A Guide to Sequencing for Long Repetitive Regions

Full-length analysis of genes with highly repetitive sequences is challenging in two respects: assembly algorithm and sequencing accuracy. The de Bruijn graph often used in short-read assembly cannot distinguish adjacent repeat units. On the other hand, the accuracy of long reads is not yet high enough to identify each and every repeat unit. In this chapter, I present an example of a strategy to solve these problems and obtain the full length of long repeats by combining the extraction and assembly of repeat units based on overlap-layout-consensus and scaffolding by long reads.

Fast and efficient Rmap assembly using the Bi-labelled de Bruijn graph

Genome wide optical maps are high resolution restriction maps that give a unique numeric representation to a genome. They are produced by assembling hundreds of thousands of single molecule optical maps, which are called Rmaps. Unfortunately, there are very few choices for assembling Rmap data. There exists only one publicly-available non-proprietary method for assembly and one proprietary software that is available via an executable. Furthermore, the publicly-available method, by Valouev et al. (Proc Natl Acad Sci USA 103(43):15770-15775, 2006), follows the overlap-layout-consensus (OLC) paradigm, and therefore, is unable to scale for relatively large genomes. The algorithm behind the proprietary method, Bionano Genomics' Solve, is largely unknown. In this paper, we extend the definition of bi-labels in the paired de Bruijn graph to the context of optical mapping data, and present the first de Bruijn graph based method for Rmap assembly. We implement our approach, which we refer to as RMAPPER, and compare its performance against the assembler of Valouev et al. (Proc Natl Acad Sci USA 103(43):15770-15775, 2006) and Solve by Bionano Genomics on data from three genomes: E. coli, human, and climbing perch fish (Anabas Testudineus). Our method was able to successfully run on all three genomes. The method of Valouev et al. (Proc Natl Acad Sci USA 103(43):15770-15775, 2006) only successfully ran on E. coli. Moreover, on the human genome RMAPPER was at least 130 times faster than Bionano Solve, used five times less memory and produced the highest genome fraction with zero mis-assemblies. Our software, RMAPPER is written in C++ and is publicly available under GNU General Public License at https://github.com/kingufl/Rmapper .

Using Apache Spark on genome assembly for scalable overlap-graph reduction

Background

De novo genome assembly is a technique that builds the genome of a specimen using overlaps of genomic fragments without additional work with reference sequence. Sequence fragments (called reads) are assembled as contigs and scaffolds by the overlaps. The quality of the de novo assembly depends on the length and continuity of the assembly. To enable faster and more accurate assembly of species, existing sequencing techniques have been proposed, for example, high-throughput next-generation sequencing and long-reads-producing third-generation sequencing. However, these techniques require a large amounts of computer memory when very huge-size overlap graphs are resolved. Also, it is challenging for parallel computation.

Results

To address the limitations, we propose an innovative algorithmic approach, called Scalable Overlap-graph Reduction Algorithms (SORA). SORA is an algorithm package that performs string graph reduction algorithms by Apache Spark. The SORA’s implementations are designed to execute de novo genome assembly on either a single machine or a distributed computing platform. SORA efficiently compacts the number of edges on enormous graphing paths by adapting scalable features of graph processing libraries provided by Apache Spark, GraphX and GraphFrames.

Conclusions

We shared the algorithms and the experimental results at our project website, https://github.com/BioHPC/SORA. We evaluated SORA with the human genome samples. First, it processed a nearly one billion edge graph on a distributed cloud cluster. Second, it processed mid-to-small size graphs on a single workstation within a short time frame. Overall, SORA achieved the linear-scaling simulations for the increased computing instances.