The Illumina DNA sequencing platform generates accurate but short reads, which can be used to produce accurate but fragmented genome assemblies. Pacific Biosciences and Oxford Nanopore Technologies DNA sequencing platforms generate long reads that can produce more complete genome assemblies, but the sequencing is more expensive and error prone. There is significant interest in combining data from these complementary sequencing technologies to generate more accurate "hybrid" assemblies. However, few tools exist that truly leverage the benefits of both types of data, namely the accuracy of short reads and the structural resolving power of long reads. Here we present Unicycler, a new tool for assembling bacterial genomes from a combination of short and long reads, which produces assemblies that are accurate, complete and cost-effective. Unicycler builds an initial assembly graph from short reads using the de novo assembler SPAdes and then simplifies the graph using information from short and long reads. Unicycler utilises a novel semi-global aligner, which is used to align long reads to the assembly graph. Tests on both synthetic and real reads show Unicycler can assemble larger contigs with fewer misassemblies than other hybrid assemblers, even when long read depth and accuracy are low. Unicycler is open source (GPLv3) and available at github.com/rrwick/Unicycler.

/pdf/unicycler-resolving-bacterial-genome-assemblies-from-short-4a7qcqh4h7.pdf

Unicycler: resolving bacterial genome assemblies from short and long sequencing reads

The Illumina DNA sequencing platform generates accurate but short reads, which can be used to produce accurate but fragmented genome assemblies. Pacific Biosciences and Oxford Nanopore Technologies DNA sequencing platforms generate long reads that can produce complete genome assemblies, but the sequencing is more expensive and error-prone. There is significant interest in combining data from these complementary sequencing technologies to generate more accurate "hybrid" assemblies. However, few tools exist that truly leverage the benefits of both types of data, namely the accuracy of short reads and the structural resolving power of long reads. Here we present Unicycler, a new tool for assembling bacterial genomes from a combination of short and long reads, which produces assemblies that are accurate, complete and cost-effective. Unicycler builds an initial assembly graph from short reads using the de novo assembler SPAdes and then simplifies the graph using information from short and long reads. Unicycler uses a novel semi-global aligner to align long reads to the assembly graph. Tests on both synthetic and real reads show Unicycler can assemble larger contigs with fewer misassemblies than other hybrid assemblers, even when long-read depth and accuracy are low. Unicycler is open source (GPLv3) and available at github.com/rrwick/Unicycler.

/pdf/unicycler-resolving-bacterial-genome-assemblies-from-short-2rdma73s0x.pdf

Unicycler: Resolving bacterial genome assemblies from short and long sequencing reads.

Motivation The emergence of high-throughput sequencing technologies revolutionized genomics in early 2000s. The next revolution came with the era of long-read sequencing. These technological advances along with novel computational approaches became the next step towards the automatic pipelines capable to assemble nearly complete mammalian-size genomes. Results In this manuscript, we demonstrate performance of the state-of-the-art genome assembly software on six eukaryotic datasets sequenced using different technologies. To evaluate the results, we developed QUAST-LG-a tool that compares large genomic de novo assemblies against reference sequences and computes relevant quality metrics. Since genomes generally cannot be reconstructed completely due to complex repeat patterns and low coverage regions, we introduce a concept of upper bound assembly for a given genome and set of reads, and compute theoretical limits on assembly correctness and completeness. Using QUAST-LG, we show how close the assemblies are to the theoretical optimum, and how far this optimum is from the finished reference. Availability and implementation http://cab.spbu.ru/software/quast-lg. Supplementary information Supplementary data are available at Bioinformatics online.

/pdf/versatile-genome-assembly-evaluation-with-quast-lg-2ri5j8mpc5.pdf

Versatile genome assembly evaluation with QUAST-LG.

Long-read, single-molecule real-time (SMRT) sequencing is routinely used to finish microbial genomes, but available assembly methods have not scaled well to larger genomes. We introduce the MinHash Alignment Process (MHAP) for overlapping noisy, long reads using probabilistic, locality-sensitive hashing. Integrating MHAP with the Celera Assembler enabled reference-grade de novo assemblies of Saccharomyces cerevisiae, Arabidopsis thaliana, Drosophila melanogaster and a human hydatidiform mole cell line (CHM1) from SMRT sequencing. The resulting assemblies are highly continuous, include fully resolved chromosome arms and close persistent gaps in these reference genomes. Our assembly of D. melanogaster revealed previously unknown heterochromatic and telomeric transition sequences, and we assembled low-complexity sequences from CHM1 that fill gaps in the human GRCh38 reference. Using MHAP and the Celera Assembler, single-molecule sequencing can produce de novo near-complete eukaryotic assemblies that are 99.99% accurate when compared with available reference genomes.

/pdf/assembling-large-genomes-with-single-molecule-sequencing-and-3owcjv6cvc.pdf

Assembling large genomes with single-molecule sequencing and locality-sensitive hashing

Genome assemblies that are accurate, complete and contiguous are essential for identifying important structural and functional elements of genomes and for identifying genetic variation. Nevertheless, most recent genome assemblies remain incomplete and fragmented. While long molecule sequencing promises to deliver more complete genome assemblies with fewer gaps, concerns about error rates, low yields, stringent DNA requirements and uncertainty about best practices may discourage many investigators from adopting this technology. Here, in conjunction with the platinum standard Drosophila melanogaster reference genome, we analyze recently published long molecule sequencing data to identify what governs completeness and contiguity of genome assemblies. We also present a hybrid meta-assembly approach that achieves remarkable assembly contiguity for both Drosophila and human assemblies with only modest long molecule sequencing coverage. Our results motivate a set of preliminary best practices for obtaining accurate and contiguous assemblies, a 'missing manual' that guides key decisions in building high quality de novo genome assemblies, from DNA isolation to polishing the assembly.

Contiguous and accurate de novo assembly of metazoan genomes with modest long read coverage.

The problem of de-novo assembly for metagenomes using only long reads is gaining attention. We study whether post-processing metagenomic assemblies with the original input long reads can result in quality improvement. Previous approaches have focused on pre-processing reads and optimizing assemblers. BIGMAC takes an alternative perspective to focus on the post-processing step. Using both the assembled contigs and original long reads as input, BIGMAC first breaks the contigs at potentially mis-assembled locations and subsequently scaffolds contigs. Our experiments on metagenomes assembled from long reads show that BIGMAC can improve assembly quality by reducing the number of mis-assemblies while maintaining or increasing N50 and N75. Moreover, BIGMAC shows the largest N75 to number of mis-assemblies ratio on all tested datasets when compared to other post-processing tools. BIGMAC demonstrates the effectiveness of the post-processing approach in improving the quality of metagenomic assemblies.

/pdf/bigmac-breaking-inaccurate-genomes-and-merging-assembled-473oqxumir.pdf

BIGMAC : breaking inaccurate genomes and merging assembled contigs for long read metagenomic assembly

The problem of de-novo assembly for metagenomes using only long reads is gaining attention. We study whether post-processing metagenomic assemblies with the original input long reads can result in quality improvement. Previous approaches have focused on pre-processing reads and optimizing assemblers. BIGMAC takes an alternative perspective to focus on the post-processing step. Using both the assembled contigs and original long reads as input, BIGMAC first breaks the contigs at potentially mis-assembled locations and subsequently scaffolds contigs. Our experiments on metagenomes assembled from long reads show that BIGMAC can improve assembly quality by reducing the number of mis-assemblies while maintaining/increasing N50 and N75. The software is available at https://github.com/kakitone/BIGMAC

https://www.biorxiv.org/content/biorxiv/early/2016/03/29/045690.full.pdf

BIGMAC : Breaking Inaccurate Genomes and Merging Assembled Contigs for long read metagenomic assembly

1TCP is a popular transport layer protocol. The performance of TCP is known to be unsatisfactory in wireless network, due to high bit error rates or drop outs. Employing error correction at the transport layer can alleviate the problem to some extent. We make three contributions in this paper. First, we optimize the efficiency of repetitive codes. In particular, we propose a good estimate on throughput and obtain a simple formula to estimate the optimal number of paths that maximizes efficiency. Second, we analyze the optimization of efficiency when using Reed-Solomon Erasure Code with TCP. We show that the improvement is dramatic for stable channels but not as significant for noisy channels. Third, we find that the bottleneck on the efficiency for multipath TCP transmission with coding is the protocol TCP. Therefore, to drastically increase efficiency in wireless network, we should use another protocol instead of TCP.

Performance analysis and optimization of multipath TCP

We introduce FinisherSC, which is a repeat-aware and scalable tool for upgrading de-novo assembly using long reads. Experiments with real data suggest that FinisherSC can provide longer and higher quality contigs than existing tools while maintaining high concordance.

https://www.biorxiv.org/content/biorxiv/early/2015/04/04/010215.full.pdf

FinisherSC : A repeat-aware tool for upgrading de-novo assembly using long reads

Author(s): Lam, Ka-Kit | Advisor(s): Rao, Satish | Abstract: We study data-efficient and also practical de-novo genome assembly algorithm. Due to the advancement in high-throughput sequencing, the cost of read data has gone down rapidly in recent years. Thus, there is a growing need to do genome assembly in a cost effective manner on a large scale. However, the state-of-the-art assemblers are not designed to be data-efficient. This leads to wastage of read data, which translates to a significant monetary loss for large scale sequencing projects. Moreover, as technology advances, the nature of the read data also evolves. This makes the old assemblers insufficient for discovering all the information behind the data. Therefore, there is a need to re-invent genome assemblers to suit the growing demand. In this dissertation, we address the data efficiency aspect of genome assembly as follows. First, we show that even when there is noise in the reads, one can successfully reconstruct the genomes with information requirements close to the noiseless fundamental limit. We develop a new assembly algorithm, X-phased Multibridging, is designed based on a probabilistic model of the genome. We show, through analysis that it performs well on the model, and through simulation that it performs well on real genomes.Second, we introduce FinisherSC, a repeat-aware and scalable tool for upgrading de-novo haploid genome assembly using long and noisy reads. Experiments with real data suggest that FinisherSC can provide longer and higher quality contigs than existing tools while maintaining high concordance. Thus, FinisherSC achieves higher data efficiency than state-of-the-art haploid genome assembly pipelines.Third, we study whether post-processing metagenomic assemblies with the original input long reads can result in quality improvement. Previous approaches have focused on pre-processing reads and optimizing assemblers. We introduce BIGMAC, which takes an alternative perspective to focus on the post-processing step. Using both the assembled contigs and original long reads as input, BIGMAC first breaks the contigs at potentially mis-assembled locations and subsequently scaffolds contigs. Our experiments on metagenomes assembled from long reads show that BIGMAC can improve assembly quality by reducing the number of mis-assemblies while maintaining/increasing N50 and N75. Thus, BIGMAC achieves higher data efficiency than state-of-the-art metagenomic assembly pipelines.Moreover, we also discuss some theoretical work regarding genome assembly in terms of data, time and space efficiency; and a promising post-processing tool POSTME in improving metagenomic assembly using both short and long reads.

https://escholarship.org/content/qt92c17743/qt92c17743.pdf?t=odi034

Data-efficient De novo Genome Assembly Algorithm : Theory and Practice

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