SPNtypeID is a Nextflow pipeline used for genome assembly and serotyping of Streptococcus pneumoniae.
Usage
Input
Parameters
Workflow outline
Read trimming and quality assessment
Genome assembly
Assembly quality assessment
Genome coverage
Contamination detection
Serotyping
Output
Results file explanation
Credits
Contributions and Support
Citations
The pipeline is designed to start from raw, paired-end Illumina reads. Start the pipeline using:
nextflow run SPNtypeID/main.nf --input [path-to-samplesheet] --outdir [path-to-outdir] --ntc_regex [what-expression-to-look-for-in-ntcs] --runname [what-to-call-run] -profile [docker,singularity,aws]
or from github using:
nextflow run wslh-bio/SPNtypeID -r [version] --input [path-to-samplesheet] --outdir [path-to-outdir] -ntc_regex [what-expression-to-look-for-in-ntcs] --runname [what-to-call-run] -profile [docker,singularity,aws]
You can also test the pipeline with example data using -profile test or -profile test_full:
nextflow run SPNtypeID/main.nf --outdir [path-to-outdir] -profile test[_full],[docker,singularity]
SPNTypeID's inputs are paired Illumina FASTQ files for each sample and a comma separated sample sheet containing the sample name, the path to the forward reads file, and the path to the reverse reads file for each sample. A sample sheet can be created using the fastq_dir_to_samplesheet.py script or by hand. An example of the sample sheet's format can be seen in the table below and found here.
| sample | fastq_1 | fastq_2 |
|---|---|---|
| sample_name | /path/to/sample_name_R1.fastq.gz | /path/to/sample_name_R2.fastq.gz |
SPNTypeID's main parameters and their defaults are shown in the table below:
| Parameter | Parameter description and default |
|---|---|
| contaminants | Path to fasta of contaminants for removal, defaults to BBDuk's adapters fasta |
| maxcontigs | Set the maximum number of contigs allowed in an assembly (default: 300) |
| maxpctother | Sets the maximum percentage of reads from other organisms (default: 1.0) |
| minavgreadq | Sets the minimum average read quality score (default: 30) |
| mincoverage | Sets the minimum coverage (default: 40) |
| minlength | Minimum read length for trimming (default: 10) |
| minpctspn | Sets the minimum percentage of reads that must be S. pneumoniae (default: 60.0) |
| minpctstrep | Sets the minimum percentage of reads that must be Streptococcus (default: 80.0) |
| ntc_regex | Regex pattern for identifying no template control (NTC) files. This is a mandatory parameter if a run has an NTC. (default: null) |
| qualitytrimscore | Sets the BBDuk trimming quality score value (default: 10) |
| trimdirection | Sets the BBDuk trimming direction (default: 'lr') |
Read repair, trimming, and cleaning are performed using BBtools v38.76 to repair fastqs with mismatched read numbers, trim reads of low quality bases, and remove PhiX contamination. Then FastQC v0.11.8 is used assess the quality of the raw and cleaned reads. Bioawk v1.0 is used to calculate the mean and median quality of the cleaned reads.
Assembly of the cleaned and trimmed reads is performed using Shovill v1.1.0.
Quality assessment of the assemblies is performed using QUAST v5.0.2.
Mean and median genome coverage is determined by mapping the cleaned reads back their the assembly using BWA v0.7.17-r1188 and calculating depth using Samtools v1.10.
Genome length is assessed by comparing the expected S. pneumoniae genome length to the observed genome length and calculating a Z score. These statistics (which can be found here were obtained from the PHoeNIx pipeline, which calculated them from 9266 publicly available S. pneumoniae genomes.
Contamination is detected by classifying reads using Kraken v1.0.0.
Serotyping is performed using SeroBA v2.0.4.
Example of pipeline output:
outdir
├── assembly_stats_summary
│ └── assembly_stats_results_summary.tsv
├── bbduk
│ ├── *.adapter.stats.txt
│ ├── *.bbduk.log
│ ├── *_repaired_1.fastq.gz
│ ├── *_repaired_2.fastq.gz
│ ├── *.repair.log
│ ├── *_singletons.fastq.gz
│ ├── *_trimmed_1.fastq.gz
│ ├── *_trimmed_2.fastq.gz
│ └── *.trim.txt
├── bbduk_summary
│ └── bbduk_results.tsv
├── bioawk
│ └── *.qual.tsv
├── calculate_assembly_stats
│ └── *_Assembly_ratio_20240124.tsv
├── coverage_stats
│ └── coverage_stats.tsv
├── fastqc
│ ├── *_fastqc.html
│ └── *_fastqc.zip
├── fastqc_summary
│ └── fastqc_summary.tsv
├── kraken_ntc
│ └── *.kraken.txt ***
├── kraken_sample
│ └── *.kraken.txt
├── kraken_summary
│ └── kraken_results.tsv
├── multiqc
│ ├── multiqc_data
│ ├── multiqc_plots
│ └── multiqc_report.html
├── percent_strep_summary
│ └── percent_strep_results.tsv
├── pipeline_info
├── quality_stats
│ └── quality_stats.tsv
├── quast
│ ├── *.quast.report.tsv
│ └── *.transposed.quast.report.tsv
├── quast_summary
│ └── quast_results.tsv
├── rejected_samples
│ └── Empty_samples.csv ***
├── report_*_ntc
│ └── *_spntypeid_report.csv
├── samtools
│ ├── *.bam
│ ├── *.depth.tsv
│ └── *.stats.txt
├── seroba
│ ├── seroba.log
│ └── *.pred.csv
├── seroba_summary
│ └── seroba_results.tsv
└── shovill
├── *.contigs.fa
├── *.sam
└── *_shovill_output
├── contigs.gfa
├── shovill.corrections
├── shovill.log
└── spades.fasta
*** = Optional output
Notable result files:
<runname>_spntypeid_report.csv - Summary table of each step in SPNtypeID
multiqc_report.html - HTML report generated by MultiQC
Empty_samples.csv - Lists any samples that are empty and were removed from the pipeline. If no samples were empty, file will be absent from output directory.
| Output header | Purpose |
|---|---|
| Sample | Unique sample identifier |
| Run | Which run the sample is on, dictated by the --runname param |
| Total Reads | How many reads identified in the sample |
| Reads Removed | How many reads removed from the total reads |
| Median Read Quality | The median value of Phred scores |
| Average Read Quality | The mean value of Phred scores |
| Contigs (#) | How many contigs present in the sample |
| N50 | Length of the shortest contig where contigs of greater than this length contain 50% of the total bases |
| Assembly Length (bp) | Total size of the assembly |
| Ratio of Actual:Expected Genome Length | Relationship between the actual genome length to the expected S. pneumoniae genome length |
| z-score | The isolate's relationship compared to the mean S. pneumo genome length |
| Median Coverage | Median amount of times each base was sequenced |
| Average Coverage | Mean amount of times each base was sequenced |
| Percent Strep | Percentage of reads from Streptococcus |
| Percent SPN | Percentage of reads specifically from S. pneumoniae |
| SecondGenus | Other genus present, if detected |
| Percent SecondGenus | Percentage of other genus, if detected |
| Serotype | Serotype determined for each sample |
| Kraken Database Version | Version of the Kraken database utilized |
| All NTC reads | List of all reads detected in all no template controls, if present. If '999999' in column, no NTC was provided |
| All NTC SPN reads | List of all S. pneumoniae reads in all no template controls, if present. If '999999' in column, no NTC was provided |
| Max NTC read | Highest amount of reads found in all no template controls. If '999999' in column, no NTC was provided |
| Max NTC SPN read | Highest amount of S. pneumoniae reads found in all no template controls. If '999999' in column, no NTC was provided |
| SPNtypeID Version | Version of the SPNTypeID pipeline used for analysis |
SPNTypeID was written by Dr. Kelsey Florek, Dr. Abigail C. Shockey, and Eva Gunawan, MS.
We thank the bioinformatics group at the Wisconsin State Laboratory of Hygiene for all of their contributions.
If you would like to contribute to this pipeline, please see the contributing guidelines.
If you use SPNtypeID for your analysis, please cite it using the following:
K. Florek, E. Gunawan, & A.C. Shockey (2025). SPNtypeID (Version 1.10.0) [https://github.com/wslh-bio/SPNtypeID/tree/main].
An extensive list of references for the tools used by Dryad can be found in the CITATIONS.md file.
This pipeline uses code and infrastructure developed and maintained by the nf-core community, reused here under the MIT license.
The nf-core framework for community-curated bioinformatics pipelines.
Philip Ewels, Alexander Peltzer, Sven Fillinger, Harshil Patel, Johannes Alneberg, Andreas Wilm, Maxime Ulysse Garcia, Paolo Di Tommaso & Sven Nahnsen.
Nat Biotechnol. 2020 Feb 13. doi: 10.1038/s41587-020-0439-x.