| ##' Make a VRanges object from a GRanges object | |
| ##' | |
| ##' makeVRangesFromDataFrame takes a \code{\linkS4class{GenomicRanges}} object as input and tries to find the specified columns required by the \code{\link{VRanges}} constructor. | |
| ##' @param gr \code{\linkS4class{GenomicRanges}} object | |
| ##' @param ... Additional arguments passed on to the \code{\link{VRanges}} constructor function. | |
| ##' @return \code{\linkS4class{VRanges}} object | |
| ##' @importFrom VariantAnnotation VRanges | |
| ##' @importFrom GenomicRanges makeGRangesFromDataFrame | |
| ##' @importMethodsFrom S4Vectors mcols | |
| ##' @importMethodsFrom GenomeInfoDb seqinfo |
| RNA-Seq QC report | |
| ----------------------------------- | |
| >>>>>>> Input | |
| bam file = /tmp/07122375.bam | |
| gff file = /mnt/annotation.gtf | |
| counting algorithm = uniquely-mapped-reads | |
| protocol = strand-specific-forward |
| accession2url <- function(x) { | |
| prefix <- "ftp://ftp.sra.ebi.ac.uk/vol1/fastq" | |
| dir1 <- paste0("/",substr(x,1,6)) | |
| dir2 <- ifelse(nchar(x) == 9, "", | |
| ifelse(nchar(x) == 10, paste0("/00",substr(x,10,10)), | |
| ifelse(nchar(x) == 11, paste0("/0",substr(x,10,11)), | |
| paste0("/",substr(x,10,12))))) | |
| paste0(prefix,dir1,dir2,"/",x) | |
| } |
The
weblogo3
website accepts motifs in many different formats, most of which represent the alignmed motifs themselves (eg fasta format).
It is also possible to submit a position-specific score matrix (PSSM) in
Transfac format.
This format is also accepted by the RWebLogo::weblogo R function.
For conversion between many different motif formats, the
cfnCluster on AWS Tutorial by AWS cnfCluster documentation
CfnCluster constructs an HPC environment with the “look and feel” of conventional HPC clusters but with the added benefit of being scalable:
A python toolkit providing best-practice pipelines for fully automated high throughput sequencing analysis. You write a high level configuration file specifying your inputs and analysis parameters. This input drives a parallel pipeline that handles distributed execution, idempotent processing restarts and safe transactional steps.
| # install the release version from CRAN | |
| install.packages(c("listviewer", "wesanderson")) | |
| library(listviewer) | |
| library(wesanderson) | |
| # explore the color palettes included in the wesanderson package | |
| jsonedit(wes_palettes) |
| # Create 3 random vectors of length 50 | |
| # We use human / mouse gene symbols to illustrate that this | |
| # reflects commonly used procedures in biomedical science, | |
| # e.g. the analysis of qPCR results with a single | |
| # reference gene. | |
| # See MIQE guidelines on how to normalize properly: | |
| # https://www.ncbi.nlm.nih.gov/pubmed/19246619 | |
| Il6 <- rnorm(mean = 10, n = 50) | |
| Il1b <- rnorm(mean = 10, n = 50) | |
| Gapdh <- rnorm(mean = 10, sd = 3, n = 50) |
| library(dplyr) | |
| library(forcats) | |
| # encode the 'Month' column of the 'airquality' dataset as a factor | |
| airquality2 <- airquality %>% | |
| dplyr::mutate( | |
| Month = factor(Month), | |
| Month = forcats::fct_recode(Month, May = "5", June = "6", July = "7", | |
| August = "8", September = "9") | |
| ) |
