Table 1.

Difficulties of working with ancient DNA and specialized methods developed to address them

Problem	Experimental solutions	Bioinformatics solutions
Degradation	Improved extraction protocols Using NGS approach (catches short DNA fragments)	Algorithms based on genotype likelihoods rather than a single best genotype for low coverage genomic positions
Base damage	Using a DNA polymerase which does not amplify through uracils (remove uracil-containing fragments from the reaction) Treatment with uracil-DNA glycosylase plus endonuclease VIII (removes uracil, then cleaves abasic sites) Single-primer extension PCR (analyses separate DNA strands)	Trimming 5-7 bases from read ends Counting and excluding C→T and G→A mutations at ultra-conserved positions Comparing frequencies of different classes of mutations in modern-modern and modern-ancient alignments Estimation of contamination or divergence based on indels and transversions only, not transitions Exclusion of common ancestor-ancient sample branches from calculation of divergence
Contamination	Special protocols for sample collection, transport and storage Special Custom pre-digestion steps (including mechanical and chemical decontamination, short-time pre-incubation) Independent replication in two labs PCR-capture with species-specific primers	Exclusion of long reads or alignments (in case of 454 or Sanger sequencing) as aDNA fragments are very short, usually <100nt Phylogenetic correctness correction (exclusion of reads based on similarity with non-target species; inclusion of reads based on similarity with the target species or a close relative) Conformity to species- or ethnicity-specific variants or haplotypes Checking homozygosity of X and Y positions in male specimens, absence of Y reads in female specimens, homozygosity of mtDNA positions Absence of haplotypes present in research team members Distinguishing mtDNA sequences from NUMTs

The solutions aimed at one or more of the problems are not mutually exclusive and are often used in combination for better results. In addition, various bioinformatics ideas for tackling contamination and base damage are sometimes integrated into a single Maximum Likelihood framework for base and genotype calling.