Insight into biases and sequencing errors for amplicon sequencing with the Illumina MiSeq platform

Abstract

With read lengths of currently up to 2 × 300 bp, high throughput and low sequencing costs Illumina's MiSeq is becoming one of the most utilized sequencing platforms worldwide. The platform is manageable and affordable even for smaller labs. This enables quick turnaround on a broad range of applications such as targeted gene sequencing, metagenomics, small genome sequencing and clinical molecular diagnostics. However, Illumina error profiles are still poorly understood and programs are therefore not designed for the idiosyncrasies of Illumina data. A better knowledge of the error patterns is essential for sequence analysis and vital if we are to draw valid conclusions. Studying true genetic variation in a population sample is fundamental for understanding diseases, evolution and origin. We conducted a large study on the error patterns for the MiSeq based on 16S rRNA amplicon sequencing data. We tested state-of-the-art library preparation methods for amplicon sequencing and showed that the library preparation method and the choice of primers are the most significant sources of bias and cause distinct error patterns. Furthermore we tested the efficiency of various error correction strategies and identified quality trimming (Sickle) combined with error correction (BayesHammer) followed by read overlapping (PANDAseq) as the most successful approach, reducing substitution error rates on average by 93%.

Double‐digest RADseq loci using standard Illumina indexes improve deep and shallow phylogenetic resolution of Lophodermium, a widespread fungal endophyte of pine needles

“…The mean quality (Q) score of merged reads ranged from 35.70 to 39.60 for the first 251 nucleotide positions, 33.60 to 31 up to position 485, and then from 30.90 to 28.70 at final position 492. The drop in Q‐score is typical for reverse reads (Schirmer et al., 2015). The mean percentage of reads that passed quality‐filtering was 90.22% and 84.47% for 30N and 15N criteria, respectively.…”

Snowball: strain aware gene assembly of metagenomes

“…The use of a library containing selfoverlapping paired-end reads is a powerful strategy for an initial error-correction (Schirmer et al, 2015), which has been employed in e.g. ALLPATHS (Butler et al, 2008).…”

Computational analysis of fitness landscapes and evolutionary networks from in vitro evolution experiments

“…Sequencing technology sometimes misreads nucleotides, which effectively blurs the sharpness of the observed distribution in sequence space. However, this blurring effect can be corrected when accurate models are developed to describe how and why nucleotides are occasionally read incorrectly [22][23][24][25][26][27][28]. Therefore, it is possible to quantify the effects of those errors on a real distribution of sequences, and thus recover true sequence distributions from observed distributions by designing appropriate algorithms.…”