nf-core/eager
A fully reproducible and state-of-the-art ancient DNA analysis pipeline
2.0.7
). The latest
stable release is 2.4.7
.
Introduction
*nf-core/eager* is a bioinformatics best-practice analysis pipeline for NGS
sequencing based ancient DNA (aDNA) data analysis.
The pipeline uses Nextflow, a bioinformatics
workflow tool. It pre-processes raw data from FASTQ inputs, aligns the reads
and performs extensive general NGS and aDNA specific quality-control on the
results. It comes with docker, singularity or conda containers making
installation trivial and results highly reproducible.
Pipeline steps
By default the pipeline currently performs the following:
* Create reference genome indices for mapping (bwa
, samtools
, and picard
)
* Sequencing quality control (FastQC
)
* Sequencing adapter removal and for paired end data merging (AdapterRemoval
)
* Read mapping to reference using (bwa aln
, bwa mem
or CircularMapper
)
* Post-mapping processing, statistics and conversion to bam (samtools
)
* Ancient DNA C-to-T damage pattern visualisation (DamageProfiler
)
* PCR duplicate removal (DeDup
or MarkDuplicates
)
* Post-mapping statistics and BAM quality control (Qualimap
)
* Library Complexity Estimation (preseq
)
* Overall pipeline statistics summaries (MultiQC
)
Additional functionality contained by the pipeline currently includes:
* Illumina two-coloured sequencer poly-G tail removal (fastp
)
* Automatic conversion of unmapped reads to FASTQ (samtools
)
* Damage removal/clipping for UDG+/UDG-half treatment protocols (BamUtil
)
* Damage reads extraction and assessment (PMDTools
)
Quick Start
-
Install
nextflow
-
Install one of
docker
,singularity
orconda
-
Download the EAGER pipeline
nextflow pull nf-core/eager
- Test the pipeline using the provided test data
nextflow run nf-core/eager -profile <docker/singularity/conda>,test --pairedEnd
- Start running your own ancient DNA analysis!
nextflow run nf-core/eager -profile <docker/singularity/conda> --reads'\*_R{1,2}.fastq.gz' --fasta '<REFERENCE>.fasta'
NB. You can see an overview of the run in the MultiQC report located at <OUTPUT_DIR>/MultiQC/multiqc_report.html
Modifications to the default pipeline are easily made using various options
as described in the documentation.
Documentation
The nf-core/eager pipeline comes with documentation about the pipeline, found in the docs/
directory or on the main homepage of the nf-core project:
-
Pipeline configuration
* Pipeline installation
* Adding your own system config
* Reference genomes
Credits
This pipeline was written by Alexander Peltzer (apeltzer),
with major contributions from Stephen Clayton, ideas and documentation from
James A. Fellows Yates, Raphael Eisenhofer, Maxime Borry and Judith Neukamm. If you want to
contribute, please open an issue and ask to be added to the project - happy to
do so and everyone is welcome to contribute here!
Contributors
- James A. Fellows-Yates
- Stephen Clayton
- Maxime Borry
- Judith Neukamm
- Raphael Eisenhofer
- Maxime Garcia
- Luc Venturini
- Hester van Schalkwyk
If you've contributed and you're missing in here, please let me know and I'll add you in.
Tool References
* *EAGER v1*, CircularMapper, DeDup* Peltzer, A., Jäger, G., Herbig, A., Seitz, A., Kniep, C., Krause, J., & Nieselt, K. (2016). EAGER: efficient ancient genome reconstruction. Genome Biology, 17(1), 1–14. https://doi.org/10.1186/s13059-016-0918-z Download: https://github.com/apeltzer/EAGER-GUI and https://github.com/apeltzer/EAGER-CLI
* *FastQC* download: https://www.bioinformatics.babraham.ac.uk/projects/fastqc/
* *AdapterRemoval v2* Schubert, M., Lindgreen, S., & Orlando, L. (2016). AdapterRemoval v2: rapid adapter trimming, identification, and read merging. BMC Research Notes, 9, 88. https://doi.org/10.1186/s13104-016-1900-2 Download: https://github.com/MikkelSchubert/adapterremoval
* *bwa* Li, H., & Durbin, R. (2009). Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics , 25(14), 1754–1760. https://doi.org/10.1093/bioinformatics/btp324 Download: http://bio-bwa.sourceforge.net/bwa.shtml
* *SAMtools* Li, H., Handsaker, B., Wysoker, A., Fennell, T., Ruan, J., Homer, N., … 1000 Genome Project Data Processing Subgroup. (2009). The Sequence Alignment/Map format and SAMtools. Bioinformatics , 25(16), 2078–2079. https://doi.org/10.1093/bioinformatics/btp352 Download: http://www.htslib.org/
* *DamageProfiler* Judith Neukamm (Unpublished)
* *QualiMap* Okonechnikov, K., Conesa, A., & García-Alcalde, F. (2016). Qualimap 2: advanced multi-sample quality control for high-throughput sequencing data. Bioinformatics , 32(2), 292–294. https://doi.org/10.1093/bioinformatics/btv566 Download: http://qualimap.bioinfo.cipf.es/
* *preseq* Daley, T., & Smith, A. D. (2013). Predicting the molecular complexity of sequencing libraries. Nature Methods, 10(4), 325–327. https://doi.org/10.1038/nmeth.2375. Download: http://smithlabresearch.org/software/preseq/
* *PMDTools* Skoglund, P., Northoff, B. H., Shunkov, M. V., Derevianko, A. P., Pääbo, S., Krause, J., & Jakobsson, M. (2014). Separating endogenous ancient DNA from modern day contamination in a Siberian Neandertal. Proceedings of the National Academy of Sciences of the United States of America, 111(6), 2229–2234. https://doi.org/10.1073/pnas.1318934111 Download: https://github.com/pontussk/PMDtools
* *MultiQC* Ewels, P., Magnusson, M., Lundin, S., & Käller, M. (2016). MultiQC: summarize analysis results for multiple tools and samples in a single report. Bioinformatics , 32(19), 3047–3048. https://doi.org/10.1093/bioinformatics/btw354 Download: https://multiqc.info/
* *BamUtils* Jun, G., Wing, M. K., Abecasis, G. R., & Kang, H. M. (2015). An efficient and scalable analysis framework for variant extraction and refinement from population-scale DNA sequence data. Genome Research, 25(6), 918–925. https://doi.org/10.1101/gr.176552.114 Download: https://genome.sph.umich.edu/wiki/BamUtil
* *FastP* Chen, S., Zhou, Y., Chen, Y., & Gu, J. (2018). fastp: an ultra-fast all-in-one FASTQ preprocessor. Bioinformatics , 34(17), i884–i890. https://doi.org/10.1093/bioinformatics/bty560 Download: https://github.com/OpenGene/fastp