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MWASTools provides a complete pipeline to perform metabolome-wide association studies. Key functionalities of the package include: quality control analysis of metabonomic data; MWAS using different association models (partial correlations; generalized linear models); model validation using non-parametric bootstrapping; visualization of MWAS results; NMR metabolite identification using STOCSY; and biological interpretation of MWAS results.

MyGene.Info_ provides simple-to-use REST web services to query/retrieve gene annotation data. It's designed with simplicity and performance emphasized. *mygene*, is an easy-to-use R wrapper to access MyGene.Info_ services.

MyVariant.info is a comprehensive aggregation of variant annotation resources. myvariant is a wrapper for querying MyVariant.info services

A parser for mzIdentML files implemented using the XML package. The parser tries to be general and able to handle all types of mzIdentML files with the drawback of having less 'pretty' output than a vendor specific parser. Please contact the maintainer with any problems and supply an mzIdentML file so the problems can be fixed quickly.

Nucleolus is an important structure inside the nucleus in eukaryotic cells. It is the site for transcribing rDNA into rRNA and for assembling ribosomes, aka ribosome biogenesis. In addition, nucleoli are dynamic hubs through which numerous proteins shuttle and contact specific non-rDNA genomic loci. Deep sequencing analyses of DNA associated with isolated nucleoli (NAD- seq) have shown that specific loci, termed nucleolus- associated domains (NADs) form frequent three- dimensional associations with nucleoli. NAD-seq has been used to study the biological functions of NAD and the dynamics of NAD distribution during embryonic stem cell (ESC) differentiation. Here, we developed a Bioconductor package NADfinder for bioinformatic analysis of the NAD-seq data, including baseline correction, smoothing, normalization, peak calling, and annotation.

NanoMethViz is a toolkit for visualising methylation data from Oxford Nanopore sequencing. It can be used to explore methylation patterns from reads derived from Oxford Nanopore direct DNA sequencing with methylation called by callers including nanopolish, f5c and megalodon. The plots in this package allow the visualisation of methylation profiles aggregated over experimental groups and across classes of genomic features.

This Package utilizes a generalized linear model(GLM) of the negative binomial family to characterize count data and allows for multi-factor design. NanoStrongDiff incorporate size factors, calculated from positive controls and housekeeping controls, and background level, obtained from negative controls, in the model framework so that all the normalization information provided by NanoString nCounter Analyzer is fully utilized.

Tools for NanoString Technologies nCounter Technology. Provides support for reading RCC files into an ExpressionSet derived object. Also includes methods for QC and normalizaztion of NanoString data.

NanoTube includes functions for the processing, quality control, analysis, and visualization of NanoString nCounter data. Analysis functions include differential analysis and gene set analysis methods, as well as postprocessing steps to help understand the results. Additional functions are included to enable interoperability with other Bioconductor NanoString data analysis packages.

Provides HDF5 storage based methods and functions for manipulation of flow cytometry data.

The purpose of ncGTW is to help XCMS for LC-MS data alignment. Currently, ncGTW can detect the misaligned feature groups by XCMS, and the user can choose to realign these feature groups by ncGTW or not.

Provides various methods to load the pathways from the NCI Pathways Database in R graph objects and to re-format them.

ncRNAtools provides a set of basic tools for handling and analyzing non-coding RNAs. These include tools to access the RNAcentral database and to predict and visualize the secondary structure of non-coding RNAs. The package also provides tools to read, write and interconvert the file formats most commonly used for representing such secondary structures.

Provides a pipeline to discern RNA structure at and proximal to the site of protein binding within regions of the transcriptome defined by the user. CLIP protein-binding data can be input as either aligned BAM or peak-called bedGraph files. RNA structure can either be predicted internally from sequence or users have the option to input their own RNA structure data. RNA structure binding profiles can be visually and quantitatively compared across multiple formats.

This package provides a enhanced visualization of single-cell data based on gene-weighted density estimation. Nebulosa recovers the signal from dropped-out features and allows the inspection of the joint expression from multiple features (e.g. genes). Seurat and SingleCellExperiment objects can be used within Nebulosa.

#' NetActivity enables to compute gene set scores from previously trained sparsely-connected autoencoders. The package contains a function to prepare the data (`prepareSummarizedExperiment`) and a function to compute the gene set scores (`computeGeneSetScores`). The package `NetActivityData` contains different pre-trained models to be directly applied to the data. Alternatively, the users might use the package to compute gene set scores using custom models.

Boosting supported network analysis for high-dimensional omics applications. This package comes bundled with the MC-UPGMA clustering package by Yaniv Loewenstein.

Package nethet is an implementation of statistical solid methodology enabling the analysis of network heterogeneity from high-dimensional data. It combines several implementations of recent statistical innovations useful for estimation and comparison of networks in a heterogeneous, high-dimensional setting. In particular, we provide code for formal two-sample testing in Gaussian graphical models (differential network and GGM-GSA; Stadler and Mukherjee, 2013, 2014) and make a novel network-based clustering algorithm available (mixed graphical lasso, Stadler and Mukherjee, 2013).

NetPathMiner is a general framework for network path mining using genome-scale networks. It constructs networks from KGML, SBML and BioPAX files, providing three network representations, metabolic, reaction and gene representations. NetPathMiner finds active paths and applies machine learning methods to summarize found paths for easy interpretation. It also provides static and interactive visualizations of networks and paths to aid manual investigation.

A model for semi-supervised prioritisation of genes integrating network data, phenotypes and additional prior knowledge about TP and TN gene labels from the literature or experts.

The NetSAM (Network Seriation and Modularization) package takes an edge-list representation of a weighted or unweighted network as an input, performs network seriation and modularization analysis, and generates as files that can be used as an input for the one-dimensional network visualization tool NetGestalt (http://www.netgestalt.org) or other network analysis. The NetSAM package can also generate correlation network (e.g. co-expression network) based on the input matrix data, perform seriation and modularization analysis for the correlation network and calculate the associations between the sample features and modules or identify the associated GO terms for the modules.

A model designed for dimensionality reduction and batch effect removal for scRNA-seq data. It is designed to be massively parallelizable using shared objects that prevent memory duplication, and it can be used with different mini-batch approaches in order to reduce time consumption. It assumes a negative binomial distribution for the data with a dispersion parameter that can be both commonwise across gene both genewise.

This package provides methods and object classes for parsing FastQC reports and output summaries from other NGS tools into R. As well as parsing files, multiple plotting methods have been implemented for visualising the parsed data. Plots can be generated as static ggplot objects or interactive plotly objects.

Computes Multiple Co-Inertia Analysis (MCIA), a dimensionality reduction (jDR) algorithm, for a multi-block dataset using a modification to the Nonlinear Iterative Partial Least Squares method (NIPALS) proposed in (Hanafi et. al, 2010). Allows multiple options for row- and table-level preprocessing, and speeds up computation of variance explained. Vignettes detail application to bulk- and single cell- multi-omics studies.

This package allows to detect and correct for spatial and intensity biases with two-channel microarray data. The normalization method implemented in this package is based on robust neural networks fitting.

Method for scalable identification of spatially variable genes (SVGs) in spatially-resolved transcriptomics data. The method is based on nearest-neighbor Gaussian processes and uses the BRISC algorithm for model fitting and parameter estimation. Allows identification and ranking of SVGs with flexible length scales across a tissue slide or within spatial domains defined by covariates. Scales linearly with the number of spatial locations and can be applied to datasets containing thousands or more spatial locations.

Analysis of RNA-seq expression data or other similar kind of data. Exploratory plots to evualuate saturation, count distribution, expression per chromosome, type of detected features, features length, etc. Differential expression between two experimental conditions with no parametric assumptions.

Methods to model and impute non-detects in the results of qPCR experiments.

While some non-coding RNAs (ncRNAs) are assigned critical regulatory roles, most remain functionally uncharacterized. This presents a challenge whenever an interesting set of ncRNAs needs to be analyzed in a functional context. Transcripts located close-by on the genome are often regulated together. This genomic proximity on the sequence can hint to a functional association. We present a tool, NoRCE, that performs cis enrichment analysis for a given set of ncRNAs. Enrichment is carried out using the functional annotations of the coding genes located proximal to the input ncRNAs. Other biologically relevant information such as topologically associating domain (TAD) boundaries, co-expression patterns, and miRNA target prediction information can be incorporated to conduct a richer enrichment analysis. To this end, NoRCE includes several relevant datasets as part of its data repository, including cell-line specific TAD boundaries, functional gene sets, and expression data for coding & ncRNAs specific to cancer. Additionally, the users can utilize custom data files in their investigation. Enrichment results can be retrieved in a tabular format or visualized in several different ways. NoRCE is currently available for the following species: human, mouse, rat, zebrafish, fruit fly, worm, and yeast.

Precise measurements are important for epigenome-wide studies investigating DNA methylation in whole blood samples, where effect sizes are expected to be small in magnitude. The 450K platform is often affected by batch effects and proper preprocessing is recommended. This package provides functions to read and normalize 450K '.idat' files. The normalization corrects for dye bias and biases related to signal intensity and methylation of probes using local regression. No adjustment for probe type bias is performed to avoid the trade-off of precision for accuracy of beta-values.

Functions for the selection of optimal reference genes and the normalisation of real-time quantitative PCR data.

Provides functionality for untargeted LC-MS metabolomics research as specified in the associated protocol article in the 'Metabolomics Data Processing and Data Analysis—Current Best Practices' special issue of the Metabolites journal (2020). This includes tabular data preprocessing and quality control, uni- and multivariate analysis as well as quality control visualizations, feature-wise visualizations and results visualizations. Raw data preprocessing and functionality related to biological context, such as pathway analysis, is not included.

Perform non-parametric analysis of response curves as described by Childs, Bach, Franken et al. (2019): Non-parametric analysis of thermal proteome profiles reveals novel drug-binding proteins.

Current gene set enrichment methods rely upon permutations for inference. These approaches are computationally expensive and have minimum achievable p-values based on the number of permutations, not on the actual observed statistics. We have derived three parametric approximations to the permutation distributions of two gene set enrichment test statistics. We are able to reduce the computational burden and granularity issues of permutation testing with our method, which is implemented in this package. npGSEA calculates gene set enrichment statistics and p-values without the computational cost of permutations. It is applicable in settings where one or many gene sets are of interest. There are also built-in plotting functions to help users visualize results.

This package predicts the gene-gene interaction network and identifies the direct transcriptional targets of the perturbation using an ODE (Ordinary Differential Equation) based method.

This package can generate a synthetic map with reads covering the nucleosome regions as well as a synthetic map with forward and reverse reads emulating next-generation sequencing. The synthetic hybridization data of “Tiling Arrays” can also be generated. The user has choice between three different distributions for the read positioning: Normal, Student and Uniform. In addition, a visualization tool is provided to explore the synthetic nucleosome maps.

Nucleosome positioning for Tiling Arrays and NGS experiments.

nuCpos, a derivative of NuPoP, is an R package for prediction of nucleosome positions. nuCpos calculates local and whole nucleosomal histone binding affinity (HBA) scores for a given 147-bp sequence. Note: This package was designed to demonstrate the use of chemical maps in prediction. As the parental package NuPoP now provides chemical-map-based prediction, the function for dHMM-based prediction was removed from this package. nuCpos continues to provide functions for HBA calculation.

NuPoP is an R package for Nucleosome Positioning Prediction.This package is built upon a duration hidden Markov model proposed in Xi et al, 2010; Wang et al, 2008. The core of the package was written in Fotran. In addition to the R package, a stand-alone Fortran software tool is also available at https://github.com/jipingw. The Fortran codes have complete functonality as the R package. Note: NuPoP has two separate functions for prediction of nucleosome positioning, one for MNase-map trained models and the other for chemical map-trained models. The latter was implemented for four species including yeast, S.pombe, mouse and human, trained based on our recent publications. We noticed there is another package nuCpos by another group for prediction of nucleosome positioning trained with chemicals. A report to compare recent versions of NuPoP with nuCpos can be found at https://github.com/jiping/NuPoP_doc. Some more information can be found and will be posted at https://github.com/jipingw/NuPoP.

Provides a suite of R functions to analyze gene expression experiments on the OpenArray real-time PCR platform. OAtools fits logistic regressions to fluorescence curves to distinguish between real amplification and false positives. OAtools supports data import, analysis, and visualization through plots and a dynamic HTML report.

Statistical tools for building random mutagenesis libraries for prokaryotes. The package has functions for handling the occupancy distribution for a multinomial and for estimating the number of essential genes in random transposon mutagenesis libraries.

This package allows to characterize the operating characteristics of a microarray experiment, i.e. the trade-off between false discovery rate and the power to detect truly regulated genes. The package includes tools both for planned experiments (for sample size assessment) and for already collected data (identification of differentially expressed genes).

OCTAD provides a platform for virtually screening compounds targeting precise cancer patient groups. The essential idea is to identify drugs that reverse the gene expression signature of disease by tamping down over-expressed genes and stimulating weakly expressed ones. The package offers deep-learning based reference tissue selection, disease gene expression signature creation, pathway enrichment analysis, drug reversal potency scoring, cancer cell line selection, drug enrichment analysis and in silico hit validation. It currently covers ~20,000 patient tissue samples covering 50 cancer types, and expression profiles for ~12,000 distinct compounds.

Performs outlier detection of sequences in a multiple sequence alignment using bootstrap of predefined distance metrics. Outlier sequences can make downstream analyses unreliable or make the alignments less accurate while they are being constructed. This package implements the OD-seq algorithm proposed by Jehl et al (doi 10.1186/s12859-015-0702-1) for aligned sequences and a variant using string kernels for unaligned sequences.

OGRE calculates overlap between user defined genomic region datasets. Any regions can be supplied i.e. genes, SNPs, or reads from sequencing experiments. Key numbers help analyse the extend of overlaps which can also be visualized at a genomic level.

A package to analyze oligonucleotide arrays (expression/SNP/tiling/exon) at probe-level. It currently supports Affymetrix (CEL files) and NimbleGen arrays (XYS files).

This package contains class definitions, validity checks, and initialization methods for classes used by the oligo and crlmm packages.

Functions for normalisation of two-color microarrays by optimised local regression and for detection of artefacts in microarray data

Graphical user interface for the OLIN package

Symptomatic heterogeneity in complex diseases reveals differences in molecular states that need to be investigated. However, selecting the numerous parameters of an exploratory clustering analysis in RNA profiling studies requires deep understanding of machine learning and extensive computational experimentation. Tools that assist with such decisions without prior field knowledge are nonexistent and further gene association analyses need to be performed independently. We have developed a suite of tools to automate these processes and make robust unsupervised clustering of transcriptomic data more accessible through automated machine learning based functions. The efficiency of each tool was tested with four datasets characterised by different expression signal strengths. Our toolkit’s decisions reflected the real number of stable partitions in datasets where the subgroups are discernible. Even in datasets with less clear biological distinctions, stable subgroups with different expression profiles and clinical associations were found.