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

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preparation should further induce tolerance to employ the resource sequencing less sparingly.
While most current studies due to economical considerations rely on no more than two replicate
experiments, gathering extensive experimental evidence in the form of many replicate data sets
could become standard in the future. Combined, these factors may entail improved statistical
power from quantitative sequencing experiments, while at the same time facilitating statistical
modeling of the sequencing and sample preparation process.
To this eect, the fundamental approaches to quantitative sequencing data analysis should
require rather gradual modication compared to genome structure and variation analysis. Reference
sequence-based analysis as implemented in our modules SHORE peak or SHORE count will
probably remain the preferred strategy in the near future, although it may become more commonplace
to generate appropriate reference genomes de-novo along with the quantitative data. A
medium-term goal might be complementing the available algorithms for quantitative data with
methods that allow to better leverage the statistical power acquired by increasing the number of
replicate experiments. Short-term improvements could possibly be achieved by investing in more
sophisticated models and algorithms to capture and estimate an increasing number of parameters
of library preparation, sequencing and data analysis. However, the characteristics of current
data sets may be the result of superimposition and complex interplay of a variety of eects, and
are furthermore subject to constant change as protocols and sequencing chemistry evolve.
With progress with respect to sequencing read length, sequence assembly and automated
genome annotation algorithms, reference-based variation detection as implemented by
SHORE consensus or SHORE qVar modules could be superseded by fundamentally dierent strategies.
However, SHORE and libshore may still constitute a valuable framework for implementation
of such future methods. While increasing read length should automatically resolve many of
the ambiguities that current analysis algorithms are confronted with, the biological questions
to address by future algorithms are likely to gain complexity. They should therefore pose
computationally intensive tasks that benet from application programming frameworks oriented
towards the ecient processing of large amounts of biological sequence data.
While the focus of this work was on the technical and algorithmic aspects of obtaining primary
analysis results from various types of high-throughput DNA sequencing data set, a future
challenge will lie with moving from elucidation of isolated properties of the cellular mechanism
to building consistent theoretical frameworks through integration of the data made accessible by
dierent sequencing applications and further complementary technologies.