split_libraries_lea_seq.py – Demultiplexes Low-Error Amplicon Sequencing (LEA-Seq) data

Description:

Implements Low-Error Amplicon Sequencing (LEA-Seq) method, described in:

Faith, Jeremiah J., et al. The long-term stability of the human gut microbiota.Science 341.6141 (2013).

This method is based on redundant sequencing of a set of linear PCR template extensions of 16S rRNA genes. The oligonucleotide primer that is used for PCR template extensions is labeled with a random barcode 5’ to the universal 16S rRNA primer sequence. This PCR pool is then amplified with exponential PCR, using primers that specifically amplify only the linear PCR molecules. An index primer is added to the amplicons along with a primer specific for each sample. This exponential PCR pool is then sequenced redundantly (20x coverage). The resulting sequences are separated by sample, using the index sequence. The amplicon sequences within each sample are separated by the random barcodes. The large number of reads for each barcode helps to create an error-corrected consensus sequence for the initial template molecule.

Usage: split_libraries_lea_seq.py [options]

Input Arguments:

Note

[REQUIRED]

-i, --sequence_read_fps

The forward and reverse sequence read fastq files (comma-separated)

-o, --output_dir

Directory to store output files

-m, --mapping_fp

Metadata mapping file

[OPTIONAL]

-b, --barcode_type

The type of barcode used. This can be an integer, e.g. 6 for length 6 barcodes, or golay_12 for golay error-correcting barcodes. Error correction will only be applied for golay_12 barcodes [default: golay_12]

--max_barcode_errors

The maximum allowable number of errors in the barcode if passing –barcode_type golay_12 [default: 1.5]

--min_consensus

Threshold for consensus score: the minimum score allowable at any position in sequence. where the score is calulated as: occurence of base in consensus sequence/ total sequences[default: 6.6]

--max_cluster_ratio

Threshold for cluster ratio: the maximum allowable cluster ratio above which you need to find the consensus sequence for the given sequences.[default: 2.5]

--min_difference_in_bcs

Threshold for selecting unique barcodes: Barcodes that are more similar to each other than this value will be discarded.[default: 0.86]

--fwd_length

Removes phasing from forward readby truncating it to standard length for the region[default: 64]

--rev_length

Removes phasing from reverse readby truncating it to standard length for the region[default: 77]

--min_difference_in_clusters

The percent identity threshold while using uclust to cluster sequence reads, which is helpfulin measuring quality of sequencing.[default: 0.98]

--min_reads_per_random_bc

Minimum number of reads per randombarcode, attempts to remove random barcodes that are sequencing errors of true barcodesmight be useful in saving memory and time[default: 1]

--header_barcode_column

Header of barcode column[default: BarcodeSequence]

--reverse_primer_column

Header of reverse primer column[default: ReversePrimer]

Output:

The split_libraries_lea_seq.py generates: A fasta file called seqs.fna which contains error corrected consensus sequence for the template DNA

General Example: Specify forward read and reverse read fasta files, use the metadata mapping file map.txt,and output the data to output_dir:

output_dir

split_libraries_lea_seq.py -i fwd_read.fq,rev_read.fq -m map.txt -o output --b 7