diff --git a/README.md b/README.md index 5487b6a..f7781ae 100644 --- a/README.md +++ b/README.md @@ -1,7 +1,7 @@ # Output directory containing the formatted manuscript The [`gh-pages`](https://github.com/apeltzer/eager2-paper/tree/gh-pages) branch hosts the contents of this directory at . -The permalink for this webpage version is . +The permalink for this webpage version is . To redirect to the permalink for the latest manuscript version at anytime, use the link . ## Files @@ -35,4 +35,4 @@ Verifying timestamps with the `ots verify` command requires running a local bitc ## Source The manuscripts in this directory were built from -[`385917553473f25918b140f8554374d9387c4eb1`](https://github.com/apeltzer/eager2-paper/commit/385917553473f25918b140f8554374d9387c4eb1). +[`9444db7443a66f375c7f2adbffbebb6541b512ea`](https://github.com/apeltzer/eager2-paper/commit/9444db7443a66f375c7f2adbffbebb6541b512ea). diff --git a/index.html b/index.html index 400bd4b..f9a9095 100644 --- a/index.html +++ b/index.html @@ -13,7 +13,7 @@ - + Reproducible, portable, and efficient ancient genome reconstruction with nf-core/eager + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+

Reproducible, portable, and efficient ancient genome reconstruction with nf-core/eager

+
+

+This manuscript +(permalink) +was automatically generated +from apeltzer/eager2-paper@9444db7 +on November 11, 2020. +

+

Authors

+
    +

  • James A. Fellows Yates
    +ORCID icon +0000-0001-5585-6277 +· GitHub icon +jfy133 +· Twitter icon +jafellowsyates
    + +Microbiome Sciences Group, Department of Archaeogenetics, Max Planck Institute for the Science of Human History, Jena, Germany; Institut für Vor- und Frühgeschichtliche Archäologie und Provinzialrömische Archäologie, Ludwig Maximilian University, Munich, Germany +· Funded by Max Planck Society +

    • +

  • Thiseas C. Lamnidis
    +ORCID icon +0000-0003-4485-8570 +· GitHub icon +TCLamnidis +· Twitter icon +TCLamnidis
    + +Population Genetics Group, Department of Archaeogenetics, Max Planck Institute for the Science of Human History, Jena, Germany +· Funded by Max Planck Society +

    • +

  • Maxime Borry
    +ORCID icon +0000-0001-9140-7559 +· GitHub icon +maxibor +· Twitter icon +notmaxib
    + +Microbiome Sciences Group, Department of Archaeogenetics, Max Planck Institute for the Science of Human History, Jena, Germany +· Funded by Max Planck Society +

    • +

  • Aida Andrades Valtueña
    +ORCID icon +0000-0002-1737-2228 +· GitHub icon +aidaanva +· Twitter icon +aidaanva
    + +Computational Pathogenomics Group, Department of Archaeogenetics, Max Planck Institute for the Science of Human History, Jena, Germany +· Funded by Max Planck Society +

    • +

  • Zandra Fagernäs
    +ORCID icon +0000-0003-2667-3556 +· GitHub icon +ZandraFagernas +· Twitter icon +ZandraSelina
    + +Microbiome Sciences Group, Department of Archaeogenetics, Max Planck Institute for the Science of Human History, Jena, Germany +· Funded by Max Planck Society +

    • +

  • Stephen Clayton
    +ORCID icon +0000-0001-5223-9695 +· GitHub icon +sc13-bioinf
    + +Department of Archaeogenetics, Max Planck Institute for the Science of Human History, Jena, Germany +· Funded by Max Planck Society +

    • +

  • Maxime U. Garcia
    +ORCID icon +0000-0003-2827-9261 +· GitHub icon +MaxUlysse +· Twitter icon +gau
    + +Department of Oncology-Pathology, Karolinska Institutet, Stockholm, Sweden +· Funded by Barncancerfonden +

    • +

  • Judith Neukamm
    +ORCID icon +0000-0001-8141-566X +· GitHub icon +JudithNeukamm +· Twitter icon +JudithNeukamm
    + +Palaeogenetics Group, Institute of Evolutionary Medicine, University of Zurich, Zürich, Switzerland +

    • +

  • Alexander Peltzer
    +ORCID icon +0000-0002-6503-2180 +· GitHub icon +apeltzer +· Twitter icon +alex_peltzer
    + +Quantitative Biology Center (QBiC), Eberhard-Karls-Universität, Tübingen, Germany; Department of Archaeogenetics, Max Planck Institute for the Science of Human History, Jena, Germany +

    • +
+

Abstract

+

The broadening utilisation of ancient DNA to address archaeological, +palaeontological, and biological questions is resulting in a rising diversity in +the size of laboratories and scale of analyses being performed. In the context +of this heterogeneous landscape, we present nf-core/eager, an advanced and +entirely redesigned and extended version of the EAGER pipeline for the analysis of ancient genomic +data. This Nextflow pipeline aims to address three main themes: accessibility +and adaptability to different computing configurations, reproducibility to +ensure robust analytical standards, and updating the pipeline to +the latest routine ancient genomic practises. This new version of EAGER has been +developed within the nf-core initiative to ensure high-quality software +development and maintenance support; contributing to a long-term lifecycle for +the pipeline. nf-core/eager will assist in ensuring that ancient DNA sequencing +data can be used by a diverse range of research groups and fields.

+

Introduction

+

Ancient DNA (aDNA) has become a widely accepted source of biological data, +helping to provide new perspectives for a range of fields including archaeology, +cultural heritage, evolutionary biology, ecology, and palaeontology. The +utilisation of short-read high-throughput sequencing has allowed the recovery of +whole genomes and genome-wide data from a wide variety of sources, including +(but not limited to), the skeletal remains of animals +[1,2,3,4], modern and archaic +humans [5,6,7,8], bacteria +[9,10,11], viruses [12,13], plants [14,15], palaeofaeces [16,17], dental calculus [18,19], sediments [20,21], medical slides [22], +parchment [23], and recently, ancient ‘chewing gum’ +[24,25]. Improvement +in laboratory protocols to increase yields of otherwise trace amounts of DNA has +at the same time led to studies that can total hundreds of ancient individuals +[26,27], spanning single +[28] to thousands of organisms [18]. +These differences of disciplines have led to a heterogeneous landscape in terms +of the types of analyses undertaken, and their computational resource +requirements [29,30]. Taking +into consideration the unequal distribution of resources (and infrastructure +such as internet connection), easy-to-deploy, streamlined and efficient +pipelines can help increase accessibility to high-quality analyses.

+

The degraded nature of aDNA poses an extra layer of complexity to standard +modern genomic analysis. Through a variety of processes [31] +DNA molecules fragment over time, resulting in ultra-short molecules +[32]. These sequences have low nucleotide complexity +making it difficult to identify with precision which part of the genome a read +(a sequenced DNA molecule) is derived from. Fragmentation without a ‘clean +break’ leads to uneven ends, consisting of single-stranded ‘overhangs’ at end of +molecules, which are susceptible to chemical processes such as deamination of +nucleotides. These damaged nucleotides then lead to misincorporation of +complementary bases during library construction for high-throughput DNA +sequencing [33]. On top of this, taphonomic processes +such as heat, moisture, and microbial- and burial-environment processes lead to +varying rates of degradation [34,35]. The original DNA content of a sample +is therefore increasingly lost over time and supplanted by younger +‘environmental’ DNA. Later handling by archaeologists, museum curators, and +other researchers can also contribute ‘modern’ contamination. While these +characteristics can help provide evidence towards the ‘authenticity’ of true +aDNA sequences (e.g. the aDNA cytosine to thymine or C to T ‘damage’ +deamination profiles [36]), they also pose +specific challenges for genome reconstruction, such as unspecific DNA alignment +and/or low coverage and miscoding lesions that can result in low-confidence +genotyping. These factors often lead to prohibitive sequencing costs when +retrieving enough data for modern high-throughput short-read sequencing data +pipelines (such as more than 1 billion reads for a 1X depth coverage Yersinia +pestis genome [37]), and thus aDNA-tailored +methods and techniques are required to overcome these challenges.

+

Two previously published and commonly used pipelines in the field are PALEOMIX +[38] and EAGER [39]. These +two pipelines take a similar approach to link together standard tools used for +Illumina high-throughput short-read data processing (sequencing quality control, +sequencing adapter removal/and or paired-end read merging, mapping of reads to a +reference genome, genotyping, etc.). However, they have a specific focus on +tools that are designed for, or well-suited for aDNA (such as the bwa aln +algorithm for ultra-short molecules [40] and +mapDamage [41] for evaluation of aDNA +characteristics). Yet, neither of these genome reconstruction pipelines have had +major updates to bring them in-line with current routine aDNA analyses. +Metagenomic screening of off-target genomic reads for pathogens or microbiomes +[18,19] has become particularly common +in palaeo- and archaeogenetics, given its role in revealing widespread +infectious disease and possible epidemics that have sometimes been previously +undetected in the archaeological record [12,13,37,42]. Without easy access to the latest field-established +analytical routines, ancient genomic studies risk being published without the +necessary quality control checks that ensure aDNA authenticity, as well as +limiting the full range of possibilities from their data. Given that material +from samples is limited, there are both ethical as well as economical interests +to maximise analytical yield [43].

+

To address these shortcomings, we have completely re-implemented the latest +version of the EAGER pipeline in Nextflow [44] (a +domain-specific-language or ‘DSL’, specifically designed for the construction of +omics analysis pipelines), introduced new features, and more flexible pipeline +configuration. In addition, the renamed pipeline - nf-core/eager +- has been developed in the context of the nf-core community framework +[45], which enforces strict guidelines for +best-practices in software development.

+

Results and Discussion

+

Scalability, Portability, and Efficiency

+

The re-implementation of EAGER into Nextflow offers a range of benefits over the +original custom pipeline framework.

+

Firstly, the new framework provides immediate integration of nf-core/eager into +various job schedulers in POSIX High-Performance-Cluster (HPC) environments, +cloud computing resources, as well as local workstations. This portability +allows users to set up nf-core/eager regardless of the type of computing +infrastructure or cluster size (if applicable), with minimal effort or +configuration. This facilitates reproducibility and therefore maintenance of +standards within the field. Portability is further assisted by the in-built +compatibility with software environments and containers such as Conda +[46], Docker [47] and Singularity +[48]. These are isolated software ‘sandbox’ environments +that include all software (with exact versions) required by the pipeline, in a +form that is installable and runnable by users regardless of the set up of their +local software environment. Another major change with nf-core/eager is that the +primary user interaction mode of a pipeline run set up is now with a +command-line interface (CLI), replacing the graphical-user-interface (GUI) of +the original EAGER pipeline. This is more portable and compatible with most HPCs (that +may not offer display of a window system), and is in line with the vast majority +of bioinformatics tools. We therefore believe this will not be a hindrance to +new researchers from outside computational biology. However, a GUI-based +pipeline set up is still avaliable via the nf-core website’s Launch page +[49], which provides a common +GUI format across multiple pipelines, as well as additional robustness checks of +input parameters for those less familiar with CLIs. Typically the output of the +launch functionality is a JSON file that can be used with a nf-core/tools launch +command as a single parameter (similar to the original EAGER), however +integration with Nextflow’s companion monitoring tool tower.nf +[50] also allows direct submission of pipelines without any +command line usage.

+

Secondly, reproducibility is made easier through the use of ‘profiles’ that can +define configuration parameters. These profiles can be managed at different +hierarchical levels. HPC-level profiles can specify parameters for the computing +environment (job schedulers, cache locations for containers, maximum memory and +CPU resources etc.), which can be centrally managed to ensure all users of a +group use the same settings. Pipeline-level profiles, specifying parameters for +nf-core/eager itself, allow fast access to routinely-run pipeline parameters via a +single flag in the nf-core/eager run command, without having to configure each +new run from scratch. Compared to the original EAGER, which utilised per-FASTQ +XML files with hardcoded filepaths for a specific user’s server, nf-core/eager +allows researchers to publish the specific profile used in their runs alongside +their publications, that can also be used by other groups to generate the same +results. Usage of profiles can also reduce mistakes caused by insufficient +‘prose’ based reporting of program settings that can be regularly found in the +literature. The default nf-core/eager profile uses parameters evaluated in +different aDNA-specific contexts (e.g. in [51]), and +will be updated in each new release as new studies are published.

+

Finally, nf-core/eager provides improved efficiency over the original EAGER +pipeline by replacing sample-by-sample sequential processing with Nextflow’s +asynchronous job parallelisation, whereby multiple pipeline steps and samples +are run in parallel (in addition to natively parallelised pipeline steps). This +is similar to the approach taken by PALEOMIX, however nf-core/eager expands this +by utilising Nextflow’s ability to customise the resource parameters for every +job in the pipeline; reducing unnecessary resource allocation that can occur +with unfamiliar users to each step of a high-throughput short-read data +processing pipeline. This is particularly pertinent given the increasing use of +centralised HPCs or cloud computing that often use per-hour cost calculations.

+

Updated Workflow

+

nf-core/eager follows a similar structural foundation to the original version of +EAGER and partially to PALEOMIX. Given Illumina short-read FASTQ and/or BAM +files and a reference FASTA file, the core functionality of nf-core/eager can be split in five +main stages:

+
    +
  1. Pre-processing +
      +
    • Sequencing quality control: FastQC +[52]
      • +
    • Sequencing artefact clean-up (merging, adapter clipping): AdapterRemoval2 +[53], fastp [54]
      • +
    • Pre-processing statistics generation: FastQC
      • +
    2. +
  2. Mapping and post-processing +
      +
    • Alignment against reference genome: BWA aln and mem +[40,55], CircularMapper [39], Bowtie2 +[57]
      • +
    • Mapping quality filtering: SAMtools [58]
      • +
    • PCR duplicate removal: DeDup [39], Picard +MarkDuplicates [59]
      • +
    • Mapping statistics generation: SAMTools, PreSeq [60], Qualimap2 +[61], bedtools [62], Sex.DetERRmine [63]
      • +
    3. +
  3. aDNA evaluation and modification +
      +
    • Damage profiling: DamageProfiler [64]
      • +
    • aDNA reads selection: PMDtools [65]
      • +
    • Damage removal/Base trimming: Bamutils [66]
      • +
    • Human nuclear contamination estimation: ANGSD [67]
      • +
    4. +
  4. Variant calling and consensus sequence generation: GATK UnifiedGenotyper and HaploTypeCaller +[59], ANGSD [67], sequenceTools pileupCaller [68], +VCF2Genome [39], MultiVCFAnalyzer [9]
    5. +
  5. Report generation: MultiQC [69]
    6. +
+

In nf-core/eager, all tools originally used in EAGER have been updated to their +latest versions, as available on Bioconda [70] and +Conda-forge [71], to ensure widespread +accessibility and stability of utilised tools. The mapDamage2 (for damage +profile generation) [36] and Schmutzi (for +mitochondrial contamination estimation) [72] methods +have not been carried over to nf-core/eager, the first because a more performant +successor method is now available (DamageProfiler), and the latter because a +stable release of the method could not be migrated to Bioconda. We anticipate +that there will be an updated version of Schmutzi in the near future that will +allow us to integrate the method again into nf-core/eager. As an alternative, +estimation of human nuclear contamination is now offered through +ANGSD [67]. Support for the Bowtie2 aligner +[57] has been updated to have default settings optimised +for aDNA [73].

+

New tools to the basic workflow include fastp +[54] for the removal of ‘poly-G’ sequencing +artefacts that are common in 2-colour Illumina sequencing machines (such as the +increasingly popular NextSeq and NovaSeq platforms +[74]). +For variant calling, we have now included FreeBayes [75] as an +alternative to the human-focused GATK tools, and have also added pileupCaller +[68] for generation of genotyping +formats commonly utilised in ancient human population analysis. We have also +maintained the possibility of using the now unsupported GATK UnifiedGenotyper, +as the supported replacement, GATK HaplotypeCaller, performs de novo assembly +around possible variants; something that may not be suitable for low-coverage +aDNA data.

+
+
+
Figure 1: Simplified schematic of the nf-core/eager workflow pipeline. Green filled +bubbles indicate new functionality added over the original EAGER +pipeline.
+
+
+

Additional functionality tailored for ancient bacterial genomics includes +integration of a SNP alignment generation tool, MultiVCFAnalyzer +[9], which includes the ability to make an assessment of +levels of cross-mapping from different related taxa to a reference genome - a +common challenge in ancient bacterial genome reconstruction +[35]. The output SNP consensus alignment +FASTA file can then be used for downstream analyses such as phylogenetic tree +construction. Simple coverage statistics of particular annotations (e.g. genes) +of an input reference is offered by bedtools +[62], which can be used in cases such as for +providing initial indications of functional differences between ancient +bacterial strains (as in [42]). When using a human +reference genome, nf-core/eager can also give estimates of the relative coverage +on the X and Y chromosomes with Sex.DetERRmine that can be used to infer the +biological sex of a given human individual [63]. A +dedicated ‘endogenous DNA’ calculator (endorS.py) is also included, to provide a +percentage estimate of the sequenced reads matching the reference (‘on-target’) +from the total number of reads sequenced per library.

+

Given the large amount of sequencing often required to yield sufficient genome +coverage from aDNA data, palaeogeneticists tend to use multiple (differently +treated) libraries, and/or merge data from multiple sequencing runs of each +library or even samples. The original EAGER pipeline could only run a single +library at a time, and in these contexts required significant manual user input +in merging different FASTQ or BAM files of related libraries. A major upgrade in +nf-core/eager is that the new pipeline supports automated processing of complex +sequencing strategies for many samples, similar to PALEOMIX. This is facilitated +by the optional use of a simple table (in TSV format, a format more +commonly used in wet-lab stages of data generation, compared to PALEOMIX’s YAML +format) that includes file paths and additional metadata such as sample name, +library name, sequencing lane, colour chemistry, and UDG treatment. This allows +automated and simultaneous processing and appropriate merging and treatment of +heterogeneous data from multiple sequencing runs and/or library types.

+

The original EAGER and PALEOMIX pipelines required users to look through many +independent output directories and files to make full assessment of their +sequencing data. This has now been replaced in nf-core/eager with a much more +extensive MultiQC report [69]. This tool +aggregates the log files of every supported tool into a single interactive +report, and assists users in making a fuller assessment of their sequencing and +analysis runs. We have developed a corresponding MultiQC module for every tool +used by nf-core/eager, where possible, to enable comprehensive evaluation of all +stages of the pipeline.

+

We have further extended the functionality of the original EAGER pipeline by +adding ancient metagenomic analysis; allowing reconstruction of the wider +taxonomic content of a sample. We have added the possibility to screen all +off-target reads (not mapped to the reference genome) with two metagenomic +profilers: MALT [76,77] and +Kraken2 [78], in parallel to the mapping to a given +reference genome (typically of the host individual, assuming the sample is a +host organism). Characterisation of properties of authentic aDNA from +metagenomic MALT alignments is carried out with MaltExtract of the HOPS pipeline +[79]. This functionality can be used either for +microbiome screening or putative pathogen detection. Ancient metagenomic studies +sometimes include comparative samples from living individuals +[80]. To support open data, whilst respecting +personal data privacy, nf-core/eager includes a ‘FASTQ host removal’ script that +creates raw FASTQ files, but with all reads successfully mapped to the reference +genome removed. This allows for safe upload of metagenomic non-host sequencing +data to public repositories after removal of identifiable (human) data, for +example for microbiome studies.

+

An overview of the entire pipeline is shown in Figure 1, +and a tabular comparison of functionality between EAGER, PALEOMIX and +nf-core/eager is in Table 1.

+

To demonstrate the simultaneous genomic analysis of human DNA and metagenomic +screening for putative pathogens, as well as improved results reporting, we +re-analysed data from Barquera et al. 2020 [81], who +performed a multi-discipline study of three 16th century individuals excavated +from a mass burial site in Mexico City. The authors reported genetic results +showing sufficient on-target human DNA (>1%) with typical aDNA damage (>20% C to +T reference mismatches in the first base of the 5’ ends of reads) for downstream +population-genetic analysis and Y-chromosome coverage indicative that the three +individuals were genetically male. In addition, one individual (Lab ID: SJN003) +contained DNA suggesting a possible infection by Treponema pallidum, a species +with a variety of strains that can cause diseases such as syphilis, bejel and +yaws, and a second individual (Lab ID: SJN001) displayed reads similar to the +Hepatitis B virus. Both results were confirmed by the authors via in-solution +enrichment approaches.

+

We were able to successfully replicate the human and pathogen screening results +in a single run of nf-core/eager. Mapping to the human reference genome (hs37d5) +with BWA aln and binning of off-target reads with MALT to the NCBI Nucleotide +database (2017-10-26), yielded the same results of all individuals having a +biological sex of male, as well as the same frequency of C to T miscoding +lesions and short fragment lengths (both characteristic of true aDNA). +Metagenomic hits to both pathogens from the corresponding individuals that also +yielded complete genomes in the original publication were also detected. Both +results and other processing statistics were identified via a single interactive +MultiQC report, excerpts of which can be seen in Figure +2. The full interactive report can be seen in the +supplementary information.

+
+
+
Figure 2: Sections of a MultiQC report (v1.10dev) with the outcome of simultaneous human +DNA and microbial pathogen screening with nf-core/eager, including A +Sex.DetERRmine output of biological sex assignment with coverages on X and Y +being half of that of autosomes, indicative of male individuals, and B HOPS +output with positive detection of both Treponema pallidum and Hepatitis B +virus reads - indicated with blue boxes. Other taxa in HOPS output represent +typical environmental contamination and oral commensal microbiota found in +archaeological teeth. Data was Illumina shotgun sequencing data from Barquera et +al. 2020 [81], and replicated results here were +originally verified in the publication via enrichment methods. The full +interactive reports for both MultiQC v1.9 and v1.10 (see methods) can be seen in +the supplementary +information.
+
+
+

Accessibility

+

Alongside the interactive MultiQC report, we have written extensive +documentation on all parts of running and interpreting the output of the +pipeline. Given that a large fraction of aDNA researchers come from fields +outside computational biology, and thus may have limited computational training, +we have written documentation and tutorials [82] +that also gives guidance on how to run the pipeline and interpret each section +of the report in the context of high-throughput sequencing data, but with with a +special focus on aDNA. This includes best practice or expected output schematic +images that are published under CC-BY licenses to allow for use in other +training material (an example can be seen in Figure 3). +We hope this open-access resource will make the study of aDNA more accessible to +researchers new to the field, by providing practical guidelines on how to +evaluate characteristics and effects of aDNA on downstream analyses.

+
+
+
Figure 3: Example schematic images of pipeline output documentation that can assist new +users in the interpretation of high-throughput sequencing aDNA +processing.
+
+
+

The development of nf-core/eager in Nextflow and the nf-core initiative will +also improve open-source development, while ensuring the high quality of +community contributions to the pipeline. While Nextflow is written primarily in +Groovy, the Nextflow DSL simplifies a number of concepts to an intermediate +level that bioinformaticians without Java/Groovy experience can easily access +(regardless of own programming language experience). Furthermore, Nextflow +places ubiquitous and more widely known command-line interfaces, such as bash, +in a prominent position within the code, rather than custom Java code and +classes (as in EAGER). We hope this will motivate further bug fixes and +feature contributions from the community, to keep the pipeline state-of-the-art +and ensure a longer life-cycle. This will also be supported by the open and +active nf-core community who provide general guidance and advice on developing +Nextflow and nf-core pipelines.

+

Comparisons with other pipelines

+

The scope of nf-core/eager is as a generic, initial data processing and +screening tool, and not to act as a tool for performing more experimental +analyses that requires extensive parameter testing such as modelling. As such, +while similar pipelines designed for aDNA have also been released, for example +ATLAS [83], these generally have been designed with specific +contexts in mind (e.g. human population genetics). We therefore have opted to +not include common downstream analysis such as Principal Component Analysis for +population genetics, or phylogenetic analysis for microbial genomics, but rather +focus on ensuring nf-core/eager produces useful files that can be easily used as +input for common but more experimental and specialised downstream analysis.

+

Therefore, we compared pipeline run-times of two functionally equivalent and +previously published pipelines to show that the new implementation of +nf-core/eager is equivalent or more efficient than EAGER or PALEOMIX.

+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Table 1: Comparison of pipeline functionality of common ancient DNA processing +pipelines. Tick represents full functionality, tilde represents partial functionality, and cross represents not implemented.
CategoryFunctionalityEAGERPALEOMIXnf-core/eager
InfrastructureReproducible software environments offered✓✗✓
HPC scheduler integration✗✗✓
Cloud computing integration✗✗✓
Per-process resource optimisation✗~✓
Pipeline-step parallelisation✗✓✓
Command line set up✗✓✓
GUI set up✓✗✓
PreprocessingSequencing lane merging✓✓✓
Sequencing quality control✓✗✓
Sequencing artefact removal✗✗✓
Adapter clipping/read merging✓✓✓
Post-processing sequencing QC✗✗✓
AlignmentReference mapping✓✓✓
Reference mapping statistics✓✓✓
Multi-reference mapping✗✓✗
PostprocessingMapped reads filtering✓✓✓
Off-target metagenomic profiling✗✗✓
Off-target metagenomic authentication✗✗✓
Library complexity estimation✓✗✓
Duplicate removal✓✗✓
BAM merging✗✓✓
AuthenticationDamage read filtering✓✗✓
Contamination estimation (Human)✓✗✓
Biological sex determination (Human)✗✗✓
Genome coverage estimation✓✓✓
Damage calculation✓✓✓
Damage rescaling✗✓~
DownstreamSNP Calling/Genotyping✓~✓
Consensus sequence generation✓~✓
Regions of interest statistics~✓✓
+
+

We ran each pipeline on a subset of Viking-age genomic data of cod (Gadus +morhua) from Star et al. 2017 [4]. This data was +originally run using PALEOMIX, and was re-run here as described, but with the +latest version of PALEOMIX (v1.2.14), and with equivalent settings for the other +two pipelines as close as possible to the original paper (EAGER with v1.92.33, +and nf-core/EAGER with v2.2.0dev, commit +830c22d). The respective benchmarking +environment and exact pipeline run settings can be seen in the Methods and +Supplementary Information. Two samples each with three Illumina paired-end +sequencing runs were analysed, with adapter clipping and merging +(AdapterRemoval), mapping (BWA aln), duplicate removal (Picard’s MarkDuplicates) +and damage profiling (PALEOMIX: mapDamage2, EAGER and nf-core/EAGER: +DamageProfiler) steps being performed. We ran the commands for each tool +sequentially, but repeated these batches of commands 10 times - to account for +variability in the cloud service’s IO connection. Run times were measured using +the GNU time tool (v1.7).

+
+ + ++++++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Table 2: Comparison of run times in minutes between three ancient DNA pipelines. +PALEOMIX and nf-core/eager have additional runs with ‘optimised’ parameters with +fairer computational resources matching modern multi-threading strategies. +Values represent mean and standard deviation of run times in minutes, calculated +from the output of the GNU time tool. Real: real time, System: cumulative CPU system-task times, +User: cumulative CPU time of all tasks.
PipelineVersionEnvironmentrealsysuser
nf-core-eager (optimised)2.2.0devsingularity105.6 ± 4.613.6 ± 0.71593 ± 79.7
PALEOMIX (optimised)1.2.14conda130.6 ± 8.712 ± 0.71820.2 ± 36.9
nf-core-eager2.2.0devsingularity209.2 ± 4.411 ± 0.91407.7 ± 30.2
EAGER1.92.37singularity224.2 ± 4.922.9 ± 0.31736.3 ± 70.2
PALEOMIX1.2.14conda314.6 ± 2.910.7 ± 11506.7 ± 14
+
+

A summary of runtimes of the benchmarking tests can be seen in Table +2. nf-core/eager showed fastest runtimes across all +three time metrics when running on default parameters. This +highlights the improved efficiency of nf-core/eager’s asynchronous processing +system and per-process resource customisation (here represented by nf-core/eager +defaults designed for typical HPC set ups).

+

As a more realistic demonstration of modern computing multi-threading set ups, +we also re-ran PALEOMIX with the flag –max-bwa-threads set to 4 (listed in +Table 2 as ‘optimised’), which is equivalent to a +single BWA aln process of nf-core/eager. This resulted in a much faster run-time +than that of default nf-core/eager, due to the approach of PALEOMIX of mapping +each lane of a library separately, whereas nf-core/eager will map all lanes of a +single library merged together. Therefore, given that each library was split +across three lanes, increasing the threads of BWA aln to 4 resulted in 12 per +library, whereas nf-core/eager only gave 4 (by default) for a single BWA aln +process of one library. While the PALEOMIX approach is valid, we opted to retain +the per-library mapping as it is often the longest running step of +high-throughput sequencing genome-mapping pipelines, and it prevents flooding of +HPC scheduling systems with many long-running jobs. Secondly, if users regularly +use multi-lane data, due to nf-core/eager’s fine-granularity control, they can +simply modify nf-core/eager’s BWA aln process resources via config files to +account for this. When we optimised parameters that were used for BWA aln’s +multi-threading, and the number of multiple lanes to the same number of BWA aln +threads as the optimised PALEOMIX run, nf-core/eager again displayed faster +runtimes. All metrics including mapped reads, percentage on-target, mean depth +coverage and mean read lengths across all pipelines were extremely similar +across all pipelines and replicates (see methods and Table +3).

+

Conclusion

+

nf-core/eager is an efficient, portable, and accessible pipeline for processing +and screening ancient (meta)genomic data. This re-implementation of EAGER into +Nextflow and nf-core will improve reproducibility and scalability of rapidly +increasing aDNA datasets, for both large and small laboratories. Extensive +documentation also enables newcomers to the field to get a practical +understanding on how to interpret aDNA in the context of NGS data processing. +Ultimately, nf-core/eager provides easier access to the latest tools and routine +screening analyses commonly used in the field, and sets up the pipeline for +remaining at the forefront of palaeogenetic analysis.

+

Methods

+

Installation

+

nf-core/eager requires only three dependencies: Java (version >= 8), Nextflow, +and either a functional Conda installation or Docker/Singularity engine +installation. A quick installation guide to follow to get started can be found +in the Quick start section of the nf-core/eager repository +[84].

+

Running

+

After installation, users can run the pipeline using standard test data by +utilising some of the test profiles we provide (e.g. using Docker):

+
nextflow run nf-core/eager -r 2.2.0 -profile test_tsv,docker
+

This will download test data automatically (as recorded in the test_tsv +profile), run the pipeline locally with all software tools containerised in a +Docker image. The pipeline will store the output of that run in the default +‘./results’ folder of the current directory.

+

The default pipeline settings assumes paired-end FASTQ data, and will run:

+
    +
  • FastQC
    • +
  • AdapterRemoval2 (merging and adapter clipping)
    • +
  • post-clipping FastQC (for AdapterRemoval2 performance evaluation)
    • +
  • BWA mapping (with the ‘aln’ algorithm)
    • +
  • samtools flagstat (for mapping statistics)
    • +
  • endorS.py (for endogenous DNA calculation)
    • +
  • Picard MarkDuplicates (for PCR amplicon deduplication)
    • +
  • PreSeq (for library complexity evaluation)
    • +
  • DamageProfiler and Qualimap2 (for genome coverage statistics)
    • +
  • MultiQC pipeline run report
    • +
+

If no additional FASTA indices are given, these will also be generated.

+

The pipeline is highly configurable and most modules can be turned on-and-off +using different flags at the request of the user, to allow a high level of +customisation to each user’s needs. For example, to include metagenomic +screening of off-target reads, and sex determination based on on-target mappings +of pre-clipped single-end data:

+
nextflow run nf-core/eager -r 2.2.0 \
+-profile conda \
+--input '/<path>/<to>/*/*R1*.fastq.gz' --single_end \
+--fasta '/<path>/<to>/<reference>.fasta.gz' \
+--skip_fastqc --skip_adapterremoval \
+--run_bam_filtering --bam_discard_unmapped --bam_unmapped_type 'fastq' \
+--run_metagenomic_screening \
+--metagenomic_tool 'malt' --database '/<path>/<to>/<malt_database>' \
+--run_sexdeterrmine
+

Profiles

+

In addition to private locally defined profiles, we utilise a central +configuration repository to enable users from various institutions to use +pipelines on their particular infrastructure more easily +[85]. There are multiple resources listed +in this repository with information on how to add a user’s own institutional +configuration profile with help from the nf-core community. These profiles can +be both generic for all nf-core pipelines, but also customised for specific +pipelines.

+

Users can customise this infrastructure profile by themselves, with the nf-core +community, or with their local system administrator to make sure that the +pipeline runs successfully, and can then rely on the Nextflow and nf-core +framework to ensure compatibility upon further infrastructure changes. For +example, in order to run the nf-core/eager pipeline at the Max Planck Institute +for the Science of Human History (MPI-SHH), users only have to run:

+
nextflow run nf-core/eager -r 2.2.0 -profile test_tsv,sdag,shh
+

This runs the testing profile of the nf-core/eager pipeline with parameters +specifically adapted to a specific HPC system at the MPI-SHH. In some cases, +similar institutional configs for other institutions may already exist +(originally utilised for different nf-core pipelines), so users need not +necessarily write their own.

+

Inputs

+

The pipeline can be started using (raw) FASTQ files from sequencing or +pre-mapped BAM files. Additionally, the pipeline requires a FASTA reference +genome. If BAM input is provided, an optional conversion to FASTQ is offered, +otherwise BAM files processing will start from the post-mapping stage.

+

If users have complex set-ups, e.g. multiple sequencing lanes that require +merging of files, the pipeline can be supplied with a tab separated value (TSV) +file to enable such complex data handling. Both FASTQs and BAMs can be provided +in this set up. FASTQs with the same library name and sequencing chemistry but +sequenced across multiple lanes will be concatenated after adapter removal and +prior mapping. Libraries with different sequencing chemistry kits (paired- vs. +single-end) will be merged after mapping. Libraries with the same sample name +and with the same UDG treatment, will be merged after deduplication. If +libraries with the sample name have different UDG treatment, these will be +merged after the aDNA modification stage (i.e. BAM trimming or PMDtools, if +turned on), prior to genotyping, as shown in Figure 4.

+
+
+
Figure 4: Schematic of different processing and merging points based on the nature of +different libraries, as specified by the metadata of a TSV file. Dashed boxes +represent optional library-specific +processes. Colours refer to each merge points, which occur at certain points +along the pipeline depending on the metadata columns defined in the TSV file.
+
+
+

As Nextflow will automatically download files from URLs, profiles and/or TSV +files, users can include links to publicly available data (e.g. the European +Bioinformatics Institutes’s ENA FTP server). This assists in reproducibility, +because if profiles or TSV files are uploaded with a publication, a researcher +wishing to re-analyse the data in the same way can use the exact settings and +file merging procedures in the original publication, without having to +reconstruct this from prose.

+

Monitoring

+

Users can either monitor their pipeline execution with the messages Nextflow +prints to the console while running, or utilise companion tools such as +Nextflow’s Tower [50] to monitor their analysis pipeline +during runtime.

+

Output

+

The pipeline produces a multitude of output files in various file formats, with +a more detailed listing available in the user documentation. These include +metrics, statistical analysis data, and standardised output files (BAM, VCF) for +close inspection and further downstream analysis, as well as a MultiQC report. +If an emailing daemon is set up on the server, the latter can be emailed to +users automatically, when starting the pipeline with a dedicated option +(--email you@yourdomain.org).

+

Benchmarking

+

Dual Screening of Human and Microbial Pathogen DNA

+

Full step-by-step instructions on the set up of the human and pathogen screening +demonstration (including input TSV file) can be seen in the supplementary +information. To demonstrate the efficiency and conciseness of nf-core/eager +pipeline in it’s dual role for both human and microbial screening of ancient +material, we replicated the results of Barquera et al. 2020 +[81] using v2.2.0 (commit: e7471a7 and +Nextflow version: 20.04.1).

+

The following command was used to run the pipeline on the in-house servers at +the MPI-SHH, including a 2 TB memory node for running MALT against the NCBI Nt +(Nucleotide) database, and therefore the centralised custom profile for this +cluster was used.

+
nextflow run nf-core/eager -r 2.2.0 \
+-profile microbiome_screening,sdag,shh \
+-with-tower \
+--input 'barquera2020_pathogenscreening.tsv' \
+--fasta 'ftp://ftp-trace.ncbi.nih.gov/1000genomes/ftp/technical/reference/phase2_reference_assembly_sequence/hs37d5.fa.gz' \
+--bwaalnn 0.01 \
+--bwaalnl 32 \
+--run_bam_filtering \
+--bam_discard_unmapped \
+--bam_unmapped_type fastq \
+--dedupper 'markduplicates' \
+--run_mtnucratio \
+--run_nuclear_contamination \
+--run_sexdeterrmine \
+--sexdeterrmine_bedfile 'https://github.com/nf-core/test-datasets/raw/eager/reference/Human/1240K.pos.list_hs37d5.0based.bed.gz' \
+--run_metagenomic_screening \
+--metagenomic_tool malt \
+--run_maltextract \
+--percent_identity 90 \
+--malt_top_percent 1 \
+--malt_min_support_mode 'reads' \
+--metagenomic_min_support_reads 1 \
+--malt_max_queries 100 \
+--malt_memory_mode load \
+--maltextract_taxon_list 'https://raw.githubusercontent.com/rhuebler/HOPS/external/Resources/default_list.txt' \
+--maltextract_filter def_anc \
+--maltextract_toppercent 0.01 \
+--maltextract_destackingoff \
+--maltextract_downsamplingoff \
+--maltextract_duplicateremovaloff \
+--maltextract_matches \
+--maltextract_megansummary \
+--maltextract_percentidentity 90.0 \
+--maltextract_topalignment \
+--database 'malt/databases/indexed/index040/full-nt_2017-10/' \
+--maltextract_ncbifiles 'resources/'
+

To include the HOPS results from metagenomic screening in the report, we also +re-ran MultiQC with the upcoming version v1.10 (to be integrated into +nf-core/eager on release). After then installing the development version of +MultiQC (commit: 7584e64), as described in the MultiQC documentation +[86], we ran the following command in the results +directory of the nf-core/eager run, using the same configuration file.

+
multiqc . -c multiqc_config.yaml -n multiqc1_10.html -o multiqc1_10
+

Until MultiQC v1.10 is released, the HOPS heatmap is exported by nf-core/eager +in the corresponding MaltExtract results directory. Reports from both versions +(and the standalone HOPS PDF) can be seen in the supplementary information.

+

Pipeline Comparison

+

Full step-by-step instructions on the set up of the pipeline run-time +benchmarking, including environment and tool versions, can be seen in the +supplementary information. EAGER (v1.92.37) and nf-core/eager (v2.2.0, +commit: 830c22d; Nextflow v20.04.1) used the provided pre-built singularity +containers for software environments, whereas for PALEOMIX (v1.2.14) we +generated a custom conda environment (see supplementary information for the +environmental.yaml file). Run time comparisons were performed on a 32 CPU (AMD +Opteron 23xx) and 256 GB memory Red Hat QEMU Virtual Machine running the Ubuntu +18.04 operating system (Linux Kernel 4.15.0-112). Resource parameters of each +tool were only modified to specify the maximum available on the server and +otherwise left as default.

+

The following commands were used for each pipeline, with the commands run 10 +times, each after cleaning up reference and results directories using a for +loop. Run times of the run commands themselves were measured using GNU Time.

+
## EAGER - description of XML files can be seen in supplementary information
+singularity exec \
+-B ~/benchmarks/output/EAGER:/data ~/.singularity/cache/EAGER-cache/EAGER-GUI_latest.sif \
+eagercli \
+/data
+
+## PALEOMIX - description of input YAML files can be seen in supplementary
+## information
+paleomix bam_pipeline run ~/benchmarks/output/paleomix/makefile_paleomix.yaml
+
+## paleomix optimised - description of input YAML files can be seen in
+## supplementary information
+paleomix bam_pipeline \
+run ~/benchmarks/output/paleomix_optimised/makefile_paleomix.yaml \
+--bwa-max-threads 4
+
+## nf-core/eager - description of resources configuration file (-c) can be seen
+## in supplementary information
+nextflow run nf-core/eager -r 2.2.0 \
+--input ~/benchmarks/output/nfcore-eager-optimised/nfcore-eager_tsv.tsv \
+-c ~/.nextflow/pub_eager_vikingfish.conf \
+-profile pub_eager_vikingfish_optimised,pub_eager_vikingfish,singularity \
+--fasta ~/benchmarks/reference/GCF_902167405.1_gadMor3.0_genomic.fasta \
+--outdir ~/benchmarks/output/nfcore-eager-optimised/results/ \
+-w ~/benchmarks/output/nfcore-eager-optimised/work/ \
+--skip_fastqc \
+--skip_preseq \
+--run_bam_filtering \
+--bam_mapping_quality_threshold 25 \
+--bam_discard_unmapped \
+--bam_unmapped_type 'discard' \
+--dedupper 'markduplicates'
+
+##nf-core/eager optimised - description of resources profile(s) with optimised
+## bwa threads setting can be seen in supplementary information
+nextflow run nf-core/eager -r 2.2.0 \
+--input ~/benchmarks/output/nfcore-eager-optimised/nfcore-eager_tsv.tsv \
+-c ~/.nextflow/pub_eager_vikingfish.conf \
+-profile pub_eager_vikingfish_optimised,pub_eager_vikingfish,singularity \
+--fasta ~/benchmarks/reference/GCF_902167405.1_gadMor3.0_genomic.fasta \
+--outdir ~/benchmarks/output/nfcore-eager-optimised/results/ \
+-w ~/benchmarks/output/nfcore-eager-optimised/work/ \
+--skip_fastqc \
+--skip_preseq \
+--run_bam_filtering \
+--bam_mapping_quality_threshold 25 \
+--bam_discard_unmapped \
+--bam_unmapped_type 'discard' \
+--dedupper 'markduplicates'
+

Mapping results across all pipelines showed very similar values, with low +variation across replicates as can be seen in Table 3.

+
+ + +++++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Table 3: Comparison of common results values of key high-throughput short-read +data processing and mapping steps across the three pipelines. ‘qf’ stands for +mapping-quality filtered reads. All values represent mean and standard deviation +across 10 replicates of each pipeline, calculated from the output of the GNU +time tool.
sample_namecategoryEAGERnf-core/eagerPALEOMIX
COD076processed_reads71388991 ± 071388991 ± 072100142 ± 0
COD092processed_reads69615709 ± 069615709 ± 070249181 ± 0
COD076mapped_qf_reads16786467.7 ± 106.516786491.1 ± 89.916686607.2 ± 91.3
COD092mapped_qf_reads16283216.3 ± 71.316283194.7 ± 37.416207986.2 ± 44.4
COD076ontarget_qf23.5 ± 023.5 ± 023.1 ± 0
COD092ontarget_qf23.4 ± 023.4 ± 023.1 ± 0
COD076dedupped_mapped_reads12107264.4 ± 87.812107293.7 ± 69.712193415.8 ± 86.7
COD092dedupped_mapped_reads13669323.7 ± 87.613669328 ± 32.413795703.3 ± 47.9
COD076mean_depth_coverage0.9 ± 00.9 ± 00.9 ± 0
COD092mean_depth_coverage1 ± 01 ± 01 ± 0
COD076mean_read_length49.4 ± 049.4 ± 049.4 ± 0
COD092mean_read_length48.8 ± 048.8 ± 048.7 ± 0
+
+

Data and software availability

+

All pipeline code is available on GitHub at +https://github.com/nf-core/eager and +archived with Zenodo under the DOI +10.5281/zenodo.1465061. The version of +nf-core/eager that this manuscript is based on was the ‘dev’ branch of the GitHub +repository (2.2.0dev), and was released as v2.2.0. Demonstration data for dual +ancient human and pathogen screening from Barquera et al. [81] is +publicly available on the European Nucleotide Archive (ENA) under project +accession PRJEB37490. The human reference genome (hs37d5) and screening database +(Nucleotide or ‘nt’, October 2017) was downloaded from National Center for +Biotechnology Information FTP server. Ancient Cod genomic data from Star et al. +[4] used for benchmarking is publicly available on +the ENA under project accession PRJEB20524. The Gadus morhua reference genome +NCBI accession ID is: GCF_902167405.1.

+

This paper was collaboratively written with Manubot +[87], and supplementary information including +demonstration and benchmarking environments descriptions and walk-through can be +seen on GitHub at +https://github.com/apeltzer/eager2-paper/ +and the supplement/ directory.

+

Competing Interests

+

No competing interests are declared.

+

Acknowledgements

+

We thank the nf-core community for general support and suggestions during the +writing of the pipeline. We also thank Arielle Munters, Hester van Schalkwyk, +Irina Velsko, Katherine Eaton, Luc Venturini, Marcel Keller, Pierre Lindenbaum, +Pontus Skoglund, Raphael Eisenhofer, Torsten Günter, Kevin Lord, and Åshild +Vågene for bug reports and feature suggestions. We are grateful to the members +of the Department of Archaeogenetics at the Max Planck Institute for the Science +of Human History who performed beta testing of the pipeline. We thank the aDNA +twitter community for responding to polls regarding design decisions during +development.

+

The Gesellschaft für wissenschaftliche Datenverarbeitung (GWDG, Göttingen) +kindly provided computational infrastructure for benchmarking. We also want to +thank Selina Carlhoff, Maria Spyrou, Elisabeth Nelson, Alexander Herbig and +Wolfgang Haak for providing comments and suggestions on this manuscript, and +acknowledge Christina Warinner, Stephan Schiffels and the Max Planck Society who +provided funds for travel to nf-core events. This project was also supported by +the ERC Starting Grant project (FoodTransforms) ERC-2015-StG 678901 funded by +the European Research Council awarded to Philipp W. Stockhammer (Ludwig +Maximilian University, Munich).

+

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+
+
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