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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Seamlessly interfaces the Basic Local Alignment Search Tool (BLAST) to search genetic sequence data bases. This work was partially supported by grant no. R21HG005912 from the National Human Genome Research Institute.
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查看安装方法安装前可以查看交给 AI 代理的指令内容。
Create a custom BLAST database from user-provided sequences and query it locally.
library(rBLAST)
library(Biostrings)
# Load example sequences
seq <- readRNAStringSet(system.file("examples/RNA_example.fasta", package = "rBLAST"))
# Write to FASTA
writeXStringSet(seq, filepath = "seqs.fasta")
# Create database
makeblastdb("seqs.fasta", db_name = "db/small", dbtype = "nucl")
Note: Input is an XStringSet object, and output is a set of local BLAST database files written to disk.
Download, extract, and query an existing NCBI BLAST database using local BLAST.
library(rBLAST)
# Download and extract 16S database
tgz_file <- blast_db_get("16S_ribosomal_RNA.tar.gz")
untar(tgz_file, exdir = "16S_rRNA_DB")
# Load existing database
bl <- blast(db = "./16S_rRNA_DB/16S_ribosomal_RNA")
# Query database
seq <- readRNAStringSet(system.file("examples/RNA_example.fasta", package = "rBLAST"))[1]
cl <- predict(bl, seq)
Note: Input is a path to a local BLAST database and a query XStringSet object; output is a data.frame of matches.
Seamlessly interfaces the Basic Local Alignment Search Tool (BLAST) to search genetic sequence data
library(rBLAST)
library(Biostrings)
# 1. Read reference sequences
seq <- readRNAStringSet(system.file("examples/RNA_example.fasta", package = "rBLAST"))
# 2. Write sequences to a FASTA file
writeXStringSet(seq, filepath = "seqs.fasta")
# 3. Create custom BLAST database
makeblastdb("seqs.fasta", db_name = "db/small", dbtype = "nucl")
# 4. Load custom database
db <- blast("db/small")
# 5. Extract a subsequence fragment
fragment <- subseq(seq[1], start = 101, end = 200)
# 6. Query custom database
res <- predict(db, fragment)
Note: Input is a set of reference sequences and a query fragment; output is a data.frame of alignment matches.
XStringSet objects (e.g., RNAStringSet, DNAStringSet).makeblastdb and querying them locally.blast_db_get.annotate::blastSequences instead).PATH.Biostrings::XStringSet objects (e.g., RNAStringSet).blast().makeblastdb().makeblastdb().predict() (e.g., "-perc_identity 99").predict().has_blast() or Sys.which("blastn").PATH environment variable using Sys.setenv if R cannot find the BLAST+ executables.db/small) in makeblastdb.unlink with recursive = TRUE to clean up temporary database directories and FASTA files.Sys.which("blastn") returns "". Fix: Set the PATH environment variable to include the BLAST+ installation directory.makeblastdb.blastSequences for querying the remote NCBI BLAST server.还没有评价。欢迎分享你的使用感受。
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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
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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
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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
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