journal
MENU ▼
Read by QxMD icon Read
search

Briefings in Bioinformatics

journal
https://www.readbyqxmd.com/read/30102374/comprehensive-comparative-analysis-of-methods-and-software-for-identifying-viral-integrations
#1
Xun Chen, Jason Kost, Dawei Li
Many viruses are capable of integrating in the human genome, particularly viruses involved in tumorigenesis. Viral integrations can be considered genetic markers for discovering virus-caused cancers and inferring cancer cell development. Next-generation sequencing (NGS) technologies have been widely used to screen for viral integrations in cancer genomes, and a number of bioinformatics tools have been developed to detect viral integrations using NGS data. However, there has been no systematic comparison of the methods or software...
August 8, 2018: Briefings in Bioinformatics
https://www.readbyqxmd.com/read/30102367/review-and-comparative-assessment-of-similarity-based-methods-for-prediction-of-drug-protein-interactions-in-the-druggable-human-proteome
#2
Chen Wang, Lukasz Kurgan
Drug-protein interactions (DPIs) underlie the desired therapeutic actions and the adverse side effects of a significant majority of drugs. Computational prediction of DPIs facilitates research in drug discovery, characterization and repurposing. Similarity-based methods that do not require knowledge of protein structures are particularly suitable for druggable genome-wide predictions of DPIs. We review 35 high-impact similarity-based predictors that were published in the past decade. We group them based on three types of similarities and their combinations that they use...
August 8, 2018: Briefings in Bioinformatics
https://www.readbyqxmd.com/read/30102366/computational-resources-associating-diseases-with-genotypes-phenotypes-and-exposures
#3
Wenliang Zhang, Haiyue Zhang, Huan Yang, Miaoxin Li, Zhi Xie, Weizhong Li
The causes of a disease and its therapies are not only related to genotypes, but also associated with other factors, including phenotypes, environmental exposures, drugs and chemical molecules. Distinguishing disease-related factors from many neutral factors is critical as well as difficult. Over the past two decades, bioinformaticians have developed many computational resources to integrate the omics data and discover associations among these factors. However, researchers and clinicians are experiencing difficulties in choosing appropriate resources from hundreds of relevant databases and software tools...
August 8, 2018: Briefings in Bioinformatics
https://www.readbyqxmd.com/read/30099485/recent-advances-and-prospects-of-computational-methods-for-metabolite-identification-a-review-with-emphasis-on-machine-learning-approaches
#4
Dai Hai Nguyen, Canh Hao Nguyen, Hiroshi Mamitsuka
Metabolomics involves studies of a great number of metabolites, which are small molecules present in biological systems. They play a lot of important functions such as energy transport, signaling, building block of cells and inhibition/catalysis. Understanding biochemical characteristics of the metabolites is an essential and significant part of metabolomics to enlarge the knowledge of biological systems. It is also the key to the development of many applications and areas such as biotechnology, biomedicine or pharmaceuticals...
August 6, 2018: Briefings in Bioinformatics
https://www.readbyqxmd.com/read/30099484/interpretation-of-differential-gene-expression-results-of-rna-seq-data-review-and-integration
#5
Adam McDermaid, Brandon Monier, Jing Zhao, Bingqiang Liu, Qin Ma
Differential gene expression (DGE) analysis is one of the most common applications of RNA-sequencing (RNA-seq) data. This process allows for the elucidation of differentially expressed genes across two or more conditions and is widely used in many applications of RNA-seq data analysis. Interpretation of the DGE results can be nonintuitive and time consuming due to the variety of formats based on the tool of choice and the numerous pieces of information provided in these results files. Here we reviewed DGE results analysis from a functional point of view for various visualizations...
August 6, 2018: Briefings in Bioinformatics
https://www.readbyqxmd.com/read/30099476/a-comparative-analysis-of-cell-type-adjustment-methods-for-epigenome-wide-association-studies-based-on-simulated-and-real-data-sets
#6
Johannes Br├Ągelmann, Justo Lorenzo Bermejo
Technological advances and reduced costs of high-density methylation arrays have led to an increasing number of association studies on the possible relationship between human disease and epigenetic variability. DNA samples from peripheral blood or other tissue types are analyzed in epigenome-wide association studies (EWAS) to detect methylation differences related to a particular phenotype. Since information on the cell-type composition of the sample is generally not available and methylation profiles are cell-type specific, statistical methods have been developed for adjustment of cell-type heterogeneity in EWAS...
August 6, 2018: Briefings in Bioinformatics
https://www.readbyqxmd.com/read/30084940/a-brief-history-of-bioinformatics
#7
Jeff Gauthier, Antony T Vincent, Steve J Charette, Nicolas Derome
It is easy for today's students and researchers to believe that modern bioinformatics emerged recently to assist next-generation sequencing data analysis. However, the very beginnings of bioinformatics occurred more than 50 years ago, when desktop computers were still a hypothesis and DNA could not yet be sequenced. The foundations of bioinformatics were laid in the early 1960s with the application of computational methods to protein sequence analysis (notably, de novo sequence assembly, biological sequence databases and substitution models)...
August 3, 2018: Briefings in Bioinformatics
https://www.readbyqxmd.com/read/30084867/lncfinder-an-integrated-platform-for-long-non-coding-rna-identification-utilizing-sequence-intrinsic-composition-structural-information-and-physicochemical-property
#8
Siyu Han, Yanchun Liang, Qin Ma, Yangyi Xu, Yu Zhang, Wei Du, Cankun Wang, Ying Li
Discovering new long non-coding RNAs (lncRNAs) has been a fundamental step in lncRNA-related research. Nowadays, many machine learning-based tools have been developed for lncRNA identification. However, many methods predict lncRNAs using sequence-derived features alone, which tend to display unstable performances on different species. Moreover, the majority of tools cannot be re-trained or tailored by users and neither can the features be customized or integrated to meet researchers' requirements. In this study, features extracted from sequence-intrinsic composition, secondary structure and physicochemical property are comprehensively reviewed and evaluated...
July 31, 2018: Briefings in Bioinformatics
https://www.readbyqxmd.com/read/30084866/recent-applications-of-deep-learning-and-machine-intelligence-on-in-silico-drug-discovery-methods-tools-and-databases
#9
Ahmet Sureyya Rifaioglu, Heval Atas, Maria Jesus Martin, Rengul Cetin-Atalay, Volkan Atalay, Tunca Dogan
The identification of interactions between drugs/compounds and their targets is crucial for the development of new drugs. In vitro screening experiments (i.e. bioassays) are frequently used for this purpose; however, experimental approaches are insufficient to explore novel drug-target interactions, mainly because of feasibility problems, as they are labour intensive, costly and time consuming. A computational field known as 'virtual screening' (VS) has emerged in the past decades to aid experimental drug discovery studies by statistically estimating unknown bio-interactions between compounds and biological targets...
July 31, 2018: Briefings in Bioinformatics
https://www.readbyqxmd.com/read/30084865/review-of-applications-of-high-throughput-sequencing-in-personalized-medicine-barriers-and-facilitators-of-future-progress-in-research-and-clinical-application
#10
Gaye Lightbody, Valeriia Haberland, Fiona Browne, Laura Taggart, Huiru Zheng, Eileen Parkes, Jaine K Blayney
There has been an exponential growth in the performance and output of sequencing technologies (omics data) with full genome sequencing now producing gigabases of reads on a daily basis. These data may hold the promise of personalized medicine, leading to routinely available sequencing tests that can guide patient treatment decisions. In the era of high-throughput sequencing (HTS), computational considerations, data governance and clinical translation are the greatest rate-limiting steps. To ensure that the analysis, management and interpretation of such extensive omics data is exploited to its full potential, key factors, including sample sourcing, technology selection and computational expertise and resources, need to be considered, leading to an integrated set of high-performance tools and systems...
July 31, 2018: Briefings in Bioinformatics
https://www.readbyqxmd.com/read/30053138/sequencing-era-methods-for-identifying-signatures-of-selection-in-the-genome
#11
Clare Horscroft, Sarah Ennis, Reuben J Pengelly, Timothy J Sluckin, Andrew Collins
Insights into genetic loci which are under selection and their functional roles contribute to increased understanding of the patterns of phenotypic variation we observe today. The availability of whole-genome sequence data, for humans and other species, provides opportunities to investigate adaptation and evolution at unprecedented resolution. Many analytical methods have been developed to interrogate these large data sets and characterize signatures of selection in the genome. We review here recently developed methods and consider the impact of increased computing power and data availability on the detection of selection signatures...
July 24, 2018: Briefings in Bioinformatics
https://www.readbyqxmd.com/read/30032279/an-efficient-multi-locus-mixed-model-framework-for-the-detection-of-small-and-linked-qtls-in-f2
#12
Yang-Jun Wen, Ya-Wen Zhang, Jin Zhang, Jian-Ying Feng, Jim M Dunwell, Yuan-Ming Zhang
In the genetic system that regulates complex traits, metabolites, gene expression levels, RNA editing levels and DNA methylation, a series of small and linked genes exist. To date, however, little is known about how to design an efficient framework for the detection of these kinds of genes. In this article, we propose a genome-wide composite interval mapping (GCIM) in F2. First, controlling polygenic background via selecting markers in the genome scanning of linkage analysis was replaced by estimating polygenic variance in a genome-wide association study...
July 18, 2018: Briefings in Bioinformatics
https://www.readbyqxmd.com/read/30020404/a-review-of-metrics-measuring-dissimilarity-for-rooted-phylogenetic-networks
#13
Juan Wang, Maozu Guo
Availability: http://bioinformatics.imu.edu.cn/distance/. Contact: guomaozu@bucea.edu.cn.
July 17, 2018: Briefings in Bioinformatics
https://www.readbyqxmd.com/read/30016397/dbcid-a-manually-curated-resource-for-exploring-the-driver-indels-in-human-cancer
#14
Zhenyu Yue, Le Zhao, Na Cheng, Hua Yan, Junfeng Xia
While recent advances in next-generation sequencing technologies have enabled the creation of a multitude of databases in cancer genomic research, there is no comprehensive database focusing on the annotation of driver indels (insertions and deletions) yet. Therefore, we have developed the database of Cancer driver InDels (dbCID), which is a collection of known coding indels that likely to be engaged in cancer development, progression or therapy. dbCID contains experimentally supported and putative driver indels derived from manual curation of literature and is freely available online at http://bioinfo...
July 16, 2018: Briefings in Bioinformatics
https://www.readbyqxmd.com/read/30010713/a-practical-guide-for-dnase-seq-data-analysis-from-data-management-to-common-applications
#15
Yongjing Liu, Liangyu Fu, Kerstin Kaufmann, Dijun Chen, Ming Chen
Deoxyribonuclease I (DNase I)-hypersensitive site sequencing (DNase-seq) has been widely used to determine chromatin accessibility and its underlying regulatory lexicon. However, exploring DNase-seq data requires sophisticated downstream bioinformatics analyses. In this study, we first review computational methods for all of the major steps in DNase-seq data analysis, including experimental design, quality control, read alignment, peak calling, annotation of cis-regulatory elements, genomic footprinting and visualization...
July 12, 2018: Briefings in Bioinformatics
https://www.readbyqxmd.com/read/30010717/the-small-peptide-world-in-long-noncoding-rnas
#16
Seo-Won Choi, Hyun-Woo Kim, Jin-Wu Nam
Long noncoding RNAs (lncRNAs) are a group of transcripts that are longer than 200 nucleotides (nt) without coding potential. Over the past decade, tens of thousands of novel lncRNAs have been annotated in animal and plant genomes because of advanced high-throughput RNA sequencing technologies and with the aid of coding transcript classifiers. Further, a considerable number of reports have revealed the existence of stable, functional small peptides (also known as micropeptides), translated from lncRNAs. In this review, we discuss the methods of lncRNA classification, the investigations regarding their coding potential and the functional significance of the peptides they encode...
June 29, 2018: Briefings in Bioinformatics
https://www.readbyqxmd.com/read/30010715/a-comprehensive-survey-of-models-for-dissecting-local-ancestry-deconvolution-in-human-genome
#17
Ephifania Geza, Jacquiline Mugo, Nicola J Mulder, Ambroise Wonkam, Emile R Chimusa, Gaston K Mazandu
Over the past decade, studies of admixed populations have increasingly gained interest in both medical and population genetics. These studies have so far shed light on the patterns of genetic variation throughout modern human evolution and have improved our understanding of the demographics and adaptive processes of human populations. To date, there exist about 20 methods or tools to deconvolve local ancestry. These methods have merits and drawbacks in estimating local ancestry in multiway admixed populations...
June 29, 2018: Briefings in Bioinformatics
https://www.readbyqxmd.com/read/30010714/physiological-rna-dynamics-in-rna-seq-analysis
#18
Zhongneng Xu, Shuichi Asakawa
Physiological RNA dynamics cause problems in transcriptome analysis. Physiological RNA accumulation affects the analysis of RNA quantification, and physiological RNA degradation affects the analysis of the RNA sequence length, feature site and quantification. In the present article, we review the effects of physiological degradation and accumulation of RNA on analysing RNA sequencing data. Physiological RNA accumulation and degradation probably led to such phenomena as incorrect estimations of transcription quantification, differential expressions, co-expressions, RNA decay rates, alternative splicing, boundaries of transcription, novel genes, new single-nucleotide polymorphisms, small RNAs and gene fusion...
June 29, 2018: Briefings in Bioinformatics
https://www.readbyqxmd.com/read/30010710/corrigendum-to-how-trees-allocate-carbon-for-optimal-growth-insight-from-a-game-theoretic-model
#19
(no author information available yet)
No abstract text is available yet for this article.
June 29, 2018: Briefings in Bioinformatics
https://www.readbyqxmd.com/read/29982337/atac-pipe-general-analysis-of-genome-wide-chromatin-accessibility
#20
Zuqi Zuo, Yonghao Jin, Wen Zhang, Yichen Lu, Bin Li, Kun Qu
https://github.com/QuKunLab/ATAC-pipe.
June 29, 2018: Briefings in Bioinformatics
journal
journal
34905
1
2
Fetch more papers »
Fetching more papers... Fetching...
Read by QxMD. Sign in or create an account to discover new knowledge that matter to you.
Remove bar
Read by QxMD icon Read
×

Search Tips

Use Boolean operators: AND/OR

diabetic AND foot
diabetes OR diabetic

Exclude a word using the 'minus' sign

Virchow -triad

Use Parentheses

water AND (cup OR glass)

Add an asterisk (*) at end of a word to include word stems

Neuro* will search for Neurology, Neuroscientist, Neurological, and so on

Use quotes to search for an exact phrase

"primary prevention of cancer"
(heart or cardiac or cardio*) AND arrest -"American Heart Association"