experiment_design: Find a set of barcode combinations with least heterogeneity...

In comoto-pasteur-fr/DNABarcodeCompatibility: A Tool for Optimizing Combinations of DNA Barcodes Used in Multiplexed Experiments on Next Generation Sequencing Platforms

Description

This function uses the Shannon Entropy to identify a set of compatible barcode combinations with least heterogeneity in barcode usage, in the context of single and dual indexing. It performs either an exhaustive or a random-greedy search of compatible DNA-barcode combinations depending on the size of the DNA-barcode population and of the number of samples to be multiplexed.

Usage

experiment_design(file1, sample_number, mplex_level, platform = 4,
file2 = NULL, export = NULL, metric = NULL, d = 3, thrs_size_comb,
max_iteration, method)

Arguments

file1	The input data file that contains 2 columns separated by a space or a tabulation, namely the sequence identifiers and corresponding DNA sequence
sample_number	Number of libraries to be sequenced.
mplex_level	The number at which the barcodes will be multiplexed.
platform	An integer representing the number of channels (1, 2, 4) of the desired Illumina platform: 1 for iSeq; 2 for NextSeq, NovaSeq, MiniSeq; 4 for HiSeq and MiSeq. 0 represents any other platform than Illumina.
file2	The input data file that contains 2 columns separated by a space or a tabulation, namely the sequence identifiers and corresponding DNA sequence; used for dual-indexing, see details below.
export	If not NULL, results are saved in a csv file at the specified path.
metric	The type of distance (hamming or seqlev).
d	The minimum value of the distance.
thrs_size_comb	The maximum size of the set of compatible combinations to be used for the greedy optimization.
max_iteration	The maximum number of iterations during the optimizing step.
method	The choice of the greedy search: 'greedy_exchange' or 'greedy_descent'.

Details

By specifying the total number of libraries and the number of libraries to be multiplexed, this function returns an optimal set of DNA-barcode combinations to be used for sequencing.

In the case of single indexing, one must only provide one input file containing a list of DNA barcodes (file1 argument). The file2 argument being optional, the program runs the optimisation for single indexing if this argument is left empty. The output shows the sample ID with its respective single barcode.

In the case of dual indexing, one must provide two input files containing DNA barcodes as two separate sets of barcodes. The program will detect these two files and automatically switch to the 'dual indexing' mode. The program then runs the optimisation for each barcode set separately. The output shows the sample ID with its respective pair of barcodes.

The inputs of the algorithm are a list of n distinct barcodes, the number N of required libraries, and the multiplex level k; N = ak, where a is the number of lanes of the flow cells to be used for the experiment.

• Step 1:

This step consists of identifying a set of compatible barcode combinations. Given the number of barcodes and the multiplex level, the total number of barcode combinations (compatible or not) reads:

{n}\choose{k}

If this number is not too large, the algorithm will perform an exhaustive search and output all compatible combinations of k barcodes. Otherwise, it will proceed by picking up combinations at random, in order to identify a large enough set of compatible barcode combinations.

• Step 2:

Finds an optimized set of barcode combinations in which barcode redundancy is minimized (see details in optimize_combinations)

Value

A dataframe containing compatible DNA-barcode combinations organized by lanes of the flow cell.

Examples

write.table(DNABarcodeCompatibility::IlluminaIndexesRaw,
txtfile <- tempfile(), row.names = FALSE, col.names = FALSE, quote=FALSE)
experiment_design(file1=txtfile, sample_number=18, mplex_level=3,
platform=4)

comoto-pasteur-fr/DNABarcodeCompatibility documentation built on June 21, 2019, 9:21 a.m.