Local de novo assembly of RAD paired-end contigs using short sequencing reads

Despite the power of massively parallel sequencing platforms, a drawback is the short length of the sequence reads produced. We demonstrate that short reads can be locally assembled into longer contigs using paired-end sequencing of restriction-site associated DNA (RAD-PE) fragments. We use this RAD-PE contig approach to identify single nucleotide polymorphisms (SNPs) and determine haplotype structure in threespine stickleback and to sequence E. coli and stickleback genomic DNA with overlapping contigs of several hundred nucleotides. We also demonstrate that adding a circularization step allows the local assembly of contigs up to 5 kilobases (kb) in length. The ease of assembly and accuracy of the individual contigs produced from each RAD site sequence suggests RAD-PE sequencing is a useful way to convert genome-wide short reads into individually-assembled sequences hundreds or thousands of nucleotides long.     

Comparing de novo genome assembly: the long and short of it

Recent advances in DNA sequencing technology and their focal role in Genome Wide Association Studies (GWAS) have rekindled a growing interest in the whole-genome sequence assembly (WGSA) problem, thereby, inundating the field with a plethora of new formalizations, algorithms, heuristics and implementations. And yet, scant attention has been paid to comparative assessments of these assemblers' quality and accuracy. No commonly accepted and standardized method for comparison exists yet. Even worse, widely used metrics to compare the assembled sequences emphasize only size, poorly capturing the contig quality and accuracy. This paper addresses these concerns: it highlights common anomalies in assembly accuracy through a rigorous study of several assemblers, compared under both standard metrics (N50, coverage, contig sizes, etc.) as well as a more comprehensive metric (Feature-Response Curves, FRC) that is introduced here; FRC transparently captures the trade-offs between contigs' quality against their sizes. For this purpose, most of the publicly available major sequence assemblers--both for low-coverage long (Sanger) and high-coverage short (Illumina) reads technologies--are compared. These assemblers are applied to microbial (Escherichia coli, Brucella, Wolbachia, Staphylococcus, Helicobacter) and partial human genome sequences (Chr. Y), using sequence reads of various read-lengths, coverages, accuracies, and with and without mate-pairs. It is hoped that, based on these evaluations, computational biologists will identify innovative sequence assembly paradigms, bioinformaticists will determine promising approaches for developing "next-generation" assemblers, and biotechnologists will formulate more meaningful design desiderata for sequencing technology platforms. A new software tool for computing the FRC metric has been developed and is available through the AMOS open-source consortium.     

MetaVelvet: an extension of Velvet assembler to de novo metagenome assembly from short sequence reads

An important step in ‘metagenomics’ analysis is the assembly of multiple genomes from mixed sequence reads of multiple species in a microbial community. Most conventional pipelines use a single-genome assembler with carefully optimized parameters. A limitation of a single-genome assembler for de novo metagenome assembly is that sequences of highly abundant species are likely misidentified as repeats in a single genome, resulting in a number of small fragmented scaffolds. We extended a single-genome assembler for short reads, known as ‘Velvet’, to metagenome assembly, which we called ‘MetaVelvet’, for mixed short reads of multiple species. Our fundamental concept was to first decompose a de Bruijn graph constructed from mixed short reads into individual sub-graphs, and second, to build scaffolds based on each decomposed de Bruijn sub-graph as an isolate species genome. We made use of two features, the coverage (abundance) difference and graph connectivity, for the decomposition of the de Bruijn graph. For simulated datasets, MetaVelvet succeeded in generating significantly higher N50 scores than any single-genome assemblers. MetaVelvet also reconstructed relatively low-coverage genome sequences as scaffolds. On real datasets of human gut microbial read data, MetaVelvet produced longer scaffolds and increased the number of predicted genes.     

Rapid hybrid de novo assembly of a microbial genome using only short reads: Corynebacterium pseudotuberculosis I19 as a case study

Due to the advent of the so-called Next-Generation Sequencing (NGS) technologies the amount of monetary and temporal resources for whole-genome sequencing has been reduced by several orders of magnitude. Sequence reads can be assembled either by anchoring them directly onto an available reference genome (classical reference assembly), or can be concatenated by overlap (de novo assembly). The latter strategy is preferable because it tends to maintain the architecture of the genome sequence the however, depending on the NGS platform used, the shortness of read lengths cause tremendous problems the in the subsequent genome assembly phase, impeding closing of the entire genome sequence. To address the problem, we developed a multi-pronged hybrid de novo strategy combining De Bruijn graph and Overlap-Layout-Consensus methods, which was used to assemble from short reads the entire genome of Corynebacterium pseudotuberculosis strain I19, a bacterium with immense importance in veterinary medicine that causes Caseous Lymphadenitis in ruminants, principally ovines and caprines. Briefly, contigs were assembled de novo from the short reads and were only oriented using a reference genome by anchoring. Remaining gaps were closed using iterative anchoring of short reads by craning to gap flanks. Finally, we compare the genome sequence assembled using our hybrid strategy to a classical reference assembly using the same data as input and show that with the availability of a reference genome, it pays off to use the hybrid de novo strategy, rather than a classical reference assembly, because more genome sequences are preserved using the former.     

Inferring short tandem repeat variation from paired-end short reads

The advances of high-throughput sequencing offer an unprecedented opportunity to study genetic variation. This is challenged by the difficulty of resolving variant calls in repetitive DNA regions. We present a Bayesian method to estimate repeat-length variation from paired-end sequence read data. The method makes variant calls based on deviations in sequence fragment sizes, allowing the analysis of repeats at lengths of relevance to a range of phenotypes. We demonstrate the method’s ability to detect and quantify changes in repeat lengths from short read genomic sequence data across genotypes. We use the method to estimate repeat variation among 12 strains of Arabidopsis thaliana and demonstrate experimentally that our method compares favourably against existing methods. Using this method, we have identified all repeats across the genome, which are likely to be polymorphic. In addition, our predicted polymorphic repeats also included the only known repeat expansion in A. thaliana, suggesting an ability to discover potential unstable repeats.     

Fragment assembly with short reads

MOTIVATION: Current DNA sequencing technology produces reads of about 500-750 bp, with typical coverage under 10x. New sequencing technologies are emerging that produce shorter reads (length 80-200 bp) but allow one to generate significantly higher coverage (30x and higher) at low cost. Modern assembly programs and error correction routines have been tuned to work well with current read technology but were not designed for assembly of short reads. RESULTS: We analyze the limitations of assembling reads generated by these new technologies and present a routine for base-calling in reads prior to their assembly. We demonstrate that while it is feasible to assemble such short reads, the resulting contigs will require significant (if not prohibitive) finishing efforts. AVAILABILITY: Available from the web at http://www.cse.ucsd.edu/groups/bioinformatics/software.html     

Gene-boosted assembly of a novel bacterial genome from very short reads

Recent improvements in technology have made DNA sequencing dramatically faster and more efficient than ever before. The new technologies produce highly accurate sequences, but one drawback is that the most efficient technology produces the shortest read lengths. Short-read sequencing has been applied successfully to resequence the human genome and those of other species but not to whole-genome sequencing of novel organisms. Here we describe the sequencing and assembly of a novel clinical isolate of Pseudomonas aeruginosa, strain PAb1, using very short read technology. From 8,627,900 reads, each 33 nucleotides in length, we assembled the genome into one scaffold of 76 ordered contiguous sequences containing 6,290,005 nucleotides, including one contig spanning 512,638 nucleotides, plus an additional 436 unordered contigs containing 416,897 nucleotides. Our method includes a novel gene-boosting algorithm that uses amino acid sequences from predicted proteins to build a better assembly. This study demonstrates the feasibility of very short read sequencing for the sequencing of bacterial genomes, particularly those for which a related species has been sequenced previously, and expands the potential application of this new technology to most known prokaryotic species.     

Targeted assembly of short sequence reads

As next-generation sequence (NGS) production continues to increase, analysis is becoming a significant bottleneck. However, in situations where information is required only for specific sequence variants, it is not necessary to assemble or align whole genome data sets in their entirety. Rather, NGS data sets can be mined for the presence of sequence variants of interest by localized assembly, which is a faster, easier, and more accurate approach. We present TASR, a streamlined assembler that interrogates very large NGS data sets for the presence of specific variants by only considering reads within the sequence space of input target sequences provided by the user. The NGS data set is searched for reads with an exact match to all possible short words within the target sequence, and these reads are then assembled stringently to generate a consensus of the target and flanking sequence. Typically, variants of a particular locus are provided as different target sequences, and the presence of the variant in the data set being interrogated is revealed by a successful assembly outcome. However, TASR can also be used to find unknown sequences that flank a given target. We demonstrate that TASR has utility in finding or confirming genomic mutations, polymorphisms, fusions and integration events. Targeted assembly is a powerful method for interrogating large data sets for the presence of sequence variants of interest. TASR is a fast, flexible and easy to use tool for targeted assembly.     

De novo assembly of the Pseudomonas syringae pv. syringae B728a genome using Illumina/Solexa short sequence reads

Illumina's Genome Analyzer generates ultra-short sequence reads, typically 36 nucleotides in length, and is primarily intended for resequencing. We tested the potential of this technology for de novo sequence assembly on the 6 Mbp genome of Pseudomonas syringae pv. syringae B728a with several freely available assembly software packages. Using an unpaired data set, velvet assembled >96% of the genome into contigs with an N50 length of 8289 nucleotides and an error rate of 0.33%. edena generated smaller contigs (N50 was 4192 nucleotides) and comparable error rates. ssake and vcake yielded shorter contigs with very high error rates. Assembly of paired-end sequence data carrying 400 bp inserts produced longer contigs (N50 up to 15 628 nucleotides), but with increased error rates (0.5%). Contig length and error rate were very sensitive to the choice of parameter values. Noncoding RNA genes were poorly resolved in de novo assemblies, while >90% of the protein-coding genes were assembled with 100% accuracy over their full length. This study demonstrates that, in practice, de novo assembly of 36-nucleotide reads can generate reasonably accurate assemblies from about 40 × deep sequence data sets. These draft assemblies are useful for exploring an organism's proteomic potential, at a very economic low cost.     

Current challenges in de novo plant genome sequencing and assembly

Genome sequencing is now affordable, but assembling plant genomes de novo remains challenging. We assess the state of the art of assembly and review the best practices for the community.     

Assisted assembly: how to improve a de novo genome assembly by using related species

We describe a new assembly algorithm, where a genome assembly with low sequence coverage, either throughout the genome or locally, due to cloning bias, is considerably improved through an assisting process via a related genome. We show that the information provided by aligning the whole-genome shotgun reads of the target against a reference genome can be used to substantially improve the quality of the resulting assembly.     

Evaluation of methods for de novo genome assembly from high-throughput sequencing reads reveals dependencies that affect the quality of the results

Recent developments in high-throughput sequencing technology have made low-cost sequencing an attractive approach for many genome analysis tasks. Increasing read lengths, improving quality and the production of increasingly larger numbers of usable sequences per instrument-run continue to make whole-genome assembly an appealing target application. In this paper we evaluate the feasibility of de novo genome assembly from short reads (≤100 nucleotides) through a detailed study involving genomic sequences of various lengths and origin, in conjunction with several of the currently popular assembly programs. Our extensive analysis demonstrates that, in addition to sequencing coverage, attributes such as the architecture of the target genome, the identity of the used assembly program, the average read length and the observed sequencing error rates are powerful variables that affect the best achievable assembly of the target sequence in terms of size and correctness.     

First de novo whole genome sequencing and assembly of the pink-footed goose

Annotated genomes can provide new perspectives on the biology of species. We present the first de novo whole genome sequencing for the pink-footed goose. In order to obtain a high-quality de novo assembly the strategy used was to combine one short insert paired-end library with two mate-pair libraries. The pink-footed goose genome was assembled de novo using three different assemblers and an assembly evaluation was subsequently performed in order to choose the best assembler. For our data, ALLPATHS-LG performed the best, since the assembly produced covers most of the genome, while introducing the fewest errors. A total of 26,134 genes were annotated, with bird species accounting for virtually all BLAST hits. We also estimated the substitution rate in the pink-footed goose, which can be of use in future demographic studies, by using a comparative approach with the genome of the chicken, the mallard and the swan goose. A substitution rate of 1.38 × 10^? 7 per nucleotide per generation was obtained when comparing the genomes of the two closely-related goose species (the pink-footed and the swan goose). Altogether, we provide a valuable tool for future genomic studies aiming at particular genes and regions of the pink-footed goose genome as well as other bird species.     

Feature-by-feature--evaluating de novo sequence assembly

The whole-genome sequence assembly (WGSA) problem is among one of the most studied problems in computational biology. Despite the availability of a plethora of tools (i.e., assemblers), all claiming to have solved the WGSA problem, little has been done to systematically compare their accuracy and power. Traditional methods rely on standard metrics and read simulation: while on the one hand, metrics like N50 and number of contigs focus only on size without proportionately emphasizing the information about the correctness of the assembly, comparisons performed on simulated dataset, on the other hand, can be highly biased by the non-realistic assumptions in the underlying read generator. Recently the Feature Response Curve (FRC) method was proposed to assess the overall assembly quality and correctness: FRC transparently captures the trade-offs between contigs' quality against their sizes. Nevertheless, the relationship among the different features and their relative importance remains unknown. In particular, FRC cannot account for the correlation among the different features. We analyzed the correlation among different features in order to better describe their relationships and their importance in gauging assembly quality and correctness. In particular, using multivariate techniques like principal and independent component analysis we were able to estimate the "excess-dimensionality" of the feature space. Moreover, principal component analysis allowed us to show how poorly the acclaimed N50 metric describes the assembly quality. Applying independent component analysis we identified a subset of features that better describe the assemblers performances. We demonstrated that by focusing on a reduced set of highly informative features we can use the FRC curve to better describe and compare the performances of different assemblers. Moreover, as a by-product of our analysis, we discovered how often evaluation based on simulated data, obtained with state of the art simulators, lead to not-so-realistic results.     

Combining de novo and reference-guided assembly with scaffold_builder

Genome sequencing has become routine, however genome assembly still remains a challenge despite the computational advances in the last decade. In particular, the abundance of repeat elements in genomes makes it difficult to assemble them into a single complete sequence. Identical repeats shorter than the average read length can generally be assembled without issue. However, longer repeats such as ribosomal RNA operons cannot be accurately assembled using existing tools. The application Scaffold_builder was designed to generate scaffolds – super contigs of sequences joined by N-bases – based on the similarity to a closely related reference sequence. This is independent of mate-pair information and can be used complementarily for genome assembly, e.g. when mate-pairs are not available or have already been exploited. Scaffold_builder was evaluated using simulated pyrosequencing reads of the bacterial genomes Escherichia coli 042, Lactobacillus salivarius UCC118 and Salmonella enterica subsp. enterica serovar Typhi str. P-stx-12. Moreover, we sequenced two genomes from Salmonella enterica serovar Typhimurium LT2 G455 and Salmonella enterica serovar Typhimurium SDT1291 and show that Scaffold_builder decreases the number of contig sequences by 53% while more than doubling their average length. Scaffold_builder is written in Python and is available at http://edwards.sdsu.edu/scaffold_builder. A web-based implementation is additionally provided to allow users to submit a reference genome and a set of contigs to be scaffolded.     

Evaluating the fidelity of de novo short read metagenomic assembly using simulated data

A frequent step in metagenomic data analysis comprises the assembly of the sequenced reads. Many assembly tools have been published in the last years targeting data coming from next-generation sequencing (NGS) technologies but these assemblers have not been designed for or tested in multi-genome scenarios that characterize metagenomic studies. Here we provide a critical assessment of current de novo short reads assembly tools in multi-genome scenarios using complex simulated metagenomic data. With this approach we tested the fidelity of different assemblers in metagenomic studies demonstrating that even under the simplest compositions the number of chimeric contigs involving different species is noticeable. We further showed that the assembly process reduces the accuracy of the functional classification of the metagenomic data and that these errors can be overcome raising the coverage of the studied metagenome. The results presented here highlight the particular difficulties that de novo genome assemblers face in multi-genome scenarios demonstrating that these difficulties, that often compromise the functional classification of the analyzed data, can be overcome with a high sequencing effort.