“…For example, running Velvet on the rice data (see Section 3.2 for details) results in contigs of mean length 245 bp only, while the mean length of transcripts is about 1700 bp. Some metagenomic (Bankevich et al , 2012) and single-cell assemblers (Vyahhi et al , 2012) try to find a path with maximum paired-end reads support; however, as the insert distance of transcriptome data usually cannot cover more than one branch (splicing junction) and there are multiple correct paths (isoforms) with paired-end reads support, these assembliers also fail to reconstruct the isoforms. Fig.…”
Motivation: RNA sequencing based on next-generation sequencing technology is effective for analyzing transcriptomes. Like de novo genome assembly, de novo transcriptome assembly does not rely on any reference genome or additional annotation information, but is more difficult. In particular, isoforms can have very uneven expression levels (e.g. 1:100), which make it very difficult to identify low-expressed isoforms. One challenge is to remove erroneous vertices/edges with high multiplicity (produced by high-expressed isoforms) in the de Bruijn graph without removing correct ones with not-so-high multiplicity from low-expressed isoforms. Failing to do so will result in the loss of low-expressed isoforms or having complicated subgraphs with transcripts of different genes mixed together due to erroneous vertices/edges.Contributions: Unlike existing tools, which remove erroneous vertices/edges with multiplicities lower than a global threshold, we use a probabilistic progressive approach to iteratively remove them with local thresholds. This enables us to decompose the graph into disconnected components, each containing a few genes, if not a single gene, while retaining many correct vertices/edges of low-expressed isoforms. Combined with existing techniques, IDBA-Tran is able to assemble both high-expressed and low-expressed transcripts and outperform existing assemblers in terms of sensitivity and specificity for both simulated and real data.Availability:
http://www.cs.hku.hk/∼alse/idba_tran.Contact:
chin@cs.hku.hkSupplementary information:
Supplementary data are available at Bioinformatics online.
“…For example, running Velvet on the rice data (see Section 3.2 for details) results in contigs of mean length 245 bp only, while the mean length of transcripts is about 1700 bp. Some metagenomic (Bankevich et al , 2012) and single-cell assemblers (Vyahhi et al , 2012) try to find a path with maximum paired-end reads support; however, as the insert distance of transcriptome data usually cannot cover more than one branch (splicing junction) and there are multiple correct paths (isoforms) with paired-end reads support, these assembliers also fail to reconstruct the isoforms. Fig.…”
Motivation: RNA sequencing based on next-generation sequencing technology is effective for analyzing transcriptomes. Like de novo genome assembly, de novo transcriptome assembly does not rely on any reference genome or additional annotation information, but is more difficult. In particular, isoforms can have very uneven expression levels (e.g. 1:100), which make it very difficult to identify low-expressed isoforms. One challenge is to remove erroneous vertices/edges with high multiplicity (produced by high-expressed isoforms) in the de Bruijn graph without removing correct ones with not-so-high multiplicity from low-expressed isoforms. Failing to do so will result in the loss of low-expressed isoforms or having complicated subgraphs with transcripts of different genes mixed together due to erroneous vertices/edges.Contributions: Unlike existing tools, which remove erroneous vertices/edges with multiplicities lower than a global threshold, we use a probabilistic progressive approach to iteratively remove them with local thresholds. This enables us to decompose the graph into disconnected components, each containing a few genes, if not a single gene, while retaining many correct vertices/edges of low-expressed isoforms. Combined with existing techniques, IDBA-Tran is able to assemble both high-expressed and low-expressed transcripts and outperform existing assemblers in terms of sensitivity and specificity for both simulated and real data.Availability:
http://www.cs.hku.hk/∼alse/idba_tran.Contact:
chin@cs.hku.hkSupplementary information:
Supplementary data are available at Bioinformatics online.
“…Another advantage of this (less tangled) assembly graph lies in applications relating to hybrid assembly, e.g., coassembly of short and long reads (Antipov et al, 2015, Wick et al, 2017. However, although some studies attempted to construct the assembly graph directly from paired reads (Vyahhi et al, 2012), the popular short read assemblers have failed to incorporate this approach into their pipelines so far.…”
Section: Appendix: Short Read Assemblers and The Challenge Of Repeat mentioning
The problem of genome assembly is ultimately linked to the problem of the characterization of all repeat families in a genome as a repeat graph. The key reason the de Bruijn graph emerged as a popular short read assembly approach is because it offered an elegant representation of all repeats in a genome that reveals their mosaic structure. However, most algorithms for assembling long error-prone reads use an alternative overlap-layout-consensus (OLC) approach that does not provide a repeat characterization. We present the Flye algorithm for constructing the A-Bruijn (assembly) graph from long error-prone reads, that, in contrast to the k-mer-based de Bruijn graph, assembles genomes using an alignment-based ABruijn graph. In difference from existing assemblers, Flye does not attempt to construct accurate contigs (at least at the initial assembly stage) but instead simply generates arbitrary paths in the (unknown) assembly graph and further constructs an assembly graph from these paths. Counter-intuitively, this fast but seemingly reckless approach results in the same graph as the assembly graph constructed from accurate contigs. Flye constructs (overlapping) contigs with possible assembly errors at the initial stage, combines them into an accurate assembly graph, resolves repeats in the assembly graph using small variations between various repeat instances that were left unresolved during the initial assembly stage, constructs a new, less tangled assembly graph based on resolved repeats, and finally outputs accurate contigs as paths in this graph. We benchmark Flye against several state-of-the-art Single Molecule Sequencing assemblers and demonstrate that it generates better or comparable assemblies for all analyzed datasets.
“…Although paired-end reads can be used to construct scaffolds, the information of the paired-end reads can also be used in assembling as they can be used to resolve branches in the graph. Some assemblers [25] try to apply paired-end reads information directly on assembling by considering a rectangle graph instead of de Bruijn graph. A contig can be represented by some traversal of the rectangle graph with the insert distance restrictions.…”
Section: Under Utilization Of Paired-end Reads Informationmentioning
Sequence assembling is an important step for bioinformatics study. With the help of next generation sequencing (NGS) technology, high throughput DNA fragment (reads) can be randomly sampled from DNA or RNA molecular sequence. However, as the positions of reads being sampled are unknown, assembling process is required for combining overlapped reads to reconstruct the original DNA or RNA sequence. Compared with traditional Sanger sequencing methods, although the throughput of NGS reads increases, the read length is shorter and the error rate is higher. It introduces several problems in assembling. Moreover, paired-end reads instead of single-end reads can be sampled which contain more information. The existing assemblers cannot fully utilize this information and fails to assemble longer contigs. In this article, we will revisit the major problems of assembling NGS reads on genomic, transcriptomic, metagenomic and metatranscriptomic data. We will also describe our IDBA package for solving these problems. IDBA package has adopted several novel ideas in assembling, including using multiple k, local assembling and progressive depth removal. Compared with existence assemblers, IDBA has better performance on many simulated and real sequencing datasets. genomic assembling, de Bruijn graph, paired-end reads, next generation sequencing
Citation:Chin FYL, Leung HCM, Yiu SM. Sequence assembly using next generation sequencing data-challenges and solutions.
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