NGS_Multi_Heal

NGS_Multi_Heal simplifies quality analysis, trimming, and healing of paired-end reads comprised of forward (R1) and reverse (R2) reads in gzipped or unzipped fastq files.
Two scripts in this package, fastxTrimmer_R1andR2.py and windowQualPrinseqLite_R1andR2.py, were employed in read-healing analysis in Wagner et al. (2021) PeerJ. 9:e12446 (https://doi.org/10.7717/peerj.12446)

Preprocessing with NGS_Multi_Heal

• Trimming 3-prime ends of reads prior to high-quality Single Nucleotide Polymorphisms (hqSNPs) analysis
• Removing ambiguous nucleotides (Ns) from reads prior to hqSNPs analysis or de novo assembly
• Removing reads under a user-specified minimum prior to de novo assembly

Prerequisites

• Perl 5.X.X or higher
• Python 3.X.X or higher
• Java 1.7.xxxx or higher
• Bpipe 0.9.8.7: bpipe.org
• FASTX-Toolkit-0.0.13: hannonlab.cshl.edu
• prinseq 1.20.X: sourceforge.net/projects/prinseq
• CG-Pipeline Perl: github.com/lskatz

Example Preprocessing of reads for hqSNPs

Method I. Uniform 3-prime trim for forward and reverse reads

Trim 3' ends of forward and reverse reads by 5 and variable (XX) base pair positions, respectively:

python fastxTrimmer_R1andR2.py reads_R1_001.fastq reads_R2_001.fastq --trimF 5 --trimR XX -outDir trimmed/

• trim forward (R1) reads by 5 bp.
• trim reverse (R2) reads by 5 bp when average PHRED quality score is > 31.00
• otherwise, trim reverse reads by 10 bp for PHRED qual. < 31.00
• trim reverse reads by 15 bp for PHRED qual. < 30.00
• trim reverse reads by 20 bp for PHRED qual. < 29.00

Method II.

Step 1. Remove reads < 40 bp and/or containing > 0 ambiguous nucleotides:

python simpPrinseqLite_R1andR2.py reads_R1_001.fastq reads_R2_001.fastq --min_len 40 --rm_ambig Y --ambig_allow 0 --outDir trimmed/

Step 2. Trim 3' ends of forward and reverse reads as shown under Method I:

python fastxTrimmer_R1andR2.py trimmed/reads_prinseq_R1_001.fastq trimmed/reads_prinseq_R2_001.fastq --trimF 5 --trimR XX -outDir trimmed/

Example Preprocessing genomeA reads for de novo assembly

Method I. Single master wrapper for prinseq-lite.pl

Remove reads < 100 bp, containing > 0 ambiguous nucleotides, and trim regions with quality < 26:

python qualPrinseqLite_R1andR2.py genomeA_reads_R1_001.fastq genomeA_reads_R2_001.fastq --min_len 100 --rm_ambig Y --ambig_allow 0 --trim_qual Y --min_score 26 --outDir trim_genomeA_reads/

Method II. Prinseq-lite.pl wrapper followed by fastx_trimmer wrapper

Step 1. Remove reads < 100 bp and/or containing > 1 ambiguous nucleotides

python simpPrinseqLite_R1andR2.py genomeA_reads_R1_001.fastq genomeA_reads_R2_001.fastq --min_len 100 --rm_ambig Y --ambig_allow 1 --outDir trim_genomeA_reads/

Step 2. Trim 3' ends of forward and reverse reads as shown under Method I for hqSNPs

python fastxQualAdaptTrimmer_R1andR2.py trim_genomeA_reads/genomeA_reads_R1_001_prinseq/genomeA_reads_R1_001_prinseq_1.fastq trim_genomeA_reads/genomeA_reads_R1_001_prinseq/genomeA_reads_R1_001_prinseq_2.fastq --trimF 5 --trimR XX --outDir trim_genomeA_reads/

Coming Soon

• Python wrapper for SPAdes BayesHammer
• Python scripts for managing DNA .fasta or .txt