UMI4Cats

The goal of UMI4Cats is to provide and easy-to-use package to analyze UMI-4C contact data.

Installation

devtools::install_gitlab("pasquali-lab/umi4cats")

Now you can load the package using library(UMI4Cats).

Basic usage

library(UMI4Cats)

## 0) Download example data
path <- downloadUMI4CexampleData()

## 1) Generate Digested genome ----------------------------
# The selected RE in this case is DpnII (|GATC), so the cs5p is "" and cs3p is GATC
hg19_dpnii <- digestGenome(cut_pos = 0,
                           res_enz = "GATC",
                           name_RE = "DpnII",
                           ref_gen = BSgenome.Hsapiens.UCSC.hg19::BSgenome.Hsapiens.UCSC.hg19,
                           out_path = "digested_genome/")

## 2) Process UMI-4C fastq files --------------------------
raw_dir <- file.path(path, "SOCS1", "fastq")

contactsUMI4C(fastq_dir = raw_dir,
              wk_dir = "SOCS1",
              bait_seq = "CCCAAATCGCCCAGACCAG",
              bait_pad = "GCGCG",
              res_enz = "GATC",
              cut_pos = 0,
              digested_genome = hg19_dpnii,
              bowtie_index = file.path(path, "ref_genome", "ucsc.hg19.chr16"),
              ref_gen = BSgenome.Hsapiens.UCSC.hg19::BSgenome.Hsapiens.UCSC.hg19,
             threads = 5)

## 3) Get filtering and alignment stats -------------------
statsUMI4C(wk_dir = system.file("extdata", "SOCS1",
                               package="UMI4Cats"))

## 4) Analyze UMI-4C results ------------------------------
# Load sample processed file paths
files <- list.files(system.file("extdata", "SOCS1", "count",
                                package="UMI4Cats"),
                    pattern="*_counts.tsv",
                    full.names=TRUE)

# Create colData including all relevant information
colData <- data.frame(sampleID = gsub("_counts.tsv.gz", "", basename(files)),
                      file = files,
                      stringsAsFactors=FALSE)

library(tidyr)
colData <- colData %>%
  separate(sampleID,
           into=c("condition", "replicate", "viewpoint"),
           remove=FALSE)

# Load UMI-4C data and generate UMI4C object
umi <- makeUMI4C(colData=colData,
                 viewpoint_name="SOCS1")

## 5) Perform differential test ---------------------------
umi <- fisherUMI4C(umi,
                   filter_low = 20)

## 6) Plot results ----------------------------------------
plotUMI4C(umi,
          ylim=c(0,10),
          xlim=c(11e6, 11.5e6))