The package allows one to obtain optimised combinations of DNA barcodes to be used for multiplex sequencing. In each barcode combination, barcodes are pooled with respect to Illumina chemistry constraints. Combinations can be filtered to keep those that are robust against substitution and insertion/deletion errors thereby facilitating the demultiplexing step. In addition, the package provides an optimiser function to further favor the selection of barcode combinations with least heterogeneity in barcode usage.

DoRothEA is a gene regulatory network containing signed transcription factor (TF) - target gene interactions. DoRothEA regulons, the collection of a TF and its transcriptional targets, were curated and collected from different types of evidence for both human and mouse. A confidence level was assigned to each TF-target interaction based on the number of supporting evidence.

The identification of novel compound-protein interaction (CPI) is important in drug discovery. Revealing unknown compound-protein interactions is useful to design a new drug for a target protein by screening candidate compounds. The accurate CPI prediction assists in effective drug discovery process. To identify potential CPI effectively, prediction methods based on machine learning and deep learning have been developed. Data for sequences are provided as discrete symbolic data. In the data, compounds are represented as SMILES (simplified molecular-input line-entry system) strings and proteins are sequences in which the characters are amino acids. The outcome is defined as a variable that indicates how strong two molecules interact with each other or whether there is an interaction between them. In this package, a deep-learning based model that takes only sequence information of both compounds and proteins as input and the outcome as output is used to predict CPI. The model is implemented by using compound and protein encoders with useful features. The CPI model also supports other modeling tasks, including protein-protein interaction (PPI), chemical-chemical interaction (CCI), or single compounds and proteins. Although the model is designed for proteins, DNA and RNA can be used if they are represented as sequences.

The R package decemedip is a novel computational paradigm developed for inferring the relative abundances of cell types and tissues measure by methylated DNA immunoprecipitation sequencing (MeDIP-Seq). This paradigm allows using reference data from other technologies such as microarray or WGBS.

An experiment data package associated with the publication Dona et al. (2013). Package contains runnable vignettes showing an example image segmentation for one posterior lateral line primordium, and also the data table and code used to analyze tissue-scale lifetime-ratio statistics.

distinct is a statistical method to perform differential testing between two or more groups of distributions; differential testing is performed via hierarchical non-parametric permutation tests on the cumulative distribution functions (cdfs) of each sample. While most methods for differential expression target differences in the mean abundance between conditions, distinct, by comparing full cdfs, identifies, both, differential patterns involving changes in the mean, as well as more subtle variations that do not involve the mean (e.g., unimodal vs. bi-modal distributions with the same mean). distinct is a general and flexible tool: due to its fully non-parametric nature, which makes no assumptions on how the data was generated, it can be applied to a variety of datasets. It is particularly suitable to perform differential state analyses on single cell data (i.e., differential analyses within sub-populations of cells), such as single cell RNA sequencing (scRNA-seq) and high-dimensional flow or mass cytometry (HDCyto) data. To use distinct one needs data from two or more groups of samples (i.e., experimental conditions), with at least 2 samples (i.e., biological replicates) per group.

DuplexDiscovereR is a package designed for analyzing data from RNA cross-linking and proximity ligation protocols such as SPLASH, PARIS, LIGR-seq, and others. DuplexDiscovereR accepts input in the form of chimerically or split-aligned reads. It includes procedures for alignment classification, filtering, and efficient clustering of individual chimeric reads into duplex groups (DGs). Once DGs are identified, the package predicts RNA duplex formation and their hybridization energies. Additional metrics, such as p-values for random ligation hypothesis or mean DG alignment scores, can be calculated to rank final set of RNA duplexes. Data from multiple experiments or replicates can be processed separately and further compared to check the reproducibility of the experimental method.

DEGseq is an R package to identify differentially expressed genes from RNA-Seq data.

This package provides expression profile and CNV data for glioblastoma from TCGA, and transcriptional and post-translational regulatory networks assembled with the ARACNe and MINDy algorithms, respectively.

Given a set of clustering labels, Dune merges pairs of clusters to increase mean ARI between labels, improving replicability.

This package discovers meso-scale chromatin remodelling from 3C data. 3C data is local in nature. It givens interaction counts between restriction enzyme digestion fragments and a preferred viewpoint region. By binning this data and using permutation testing, this package can test whether there are statistically significant changes in the interaction counts between the data from two cell types or two treatments.

DMCFB is a pipeline for identifying differentially methylated cytosines using a Bayesian functional regression model in bisulfite sequencing data. By using a functional regression data model, it tries to capture position-specific, group-specific and other covariates-specific methylation patterns as well as spatial correlation patterns and unknown underlying models of methylation data. It is robust and flexible with respect to the true underlying models and inclusion of any covariates, and the missing values are imputed using spatial correlation between positions and samples. A Bayesian approach is adopted for estimation and inference in the proposed method.

Detects differential interactions across biological conditions in a Hi-C experiment. Methods are provided for read alignment and data pre-processing into interaction counts. Statistical analysis is based on edgeR and supports normalization and filtering. Several visualization options are also available.

Data for the dyebias package, consisting of 4 self-self hybrizations of self-spotted yeast slides, as well as data from Array Express accession E-MTAB-32.

This package detects significant differentially methylated regions (for both qualitative and quantitative traits), using a scan statistic with underlying Poisson heuristics. The scan statistic will depend on a sequence of window sizes (# of CpGs within each window) and on a threshold for each window size. This threshold can be calculated by three different means: i) analytically using Siegmund et.al (2012) solution (preferred), ii) an important sampling as suggested by Zhang (2008), and a iii) full MCMC modeling of the data, choosing between a number of different options for modeling the dependency between each CpG.

Data package which provides default disease expression profiles, clusters and annotation information for use with the DrugVsDisease package.

This package contains the data for the paper by L. David et al. in PNAS 2006 (PMID 16569694): 8 CEL files of Affymetrix genechips, an ExpressionSet object with the raw feature data, a probe annotation data structure for the chip and the yeast genome annotation (GFF file) that was used. In addition, some custom-written analysis functions are provided, as well as R scripts in the scripts directory.

This package implements methods and an evaluation framework to infer differential co-expression/association networks. Various methods are implemented and can be evaluated using simulated datasets. Inference of differential co-expression networks can allow identification of networks that are altered between two conditions (e.g., health and disease).

DeMixT is a software package that performs deconvolution on transcriptome data from a mixture of two or three components.

DEsingle is an R package for differential expression (DE) analysis of single-cell RNA-seq (scRNA-seq) data. It defines and detects 3 types of differentially expressed genes between two groups of single cells, with regard to different expression status (DEs), differential expression abundance (DEa), and general differential expression (DEg). DEsingle employs Zero-Inflated Negative Binomial model to estimate the proportion of real and dropout zeros and to define and detect the 3 types of DE genes. Results showed that DEsingle outperforms existing methods for scRNA-seq DE analysis, and can reveal different types of DE genes that are enriched in different biological functions.

Visualize significant conserved amino acid sequence pattern in groups based on probability theory.

This package provides a package containing an environment representing the DrosGenome1.CDF file.

This package provides a reproducible and modular workflow for absolute microbial quantification using spike-in controls. Supports both single spike-in taxa and synthetic microbial communities with user-defined spike-in volumes and genome copy numbers. Compatible with phyloseq and TreeSummarizedExperiment (TSE) data structures. The package implements methods for spike-in validation, preprocessing, scaling factor estimation, absolute abundance conversion, bias correction, and normalization. Facilitates downstream statistical analyses with DESeq2', edgeR', and other Bioconductor-compatible methods. Visualization tools are provided via ggplot2', ggtree', and related packages. Includes detailed vignettes, case studies, and function-level documentation to guide users through experimental design, quantification, and interpretation.

Bioinformatics platform containing interactive plots and tables for differential gene and region expression studies. Allows visualizing expression data much more deeply in an interactive and faster way. By changing the parameters, users can easily discover different parts of the data that like never have been done before. Manually creating and looking these plots takes time. With DEBrowser users can prepare plots without writing any code. Differential expression, PCA and clustering analysis are made on site and the results are shown in various plots such as scatter, bar, box, volcano, ma plots and Heatmaps.