Needlestack: an ultra-sensitive variant caller for multi-sample next generation sequencing data

Abstract

<jats:title>ABSTRACT</jats:title><jats:p>The emergence of Next-Generation Sequencing (NGS) has revolutionized the way of reaching a genome sequence, with the promise of potentially providing a comprehensive characterization of DNA variations. Nevertheless, detecting somatic mutations is still a difficult problem, in particular when trying to identify low abundance mutations such as subclonal mutations, tumour-derived alterations in body fluids or somatic mutations from histological normal tissue. The main challenge is to precisely distinguish between sequencing artefacts and true mutations, particularly when the latter are so rare they reach similar abundance levels as artefacts. Here, we present needlestack, a highly sensitive variant caller, which directly learns from the data the level of systematic sequencing errors to accurately call mutations. Needlestack is based on the idea that the sequencing error rate can be dynamically estimated from analyzing multiple samples together. We show that the sequencing error rate varies across alterations, illustrating the need to precisely estimate it. We evaluate the performance of needlestack for various types of variations, and we show that needlestack is robust among positions and outperforms existing state-of-the-art method for low abundance mutations. Needlestack, along with its source code is freely available on the GitHub plateform: <jats:ext-link xmlns:xlink="http://www.w3.org/1999/xlink" ext-link-type="uri" xlink:href="https://github.com/IARCbioinfo/needlestack">https://github.com/IARCbioinfo/needlestack</jats:ext-link>.</jats:p>

Urinary TERT promoter mutations are detectable up to 10 years prior to clinical diagnosis of bladder cancer: Evidence from the Golestan Cohort Study

“…The detailed protocol for amplicon-based Ion Torrent Proton sequencing is provided in the Supplementary data. Variant calling was performed using our Needlestack algorithm based on negative binomial regression analysis and specifically designed for the detection of low-abundance mutations (https://github.com/IARC bioinfo/needlestack) [21]. A p-value for being a variant (outlier from the regression) was calculated for each sample and further transformed into q-values to account for multiple hypotheses testing (Supplementary Figure 1).…”

TP53 Targeted Deep Sequencing of Cell-Free DNA in Esophageal Squamous Cell Carcinoma Using Low-Quality Serum: Concordance with Tumor Mutation

“…It could be a random error and we did not determine a reason for it. Among the filtering steps, we removed variants with an allelic fraction of 10 times higher than candidate variants in 5-nt to 10-nt distance from the target candidate [ 28 ]. It could indicate that using whole coding region for genes and cancers with prevalent adjacent mutations (<10 nt), may cause some limitations, particularly among variant with too low allelic fraction.…”

“…Out of 107 ESCC cases with available data on tumor TP53 mutations, we selected 30 ESCC cases based on an in silico experiment. We used a database of TP53 variants in cfDNA of a series of patients with small-cell lung cancer to determine the error rate and proportion of false-positive detection per each genomic concordance [ 28 ]. We have added 12 further cases with more than one TP53 mutations in tumors to our selected cases.…”

“…We used Needlestack, a multi-sample variant caller designed for the detection of low abundance mutations. Details of the algorithm were mentioned in its methodological paper [ 28 ]. Briefly, needlestack estimated the sequencing error rate, at a particular position for a particular base change, using a negative binomial regression (NB) [ 36 ].…”