  Recent emergence of next-generation DNA sequencing technology has enabled
acquisition of genetic information at unprecedented scales. In order to
determine the genetic blueprint of an organism, sequencing platforms typically
employ so-called shotgun sequencing strategy to oversample the target genome
with a library of relatively short overlapping reads. The order of nucleotides
in the reads is determined by processing the acquired noisy signals generated
by the sequencing instrument. Assembly of a genome from potentially erroneous
short reads is a computationally daunting task even in the scenario where a
reference genome exists. Errors and gaps in the reference, and perfect repeat
regions in the target, further render the assembly challenging and cause
inaccuracies. In this paper, we formulate the reference-guided sequence
assembly problem as the inference of the genome sequence on a bipartite graph
and solve it using a message-passing algorithm. The proposed algorithm can be
interpreted as the well-known classical belief propagation scheme under a
certain prior. Unlike existing state-of-the-art methods, the proposed algorithm
combines the information provided by the reads without needing to know
reliability of the short reads (so-called quality scores). Relation of the
message-passing algorithm to a provably convergent power iteration scheme is
discussed. To evaluate and benchmark the performance of the proposed technique,
we find an analytical expression for the probability of error of a genie-aided
maximum a posteriori (MAP) decision scheme. Results on both simulated and
experimental data demonstrate that the proposed message-passing algorithm
outperforms commonly used state-of-the-art tools, and it nearly achieves the
performance of the aforementioned MAP decision scheme.
