SHARCGS, a fast and highly accurate short-read assembly algorithm for de novo genomic sequencing

Pebble and rock band: heuristic resolution of repeats and scaffolding in the velvet short-read de novo assembler

Background

Despite the short length of their reads, micro-read sequencing technologies have shown their usefulness for de novo sequencing. However, especially in eukaryotic genomes, complex repeat patterns are an obstacle to large assemblies.

Principal Findings

We present a novel heuristic algorithm, Pebble, which uses paired-end read information to resolve repeats and scaffold contigs to produce large-scale assemblies. In simulations, we can achieve weighted median scaffold lengths (N50) of above 1 Mbp in Bacteria and above 100 kbp in more complex organisms. Using real datasets we obtained a 96 kbp N50 in Pseudomonas syringae and a unique 147 kbp scaffold of a ferret BAC clone. We also present an efficient algorithm called Rock Band for the resolution of repeats in the case of mixed length assemblies, where different sequencing platforms are combined to obtain a cost-effective assembly.

Conclusions

These algorithms extend the utility of short read only assemblies into large complex genomes. They have been implemented and made available within the open-source Velvet short-read de novo assembler.

De novo assembly of human genomes with massively parallel short read sequencing

Next-generation massively parallel DNA sequencing technologies provide ultrahigh throughput at a substantially lower unit data cost; however, the data are very short read length sequences, making de novo assembly extremely challenging. Here, we describe a novel method for de novo assembly of large genomes from short read sequences. We successfully assembled both the Asian and African human genome sequences, achieving an N50 contig size of 7.4 and 5.9 kilobases (kb) and scaffold of 446.3 and 61.9 kb, respectively. The development of this de novo short read assembly method creates new opportunities for building reference sequences and carrying out accurate analyses of unexplored genomes in a cost-effective way.

BOAT: Basic Oligonucleotide Alignment Tool

Background

Next-generation DNA sequencing technologies generate tens of millions of sequencing reads in one run. These technologies are now widely used in biology research such as in genome-wide identification of polymorphisms, transcription factor binding sites, methylation states, and transcript expression profiles. Mapping the sequencing reads to reference genomes efficiently and effectively is one of the most critical analysis tasks. Although several tools have been developed, their performance suffers when both multiple substitutions and insertions/deletions (indels) occur together.

Results

We report a new algorithm, Basic Oligonucleotide Alignment Tool (BOAT) that can accurately and efficiently map sequencing reads back to the reference genome. BOAT can handle several substitutions and indels simultaneously, a useful feature for identifying SNPs and other genomic structural variations in functional genomic studies. For better handling of low-quality reads, BOAT supports a "3'-end Trimming Mode" to build local optimized alignment for sequencing reads, further improving sensitivity. BOAT calculates an E-value for each hit as a quality assessment and provides customizable post-mapping filters for further mapping quality control.

Conclusion

Evaluations on both real and simulation datasets suggest that BOAT is capable of mapping large volumes of short reads to reference sequences with better sensitivity and lower memory requirement than other currently existing algorithms. The source code and pre-compiled binary packages of BOAT are publicly available for download at http://boat.cbi.pku.edu.cn under GNU Public License (GPL). BOAT can be a useful new tool for functional genomics studies.

Maximum likelihood genome assembly

Whole genome shotgun assembly is the process of taking many short sequenced segments (reads) and reconstructing the genome from which they originated. We demonstrate how the technique of bidirected network flow can be used to explicitly model the double-stranded nature of DNA for genome assembly. By combining an algorithm for the Chinese Postman Problem on bidirected graphs with the construction of a bidirected de Bruijn graph, we are able to find the shortest double-stranded DNA sequence that contains a given set of k-long DNA molecules. This is the first exact polynomial time algorithm for the assembly of a double-stranded genome. Furthermore, we propose a maximum likelihood framework for assembling the genome that is the most likely source of the reads, in lieu of the standard maximum parsimony approach (which finds the shortest genome subject to some constraints). In this setting, we give a bidirected network flow-based algorithm that, by taking advantage of high coverage, accurately estimates the copy counts of repeats in a genome. Our second algorithm combines these predicted copy counts with matepair data in order to assemble the reads into contigs. We run our algorithms on simulated read data from Escherichia coli and predict copy counts with extremely high accuracy, while assembling long contigs.

RNA-seq: from technology to biology

Next-generation sequencing technologies are now being exploited not only to analyse static genomes, but also dynamic transcriptomes in an approach termed RNA-seq. Although these powerful and rapidly evolving technologies have only been available for a couple of years, they are already making substantial contributions to our understanding of genome expression and regulation. Here, we briefly describe technical issues accompanying RNA-seq data generation and analysis, highlighting differences to array-based approaches. We then review recent biological insight gained from applying RNA-seq and related approaches to deeply sample transcriptomes in different cell types or physiological conditions. These approaches are providing fascinating information about transcriptional and post-transcriptional gene regulation, and they are also giving unique insight into the richness of transcript structures and processing on a global scale and at unprecedented resolution.

Large scale single nucleotide polymorphism discovery in unsequenced genomes using second generation high throughput sequencing technology: applied to turkey

BACKGROUND

The development of second generation sequencing methods has enabled large scale DNA variation studies at moderate cost. For the high throughput discovery of single nucleotide polymorphisms (SNPs) in species lacking a sequenced reference genome, we set-up an analysis pipeline based on a short read de novo sequence assembler and a program designed to identify variation within short reads. To illustrate the potential of this technique, we present the results obtained with a randomly sheared, enzymatically generated, 2-3 kbp genome fraction of six pooled Meleagris gallopavo (turkey) individuals.

RESULTS

A total of 100 million 36 bp reads were generated, representing approximately 5-6% (approximately 62 Mbp) of the turkey genome, with an estimated sequence depth of 58. Reads consisting of bases called with less than 1% error probability were selected and assembled into contigs. Subsequently, high throughput discovery of nucleotide variation was performed using sequences with more than 90% reliability by using the assembled contigs that were 50 bp or longer as the reference sequence. We identified more than 7,500 SNPs with a high probability of representing true nucleotide variation in turkeys. Increasing the reference genome by adding publicly available turkey BAC-end sequences increased the number of SNPs to over 11,000. A comparison with the sequenced chicken genome indicated that the assembled turkey contigs were distributed uniformly across the turkey genome. Genotyping of a representative sample of 340 SNPs resulted in a SNP conversion rate of 95%. The correlation of the minor allele count (MAC) and observed minor allele frequency (MAF) for the validated SNPs was 0.69.

CONCLUSION

We provide an efficient and cost-effective approach for the identification of thousands of high quality SNPs in species currently lacking a sequenced genome and applied this to turkey. The methodology addresses a random fraction of the genome, resulting in an even distribution of SNPs across the targeted genome.

Novel software package for cross-platform transcriptome analysis (CPTRA)

Background

Next-generation sequencing techniques enable several novel transcriptome profiling approaches. Recent studies indicated that digital gene expression profiling based on short sequence tags has superior performance as compared to other transcriptome analysis platforms including microarrays. However, the transcriptomic analysis with tag-based methods often depends on available genome sequence. The use of tag-based methods in species without genome sequence should be complemented by other methods such as cDNA library sequencing. The combination of different next generation sequencing techniques like 454 pyrosequencing and Illumina Genome Analyzer (Solexa) will enable high-throughput and accurate global gene expression profiling in species with limited genome information. The combination of transcriptome data acquisition methods requires cross-platform transcriptome data analysis platforms, including a new software package for data processing.

Results

Here we presented a software package, CPTRA: Cross-Platform TRanscriptome Analysis, to analyze transcriptome profiling data from separate methods. The software package is available at http://people.tamu.edu/~syuan/cptra/cptra.html. It was applied to the case study of non-target site glyphosate resistance in horseweed; and the data was mined to discover resistance target gene(s). For the software, the input data included a long-read sequence dataset with proper annotation, and a short-read sequence tag dataset for the quantification of transcripts. By combining the two datasets, the software carries out the unique sequence tag identification, tag counting for transcript quantification, and cross-platform sequence matching functions, whereby the short sequence tags can be annotated with a function, level of expression, and Gene Ontology (GO) classification. Multiple sequence search algorithms were implemented and compared. The analysis highlighted the importance of transport genes in glyphosate resistance and identified several candidate genes for down-stream analysis.

Conclusion

CPTRA is a powerful software package for next generation sequencing-based transcriptome profiling in species with limited genome information. According to our case study, the strategy can greatly broaden the application of the next generation sequencing for transcriptome analysis in species without reference genome sequence.

ALLPATHS 2: small genomes assembled accurately and with high continuity from short paired reads

Allpaths2, a method for accurately assembling small genomes with high continuity using short paired reads.

We demonstrate that genome sequences approaching finished quality can be generated from short paired reads. Using 36 base (fragment) and 26 base (jumping) reads from five microbial genomes of varied GC composition and sizes up to 40 Mb, ALLPATHS2 generated assemblies with long, accurate contigs and scaffolds. Velvet and EULER-SR were less accurate. For example, for Escherichia coli, the fraction of 10-kb stretches that were perfect was 99.8% (ALLPATHS2), 68.7% (Velvet), and 42.1% (EULER-SR).

Parametric complexity of sequence assembly: theory and applications to next generation sequencing

In recent years, a flurry of new DNA sequencing technologies have altered the landscape of genomics, providing a vast amount of sequence information at a fraction of the costs that were previously feasible. The task of assembling these sequences into a genome has, however, still remained an algorithmic challenge that is in practice answered by heuristic solutions. In order to design better assembly algorithms and exploit the characteristics of sequence data from new technologies, we need an improved understanding of the parametric complexity of the assembly problem. In this article, we provide a first theoretical study in this direction, exploring the connections between repeat complexity, read lengths, overlap lengths and coverage in determining the "hard" instances of the assembly problem. Our work suggests at least two ways in which existing assemblers can be extended in a rigorous fashion, in addition to delineating directions for future theoretical investigations.

PAVE: program for assembling and viewing ESTs

Background

New sequencing technologies are rapidly emerging. Many laboratories are simultaneously working with the traditional Sanger ESTs and experimenting with ESTs generated by the 454 Life Science sequencers. Though Sanger ESTs have been used to generate contigs for many years, no program takes full advantage of the 5' and 3' mate-pair information, hence, many tentative transcripts are assembled into two separate contigs. The new 454 technology has the benefit of high-throughput expression profiling, but introduces time and space problems for assembling large contigs.

Results

The PAVE (Program for Assembling and Viewing ESTs) assembler takes advantage of the 5' and 3' mate-pair information by requiring that the mate-pairs be assembled into the same contig and joined by n's if the two sub-contigs do not overlap. It handles the depth of 454 data sets by "burying" similar ESTs during assembly, which retains the expression level information while circumventing time and space problems. PAVE uses MegaBLAST for the clustering step and CAP3 for assembly, however it assembles incrementally to enforce the mate-pair constraint, bury ESTs, and reduce incorrect joins and splits. The PAVE data management system uses a MySQL database to store multiple libraries of ESTs along with their metadata; the management system allows multiple assemblies with variations on libraries and parameters. Analysis routines provide standard annotation for the contigs including a measure of differentially expressed genes across the libraries. A Java viewer program is provided for display and analysis of the results. Our results clearly show the benefit of using the PAVE assembler to explicitly use mate-pair information and bury ESTs for large contigs.

Conclusion

The PAVE assembler provides a software package for assembling Sanger and/or 454 ESTs. The assembly software, data management software, Java viewer and user's guide are freely available.

A draft genome sequence and functional screen reveals the repertoire of type III secreted proteins of Pseudomonas syringae pathovar tabaci 11528

Background

Pseudomonas syringae is a widespread bacterial pathogen that causes disease on a broad range of economically important plant species. Pathogenicity of P. syringae strains is dependent on the type III secretion system, which secretes a suite of up to about thirty virulence 'effector' proteins into the host cytoplasm where they subvert the eukaryotic cell physiology and disrupt host defences. P. syringae pathovar tabaci naturally causes disease on wild tobacco, the model member of the Solanaceae, a family that includes many crop species as well as on soybean.

Results

We used the 'next-generation' Illumina sequencing platform and the Velvet short-read assembly program to generate a 145X deep 6,077,921 nucleotide draft genome sequence for P. syringae pathovar tabaci strain 11528. From our draft assembly, we predicted 5,300 potential genes encoding proteins of at least 100 amino acids long, of which 303 (5.72%) had no significant sequence similarity to those encoded by the three previously fully sequenced P. syringae genomes. Of the core set of Hrp Outer Proteins that are conserved in three previously fully sequenced P. syringae strains, most were also conserved in strain 11528, including AvrE1, HopAH2, HopAJ2, HopAK1, HopAN1, HopI, HopJ1, HopX1, HrpK1 and HrpW1. However, the hrpZ1 gene is partially deleted and hopAF1 is completely absent in 11528. The draft genome of strain 11528 also encodes close homologues of HopO1, HopT1, HopAH1, HopR1, HopV1, HopAG1, HopAS1, HopAE1, HopAR1, HopF1, and HopW1 and a degenerate HopM1'. Using a functional screen, we confirmed that hopO1, hopT1, hopAH1, hopM1', hopAE1, hopAR1, and hopAI1' are part of the virulence-associated HrpL regulon, though the hopAI1' and hopM1' sequences were degenerate with premature stop codons. We also discovered two additional HrpL-regulated effector candidates and an HrpL-regulated distant homologue of avrPto1.

Conclusion

The draft genome sequence facilitates the continued development of P. syringae pathovar tabaci on wild tobacco as an attractive model system for studying bacterial disease on plants. The catalogue of effectors sheds further light on the evolution of pathogenicity and host-specificity as well as providing a set of molecular tools for the study of plant defence mechanisms. We also discovered several large genomic regions in Pta 11528 that do not share detectable nucleotide sequence similarity with previously sequenced Pseudomonas genomes. These regions may include horizontally acquired islands that possibly contribute to pathogenicity or epiphytic fitness of Pta 11528.

Next-generation sequencing technologies and their implications for crop genetics and breeding

Using next-generation sequencing technologies it is possible to resequence entire plant genomes or sample entire transcriptomes more efficiently and economically and in greater depth than ever before. Rather than sequencing individual genomes, we envision the sequencing of hundreds or even thousands of related genomes to sample genetic diversity within and between germplasm pools. Identification and tracking of genetic variation are now so efficient and precise that thousands of variants can be tracked within large populations. In this review, we outline some important areas such as the large-scale development of molecular markers for linkage mapping, association mapping, wide crosses and alien introgression, epigenetic modifications, transcript profiling, population genetics and de novo genome/organellar genome assembly for which these technologies are expected to advance crop genetics and breeding, leading to crop improvement.

Comparison of next generation sequencing technologies for transcriptome characterization

Background

We have developed a simulation approach to help determine the optimal mixture of sequencing methods for most complete and cost effective transcriptome sequencing. We compared simulation results for traditional capillary sequencing with "Next Generation" (NG) ultra high-throughput technologies. The simulation model was parameterized using mappings of 130,000 cDNA sequence reads to the Arabidopsis genome (NCBI Accession SRA008180.19). We also generated 454-GS20 sequences and de novo assemblies for the basal eudicot California poppy (Eschscholzia californica) and the magnoliid avocado (Persea americana) using a variety of methods for cDNA synthesis.

Results

The Arabidopsis reads tagged more than 15,000 genes, including new splice variants and extended UTR regions. Of the total 134,791 reads (13.8 MB), 119,518 (88.7%) mapped exactly to known exons, while 1,117 (0.8%) mapped to introns, 11,524 (8.6%) spanned annotated intron/exon boundaries, and 3,066 (2.3%) extended beyond the end of annotated UTRs. Sequence-based inference of relative gene expression levels correlated significantly with microarray data. As expected, NG sequencing of normalized libraries tagged more genes than non-normalized libraries, although non-normalized libraries yielded more full-length cDNA sequences. The Arabidopsis data were used to simulate additional rounds of NG and traditional EST sequencing, and various combinations of each. Our simulations suggest a combination of FLX and Solexa sequencing for optimal transcriptome coverage at modest cost. We have also developed ESTcalc http://fgp.huck.psu.edu/NG_Sims/ngsim.pl, an online webtool, which allows users to explore the results of this study by specifying individualized costs and sequencing characteristics.

Conclusion

NG sequencing technologies are a highly flexible set of platforms that can be scaled to suit different project goals. In terms of sequence coverage alone, the NG sequencing is a dramatic advance over capillary-based sequencing, but NG sequencing also presents significant challenges in assembly and sequence accuracy due to short read lengths, method-specific sequencing errors, and the absence of physical clones. These problems may be overcome by hybrid sequencing strategies using a mixture of sequencing methodologies, by new assemblers, and by sequencing more deeply. Sequencing and microarray outcomes from multiple experiments suggest that our simulator will be useful for guiding NG transcriptome sequencing projects in a wide range of organisms.

Integrating sequencing technologies in personal genomics: optimal low cost reconstruction of structural variants

The goal of human genome re-sequencing is obtaining an accurate assembly of an individual's genome. Recently, there has been great excitement in the development of many technologies for this (e.g. medium and short read sequencing from companies such as 454 and SOLiD, and high-density oligo-arrays from Affymetrix and NimbelGen), with even more expected to appear. The costs and sensitivities of these technologies differ considerably from each other. As an important goal of personal genomics is to reduce the cost of re-sequencing to an affordable point, it is worthwhile to consider optimally integrating technologies. Here, we build a simulation toolbox that will help us optimally combine different technologies for genome re-sequencing, especially in reconstructing large structural variants (SVs). SV reconstruction is considered the most challenging step in human genome re-sequencing. (It is sometimes even harder than de novo assembly of small genomes because of the duplications and repetitive sequences in the human genome.) To this end, we formulate canonical problems that are representative of issues in reconstruction and are of small enough scale to be computationally tractable and simulatable. Using semi-realistic simulations, we show how we can combine different technologies to optimally solve the assembly at low cost. With mapability maps, our simulations efficiently handle the inhomogeneous repeat-containing structure of the human genome and the computational complexity of practical assembly algorithms. They quantitatively show how combining different read lengths is more cost-effective than using one length, how an optimal mixed sequencing strategy for reconstructing large novel SVs usually also gives accurate detection of SNPs/indels, how paired-end reads can improve reconstruction efficiency, and how adding in arrays is more efficient than just sequencing for disentangling some complex SVs. Our strategy should facilitate the sequencing of human genomes at maximum accuracy and low cost.

In recent years, the development of high throughput sequencing and array technologies has enabled the accurate re-sequencing of individual genomes, especially in identifying and reconstructing the variants in an individual's genome compared to a “reference”. The costs and sensitivities of these technologies differ considerably from each other, and even more technologies are expected to appear in the near future. To both reduce the total cost of re-sequencing to an affordable point and be adaptive to these constantly evolving bio-technologies, we propose to build a computationally efficient simulation framework that can help us optimize the combination of different technologies to perform low cost comparative genome re-sequencing, especially in reconstructing large structural variants, which is considered in many respects the most challenging step in genome re-sequencing. Our simulation results quantitatively show how much improvement one can gain in reconstructing large structural variants by integrating different technologies in optimal ways. We envision that in the future, more experimental technologies will be incorporated into this simulation framework and its results can provide informative guidelines for the actual experimental design to achieve optimal genome re-sequencing output at low costs.

SHREC: a short-read error correction method

MOTIVATION

Second-generation sequencing technologies produce a massive amount of short reads in a single experiment. However, sequencing errors can cause major problems when using this approach for de novo sequencing applications. Moreover, existing error correction methods have been designed and optimized for shotgun sequencing. Therefore, there is an urgent need for the design of fast and accurate computational methods and tools for error correction of large amounts of short read data.

RESULTS

We present SHREC, a new algorithm for correcting errors in short-read data that uses a generalized suffix trie on the read data as the underlying data structure. Our results show that the method can identify erroneous reads with sensitivity and specificity of over 99% and 96% for simulated data with error rates of up to 3% as well as for real data. Furthermore, it achieves an error correction accuracy of over 80% for simulated data and over 88% for real data. These results are clearly superior to previously published approaches. SHREC is available as an efficient open-source Java implementation that allows processing of 10 million of short reads on a standard workstation.

A fast hybrid short read fragment assembly algorithm

SUMMARY

The shorter and vastly more numerous reads produced by second-generation sequencing technologies require new tools that can assemble massive numbers of reads in reasonable time. Existing short-read assembly tools can be classified into two categories: greedy extension-based and graph-based. While the graph-based approaches are generally superior in terms of assembly quality, the computer resources required for building and storing a huge graph are very high. In this article, we present Taipan, an assembly algorithm which can be viewed as a hybrid of these two approaches. Taipan uses greedy extensions for contig construction but at each step realizes enough of the corresponding read graph to make better decisions as to how assembly should continue. We show that this approach can achieve an assembly quality at least as good as the graph-based approaches used in the popular Edena and Velvet assembly tools using a moderate amount of computing resources.

Genome assembly reborn: recent computational challenges

Research into genome assembly algorithms has experienced a resurgence due to new challenges created by the development of next generation sequencing technologies. Several genome assemblers have been published in recent years specifically targeted at the new sequence data; however, the ever-changing technological landscape leads to the need for continued research. In addition, the low cost of next generation sequencing data has led to an increased use of sequencing in new settings. For example, the new field of metagenomics relies on large-scale sequencing of entire microbial communities instead of isolate genomes, leading to new computational challenges. In this article, we outline the major algorithmic approaches for genome assembly and describe recent developments in this domain.

Application of 'next-generation' sequencing technologies to microbial genetics

New sequencing methods generate data that can allow the assembly of microbial genome sequences in days. With such revolutionary advances in technology come new challenges in methodologies and informatics. In this article, we review the capabilities of high-throughput sequencing technologies and discuss the many options for getting useful information from the data.

ABySS: a parallel assembler for short read sequence data

Widespread adoption of massively parallel deoxyribonucleic acid (DNA) sequencing instruments has prompted the recent development of de novo short read assembly algorithms. A common shortcoming of the available tools is their inability to efficiently assemble vast amounts of data generated from large-scale sequencing projects, such as the sequencing of individual human genomes to catalog natural genetic variation. To address this limitation, we developed ABySS (Assembly By Short Sequences), a parallelized sequence assembler. As a demonstration of the capability of our software, we assembled 3.5 billion paired-end reads from the genome of an African male publicly released by Illumina, Inc. Approximately 2.76 million contigs > or =100 base pairs (bp) in length were created with an N50 size of 1499 bp, representing 68% of the reference human genome. Analysis of these contigs identified polymorphic and novel sequences not present in the human reference assembly, which were validated by alignment to alternate human assemblies and to other primate genomes.

Next-generation sequencing: from basic research to diagnostics

BACKGROUND

For the past 30 years, the Sanger method has been the dominant approach and gold standard for DNA sequencing. The commercial launch of the first massively parallel pyrosequencing platform in 2005 ushered in the new era of high-throughput genomic analysis now referred to as next-generation sequencing (NGS).

CONTENT

This review describes fundamental principles of commercially available NGS platforms. Although the platforms differ in their engineering configurations and sequencing chemistries, they share a technical paradigm in that sequencing of spatially separated, clonally amplified DNA templates or single DNA molecules is performed in a flow cell in a massively parallel manner. Through iterative cycles of polymerase-mediated nucleotide extensions or, in one approach, through successive oligonucleotide ligations, sequence outputs in the range of hundreds of megabases to gigabases are now obtained routinely. Highlighted in this review are the impact of NGS on basic research, bioinformatics considerations, and translation of this technology into clinical diagnostics. Also presented is a view into future technologies, including real-time single-molecule DNA sequencing and nanopore-based sequencing.

SUMMARY

In the relatively short time frame since 2005, NGS has fundamentally altered genomics research and allowed investigators to conduct experiments that were previously not technically feasible or affordable. The various technologies that constitute this new paradigm continue to evolve, and further improvements in technology robustness and process streamlining will pave the path for translation into clinical diagnostics.

QSRA: a quality-value guided de novo short read assembler

BACKGROUND

New rapid high-throughput sequencing technologies have sparked the creation of a new class of assembler. Since all high-throughput sequencing platforms incorporate errors in their output, short-read assemblers must be designed to account for this error while utilizing all available data.

RESULTS

We have designed and implemented an assembler, Quality-value guided Short Read Assembler, created to take advantage of quality-value scores as a further method of dealing with error. Compared to previous published algorithms, our assembler shows significant improvements not only in speed but also in output quality.

CONCLUSION

QSRA generally produced the highest genomic coverage, while being faster than VCAKE. QSRA is extremely competitive in its longest contig and N50/N80 contig lengths, producing results of similar quality to those of EDENA and VELVET. QSRA provides a step closer to the goal of de novo assembly of complex genomes, improving upon the original VCAKE algorithm by not only drastically reducing runtimes but also increasing the viability of the assembly algorithm through further error handling capabilities.

Short read DNA fragment anchoring algorithm

Background

The emerging next-generation sequencing method based on PCR technology boosts genome sequencing speed considerably, the expense is also get decreased. It has been utilized to address a broad range of bioinformatics problems. Limited by reliable output sequence length of next-generation sequencing technologies, we are confined to study gene fragments with 30~50 bps in general and it is relatively shorter than traditional gene fragment length. Anchoring gene fragments in long reference sequence is an essential and prerequisite step for further assembly and analysis works. Due to the sheer number of fragments produced by next-generation sequencing technologies and the huge size of reference sequences, anchoring would rapidly becoming a computational bottleneck.

Results and discussion

We compared algorithm efficiency on BLAT, SOAP and EMBF. The efficiency is defined as the count of total output results divided by time consumed to retrieve them. The data show that our algorithm EMBF have 3~4 times efficiency advantage over SOAP, and at least 150 times over BLAT. Moreover, when the reference sequence size is increased, the efficiency of SOAP will get degraded as far as 30%, while EMBF have preferable increasing tendency.

Conclusion

In conclusion, we deem that EMBF is more suitable for short fragment anchoring problem where result completeness and accuracy is predominant and the reference sequences are relatively large.

Parallel short sequence assembly of transcriptomes

Background

The de novo assembly of genomes and transcriptomes from short sequences is a challenging problem. Because of the high coverage needed to assemble short sequences as well as the overhead of modeling the assembly problem as a graph problem, the methods for short sequence assembly are often validated using data from BACs or small sized prokaryotic genomes.

Results

We present a parallel method for transcriptome assembly from large short sequence data sets. Our solution uses a rigorous graph theoretic framework and tames the computational and space complexity using parallel computers. First, we construct a distributed bidirected graph that captures overlap information. Next, we compact all chains in this graph to determine long unique contigs using undirected parallel list ranking, a problem for which we present an algorithm. Finally, we process this compacted distributed graph to resolve unique regions that are separated by repeats, exploiting the naturally occurring coverage variations arising from differential expression.

Conclusion

We demonstrate the validity of our method using a synthetic high coverage data set generated from the predicted coding regions of Zea mays. We assemble 925 million sequences consisting of 40 billion nucleotides in a few minutes on a 1024 processor Blue Gene/L. Our method is the first fully distributed method for assembling a non-hierarchical short sequence data set and can scale to large problem sizes.

Crystallizing short-read assemblies around seeds

Background

New short-read sequencing technologies produce enormous volumes of 25–30 base paired-end reads. The resulting reads have vastly different characteristics than produced by Sanger sequencing, and require different approaches than the previous generation of sequence assemblers. In this paper, we present a short-read de novo assembler particularly targeted at the new ABI SOLiD sequencing technology.

Results

This paper presents what we believe to be the first de novo sequence assembly results on real data from the emerging SOLiD platform, introduced by Applied Biosystems. Our assembler SHORTY augments short-paired reads using a trivially small number (5 – 10) of seeds of length 300 – 500 bp. These seeds enable us to produce significant assemblies using short-read coverage no more than 100×, which can be obtained in a single run of these high-capacity sequencers. SHORTY exploits two ideas which we believe to be of interest to the short-read assembly community: (1) using single seed reads to crystallize assemblies, and (2) estimating intercontig distances accurately from multiple spanning paired-end reads.

Conclusion

We demonstrate effective assemblies (N50 contig sizes ~40 kb) of three different bacterial species using simulated SOLiD data. Sequencing artifacts limit our performance on real data, however our results on this data are substantially better than those achieved by competing assemblers.

Finding optimal threshold for correction error reads in DNA assembling

BACKGROUND

DNA assembling is the problem of determining the nucleotide sequence of a genome from its substrings, called reads. In the experiments, there may be some errors on the reads which affect the performance of the DNA assembly algorithms. Existing algorithms, e.g. ECINDEL and SRCorr, correct the error reads by considering the number of times each length-k substring of the reads appear in the input. They treat those length-k substrings appear at least M times as correct substring and correct the error reads based on these substrings. However, since the threshold M is chosen without any solid theoretical analysis, these algorithms cannot guarantee their performances on error correction.

RESULTS

In this paper, we propose a method to calculate the probabilities of false positive and false negative when determining whether a length-k substring is correct using threshold M. Based on this optimal threshold M that minimizes the total errors (false positives and false negatives). Experimental results on both real data and simulated data showed that our calculation is correct and we can reduce the total error substrings by 77.6% and 65.1% when compared to ECINDEL and SRCorr respectively.

CONCLUSION

We introduced a method to calculate the probability of false positives and false negatives of the length-k substring using different thresholds. Based on this calculation, we found the optimal threshold to minimize the total error of false positive plus false negative.

Profiling model T-cell metagenomes with short reads

MOTIVATION

T-cell receptor (TCR) diversity in peripheral blood has not yet been fully profiled with sequence level resolution. Each T-cell clonotype expresses a unique receptor, generated by somatic recombination of TCR genes and the enormous potential for T-cell diversity makes repertoire analysis challenging. We developed a sequencing approach and assembly software (immuno-SSAKE or iSSAKE) for profiling T-cell metagenomes using short reads from the massively parallel sequencing platforms.

RESULTS

Models of sequence diversity for the TCR beta-chain CDR3 region were built using empirical data and used to simulate, at random, distinct TCR clonotypes at 1-20 p.p.m. Using simulated TCRbeta (sTCRbeta) sequences, we randomly created 20 million 36 nt reads having 1-2% random error, 20 million 42 or 50 nt reads having 1% random error and 20 million 36 nt reads with 1% error modeled on real short read data. Reads aligning to the end of known TCR variable (V) genes and having consecutive unmatched bases in the adjacent CDR3 were used to seed iSSAKE de novo assemblies of CDR3. With assembled 36 nt reads, we detect over 51% and 63% of rare (1 p.p.m.) clonotypes using a random or modeled error distribution, respectively. We detect over 99% of more abundant clonotypes (6 p.p.m. or higher) using either error distribution. Longer reads improve sensitivity, with assembled 42 and 50 nt reads identifying 82.0% and 94.7% of rare 1 p.p.m. clonotypes, respectively. Our approach illustrates the feasibility of complete profiling of the TCR repertoire using new massively parallel short read sequencing technology.

AVAILABILITY

ftp://ftp.bcgsc.ca/supplementary/iSSAKE.

Applications of ultra-high-throughput sequencing

The genomics era has enabled scientists to more readily pose truly global questions regarding mutation, evolution, gene and genome structure, function, and regulation. Just as Sanger sequencing ushered in a paradigm shift that enabled the molecular basis of biological questions to be directly addressed, to an even greater degree, ultra-high-throughput DNA sequencing is poised to dramatically change the nature of biological research. New sequencing technologies have opened the door for novel questions to be addressed at the level of the entire genome in the areas of comparative genomics, systems biology, metagenomics, and genome biology. These new sequencing technologies provide a tremendous amount of DNA sequence data to be collected at an astounding pace, with reduced costs, effort, and time as compared to Sanger sequencing. Applications of ultra-high-throughput sequencing (UHTS) are essentially limited only by the imaginations of researchers, and include genome sequencing/resequencing, small RNA discovery, deep SNP discovery, chromatin immunoprecipitation (ChIP) and RNA immunoprecipitation (RIP) coupled with sequence identification, transcriptome analysis including empirical annotation, discovery and characterization of alternative splicing, and gene expression profiling. This technology will have a profound impact on plant breeding, biotechnology, and our fundamental understanding of plant evolution, development, and environmental responses. In this chapter, we provide an overview of UHTS approaches and their applications. We also describe a protocol we have developed for deep sequencing of plant transcriptomes using the Illumina/Solexa sequencing platform.

Next-generation DNA sequencing

DNA sequence represents a single format onto which a broad range of biological phenomena can be projected for high-throughput data collection. Over the past three years, massively parallel DNA sequencing platforms have become widely available, reducing the cost of DNA sequencing by over two orders of magnitude, and democratizing the field by putting the sequencing capacity of a major genome center in the hands of individual investigators. These new technologies are rapidly evolving, and near-term challenges include the development of robust protocols for generating sequencing libraries, building effective new approaches to data-analysis, and often a rethinking of experimental design. Next-generation DNA sequencing has the potential to dramatically accelerate biological and biomedical research, by enabling the comprehensive analysis of genomes, transcriptomes and interactomes to become inexpensive, routine and widespread, rather than requiring significant production-scale efforts.

Annotating genomes with massive-scale RNA sequencing

A method for de novo genome annotation using high-throughput cDNA sequencing data.

Next generation technologies enable massive-scale cDNA sequencing (so-called RNA-Seq). Mainly because of the difficulty of aligning short reads on exon-exon junctions, no attempts have been made so far to use RNA-Seq for building gene models de novo, that is, in the absence of a set of known genes and/or splicing events. We present G-Mo.R-Se (Gene Modelling using RNA-Seq), an approach aimed at building gene models directly from RNA-Seq and demonstrate its utility on the grapevine genome.

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 x deep sequence data sets. These draft assemblies are useful for exploring an organism's proteomic potential, at a very economic low cost.

Sequence assembly

Despite the rapidly increasing number of sequenced and re-sequenced genomes, many issues regarding the computational assembly of large-scale sequencing data have remain unresolved. Computational assembly is crucial in large genome projects as well for the evolving high-throughput technologies and plays an important role in processing the information generated by these methods. Here, we provide a comprehensive overview of the current publicly available sequence assembly programs. We describe the basic principles of computational assembly along with the main concerns, such as repetitive sequences in genomic DNA, highly expressed genes and alternative transcripts in EST sequences. We summarize existing comparisons of different assemblers and provide a detailed descriptions and directions for download of assembly programs at: http://genome.ku.dk/resources/assembly/methods.html.

De novo fragment assembly with short mate-paired reads: Does the read length matter?

Increasing read length is currently viewed as the crucial condition for fragment assembly with next-generation sequencing technologies. However, introducing mate-paired reads (separated by a gap of length, GapLength) opens a possibility to transform short mate-pairs into long mate-reads of length approximately GapLength, and thus raises the question as to whether the read length (as opposed to GapLength) even matters. We describe a new tool, EULER-USR, for assembling mate-paired short reads and use it to analyze the question of whether the read length matters. We further complement the ongoing experimental efforts to maximize read length by a new computational approach for increasing the effective read length. While the common practice is to trim the error-prone tails of the reads, we present an approach that substitutes trimming with error correction using repeat graphs. An important and counterintuitive implication of this result is that one may extend sequencing reactions that degrade with length "past their prime" to where the error rate grows above what is normally acceptable for fragment assembly.

De novo assembly using low-coverage short read sequence data from the rice pathogen Pseudomonas syringae pv. oryzae

We developed a novel approach for de novo genome assembly using only sequence data from high-throughput short read sequencing technologies. By combining data generated from 454 Life Sciences (Roche) and Illumina (formerly known as Solexa sequencing) sequencing platforms, we reliably assembled genomes into large scaffolds at a fraction of the traditional cost and without use of a reference sequence. We applied this method to two isolates of the phytopathogenic bacteria Pseudomonas syringae. Sequencing and reassembly of the well-studied tomato and Arabidopsis pathogen, Pto(DC3000), facilitated development and testing of our method. Sequencing of a distantly related rice pathogen, Por(1_)(6), demonstrated our method's efficacy for de novo assembly of novel genomes. Our assembly of Por(1_6) yielded an N50 scaffold size of 531,821 bp with >75% of the predicted genome covered by scaffolds over 100,000 bp. One of the critical phenotypic differences between strains of P. syringae is the range of plant hosts they infect. This is largely determined by their complement of type III effector proteins. The genome of Por(1_6) is the first sequenced for a P. syringae isolate that is a pathogen of monocots, and, as might be predicted, its complement of type III effectors differs substantially from the previously sequenced isolates of this species. The genome of Por(1_6) helps to define an expansion of the P. syringae pan-genome, a corresponding contraction of the core genome, and a further diversification of the type III effector complement for this important plant pathogen species.

MetaGeneAnnotator: detecting species-specific patterns of ribosomal binding site for precise gene prediction in anonymous prokaryotic and phage genomes

Recent advances in DNA sequencers are accelerating genome sequencing, especially in microbes, and complete and draft genomes from various species have been sequenced in rapid succession. Here, we present a comprehensive gene prediction tool, the MetaGeneAnnotator (MGA), which precisely predicts all kinds of prokaryotic genes from a single or a set of anonymous genomic sequences having a variety of lengths. The MGA integrates statistical models of prophage genes, in addition to those of bacterial and archaeal genes, and also uses a self-training model from input sequences for predictions. As a result, the MGA sensitively detects not only typical genes but also atypical genes, such as horizontally transferred and prophage genes in a prokaryotic genome. In this paper, we also propose a novel approach for analyzing the ribosomal binding site (RBS), which enables us to detect species-specific patterns of the RBSs. The MGA has the ingenious RBS model based on this approach, and precisely predicts translation starts of genes. The MGA also succeeds in improving prediction accuracies for short sequences by using the adapted RBS models (96% sensitivity and 93% specificity for 700 bp fragments). These features of the MGA expedite wide ranges of microbial genome studies, such as genome annotations and metagenome analyses.

The new paradigm of flow cell sequencing

DNA sequencing is in a period of rapid change, in which capillary sequencing is no longer the technology of choice for most ultra-high-throughput applications. A new generation of instruments that utilize primed synthesis in flow cells to obtain, simultaneously, the sequence of millions of different DNA templates has changed the field. We compare and contrast these new sequencing platforms in terms of stage of development, instrument configuration, template format, sequencing chemistry, throughput capability, operating cost, data handling issues, and error models. While these platforms outperform capillary instruments in terms of bases per day and cost per base, the short length of sequence reads obtained from most instruments and the limited number of samples that can be run simultaneously imposes some practical constraints on sequencing applications. However, recently developed methods for paired-end sequencing and for array-based direct selection of desired templates from complex mixtures extend the utility of these platforms for genome analysis. Given the ever increasing demand for DNA sequence information, we can expect continuous improvement of this new generation of instruments and their eventual replacement by even more powerful technology.

Substantial biases in ultra-short read data sets from high-throughput DNA sequencing

Novel sequencing technologies permit the rapid production of large sequence data sets. These technologies are likely to revolutionize genetics and biomedical research, but a thorough characterization of the ultra-short read output is necessary. We generated and analyzed two Illumina 1G ultra-short read data sets, i.e. 2.8 million 27mer reads from a Beta vulgaris genomic clone and 12.3 million 36mers from the Helicobacter acinonychis genome. We found that error rates range from 0.3% at the beginning of reads to 3.8% at the end of reads. Wrong base calls are frequently preceded by base G. Base substitution error frequencies vary by 10- to 11-fold, with A > C transversion being among the most frequent and C > G transversions among the least frequent substitution errors. Insertions and deletions of single bases occur at very low rates. When simulating re-sequencing we found a 20-fold sequencing coverage to be sufficient to compensate errors by correct reads. The read coverage of the sequenced regions is biased; the highest read density was found in intervals with elevated GC content. High Solexa quality scores are over-optimistic and low scores underestimate the data quality. Our results show different types of biases and ways to detect them. Such biases have implications on the use and interpretation of Solexa data, for de novo sequencing, re-sequencing, the identification of single nucleotide polymorphisms and DNA methylation sites, as well as for transcriptome analysis.

Velvet: algorithms for de novo short read assembly using de Bruijn graphs

We have developed a new set of algorithms, collectively called "Velvet," to manipulate de Bruijn graphs for genomic sequence assembly. A de Bruijn graph is a compact representation based on short words (k-mers) that is ideal for high coverage, very short read (25-50 bp) data sets. Applying Velvet to very short reads and paired-ends information only, one can produce contigs of significant length, up to 50-kb N50 length in simulations of prokaryotic data and 3-kb N50 on simulated mammalian BACs. When applied to real Solexa data sets without read pairs, Velvet generated contigs of approximately 8 kb in a prokaryote and 2 kb in a mammalian BAC, in close agreement with our simulated results without read-pair information. Velvet represents a new approach to assembly that can leverage very short reads in combination with read pairs to produce useful assemblies.

ALLPATHS: de novo assembly of whole-genome shotgun microreads

New DNA sequencing technologies deliver data at dramatically lower costs but demand new analytical methods to take full advantage of the very short reads that they produce. We provide an initial, theoretical solution to the challenge of de novo assembly from whole-genome shotgun "microreads." For 11 genomes of sizes up to 39 Mb, we generated high-quality assemblies from 80x coverage by paired 30-base simulated reads modeled after real Illumina-Solexa reads. The bacterial genomes of Campylobacter jejuni and Escherichia coli assemble optimally, yielding single perfect contigs, and larger genomes yield assemblies that are highly connected and accurate. Assemblies are presented in a graph form that retains intrinsic ambiguities such as those arising from polymorphism, thereby providing information that has been absent from previous genome assemblies. For both C. jejuni and E. coli, this assembly graph is a single edge encompassing the entire genome. Larger genomes produce more complicated graphs, but the vast majority of the bases in their assemblies are present in long edges that are nearly always perfect. We describe a general method for genome assembly that can be applied to all types of DNA sequence data, not only short read data, but also conventional sequence reads.

De novo bacterial genome sequencing: millions of very short reads assembled on a desktop computer

Novel high-throughput DNA sequencing technologies allow researchers to characterize a bacterial genome during a single experiment and at a moderate cost. However, the increase in sequencing throughput that is allowed by using such platforms is obtained at the expense of individual sequence read length, which must be assembled into longer contigs to be exploitable. This study focuses on the Illumina sequencing platform that produces millions of very short sequences that are 35 bases in length. We propose a de novo assembler software that is dedicated to process such data. Based on a classical overlap graph representation and on the detection of potentially spurious reads, our software generates a set of accurate contigs of several kilobases that cover most of the bacterial genome. The assembly results were validated by comparing data sets that were obtained experimentally for Staphylococcus aureus strain MW2 and Helicobacter acinonychis strain Sheeba with that of their published genomes acquired by conventional sequencing of 1.5- to 3.0-kb fragments. We also provide indications that the broad coverage achieved by high-throughput sequencing might allow for the detection of clonal polymorphisms in the set of DNA molecules being sequenced.