---
title: "Introduction to DNABarcodeCompatibility"
author: "Céline Trébeau, Jacques Boutet de Monvel,
Fabienne Wong Jun Tai, Raphaël Etournay"
date: "`r Sys.Date()`"
output:
BiocStyle::html_document:
toc: true
BiocStyle::pdf_document:
toc: true
vignette: >
%\VignetteIndexEntry{Introduction to DNABarcodeCompatibility}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
\usepackage[utf8]{inputenc}
---
```{r style, echo = FALSE, results = 'asis', warnings=FALSE, messages=FALSE}
# <style>
# body {
# text-align: justify}
# </style>
BiocStyle::markdown()
```
```{r setup, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE #,
# comment = "#>"
)
options(width=120)
```
This document gives an overview of the DNABarcodeCompatibility R package with a
brief description of the set of tools that it contains. The package includes
six main functions that are briefly described below with examples.
These functions allow one to load a list of DNA barcodes (such as the Illumina
TruSeq small RNA kits), to filter these barcodes according to distance and
nucleotide content criteria, to generate sets of compatible barcode
combinations out of the filtered barcode list, and finally to generate an
optimized selection of barcode combinations for multiplex sequencing
experiments. In particular, the package provides an optimizer function to
favour the selection of compatible barcode combinations with
**least heterogeneity in the frequencies of DNA barcodes**, and allows one to
keep barcodes that are **robust against substitution and insertion/deletion
errors**, thereby facilitating the demultiplexing step.
The DNABarcodeCompatibility package also contains:
* one workflow called `experiment_design()` allowing one to perform all steps
in one go.
* two data sets called `IlluminaIndexesRaw` and `IlluminaIndexes` for running
and testing examples.
* a series of API to build your own workflow.
The package deals with the three existing sequencing-by-synthesis chemistries
from Illumina:
* Four-Channel SBS Chemistry: MiSeq, HiSeq systems
* Two-Channel SBS Chemistry: MiniSeq, NextSeq, NovaSeq systems
* One-Channel SBS Chemistry: iSeq system
# Load the package
```{r, echo=TRUE}
library("DNABarcodeCompatibility")
```
# Define a helper function to save the raw dataset as a temporary text file
```{r, echo=TRUE}
# This function is created for the purpose of the documentation
export_dataset_to_file =
function(dataset = DNABarcodeCompatibility::IlluminaIndexesRaw) {
if ("data.frame" %in% is(dataset)) {
write.table(dataset,
textfile <- tempfile(),
row.names = FALSE, col.names = FALSE, quote=FALSE)
return(textfile)
} else print(paste("The input dataset isn't a data.frame:",
"NOT exported into file"))
}
```
# Design an experiment
The function `experiment_design()` uses a Shannon-entropy maximization approach
to identify a set of compatible barcode combinations in which the frequencies
of occurrences of the various DNA barcodes are as uniform as possible.
The optimization can be performed in the contexts of single and dual barcoding.
It performs either an exhaustive or a random search of compatible DNA-barcode
combinations, depending on the size of the DNA-barcode set used, and on the
number of samples to be multiplexed.
## Examples for single indexing
* 12 libraries sequenced in multiplex of 3 on a HiSeq (4 channels) platform
```{r, echo=TRUE}
txtfile <- export_dataset_to_file (
dataset = DNABarcodeCompatibility::IlluminaIndexesRaw
)
experiment_design(file1=txtfile,
sample_number=12,
mplex_level=3,
platform=4)
```
* 12 libraries sequenced in multiplex of 3 on a NextSeq (2 channels) platform
```{r, echo=TRUE}
txtfile <- export_dataset_to_file (
dataset = DNABarcodeCompatibility::IlluminaIndexesRaw
)
experiment_design(file1=txtfile,
sample_number=12,
mplex_level=3,
platform=2)
```
* 12 libraries sequenced in multiplex of 3 on a iSeq (1 channels) platform
```{r, echo=TRUE}
txtfile <- export_dataset_to_file (
dataset = DNABarcodeCompatibility::IlluminaIndexesRaw
)
experiment_design(file1=txtfile,
sample_number=12,
mplex_level=3,
platform=1)
```
* 12 libraries sequenced in multiplex of 3 on a HiSeq platform using barcodes
robust against 1 substitution error
```{r, echo=TRUE}
txtfile <- export_dataset_to_file (
dataset = DNABarcodeCompatibility::IlluminaIndexesRaw
)
experiment_design(file1=txtfile,
sample_number=12,
mplex_level=3,
platform=4,
metric = "hamming",
d = 3)
```
## Examples for dual indexing
* 12 libraries sequenced in multiplex of 3 on a HiSeq platform
```{r, echo=TRUE}
# Select the first half of barcodes from the dataset
txtfile1 <- export_dataset_to_file (
DNABarcodeCompatibility::IlluminaIndexesRaw[1:24,]
)
# Select the second half of barcodes from the dataset
txtfile2 <- export_dataset_to_file (
DNABarcodeCompatibility::IlluminaIndexesRaw[25:48,]
)
# Get compatibles combinations of least redundant barcodes
experiment_design(file1=txtfile1,
sample_number=12,
mplex_level=3,
platform=4,
file2=txtfile2)
```
* 12 libraries sequenced in multiplex of 3 on a HiSeq platform using barcodes
robust against 1 substitution error
```{r, echo=TRUE}
# Select the first half of barcodes from the dataset
txtfile1 <- export_dataset_to_file (
DNABarcodeCompatibility::IlluminaIndexesRaw[1:24,]
)
# Select the second half of barcodes from the dataset
txtfile2 <- export_dataset_to_file (
DNABarcodeCompatibility::IlluminaIndexesRaw[25:48,]
)
# Get compatibles combinations of least redundant barcodes
experiment_design(file1=txtfile1, sample_number=12, mplex_level=3, platform=4,
file2=txtfile2, metric="hamming", d=3)
```
# Build your own workflow
This section guides you through the detailed API of the package with the aim to
help you build your own workflow. The package is designed to be flexible and
should be easily adaptable to most experimental contexts, using the
`experiment_design()` function as a template, or building your own workflow
from scratch.
## Load and check a dataset of barcodes
The `file_loading_and_checking()` function loads the file containing the DNA
barcodes set and analyzes its content. In particular, it checks that each
barcode in the set is unique and uniquely identified (removing any repetition
that occurs). It also checks the homogeneity of size of the barcodes,
calculates their GC content and detects the presence of homopolymers of
length >= 3.
```{r, echo=TRUE}
file_loading_and_checking(
file = export_dataset_to_file(
dataset = DNABarcodeCompatibility::IlluminaIndexesRaw
)
)
```
## Examples of an exhaustive search of compatible barcode combinations
The total number of combinations depends on the number of available barcodes
and of the multiplex level. For 48 barcodes and a multiplex level of 3, the
total number of combinations (compatible or not) can be calculated using
`choose(48,3)`, which gives `r format(choose(48,3))` combinations. In many
cases the total number of combinations can become much larger (even gigantic),
and one cannot perform an exhaustive search
(see `get_random_combinations()` below).
* 48 barcodes, multiplex level of 2, HiSeq platform
```{r, echo=TRUE}
# Total number of combinations
choose(48,2)
# Load barcodes
barcodes <- DNABarcodeCompatibility::IlluminaIndexes
# Time for an exhaustive search
system.time(m <- get_all_combinations(index_df = barcodes,
mplex_level = 2,
platform = 4))
# Each line represents a compatible combination of barcodes
head(m)
```
* 48 barcodes, multiplex level of 3, HiSeq platform
```{r, echo=TRUE}
# Total number of combinations
choose(48,3)
# Load barcodes
barcodes <- DNABarcodeCompatibility::IlluminaIndexes
# Time for an exhaustive search
system.time(m <- get_all_combinations(index_df = barcodes,
mplex_level = 3,
platform = 4))
# Each line represents a compatible combination of barcodes
head(m)
```
## Examples of a random search of compatible barcode combinations
When the total number of combinations is too high, it is recommended to pick
combinations at random and then select those that are compatible.
* 48 barcodes, multiplex level of 3, HiSeq platform
```{r, echo=TRUE}
# Total number of combinations
choose(48,3)
# Load barcodes
barcodes <- DNABarcodeCompatibility::IlluminaIndexes
# Time for a random search
system.time(m <- get_random_combinations(index_df = barcodes,
mplex_level = 2,
platform = 4))
# Each line represents a compatible combination of barcodes
head(m)
```
* 48 barcodes, multiplex level of 4, HiSeq platform
```{r, echo=TRUE}
# Total number of combinations
choose(48,4)
# Load barcodes
barcodes <- DNABarcodeCompatibility::IlluminaIndexes
# Time for a random search
system.time(m <- get_random_combinations(index_df = barcodes,
mplex_level = 4,
platform = 4))
# Each line represents a compatible combination of barcodes
head(m)
```
* 48 barcodes, multiplex level of 6, HiSeq platform
```{r, echo=TRUE}
# Total number of combinations
choose(48,6)
# Load barcodes
barcodes <- DNABarcodeCompatibility::IlluminaIndexes
# Time for a random search
system.time(m <- get_random_combinations(index_df = barcodes,
mplex_level = 6,
platform = 4))
# Each line represents a compatible combination of barcodes
head(m)
```
## Constrain barcodes to be robust against one substitution error
```{r, echo=TRUE}
# Load barcodes
barcodes <- DNABarcodeCompatibility::IlluminaIndexes
# Perform a random search of compatible combinations
m <- get_random_combinations(index_df = barcodes,
mplex_level = 3,
platform = 4)
# Keep barcodes that are robust against one substitution error
filtered_m <- distance_filter(index_df = barcodes,
combinations_m = m,
metric = "hamming",
d = 3)
# Each line represents a compatible combination of barcodes
head(filtered_m)
```
## Optimize the set of compatible combinations to reduce barcode redundancy
```{r, echo=TRUE}
# Keep set of compatible barcodes that are robust against one substitution
# error
filtered_m <- distance_filter(
index_df = DNABarcodeCompatibility::IlluminaIndexes,
combinations_m = get_random_combinations(index_df = barcodes,
mplex_level = 3,
platform = 4),
metric = "hamming", d = 3)
# Use a Shannon-entropy maximization approach to reduce barcode redundancy
df <- optimize_combinations(combination_m = filtered_m,
nb_lane = 12,
index_number = 48)
# Each line represents a compatible combination of barcodes and each row a lane
# of the flow cell
df
```
## The optimized result isn't an optimum when filtering out too many barcodes
* Increased distance between barcode sequences: redundancy may become
inevitable
```{r, echo=TRUE}
# Keep set of compatible barcodes that are robust against multiple substitution
# and insertion/deletion errors
filtered_m <- distance_filter(
index_df = DNABarcodeCompatibility::IlluminaIndexes,
combinations_m = get_random_combinations(index_df = barcodes,
mplex_level = 3,
platform = 4),
metric = "seqlev", d = 4)
# Use a Shannon-entropy maximization approach to reduce barcode redundancy
df <- optimize_combinations(combination_m = filtered_m,
nb_lane = 12,
index_number = 48)
# Each line represents a compatible combination of barcodes and each row a
# lane of the flow cell
df
```