Enter the query into the form above. You can look for specific version of a package by using @ symbol like this: gcc@10.
API method:
GET /api/packages?search=hello&page=1&limit=20
where search is your query, page is a page number and limit is a number of items on a single page. Pagination information (such as a number of pages and etc) is returned
in response headers.
If you'd like to join our channel webring send a patch to ~whereiseveryone/toys@lists.sr.ht adding your channel as an entry in channels.scm.
This package was automatically created by package AnnotationForge version 1.11.21. The probe sequence data was obtained from http://www.affymetrix.com. The file name was E\_coli\_probe\_tab.
This package allows user to quickly access ENCODE project files metadata and give access to helper functions to query the ENCODE rest api, download ENCODE datasets and save the database in SQLite format.
Serve data from Bioconductor Objects through a WebSocket connection.
Experimental data with Affymetrix E. coli chips, as reported in She-pin Hung, Pierre Baldi, and G. Wesley Hatfield, J. Biol. Chem., Vol. 277, Issue 43, 40309-40323, October 25, 2002.
ExCluster flattens Ensembl and GENCODE GTF files into GFF files, which are used to count reads per non-overlapping exon bin from BAM files. This read counting is done using the function featureCounts from the package Rsubread. Library sizes are normalized across all biological replicates, and ExCluster then compares two different conditions to detect signifcantly differentially spliced genes. This process requires at least two independent biological repliates per condition, and ExCluster accepts only exactly two conditions at a time. ExCluster ultimately produces false discovery rates (FDRs) per gene, which are used to detect significance. Exon log2 fold change (log2FC) means and variances may be plotted for each significantly differentially spliced gene, which helps scientists develop hypothesis and target differential splicing events for RT-qPCR validation in the wet lab.
This package provides a framework and complete preset pipeline for quantification and analysis of ATAC-seq Reads. It covers raw sequencing reads preprocessing (FASTQ files), reads alignment (Rbowtie2), aligned reads file operations (SAM, BAM, and BED files), peak calling (F-seq), genome annotations (Motif, GO, SNP analysis) and quality control report. The package is managed by dataflow graph. It is easy for user to pass variables seamlessly between processes and understand the workflow. Users can process FASTQ files through end-to-end preset pipeline which produces a pretty HTML report for quality control and preliminary statistical results, or customize workflow starting from any intermediate stages with esATAC functions easily and flexibly.
This package provides reference data required for ewce. Expression Weighted Celltype Enrichment (EWCE) is used to determine which cell types are enriched within gene lists. The package provides tools for testing enrichments within simple gene lists (such as human disease associated genes) and those resulting from differential expression studies. The package does not depend upon any particular Single Cell Transcriptome dataset and user defined datasets can be loaded in and used in the analyses.
Exon-intron split analysis (EISA) uses ordinary RNA-seq data to measure changes in mature RNA and pre-mRNA reads across different experimental conditions to quantify transcriptional and post-transcriptional regulation of gene expression. For details see Gaidatzis et al., Nat Biotechnol 2015. doi: 10.1038/nbt.3269. eisaR implements the major steps of EISA in R.
Calculates the coverage of high-throughput short-reads against a genome of reference and summarizes it per feature of interest (e.g. exon, gene, transcript). The data can be normalized as RPKM or by the DESeq or edgeR package.
This package provides objects to manage WebSocket connections to epiviz apps. Other epivizr package use this infrastructure.
This package runs the GADGETS method to identify epistatic effects in nuclear family studies. It also provides functions for permutation-based inference and graphical visualization of the results.
epigraHMM provides a set of tools for the analysis of epigenomic data based on hidden Markov Models. It contains two separate peak callers, one for consensus peaks from biological or technical replicates, and one for differential peaks from multi-replicate multi-condition experiments. In differential peak calling, epigraHMM provides window-specific posterior probabilities associated with every possible combinatorial pattern of read enrichment across conditions.
Base annotation databases for E coli K12 Strain, intended ONLY to be used by AnnotationDbi to produce regular annotation packages.
Affymetrix Affymetrix E_coli_2 Array annotation data (chip ecoli2) assembled using data from public repositories.
Base-resolution copy number analysis of viral genome. Utilizes base-resolution read depth data over viral genome to find copy number segments with two-dimensional segmentation approach. Provides publish-ready figures, including histograms of read depths, coverage line plots over viral genome annotated with copy number change events and viral genes, and heatmaps showing multiple types of data with integrative clustering of samples.
Profile maximum likelihood estimation of parameters for flow cytometry data transformations.
FANTOM4 promoters, liftOver'ed from hg18 to hg19, CpGs quantified.
FeatSeekR performs unsupervised feature selection using replicated measurements. It iteratively selects features with the highest reproducibility across replicates, after projecting out those dimensions from the data that are spanned by the previously selected features. The selected a set of features has a high replicate reproducibility and a high degree of uniqueness.
An interactive web application for quality control, filtering and trimming of FASTQ files. This user-friendly tool combines a pipeline for data processing based on Biostrings and ShortRead infrastructure, with a cutting-edge visual environment. Single-Read and Paired-End files can be locally processed. Diagnostic interactive plots (CG content, per-base sequence quality, etc.) are provided for both the input and output files.
Fragment-level analysis of gas chromatography-massspectrometry metabolomics data.
FrenchFISH comprises a nuclear volume correction method coupled with two types of Poisson models: either a Poisson model for improved manual spot counting without the need for control probes; or a homogenous Poisson Point Process model for automated spot counting.
This package provides a subset of GSE17565 (April et al. 2009) containing 32 FFPE samples of Burkitts Lymphoma and Breast Adenocarcinoma, with a dilution series in technical duplicate.
This package facilitates analysis of both timecourse and steady state flow cytometry experiments. This package was originially developed for quantifying the function of gene regulatory networks in yeast (strain W303) expressing fluorescent reporter proteins using BD Accuri C6 and SORP cytometers. However, the functions are for the most part general and may be adapted for analysis of other organisms using other flow cytometers. Functions in this package facilitate the annotation of flow cytometry data with experimental metadata, as often required for publication and general ease-of-reuse. Functions for creating, saving and loading gate sets are also included. In the past, we have typically generated summary statistics for each flowset for each timepoint and then annotated and analyzed these summary statistics. This method loses a great deal of the power that comes from the large amounts of individual cell data generated in flow cytometry, by essentially collapsing this data into a bulk measurement after subsetting. In addition to these summary functions, this package also contains functions to facilitate annotation and analysis of steady-state or time-lapse data utilizing all of the data collected from the thousands of individual cells in each sample.
This package provides a quality control tool for flow cytometry data based on compositional data analysis.