BASE: a practical de novo assembler for large genomes using long NGS reads.

De novo genome assembly using NGS data remains a computation-intensive task especially for large genomes. In practice, efficiency is often a primary concern and favors using a more efficient assembler like SOAPdenovo2. Yet SOAPdenovo2, based on de Bruijn graph, fails to take full advantage of longer NGS reads (say, 150 bp to 250 bp from Illumina HiSeq and MiSeq). Assemblers that are based on string graphs (e.g., SGA), though less popular and also very slow, are more favorable for longer reads. This paper shows a new de novo assembler called BASE. It enhances the classic seed-extension approach by indexing the reads efficiently to generate adaptive seeds that have high probability to appear uniquely in the genome. Such seeds form the basis for BASE to build extension trees and then to use reverse validation to remove the branches based on read coverage and paired-end information, resulting in high-quality consensus sequences of reads sharing the seeds. Such consensus sequences are then extended to contigs. Experiments on two bacteria and four human datasets shows the advantage of BASE in both contig quality and speed in dealing with longer reads. In the experiment on bacteria, two datasets with read length of 100 bp and 250 bp were used.. Especially for the 250 bp dataset, BASE gives much better quality than SOAPdenovo2 and SGA and is simlilar to SPAdes. Regarding speed, BASE is consistently a few times faster than SPAdes and SGA, but still slower than SOAPdenovo2. BASE and Soapdenov2 are further compared using human datasets with read length 100 bp, 150 bp and 250 bp. BASE shows a higher N50 for all datasets, while the improvement becomes more significant when read length reaches 250 bp. Besides, BASE is more-meory efficent than SOAPdenovo2 when sequencing data with error rate. BASE is a practically efficient tool for constructing contig, with significant improvement in quality for long NGS reads. It is relatively easy to extend BASE to include scaffolding.

Liu B ，Liu CM ，Li D ，Li Y ，Ting HF ，Yiu SM ，Luo R ，Lam TW ... -  《BMC GENOMICS》

Benchmarking of de novo assembly algorithms for Nanopore data reveals optimal performance of OLC approaches.

Improved DNA sequencing methods have transformed the field of genomics over the last decade. This has become possible due to the development of inexpensive short read sequencing technologies which have now resulted in three generations of sequencing platforms. More recently, a new fourth generation of Nanopore based single molecule sequencing technology, was developed based on MinION(®) sequencer which is portable, inexpensive and fast. It is capable of generating reads of length greater than 100 kb. Though it has many specific advantages, the two major limitations of the MinION reads are high error rates and the need for the development of downstream pipelines. The algorithms for error correction have already emerged, while development of pipelines is still at nascent stage. In this study, we benchmarked available assembler algorithms to find an appropriate framework that can efficiently assemble Nanopore sequenced reads. To address this, we employed genome-scale Nanopore sequenced datasets available for E. coli and yeast genomes respectively. In order to comprehensively evaluate multiple algorithmic frameworks, we included assemblers based on de Bruijn graphs (Velvet and ABySS), Overlap Layout Consensus (OLC) (Celera) and Greedy extension (SSAKE) approaches. We analyzed the quality, accuracy of the assemblies as well as the computational performance of each of the assemblers included in our benchmark. Our analysis unveiled that OLC-based algorithm, Celera, could generate a high quality assembly with ten times higher N50 & mean contig values as well as one-fifth the number of total number of contigs compared to other tools. Celera was also found to exhibit an average genome coverage of 12 % in E. coli dataset and 70 % in Yeast dataset as well as relatively lesser run times. In contrast, de Bruijn graph based assemblers Velvet and ABySS generated the assemblies of moderate quality, in less time when there is no limitation on the memory allocation, while greedy extension based algorithm SSAKE generated an assembly of very poor quality but with genome coverage of 90 % on yeast dataset. OLC can be considered as a favorable algorithmic framework for the development of assembler tools for Nanopore-based data, followed by de Bruijn based algorithms as they consume relatively less or similar run times as OLC-based algorithms for generating assembly, irrespective of the memory allocated for the task. However, few improvements must be made to the existing de Bruijn implementations in order to generate an assembly with reasonable quality. Our findings should help in stimulating the development of novel assemblers for handling Nanopore sequence data.

Cherukuri Y ，Janga SC 《BMC GENOMICS》

TraRECo: a greedy approach based de novo transcriptome assembler with read error correction using consensus matrix.

Yoon S ，Kim D ，Kang K ，Park WJ ... -  《BMC GENOMICS》

Clover: a clustering-oriented de novo assembler for Illumina sequences.

Next-generation sequencing technologies revolutionized genomics by producing high-throughput reads at low cost, and this progress has prompted the recent development of de novo assemblers. Multiple assembly methods based on de Bruijn graph have been shown to be efficient for Illumina reads. However, the sequencing errors generated by the sequencer complicate analysis of de novo assembly and influence the quality of downstream genomic researches. In this paper, we develop a de Bruijn assembler, called Clover (clustering-oriented de novo assembler), that utilizes a novel k-mer clustering approach from the overlap-layout-consensus concept to deal with the sequencing errors generated by the Illumina platform. We further evaluate Clover's performance against several de Bruijn graph assemblers (ABySS, SOAPdenovo, SPAdes and Velvet), overlap-layout-consensus assemblers (Bambus2, CABOG and MSR-CA) and string graph assembler (SGA) on three datasets (Staphylococcus aureus, Rhodobacter sphaeroides and human chromosome 14). The results show that Clover achieves a superior assembly quality in terms of corrected N50 and E-size while remaining a significantly competitive in run time except SOAPdenovo. In addition, Clover was involved in the sequencing projects of bacterial genomes Acinetobacter baumannii TYTH-1 and Morganella morganii KT. The marvel clustering-based approach of Clover that integrates the flexibility of the overlap-layout-consensus approach and the efficiency of the de Bruijn graph method has high potential on de novo assembly. Now, Clover is freely available as open source software from https://oz.nthu.edu.tw/~d9562563/src.html .

Hsieh MF ，Lu CL ，Tang CY 《BMC BIOINFORMATICS》

Subset selection of high-depth next generation sequencing reads for de novo genome assembly using MapReduce framework.

Recent progress in next-generation sequencing technology has afforded several improvements such as ultra-high throughput at low cost, very high read quality, and substantially increased sequencing depth. State-of-the-art high-throughput sequencers, such as the Illumina MiSeq system, can generate ~15 Gbp sequencing data per run, with >80% bases above Q30 and a sequencing depth of up to several 1000x for small genomes. Illumina HiSeq 2500 is capable of generating up to 1 Tbp per run, with >80% bases above Q30 and often >100x sequencing depth for large genomes. To speed up otherwise time-consuming genome assembly and/or to obtain a skeleton of the assembly quickly for scaffolding or progressive assembly, methods for noise removal and reduction of redundancy in the original data, with almost equal or better assembly results, are worth studying. We developed two subset selection methods for single-end reads and a method for paired-end reads based on base quality scores and other read analytic tools using the MapReduce framework. We proposed two strategies to select reads: MinimalQ and ProductQ. MinimalQ selects reads with minimal base-quality above a threshold. ProductQ selects reads with probability of no incorrect base above a threshold. In the single-end experiments, we used Escherichia coli and Bacillus cereus datasets of MiSeq, Velvet assembler for genome assembly, and GAGE benchmark tools for result evaluation. In the paired-end experiments, we used the giant grouper (Epinephelus lanceolatus) dataset of HiSeq, ALLPATHS-LG genome assembler, and QUAST quality assessment tool for comparing genome assemblies of the original set and the subset. The results show that subset selection not only can speed up the genome assembly but also can produce substantially longer scaffolds. The software is freely available at https://github.com/moneycat/QReadSelector.

Fang CH ，Chang YJ ，Chung WC ，Hsieh PH ，Lin CY ，Ho JM ... -  《BMC GENOMICS》