BACKGROUND: De novo genome assembly poses challenges when dealing with highly degraded DNA samples or ultrashort sequencing reads. Probabilistic approaches have been offered to enhance the algorithms, though existing methods rely solely on expected k-meric frequencies in the assemblies, neglecting the broader sequence context that strongly influences DNA fragmentation patterns. RESULTS: Here, we present a proof of concept showing that prior knowledge on sequence context-driven DNA breakage propensities, through the dedicated parameterisation of k-mer assigned breakage probabilities, can be utilised to recover DNA assemblies that originate from fragmentation patterns more likely to have happened. Our approach is beneficial even for read lengths below the common ∼ 25 bp threshold of modern de novo genome assembly algorithms, and well below the threshold used for ultrashort fragments used in ancient DNA research. CONCLUSIONS: This work could lay the groundwork for future enhanced de novo genome assembly algorithms, with improved ability to effectively assemble and evaluate ultrashort DNA fragments relevant for cell-free, ancient, and forensic DNA research.

MOTIVATION: Various computational biology calculations require a probabilistic optimization protocol to determine the parameters that capture the system at a desired state in the configurational space. Many existing methods excel at certain scenarios, but fail in others due, in part, to an inefficient exploration of the parameter space and easy trapping into local minima. Here, we developed a general-purpose optimization engine in R that can be plugged to any, simple or complex, modelling initiative through a few lucid interfacing functions, to perform a seamless optimization with rigorous parameter sampling. RESULTS: ROptimus features simulated annealing and replica exchange implementations equipped with adaptive thermoregulation to drive Monte Carlo optimization process in a flexible manner, through constrained acceptance frequency but unconstrained adaptive pseudo temperature regimens. We exemplify the applicability of our R optimizer to a diverse set of problems spanning data analyses and computational biology tasks. AVAILABILITY AND IMPLEMENTATION: ROptimus is written and implemented in R, and is freely available from CRAN (http://cran.r-project.org/web/packages/ROptimus/index.html) and GitHub (http://github.com/SahakyanLab/ROptimus).