Abstract
Reads from paired-end and mate-pair libraries are often utilized to find structural variation in genomes, and one common approach is to use their fragment length for detection. After aligning read-pairs to the reference, read-pair distances are analyzed for statistically significant deviations. However, previously proposed methods are based on a simplified model of observed fragment lengths that does not agree with data. We show how this model limits statistical analysis of identifying variants and propose a new model, by adapting a model we have previously introduced for contig scaffolding, which agrees with data. From this model we derive an improved null hypothesis that, when applied in the variant caller CLEVER, reduces the number of false positives and corrects a bias that contributes to more deletion calls than insertion calls. A reference implementation is freely available at https://github.com/ksahlin/GetDistr.
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Notes
- 1.
With some modifications to account for heterozygous variants. Only reads that have enough overlap and similar fragment lengths are grouped together.
- 2.
Under a normal distribution, 100 continuous observations are statistically equivalent to 158 binary observations for the best possible “cut point”, which is the mean. The loss of information becomes worse the further away the cut point is from the mean, e.g., \(\mu \pm k\sigma \), as k increases. In practice \(k \in [3,6]\) in variant detection tools.
- 3.
We call the side of the read that is closest to its mate “inner”.
- 4.
- 5.
n to obtain sample mean \(\bar{o}\), and \(\log t\) to search the convex ML curve.
- 6.
http://dx.doi.org/10.1101/023929, Sect. 5.5.
- 7.
- 8.
Even small variants \(\delta \ll \sigma \) will not affect the model much.
- 9.
Estimated as \(250 \cdot \frac{(114-16)\text {Mbp}}{3~\text {Gbp}}=8\), including compensation for the 16M N’s at the start of the reference sequence for chr 13.
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Sahlin, K., Frånberg, M., Arvestad, L. (2016). Structural Variation Detection with Read Pair Information—An Improved Null-Hypothesis Reduces Bias. In: Singh, M. (eds) Research in Computational Molecular Biology. RECOMB 2016. Lecture Notes in Computer Science(), vol 9649. Springer, Cham. https://doi.org/10.1007/978-3-319-31957-5_13
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