EC: an efficient error correction algorithm for short reads

Genome sequence assembly algorithms and misassembly identification methods

The sequence assembly algorithms have rapidly evolved with the vigorous growth of genome sequencing technology over the past two decades. Assembly mainly uses the iterative expansion of overlap relationships between sequences to construct the target genome. The assembly algorithms can be typically classified into several categories, such as the Greedy strategy, Overlap-Layout-Consensus (OLC) strategy, and de Bruijn graph (DBG) strategy. In particular, due to the rapid development of third-generation sequencing (TGS) technology, some prevalent assembly algorithms have been proposed to generate high-quality chromosome-level assemblies. However, due to the genome complexity, the length of short reads, and the high error rate of long reads, contigs produced by assembly may contain misassemblies adversely affecting downstream data analysis. Therefore, several read-based and reference-based methods for misassembly identification have been developed to improve assembly quality. This work primarily reviewed the development of DNA sequencing technologies and summarized sequencing data simulation methods, sequencing error correction methods, various mainstream sequence assembly algorithms, and misassembly identification methods. A large amount of computation makes the sequence assembly problem more challenging, and therefore, it is necessary to develop more efficient and accurate assembly algorithms and alternative algorithms.

Molecular Microbial Community Analysis as an Analysis Tool for Optimal Biogas Production

The microbial diversity in anaerobic digestion (AD) is important because it affects process robustness. High-throughput sequencing offers high-resolution data regarding the microbial diversity and robustness of biological systems including AD; however, to understand the dynamics of microbial processes, knowing the microbial diversity is not adequate alone. Advanced meta-omic techniques have been established to determine the activity and interactions among organisms in biological processes like AD. Results of these methods can be used to identify biomarkers for AD states. This can aid a better understanding of system dynamics and be applied to producing comprehensive models for AD. The paper provides valuable knowledge regarding the possibility of integration of molecular methods in AD. Although meta-genomic methods are not suitable for on-line use due to long operating time and high costs, they provide extensive insight into the microbial phylogeny in AD. Meta-proteomics can also be explored in the demonstration projects for failure prediction. However, for these methods to be fully realised in AD, a biomarker database needs to be developed.

Overlooked: Extrachromosomal DNA and Their Possible Impact on Whole Genome Sequencing

Extrachromosomal (ec) DNA in eukaryotic cells has been known for decades. The structures described range from linear double stranded (ds) DNA to circular dsDNA, distinct from mitochondrial (mt) DNA. The sizes of circular forms are described from some hundred base pairs (bp) up to more than 150 kbp. The number of molecules per cell ranges from several hundred to a thousand. Semi-quantitative determinations of circular dsDNA show proportions as high as several percentages of the total DNA per cell. These ecDNA fractions harbor sequences that are known to be present in chromosomal DNA (chrDNA) too. Sequencing projects on, for example the human genome, have to take into account the ecDNA sequences which are simultaneously ascertained; corrections cannot be performed retrospectively. Concerning the results of sequencings derived from extracted whole DNA: if the ecDNA fractions contained therein are not taken into account, erroneous conclusions at the chromosomal level may result.

Analytical parameters and validation of homopolymer detection in a pyrosequencing-based next generation sequencing system

Background

Current technologies in next-generation sequencing are offering high throughput reads at low costs, but still suffer from various sequencing errors. Although pyro- and ion semiconductor sequencing both have the advantage of delivering long and high quality reads, problems might occur when sequencing homopolymer-containing regions, since the repeating identical bases are going to incorporate during the same synthesis cycle, which leads to uncertainty in base calling. The aim of this study was to evaluate the analytical performance of a pyrosequencing-based next-generation sequencing system in detecting homopolymer sequences using homopolymer-preintegrated plasmid constructs and human DNA samples originating from patients with cystic fibrosis.

Results

In the plasmid system average correct genotyping was 95.8% in 4-mers, 87.4% in 5-mers and 72.1% in 6-mers. Despite the experienced low genotyping accuracy in 5- and 6-mers, it was possible to generate amplicons with more than a 90% adequate detection rate in every homopolymer tract. When homopolymers in the CFTR gene were sequenced average accuracy was 89.3%, but varied in a wide range (52.2 – 99.1%). In all but one case, an optimal amplicon-sequencing primer combination could be identified. In that single case (7A tract in exon 14 (c.2046_2052)), none of the tested primer sets produced the required analytical performance.

Conclusions

Our results show that pyrosequencing is the most reliable in case of 4-mers and as homopolymer length gradually increases, accuracy deteriorates. With careful primer selection, the NGS system was able to correctly genotype all but one of the homopolymers in the CFTR gene. In conclusion, we configured a plasmid test system that can be used to assess genotyping accuracy of NGS devices and developed an accurate NGS assay for the molecular diagnosis of CF using self-designed primers for amplification and sequencing.

Electronic supplementary material

The online version of this article (10.1186/s12864-018-4544-x) contains supplementary material, which is available to authorized users.

Evaluation of the impact of Illumina error correction tools on de novo genome assembly

BACKGROUND

Recently, many standalone applications have been proposed to correct sequencing errors in Illumina data. The key idea is that downstream analysis tools such as de novo genome assemblers benefit from a reduced error rate in the input data. Surprisingly, a systematic validation of this assumption using state-of-the-art assembly methods is lacking, even for recently published methods.

RESULTS

For twelve recent Illumina error correction tools (EC tools) we evaluated both their ability to correct sequencing errors and their ability to improve de novo genome assembly in terms of contig size and accuracy.

CONCLUSIONS

We confirm that most EC tools reduce the number of errors in sequencing data without introducing many new errors. However, we found that many EC tools suffer from poor performance in certain sequence contexts such as regions with low coverage or regions that contain short repeated or low-complexity sequences. Reads overlapping such regions are often ill-corrected in an inconsistent manner, leading to breakpoints in the resulting assemblies that are not present in assemblies obtained from uncorrected data. Resolving this systematic flaw in future EC tools could greatly improve the applicability of such tools.