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Supplementary data are available at Bioinformatics online.
Summary Third-generation sequencing technologies from companies such as Oxford Nanopore and Pacific Biosciences have paved the way for building more contiguous and potentially gap-free assemblies. The larger effective length of their reads has provided a means to overcome the challenges of short to mid-range repeats. Currently, accurate long read assemblers are computationally expensive, whereas faster methods are not as accurate. Moreover, despite recent advances in third-generation sequencing, researchers still tend to generate accurate short reads for many of the analysis tasks. Here, we present HASLR, a hybrid assembler that uses error-prone long reads together with high-quality short reads to efficiently generate accurate genome assemblies. Our experiments show that HASLR is not only the fastest assembler but also the one with the lowest number of misassemblies on most of the samples, while being on par with other assemblers in terms of contiguity and accuracy.
Motivation: Recent advances in genomics and precision medicine have been made possible through the application of high throughput sequencing (HTS) to large collections of human genomes. Although HTS technologies have proven their use in cataloging human genome variation, computational analysis of the data they generate is still far from being perfect. The main limitation of Illumina and other popular sequencing technologies is their short read length relative to the lengths of (common) genomic repeats. Newer (single molecule sequencing-SMS) technologies such as Pacific Biosciences and Oxford Nanopore are producing longer reads, making it theoretically possible to overcome the difficulties imposed by repeat regions. Unfortunately, because of their high sequencing error rate, reads generated by these technologies are very difficult to work with and cannot be used in many of the standard downstream analysis pipelines. Note that it is not only difficult to find the correct mapping locations of such reads in a reference genome, but also to establish their correct alignment so as to differentiate sequencing errors from real genomic variants. Furthermore, especially since newer SMS instruments provide higher throughput, mapping and alignment need to be performed much faster than before, maintaining high sensitivity. Results: We introduce lordFAST, a novel long-read mapper that is specifically designed to align reads generated by PacBio and potentially other SMS technologies to a reference. lordFAST not only has higher sensitivity than the available alternatives, it is also among the fastest and has a very low memory footprint. Availability and implementation: lordFAST is implemented in Cþþ and supports multi-threading.
Recent technological advances in single cell sequencing (SCS) provide high resolution data for studying intra-tumor heterogeneity and tumor evolution. Available computational methods for tumor phylogeny inference via SCS typically aim to identify the most likely perfect phylogeny tree satisfying infinite sites assumption (ISA). However limitations of SCS technologies such as frequent allele dropout or highly variable sequence coverage, commonly result in mutational call errors and prohibit a perfect phylogeny. In addition, ISA violations are commonly observed in tumor phylogenies due to the loss of heterozygosity, deletions and convergent evolution. In order to address such limitations, we, for the first time, introduce a new combinatorial formulation that integrates single cell sequencing data with matching bulk sequencing data, with the objective of minimizing a linear combination of (i) potential false negatives (due to e.g. allele dropout or variance in sequence coverage) and (ii) potential false positives (due to e.g. read errors) among mutation calls, as well as (iii) the number of mutations that violate ISA -to define the optimal sub-perfect phylogeny. Our formulation ensures that several lineage constraints imposed by the use of variant allele frequencies (VAFs, derived from bulk sequence data) are satisfied. We express our formulation both in the form of an integer linear program (ILP) and -for the first time in the context of tumor phylogeny reconstruction -a boolean constraint satisfaction problem (CSP) and solve them by leveraging state-of-the-art ILP/CSP solvers. The resulting method, which we name PhISCS, is the first to integrate SCS and bulk sequencing data under the finite sites model. Using several simulated and real SCS data sets, we demonstrate that PhISCS is not only more general but also more accurate than the alternative tumor phylogeny inference tools. PhISCS is very fast especially when its CSP based variant is used returns the optimal solution, except in rare instances for which it provides an optimality gap. PhISCS is available at https://github.com/haghshenas/PhISCS.
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