Sequencing

11th Annual Sequencing, Finishing, and Analysis in the Future Meeting
ESTIMATING THE EFFECTS OF REPEATS ON
ASSEMBLY CONTIGUITY
Thursday, 2nd June 10:00 La Fonda Ballroom Talk (OS‐4.01)
Shoudan Liang, Jason Chin
Pacific Biosciences of California
For a perfect assembler and at a high coverage, the contiguity of the assembly at a finite read length
is limited by repetitive sequences. We study the limit imposed by repeat structures in plants, and
contrast it to human, as the read length is increased. We started with assembled contigs from long
reads and perform an all‐against‐all alignment. Non‐unique regions of the contigs define repeats.
We require each alignment to be longer than a minimum length, S. Repeats shorter than S will not
align. Therefore, as the minimum overlap S is increased, we observed a decrease in the number of
repeat regions. For example, for coffee genome, when the minimum allowed overlap increases from
500 to 5,000 bp, the number of distinct repetitive regions is reduced by more a factor of 10. This
is partially due to long repeats being less abundant and partially because the short repeats are
occurring in clusters that are seen as unique sequences in the alignment. We developed a method
to separate these two effects. We show the tendency of repeats to cluster in several plant genomes.
Clustered repeats are especially difficult to assemble from short reads because even when all short
reads are identified to be from the same 100 kb region, they are still repetitive in the repeat‐cluster.
A related method to estimate the repeats is by counting the abundance of two k‐mers separated by a
fixed distance. The distance between the k‐mers is a proxy for the repeat length. This method has an
advantage of potentially being directly applied to the long‐read data before assembly. We compare
the direct estimate from the read with the estimate from the contigs for several plant genomes.
A third way of estimating repeat abundance from long reads is by performing an all‐against‐all
alignment using about 1% of data. This, when compared to the expected alignment of an idealized
genome of the same size that does not have any repeatitive regions, reveals excessive alignments
related to repeats at different lengths. This can be helpful in choosing assembly parameters. The
method we developed is available at https://github.com/pb‐sliang/TAP.
103