NGS-eval: NGS Error analysis and novel sequence VAriant detection tooL

Abstract

Massively parallel sequencing of microbial genetic markers (MGMs) is used to uncover the species composition in a multitude of ecological niches. These sequencing runs often contain a sample with known composition that can be used to evaluate the sequencing quality or to detect novel sequence variants. With NGS-eval, the reads from such (mock) samples can be used to (i) explore the differences between the reads and their references and to (ii) estimate the sequencing error rate. This tool maps these reads to references and calculates as well as visualizes the different types of sequencing errors. Clearly, sequencing errors can only be accurately calculated if the reference sequences are correct. However, even with known strains, it is not straightforward to select the correct references from databases. We previously analysed a pyrosequencing dataset from a mock sample to estimate sequencing error rates and detected sequence variants in our mock community, allowing us to obtain an accurate error estimation. Here, we demonstrate the variant detection and error analysis capability of NGS-eval with Illumina MiSeq reads from the same mock community. While tailored towards the field of metagenomics, this server can be used for any type of MGM-based reads. NGS-eval is available at http://www.ibi.vu.nl/programs/ngsevalwww/.

Sequencing‐based microsatellite instability testing using as few as six markers for high‐throughput clinical diagnostics

“…By inclusion of a smMIP targeting BRAF in the assay, these 14 samples were found to have c.1799T>A variant allele frequencies (VAFs) >5%, a threshold generally considered to be clinically relevant in cancer diagnostics (Jennings et al, ). Of the 32 CRCs that tested negative, 30 had VAFs ≤0.60%, in line with observed MiSeq base‐calling error rates of 0.62% (May et al, ). The remaining two samples had VAFs of 1.67% and 1.72%, suggesting they may contain low‐frequency variants below the detection limit of HRM (Nikiforov et al, ).…”

Production of individualized V gene databases reveals high levels of immunoglobulin genetic diversity

“…In our case, the Illumina MiSeq platform was chosen as it can produce sequence reads of 2 × 300 bases and is known to have one of the lowest error rates, approximately 1.2% (refs 29, 30), of current NGS sequencing technologies. Because IgDiscover works on the basis of identifying consensus sequences based on clusters assigned to an initial database, the process requires approximately 400,000 initial paired sequences in order to identify a full expressed germline repertoire from an individual.…”

Optimized metabarcoding with Pacific biosciences enables semi‐quantitative analysis of fungal communities

“…(2018) and Tedersoo & Anslan (2019) used Pacific Biosciences (PacBio) to sequence longer fragments (1200–2500 bases), which can provide better taxonomic resolution (Singer et al ., 2016), but the PacBio platform has also been used to sequence shorter ITS (800–900 bases: Redondo et al ., 2018) and ITS2 markers (200–450 bases: Kyaschenko et al ., 2017a,b; Varenius et al ., 2017; Castaño et al ., 2018; Sterkenburg et al ., 2018). Despite higher error rates of single reads for PacBio ( c. 11%) as compared with Illumina MiSeq ( c. 0.1–2.6%) (May et al ., 2015; Pfeiffer et al ., 2018), the PacBio SMRT technology enables correction of random errors by calculating a consensus sequence based on typically > 30 separate sequencing passes, reducing errors to < 1% when short fragments are sequenced (Reuter et al ., 2015). However, one constraint of the PacBio technology is the lower sequencing output compared to MiSeq – the output of Illumina MiSeq is around 20–50 times higher than that of PacBio RS II (at the same cost).…”