split_libraries_fastq.py – This script performs demultiplexing of Fastq sequence data where barcodes and sequences are contained in two separate fastq files (common on Illumina runs).

Description:

Usage: split_libraries_fastq.py [options]

Input Arguments:

Note

[REQUIRED]

-i, --sequence_read_fps

The sequence read fastq files (comma-separated if more than one)

-o, --output_dir

Directory to store output files

[OPTIONAL]

-m, --mapping_fps

Metadata mapping files (comma-separated if more than one) [default: None]

-b, --barcode_read_fps

The barcode read fastq files (comma-separated if more than one) [default: None]

--store_qual_scores

Store qual strings in .qual files [default: False]

--sample_ids

Comma-separated list of samples ids to be applied to all sequences, must be one per input file path (used when data is not multiplexed) [default: None]

--store_demultiplexed_fastq

Write demultiplexed fastq files [default: False]

--retain_unassigned_reads

Retain sequences which don’t map to a barcode in the mapping file (sample ID will be “Unassigned”) [default: False]

-r, --max_bad_run_length

Max number of consecutive low quality base calls allowed before truncating a read [default: 3]

-p, --min_per_read_length_fraction

Min number of consecutive high quality base calls to include a read (per single end read) as a fraction of the input read length [default: 0.75]

-n, --sequence_max_n

Maximum number of N characters allowed in a sequence to retain it – this is applied after quality trimming, and is total over combined paired end reads if applicable [default: 0]

-s, --start_seq_id

Start seq_ids as ascending integers beginning with start_seq_id [default: 0]

--rev_comp_barcode

Reverse complement barcode reads before lookup [default: False]

--rev_comp_mapping_barcodes

Reverse complement barcode in mapping before lookup (useful if barcodes in mapping file are reverse complements of golay codes) [default: False]

--rev_comp

Reverse complement sequence before writing to output file (useful for reverse-orientation reads) [default: False]

-q, --phred_quality_threshold

The maximum unacceptable Phred quality score (e.g., for Q20 and better, specify -q 19) [default: 3]

--last_bad_quality_char

DEPRECATED: use -q instead. This method of setting is not robust to different versions of CASAVA.

--barcode_type

The type of barcode used. This can be an integer, e.g. for length 6 barcodes, or “golay_12” for golay error-correcting barcodes. Error correction will only be applied for “golay_12” barcodes. If data is not barcoded, pass “not-barcoded”. [default: golay_12]

--max_barcode_errors

Maximum number of errors in barcode [default: 1.5]

--phred_offset

The ascii offset to use when decoding phred scores (either 33 or 64). Warning: in most cases you don’t need to pass this value [default: determined automatically]

Output:

Demultiplex and quality filter (at Phred >= Q20) one lane of Illumina fastq data and write results to ./slout_q20.:

split_libraries_fastq.py -i lane1_read1.fastq.gz -b lane1_barcode.fastq.gz --rev_comp_mapping_barcodes -o slout_q20/ -m map.txt -q 19

Demultiplex and quality filter (at Phred >= Q20) one lane of Illumina fastq data and write results to ./slout_q20. Store trimmed quality scores in addition to sequence data.:

split_libraries_fastq.py -i lane1_read1.fastq.gz -b lane1_barcode.fastq.gz --rev_comp_mapping_barcodes -o slout_q20/ -m map.txt --store_qual_scores -q 19

Demultiplex and quality filter (at Phred >= Q20) two lanes of Illumina fastq data and write results to ./slout_q20.:

split_libraries_fastq.py -i lane1_read1.fastq.gz,lane2_read1.fastq.gz -b lane1_barcode.fastq.gz,lane2_barcode.fastq.gz --rev_comp_mapping_barcodes -o slout_q20/ -m map.txt,map.txt --store_qual_scores -q 19

Quality filter (at Phred >= Q20) one non-multiplexed lane of Illumina fastq data and write results to ./slout_single_sample_q20.:

split_libraries_fastq.py -i lane1_read1.fastq.gz --sample_ids my.sample.1 -o slout_single_sample_q20/ -q 19 --barcode_type 'not-barcoded'

Quality filter (at Phred >= Q20) two non-multiplexed lanes of Illumina fastq data with different samples in each and write results to ./slout_not_multiplexed_q20.:

split_libraries_fastq.py -i lane1_read1.fastq.gz,lane2_read1.fastq.gz --sample_ids my.sample.1,my.sample.2 -o slout_not_multiplexed_q20/ -q 19 --barcode_type 'not-barcoded'