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.2016 Mar 31:17:60.
doi: 10.1186/s13059-016-0918-z.

EAGER: efficient ancient genome reconstruction

Affiliations

EAGER: efficient ancient genome reconstruction

Alexander Peltzer et al. Genome Biol..

Abstract

Background: The automated reconstruction of genome sequences in ancient genome analysis is a multifaceted process.

Results: 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.

Conclusions: 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.

Keywords: Authentication; Bioinformatics; Genome reconstruction; aDNA; aDNA analysis.

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Figures

Fig. 1
Fig. 1
Workflow diagram of the EAGER pipeline. The pipeline consists of three distinct main components for processing and analysis of NGS data: preprocessing, read mapping, and genotyping
Fig. 2
Fig. 2
The GUI of the EAGER pipeline. The methods that can be used in the EAGER pipeline can be selected by the user and settings for each method can be adapted via the advanced buttons
Fig. 3
Fig. 3
Conceptual idea of the DeDup method. Paired-end forward and reverse reads resulting from two fragments are drawn inred and merged reads are drawn inblue. Although the two merged reads stem from two different DNA fragments, SAMtools rmdup removes the read with the lower overall sum of base qualities, as only the starting position of the mapped reads is taken into account. DeDup takes both mapping positions (start and end) into account, and in this case would keep both reads
Fig. 4
Fig. 4
Run-time comparison of EAGER and PALEOMIX. Normalized run times are shown for six data sets: five ancient leprosy data sets [2] and an ancient human sample [19]. EAGER (red) performs on average 1.53 times faster than the PALEOMIX (turquoise) pipeline (see Table 2 for the absolute run times and respective factors of each sample)
Fig. 5
Fig. 5
Run-time comparison of several read merging tools. Our own method Clip&Merge (green) was compared to MergeTrimReads (red), CutAdapt + FLASH (blue), SeqPrep (purple), LeeHom (light green), and AdapterRemoval (yellow). The evaluation was performed on fiveMycobacterium leprae data sets and one exemplary human data set (LBK1). Clip&Merge outperforms the other available methods in terms of speed, except for the combination of CutAdapt and FLASH. MergeReadsFastQ was not evaluated on the LBK1 data set, due to the run-time limitations posed by the method, which is shown as a run time of zero for this case
Fig. 6
Fig. 6
Comparison of coverage of CircularMapper and BWA. The plot illustrates the coverage of the CircularMapper method (red) in comparison with the coverage obtained using only the BWA method (blue) to reconstruct the SK8Mycobacterium leprae sample. The coverages have been log2 transformed. The average coverage over the whole genome is shown ingreen. The first 200 (left) and the last 200 bases (right) of the genome are shown here to demonstrate the effect of the CircularMapper method. Because of the specific fragment length within the sample, the effect is restricted to the first and last approximately 80 bases
Fig. 7
Fig. 7
Comparison of duplicate removal methods. Coverages obtained when applying the SAMtools rmdup method (green) and the DeDup method (red) to five ancient leprosy samples and one ancient human sample
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References

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