Jump to Navigation

DEMoS: A Deep Learning-based Ensemble Approach for Predicting the Molecular Subtypes of Gastric Adenocarcinomas from Histopathological Images

Bioinformatics Oxford Journals - Fri, 08/07/2022 - 5:30am
AbstractMotivationThe molecular subtyping of gastric cancer (adenocarcinoma) into four main subtypes based on integrated multiomics profiles, as proposed by The Cancer Genome Atlas (TCGA) initiative, represents an effective strategy for patient stratification. However, this approach requires the use of multiple technological platforms, and is quite expensive and time consuming to perform. A computational approach that uses histopathological image data to infer molecular subtypes could be a practical, cost- and time-efficient complementary tool for prognostic and clinical management purposes.ResultsHere, we propose a deep learning ensemble learning approach (called DEMoS) capable of predicting the four recognized molecular subtypes of gastric cancer directly from histopathological images. DEMoS achieved tile-level area under the receiver-operating characteristic curve (AUROC) values of 0.785, 0.668, 0.762, and 0.811 for the prediction of these four subtypes of gastric cancer (i.e. Epstein-Barr (EBV)-infected, (2) microsatellite instability (MSI), (3) genomically-stable (GS), and (4) chromosomally unstable tumors (CIN)) using an independent test dataset, respectively. At the patient-level, it achieved AUROC values of 0.897, 0.764, 0.890, and 0.898, respectively. Thus, these four subtypes are well-predicted by DEMoS. Benchmarking experiments further suggest that DEMoS is able to achieve an improved classification performance for image-based subtyping and prevent model overfitting. This study highlights the feasibility of using a deep learning ensemble-based method to rapidly and reliably subtype gastric cancer (adenocarcinoma) solely using features from histopathological images.AvailabilityAll WSIs used in this study was collected from the TCGA database. This study builds upon our previously published HEAL framework, with related documentation and tutorials available at http://heal.erc.monash.edu.au. The source code and related models are freely accessible at https://github.com/Docurdt/DEMoS.git.Supplementary informationSupplementary dataSupplementary data are available at Bioinformatics online.
Categories: Bioinformatics Trends

pkgndep: a tool for analyzing dependency heaviness of R packages

Bioinformatics Oxford Journals - Fri, 08/07/2022 - 5:30am
AbstractSummaryNumerous R packages have been developed for bioinformatics analysis in the last decade and dependencies among packages have become critical issues to consider. In this work, we proposed a new metric named dependency heaviness that measures the number of dependencies that a parent uniquely brings to a package, and we proposed possible solutions for reducing the complexity of dependencies by optimizing the use of heavy parents. We implemented the metric in a new R package pkgndep which provides an intuitive way for dependency heaviness analysis. Based on pkgndep, we additionally performed a global analysis of dependency heaviness on CRAN and Bioconductor ecosystems and we revealed top packages that have significant contributions of high dependency heaviness to their child packages.Availability and implementationThe package pkgndep and documentations are freely available from the Comprehensive R Archive Network (CRAN) https://cran.r-project.org/package=pkgndep. The dependency heaviness analysis for all 22,076 CRAN and Bioconductor packages retrieved on 2022-06-08 are available at https://pkgndep.github.io/.Supplementary informationSupplementary dataSupplementary data are available at Bioinformatics online.
Categories: Bioinformatics Trends

HARIBOSS: a curated database of RNA-small molecules structures to aid rational drug design

Bioinformatics Oxford Journals - Thu, 07/07/2022 - 5:30am
AbstractMotivationRNA molecules are implicated in numerous fundamental biological processes and many human pathologies, such as cancer, neurodegenerative disorders, muscular diseases, and bacterial infections. Modulating the mode of action of disease-implicated RNA molecules can lead to the discovery of new therapeutical agents and even address pathologies linked to ‘undruggable’ protein targets. This modulation can be achieved by direct targeting of RNA with small molecules. As of today, only a few RNA-targeting small molecules are used clinically. One of the main obstacles that has hampered the development of a rational drug design protocol to target RNA with small molecules is the lack of a comprehensive understanding of the molecular mechanisms at the basis of RNA-small molecule recognition.ResultsHere, we present HARIBOSS, a curated collection of RNA-small molecule structures determined by X-ray crystallography, Nuclear Magnetic Resonance spectroscopy and cryo-electron microscopy. HARIBOSS facilitates the exploration of drug-like compounds known to bind RNA, the analysis of ligands and pockets properties, and ultimately the development of in silico strategies to identify RNA-targeting small molecules.AvailabilityHARIBOSS can be explored via a web interface available at http://hariboss.pasteur.cloud.Supplementary informationSupplementary dataSupplementary data are available at Bioinformatics online.
Categories: Bioinformatics Trends

Gfastats: conversion, evaluation and manipulation of genome sequences using assembly graphs

Bioinformatics Oxford Journals - Thu, 07/07/2022 - 5:30am
AbstractMotivationWith the current pace at which reference genomes are being produced, the availability of tools that can reliably and efficiently generate genome assembly summary statistics has become critical. Additionally, with the emergence of new algorithms and data types, tools that can improve the quality of existing assemblies through automated and manual curation are required.ResultsWe sought to address both these needs by developing gfastats, as part of the Vertebrate Genomes Project (VGP) effort to generate high-quality reference genomes at scale. Gfastats is a standalone tool to compute assembly summary statistics and manipulate assembly sequences in fasta, fastq, or gfa [.gz] format. Gfastats stores assembly sequences internally in a gfa-like format. This feature allows gfastats to seamlessly convert fast* to and from gfa [.gz] files. Gfastats can also build an assembly graph that can in turn be used to manipulate the underlying sequences following instructions provided by the user, while simultaneously generating key metrics for the new sequences.AvailabilityGfastats is implemented in C ++. Precompiled releases (Linux, MacOS, Windows) and commented source code for gfastats are available under MIT licence at https://github.com/vgl-hub/gfastats. Examples of how to run gfastats are provided in the Github. Gfastats is also available in Bioconda, in Galaxy (https://assembly.usegalaxy.eu) and as a MultiQC module (Ewels et al., 2016) (https://github.com/ewels/MultiQC). An automated test workflow is available to ensure consistency of software updates.Supplementary informationSupplementary dataSupplementary data are available at Bioinformatics online.
Categories: Bioinformatics Trends

DeepGenGrep: a general deep learning-based predictor for multiple genomic signals and regions

Bioinformatics Oxford Journals - Thu, 07/07/2022 - 5:30am
AbstractMotivationAccurate annotation of different genomic signals and regions (GSRs) from DNA sequences is fundamentally important for understanding gene structure, regulation, and function. Numerous efforts have been made to develop machine learning-based predictors for in silico identification of GSRs. However, it remains a great challenge to identify GSRs as the performance of most existing approaches is unsatisfactory. As such, it is highly desirable to develop more accurate computational methods for GSRs prediction.ResultsIn this study, we propose a general deep learning framework termed DeepGenGrep, a general predictor for the systematic identification of multiple different GSRs from genomic DNA sequences. DeepGenGrep leverages the power of hybrid neural networks comprising a three-layer convolutional neural network and a two-layer long short-term memory to effectively learn useful feature representations from sequences. Benchmarking experiments demonstrate that DeepGenGrep outperforms several state-of-the-art approaches on identifying polyadenylation signals, translation initiation sites, and splice sites across four eukaryotic species including Homo sapiens, Mus musculus, Bos taurus, and Drosophila melanogaster. Overall, DeepGenGrep represents a useful tool for the high-throughput and cost-effective identification of potential GSRs in eukaryotic genomes.Availability and ImplementationThe webserver and source code are freely available at http://bigdata.biocie.cn/deepgengrep/home and Github (https://github.com/wx-cie/DeepGenGrep/).Supplementary informationSupplementary dataSupplementary data are available at Bioinformatics online.
Categories: Bioinformatics Trends

DelaySSAToolkit.jl: stochastic simulation of reaction systems with time delays in Julia

Bioinformatics Oxford Journals - Thu, 07/07/2022 - 5:30am
AbstractSummaryDelaySSAToolkit.jl is a Julia package for modelling reaction systems with non-Markovian dynamics, specifically those with time delays. These delays implicitly capture multiple intermediate reaction steps and hence serve as an effective model reduction technique for complex systems in biology, chemistry, ecology and genetics. The package implements a variety of exact formulations of the delay stochastic simulation algorithm.Availability and ImplementationThe source code and documentation of DelaySSAToolkit.jl are available at https://github.com/palmtree2013/DelaySSAToolkit.jl.
Categories: Bioinformatics Trends

PCN-Miner: An open-source extensible tool for the Analysis of Protein Contact Networks

Bioinformatics Oxford Journals - Thu, 07/07/2022 - 5:30am
AbstractMotivationProtein Contact Network (PCN) is a powerful method for analysing the structure and function of proteins, with a specific focus on disclosing the molecular features of allosteric regulation through the discovery of modular substructures. The importance of PCN analysis has been shown in many contexts, such as the analysis of SARS-CoV-2 Spike protein and its complexes with the ACE human receptors. Even if there exist many software tools implementing such methods, there is a growing need tools integrating existing approaches.ResultsWe present PCN-Miner a software tool, implemented in the Python programming language, able to (i) import protein structures from the Protein Data Bank; (ii) generate the corresponding Protein Contact Network; (iii) model, analyse and graphically represent PCNs and related protein structures by using a set of known algorithms and metrics. The PCN-Miner can cover a large set of applications: from clustering to embedding and subsequent analysis.AvailabilityThe PCN-Miner tool is freely available at the following GitHub repository: https://github.com/hguzzi/ProteinContactNetworks. Tool is also available as package in the Python Package Index (PyPI) repository.Supplementary informationUse cases and support files are available in the GitHub repository.
Categories: Bioinformatics Trends

BubbleGun: Enumerating Bubbles and Superbubbles in Genome Graphs

Bioinformatics Oxford Journals - Thu, 07/07/2022 - 5:30am
AbstractMotivationWith the fast development of sequencing technology, accurate de novo genome assembly is now possible even for larger genomes. Graph-based representations of genomes arise both as part of the assembly process, but also in the context of pangenomes representing a population. In both cases, polymorphic loci lead to bubble structures in such graphs. Detecting bubbles is hence an important task when working with genomic variants in the context of genome graphs.ResultsHere, we present a fast general-purpose tool, called BubbleGun, for detecting bubbles and superbubbles in genome graphs. Furthermore, BubbleGun detects and outputs runs of linearly connected bubbles and superbubbles, which we call bubble chains. We showcase its utility on de Bruijn graphs and compare our results to vg’s snarl detection. We show that BubbleGun is considerably faster than vg especially in bigger graphs, where it reports all bubbles in less than 30 minutes on a human sample de Bruijn graph of around 2 million nodes.AvailabilityBubbleGun is available and documented as a Python3 package at https://github.com/fawaz-dabbaghieh/bubble_gun under MIT license.Supplementary informationSupplementary dataSupplementary data are available at Bioinformatics online.
Categories: Bioinformatics Trends

CovRadar: Continuously tracking and filtering SARS-CoV-2 mutations for genomic surveillance

Bioinformatics Oxford Journals - Thu, 07/07/2022 - 5:30am
AbstractSummaryThe ongoing pandemic caused by SARS-CoV-2 emphasizes the importance of genomic surveillance to understand the evolution of the virus, to monitor the viral population, and plan epidemiological responses. Detailed analysis, easy visualization, and intuitive filtering of the latest viral sequences are powerful for this purpose. We present CovRadar, a tool for genomic surveillance of the SARS-CoV-2 Spike protein. CovRadar consists of an analytical pipeline and a web application that enable the analysis and visualization of hundreds of thousands sequences. First, CovRadar extracts the regions of interest using local alignment, then builds a multiple sequence alignment, infers variants and consensus, and finally presents the results in an interactive app, making accessing and reporting simple, flexible and fast.Availability and implementationCovRadar is freely accessible at https://covradar.net, its open-source code is available at https://gitlab.com/dacs-hpi/covradar.Supplementary informationSupplementary dataSupplementary data are available at Bioinformatics online.
Categories: Bioinformatics Trends

Identification of Cell-Type-Specific Spatially Variable Genes Accounting for Excess Zeros

Bioinformatics Oxford Journals - Wed, 06/07/2022 - 5:30am
AbstractMotivationSpatial transcriptomic techniques can profile gene expressions while retaining the spatial information, thus offering unprecedented opportunities to explore the relationship between gene expression and spatial locations. The spatial relationship may vary across cell types, but there is a lack of statistical methods to identify cell-type-specific spatially variable (SV) genes by simultaneously modeling excess zeros and cell-type proportions.ResultsWe develop a statistical approach CTSV to detect cell-type-specific SV genes. CTSV directly models spatial raw count data and considers zero-inflation as well as overdispersion using a zero-inflated negative binomial distribution. It then incorporates cell-type proportions and spatial effect functions in the zero-inflated negative binomial regression framework. The R package pscl (Zeileis et al., 2008) is employed to fit the model. For robustness, a Cauchy combination rule is applied to integrate p-values from multiple choices of spatial effect functions. Simulation studies show that CTSV not only outperforms competing methods at the aggregated level but also achieves more power at the cell-type level. By analyzing pancreatic ductal adenocarcinoma spatial transcriptomic data, SV genes identified by CTSV reveal biological insights at the cell-type level.AvailabilityThe R package of CTSV is available on https://bioconductor.org/packages/devel/bioc/html/CTSV.html.Supplementary informationSupplementary dataSupplementary data are available at Bioinformatics online.
Categories: Bioinformatics Trends

flashfm-ivis: interactive visualisation for fine-mapping of multiple quantitative traits

Bioinformatics Oxford Journals - Wed, 06/07/2022 - 5:30am
AbstractSummaryflashfm-ivis provides a suite of interactive visualisation plots to view potential causal genetic variants that underlie associations that are shared or distinct between multiple quantitative traits and compares results between single- and multi-trait fine-mapping. Unique features include network diagrams that show joint effects between variants for each trait and regional association plots that integrate fine-mapping results, all with user-controlled zoom features for an interactive exploration of potential causal variants across traits.Availability and Implementationflashfm-ivis is an open-source software under the MIT license. It is available as an interactive web-based tool (http://shiny.mrc-bsu.cam.ac.uk/apps/flashfm-ivis/) and as an R package. Code and documentation are available at https://github.com/fz-cambridge/flashfm-ivis and https://zenodo.org/record/6376244#.YjnarC-l2X0. Additional features can be downloaded as standalone R libraries to encourage reuse.Supplementary informationSupplementary informationSupplementary information are available at Bioinformatics online.
Categories: Bioinformatics Trends

DockingPie: a Consensus Docking Plugin for PyMOL

Bioinformatics Oxford Journals - Wed, 06/07/2022 - 5:30am
AbstractMotivationThe primary strategy for predicting the binding mode of small molecules to their receptors and for performing receptor-based virtual screening studies is protein-ligand docking, which is undoubtedly the most popular and successful approach in computer-aided drug discovery. The increased popularity of docking has resulted in the development of different docking algorithms and scoring functions. Nonetheless, it is unlikely that a single approach outperforms the others in terms of reproducibility and precision. In this ground, consensus docking techniques are taking hold.ResultsWe have developed DockingPie, an open source PyMOL plugin for individual, as well as consensus docking analyses. Smina, AutoDock Vina, ADFR, and RxDock are the four docking engines that DockingPie currently supports in an easy and extremely intuitive way, thanks to its integrated docking environment and its GUI, fully integrated within PyMOL.Availabilityhttps://github.com/paiardin/DockingPieSupplementary informationSupplementary dataSupplementary data are available at Bioinformatics online.
Categories: Bioinformatics Trends

Biological Random Walks: multi-omics integration for disease gene prioritization

Bioinformatics Oxford Journals - Wed, 06/07/2022 - 5:30am
AbstractMotivationOver the past decade, network-based approaches have proven useful in identifying disease modules within the human interactome, often providing insights into key mechanisms and guiding the quest for therapeutic targets. This is all the more important, since experimental investigation of potential gene candidates is an expensive task, thus not always a feasible option. On the other hand, many sources of biological information exist beyond the interactome and an important research direction is the design of effective techniques for their integration.ResultsIn this work, we introduce the Biological Random Walks (BRW) approach for disease gene prioritization in the human interactome. The proposed framework leverages multiple biological sources within an integrated framework. We perform an extensive, comparative study of BRW’s performance against well-established baselines.Availability and implementationAll code is publicly available and can be downloaded at https://github.com/LeoM93/BiologicalRandomWalks. We used publicly available datasets, details on their retrieval and preprocessing are provided in the supplementary materialsupplementary material. Supplementary materialSupplementary materialSupplementary material available.
Categories: Bioinformatics Trends

Gene Regulatory Network Inference Methodology for Genomic and Transcriptomic Data Acquired in Genetically Related Heterozygote Individuals

Bioinformatics Oxford Journals - Wed, 06/07/2022 - 5:30am
AbstractMotivationInferring gene regulatory networks in non-independent genetically-related panels is a methodological challenge. This hampers evolutionary and biological studies using heterozygote individuals such as in wild sunflower populations or cultivated hybrids.ResultsFirst, we simulated 100 datasets of gene expressions and polymorphisms, displaying the same gene expression distributions, heterozygosities and heritabilities as in our dataset including 173 genes and 353 genotypes measured in sunflower hybrids. Secondly, we performed a meta-analysis based on six inference methods (Lasso, Random Forests, Bayesian Networks, Markov Random Fields, Ordinary Least Square and Findr) and selected the minimal density networks for better accuracy with 64 edges connecting 79 genes and 0.35 AUPR score on average. We identified that triangles and mutual edges are prone to errors in the inferred networks. Applied on classical datasets without heterozygotes, our strategy produced a 0.65 AUPR score for one dataset of the DREAM5 Systems Genetics Challenge. Finally, we applied our method to an experimental dataset from sunflower hybrids. We successfully inferred a network composed of 105 genes connected by 106 putative regulations with a major connected component.AvailabilityOur inference methodology dedicated to genomic and transcriptomic data is available at https://forgemia.inra.fr/sunrise/inference_methods.Supplementary informationThe data are available in the Data INRAE, at https://doi.org/10.15454/vrgwz2 (simulated datasets and also the output of meta-analysis) and https://doi.org/10.15454/HESVA0 (experimental sunflower dataset), and the complete descriptions of the inference methods used by the meta-analysis, the gene selection procedure related to drought and heterosis are available online.
Categories: Bioinformatics Trends

LRLoop: A method to predict feedback loops in cell-cell communication

Bioinformatics Oxford Journals - Tue, 05/07/2022 - 5:30am
AbstractMotivationIntercellular communication (i.e. cell-cell communication) plays an essential role in multicellular organisms coordinating various biological processes. Previous studies discovered that feedback loops between two cell types are a widespread and vital signaling motif regulating development, regeneration, and cancer progression. While many computational methods have been developed to predict cell-cell communication based on gene expression datasets, these methods often predict one-directional ligand-receptor interactions from sender to receiver cells and are not suitable to identify feedback loops.ResultsHere we describe LRLoop, a new method for analyzing cell-cell communication based on bi-directional ligand-receptor interactions, where two pairs of ligand-receptor interactions are identified that are responsive to each other, and thereby form a closed feedback loop. We first assessed LRLoop using bulk datasets and found our method significantly reduces the false positive rate seen with existing methods. Furthermore, we developed a new strategy to assess the performance of these methods in single-cell datasets. We used the between-tissue interactions as an indicator of potential false-positive prediction and found that LRLoop produced a lower fraction of between-tissue interactions than traditional methods. Finally, we applied LRLoop to the single-cell datasets obtained from retinal development. We discovered many new bi-directional ligand-receptor interactions among individual cell types that potentially control proliferation, neurogenesis, and/or cell fate specification.AvailabilityAn R package is available at https://github.com/Pinlyu3/LRLoop. The source code can be found at figureshare (https://doi.org/10.6084/m9.figshare.20126138.v1). The datasets can be found at figureshare (https://doi.org/10.6084/m9.figshare.20126021.v1).Supplementary informationSupplementary dataSupplementary data are available at Bioinformatics online.
Categories: Bioinformatics Trends

gofasta: command-line utilities for genomic epidemiology research

Bioinformatics Oxford Journals - Tue, 05/07/2022 - 5:30am
AbstractSummarygofasta comprises a set of command-line utilities for handling alignments of short assembled genomes in a genomic epidemiology context. It was developed for processing large numbers of closely related SARS-CoV-2 viral genomes, and should be useful with other densely sampled pathogen genomic datasets. It provides functions to convert sam-format pairwise alignments between assembled genomes to fasta format; to annotate mutations in multiple sequence alignments, and to extract sets of sequences by genetic distance measures for use in outbreak investigations.Availability and Implementationgofasta is an open-source project distributed under the MIT license. Binaries are available at https://github.com/virus-evolution/gofasta, from Bioconda, and through the Go programming language’s package management system. Source code and further documentation, including walkthroughs for common use cases, are available on the GitHub repository.
Categories: Bioinformatics Trends

ChromDMM: A Dirichlet-Multinomial Mixture Model For Clustering Heterogeneous Epigenetic Data

Bioinformatics Oxford Journals - Mon, 04/07/2022 - 5:30am
AbstractMotivationResearch on epigenetic modifications and other chromatin features at genomic regulatory elements elucidates essential biological mechanisms including the regulation of gene expression. Despite the growing number of epigenetic datasets, new tools are still needed to discover novel distinctive patterns of heterogeneous epigenetic signals at regulatory elements.ResultsWe introduce ChromDMM, a product Dirichlet-multinomial mixture model for clustering genomic regions that are characterised by multiple chromatin features. ChromDMM extends the mixture model framework by profile shifting and flipping that can probabilistically account for inaccuracies in the position and strand-orientation of the genomic regions. Owing to hyper-parameter optimisation, ChromDMM can also regularise the smoothness of the epigenetic profiles across the consecutive genomic regions. With simulated data, we demonstrate that ChromDMM clusters, shifts, and strand-orients the profiles more accurately than previous methods. With ENCODE data, we show that the clustering of enhancer regions in the human genome reveals distinct patterns in several chromatin features. We further validate the enhancer clusters by their enrichment for transcriptional regulatory factor binding sites.AvailabilityChromDMM is implemented as an R package and is available at https://github.com/MariaOsmala/ChromDMMSupplementary informationSupplementary dataSupplementary data are available at Bioinformatics online.
Categories: Bioinformatics Trends

Random Field Modeling of Multi-trait Multi-locus Association for Detecting Methylation Quantitative Trait Loci

Bioinformatics Oxford Journals - Mon, 04/07/2022 - 5:30am
AbstractMotivationCpG sites within the same genomic region often share similar methylation patterns and tend to be co-regulated by multiple genetic variants that may interact with one another.ResultsWe propose a multi-trait methylation random field (multi-MRF) method to evaluate the joint association between a set of CpG sites and a set of genetic variants. The proposed method has several advantages. First, it is a multi-trait method that allows flexible correlation structures between neighboring CpG sites (e.g., distance-based correlation). Second, it is also a multi-locus method that integrates the effect of multiple common and rare genetic variants. Third, it models the methylation traits with a beta distribution to characterize their bimodal and interval properties. Through simulations, we demonstrated that the proposed method had improved power over some existing methods under various disease scenarios. We further illustrated the proposed method via an application to a study of congenital heart defects (CHD) with 83 cardiac tissue samples. Our results suggested that gene BACE2, a mQTL candidate, colocalized with expression QTLs in artery tibial and harbored genetic variants with nominal significant associations in two genome-wide association studies of CHD.Availabilityhttps://github.com/chenlyu2656/Multi-MRF.Supplementary informationSupplementary dataSupplementary data are available at Bioinformatics online.
Categories: Bioinformatics Trends

HiC-TE: a computational pipeline for Hi-C data analysis to study the role of repeat family interactions in the genome 3D organization

Bioinformatics Oxford Journals - Mon, 04/07/2022 - 5:30am
AbstractMotivationThe role of repetitive DNA in the 3D organization of the interphase nucleus is a subject of intensive study. In studies of 3D nucleus organization, mutual contacts of various loci can be identified by Hi-C sequencing. Typical analyses use binning of read pairs by location to reduce noise. We use binning by repeat families instead to make similar conclusions about repeat regions.ResultsTo achieve this, we combined Hi-C data, reference genome data and tools for repeat analysis into a Nextflow pipeline identifying and quantifying the contacts of specific repeat families. As an output, our pipeline produces heatmaps showing contact frequency and circular diagrams visualizing repeat contact localization. Using our pipeline with tomato data, we revealed the preferential homotypic interactions of ribosomal DNA, centromeric satellites and some LTR retrotransposon families and, as expected, little contact between organellar and nuclear DNA elements. While the pipeline can be applied to any eukaryotic genome, results in plants provide better coverage, since the built-in TE-greedy-nester software only detects tandems and LTR retrotransposons. Other repeats can be fed via GFF3 files. This pipeline represents a novel and reproducible way to analyze the role of repetitive elements in the 3D organization of genomes.Availabilityhttps://gitlab.fi.muni.cz/lexa/hic-te/Supplementary informationSupplementary dataSupplementary data are available at Bioinformatics online.
Categories: Bioinformatics Trends

MobilityTransformR: An R package for effective mobility transformation of CE-MS data

Bioinformatics Oxford Journals - Mon, 04/07/2022 - 5:30am
AbstractSummaryWe present MobilityTransformR, an R/Bioconductor package for the effective mobility scaling of capillary zone electrophoresis-mass spectrometry (CE-MS) data. It uses functionality from different R packages that are frequently used for data processing and analysis in MS-based metabolomics workflows, allowing the subsequent use of reproducible transformed CE-MS data in existing workflows.Availability and ImplementationMobilityTransformR is implemented in R (Version > = 4.2) and can be downloaded directly from the Bioconductor database (https://bioconductor.org/packages/MobilityTransformR) or GitHub (https://github.com/LiesaSalzer/MobilityTransformR).Supplementary informationSupplementary dataSupplementary data are available at Bioinformatics online.
Categories: Bioinformatics Trends

Pages

Subscribe to Centre for Bioinformatics aggregator - Bioinformatics Trends

Calendar

Mon
Tue
Wed
Thu
Fri
Sat
Sun
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
 
August 2022