Effects of error-correction of heterozygous next-generation sequencing data

Abstract

BackgroundError correction is an important step in increasing the quality of next-generation sequencing data for downstream analysis and use. Polymorphic datasets are a challenge for many bioinformatic software packages that are designed for or assume homozygosity of an input dataset. This assumption ignores the true genomic composition of many organisms that are diploid or polyploid. In this survey, two different error correction packages, Quake and ECHO, are examined to see how they perform on next-generation sequence data from heterozygous genomes.ResultsQuake and ECHO perform well and were able to correct many errors found within the data. However, errors that occur at heterozygous positions had unique trends. Errors at these positions were sometimes corrected incorrectly, introducing errors into the dataset with the possibility of creating a chimeric read. Quake was much less likely to create chimeric reads. Quake's read trimming removed a large portion of the original data and often left reads with few heterozygous markers. ECHO resulted in more chimeric reads and introduced more errors than Quake but preserved heterozygous markers.Using real E. coli sequencing data and their assemblies after error correction, the assembly statistics improved. It was also found that segregating reads by haplotype can improve the quality of an assembly.ConclusionsThese findings suggest that Quake and ECHO both have strengths and weaknesses when applied to heterozygous data. With the increased interest in haplotype specific analysis, new tools that are designed to be haplotype-aware are necessary that do not have the weaknesses of Quake and ECHO.

Computational problems of analysis of short next generation sequencing reads

“…Additionally, it also does not look promising to correct reads without understanding causes of sequencing/library errors. The work (Fujimoto et al, 2014) confirms that error correction methods can not handle heterozygosity, and they introduce new mis-correction errors.…”

“…However, an error correction might introduce new type of errors: mis-correction errors Fujimoto et al, 2014). And these errors are more difficult to correct back than technological errors.…”

Computational Errors and Biases in Short Read Next Generation Sequencing

“…However, the paper's strong points surprisingly lead a reader to a negative conclusion: the paper sounds very convincing, that one should not introduce new mis-correcting errors. From Fujimoto et al research [245], one can find a conclusion similar to above: error correction methods cannot handle heterozygosity and introduce new mis-corrected errors.…”

“…An error correction might introduce new type of errors: miscorrection errors [245,246]. And these errors are harder to correct back than technological errors.…”

Comparative analysis of de novo assemblers for variation discovery in personal genomes

“…In NA12878 WGS, a 10.1 kb region from HLA-DQB1 harbors 584 phased SNPs, and another 11.7 kb region within HLA-DRB1 contains 249 phased SNPs. This finding has significant implication in biomedical studies, as there is increasing evidence that some diseases are associated with multiple linked variants [ 14 , 52 , 53 ]. Although short reads carry limited phase information if they contain two or more variation sites [ 54 ], long-range phasing through de novo assembly increases the chance of identifying variants linked to complex diseases.…”