「bioc」の検索結果

Graph objects from pathway topology derived from KEGG, Panther, PathBank, PharmGKB, Reactome SMPDB and WikiPathways databases.

日本語の概要は準備中です。原文の説明を表示しています。

Provides Bayesian PCA, Probabilistic PCA, Nipals PCA, Inverse Non-Linear PCA and the conventional SVD PCA. A cluster based method for missing value estimation is included for comparison. BPCA, PPCA and NipalsPCA may be used to perform PCA o

日本語の概要は準備中です。原文の説明を表示しています。

Client for the gypsum REST API (https://gypsum.artifactdb.com), a cloud-based file store in the ArtifactDB ecosystem. This package provides functions for uploads, downloads, and various adminstrative and management tasks. Check out the docu

日本語の概要は準備中です。原文の説明を表示しています。

seqLogo takes the position weight matrix of a DNA sequence motif and plots the corresponding sequence logo as introduced by Schneider and Stephens (1990).

日本語の概要は準備中です。原文の説明を表示しています。

topGO package provides tools for testing GO terms while accounting for the topology of the GO graph. Different test statistics and different methods for eliminating local similarities and dependencies between GO terms can be implemented and

日本語の概要は準備中です。原文の説明を表示しています。

This package takes a list of p-values resulting from the simultaneous testing of many hypotheses and estimates their q-values and local FDR values. The q-value of a test measures the proportion of false positives incurred (called the false

日本語の概要は準備中です。原文の説明を表示しています。

Implements the circular binary segmentation (CBS) algorithm to segment DNA copy number data and identify genomic regions with abnormal copy number.

日本語の概要は準備中です。原文の説明を表示しています。

The ability to efficiently represent and manipulate genomic annotations and alignments is playing a central role when it comes to analyzing high-throughput sequencing data (a.k.a. NGS data). The GenomicRanges package defines general purpose

日本語の概要は準備中です。原文の説明を表示しています。

The 'enrichplot' package implements several visualization methods for interpreting functional enrichment results obtained from ORA or GSEA analysis. It is mainly designed to work with the 'clusterProfiler' package suite. All the visualizati

日本語の概要は準備中です。原文の説明を表示しています。

Tools for finding bumps in genomic data

日本語の概要は準備中です。原文の説明を表示しています。

MHC (major histocompatibility complex) molecules are cell surface complexes that present antigens to T cells. The repertoire of antigens presented in a given genetic background largely depends on the sequence of the encoded MHC molecules, and thus, in humans, on the highly variable HLA (human leukocyte antigen) genes of the hyperpolymorphic HLA locus. More than 28,000 different HLA alleles have been reported, with significant differences in allele frequencies between human populations worldwide

日本語の概要は準備中です。原文の説明を表示しています。

This package is a implementation of biclustering ensemble method MoSBi (Molecular signature Identification from Biclustering). MoSBi provides standardized interfaces for biclustering results and can combine their results with a multi-algorithm ensemble approach to compute robust ensemble biclusters on molecular omics data. This is done by computing similarity networks of biclusters and filtering for overlaps using a custom error model. After that, the louvain modularity it used to extract biclus

日本語の概要は準備中です。原文の説明を表示しています。

Provides efficient containers for storing and manipulating short genomic alignments (typically obtained by aligning short reads to a reference genome). This includes read counting, computing the coverage, junction detection, and working wit

日本語の概要は準備中です。原文の説明を表示しています。

Epialleles are specific DNA methylation patterns that are mitotically and/or meiotically inherited. This package calls and reports cytosine methylation as well as frequencies of hypermethylated epialleles at the level of genomic regions or individual cytosines in next-generation sequencing data using binary alignment map (BAM) files as an input. Among other things, this package can also extract and visualise methylation patterns and assess allele specificity of methylation.

日本語の概要は準備中です。原文の説明を表示しています。

doubletrouble aims to identify duplicated genes from whole-genome protein sequences and classify them based on their modes of duplication. The duplication modes are i. segmental duplication (SD); ii. tandem duplication (TD); iii. proximal duplication (PD); iv. transposed duplication (TRD) and; v. dispersed duplication (DD). Transposon-derived duplicates (TRD) can be further subdivided into rTRD (retrotransposon-derived duplication) and dTRD (DNA transposon-derived duplication). If users want a s

日本語の概要は準備中です。原文の説明を表示しています。

A toolset for deciphering and managing biological sequences.

日本語の概要は準備中です。原文の説明を表示しています。

algorithm for determining cluster count and membership by stability evidence in unsupervised analysis

日本語の概要は準備中です。原文の説明を表示しています。

Complex heatmaps are efficient to visualize associations between different sources of data sets and reveal potential patterns. Here the ComplexHeatmap package provides a highly flexible way to arrange multiple heatmaps and supports various

日本語の概要は準備中です。原文の説明を表示しています。

cogeqc aims to facilitate systematic quality checks on standard comparative genomics analyses to help researchers detect issues and select the most suitable parameters for each data set. cogeqc can be used to asses: i. genome assembly and annotation quality with BUSCOs and comparisons of statistics with publicly available genomes on the NCBI; ii. orthogroup inference using a protein domain-based approach and; iii. synteny detection using synteny network properties. There are also data visualizat

日本語の概要は準備中です。原文の説明を表示しています。

This package supports functional characteristics of both coding and non-coding genomics data for thousands of species with up-to-date gene annotation. It provides a univeral interface for gene functional annotation from a variety of sources

日本語の概要は準備中です。原文の説明を表示しています。

Infrastructure shared by all the Biostrings-based genome data packages.

日本語の概要は準備中です。原文の説明を表示しています。

In recent years a wealth of biological data has become available in public data repositories. Easy access to these valuable data resources and firm integration with data analysis is needed for comprehensive bioinformatics data analysis. bio

日本語の概要は準備中です。原文の説明を表示しています。

BEER implements a Bayesian model for analyzing phage-immunoprecipitation sequencing (PhIP-seq) data. Given a PhIPData object, BEER returns posterior probabilities of enriched antibody responses, point estimates for the relative fold-change in comparison to negative control samples, and more. Additionally, BEER provides a convenient implementation for using edgeR to identify enriched antibody responses.

日本語の概要は準備中です。原文の説明を表示しています。

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.

日本語の概要は準備中です。原文の説明を表示しています。