SSPACE-LongRead effectively reconstructs (nearly) complete genomes

The assembly statistics show that both AHA and SSPACE-LongRead are able to significantly reduce the amount of input draft contigs using error-prone PacBio RS CLR reads as guidance. Overall the total assembly length remains relatively stable. It is apparent that SSPACE-LongRead is better able to reconstruct continuous genome segments given the final number of scaffolds is generally lower than 10. In practice this generally means a reduction of the initial amount of contigs by at least 90%. It should also be remarked that the runtime of these tools differs by a factor of 7, making our software more suited for scaffolding genomes on smaller computing systems. In terms of accuracy (through comparison with the corresponding reference genomes, not available for B. trehalosi and M. haemolytica) our software introduces some more errors compared to AHA but this is clearly explained by the more conservative approach of AHA (leading to less contig connections). Also it should be remarked that some of the apparent errors are actually true variations between the sequenced strain and the reference genome, an issue which is also explained by Koren et al. [19] as for each sequenced strain a close (but not necessarily the exact) reference was selected for quality assessment.

Not all input contigs were covered by PacBio reads (i.e. no significant alignment could be found between the draft assembly and the PacBio reads). This may be explained by the possible presence of short sequences such as plasmids which can not be captured by the long insert PacBio libraries. For this reason a hybrid assembly approach provides more complete assemblies than experiments that include only one PacBio library (as a compromise needs to be made between a large insert library – generating larger reads and better genome closure – and a short insert library to capture also short plasmids). Another explanation can be given by presence of different DNA sources in the NGS library. For instance, in the case of S. enterica a relatively high number of sequences is generated after scaffolding the Newbler draft assembly. However 12 of these input contigs are not covered by PacBio reads and surprisingly most of these show a high similarity with B. Taurus after a BLAST [20] analysis on the NCBI nr database. Other contigs uncovered by PacBio show similarity with the 5386 bp genome of phi X 174 (phiX), which is used by Illumina as a control to validate the quality of a run. In principle the corresponding reads should be removed from the MiSeq runs prior to downstream analysis.

Genomes become less fragmented also at a low PacBio RS CLR read coverage

In Figure 1 it can be observed that a higher PacBio RS long read coverage leads to less fragmented genomes. Nonetheless a coverage of 50× is generally sufficient to reduce the number of genome fragments to less than 10 scaffolds. The best assembly results are yielded at a coverage value between 110-160×. A higher coverage does not seem to improve the outcomes, instead this leads to more fragmented genomes. From a cost-perspective, the PacBio XL-C2 chemistry specifications (about 300 Mbases per SMRT cell) imply that for a small bacteria genome closure can be achieved using one SMRT cell in combination with e.g. one MiSeq paired-end library. More recent improvements of the sequencing chemistry (P4-C2 and P5-C3) aim to further enhance the data throughput (>500 Mbases) and the read length (>20 kbp). Nonetheless still a relatively high coverage is needed to completely finish a bacterial genome which is partly explained by the high error-rate which needs to be corrected for. Generally the alignment of PacBio RS long reads to a set of contigs yields low alignment scores and a non-conservative approach (based only a few reads that span consecutive contigs) will likely lead to misassemblies. Moreover it should be considered that after sequencing the number of long reads (e.g. more than 5 or 10 kbp), which are essential to overcome large repeats, is relatively small compared to the number of short reads (the median read length is currently between 2 and 5 kbp). Thus the contribution of long reads to the overall sequencing coverage is only limited. It is to be expected though that the total data yield per SMRT cell, as well as the mean read length, will significantly increase in the near future. Thus costs can be further reduced if samples are barcoded and sequenced together on the same SMRT cell. At this point a hybrid approach consisting of one short read paired-end and one long read PacBio library may eventually be the method of choice in terms of accuracy and costs compared to an alternative approach involving mate paired-end libraries. Last but not least, the computational time involved to close bacterial genomes allows high-throughput assemblies even on small compute systems. It should be underscored that the current study was performed using uncorrected PacBio RS CLR reads, thus bypassing a time-consuming error-correction step with short reads.

Both the quality of the draft assembly and the selection of the assembly strategy are crucial for the success of an experiment

From Table 2 it can be observed that the assembly results are also largely dependent on the strategy chosen for constructing the draft assembly. Common criteria used to judge a draft assembly are the number of contigs (should be low) and the N50 value (should be high to indicate that at least 50% of the assembly is in contigs of at least this length/value). Nonetheless these quantitative measures do not necessarily guarantee the choice of the best assembly.It is therefore adviced to assess the quality of an assembly using a dedicated tool, such as QUAST [21], which gives a complete overview of different quality metrics. Yet also other factors can play an important role in the selection of the most appropriate input assembly. For example, assemblies constructed with Ray do show a higher N50 value and a lower number of contigs compared to those constructed with CLCbio or Newbler. Therefore it might be intuitive to choose the Ray contigs as input for SSPACE-LongRead scaffolding. Nonetheless from the SSPACE-LongRead statistics it appears to be easier to reconstruct complete genomes from CLCbio or Newbler contigs. From further investigations (data not shown) we noticed that assemblies constructed with Ray contain repeated segments which are especially present at the contig edges. Similar observations were made for draft assemblies constructed from Illumina MiSeq reads with SPAdes [22] and MaSuRCA [23] (data not shown): the amount of contigs was lower or similar to that observed for CLCbio and Newbler, though the errors in the draft assembly led to more (erroneous) scaffolds. Although SSPACE-LongRead performs well on all tested draft assembly strategies, the user should preferably choose an assembly method that splits contigs at repeat boundaries. Consequently the assessment of the most appropriate draft assembly should not be solely based on the assembly quality-metrics, as the extent of the genome fragment reduction also depends on the draft assembly strategy.

Availability and requirements

Software input

The user needs to create a draft assembly using a de novo assembly method of choice (e.g. Velvet [1], SOAPdenovo [2], Ray [18], CLCbio (CLC bio, Aarhus, Denmark) or Newbler (Roche)). Optionally the user may also provide scaffold sequences generated with dedicated software (e.g. SSPACE [5] or SOPRA [4]). The resulting contigs or scaffolds (in FASTA format) are to be provided as input to SSPACE-LongRead software together with a set of long reads in FASTQ or FASTA format (e.g. PacBio CLR reads). Note that in our study we observe that SSPACE-LongRead obtains the best results if the draft assembly is constructed with CLCbio or Newbler as these tend to better split contigs at repeat boundaries (see the Results and Discussion section for additional explanations).

Alignment of long reads against the pre-assembled contigs (or scaffolds)

Each long read is aligned against the pre-assembled contigs with BLASR [25] resulting in a local alignment and corresponding similarity score. For gap-estimation purposes, the local alignments are extended to generate a full contig match. In order to remove false-positive alignments, contigs that display a (partial) overlap with a contig that has obtained a higher alignment score are iteratively removed from the dataset (Figure 2, step B). The minimum overlap (in bp) required to remove a contig from the alignment is defined by parameter -g (default value = 200).

Computation of contig linkage from the alignment order

The remaining contigs are sorted based on their alignment position on the long reads. Subsequently the contig distance and orientation is computed. Each contig-pairing and multi-contig linkage is stored in a hash: the preferred pairings are retained by removing contig-links which are also found within a multi-contig path of another contig-link. For example, if there are two paths, A- > B- > C- > D and A- > C- > D, the linkage of A- > C is removed. The ambiguous paths are mainly explained by the fact that the BLASR alignment tool generally can not resolve (align) low-quality alignment regions of PacBio reads. If multiple paths remain, a ratio is calculated between the two best alternatives. Ambiguous pairings are solved using the multi-contig linkage information, the unsolved pairings are flagged as repeated elements.

Scaffolding contigs into scaffolds

The contigs are now connected into linear stretches where repeated elements are placed based on the multi-contig linkage information. Repeated elements on the edges of the linear stretches are removed. As a post-processing step, the non-repeated edges of the preliminary scaffolds are reused to find connections between other preliminary scaffolds. Finally the gap-size between each contig is calculated: if this value is negative, and an overlap is found, contigs are merged; if this value is positive, a gap is inserted between contigs (the gap is represented by one or more undefined ‘N’ nucleotides depending on the gap-size).

Software output

The final linear assembly is represented in an easily interpretable FASTA file. In addition a AGP (Accessioned Golden Path) file is generated which describes the contig order within the scaffolds. The latter files can be readily used for NCBI genome submissions. Also a summary file containing statistics of the assembly process and final assembly structure is provided in TEXT format.

Datasets

In total six bacterial datasets were used for testing the performance of the software. These comprise Illumina MiSeq, Roche-454 and PacBio RS reads from Escherichia coli (K12 MG1655), Escherichia coli (O157:H7 F8092B), Bibersteina trehalosi (USDA-ARS-USMARC-192), Mannheimia haemolytica (USDA-ARS-USMARC-2286), Francisella tularensis (99A-2628) and Salmonella enterica (Newport SN31241). Datasets are downloaded from http://www.cbcb.umd.edu/software/PBcR/closure/index.html and further described in Koren et al. (2013). Dataset statistics are displayed in Table 1. To assess the assembly correctness we used close reference genomes deposited in the NCBI database (E. coli K12 MG1655 = NC_000913, E. coli O157:H7 = NC_002127, NC_002128, NC_002695, F. tularensis = NC_008369, S. enterica = NC_011079, NC_011080, NC_009140). For B. trehalosi and M. haemolytica no reference genome is currently available.

Assembly procedure

Draft assemblies of Illumina MiSeq data were constructed using Ray version 2.3.0 [18] and the CLCbio de novo assembler version 6.5.1 (CLC bio, Aarhus, Denmark) using for each program a k-mer setting of 31. Draft assemblies of Roche-454 data were constructed using Newbler version 2.8 (Roche) as described in Koren et al. [19]. Scaffolding was performed using AHA [14] which is part of the SMRT Analysis Package version 2.0 and SSPACE-LongRead. For the latter software we required a minimal estimated overlap of 200 bp (option -g) between the contigs in order to avoid false positive alignments. For Ray the minimal estimated overlap was set to 500 bp since Ray tends to include repeated elements on contigs edges: as a result a larger overlap between the contigs is observed.

System

All analysis were performed on a 48 Gb Linux machine (Intel Xeon X5650, 2.67 GHz).

Sourcecode

SSPACE-LongRead is written in Perl and runs on all major Linux platforms. A pseudocode of the algorithm is given in Additional file 1: Figure S1.