have been proposed. Among these methods, free energy-based simulations and machine learning-based scoring functions can potentially provide accurate predictions. Read More

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http://dx.doi.org/10.1093/bib/bbaa107DOI Listing

ExoBCD: a comprehensive database for exosomal biomarker discovery in breast cancer.

Brief Bioinform 2020 Jun 26. Epub 2020 Jun 26.

Key Laboratory of Clinical Laboratory Diagnostics, College of Laboratory Medicine, Chongqing Medical University, Chongqing, China.

Effective and safe implementation of precision oncology for breast cancer is a vital strategy to improve patient outcomes, which relies on the application of reliable biomarkers. As 'liquid biopsy' and novel resource for biomarkers, exosomes provide a promising avenue for the diagnosis and treatment of breast cancer. Although several exosome-related databases have been developed, there is still lacking of an integrated database for exosome-based biomarker discovery. Read More

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http://dx.doi.org/10.1093/bib/bbaa088DOI Listing

Enriching contextualized language model from knowledge graph for biomedical information extraction.

Brief Bioinform 2020 Jun 26. Epub 2020 Jun 26.

Biomedical information extraction (BioIE) is an important task. The aim is to analyze biomedical texts and extract structured information such as named entities and semantic relations between them. In recent years, pre-trained language models have largely improved the performance of BioIE. Read More

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http://dx.doi.org/10.1093/bib/bbaa110DOI Listing

Using off-target data from whole-exome sequencing to improve genotyping accuracy, association analysis and polygenic risk prediction.

Brief Bioinform 2020 Jun 17. Epub 2020 Jun 17.

Ministry of Education Key Laboratory for Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.

Whole-exome sequencing (WES) has been widely used to study the role of protein-coding variants in genetic diseases. Non-coding regions, typically covered by sparse off-target data, are often discarded by conventional WES analyses. Here, we develop a genotype calling pipeline named WEScall to analyse both target and off-target data. Read More

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http://dx.doi.org/10.1093/bib/bbaa084DOI Listing

Evaluation of gene-drug common module identification methods using pharmacogenomics data.

Brief Bioinform 2020 Jun 26. Epub 2020 Jun 26.

South China University of Technology, School of Computer Science and Engineering, Guangzhou, 510006, China.

Accurately identifying the interactions between genomic factors and the response of cancer drugs plays important roles in drug discovery, drug repositioning and cancer treatment. A number of studies revealed that interactions between genes and drugs were 'many-genes-to-many drugs' interactions, i.e. Read More

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http://dx.doi.org/10.1093/bib/bbaa087DOI Listing

A novel privacy-preserving federated genome-wide association study framework and its application in identifying potential risk variants in ankylosing spondylitis.

Brief Bioinform 2020 Jun 26. Epub 2020 Jun 26.

Department of Rheumatology and Immunology, Shanghai Changzheng Hospital.

Genome-wide association studies (GWAS) have been widely used for identifying potential risk variants in various diseases. A statistically meaningful GWAS typically requires a large sample size to detect disease-associated single nucleotide polymorphisms (SNPs). However, a single institution usually only possesses a limited number of samples. Read More

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http://dx.doi.org/10.1093/bib/bbaa090DOI Listing
June 2020
9.617 Impact Factor

SMNN: batch effect correction for single-cell RNA-seq data via supervised mutual nearest neighbor detection.

Brief Bioinform 2020 Jun 26. Epub 2020 Jun 26.

Departments of Genetics, Biostatistics and Computer Science at the University of North Carolina at Chapel Hill.

Batch effect correction has been recognized to be indispensable when integrating single-cell RNA sequencing (scRNA-seq) data from multiple batches. State-of-the-art methods ignore single-cell cluster label information, but such information can improve the effectiveness of batch effect correction, particularly under realistic scenarios where biological differences are not orthogonal to batch effects. To address this issue, we propose SMNN for batch effect correction of scRNA-seq data via supervised mutual nearest neighbor detection. Read More

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http://dx.doi.org/10.1093/bib/bbaa097DOI Listing

PredCID: prediction of driver frameshift indels in human cancer.

Brief Bioinform 2020 Jun 26. Epub 2020 Jun 26.

Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, Institutes of Physical Science and Information Technology, Anhui University.

The discrimination of driver from passenger mutations has been a hot topic in the field of cancer biology. Although recent advances have improved the identification of driver mutations in cancer genomic research, there is no computational method specific for the cancer frameshift indels (insertions or/and deletions) yet. In addition, existing pathogenic frameshift indel predictors may suffer from plenty of missing values because of different choices of transcripts during the variant annotation processes. Read More

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http://dx.doi.org/10.1093/bib/bbaa119DOI Listing

Sequence repetitiveness quantification and de novo repeat detection by weighted k-mer coverage.

Brief Bioinform 2020 Jun 26. Epub 2020 Jun 26.

Department of Bioinformatics, College of Life Sciences, Zhejiang University.

DNA repeats are abundant in eukaryotic genomes and have been proved to play a vital role in genome evolution and regulation. A large number of approaches have been proposed to identify various repeats in the genome. Some de novo repeat identification tools can efficiently generate sequence repetitive scores based on k-mer counting for repeat detection. Read More

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http://dx.doi.org/10.1093/bib/bbaa086DOI Listing

Deep4mC: systematic assessment and computational prediction for DNA N4-methylcytosine sites by deep learning.

Brief Bioinform 2020 Jun 24. Epub 2020 Jun 24.

Center for Precision Health, School of Biomedical Informatics.

DNA N4-methylcytosine (4mC) modification represents a novel epigenetic regulation. It involves in various cellular processes, including DNA replication, cell cycle and gene expression, among others. In addition to experimental identification of 4mC sites, in silico prediction of 4mC sites in the genome has emerged as an alternative and promising approach. Read More

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http://dx.doi.org/10.1093/bib/bbaa099DOI Listing

Joint reconstruction of cis-regulatory interaction networks across multiple tissues using single-cell chromatin accessibility data.

Brief Bioinform 2020 Jun 24. Epub 2020 Jun 24.

Academy of Mathematics and Systems Science, Chinese Academy of Sciences.

The rapid accumulation of single-cell chromatin accessibility data offers a unique opportunity to investigate common and specific regulatory mechanisms across different cell types. However, existing methods for cis-regulatory network reconstruction using single-cell chromatin accessibility data were only designed for cells belonging to one cell type, and resulting networks may be incomparable directly due to diverse cell numbers of different cell types. Here, we adopt a computational method to jointly reconstruct cis-regulatory interaction maps (JRIM) of multiple cell populations based on patterns of co-accessibility in single-cell data. Read More

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http://dx.doi.org/10.1093/bib/bbaa120DOI Listing

Comparison of microbiome samples: methods and computational challenges.

Brief Bioinform 2020 Jun 24. Epub 2020 Jun 24.

University of Padova.

The study of microbial communities crucially relies on the comparison of metagenomic next-generation sequencing data sets, for which several methods have been designed in recent years. Here, we review three key challenges in the comparison of such data sets: species identification and quantification, the efficient computation of distances between metagenomic samples and the identification of metagenomic features associated with a phenotype such as disease status. We present current solutions for such challenges, considering both reference-based methods relying on a database of reference genomes and reference-free methods working directly on all sequencing reads from the samples. Read More

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http://dx.doi.org/10.1093/bib/bbaa121DOI Listing

RNCE: network integration with reciprocal neighbors contextual encoding for multi-modal drug community study on cancer targets.

Brief Bioinform 2020 Jun 24. Epub 2020 Jun 24.

Department of Computer Science, City University of Hong Kong.

Mining drug targets and mechanisms of action (MoA) for novel anticancer drugs from pharmacogenomic data is a path to enhance the drug discovery efficiency. Recent approaches have successfully attempted to discover targets/MoA by characterizing drug similarities and communities with integrative methods on multi-modal or multi-omics drug information. However, the sparse and imbalanced community size structure of the drug network is seldom considered in recent approaches. Read More

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http://dx.doi.org/10.1093/bib/bbaa118DOI Listing

Dr AFC: drug repositioning through anti-fibrosis characteristic.

Brief Bioinform 2020 Jun 22. Epub 2020 Jun 22.

Tongji University, Shanghai, China.

Fibrosis is a key component in the pathogenic mechanism of a variety of diseases. These diseases involving fibrosis may share common mechanisms and therapeutic targets, and therefore common intervention strategies and medicines may be applicable for these diseases. For this reason, deliberately introducing anti-fibrosis characteristics into predictive modeling may lead to more success in drug repositioning. Read More

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http://dx.doi.org/10.1093/bib/bbaa115DOI Listing

The impact of compound library size on the performance of scoring functions for structure-based virtual screening.

Brief Bioinform 2020 Jun 22. Epub 2020 Jun 22.

Larger training datasets have been shown to improve the accuracy of machine learning (ML)-based scoring functions (SFs) for structure-based virtual screening (SBVS). In addition, massive test sets for SBVS, known as ultra-large compound libraries, have been demonstrated to enable the fast discovery of selective drug leads with low-nanomolar potency. This proof-of-concept was carried out on two targets using a single docking tool along with its SF. Read More

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http://dx.doi.org/10.1093/bib/bbaa095DOI Listing

Epidemiological data analysis of viral quasispecies in the next-generation sequencing era.

Brief Bioinform 2020 Jun 22. Epub 2020 Jun 22.

Belarusian State University.

The unprecedented coverage offered by next-generation sequencing (NGS) technology has facilitated the assessment of the population complexity of intra-host RNA viral populations at an unprecedented level of detail. Consequently, analysis of NGS datasets could be used to extract and infer crucial epidemiological and biomedical information on the levels of both infected individuals and susceptible populations, thus enabling the development of more effective prevention strategies and antiviral therapeutics. Such information includes drug resistance, infection stage, transmission clusters and structures of transmission networks. Read More

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http://dx.doi.org/10.1093/bib/bbaa101DOI Listing

Survey and comparative assessments of computational multi-omics integrative methods with multiple regulatory networks identifying distinct tumor compositions across pan-cancer data sets.

Brief Bioinform 2020 Jun 12. Epub 2020 Jun 12.

Computer Science and Engineering, South China University of Technology.

The significance of pan-cancer categories has recently been recognized as widespread in cancer research. Pan-cancer categorizes a cancer based on its molecular pathology rather than an organ. The molecular similarities among multi-omics data found in different cancer types can play several roles in both biological processes and therapeutic developments. Read More

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http://dx.doi.org/10.1093/bib/bbaa102DOI Listing

Letter to the Editor: Methods for mapping quantitative trait loci in autotetraploid species.

Brief Bioinform 2020 Jun 12. Epub 2020 Jun 12.

Institute of Biostatistics, SKLGE, School of Life Sciences, Fudan University, Shanghai 200433, China.

Mapping quantitative trait loci (QTL) in autotetraploid species represents a timely and challenging task. Two papers published by Wu and his colleagues proposed statistical methods for QTL mapping in these evolutionarily and economically important species. In this Letter to the Editor, we present critical comments on the fundamental conceptual errors involved, from both statistical and genetic points of view. Read More

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http://dx.doi.org/10.1093/bib/bbz148DOI Listing

Using conceptual modeling to improve genome data management.

Brief Bioinform 2020 Jun 12. Epub 2020 Jun 12.

Universitat Politècnica de València.

With advances in genomic sequencing technology, a large amount of data is publicly available for the research community to extract meaningful and reliable associations among risk genes and the mechanisms of disease. However, this exponential growth of data is spread in over thousand heterogeneous repositories, represented in multiple formats and with different levels of quality what hinders the differentiation of clinically valid relationships from those that are less well-sustained and that could lead to wrong diagnosis. This paper presents how conceptual models can play a key role to efficiently manage genomic data. Read More

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http://dx.doi.org/10.1093/bib/bbaa100DOI Listing

RaacLogo: a new sequence logo generator by using reduced amino acid clusters.

Brief Bioinform 2020 Jun 10. Epub 2020 Jun 10.

State key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of life sciences, Inner Mongolia University.

Sequence logos give a fast and concise display in visualizing consensus sequence. Protein exhibits greater complexity and diversity than DNA, which usually affects the graphical representation of the logo. Reduced amino acids perform powerful ability for simplifying complexity of sequence alignment, which motivated us to establish RaacLogo. Read More

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http://dx.doi.org/10.1093/bib/bbaa096DOI Listing

Proper imputation of missing values in proteomics datasets for differential expression analysis.

Brief Bioinform 2020 Jun 10. Epub 2020 Jun 10.

Label-free shotgun proteomics is an important tool in biomedical research, where tandem mass spectrometry with data-dependent acquisition (DDA) is frequently used for protein identification and quantification. However, the DDA datasets contain a significant number of missing values (MVs) that severely hinders proper analysis. Existing literature suggests that different imputation methods should be used for the two types of MVs: missing completely at random or missing not at random. Read More

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http://dx.doi.org/10.1093/bib/bbaa112DOI Listing

MLCDForest: multi-label classification with deep forest in disease prediction for long non-coding RNAs.

Brief Bioinform 2020 Jun 10. Epub 2020 Jun 10.

The long non-coding RNAs (lncRNAs) are subject of intensive recent studies due to its association with various human diseases. It is desirable to build the artificial intelligence-based models for prediction of diseases or tissues based on the lncRNAs data, which will be useful in disease diagnosis and therapy. The accuracy and robustness of existing models based on the machine learning techniques are subject to further improvement. Read More

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http://dx.doi.org/10.1093/bib/bbaa104DOI Listing
June 2020
9.617 Impact Factor

Docking of peptides to GPCRs using a combination of CABS-dock with FlexPepDock refinement.

Brief Bioinform 2020 Jun 10. Epub 2020 Jun 10.

The structural description of peptide ligands bound to G protein-coupled receptors (GPCRs) is important for the discovery of new drugs and deeper understanding of the molecular mechanisms of life. Here we describe a three-stage protocol for the molecular docking of peptides to GPCRs using a set of different programs: (1) CABS-dock for docking fully flexible peptides; (2) PD2 method for the reconstruction of atomistic structures from C-alpha traces provided by CABS-dock and (3) Rosetta FlexPepDock for the refinement of protein-peptide complex structures and model scoring. We evaluated the proposed protocol on the set of seven different GPCR-peptide complexes (including one containing a cyclic peptide), for which crystallographic structures are available. Read More

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http://dx.doi.org/10.1093/bib/bbaa109DOI Listing

CCLA: an accurate method and web server for cancer cell line authentication using gene expression profiles.

Brief Bioinform 2020 Jun 8. Epub 2020 Jun 8.

Cancer cell lines (CCLs) as important model systems play critical roles in cancer research. The misidentification and contamination of CCLs are serious problems, leading to unreliable results and waste of resources. Current methods for CCL authentication are mainly based on the CCL-specific genetic polymorphism, whereas no method is available for CCL authentication using gene expression profiles. Read More

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http://dx.doi.org/10.1093/bib/bbaa093DOI Listing

Landscape of drug-resistance mutations in kinase regulatory hotspots.

Brief Bioinform 2020 Jun 8. Epub 2020 Jun 8.

More than 48 kinase inhibitors (KIs) have been approved by Food and Drug Administration. However, drug-resistance (DR) eventually occurs, and secondary mutations have been found in the previously targeted primary-mutated cancer cells. Cancer and drug research communities recognize the importance of the kinase domain (KD) mutations for kinasopathies. Read More

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http://dx.doi.org/10.1093/bib/bbaa108DOI Listing

MetaFS: Performance assessment of biomarker discovery in metaproteomics.

Brief Bioinform 2020 Jun 8. Epub 2020 Jun 8.

Metaproteomics suffers from the issues of dimensionality and sparsity. Data reduction methods can maximally identify the relevant subset of significant differential features and reduce data redundancy. Feature selection (FS) methods were applied to obtain the significant differential subset. Read More

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http://dx.doi.org/10.1093/bib/bbaa105DOI Listing

Comprehensive fundamental somatic variant calling and quality management strategies for human cancer genomes.

Brief Bioinform 2020 Jun 8. Epub 2020 Jun 8.

Next-generation sequencing (NGS) technology has revolutionised human cancer research, particularly via detection of genomic variants with its ultra-high-throughput sequencing and increasing affordability. However, the inundation of rich cancer genomics data has resulted in significant challenges in its exploration and translation into biological insights. One of the difficulties in cancer genome sequencing is software selection. Read More

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http://dx.doi.org/10.1093/bib/bbaa083DOI Listing

DenvInD: dengue virus inhibitors database for clinical and molecular research.

Brief Bioinform 2020 Jun 8. Epub 2020 Jun 8.

Dengue virus (DENV) researchers often face challenges with the highly time-consuming process of collecting and curating information on known inhibitors during the standard drug discovery process. To this end, however, required collective information is not yet available on a single platform. Hence, we have developed the DenvInD database for experimentally validated DENV inhibitors against its known targets presently hosted at https://webs. Read More

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http://dx.doi.org/10.1093/bib/bbaa098DOI Listing
June 2020
9.617 Impact Factor

Different molecular enumeration influences in deep learning: an example using aqueous solubility.

Brief Bioinform 2020 Jun 5. Epub 2020 Jun 5.

Aqueous solubility is the key property driving many chemical and biological phenomena and impacts experimental and computational attempts to assess those phenomena. Accurate prediction of solubility is essential and challenging, even with modern computational algorithms. Fingerprint-based, feature-based and molecular graph-based representations have all been used with different deep learning methods for aqueous solubility prediction. Read More

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http://dx.doi.org/10.1093/bib/bbaa092DOI Listing

Improving structure-based virtual screening performance via learning from scoring function components.

Brief Bioinform 2020 Jun 4. Epub 2020 Jun 4.

Scoring functions (SFs) based on complex machine learning (ML) algorithms have gradually emerged as a promising alternative to overcome the weaknesses of classical SFs. However, extensive efforts have been devoted to the development of SFs based on new protein-ligand interaction representations and advanced alternative ML algorithms instead of the energy components obtained by the decomposition of existing SFs. Here, we propose a new method named energy auxiliary terms learning (EATL), in which the scoring components are extracted and used as the input for the development of three levels of ML SFs including EATL SFs, docking-EATL SFs and comprehensive SFs with ascending VS performance. Read More

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http://dx.doi.org/10.1093/bib/bbaa094DOI Listing

Predicting the stability of mutant proteins by computational approaches: an overview.

Brief Bioinform 2020 Jun 3. Epub 2020 Jun 3.

Institute of Food Sciences, CNR Italy.

A very large number of computational methods to predict the change in thermodynamic stability of proteins due to mutations have been developed during the last 30 years, and many different web servers are currently available. Nevertheless, most of them suffer from severe drawbacks that decrease their general reliability and, consequently, their applicability to different goals such as protein engineering or the predictions of the effects of mutations in genetic diseases. In this review, we have summarized all the main approaches used to develop these tools, with a survey of the web servers currently available. Read More

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http://dx.doi.org/10.1093/bib/bbaa074DOI Listing

gutMEGA: a database of the human gut MEtaGenome Atlas.

Brief Bioinform 2020 Jun 4. Epub 2020 Jun 4.

The gut microbiota plays important roles in human health through regulating both physiological homeostasis and disease emergence. The accumulation of metagenomic sequencing studies enables us to better understand the temporal and spatial variations of the gut microbiota under different physiological and pathological conditions. However, it is inconvenient for scientists to query and retrieve published data; thus, a comprehensive resource for the quantitative gut metagenome is urgently needed. Read More

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http://dx.doi.org/10.1093/bib/bbaa082DOI Listing
June 2020
9.617 Impact Factor

Computational principles and practice for decoding immune contexture in the tumor microenvironment.

Brief Bioinform 2020 Jun 3. Epub 2020 Jun 3.

School of Biomedical Engineering, Wenzhou Medical University.

Tumor-infiltrating immune cells (TIICs) have been recognized as crucial components of the tumor microenvironment (TME) and induced both beneficial and adverse consequences for tumorigenesis as well as outcome and therapy (particularly immunotherapy). Computer-aided investigation of immune cell components in the TME has become a promising avenue to better understand the interplay between the immune system and tumors. In this study, we presented an overview of data sources, computational methods and software tools, as well as their application in inferring the composition of tumor-infiltrating immune cells in the TME. Read More

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http://dx.doi.org/10.1093/bib/bbaa075DOI Listing

The road towards data integration in human genomics: players, steps and interactions.

Brief Bioinform 2020 Jun 4. Epub 2020 Jun 4.

Thousands of new experimental datasets are becoming available every day; in many cases, they are produced within the scope of large cooperative efforts, involving a variety of laboratories spread all over the world, and typically open for public use. Although the potential collective amount of available information is huge, the effective combination of such public sources is hindered by data heterogeneity, as the datasets exhibit a wide variety of notations and formats, concerning both experimental values and metadata. Thus, data integration is becoming a fundamental activity, to be performed prior to data analysis and biological knowledge discovery, consisting of subsequent steps of data extraction, normalization, matching and enrichment; once applied to heterogeneous data sources, it builds multiple perspectives over the genome, leading to the identification of meaningful relationships that could not be perceived by using incompatible data formats. Read More

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http://dx.doi.org/10.1093/bib/bbaa080DOI Listing

Scoring functions for drug-effect similarity.

Brief Bioinform 2020 Jun 2. Epub 2020 Jun 2.

IBIMA, Rostock University Medical Center, Rostock, 18041, Germany.

Motivation: The difficulty to find new drugs and bring them to the market has led to an increased interest to find new applications for known compounds. Biological samples from many disease contexts have been extensively profiled by transcriptomics, and, intuitively, this motivates to search for compounds with a reversing effect on the expression of characteristic disease genes. However, disease effects may be cell line-specific and also depend on other factors, such as genetics and environment. Read More

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http://dx.doi.org/10.1093/bib/bbaa072DOI Listing