scholarly journals msgbsR: An R package for analysing methylation-sensitive restriction enzyme sequencing data

2018 ◽  
Vol 8 (1) ◽  
Author(s):  
Benjamin T. Mayne ◽  
Shalem Y. Leemaqz ◽  
Sam Buckberry ◽  
Carlos M. Rodriguez Lopez ◽  
Claire T. Roberts ◽  
...  
Keyword(s):  
Restriction Enzyme ◽  
R Package ◽  
Sequencing Data ◽  
Methylation Sensitive Restriction Enzyme

A zero-inflated non-negative matrix factorization for the deconvolution of mixed signals of biological data

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Yixin Kong ◽  
Ariangela Kozik ◽  
Cindy H. Nakatsu ◽  
Yava L. Jones-Hall ◽  
Hyonho Chun
Keyword(s):  
Matrix Factorization ◽  
Factor Model ◽  
R Package ◽  
Biological Data ◽  
Superior Performance ◽  
Sequencing Data ◽  
Fecal Microbiome ◽  
Brain Gene Expression ◽  
Cell Transcriptome ◽  
Non Negative Matrix Factorization

Abstract A latent factor model for count data is popularly applied in deconvoluting mixed signals in biological data as exemplified by sequencing data for transcriptome or microbiome studies. Due to the availability of pure samples such as single-cell transcriptome data, the accuracy of the estimates could be much improved. However, the advantage quickly disappears in the presence of excessive zeros. To correctly account for this phenomenon in both mixed and pure samples, we propose a zero-inflated non-negative matrix factorization and derive an effective multiplicative parameter updating rule. In simulation studies, our method yielded the smallest bias. We applied our approach to brain gene expression as well as fecal microbiome datasets, illustrating the superior performance of the approach. Our method is implemented as a publicly available R-package, iNMF.

scholarly journals An R Package for Divergence Analysis of Omics Data

2019 ◽  
Author(s):  
Wikum Dinalankara ◽  
Qian Ke ◽  
Donald Geman ◽  
Luigi Marchionni
Keyword(s):  
High Throughput Sequencing ◽  
R Package ◽  
The Cancer Genome Atlas ◽  
High Dimensional ◽  
Omics Data ◽  
Sequencing Data ◽  
High Throughput Sequencing Data ◽  
Ternary Code ◽  
Cancer Genome Atlas ◽  
Level Analysis

AbstractGiven the ever-increasing amount of high-dimensional and complex omics data becoming available, it is increasingly important to discover simple but effective methods of analysis. Divergence analysis transforms each entry of a high-dimensional omics profile into a digitized (binary or ternary) code based on the deviation of the entry from a given baseline population. This is a novel framework that is significantly different from existing omics data analysis methods: it allows digitization of continuous omics data at the univariate or multivariate level, facilitates sample level analysis, and is applicable on many different omics platforms. The divergence package, available on the R platform through the Bioconductor repository collection, provides easy-to-use functions for carrying out this transformation. Here we demonstrate how to use the package with sample high throughput sequencing data from the Cancer Genome Atlas.

scholarly journals DNA Methylation Landscape of 16 Canine Somatic Tissues by Methylation-Sensitive Restriction Enzyme-Based Next Generation Sequencing

2021 ◽  
Author(s):  
Jumpei Yamazaki ◽  
Yuki Matsumoto ◽  
Jaroslav Jelinek ◽  
Teita Ishizaki ◽  
Shingo Maeda ◽  
...  
Keyword(s):  
Gene Expression ◽  
Dna Methylation ◽  
Next Generation Sequencing ◽  
Restriction Enzyme ◽  
Companion Animals ◽  
Next Generation ◽  
Cpg Sites ◽  
Somatic Tissues ◽  
Methylation Sensitive Restriction Enzyme ◽  
Generation Sequencing

Abstract Background: DNA methylation plays important functions in gene expression regulation that is involved in individual development and various diseases. DNA methylation has been well studied in human and model organisms, but only limited data exist in companion animals like dog. Results: Using methylation-sensitive restriction enzyme-based next generation sequencing (Canine DREAM), we obtained canine DNA methylation maps from 16 somatic tissues. In total, we evaluated 130,861 CpG sites. The majority of CpG sites were either highly methylated (>70%, 52.5%-64.6% of all CpG sites analyzed) or unmethylated (<30%, 22.5%-28.0% of all CpG sites analyzed) which are methylation patterns similar to other species. The overall methylation status of CpG sites across the 32 methylomes were remarkably similar. However, the tissue types were clearly defined by principle component analysis and hierarchical clustering analysis with DNA methylome. We found 6416 CpG sites located closely at promoter region of genes and inverse correlation between DNA methylation and gene expression of these genes. Conclusions: Our study provides basic dataset for DNA methylation profiles in dogs.

scholarly journals TrancriptomeReconstructoR, A Data-Driven Annotation of Complex Transcriptomes

2020 ◽  
Author(s):  
Maxim Ivanov ◽  
Albin Sandelin ◽  
Sebastian Marquardt
Keyword(s):  
De Novo ◽  
Gene Annotation ◽  
R Package ◽  
Sequence Information ◽  
Rna Seq ◽  
Sequencing Data ◽  
Gene Model ◽  
Preparation Methods ◽  
Downstream Analysis

Abstract Background: The quality of gene annotation determines the interpretation of results obtained in transcriptomic studies. The growing number of genome sequence information calls for experimental and computational pipelines for de novo transcriptome annotation. Ideally, gene and transcript models should be called from a limited set of key experimental data. Results: We developed TranscriptomeReconstructoR, an R package which implements a pipeline for automated transcriptome annotation. It relies on integrating features from independent and complementary datasets: i) full-length RNA-seq for detection of splicing patterns and ii) high-throughput 5' and 3' tag sequencing data for accurate definition of gene borders. The pipeline can also take a nascent RNA-seq dataset to supplement the called gene model with transient transcripts.We reconstructed de novo the transcriptional landscape of wild type Arabidopsis thaliana seedlings as a proof-of-principle. A comparison to the existing transcriptome annotations revealed that our gene model is more accurate and comprehensive than the two most commonly used community gene models, TAIR10 and Araport11. In particular, we identify thousands of transient transcripts missing from the existing annotations. Our new annotation promises to improve the quality of A.thaliana genome research.Conclusions: Our proof-of-concept data suggest a cost-efficient strategy for rapid and accurate annotation of complex eukaryotic transcriptomes. We combine the choice of library preparation methods and sequencing platforms with the dedicated computational pipeline implemented in the TranscriptomeReconstructoR package. The pipeline only requires prior knowledge on the reference genomic DNA sequence, but not the transcriptome. The package seamlessly integrates with Bioconductor packages for downstream analysis.

scholarly journals Diat.barcode: a DNA tool to decipher diatom communities for the evaluation environmental pressures

2021 ◽  
Vol 4 ◽  
Author(s):  
Frederic Rimet ◽  
Teofana Chonova ◽  
Gilles Gassiole ◽  
Maria Kahlert ◽  
François Keck ◽  
...  
Keyword(s):  
Taxonomic Diversity ◽  
R Package ◽  
Life Forms ◽  
Reference Database ◽  
Sequencing Data ◽  
Culture Collections ◽  
Environmental Pressures ◽  
Ecological Features ◽  
Dna Metabarcoding ◽  
Environmental Sequences

Diatoms (Bacillariophyta) are ubiquitous microalgae, which present a huge taxonomic diversity, changing in correlation with differing environmental conditions. This makes them excellent ecological indicators for various ecosystems and ecological problematics (ecotoxicology, biomonitoring, paleo-environmental reconstruction …). Current standardized methodologies for diatoms are based on microscopic determinations, which is time consuming and prone to identification uncertainties. DNA metabarcoding has been proposed as a way to avoid these flaws, enabling the sequencing of a large quantity of barcodes from natural samples. A taxonomic identity is given to these barcodes by comparing their sequences to a barcoding reference library. However, to identify environmental sequences correctly, the reference database should contain a representative number of reference sequences to ensure a good coverage of diatom diversity. Moreover, the reference database needs to be carefully taxonomically curated by experts, as its content has an obvious impact on species detection. Diat.barcode is an open-access library for diatoms linking diatom taxonomic identities to rbcL barcode sequences (a chloroplast marker suitable for species-level identification of diatoms), which has been maintained since 2012. Data are accumulated from three sources: (1) the NCBI nucleotide database, (2) unpublished sequencing data of culture collections and more recently (3) environmental sequences. Since 2017, an international network of experts in diatom taxonomy curate this library. The last version of the database (version 9.2), includes 8066 entries that correspond to more than 280 different genera and 1490 different species. In addition to the taxonomic information, morphological features (e.g. biovolumes, chloroplasts, etc.), life-forms (mobility, colony-type) and ecological features (taxa preferences to pollution) are given. The database can be downloaded from the website (www6.inrae.fr/carrtel-collection/Barcoding-database/) or directly through the R package diatbarcode. Ready-to-use files for commonly used metabarcoding pipelines (Mothur and DADA2) are also available.

scholarly journals hypeR: An R Package for Geneset Enrichment Workflows

2019 ◽  
Author(s):  
Anthony Federico ◽  
Stefano Monti
Keyword(s):  
High Throughput Sequencing ◽  
R Package ◽  
Supplementary Information ◽  
Sequencing Data ◽  
Wide Audience ◽  
Popular Method ◽  
Link Type ◽  
High Throughput Sequencing Data ◽  
One Stop ◽  
Recent Version

ABSTRACTSummaryGeneset enrichment is a popular method for annotating high-throughput sequencing data. Existing tools fall short in providing the flexibility to tackle the varied challenges researchers face in such analyses, particularly when analyzing many signatures across multiple experiments. We present a comprehensive R package for geneset enrichment workflows that offers multiple enrichment, visualization, and sharing methods in addition to novel features such as hierarchical geneset analysis and built-in markdown reporting. hypeR is a one-stop solution to performing geneset enrichment for a wide audience and range of use cases.Availability and implementationThe most recent version of the package is available at https://github.com/montilab/hypeR.Supplementary informationComprehensive documentation and tutorials, are available at https://montilab.github.io/hypeR-docs.

scholarly journals hypeR: an R package for geneset enrichment workflows

2019 ◽  
Author(s):  
Anthony Federico ◽  
Stefano Monti
Keyword(s):  
High Throughput ◽  
High Throughput Sequencing ◽  
R Package ◽  
Use Cases ◽  
Sequencing Data ◽  
Wide Audience ◽  
Popular Method ◽  
High Throughput Sequencing Data ◽  
One Stop ◽  
Recent Version

Abstract Summary Geneset enrichment is a popular method for annotating high-throughput sequencing data. Existing tools fall short in providing the flexibility to tackle the varied challenges researchers face in such analyses, particularly when analyzing many signatures across multiple experiments. We present a comprehensive R package for geneset enrichment workflows that offers multiple enrichment, visualization, and sharing methods in addition to novel features such as hierarchical geneset analysis and built-in markdown reporting. hypeR is a one-stop solution to performing geneset enrichment for a wide audience and range of use cases. Availability and implementation The most recent version of the package is available at https://github.com/montilab/hypeR. Contact [email protected] or [email protected]

scholarly journals DNA methylation estimation using methylation-sensitive restriction enzyme bisulfite sequencing (MREBS)

PLoS ONE ◽  
2019 ◽  
Vol 14 (4) ◽  
pp. e0214368 ◽  
Author(s):  
Giancarlo Bonora ◽  
Liudmilla Rubbi ◽  
Marco Morselli ◽  
Feiyang Ma ◽  
Constantinos Chronis ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Restriction Enzyme ◽  
Bisulfite Sequencing ◽  
Methylation Sensitive Restriction Enzyme
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