Misassembly Detection using Paired-End Sequence Reads and Optical Mapping Data
This arxiv paper appears to be promising (h/t: @lexnederbragt). We are not sure whether having optical map data is an absolute requirement. (Edit. Please see comment from senior author).
A crucial problem in genome assembly is the discovery and correction of misassembly errors in draft genomes. We develop a method that will enhance the quality of draft genomes by identifying and removing misassembly errors using paired short read sequence data and optical mapping data. We apply our method to various assemblies of the loblolly pine and Francisella tularensis genomes. Our results demonstrate that we detect more than 54% of extensively misassembled contigs and more than 60% of locally misassembed contigs in an assembly of Francisella tularensis, and between 31% and 100% of extensively misassembled contigs and between 57% and 73% of locally misassembed contigs in the assemblies of loblolly pine. MISSEQUEL can be downloaded at this http URL.
If you are wondering how it relates to other available tools, the following section is helpful.
Related Work. Both amosvalidate [31] and REAPR [32] are capable of identifying and correcting misassembly errors. REAPR is designed to use both short insert and long insert paired-end sequencing libraries, however, it can operate with only one of these types of sequencing data. Amosvalidate, which is included as part of the AMOS assembly package [33], was developed speci^Lcally for first generation sequencing libraries [31]. iMetAMOS [34] is an automated assembly pipeline that provides error correction and validation of the assembly. It packages several open-source tools and provides annotated assemblies that result from an ensemble of tools and assemblers. Currently, it uses REAPR for misassembly error correction.
Many optical mapping tools exist and deserve mentioning, including AGORA [35], SOMA [36], and Twin [30]. AGORA [35] uses the optical map information to constrain de Bruijn graph construction with the aim of improving the resulting assembly. SOMA [36] uses dynamic programming to align in silico digested contigs to an optical map. Twin [30] is an index-based method for align-ing contigs to an optical map. Due to its use of an index data structure it is capable of aligning in silico digested contigs orders of magnitude faster than competing methods. Xavier et al. [37] demonstrated misassembly errors in bacterial genomes can be detected using proprietary software. Lastly, there are special purpose tools that have some relation to misSEQuel in their algorith-mic approach. Numerous assembly tools use a finishing process after assembly, including Hapsem-bler [38], LOCAS [39], Meraculous [40], and the \assisted assembly” algorithm [41]. Hapsembler [38] is a haplotype-specific genome assembly toolkit that is designed for genomes that are highly- polymorphic. Both RACA [42], and SCARPA [43] perform paired-end alignment to the contigs as an initial step, and thus, are similar to our algorithm in that respect.
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The following program by Heng Li is also worth noting.
Abreak: evaluating de novo assemblies
Abreak is a subcommand of htsbox, which is a little toy forked from on the lite branch of htslib. It takes an assembly-to-reference alignment as input and counts the number of alignment break points. An earlier version was used in my fermi paper to measure the missassembly rate of human de novo assemblies. A typical output looks like:
Number of unmapped contigs: 239
Total length of unmapped contigs: 54588
Number of alignments dropped due to excessive overlaps: 0
Mapped contig bases: 2933399461
Mapped N50: 6241
Number of break points: 102146
Number of Q10 break points longer than (0,100,200,500)bp: (28719,7206,4644,3222)
Number of break points after patching gaps short than 500bp: 94298
Number of Q10 break points longer than (0,100,200,500)bp after gap patching: (23326,5320,3369,2194)
