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ncHMR detector: a computational framework to systematically reveal non-classical functions of histone modification regulators

Genome Biology - BiomedCentral - Mon, 24/02/2020 - 5:30am
Recently, several non-classical functions of histone modification regulators (HMRs), independent of their known histone modification substrates and products, have been reported to be essential for specific cel...
Categories: Bioinformatics Trends

Carnelian uncovers hidden functional patterns across diverse study populations from whole metagenome sequencing reads

Genome Biology - BiomedCentral - Mon, 24/02/2020 - 5:30am
Microbial populations exhibit functional changes in response to different ambient environments. Although whole metagenome sequencing promises enough raw data to study those changes, existing tools are limited ...
Categories: Bioinformatics Trends

Author Correction: 547 transcriptomes from 44 brain areas reveal features of the aging brain in non-human primates

Genome Biology - BiomedCentral - Mon, 24/02/2020 - 5:30am
Following publication of the original paper [1], the authors reported an error in the affiliation of Xin-Tian Hu, who is also affiliated with “Kunming Primate Research Center, Kunming Institute of Zoology, Chi...
Categories: Bioinformatics Trends

Combining feature selection and shape analysis uncovers precise rules for miRNA regulation in Huntington’s disease mice

BMC Bioinformatics - Mon, 24/02/2020 - 5:30am
MicroRNA (miRNA) regulation is associated with several diseases, including neurodegenerative diseases. Several approaches can be used for modeling miRNA regulation. However, their precision may be limited for ...
Categories: Bioinformatics Trends

Pre- and post-sequencing recommendations for functional annotation of human fecal metagenomes

BMC Bioinformatics - Mon, 24/02/2020 - 5:30am
Shotgun metagenomes are often assembled prior to annotation of genes which biases the functional capacity of a community towards its most abundant members. For an unbiased assessment of community function, sho...
Categories: Bioinformatics Trends

proMAD: semiquantitative densitometric measurement of protein microarrays

BMC Bioinformatics - Mon, 24/02/2020 - 5:30am
Protein microarrays are a versatile and widely used tool for analyzing complex protein mixtures. Membrane arrays utilize antibodies which are captured on a membrane to specifically immobilize several proteins ...
Categories: Bioinformatics Trends

Prediction of new associations between ncRNAs and diseases exploiting multi-type hierarchical clustering

BMC Bioinformatics - Mon, 24/02/2020 - 5:30am
The study of functional associations between ncRNAs and human diseases is a pivotal task of modern research to develop new and more effective therapeutic approaches. Nevertheless, it is not a trivial task sinc...
Categories: Bioinformatics Trends

GenEpi: gene-based epistasis discovery using machine learning

BMC Bioinformatics - Mon, 24/02/2020 - 5:30am
Genome-wide association studies (GWAS) provide a powerful means to identify associations between genetic variants and phenotypes. However, GWAS techniques for detecting epistasis, the interactions between gene...
Categories: Bioinformatics Trends

ncHMR detector: a computational framework to systematically reveal non-classical functions of histone modification regulators

Genome Biology - Mon, 24/02/2020 - 5:30am
Recently, several non-classical functions of histone modification regulators (HMRs), independent of their known histone modification substrates and products, have been reported to be essential for specific cel...
Categories: Bioinformatics Trends

Carnelian uncovers hidden functional patterns across diverse study populations from whole metagenome sequencing reads

Genome Biology - Mon, 24/02/2020 - 5:30am
Microbial populations exhibit functional changes in response to different ambient environments. Although whole metagenome sequencing promises enough raw data to study those changes, existing tools are limited ...
Categories: Bioinformatics Trends

Author Correction: 547 transcriptomes from 44 brain areas reveal features of the aging brain in non-human primates

Genome Biology - Mon, 24/02/2020 - 5:30am
Following publication of the original paper [1], the authors reported an error in the affiliation of Xin-Tian Hu, who is also affiliated with “Kunming Primate Research Center, Kunming Institute of Zoology, Chi...
Categories: Bioinformatics Trends

Nubeam-dedup: a fast and RAM-efficient tool to de-duplicate sequencing reads without mapping

Bioinformatics Oxford Journals - Mon, 24/02/2020 - 5:30am
AbstractSummaryWe present Nubeam-dedup, a fast and RAM-efficient tool to de-duplicate sequencing reads without reference genome. Nubeam-dedup represents nucleotides by matrices, transforms reads into products of matrices, and based on which assigns a unique number to a read. Thus, duplicate reads can be efficiently removed by using a collisionless hash function. Compared with other state-of-the-art reference-free tools, Nubeam-dedup uses 50-70% of CPU time and 10-15% of RAM.Availability and implementationSource code in C ++ and manual are available at https://github.com/daihang16/nubeamdedup and https://haplotype.org.Supplementary informationSupplementary dataSupplementary data are available at Bioinformatics online.
Categories: Bioinformatics Trends

Protein-ligand binding residue prediction enhancement through hybrid deep heterogeneous learning of sequence and structure data

Bioinformatics Oxford Journals - Mon, 24/02/2020 - 5:30am
AbstractMotivationKnowledge of protein-ligand binding residues is important for understanding the functions of proteins and their interaction mechanisms. From experimentally solved protein structures, how to accurately identify its potential binding sites of a specific ligand on the protein is still a challenging problem. Compared with structure-alignment-based methods, machine learning algorithms provide an alternative flexible solution which is less dependent on annotated homogeneous protein structures. Several factors are important for an efficient protein-ligand prediction model, e.g. discriminative feature representation and effective learning architecture to deal with both the large-scale and severe imbalanced data.ResultsIn this study, we propose a novel deep-learning-based method called DELIA for protein-ligand binding residue prediction. In DELIA, a hybrid deep neural network is designed to integrate 1D sequence-based features with 2D structure-based amino acid distance matrices. In order to overcome the problem of severe data imbalance between the binding and non-binding residues, strategies of oversampling in mini-batch, random under-sampling, and stacking ensemble strategy are designed to enhance the model. Experimental results on five benchmark datasets demonstrate the effectiveness of proposed DELIA pipeline.AvailabilityThe web server of DELIA is available at www.csbio.sjtu.edu.cn/bioinf/delia/.Supplementary informationSupplementary dataSupplementary data are available at Bioinformatics online.
Categories: Bioinformatics Trends

DL4papers: a deep learning approach for the automatic interpretation of scientific articles

Bioinformatics Oxford Journals - Mon, 24/02/2020 - 5:30am
AbstractMotivationIn precision medicine, next-generation sequencing and novel preclinical reports have led to an increasingly large amount of results, published in the scientific literature. However, identifying novel treatments or predicting a drug response in, for example, cancer patients, from the huge amount of papers available remains a laborious and challenging work. This task can be considered a text mining problem that requires reading a lot of academic documents for identifying a small set of papers describing specific relations between key terms. Due to the infeasibility of the manual curation of these relations, computational methods that can automatically identify them from the available literature are urgently needed.ResultsWe present DL4papers, a new method based on deep learning that is capable of analyzing and interpreting papers in order to automatically extract relevant relations between specific keywords. DL4papers receives as input a query with the desired keywords, and it returns a ranked list of papers that contain meaningful associations between the keywords. The comparison against related methods showed that our proposal outperformed them in a cancer corpus. The reliability of the DL4papers output list was also measured, revealing that 100% of the first two documents retrieved for a particular search have relevant relations, in average. This shows that our model can guarantee that in the top-2 papers of the ranked list, the relation can be effectively found. Furthermore, the model is capable of highlighting, within each document, the specific fragments that have the associations of the input keywords. This can be very useful in order to pay attention only to the highlighted text, instead of reading the full paper. We believe that our proposal could be used as an accurate tool for rapidly identifying relationships between genes and their mutations, drug responses and treatments in the context of a certain disease. This new approach can certainly be a very useful and valuable resource for the advancement of the precision medicine field.Availability and implementationA web-demo is available at: http://sinc.unl.edu.ar/web-demo/dl4papers/. Full source code and data are available at: https://sourceforge.net/projects/sourcesinc/files/dl4papers/
Categories: Bioinformatics Trends

OrgDyn: Feature and model based characterization of spatial and temporal organoid dynamics

Bioinformatics Oxford Journals - Mon, 24/02/2020 - 5:30am
AbstractSummaryOrganoid model systems recapitulate key features of mammalian tissues and enable high throughput experiments. However, the impact of these experiments may be limited by manual, non-standardized, static, or qualitative phenotypic analysis. OrgDyn is an open-source and modular pipeline to quantify organoid shape dynamics using a combination of feature and model based approaches on time series of 2D organoid contour images. Our pipeline consists of i) geometrical and signal processing feature extraction, ii) dimensionality reduction to differentiate dynamical paths, iii) time series clustering to identify coherent groups of organoids, and iv) dynamical modeling using point distribution models to explain temporal shape variation. OrgDyn can characterize, cluster, and model differences among unique dynamical paths that define diverse final shapes, thus enabling quantitative analysis of the molecular basis of tissue development and disease.Availability and Implementationhttps://github.com/zakih/organoidDynamics (BSD 3-Clause License)Supplementary informationSupplementary dataSupplementary data are available at Bioinformatics online.
Categories: Bioinformatics Trends

PlasGUN: Gene prediction in plasmid metagenomic short reads using deep learning

Bioinformatics Oxford Journals - Mon, 24/02/2020 - 5:30am
AbstractSummaryWe present the first tool of gene prediction, PlasGUN, for plasmid metagenomic short read data. The tool, developed based on deep learning algorithm of multiple input Convolutional Neural Network, demonstrates much better performance when tested on a benchmark dataset of artificial short reads and presents more reliable results for real plasmid metagenomic data than traditional gene prediction tools designed primarily for chromosome-derived short reads.AvailabilityThe PlasGUN software is available at http://cqb.pku.edu.cn/ZhuLab/PlasGUN/ or https://github.com/zhenchengfang/PlasGUN/.Supplementary informationSupplementary dataSupplementary data are available at Bioinformatics online.
Categories: Bioinformatics Trends

6mA-Finder: a novel online tool for predicting DNA N6-methyladenine sites in genomes

Bioinformatics Oxford Journals - Mon, 24/02/2020 - 5:30am
AbstractMotivationDNA N6-methyladenine (6mA) has recently been found as an essential epigenetic modification, playing its roles in a variety of cellular processes. The abnormal status of DNA 6mA modification has been reported in cancer and other disease. The annotation of 6mA marks in genome is the first crucial step to explore the underlying molecular mechanisms including its regulatory roles.ResultsWe present a novel online DNA 6mA site tool, 6mA-Finder, by incorporating seven sequence-derived information and three physicochemical-based features through recursive feature elimination (RFE) strategy. Our multiple cross-validations indicate the promising accuracy and robustness of our model. 6mA-Finder outperforms its peer tools in general and species-specific 6mA site prediction, suggesting it can provide a useful resource for further experimental investigation of DNA 6mA modification.Availabilityhttps://bioinfo.uth.edu/6mA_Finder.Supplementary informationSupplementary dataSupplementary data are available at Bioinformatics online.
Categories: Bioinformatics Trends

Histone H3K27 acetylation is dispensable for enhancer activity in mouse embryonic stem cells

Genome Biology - BiomedCentral - Fri, 21/02/2020 - 5:30am
H3K27ac is well recognized as a marker for active enhancers and a great indicator of enhancer activity. However, its functional impact on transcription has not been characterized. By substituting lysine 27 in ...
Categories: Bioinformatics Trends

Histone H3K27 acetylation is dispensable for enhancer activity in mouse embryonic stem cells

Genome Biology - Fri, 21/02/2020 - 5:30am
H3K27ac is well recognized as a marker for active enhancers and a great indicator of enhancer activity. However, its functional impact on transcription has not been characterized. By substituting lysine 27 in ...
Categories: Bioinformatics Trends

FastMM: an efficient toolbox for personalized constraint-based metabolic modeling

BMC Bioinformatics - Fri, 21/02/2020 - 5:30am
Constraint-based metabolic modeling has been applied to understand metabolism related disease mechanisms, to predict potential new drug targets and anti-metabolites, and to identify biomarkers of complex disea...
Categories: Bioinformatics Trends

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