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      DBG2OLC: Efficient Assembly of Large Genomes Using the Compressed Overlap Graph

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          Abstract

          The highly anticipated transition to the third generation DNA sequencing (3rdGS) technology have reached a stalemate primarily due to the high error rates (15-45%), which make the assembly of long erroneous reads extremely challenging because existing software solutions for 3rdGS assembly are often overwhelmed by error correction tasks. We report three significant breakthroughs that push the envelope of genome assembly and offer an enabling software solution to overcome the current 3rdGS stalemate. Firstly, we take a counter-intuitive strategy and develop a base-level correction-free assembly algorithm, which resorts to data compression technology and the assembly was performed with the compressed reads. Magnitudes of compression lead to magnitudes of reduction in read lengths, enabling magnitudes of savings in computational time and memory space. We implement the new algorithm in a proof-of-concept software package DBG2OLC. Experiments with the 3rdGS data including PacBio and Oxford Nanopore show that our method is able to assemble large genomes magnitudes more efficiently than existing methods. For example, on a large PacBio human genome dataset we calculated the all-pair alignment of 54x erroneous long reads in 6 hours compared to the 405,000 CPU hours previously reported by Pacific Biosciences. Secondly, while maintaining comparable high quality assemblies, our approach requires significantly lower sequencing coverage (10x-20x) than existing assemblers, which translates to significant cost-cut for genome sequencing. Thirdly, our method is highly adaptive and is the only one to date that demonstrates ultra efficiencies not only for the 3rdGS PacBio and Nanapore sequences, but also for the latest NGS data.

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          The sequence of the human genome.

          A 2.91-billion base pair (bp) consensus sequence of the euchromatic portion of the human genome was generated by the whole-genome shotgun sequencing method. The 14.8-billion bp DNA sequence was generated over 9 months from 27,271,853 high-quality sequence reads (5.11-fold coverage of the genome) from both ends of plasmid clones made from the DNA of five individuals. Two assembly strategies-a whole-genome assembly and a regional chromosome assembly-were used, each combining sequence data from Celera and the publicly funded genome effort. The public data were shredded into 550-bp segments to create a 2.9-fold coverage of those genome regions that had been sequenced, without including biases inherent in the cloning and assembly procedure used by the publicly funded group. This brought the effective coverage in the assemblies to eightfold, reducing the number and size of gaps in the final assembly over what would be obtained with 5.11-fold coverage. The two assembly strategies yielded very similar results that largely agree with independent mapping data. The assemblies effectively cover the euchromatic regions of the human chromosomes. More than 90% of the genome is in scaffold assemblies of 100,000 bp or more, and 25% of the genome is in scaffolds of 10 million bp or larger. Analysis of the genome sequence revealed 26,588 protein-encoding transcripts for which there was strong corroborating evidence and an additional approximately 12,000 computationally derived genes with mouse matches or other weak supporting evidence. Although gene-dense clusters are obvious, almost half the genes are dispersed in low G+C sequence separated by large tracts of apparently noncoding sequence. Only 1.1% of the genome is spanned by exons, whereas 24% is in introns, with 75% of the genome being intergenic DNA. Duplications of segmental blocks, ranging in size up to chromosomal lengths, are abundant throughout the genome and reveal a complex evolutionary history. Comparative genomic analysis indicates vertebrate expansions of genes associated with neuronal function, with tissue-specific developmental regulation, and with the hemostasis and immune systems. DNA sequence comparisons between the consensus sequence and publicly funded genome data provided locations of 2.1 million single-nucleotide polymorphisms (SNPs). A random pair of human haploid genomes differed at a rate of 1 bp per 1250 on average, but there was marked heterogeneity in the level of polymorphism across the genome. Less than 1% of all SNPs resulted in variation in proteins, but the task of determining which SNPs have functional consequences remains an open challenge.
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            Versatile and open software for comparing large genomes

            Background Genome sequence comparison has been an important method for understanding gene function and genome evolution since the early days of gene sequencing. The pairwise sequence-comparison methods implemented in BLAST [1] and FASTA [2] have proved invaluable in discovering the evolutionary relationships and functions of thousands of proteins from hundreds of different species. The most commonly used application of these sequence-analysis programs is for comparing a single gene (either a DNA sequence or the protein translation of that sequence) to a large database of other genes. The results of such protein and nucleotide database searches have been used in recent years as the basis for assigning function to most of the newly discovered genes emerging from genome projects. In recent years, an important new sequence-analysis task has emerged: comparing an entire genome with another. Until 1999, each new genome published was so distant from all previous genomes that aligning them would not yield interesting results. With the publication of the second strain of Helicobacter pylori [3] in 1999, following the publication of the first strain [4] in 1997, the scientific world had its first chance to look at two complete bacterial genomes whose DNA sequences lined up very closely. Comparison of these genomes revealed an overall genomic structure that was very similar, but showed evidence of two large inversion events centered on the replication origin. The comparison also made it clear that a new type of bioinformatics program was needed, one that could efficiently compare two megabase-scale sequences, something that BLAST cannot do. In response to this need, TIGR released MUMmer 1.0, the first system that could perform genome comparisons of this scale [5]. The first two releases of MUMmer had over 1,600 site licensees, a number that has grown since moving to an open-source license in May 2003. The number of pairs of closely related genomes has increased dramatically in recent years, with a corresponding increase in the number of scientific studies of genome structure and evolution, facilitated by new software that permits the comparisons of these genomes. As of mid-2003, there are more than 150 complete published genomes, with over 380 prokaryotic genome projects and 240 eukaryotic projects under way. Many of these involve species that are closely related to published genomes. The published databases already include 33 species for which at least one other closely related species has been sequenced; for a detailed list see [6]. More distantly related pairs of species, for example, Plasmodium falciparum and P. yoelii, fail to show DNA sequence similarity but do show large-scale similarity when their translated protein sequences are aligned, as described in earlier studies [7,8]. Related to the growing number of closely related species that have been sequenced is a rapid growth in the number of known species whose genomes are similar but have undergone significant rearrangement. The human and mouse genomes, for example, are both available in draft form, and the chromosomes of either species can be aligned with the other at the DNA level. Various lines of evidence in the past have pointed to massive genome rearrangements separating the species, and the latest analysis indicates that the mouse genome can be split into 217 large segments that can be rearranged to produce the same gene order as in the human genome [9]. This very large-scale similarity interrupted by rearrangements places additional demands on genome-comparison programs: essentially, one must produce all pairs of similar regions in the sequences (in form of local alignments), not merely a single 'best' or longest global alignment of the entire sequences. In addition to the need for whole-genome alignment programs, another need has become evident recently - a means of reliably evaluating and comparing genome assemblies. The explosion of genome sequencing has brought with it an explosion in genome-assembly programs, with several new assemblers either under development or recently released [10-12]. Unlike the previous generation of assemblers (TIGR Assembler [13], phrap [14], and CAP3 [15]), these second-generation assemblers are designed to handle large eukaryotic genomes. Assembly of large genomes is a major technical challenge, and once an assembly has been produced, evaluating it can be almost as difficult. Debates over the relative quality of assemblies produced by different assemblers are ongoing, and whole-genome comparison algorithms represent a critical tool in these analyses. Different assemblies of the same data should be nearly 100% identical, making the comparison problem analogous to the problem of comparing closely related species. Assembly differences may represent errors in one of the algorithms, and are useful for providing insights into the strengths and weaknesses of different methods. The large-scale comparison problem also occurs for assemblies delivered by the same software but from different inputs; for example, assemblies at threefold (3×) coverage and sixfold (6×) coverage of the same genome. With larger eukaryotic projects, multiple assemblies are run at different stages of the project, and comparisons of the successive assemblies provide a map showing how to transfer any analyses (such as gene predictions) from one assembly to another. A third use for rapid, large-scale alignment programs has come up in our own applications. As part of our annotation 'pipeline' at TIGR, we routinely rebuild a database containing the results of all-against-all BLAST searches for all known proteins. Each time a new genome is added to the public archives, many thousands of searches need to be re-run to incorporate the newly sequenced genes. Because of the size of the archive, these additional searches take a relatively long time. A rapid method for identifying potential hits is used as a pre-screen as follows: for each new gene that is being added to the database, we use the high-speed method (MUMmer) to determine if it has any potential hits. If it does not, then it can be omitted from subsequent BLAST searches. If a new genome has a large number of novel proteins, this pre-screening step can substantially reduce the time required to search it against the database. The new MUMmer system, version 3.0, addresses all of the above uses and more, including new graphical modules for viewing assembly comparisons and for looking at more distantly related species alignments. In addition, the implementations of all the fundamental search operations are now either optimal or nearly optimal, in the sense of running in time proportional to the sum of their input and output sizes. Other parts of the code have also been rewritten to improve their efficiency. What may be the most significant change with MUMmer 3.0 is that it is now an open-source system. All code is publicly available without restriction on its use or redistribution, and we encourage others to add to the code base and distribute their own improvements. The modularity of the code base makes it easily extendable as well. Others can build on our matching algorithm, for example, and create their own clustering and extension steps. Results Since the development of MUMmer 1.0 in 1999, several other programs for large-scale genome comparison have been developed, for example, SSAHA [16], AVID [17], MGA [18], BLASTZ [19], and LAGAN [20] (see also [21] for a review). Most of these programs follow an anchor-based approach, which can be divided into three phases: computation of potential anchors; computation of a colinear sequence of non-overlapping potential anchors - these anchors form the basis of the alignment; and alignment of the gaps in between the anchors. The traditional methods to compute potential anchors, that is, maximal matches of some length l or longer, use a generate-and-test approach. In a first step, all matches of some fixed length k c.) The parameters l, g, and c can all be set on the command line. The chain matches are then extended using an implementation of the Smith-Waterman dynamic programming algorithm [28], which is applied to the regions between the exact matches and also to the boundaries of the chains, which may be extended outward. This 'match and extend' step in the algorithm is essentially the same as that used by FASTA [29], BLAST [30], and many other sequence-alignment programs. When two species are very similar, such as the two isolates of the Bacillus anthracis Ames strain sequenced at TIGR [31-33], then MUMmer is ideally suited for aligning the genomes. In that comparison of anthrax isolates, only four single-nucleotide differences separated the two 5.3 Mbp main chromosomes from one another. Similarly, in our comparison of a clinical isolate of Mycobacterium tuberculosis to a laboratory strain [31], MUMmer quickly found the approximately 1,100 SNPs and a handful of IS elements that distinguished the strains. However, when the species being compared are more distant, Nucmer and Promer provide much more detailed and more useful alignments than MUMmer alone. In the examples described below, we show how each of the programs described here may be run for genomes at different evolutionary distances Fly versus fly The 130 Mbp genome of D. melanogaster is largely complete, with the six main chromosome arms containing only a few gaps. Recently, the Human Genome Sequencing Center at Baylor College of Medicine completed the shotgun sequencing of D. pseudoobscura, a closely related species with a genome of approximately the same size. These two species are close enough that almost all genes are shared, and exons show a high level of sequence identity. However, they are sufficiently distant that intergenic regions and introns do not align well, and there have been hundreds of large-scale chromosomal rearrangements since the species diverged. Thus, one cannot simply align each chromosome arm to its counterpart. Complicating matters further, the D. pseudoobscura shotgun assembly consists of thousands of scaffolds and contigs. To facilitate comparison, the first computational task is to align all the scaffolds to each of the D. melanogaster arms. (The comprehensive analysis of D. pseudoobscura, organized by the sequencing center scientists and their collaborators, will appear in a future paper. The description here is primarily intended to illustrate the use and capabilities of Nucmer.) We ran the Nucmer program with a minimum match length of 25, which was adequate to capture virtually all matching exons. Because matching genes are much longer, we required cluster chains to contain at least 100 matching nucleotides. To account for long introns and to allow the program to cluster together multiple genes, we allowed the gap between exact matches to be as long as 3,000 bp. At the time of our analysis (before completion of the sequencing project), the D. pseudoobscura assembly contained 4,653 scaffolds spanning 150 Mbp. We ran Nucmer separately to align the full set of scaffolds to each D. melanogaster chromosome arm. Using these settings, the program takes about 6 minutes per arm and uses approximately 490 Mb of memory on a 2.8 GHz desktop Pentium 4 PC running Linux. Fly versus mosquito When the two species are more distantly related, the only means of detecting large-scale similarity is through comparisons on the amino acid level. One example of this phenomenon arose during our comparison of the genomes of the malaria mosquito, Anopheles gambiae, and the fruit fly D. melanogaster. Because Anopheles was the second insect genome to be sequenced, the only available species for comparison was fruit fly. Our detailed analysis, done jointly with colleagues at the European Molecular Biology Laboratory in Heidelberg, was based on a combination of BLAST and MUMmer analysis [34]. These two species diverged about 250 million years ago, and they have an average protein sequence identity of 56%, less than that shared between humans and pufferfish. Although the two insects have the same number of chromosomes, the Anopheles genome is approximately twice as large, and the gene order has been almost completely shuffled, as our alignments revealed. Only small, but numerous, regions of 'microsynteny' remain: we reported 948 regions, the largest containing 8 genes in Anopheles and 31 in Drosophila. An interesting finding, though, was that despite extensive shuffling, each chromosome arm had a clear predominance of homologs on a single arm in the other species, indicating that intrachromosome gene shuffling was the primary force affecting gene order (see Figure 7 of [34]). Fungus versus fungus In a current application, we are using both Nucmer and Promer to compare two related fungal genomes, Aspergillus fumigatus (a human pathogen) and A. nidulans (a non-pathogenic model organism). Shotgun sequencing of these two genomes has been completed, and A. fumigatus is in the process of being completely finished; that is, all gaps are being closed. (A. fumigatus is a joint sequencing project of TIGR and The Sanger Institute, while A. nidulans is being sequenced at the Whitehead/MIT Genome Center.) At the time of our most recent comparison, the A. fumigatus genome had progressed to the point where it was assembled into 19 scaffolds spanning 28 Mbp, and the A. nidulans genome was assembled into 238 contigs spanning 30 Mbp. For this comparison, we first ran Nucmer and found that most of the two genomes mapped onto one another quite clearly: there are sufficient matches to reveal large segments of similarity in a simple dot plot. There has been extensive rearrangement of the chromosomes, but large-scale synteny is still present. For example, the largest contig (A1058) in A. fumigatus, at 2.9 Mbp, representing an essentially complete chromosome, maps onto five different scaffolds in A. nidulans. If one looks only at the Nucmer alignment of the largest of these, a 2.1 Mbp scaffold containing 10 contigs, it appears to be rearranged into multiple segments, but the matches are so scattered that it is difficult to tell how many segments there are (Figure 1, left-hand side). The syntenic alignment is much more clearly visible, however, if we use Promer instead. The simplest summary is just the number of bases included in the alignments: if we look at the Nucmer alignment between the scaffolds, the total number of matching bases is 81 kbp. In contrast, the Promer alignment covers 1.87 Mbp of A1058, beginning at nucleotide position 1,000,000 and continuing to the end of the chromosome. A graphical illustration is shown in Figure 1, which displays both the Promer and Nucmer alignments between the 2.1 Mbp scaffold from A. nidulans and scaffold A1058 of A. fumigatus. As the figure makes clear, the amino-acid-based alignment covers much more of the sequence of both species, and is therefore much more useful for determining homologous relationships between genes and chromosomal relationships. Human versus human One of the most challenging computational tasks one can perform today is the cross-comparison of mammalian genomes. The human and mouse genomes are sufficiently complete that much ongoing research is based on mappings between these two species. As shown in Table 1, MUMmer 3.0 can compare human and mouse chromosomes in a matter of minutes. The table shows the time (7 minutes 10 seconds, on a 2.4 GHz Pentium processor) required to align mouse chromosome 16 (Mm16) to human chromosome 21 (Hs21). These two were chosen because nearly all of Hs21 maps to one end of Mm16; in fact, researchers have developed a mouse model of Down syndrome that has an extra copy of this part of Mm16. We ran a benchmark test of MUMmer 3.0 in which we compared the human genome (version of 3 January 2003, downloaded from GenBank) to itself by computing all maximal matches of length at least 300 between each chromosome and all the others. The resulting 631,975 matches allow one to identify both large- and small-scale interchromosomal duplications. Note that the run-times reported in [6] are only for the match-finding part of MUMmer. The time for processing clusters and performing alignments in the gaps between matches are omitted as these vary widely depending on the parameters used. For this test, we needed a maximum of about 4 GB of memory. As we did not have a PC available with this amount of memory, we used a Sun-Sparc computer running the Solaris operating system, with 64 GB of memory and a 950 MHz processor. We ran the alignment as follows. Each human chromosome was used as a reference, and the rest of the genome was used as a query and streamed against it. To avoid duplication, we only included chromosomes in the query if they had not already been compared; thus we first used chromosome 1 as a reference, and streamed the other 23 chromosomes against it. Then we used chromosome 2 as a reference, and streamed chromosomes 3-22, X, and Y against that, and so on. The total length of all human chromosomes for this test was 2,839 Mbp. The time required to build all the suffix trees was 4.7 hours. The space requirement for the suffix tree was remarkably constant, with about 15.5 bytes per base-pair (with only one exception). The total query time was 101.5 hours, and memory usage never exceeded 3.9 GB (see [6] for details). Thus, in approximately 4.5 days on a single processor, we matched the human genome against itself. This could easily be divided up among multiple computers, with each chromosome handled separately, bringing the time down to just 11 hours. Graphical viewers Because the text-format output of MUMmer 3.0 is often voluminous, we have developed two graphical viewers, one for the purpose of comparing two genome assemblies or near-identical sequences, and the other for comparing more distantly related genomes, such as two distinct species. The first viewer, DisplayMUMs, is an open-source, platform-independent Java program. It has been tested on a variety of Unix/Linux platforms and also runs on Apple Macintosh (OS X) or Microsoft Windows computers. The program, which takes as input the results of running MUMmer, allows the user to align and view the results of two different assemblies of the same or very closely related genomes and to tile one set of contigs onto the other. This provides a powerful graphical front end for assembly comparison, a function that is frequently used in the process of assembling and finishing genomes. It allows a user to visualize the tiling of sequence reads onto an assembly in order to understand why contigs might not have properly merged together. Alternatively, one can compare the output of different genome assemblers on the same data, a task that can be quite bewildering when the genome is large and the assemblers disagree. DisplayMUMs creates a stand-alone display, illustrated in Figure 2. It contains three main areas. The upper area can show a variety of types of information, including zoomed-in nucleotide alignments. The central panel shows a summary of the alignment, with the reference shown as a gray bar. The matches of the queries to the reference are shown as green (forward) and red (reverse) rectangles, with gaps indicated in gray. A second gray bar shows the gaps in blue, which may seem redundant but is useful when the scale is zoomed out; for example, if the sequence has only one small gap and the scale shows 1 Mbp, then the small gap will be invisible in the upper bar but will still be visible on the lower bar. The lower panel shows the tiling of all the query sequences on the reference, with red and green colors indicating the forward and reverse matching substrings. As Figure 2 shows, some sequences might match for only a small portion of their length, while others will match across their entire length. DisplayMUMs has many other features, including mouse-over and searching functions, all of which are documented in the software. As this example makes clear, its primary purpose is to improve the utility of MUMmer for genome-assembly analysis. The second viewer, MapView, creates a picture of the mapping between two species based on Nucmer or Promer output. The motivation for creating this viewer was the rapidly increasing number of genome projects that are undertaken to enhance our understanding of another, already completed genome. In these projects, the second genome may have only faint DNA sequence similarity to the first, and in some cases the similarity may be detectable only through protein sequence alignments, such as those produced by Promer. A good example of such a project is the recent effort to sequence D. pseudoobscura mentioned above. The primary motivation for this project is to improve the annotation of D. melanogaster, and MUMmer is one of the tools being used to map the newly assembled D. pseudoobscura onto it. Because the reference genome is well annotated, we included in the viewer the option to display the locations of the genes (and their identifiers) along with the mapping at either the DNA or amino acid sequence level. A snapshot of this alignment by MapView is in Figure 3, which makes it clear that the amino acid conservation between these two species closely matches the annotated exon structure. This viewer can be used to highlight areas of a genome where exons might have been missed in previous analyses. The MapView program can produce output in three formats: fig (for viewing with the Unix xfig program), PostScript, or PDF. The most flexible format, fig, allows for unlimited scrolling and zooming, and for export to a wide range of additional formats. This makes it easy to view the mapping between a large collection of contigs and a large chromosome. Conclusions As the examples above show, the capabilities of MUMmer 3.0 enable a researcher to compare virtually any two genomes, or collections of genomic sequences, using computers widely available today. Bacterial genomes and relatively small eukaryotes can be aligned on a standard desktop computer, while larger genomes may require larger, server-class machines. With the state of the art representation of the suffix-tree data structure, the memory usage of MUMmer 3.0 is close to the minimum possible, while retaining optimal or near-optimal worst-case run time, depending on the match algorithm used. The additional features in MUMmer 3.0 allow one to find non-unique and non-exact matches, greatly enhancing the flexibility of the system. Finally, by making the system open source, we hope to encourage others to expand upon and improve the code base, which is freely available to all.
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              Nonhybrid, finished microbial genome assemblies from long-read SMRT sequencing data.

              We present a hierarchical genome-assembly process (HGAP) for high-quality de novo microbial genome assemblies using only a single, long-insert shotgun DNA library in conjunction with Single Molecule, Real-Time (SMRT) DNA sequencing. Our method uses the longest reads as seeds to recruit all other reads for construction of highly accurate preassembled reads through a directed acyclic graph-based consensus procedure, which we follow with assembly using off-the-shelf long-read assemblers. In contrast to hybrid approaches, HGAP does not require highly accurate raw reads for error correction. We demonstrate efficient genome assembly for several microorganisms using as few as three SMRT Cell zero-mode waveguide arrays of sequencing and for BACs using just one SMRT Cell. Long repeat regions can be successfully resolved with this workflow. We also describe a consensus algorithm that incorporates SMRT sequencing primary quality values to produce de novo genome sequence exceeding 99.999% accuracy.
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                Author and article information

                Journal
                2014-10-10
                2015-05-22
                1410.2801

                http://arxiv.org/licenses/nonexclusive-distrib/1.0/

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                q-bio.GN

                Genetics

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