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
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Assess genome assembly and annotation quality by comparing custom statistics against NCBI genomes and evaluating gene space completeness with BUSCO.
library(cogeqc)
# 1. Retrieve and compare genome assembly statistics
maize_stats <- get_genome_stats(taxon = "Zea mays")
my_stats <- data.frame(
accession = "my_lovely_maize",
sequence_length = 2.4 * 1e9,
gene_count_total = 50000,
CC_ratio = 2
)
comparison <- compare_genome_stats(ncbi_stats = maize_stats, user_stats = my_stats)
plot_genome_stats(ncbi_stats = maize_stats, user_stats = my_stats)
# 2. Read and plot BUSCO summary statistics
output_dir <- system.file("extdata", package = "cogeqc")
busco_summary <- read_busco(output_dir)
plot_busco(busco_summary)
Input: Custom genome assembly statistics and BUSCO output directories; Output: Quality control comparison tables and summary visualization plots.
get_genome_stats, compare_genome_stats, and plot_genome_stats.run_busco, read_busco, and plot_busco.read_orthogroups and assess_orthogroups.cogeqc for downstream quality control.accession column and other matching NCBI fields (e.g., sequence_length, gene_count_total, CC_ratio).Orthogroups.tsv file or a parsed data frame with columns Orthogroup, Species, and Gene.get_genome_stats.get_genome_stats.compare_genome_stats and plot_genome_stats.run_busco.run_busco.run_busco.run_busco.CC_ratio (ratio of contigs to chromosome pairs) as a robust measurement of contiguity that allows cross-species comparisons.filters parameter in get_genome_stats.busco_is_installed before calling run_busco.plot_busco.run_busco failing due to missing system dependencies. Fix: Ensure BUSCO is installed and in your system PATH, or check with busco_is_installed().get_genome_stats (e.g., sequence_length instead of genome_size).quast (external tool) for general genome assembly quality metrics.OrthoFinder (external tool) for orthogroup inference.まだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
The package contains functions for exploratory oligonucleotide array analysis. The dependence on tkWidgets only concerns few convenience functions. 'affy' is fully functional without it.
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This tool uses the label-tree function from HyPhy to annotate a phylogenetic tree. It allows users to select a subset of leaves using either a regular expression or a list of sequence names, and then apply a specified label to these selecte
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 provides a client for the Bioconductor AnnotationHub web resource. The AnnotationHub web resource provides a central location where genomic files (e.g., VCF, bed, wig) and other resources from standard locations (e.g., UCSC, En
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。
apeglm provides Bayesian shrinkage estimators for effect sizes for a variety of GLM models, using approximation of the posterior for individual coefficients.
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ASURAT is a software for single-cell data analysis. Using ASURAT, one can simultaneously perform unsupervised clustering and biological interpretation in terms of cell type, disease, biological process, and signaling pathway activity. Inputting a single-cell RNA-seq data and knowledge-based databases, such as Cell Ontology, Gene Ontology, KEGG, etc., ASURAT transforms gene expression tables into original multivariate tables, termed sign-by-sample matrices (SSMs).
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AUCell allows to identify cells with active gene sets (e.g. signatures, gene modules...) in single-cell RNA-seq data. AUCell uses the "Area Under the Curve" (AUC) to calculate whether a critical subset of the input gene set is enriched with
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。