EAGER: efficient ancient genome reconstruction
© Peltzer et al. 2016
Received: 25 January 2016
Accepted: 9 March 2016
Published: 31 March 2016
The automated reconstruction of genome sequences in ancient genome analysis is a multifaceted process.
Here we introduce EAGER, a time-efficient pipeline, which greatly simplifies the analysis of large-scale genomic data sets. EAGER provides features to preprocess, map, authenticate, and assess the quality of ancient DNA samples. Additionally, EAGER comprises tools to genotype samples to discover, filter, and analyze variants.
EAGER encompasses both state-of-the-art tools for each step as well as new complementary tools tailored for ancient DNA data within a single integrated solution in an easily accessible format.
KeywordsaDNA Bioinformatics Authentication aDNA analysis Genome reconstruction
In ancient DNA (aDNA) studies, often billions of sequence reads are analyzed to determine the genomic sequence of ancient organisms [1–3]. Newly developed enrichment techniques utilizing tailored baits to capture aDNA fragments, even make samples accessible that were previously both economically as well as technically unsuitable to be analyzed. The crucial step is no longer the production of genomic data from the past, but the computational reconstruction of ancient genomes using high-throughput sequencing (HTS) data, which is usually done employing short read alignment methods such as BWA  and standard analysis toolboxes such as SAMtools  or the Genome Analysis Toolkit (GATK) . However, aDNA shows several characteristics, such as low endogenous DNA content, short fragment lengths, and misincorporation patterns , making the application of modern alignment methods with default parameters difficult. Therefore, specialized methods tailored to address the characteristics of aDNA need to be applied, to reconstruct ancient genomes successfully.
Until today, there have only been a few contributions towards a general framework for this task, such as the collection of tools and respective parameters proposed by Martin Kircher . However, most of these methods have been developed for mitochondrial data in the context of the Neanderthal project [1, 9], and therefore do not scale well to larger genomes. Another contribution towards a fully automated approach has been made with PALEOMIX . PALEOMIX offers separate toolkits for the analysis of aDNA samples, mapping reads, and subsequent genotyping combined with taxonomic as well as metagenomic profiling. Therefore, PALEOMIX is already a great improvement over simple scripts in a way that it provides users with access to more advanced methods and keeping these utilizable in a standardized way.
Especially for ancient bacterial research projects, this provides an improvement over former methods, which solely consist of sets of small scripts and which were therefore more prone to error and not very user-friendly. In this highly interdisciplinary field, where many users have a background in molecular biology or archaeology, the practical applicability of available methods is of high importance. The execution of scripts and their complex configurations are difficult for many researchers in this field, in particular if this requires learning programming language syntax for execution. Further barriers include the necessity to compile the source code of the underlying methods, making the installation and maintenance of sophisticated pipelines difficult even for administrative users with more bioinformatics expertise. User-friendly bioinformatic tools and methods with an interactive interface provide archaeologists and biologists with the ability to analyze large HTS data sets. For these reasons, a data-processing pipeline should be designed as comprehensively as possible, to make the underlying methods easily accessible for a wide range of users.
To address this need, we have developed EAGER, a fast and highly user-friendly next-generation sequencing (NGS) analysis pipeline for the efficient reconstruction of ancient genomes, which is designed to be used by researchers without the requirement to apply scripting languages or obtain further programming knowledge.
For the preprocessing, EAGER encompasses all steps necessary to process HTS raw data in FastQ format, using methods for quality assessment and prefiltering as well as newly designed methods for efficient and fast read merging and clipping. Furthermore, the pipeline provides features to map reads against a reference genome using a set of state-of-the-art mapping methods such as BWA-aln , Bowtie2 , and BWA-mem . The pipeline can authenticate aDNA samples based on DNA damage patterns with mapDamage  and includes methods that are offered by Preseq  to determine the complexity of sequencing libraries. Furthermore, contamination estimation and subsequent consensus sequence generation in FastA format can be done within the pipeline using schmutzi . In addition, EAGER has tools to perform genotyping  for mid to high coverage samples, to discover, filter, and analyze variants within a single integrated solution. For low coverage samples, the pipeline encompasses the ANGSD method to generate genotype likelihoods . Furthermore, methods specifically designed for aDNA projects can be turned off, permitting the same pipeline to be used for modern DNA projects as well.
Within EAGER, we have also added four new tools, Clip&Merge, the CircularMapper, DeDup, and VCF2Genome, replacing or complementing existing tools for preprocessing, mapping, PCR duplicate removal, and genome reconstruction, respectively.
The Clip&Merge method performs a highly efficient adapter clipping of sequencing reads and subsequent merging of paired-end reads with negative insert sizes (an overlap between two sequencing reads derived from a single DNA fragment) into a single “collapsed” read.
The CircularMapper method performs an improved mapping of sequencing reads to circular reference genomes. Using the CircularMapper enables researchers to apply, for example, mitochondrial (mtDNA) haplogroup assignment methods such as HaploFind  with higher certainty, as many phylogenetically informative positions can be found at the beginning and the end of the mtDNA reference sequence.
Finally, we incorporated our new VCF2Genome tool into the EAGER pipeline, which can take variant calls from the genotyping step and generate a draft genome sequence, with specific filtering criteria applied to each call performed by the genotyping method. The generated draft sequence can then be used by other methods, e.g., for performing phylogenetic analyses.
The pipeline also has a method that automatically produces a comprehensive report of the processed data, making the retrieval of statistics about generated data as easy as possible.
EAGER has been implemented in the Java programming language and can be run on several types of operating system, including but not limited to desktop workstations. Setting up the pipeline has been realized using Linux containers via Docker , to provide users and administrators with a portable and flexible distribution of the pipeline, without complex configuration scripts or the need to compile the source code themselves. Once set up, the pipeline can be accessed via a GUI (see Fig. 2). The GUI is applied to configure the analysis tasks, hiding most of the complexity from the user. For advanced users, options for more detailed parameter adjustments exist. Previously published protocols, such as PALEOMIX , partially overlap in terms of features; however, EAGER offers an improved user experience by providing a GUI, swift setup, and short processing time.
Results and discussion
Sample names, corresponding SRA/ENA identifiers, number of reads, read length, number of bases, and the average fragment length for the samples used for evaluation of the EAGER pipeline
SRA ID/ENA ID
# of reads
# of bases
Avg. fragment length
Execution times (in seconds) of the EAGER and PALEOMIX pipeline applied to five ancient Mycobacterium leprosy data sets and eight Homo sapiens data sets (LBK1–LBK8) (see Table 1)
Mapping rates for different adapter clipping and read merging methods
CutAdapt + FLASH
CutAdapt + FLASH
CutAdapt + FLASH
CutAdapt + FLASH
CutAdapt + FLASH
CutAdapt + FLASH
Merging accuracy on simulated test data sets with ranging error rates from 0–5 %
Run-time evaluation and performance evaluation of DeDup compared to SAMtools rmdup on five leprosy and eight human data sets
Run time in seconds
Total # of mapped reads
Comparison of DeDup with the SAMtools rmdup method
Downsampling experiment for simulation of low coverage data
Expected mean coverage
Achieved mean coverage
Cov % of genome ≥1 read
Cov % of genome by ANGSD
% of correctly called bases
Evaluation of the EAGER pipeline in comparison with already published data (ENA SAMEA2697125)
Total variants predicted
Variants annotated in dbSNP
Comparison of EAGER to a benchmark data set from GIAB
In summary, we have developed EAGER, a user-friendly and integrated pipeline for the efficient reconstruction of ancient genomes, providing users with easy access to a large number of state-of-the-art and complementary methods. EAGER is an actively developed pipeline that has been designed as a modular framework. Therefore, while keeping the usability aspect as its first and foremost priority, it allows for the easy integration of extended features and new tools that will contribute to high-throughput DNA sequencing data analysis in the future.
EAGER can perform several raw read preprocessing steps, including the initial analysis of raw sequencing reads using FastQC (Andrews, S.; FastQC: A quality control tool for high throughput sequence data, unpublished, 2010) to assess the basic quality of the generated NGS data. aDNA usually suffers from post-mortem damage, with decreasing read lengths and increasing misincorporation patterns, rendering the analysis of aDNA data difficult with the currently applied NGS methods. Furthermore, the fragments are typically of smaller length than in modern data sets, making the reconstruction of a full genome even more difficult. Read merging is therefore a necessary step to improve the overall quality of reads from aDNA. Furthermore, some mapping algorithms, for example BWA-aln, have difficulties in mapping paired-end data with negative insert sizes.
The newly developed Clip&Merge method is capable of clipping adapter sequences, merging clipped paired-end reads if possible, and trimming non-merged reads based on a user-defined quality threshold. To achieve this, a clipping strategy that was motivated by the technique implemented in the FASTX-Toolkit (Gordon A, Hannon GJ: Fastx-toolkit. FastQ/A short-reads pre-processing tools, unpublished) was developed, making use of multi-core systems by running the clipping on forward and reverse reads in parallel. To identify adapter sequences at the ends of the reads, a local alignment based on the Smith–Waterman algorithm  between the adapter sequence and the read is calculated. All bases between the start position of the alignment and the end of the read are then removed, if the alignment fulfills the requirements defined by the user, including an allowed number of mismatches and a minimum length of the overlap region. If the start position of the alignment and the adapter are different, the start position is moved towards the 5′ end of the read by the number of unaligned bases at the start of the adapter sequence. This ensures that there are no adapter bases left in the read sequence, avoiding the merging of adapter sequences in the subsequent step. The merging step calculates the reverse complement of the reverse read and then performs a maximal overlap search between the ends of the forward and the reverse complemented reverse reads, starting with a maximal overlap and a pairwise comparison of the nucleotides in the overlap region. If the edit distance in the overlap region is lower than a defined threshold and the size of the overlap region is larger than a defined minimal overlap size, the merging is accepted. Bases with very low sequencing quality are treated as undefined nucleotides and do not contribute to the edit distance in the temporary overlap region. If the criteria for an overlap cannot be fulfilled properly, the temporary overlap is shifted by one base and the calculations are repeated, until either a satisfying overlap has been found or no overlap could be identified.
EAGER features several mapping algorithms that can be accessed and configured easily via the integrated GUI. Currently, BWA , BWA-mem , Bowtie , and Stampy  are available. Many available mapping methods are optimized towards mapping NGS reads to a linear reference genome. However, the majority of bacterial genomes as well as the human mitochondrion are circular. Methods like BWA try to map sequencing reads completely against the reference genomes and mark reads that cannot be mapped completely as unmapped. Even improved methods that allow for soft-clipping, for example BWA-mem, have not solved these issues completely. Although this does not pose an issue for reads falling into the interior regions of a circular genome, the first as well as the last couple of hundred bases of circular genomes are usually reconstructed poorly due to the inability to map reads to the respective regions. The resulting coverage in such regions has been observed to be significantly lower than the average coverage on the whole genome of the respective organism, which poses difficulties for some downstream analysis tasks such as haplotyping or full-genome reconstruction, where an even coverage of the whole genome is required . To overcome these issues, the CircularMapper method has been developed.
CircularMapper performs two independent steps: It first creates an elongated reference genome, by adding the first k bases of the reference genome to the end of the genome and then mapping the sequencing reads against that elongated reference genome. Typically, k is chosen to be a default of 500 bases but can be set by the user. The elongation value k should be chosen to be at least the maximal read length observed in the preprocessed sequencing data set used as input. After this, reads are categorized by the second CircularMapper component into three different categories. The first category of reads is found in the region ranging from k to the unmodified genome length and reads in the category do not require any changes. The second category of reads is found in the first or in the last k bases of the modified genome. These are remapped in a separate step. This is important, as reads that are found to fit two or more regions on a reference genome are usually marked ambiguous by the mapping algorithm. As they clearly result from the modifications introduced by the CircularMapper, these reads can be remapped safely against an unmodified reference and then taken into the final mapping results. Lastly, reads that have a starting position within the unmodified reference genome and simultaneously have an end position in the modified region are considered as overlapping reads, spanning the circular overlap region of the reference. These reads are split according to their overlap and are afterward placed at their correct positions by the second component of the CircularMapper method. For human genomes, where the mitochondrion is the only part of the genome to be organized as a circular chromosome, the method can perform this extension and split approach on the whole genome, but only modifying the mitochondrion reference in such a case. This is required, because the human genome has nuclear mitochondrion DNA (NUMTs) regions . Mapping DNA against only the mitochondrion reference genome would therefore result in an overestimation of actual coverage, as reads that would otherwise map to NUMTs, are mapped against the mitochondrion reference in such cases. Thus, a mitochondrion DNA reconstruction should always be performed on the full human genome to take these NUMTs into account.
To ensure that the resulting SAM and/or BAM files of aDNA sequences are processed appropriately, we developed an improved duplicate removal method called DeDup, which is integrated in the pipeline, too. As aDNA samples often show very low amounts of endogenous DNA, enrichment and amplification methods are often used to increase the number of DNA reads retrieved from the given DNA fragments [2, 3]. Unfortunately, these methods increase the number of sequencing duplicates stemming from the same fragments. Since the coverage of specific genomic loci is important for downstream analysis, the statistics of the respective loci, such as duplicates, can convey a false-positive trust in a specific region that might only result from a high number of duplicate entries. This is undesired, and therefore in silico methods are utilized to remove duplicated sequencing reads. Several methods to achieve this have been proposed, with the most prominently used being rmdup in SAMtools . This method works well on regular paired-end sequencing data, where the 3′ end of the forward reads and 5′ end of the reverse reads are known. Since rmdup only considers the 5′ positions of the respective reads, the assumption regarding equal 3′ ends fails for merged paired-end reads, where the 3′ end is not known in advance. Thus, the method may also remove reads that stem from different fragments. To compensate for this, the DeDup method has been implemented following a principle described by Green et al. , which considers both the 5′ and the 3′ positions of the respective reads and thus, keeps merged reads that have different lengths (see Fig. 3). When two reads are mapped to the same start and end positions, the read with the higher sum of base qualities is kept, whereas the read with the inferior sum of base qualities is discarded accordingly. For unmerged reads, the method performs the same duplicate removal procedure as the SAMtools rmdup method for single-end reads. DeDup has been optimized to work correctly on single-end data as well as (partially) merged paired-end data with negative insert sizes or collapsed reads. For paired-end data with positive insert sizes, as for typical modern data, the EAGER pipeline features the MarkDuplicates method from the Picard toolkit to enable paired-end de-duplication for non-merged data, too. Finally, the method QualiMap , which reviews the overall mapping results, has been made accessible in the pipeline.
An important step during aDNA analysis is authentication. This can be addressed by damage pattern analysis and fragment length calculation. In EAGER, we have, therefore, integrated mapDamage  for an automated damage pattern analysis to authenticate ancient samples. Furthermore, the mapping module contains the Preseq tool  to determine the complexity of the sequencing library. To enable researchers to perform contamination estimation on aDNA data, which is a crucial step for assessing whether data has been contaminated with DNA from foreign sources, we also integrated the recently published method schmutzi  into the EAGER pipeline. Schmutzi estimates contamination based on a maximum likelihood approach using deamination patterns and fragment lengths typical for aDNA. In addition, schmutzi can be used to compute an improved endogenous human mitochondrial genome sequence by taking the estimated contamination into account.
The pipeline can be used to perform a full genotyping of a given sample using GATK , including both available genotypers (the UnifiedGenotyper and the HaplotypeCaller) in GATK along with the GATK variant filtration method to perform downstream analysis of called variants inside the pipeline. Within EAGER, the GATK Best Practice’s Guidelines are followed , including IndelRealignment but excluding the Base Score Recalibration procedures. As Base Score Recalibration requires some reference VCF file to perform the recalibration properly, which rarely exists for the application on ancient genomes and applications that involve species other than humans, we excluded the method, as it could hinder the detection of potentially ancient variants that are not present in modern populations when used in aDNA projects. Furthermore, since modern sequencing machines produce very reliable base quality scores, we decided to remove the Base Score Recalibration step from the EAGER pipeline.
Furthermore, we developed the VCF2Genome method, which reads a VCF file produced by the genotyping method of choice. For each call, it incorporates one nucleotide into a new draft genome sequence. By default, if the genotyper calls a reference base and the quality of the respective call was at least 30 with a minimal coverage of the respective position of at least five reads, then a reference base is included in the draft genome sequence at this position. If a variant was called (SNP), it is included if the same quality threshold is fulfilled, at least five reads covering the respective locus contain the respective SNP, and the fraction of mapped reads containing the SNP was at least 90 %. If not all of these requirements are fulfilled, but the quality threshold is still reached, the reference base is called instead, but only if it is confirmed by at least five reads and contained in 90 % of the reads covering the locus. The stated thresholds and filtering criteria are the current default values set by the pipeline and can be configured by the user. If neither the reference call nor a variant call can be made, the character “N” is incorporated at the position. To keep the potential introduction of too many “N” characters due to sequencing errors as low as possible for low coverage genomes, the major allele is still regarded as being confirmed by 100 % of the reads, if there is only a single read confirming the minor allele. Additionally, the tool produces two further draft sequences. The first contains the reference base instead of “N” in all cases, whereas the second contains a special uncertainty encoding. Instead of the “N” character, it contains lower-case letters “a”, “c”, “g” and “t” at positions where a call was rejected, for example due to low coverage but the reads covering the respective position unambiguously indicate a SNP call. For uncertain reference calls, an “R” is inserted. Using this approach, users can differentiate between a clear SNP call, a weak SNP call, a clear/weak reference call, and no call at a certain position more effectively. As many samples in aDNA projects only show low coverages, EAGER also features the ANGSD method  to create genotype-likelihood-based output on low coverage data, using an already established method.
Excerpt of the report table automatically generated by EAGER
# reads after C&M
# mapped reads
prior to mapping
prior to DeDup
removed with DeDup
Software availability and requirements
The EAGER pipeline is available in several types of flavors. For testing, a VirtualBox-based image is available, with all the required tools that can be executed on any platform supporting VirtualBox . Note that this has some performance drawbacks, so that this image should be used only for testing. For more advanced users, a manual is available from our website (it.informatik.uni-tuebingen.deit.informatik.uni-tuebingen.de) with instructions on how to set EAGER up on different kinds of Linux/Unix-based operating systems, such as CPU clusters where a Docker-based installation is not feasible, due to access rights for example. We were successfully able to run EAGER on systems with 4–8 GB of RAM and four CPUs, ranging up to workstations with 500 GB of RAM and 64 CPUs, as well as a typical cluster grid infrastructure. Many state-of-the-art methods are used less by end users because of several dependencies that need to be fulfilled before a provided software method can be used. Especially when dealing with newly designed workflows, end users are often faced with highly complex software packages that need to be installed, used, and maintained on their respective infrastructure of choice. Though most of EAGER has been developed in the Java programming language, which is portable to many different types of operating systems, there still exist several necessary tools in EAGER that need to be included in such an environment. Subsequently, an end user would be forced to install these tools by manually compiling them or finding and installing appropriate executable versions of these tools.
To overcome these dependency-related issues and hide most of the technical dependencies of the EAGER pipeline, a Docker-based image (docker.iodocker.io) with all the dependencies of EAGER has been set up. For end users, this means that there is a single requirement in the form of a working Docker installation necessary to run the EAGER pipeline, making the installation and setup as well as the maintenance of EAGER as easy as possible and less prone to error. A further improvement is the centralized architecture of the Docker-image-based system, as fixes for errors in the pipeline can be easily distributed to any installation worldwide. The users can then update their installation to any published revision of the pipeline with a single command at any time, while Docker guarantees that the image pulled from the server contains exactly the software the user wanted to pull. Furthermore, the EAGER images are stored in a tagged archive on our web server, enabling users to stay with older versions of the pipeline or step back to a previously published version of the pipeline at any given time point. This can be useful, for example, when results from former publications need to be reproduced. For some end users, the possibility of running Docker images on a cloud computing infrastructure, such as Amazon EC/2 or Google Cloud instances, might be a good alternative to buying and installing their own hardware, especially when the analysis of aDNA data is only done, e.g., on a per project basis and the computing resources would lie idle for most of the time. In such cases, the renting of an infrastructure as a service (IaaS) cloud computing unit together with the EAGER Docker image could be beneficial in terms of overall analysis costs. To enable administrators to install and set up the pipeline on different types of infrastructure, we also provide access to the executables used in the pipeline as well as the main pipeline components. These can be used to set up the pipeline, for example, on grid computing infrastructures that do not rely on Docker or cloud computing instances for task execution. Note that this requires end users to download and install all the subsequent tools used by the pipeline as well, making most of the installation more complex than the setup of solely a Docker container. A set of links to download the required tools for the EAGER pipeline as well as the Docker-based image of EAGER is available on our website (https://it.informatik.uni-tuebingen.deit.informatik.uni-tuebingen.de). EAGER and all its components are published under GPLv3, and the source code is available on GitHub (https://github.com/apeltzer/EAGER-GUI).
All ancient genome data sets are available from SRA and/or ENA (accession IDs in Table 1). To test our Clip&Merge tool, we used an artificial data set provided by the authors of FLASH, which can be downloaded from their webpage (https://ccb.jhu.edu/software/FLASH/). The modern data set to compare EAGER and PALEOMIX can be downloaded from bioplanet.com. Here, we used the illumina-100bp-pe-exome-30x data set available from GCAT. Finally, the simulated low coverage can be reproduced by merging BAM files from LBK1 to LBK8 into a single BAM file after mapping to hg19 and then sub-sampling from these BAM files with a random seed and varying s i =(0.005,0.01,0.02,0.05,0.10,0.20,0.30,0.40) using SAMtools (command ‘samtools view -s s i input.bam > output.bam’.)
No ethical approval was required for this study.
We wish to thank André Hennig for his help with the implementation of the pipeline, Sven Fillinger for helpful corrections and improvements to the software, and Aydın Can Polatkan for the EAGER logo. Furthermore, we would like to thank the people of the Archaeo- & Palaeogenetics workgroup at the University of Tübingen and now the Max Planck Institute for the Science of Human History, for their valuable feedback and suggestions on how to improve the pipeline. We would also like to acknowledge the feedback we received from Marcel Nutsua and Ben Krause-Kyora at the University of Kiel, which enabled us to make the pipeline even more user-friendly. Last but not least, we thank the reviewers for their constructive criticisms, which greatly improved both the paper and EAGER.
Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
- Green RE, Malaspinas AS, Krause J, Briggs AW, Johnson PL, Uhler C, et al.A complete Neandertal mitochondrial genome sequence determined by high-throughput sequencing. Cell. 2008; 134(3):416–26.View ArticlePubMedPubMed CentralGoogle Scholar
- Schuenemann VJ, Singh P, Mendum TA, Krause-Kyora B, Jäger G, Bos KI, et al.Genome-wide comparison of medieval and modern Mycobacterium leprae. Science. 2013; 341(6142):179–83.View ArticlePubMedGoogle Scholar
- Bos KI, Harkins KM, Herbig A, Coscolla M, Weber N, Comas I, Krause J. Pre-Columbian mycobacterial genomes reveal seals as a source of New World human tuberculosis. Nature. 2014; 514(7523):494–497.View ArticlePubMedPubMed CentralGoogle Scholar
- Li H, Durbin R. Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics. 2009; 25(14):1754–60.View ArticlePubMedPubMed CentralGoogle Scholar
- Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, et al.The Sequence Alignment/Map format and SAMtools. Bioinformatics. 2009; 25(16):2078–9.View ArticlePubMedPubMed CentralGoogle Scholar
- McKenna A, Hanna M, Banks E, Sivachenko A, Cibulskis K, Kernytsky A, et al.The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 2010; 20(9):1297–303.View ArticlePubMedPubMed CentralGoogle Scholar
- Sawyer S, Krause J, Guschanski K, Savolainen V, Pääbo S. Temporal patterns of nucleotide misincorporations and DNA fragmentation in ancient DNA. PloS One. 2012; 7(3):e34131.View ArticlePubMedPubMed CentralGoogle Scholar
- Kircher M. Analysis of high-throughput ancient DNA sequencing data In: Shapiro B, Hofreiter M, editors. Ancient DNA SE - 23. Vol. 840 of methods in molecular biology. New York: Humana Press: 2012. p. 197–228.Google Scholar
- Green RE, Krause J, Briggs AW, Maricic T, Stenzel U, Kircher M, et al.A draft sequence of the Neandertal genome. Science. 2010; 328(5979):710–22.View ArticlePubMedGoogle Scholar
- Schubert M, Ermini L, Sarkissian CD, Jónson H, Ginolhac A, Schaefer R, et al.Characterization of ancient and modern genomes by SNP detection and phylogenomic and metagenomic analysis using PALEOMIX. Nat Protoc. 2014; 9(5):1056–82.View ArticlePubMedGoogle Scholar
- Langmead B, Salzberg SL. Fast gapped-read alignment with Bowtie 2. Nat Methods. 2012; 9(4):357–9.View ArticlePubMedPubMed CentralGoogle Scholar
- Li H. Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM. arXiv preprint arXiv:13033997. 2013.
- Ginolhac A, Rasmussen M, Gilbert MTP, Willerslev E, Orlando L. mapDamage: testing for damage patterns in ancient DNA sequences. Bioinformatics. 2011; 27(15):2153–5.View ArticlePubMedGoogle Scholar
- Daley T, Smith AD. Predicting the molecular complexity of sequencing libraries. Nat Methods. 2013; 10(4):325–7.View ArticlePubMedPubMed CentralGoogle Scholar
- Renaud G, Slon V, Duggan AT, Kelso J. Schmutzi: estimation of contamination and endogenous mitochondrial consensus calling for ancient DNA. Genome Biol. 2015; 16(1):224.View ArticlePubMedPubMed CentralGoogle Scholar
- Korneliussen TS, Albrechtsen A, Nielsen R. ANGSD: analysis of next generation sequencing data. BMC Bioinform. 2014; 15(1):356.View ArticleGoogle Scholar
- Vianello D, Sevini F, Castellani G, Lomartire L, Capri M, Franceschi C. HAPLOFIND: a new method for high-throughput mtDNA haplogroup assignment. Hum Mutat. 2013; 34(9):1189–94.View ArticlePubMedGoogle Scholar
- Merkel D. Docker: lightweight Linux containers for consistent development and deployment. Linux J. 2014; 2014(239):2.Google Scholar
- Lazaridis I, Patterson N, Mittnik A, Renaud G, Mallick S, Kirsanow K, et al.Ancient human genomes suggest three ancestral populations for present-day Europeans. Nature. 2014; 513(7518):409–13.View ArticlePubMedPubMed CentralGoogle Scholar
- Magoč T, Salzberg SL. FLASH: Fast length adjustment of short reads to improve genome assemblies. Bioinformatics. 2011; 27:2957–63.View ArticlePubMedPubMed CentralGoogle Scholar
- Zook JM, Chapman B, Wang J, Mittelman D, Hofmann O, Hide W, et al.Integrating human sequence data sets provides a resource of benchmark SNP and indel genotype calls. Nat Biotech. 2014; 32(3):246–51.View ArticleGoogle Scholar
- Sherry ST. dbSNP: the NCBI database of genetic variation. Nucleic Acids Res. 2001; 29(1):308–11.View ArticlePubMedPubMed CentralGoogle Scholar
- Yang Z, Yoder AD. Estimation of the transition/transversion rate bias and species sampling. J Mol Evol. 1999; 48(3):274–83.View ArticlePubMedGoogle Scholar
- Smith TF, Waterman MS. Identification of common molecular subsequences. J Mol Biol. 1981; 147(1):195–7.View ArticlePubMedGoogle Scholar
- Langmead B, Trapnell C, Pop M, Salzberg SL. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol. 2009; 10(3):R25.View ArticlePubMedPubMed CentralGoogle Scholar
- Lunter G, Goodson M. Stampy: a statistical algorithm for sensitive and fast mapping of Illumina sequence reads. Genet Res. 2011; 21(6):936–9.View ArticleGoogle Scholar
- Lopez JV, Yuhki N, Masuda R, Modi W, O’Brien SJ. Numt, a recent transfer and tandem amplification of mitochondrial DNA to the nuclear genome of the domestic cat. J Mol Evol. 1994; 39(2):174–90.PubMedGoogle Scholar
- Okonechnikov K, Conesa A, García-Alcalde F. QualiMap 2: advanced multi-sample quality control for high-throughput sequencing data. Bioinformatics (Oxford, England). 2016; 32(2):292–4.Google Scholar
- Van der Auwera GA, Carneiro MO, Hartl C, Poplin R, del Angel G, Levy-Moonshine A, et al.From FastQ data to high-confidence variant calls: the genome analysis toolkit best practices pipeline. In: Current protocols in bioinformatics. New Jersey: John Wiley & Sons, Inc: 2013. p. 11.10.1–33.Google Scholar
- Watson J. Virtualbox: bits and bytes masquerading as machines. Linux J. 2008; 2008(166):1.Google Scholar