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sAMPpred-GAT: Prediction of Antimicrobial Peptide by Graph Attention Network and Predicted Peptide Structure

Bioinformatics Oxford Journals - Mon, 07/11/2022 - 5:30am
AbstractMotivationAntimicrobial peptides (AMPs) are essential components of therapeutic peptides for innate immunity. Researchers have developed several computational methods to predict the potential AMPs from many candidate peptides. With the development of artificial intelligent techniques, the protein structures can be accurately predicted, which are useful for protein sequence and function analysis. Unfortunately, the predicted peptide structure information has not been applied to the field of AMP prediction so as to improve the predictive performance.ResultsIn this study, we proposed a computational predictor called sAMPpred-GAT for AMP identification. To the best of our knowledge, sAMPpred-GAT is the first approach based on the predicted peptide structures for AMP prediction. The sAMPpred-GAT predictor constructs the graphs based on the predicted peptide structures, sequence information and evolutionary information. The Graph Attention Network (GAT) is then performed on the graphs to learn the discriminative features. Finally, the full connection networks are utilized as the output module to predict whether the peptides are AMP or not. Experimental results show that sAMPpred-GAT outperforms the other state-of-the-art methods in terms of AUC, and achieves better or highly comparable performance in terms of the other metrics on the eight independent test datasets, demonstrating that the predicted peptide structure information is important for AMP prediction.AvailabilityA user-friendly webserver of sAMPpred-GAT can be accessed at http://bliulab.net/sAMPpred-GAT and the source code is available at https://github.com/HongWuL/sAMPpred-GAT/.Supplementary informationSupplementary dataSupplementary data are available at Bioinformatics online.
Categories: Bioinformatics Trends

Vaeda computationally annotates doublets in single-cell RNA sequencing data

Bioinformatics Oxford Journals - Mon, 07/11/2022 - 5:30am
AbstractMotivationSingle-cell RNA sequencing (scRNA-seq) continues to expand our knowledge by facilitating the study of transcriptional heterogeneity at the level of single cells. Despite this technology’s utility and success in biomedical research, technical artifacts are present in scRNA-seq data. Doublets/multiplets are a type of artifact that occurs when two or more cells are tagged by the same barcode, and therefore they appear as a single cell. Because this introduces non-existent transcriptional profiles, doublets can bias and mislead downstream analysis. To address this limitation computational methods to annotate and remove doublets form scRNA-seq datasets are needed.ResultsWe introduce vaeda, a new approach for computational annotation of doublets in scRNA-seq data. Vaeda integrates a variational auto-encoder and Positive-Unlabeled learning to produce doublet scores and binary doublet calls. We apply vaeda, along with seven existing doublet annotation methods, to sixteen benchmark datasets and find that vaeda performs competitively in terms of doublet scores and doublet calls. Notably, vaeda outperforms other python-based methods for doublet annotation. All together, vaeda is a robust and competitive method for scRNA-seq doublet annotation and may be of particular interest in the context of python-based workflows.AvailabilityVaeda is available at https://github.com/kostkalab/vaeda and the version used for results we present here is archived at zenodo (https://doi.org/10.5281/zenodo.7199783).Supplementary informationSupplementary dataSupplementary data are available at Bioinformatics online.
Categories: Bioinformatics Trends

An Approach of Gene Regulatory Network Construction Using Mixed Entropy Optimizing Context-Related Likelihood Mutual Information

Bioinformatics Oxford Journals - Mon, 07/11/2022 - 5:30am
AbstractMotivationThe question of how to construct gene regulatory networks has long been a focus of biological research. Mutual information can be used to measure nonlinear relationships, and it has been widely used in the construction of gene regulatory networks. However, this method cannot measure indirect regulatory relationships under the influence of multiple genes, which reduces the accuracy of inferring gene regulatory networks.ApproachThis work proposes a method for constructing gene regulatory networks based on mixed entropy optimizing context-related likelihood mutual information (MEOMI). First, two entropy estimators were combined to calculate the mutual information between genes. Then, distribution optimization was performed using a context-related likelihood algorithm to eliminate some indirect regulatory relationships and obtain the initial gene regulatory network. To obtain the complex interaction between genes and eliminate redundant edges in the network, the initial gene regulatory network was further optimized by calculating the conditional mutual inclusive information (CMI2) between gene pairs under the influence of multiple genes. The network was iteratively updated to reduce the impact of mutual information on the overestimation of the direct regulatory intensity.ResultsThe experimental results show that the MEOMI method performed better than several other kinds of gene network construction methods on DREAM challenge simulated datasets (DREAM3 and DREAM5), three real Escherichia coli datasets (E. coli SOS pathway network, E. coli SOS DNA repair network, and E. coli community network) and two human datasets.Availability and implementationSource code and dataset are available at https://github.com/Dalei-Dalei/MEOMI/ and http://122.205.95.139/MEOMI/.Supplementary informationSupplementary dataSupplementary data are available at Bioinformatics online.
Categories: Bioinformatics Trends

Global FDR control across multiple RNAseq experiments

Bioinformatics Oxford Journals - Thu, 03/11/2022 - 5:30am
AbstractMotivationWhile classical approaches for controlling the false discovery rate (FDR) of RNAseq experiments have been well described, modern research workflows and growing databases enable a new paradigm of controlling the FDR globally across RNAseq experiments in the past, present, and future. The simplest analysis strategy that analyses each RNAseq experiment separately and applies an FDR correction method can lead to inflation of the overall FDR. We propose applying recently developed methodology for online multiple hypothesis testing to control the global FDR in a principled way across multiple RNAseq experiments.ResultsWe show that repeated application of classical repeated offline approaches has variable control of global FDR of RNAseq experiments over time. We demonstrate that the online FDR algorithms are a principled way to control FDR. Furthermore, in certain simulation scenarios, we observe empirically that online approaches have comparable power to repeated offline approaches.Availability and ImplementationThe onlineFDR package is freely available at http://www.bioconductor.org/packages/onlineFDR. Additional code used for the simulation studies can be found at https://github.com/latlio/onlinefdr_rnaseq_simulationSupplementary InformationSupplementary AppendixSupplementary Appendix is available in Bioinformatics online.
Categories: Bioinformatics Trends

Integrating transformer and imbalanced multi-label learning to identify antimicrobial peptides and their functional activities

Bioinformatics Oxford Journals - Thu, 03/11/2022 - 5:30am
AbstractMotivationAntimicrobial peptides (AMP) have the potential to inhibit multiple types of pathogens and to heal infections. Computational strategies can assist in characterizing novel AMPs from proteome or collections of synthetic sequences and discovering their functional abilities towards different microbial targets without intensive labor.ResultsHere, we present a deep learning-based method for computer-aided novel AMP discovery that utilizes the transformer neural network architecture with knowledge from natural language processing to extract peptide sequence information. We implemented the method for two AMP-related tasks: the first is to discriminate AMPs from other peptides, and the second task is identifying AMPs functional activities related to seven different targets (gram-negative bacteria, gram-positive bacteria, fungi, viruses, cancer cells, parasites, and mammalian cell inhibition), which is a multi-label problem. In addition, asymmetric loss was adopted to resolve the intrinsic imbalance of dataset, particularly for the multi-label scenarios. The evaluation showed that our proposed scheme achieves the best performance for the first task (96.85% balanced accuracy) and has a more unbiased prediction for the second task (79.83% balanced accuracy averaged across all functional activities) when compared to that of strategies without imbalanced learning or deep learning.AvailabilityThe source code of this study is available at https://github.com/BiOmicsLab/TransImbAMP.Supplementary informationSupplementary dataSupplementary data are available at Bioinformatics online.
Categories: Bioinformatics Trends

PEMT: a patent enrichment tool for drug discovery

Bioinformatics Oxford Journals - Wed, 02/11/2022 - 5:30am
AbstractMotivationDrug discovery practitioners in industry and academia use semantic tools to extract information from online scientific literature to generate new insights into targets, therapeutics and diseases. However, due to complexities in access and analysis, patent-based literature is often overlooked as a source of information. As drug discovery is a highly competitive field, naturally, tools that tap into patent literature can provide any actor in the field an advantage in terms of better informed decision making. Hence, we aim to facilitate access to patent literature through the creation of an automatic tool for extracting information from patents described in existing public resources.ResultsHere, we present PEMT, a novel patent enrichment tool, that takes advantage of public databases like ChEMBL and SureChEMBL to extract relevant patent information linked to chemical structures and/or gene names described through FAIR principles and metadata annotations. PEMT aims at supporting drug discovery and research by establishing a patent landscape around genes of interest. The pharmaceutical focus of the tool is mainly due to the subselection of International Patent Classification (IPC) codes, but in principle, it can be used for other patent fields, provided that a link between a concept and chemical structure is investigated. Finally, we demonstrate a use-case in rare diseases by generating a gene-patent list based on the epidemiological prevalence of these diseases and exploring their underlying patent landscapes.Availability and implementationPEMT is an open-source Python tool and its source code and PyPi package are available at https://github.com/Fraunhofer-ITMP/PEMT and https://pypi.org/project/PEMT/ respectively.Supplementary informationSupplementary dataSupplementary data are available at Bioinformatics online.
Categories: Bioinformatics Trends

MIDAS2: Metagenomic Intra-species Diversity Analysis System

Bioinformatics Oxford Journals - Wed, 02/11/2022 - 5:30am
AbstractSummaryThe Metagenomic Intra-Species Diversity Analysis System (MIDAS) is a scalable metagenomic pipeline that identifies single nucleotide variants (SNVs) and gene copy number variants (CNVs) in microbial populations. Here, we present MIDAS2, which addresses the computational challenges presented by increasingly large reference genome databases, while adding functionality for building custom databases and leveraging paired-end reads to improve SNV accuracy. This fast and scalable reengineering of the MIDAS pipeline enables thousands of metagenomic samples to be efficiently genotyped.AvailabilityThe source code is available at https://github.com/czbiohub/MIDAS2.Supplementary informationSupplementary dataSupplementary data are available at Bioinformatics online.
Categories: Bioinformatics Trends

GAVISUNK: Genome assembly validation via inter-SUNK distances in Oxford Nanopore reads

Bioinformatics Oxford Journals - Wed, 02/11/2022 - 5:30am
AbstractMotivationHighly contiguous de novo phased diploid genome assemblies are now feasible for large numbers of species and individuals. Methods are needed to validate assembly accuracy and detect misassemblies with orthologous sequencing data to allow for confident downstream analyses.ResultsWe developed GAVISUNK, an open-source pipeline that detects misassemblies and produces a set of reliable regions genome-wide by assessing concordance of distances between unique k-mers in Pacific Biosciences high-fidelity (HiFi) assemblies and raw Oxford Nanopore Technologies reads.AvailabilityGAVISUNK is available at https://github.com/pdishuck/GAVISUNK.
Categories: Bioinformatics Trends

BraneMF: Integration of Biological Networks for Functional Analysis of Proteins

Bioinformatics Oxford Journals - Wed, 02/11/2022 - 5:30am
AbstractMotivationThe cellular system of a living organism is composed of interacting bio-molecules that control cellular processes at multiple levels. Their correspondences are represented by tightly regulated molecular networks. The increase of omics technologies has favored the generation of large-scale disparate data and the consequent demand for simultaneously using molecular and functional interaction networks: gene co-expression, protein-protein interaction (PPI), genetic interaction, and metabolic networks. They are rich sources of information at different molecular levels, and their effective integration is essential to understand cell functioning and their building blocks (proteins). Therefore, it is necessary to obtain informative representations of proteins and their proximity, that are not fully captured by features extracted directly from a single informational level. We propose BraneMF, a novel random walk-based matrix factorization method for learning node representation in a multilayer network, with application to omics data integration.ResultsWe test BraneMF with PPI networks of Saccharomyces cerevisiae, a well-studied yeast model organism. We demonstrate the applicability of the learned features for essential multi-omics inference tasks: clustering, function and PPI prediction. We compare it to state-of-the-art integration methods for multilayer network. BraneMF outperforms baseline methods by achieving high prediction scores for a variety of downstream tasks. The robustness of results is assessed by an extensive parameter sensitivity analysis.AvailabilityBraneMF’s code is freely available at: https://github.com/Surabhivj/BraneMF, along with datasets, embeddings, and result files.Supplementary informationSupplementary dataSupplementary data are available at Bioinformatics online.
Categories: Bioinformatics Trends

An in silico genome-wide screen for circadian clock strength in human samples

Bioinformatics Oxford Journals - Wed, 02/11/2022 - 5:30am
AbstractMotivationYears of time-series gene expression studies have built a strong understanding of clock-controlled pathways across species. However, comparatively little is known about how ‘non-clock’ pathways influence clock function. We need a strong understanding of clock-coupled pathways in human tissues to better appreciate the links between disease and clock function.ResultsWe developed a new computational approach to explore candidate pathways coupled to the clock in human tissues. This method, termed LTM, is an in silico screen to infer genetic influences on circadian clock function. LTM uses natural variation in gene expression in human data and directly links gene expression variation to clock strength independent of longitudinal data. We applied LTM to three human skin and one melanoma datasets and found that the cell cycle is the top candidate clock-coupled pathway in healthy skin. In addition, we applied LTM to thousands of tumor samples from 11 cancer types in the TCGA database and found that extracellular matrix organization-related pathways are tightly associated with the clock strength in humans. Further analysis shows that clock strength in tumor samples are correlated with the proportion of cancer-associated fibroblasts and endothelial cells. Therefore, we show both the power of LTM in predicting clock-coupled pathways and classify factors associated with clock strength in human tissues.AvailabilityLTM is available on GitHub (https://github.com/gangwug/LTMR) and figshare (https://figshare.com/articles/software/LTMR/21217604) to facilitate its use.Supplementary informationSupplementary dataSupplementary data are available at Bioinformatics online.
Categories: Bioinformatics Trends

ConsensuSV—from the whole genome sequencing data to the complete variant list

Bioinformatics Oxford Journals - Mon, 31/10/2022 - 5:30am
AbstractSummaryThe detection of the Structural Variants using Illumina sequencing of human DNA is not an easy task. Multiple approaches have been proposed; however, all the methods have their limitations. In this paper we present ConsensuSV pipeline, that aids the research in complex variant detection. By using consensus meta-approach, eight independent SV callers are being used to identify a uniform set of high-quality structural variants. The pipeline works using raw sequencing data, and performs all the necessary steps automatically, significantly reducing the researchers’ time required for processing the data. The output files contain Structural Variants, Single Nucleotide Polymorphisms and Indels. The pipeline uses luigi framework, allowing the software to be run efficiently and parallelly using the high-performance computing (HPC) infrastructure. We strongly believe that the software is useful to the scientific community interested in the germline variant detection.Availabilityhttps://github.com/SFGLab/ConsensuSV-pipelineSupplementary informationSupplementary dataSupplementary data are available at Bioinformatics online.
Categories: Bioinformatics Trends

Convex hull as diagnostic tool in single-molecule localization microscopy

Bioinformatics Oxford Journals - Mon, 31/10/2022 - 5:30am
AbstractMotivationSingle-molecule localization microscopy resolves individual fluorophores or fluorescence-labeled biomolecules. Data is provided as a set of localizations that distribute normally around the true fluorophore position with a variance determined by the localization precision. Characterizing the spatial fluorophore distribution to differentiate between resolution-limited localization clusters, which resemble individual biomolecules, and extended structures, which represent aggregated molecular complexes, is a common challenge.ResultsWe demonstrate use of the convex hull and related hull properties of localization clusters for diagnostic purposes, as a parameter for cluster selection, or as a tool to determine localization precision.Availabilityhttps://github.com/super-resolution/Ebert-et-al-2022-supplement.Supplementary informationSupplementary dataSupplementary data are available at Bioinformatics online.
Categories: Bioinformatics Trends

kmdiff, large-scale and user-friendly differential k-mer analyses

Bioinformatics Oxford Journals - Mon, 31/10/2022 - 5:30am
Abstract Genome Wide Association Studies (GWAS) elucidate links between genotypes and phenotypes. Recent studies point out the interest of conducting such experiments using k-mers as the base signal instead of single-nucleotide polymorphisms. We propose a tool, kmdiff, that performs differential k-mer analyses on large sequencing cohorts in an order of magnitude less time and memory than previously possible.Availabilityhttps://github.com/tlemane/kmdiffFundingThe work was funded by IPL Inria Neuromarkers, ANR Inception (ANR-16-CONV-0005), ANR Prairie (ANR-19-P3IA-0001), ANR SeqDigger (ANR-19-CE45-0008), H2020 ITN ALPACA grant 956229.
Categories: Bioinformatics Trends

HaploDMF: viral Haplotype reconstruction from long reads via Deep Matrix Factorization

Bioinformatics Oxford Journals - Sat, 29/10/2022 - 5:30am
AbstractMotivationLacking strict proofreading mechanisms, many RNA viruses can generate progeny with slightly changed genomes. Being able to characterize highly similar genomes (i.e. haplotypes) in one virus population helps study the viruses’ evolution and their interactions with the host/other microbes. High-throughput sequencing data has become the major source for characterizing viral populations. However, the inherent limitation on read length by next-generation sequencing (NGS) makes complete haplotype reconstruction difficult.ResultsIn this work, we present a new tool named HaploDMF that can construct complete haplotypes using third-generation sequencing (TGS) data. HaploDMF utilizes a deep matrix factorization model with an adapted loss function to learn latent features from aligned reads automatically. The latent features are then used to cluster reads of the same haplotype. Unlike existing tools whose performance can be affected by the overlap size between reads, HaploDMF is able to achieve highly robust performance on data with different coverage, haplotype number, and error rates. In particular, it can generate more complete haplotypes even when the sequencing coverage drops in the middle. We benchmark HaploDMF against the state-of-the-art tools on simulated and real sequencing TGS data on different viruses. The results show that HaploDMF competes favorably against all others.AvailabilityThe source code and the documentation of HaploDMF are available at https://github.com/dhcai21/HaploDMF.Supplementary informationSupplementary dataSupplementary data are available at Bioinformatics online.
Categories: Bioinformatics Trends

ADViSELipidomics: a workflow for analyzing lipidomics data

Bioinformatics Oxford Journals - Sat, 29/10/2022 - 5:30am
AbstractSummaryADViSELipidomics is a novel Shiny app for preprocessing, analyzing, and visualizing lipidomics data. It handles the outputs from LipidSearch and LIQUID for lipid identification and quantification and the data from the Metabolomics Workbench. ADViSELipidomics extracts information by parsing lipid species (using LIPID MAPS classification) and, together with information available on the samples, performs several exploratory and statistical analyses. When the experiment includes internal lipid standards, ADViSELipidomics can normalize the data matrix, providing normalized concentration values per lipids and samples. Moreover, it identifies differentially abundant lipids in simple and complex experimental designs, dealing with batch effect correction. Finally, ADViSELipidomics has a user-friendly Graphical User Interface (GUI) and supports an extensive series of interactive graphics.Availability and ImplementationADViSELipidomics is freely available at https://github.com/ShinyFabio/ADViSELipidomicsSupplementary informationSupplementary dataSupplementary data are available at Bioinformatics online.
Categories: Bioinformatics Trends

HAT: Haplotype Assembly Tool using short and error-prone long reads

Bioinformatics Oxford Journals - Sat, 29/10/2022 - 5:30am
AbtractMotivationHaplotypes are the set of alleles co-occurring on a single chromosome and inherited together to the next generation. Because a monoploid reference genome loses this co-occurrence information, it has limited use in associating phenotypes with allelic combinations of genotypes. Therefore, methods to reconstruct the complete haplotypes from DNA sequencing data are crucial.Recently, several attempts have been made at haplotype reconstructions, but significant limitations remain. High-quality continuous haplotypes cannot be created reliably, particularly when there are few differences between the homologous chromosomes.ResultsHere, we introduce HAT, a haplotype assembly tool that exploits short and long reads along with a reference genome to reconstruct haplotypes. HAT tries to take advantage of the accuracy of short reads and the length of the long reads to reconstruct haplotypes. We tested HAT on the aneuploid yeast strain Saccharomyces pastorianus CBS1483 and multiple simulated polyploid data sets of the same strain, showing that it outperforms existing tools.Availabilityhttps://github.com/AbeelLab/hat/Supplementary informationSupplementary dataSupplementary data are available at Bioinformatics online.
Categories: Bioinformatics Trends

Common data model for COVID-19 datasets

Bioinformatics Oxford Journals - Thu, 27/10/2022 - 5:30am
AbstractMotivationA global medical crisis like the COVID-19 pandemic requires interdisciplinary and highly collaborative research from all over the world. One of the key challenges for collaborative research is a lack of interoperability among various heterogeneous data sources. Interoperability, standardization and mapping of datasets is necessary for data analysis and applications in advanced algorithms such as developing personalized risk prediction modeling.ResultsTo ensure the interoperability and compatibility among COVID-19 datasets, we present here a Common Data Model (CDM) which has been built from 11 different COVID-19 datasets from various geographical locations. The current version of the CDM holds 4639 data variables related to COVID-19 such as basic patient information (age, biological sex, and diagnosis) as well as disease-specific data variables, for example, Anosmia and Dispnea. Each of the data variables in the data model is associated with specific data types, variable mappings, value ranges, data units, and data encodings that could be used for standardizing any dataset. Moreover, the compatibility with established data standards like OMOP and FHIR makes the CDM a well-designed common data model for COVID-19 data interoperability.AvailabilityThe CDM is available in a public repo here: https://github.com/Fraunhofer-SCAI-Applied-Semantics/COVID-19-Global-ModelSupplementary informationSupplementary dataSupplementary data are available at Bioinformatics online.
Categories: Bioinformatics Trends

Neuron Tracing from Light Microscopy Images: Automation, Deep Learning, and Bench Testing

Bioinformatics Oxford Journals - Thu, 27/10/2022 - 5:30am
AbstractMotivationLarge-scale neuronal morphologies are essential to neuronal typing, connectivity characterization and brain modeling. It is widely accepted that automation is critical to the production of neuronal morphology. Despite previous survey papers about neuron tracing from light microscopy data in the last decade, thanks to the rapid development of the field, there is a need to update recent progress in a review focusing on new methods and remarkable applications.ResultsThis review outlines neuron tracing in various scenarios with the goal to help the community understand and navigate tools and resources. We describe the status, examples, and accessibility of automatic neuron tracing. We survey recent advances of the increasingly popular deep learning enhanced methods. We highlight the semi-automatic methods for single neuron tracing of mammalian whole brains as well as the resulting datasets, each containing thousands of full neuron morphologies. Finally, we exemplify the commonly used datasets and metrics for neuron tracing bench testing.
Categories: Bioinformatics Trends

EvAM-Tools: tools for evolutionary accumulation and cancer progression models

Bioinformatics Oxford Journals - Wed, 26/10/2022 - 5:30am
AbstractSummaryEvAM-Tools is an R package and web application that provides a unified interface to state-of-the-art cancer progression models (CPMs) and, more generally, evolutionary models of event accumulation. The output includes, in addition to the fitted models, the transition (and transition rate) matrices between genotypes and the probabilities of evolutionary paths. Generation of random cancer progression models is also available. Using the GUI in the web application, users can easily construct models (modifying Directed Acyclic Graphs —DAGs— of restrictions, matrices of mutual hazards, or specifying genotype composition), generate data from them (with user-specified observational/genotyping error), and analyze the data.Availability and ImplementationImplemented in R and C; open source code available under the GNU Affero General Public License v3.0 at https://github.com/rdiaz02/EvAM-Tools. Docker images freely available from https://hub.docker.com/u/rdiaz02. Web app freely accessible at https://iib.uam.es/evamtools.Supplementary informationSupplementary dataSupplementary data are available at Bioinformatics online.
Categories: Bioinformatics Trends

Identifying the critical state of complex biological systems by the directed-network rank score method

Bioinformatics Oxford Journals - Tue, 25/10/2022 - 5:30am
AbstractMotivationCatastrophic transitions are ubiquitous in the dynamic progression of complex biological systems; that is, a critical transition at which complex systems suddenly shift from one stable state to another occurs. Identifying such a critical point or tipping point is essential for revealing the underlying mechanism of complex biological systems. However, it is difficult to identify the tipping point since few significant differences in the critical state are detected in terms of traditional static measurements.ResultsIn this study, by exploring the dynamic changes in gene cooperative effects between the before-transition and critical states, we presented a model-free approach, the directed-network rank score (DNRS), to detect the early-warning signal of critical transition in complex biological systems. The proposed method is applicable to both bulk and single-cell RNA-sequencing (scRNA-seq) data. This computational method was validated by the successful identification of the critical or pre-transition state for both simulated and six real datasets, including three scRNA-seq datasets of embryonic development and three tumor datasets. In addition, the functional and pathway enrichment analyses suggested that the corresponding DNRS signaling biomarkers were involved in key biological processes.AvailabilityThe source code is freely available at https://github.com/zhongjiayuan/DNRSSupplementary informationSupplementary dataSupplementary data are available at Bioinformatics online.
Categories: Bioinformatics Trends

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