The impact of contaminants on the accuracy of genome skimming and the effectiveness of exclusion read filters

The ability to detect the identity of a sample obtained from its environment is a cornerstone of molecular ecological research. Thanks to the falling price of shotgun sequencing, genome skimming, the acquisition of short reads spread across the genome at low coverage, is emerging as an alternative to traditional barcoding. By obtaining far more data across the whole genome, skimming has the promise to increase the precision of sample identification beyond traditional barcoding while keeping the costs manageable. While methods for assembly‐free sample identification based on genome skims are now available, little is known about how these methods react to the presence of DNA from organisms other than the target species. In this paper, we show that the accuracy of distances computed between a pair of genome skims based on k‐mer similarity can degrade dramatically if the skims include contaminant reads; i.e., any reads originating from other organisms. We establish a theoretical model of the impact of contamination. We then suggest and evaluate a solution to the contamination problem: Query reads in a genome skim against an extensive database of possible contaminants (e.g., all microbial organisms) and filter out any read that matches. We evaluate the effectiveness of this strategy when implemented using Kraken‐II, in detailed analyses. Our results show substantial improvements in accuracy as a result of filtering but also point to limitations, including a need for relatively close matches in the contaminant database.     

Mapping reads on a genomic sequence: an algorithmic overview and a practical comparative analysis

Mapping short reads against a reference genome is classically the first step of many next-generation sequencing data analyses, and it should be as accurate as possible. Because of the large number of reads to handle, numerous sophisticated algorithms have been developped in the last 3 years to tackle this problem. In this article, we first review the underlying algorithms used in most of the existing mapping tools, and then we compare the performance of nine of these tools on a well controled benchmark built for this purpose. We built a set of reads that exist in single or multiple copies in a reference genome and for which there is no mismatch, and a set of reads with three mismatches. We considered as reference genome both the human genome and a concatenation of all complete bacterial genomes. On each dataset, we quantified the capacity of the different tools to retrieve all the occurrences of the reads in the reference genome. Special attention was paid to reads uniquely reported and to reads with multiple hits.     

The fragment assembly string graph

We present a concept and formalism, the string graph, which represents all that is inferable about a DNA sequence from a collection of shotgun sequencing reads collected from it. We give time and space efficient algorithms for constructing a string graph given the collection of overlaps between the reads and, in particular, present a novel linear expected time algorithm for transitive reduction in this context. The result demonstrates that the decomposition of reads into kmers employed in the de Bruijn graph approach described earlier is not essential, and exposes its close connection to the unitig approach we developed at Celera. This paper is a preliminary piece giving the basic algorithm and results that demonstrate the efficiency and scalability of the method. These ideas are being used to build a next-generation whole genome assembler called BOA (Berkeley Open Assembler) that will easily scale to mammalian genomes.     

Correcting base-assignment errors in repeat regions of shotgun assembly

Accurate base-assignment in repeat regions of a whole genome shotgun assembly is an unsolved problem. Since reads in repeat regions cannot be easily attributed to a unique location in the genome, current assemblers may place these reads arbitrarily. As a result, the base-assignment error rate in repeats is likely to be much higher than that in the rest of the genome. We developed an iterative algorithm, EULER-AIR, that is able to correct base-assignment errors in finished genome sequences in public databases. The Wolbachia genome is among the best finished genomes. Using this genome project as an example, we demonstrated that EULER-AIR can 1) discover and correct base-assignment errors, 2) provide accurate read assignments, 3) utilize finishing reads for accurate base-assignment, and 4) provide guidance for designing finishing experiments. In the genome of Wolbachia, EULER-AIR found 16 positions with ambiguous base-assignment and two positions with erroneous bases. Besides Wolbachia, many other genome sequencing projects have significantly fewer finishing reads and, hence, are likely to contain more base-assignment errors in repeats. We demonstrate that EULER-AIR is a software tool that can be used to find and correct base-assignment errors in a genome assembly project     

Occupancy Modeling,Maximum Contig Size Probabilities and Designing Metagenomics Experiments

Mathematical aspects of coverage and gaps in genome assembly have received substantial attention by bioinformaticians. Typical problems under consideration suppose that reads can be experimentally obtained from a single genome and that the number of reads will be set to cover a large percentage of that genome at a desired depth. In metagenomics experiments genomes from multiple species are simultaneously analyzed and obtaining large numbers of reads per genome is unlikely. We propose the probability of obtaining at least one contig of a desired minimum size from each novel genome in the pool without restriction based on depth of coverage as a metric for metagenomic experimental design. We derive an approximation to the distribution of maximum contig size for single genome assemblies using relatively few reads. This approximation is verified in simulation studies and applied to a number of different metagenomic experimental design problems, ranging in difficulty from detecting a single novel genome in a pool of known species to detecting each of a random number of novel genomes collectively sized and with abundances corresponding to given distributions in a single pool.     

MetaGeniE: Characterizing Human Clinical Samples Using Deep Metagenomic Sequencing

With the decreasing cost of next-generation sequencing, deep sequencing of clinical samples provides unique opportunities to understand host-associated microbial communities. Among the primary challenges of clinical metagenomic sequencing is the rapid filtering of human reads to survey for pathogens with high specificity and sensitivity. Metagenomes are inherently variable due to different microbes in the samples and their relative abundance, the size and architecture of genomes, and factors such as target DNA amounts in tissue samples (i.e. human DNA versus pathogen DNA concentration). This variation in metagenomes typically manifests in sequencing datasets as low pathogen abundance, a high number of host reads, and the presence of close relatives and complex microbial communities. In addition to these challenges posed by the composition of metagenomes, high numbers of reads generated from high-throughput deep sequencing pose immense computational challenges. Accurate identification of pathogens is confounded by individual reads mapping to multiple different reference genomes due to gene similarity in different taxa present in the community or close relatives in the reference database. Available global and local sequence aligners also vary in sensitivity, specificity, and speed of detection. The efficiency of detection of pathogens in clinical samples is largely dependent on the desired taxonomic resolution of the organisms. We have developed an efficient strategy that identifies “all against all” relationships between sequencing reads and reference genomes. Our approach allows for scaling to large reference databases and then genome reconstruction by aggregating global and local alignments, thus allowing genetic characterization of pathogens at higher taxonomic resolution. These results were consistent with strain level SNP genotyping and bacterial identification from laboratory culture.     

<Emphasis Type="Italic">Tracembler</Emphasis> – software for <Emphasis Type="Italic">in-silico</Emphasis> chromosome walking in unassembled genomes

Background  

Whole genome shotgun sequencing produces increasingly higher coverage of a genome with random sequence reads. Progressive whole genome assembly and eventual finishing sequencing is a process that typically takes several years for large eukaryotic genomes. In the interim, all sequence reads of public sequencing projects are made available in repositories such as the NCBI Trace Archive. For a particular locus, sequencing coverage may be high enough early on to produce a reliable local genome assembly. We have developed software, Tracembler, that facilitates in silico chromosome walking by recursively assembling reads of a selected species from the NCBI Trace Archive starting with reads that significantly match sequence seeds supplied by the user.     

Multiple sequence assembly from reads alignable to a common reference genome

We describe a set of computational problems motivated by certain analysis tasks in genome resequencing. These are assembly problems for which multiple distinct sequences must be assembled, but where the relative positions of reads to be assembled are already known. This information is obtained from a common reference genome and is characteristic of resequencing experiments. The simplest variant of the problem aims at determining a minimum set of superstrings such that each sequenced read matches at least one superstring. We give an algorithm with time complexity O(N), where N is the sum of the lengths of reads, substantially improving on previous algorithms for solving the same problem. We also examine the problem of finding the smallest number of reads to remove such that the remaining reads are consistent with k superstrings. By exploiting a surprising relationship with the minimum cost flow problem, we show that this problem can be solved in polynomial time when nested reads are excluded. If nested reads are permitted, this problem of removing the minimum number of reads becomes NP-hard. We show that permitting mismatches between reads and their nearest superstrings generally renders these problems NP-hard.     

Phylogenetic comparative assembly

Background  

Recent high throughput sequencing technologies are capable of generating a huge amount of data for bacterial genome sequencing projects. Although current sequence assemblers successfully merge the overlapping reads, often several contigs remain which cannot be assembled any further. It is still costly and time consuming to close all the gaps in order to acquire the whole genomic sequence.     

Sequencing and assembly of highly heterozygous genome of Vitis vinifera L. cv Pinot Noir: problems and solutions

A new approach to sequencing and assembling a highly heterozygous genome, that of grape, species Vitis vinifera cv Pinot Noir, is described. The combining of genome shotgun of paired reads produced by Sanger sequencing and sequencing by synthesis of unpaired reads was shown to be an efficient procedure for decoding a complex genome. About 2 million SNPs and more than a million heterozygous gaps have been identified in the 500Mb genome of grape. More than 91% of the sequence assembled into 58,611 contigs is now anchored to the 19 linkage groups of V. vinifera.     

Genome assembly using Nanopore-guided long and error-free DNA reads

Background

Long-read sequencing technologies were launched a few years ago, and in contrast with short-read sequencing technologies, they offered a promise of solving assembly problems for large and complex genomes. Moreover by providing long-range information, it could also solve haplotype phasing. However, existing long-read technologies still have several limitations that complicate their use for most research laboratories, as well as in large and/or complex genome projects. In 2014, Oxford Nanopore released the MinION® device, a small and low-cost single-molecule nanopore sequencer, which offers the possibility of sequencing long DNA fragments.

Results

The assembly of long reads generated using the Oxford Nanopore MinION® instrument is challenging as existing assemblers were not implemented to deal with long reads exhibiting close to 30% of errors. Here, we presented a hybrid approach developed to take advantage of data generated using MinION® device. We sequenced a well-known bacterium, Acinetobacter baylyi ADP1 and applied our method to obtain a highly contiguous (one single contig) and accurate genome assembly even in repetitive regions, in contrast to an Illumina-only assembly. Our hybrid strategy was able to generate NaS (Nanopore Synthetic-long) reads up to 60 kb that aligned entirely and with no error to the reference genome and that spanned highly conserved repetitive regions. The average accuracy of NaS reads reached 99.99% without losing the initial size of the input MinION® reads.

Conclusions

We described NaS tool, a hybrid approach allowing the sequencing of microbial genomes using the MinION® device. Our method, based ideally on 20x and 50x of NaS and Illumina reads respectively, provides an efficient and cost-effective way of sequencing microbial or small eukaryotic genomes in a very short time even in small facilities. Moreover, we demonstrated that although the Oxford Nanopore technology is a relatively new sequencing technology, currently with a high error rate, it is already useful in the generation of high-quality genome assemblies.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-1519-z) contains supplementary material, which is available to authorized users.     

GPU-BSM: A GPU-Based Tool to Map Bisulfite-Treated Reads

Cytosine DNA methylation is an epigenetic mark implicated in several biological processes. Bisulfite treatment of DNA is acknowledged as the gold standard technique to study methylation. This technique introduces changes in the genomic DNA by converting cytosines to uracils while 5-methylcytosines remain nonreactive. During PCR amplification 5-methylcytosines are amplified as cytosine, whereas uracils and thymines as thymine. To detect the methylation levels, reads treated with the bisulfite must be aligned against a reference genome. Mapping these reads to a reference genome represents a significant computational challenge mainly due to the increased search space and the loss of information introduced by the treatment. To deal with this computational challenge we devised GPU-BSM, a tool based on modern Graphics Processing Units. Graphics Processing Units are hardware accelerators that are increasingly being used successfully to accelerate general-purpose scientific applications. GPU-BSM is a tool able to map bisulfite-treated reads from whole genome bisulfite sequencing and reduced representation bisulfite sequencing, and to estimate methylation levels, with the goal of detecting methylation. Due to the massive parallelization obtained by exploiting graphics cards, GPU-BSM aligns bisulfite-treated reads faster than other cutting-edge solutions, while outperforming most of them in terms of unique mapped reads.     

Whole Genome Complete Resequencing of Bacillus subtilis Natto by Combining Long Reads with High-Quality Short Reads

De novo microbial genome sequencing reached a turning point with third-generation sequencing (TGS) platforms, and several microbial genomes have been improved by TGS long reads. Bacillus subtilis natto is closely related to the laboratory standard strain B. subtilis Marburg 168, and it has a function in the production of the traditional Japanese fermented food “natto.” The B. subtilis natto BEST195 genome was previously sequenced with short reads, but it included some incomplete regions. We resequenced the BEST195 genome using a PacBio RS sequencer, and we successfully obtained a complete genome sequence from one scaffold without any gaps, and we also applied Illumina MiSeq short reads to enhance quality. Compared with the previous BEST195 draft genome and Marburg 168 genome, we found that incomplete regions in the previous genome sequence were attributed to GC-bias and repetitive sequences, and we also identified some novel genes that are found only in the new genome.     

Hybrid assembly of ultra-long Nanopore reads augmented with 10x-Genomics contigs: Demonstrated with a human genome

The 3rd generation of sequencing (3GS) technologies generate ultra-long reads (up to 1 Mb), which makes it possible to eliminate gaps and effectively resolve repeats in genome assembly. However, the 3GS technologies suffer from the high base-level error rates (15%–40%) and high sequencing costs. To address these issues, the hybrid assembly strategy, which utilizes both 3GS reads and inexpensive NGS (next generation sequencing) short reads, was invented. Here, we use 10×-Genomics® technology, which integrates a novel bar-coding strategy with Illumina® NGS with an advantage of revealing long-range sequence information, to replace common NGS short reads for hybrid assembly of long erroneous 3GS reads. We demonstrate the feasibility of integrating the 3GS with 10×-Genomics technologies for a new strategy of hybrid de novo genome assembly by utilizing DBG2OLC and Sparc software packages, previously developed by the authors for regular hybrid assembly. Using a human genome as an example, we show that with only 7× coverage of ultra-long Nanopore® reads, augmented with 10× reads, our approach achieved nearly the same level of quality, compared with non-hybrid assembly with 35× coverage of Nanopore reads. Compared with the assembly with 10×-Genomics reads alone, our assembly is gapless with slightly high cost. These results suggest that our new hybrid assembly with ultra-long 3GS reads augmented with 10×-Genomics reads offers a low-cost (less than ¼ the cost of the non-hybrid assembly) and computationally light-weighted (only took 109 calendar hours with peak memory-usage = 61GB on a dual-CPU office workstation) solution for extending the wide applications of the 3GS technologies.     

SMRT sequencing only de novo assembly of the sugar beet (Beta vulgaris) chloroplast genome

Background

Third generation sequencing methods, like SMRT (Single Molecule, Real-Time) sequencing developed by Pacific Biosciences, offer much longer read length in comparison to Next Generation Sequencing (NGS) methods. Hence, they are well suited for de novo- or re-sequencing projects. Sequences generated for these purposes will not only contain reads originating from the nuclear genome, but also a significant amount of reads originating from the organelles of the target organism. These reads are usually discarded but they can also be used for an assembly of organellar replicons. The long read length supports resolution of repetitive regions and repeats within the organelles genome which might be problematic when just using short read data. Additionally, SMRT sequencing is less influenced by GC rich areas and by long stretches of the same base.

Results

We describe a workflow for a de novo assembly of the sugar beet (Beta vulgaris ssp. vulgaris) chloroplast genome sequence only based on data originating from a SMRT sequencing dataset targeted on its nuclear genome. We show that the data obtained from such an experiment are sufficient to create a high quality assembly with a higher reliability than assemblies derived from e.g. Illumina reads only. The chloroplast genome is especially challenging for de novo assembling as it contains two large inverted repeat (IR) regions. We also describe some limitations that still apply even though long reads are used for the assembly.

Conclusions

SMRT sequencing reads extracted from a dataset created for nuclear genome (re)sequencing can be used to obtain a high quality de novo assembly of the chloroplast of the sequenced organism. Even with a relatively small overall coverage for the nuclear genome it is possible to collect more than enough reads to generate a high quality assembly that outperforms short read based assemblies. However, even with long reads it is not always possible to clarify the order of elements of a chloroplast genome sequence reliantly which we could demonstrate with Fosmid End Sequences (FES) generated with Sanger technology. Nevertheless, this limitation also applies to short read sequencing data but is reached in this case at a much earlier stage during finishing.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-015-0726-6) contains supplementary material, which is available to authorized users.     

An Efficient Algorithm for Mapping of Reads to a Genome Graph Using an Index Based on Hash Tables and Dynamic Programming

The problem of storage of the sequences of a number of closely related genomes and analysis of genome variations is considered. A genome graph with the structure of an acyclic directed graph is used to store matching sections of sequences and known variants. An algorithm for rapid mapping of reads to the genome graph is developed to align the individual nucleotide sequence fragments to the genome graph. The algorithm combines rapid searching using hash tables with the algorithm of dynamic programming and solves the problem of exponential growth in the number of paths on the graph. The implementation of the genome graph and the algorithm of the alignment of reads is developed. A comparison with the best-known programs with similar functionality is made.