An Integrated Data Analysis Suite and Programming ... - TOBIAS-lib

1.5. SEQUENCING DATA ANALYSIS 11
the most likely combination of alleles to give rise to the observed data, along with a measure of
condence for the provided solution.
Identication of this most likely combination of alleles at each reference genome position is
therefore the main problem to be solved by analysis algorithms. A major issue to be overcome
by these algorithms is to achieve an integration of models for sequencing errors, read mapping
and base alignment errors. While the probability of sequencing errors may be systematically
inuenced by the local sequence context, individual error events in dierent reads may be considered
as largely independent. Sources of mapping and base alignment error are however mostly
systematic in nature. Reads and aligned bases therefore do not represent independent evidence
for a certain allele conguration, preventing these types of error from being as readily captured
by statistical modeling.
The majority of consensus and genotype calling algorithms commence by examining the
pileup of base calls and base qualities provided by the mapped reads overlapping a position.
Given sucient depth of coverage, eventual sequencing errors can be thereby identied at high
condence.
However, sequencing error may not only cause invalid readout of single positions, but also
spurious cross-mappings to loci with similar sequence to that of the true origin of a read. The
likelihood of such spurious mappings may be assessed using measures like the mapping quality
provided by some alignment tools (section 1.5.3). Furthermore, heuristic parts of the mapping
algorithms used to achieve improved mapping performance may also cause systematic crossmappings
that can be indistinguishable from true variants or give rise to invalid multi-allelic
calls. Such pseudo multi-allelic regions may further arise due to copy number variants, underrepresentation
of repetitive sequence in the reference assembly or sample contamination.
Another source of systematic miscalls presents base alignment error. Base alignment error
is introduced by reads, albeit mapped to the locus on the reference genome from which they
originate, whose alignment provided by the read mapping tool does not suggest the correct set
of base pairings. This type of error mainly occurs at transition points to insertions, deletions,
copy number variants or other types of rearrangement or at exon-intron boundaries in mRNA
sequencing. Reads overlapping these transition points only with a small number of positions do
not contain enough information to support the correct type of variant, causing spurious alignment
of terminal nucleotides.
For these reasons, current consensus and genotype call algorithms employ combinations of
statistical models as well as alignment correction algorithms and heuristics for removing or
penalizing read alignment issues.
The Genome Analysis Toolkit (GATK) [97] combines Bayesian heterozygous genotype calling
based on quality score recalibration with indel realignment and various heuristics for penalizing
mapping artifacts [98].
SHORE [18] implements a exibly calibratable scoring matrix approach [1, 99] that calculates
SNP, indel and reference call scores based on a user-denable weighting of various properties of
the read alignments overlapping a position. The method allows to impose a penalty on, or to
ignore a number of terminal nucleotides towards the ends of each alignment, ruling out many
cases of base alignment error at the cost of an eective loss of sequencing depth.
The SAMtools package [100] implements a prole HMM for estimation of a Base Alignment
Quality (BAQ) for the assessment of potential mapping artifacts [101].
1.5.5 Quantication by Deep Sequencing
In principle, deep sequencing constitutes statistical sampling of the population of molecules that
make up the sequencing library. Therefore, obtained sequencing data may be transformed into