tomsing1

##' 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

tomsing1

RNA-Seq QC report
-----------------------------------

>>>>>>> Input

    bam file = /tmp/07122375.bam
    gff file = /mnt/annotation.gtf
    counting algorithm = uniquely-mapped-reads
    protocol = strand-specific-forward

tomsing1

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)
}

tomsing1

WebLogo3

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

tomsing1

HPC cluster deployment on AWS

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:

• Jobs are submitted to a queue

tomsing1

bcbio

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.

• bcbio documentation
• bcbio github page

Installation

tomsing1

Docker machine

Documentation

prerequisites

• docker
• AWS credentials in ~/.aws/credentials

Documentation

tomsing1

# 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)

tomsing1

# 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)

tomsing1

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")
    )