Additional file 2 of BBCAnalyzer: a visual approach to facilitate variant calling

Supplement_2. Scripts and documentation of the web application “BBCAnalyzer”. (ZIP 709 kb

GLM-based optimization of NGS data analysis: A case study of Roche 454, Ion Torrent PGM and Illumina NextSeq sequencing data

<div><p>Background</p><p>There are various next-generation sequencing techniques, all of them striving to replace Sanger sequencing as the gold standard. However, false positive calls of single nucleotide variants and especially indels are a widely known problem of basically all sequencing platforms.</p><p>Methods</p><p>We considered three common next-generation sequencers—Roche 454, Ion Torrent PGM and Illumina NextSeq—and applied standard as well as optimized variant calling pipelines. Optimization was achieved by combining information of 23 diverse parameters characterizing the reported variants and generating individually calibrated generalized linear models. Models were calibrated using amplicon-based targeted sequencing data (19 genes, 28,775 bp) from seven to 12 myelodysplastic syndrome patients. Evaluation of the optimized pipelines and platforms was performed using sequencing data from three additional myelodysplastic syndrome patients.</p><p>Results</p><p>Using standard analysis methods, true mutations were missed and the obtained results contained many artifacts—no matter which platform was considered. Analysis of the parameters characterizing the true and false positive calls revealed significant platform- and variant specific differences. Application of optimized variant calling pipelines considerably improved results. 76% of all false positive single nucleotide variants and 97% of all false positive indels could be filtered out. Positive predictive values could be increased by factors of 1.07 to 1.27 in case of single nucleotide variant calling and by factors of 3.33 to 53.87 in case of indel calling. Application of the optimized variant calling pipelines leads to comparable results for all next-generation sequencing platforms analyzed. However, regarding clinical diagnostics it needs to be considered that even the optimized results still contained false positive as well as false negative calls.</p></div

Overview of the variant calling pipeline (steps marked by dashed frames are only performed in case of the variant calling pipeline with GLM).

<p>Overview of the variant calling pipeline (steps marked by dashed frames are only performed in case of the variant calling pipeline with GLM).</p

Normalized relative variable importance for all parameters characterizing indels, considering 454, Ion Torrent and Illumina NextSeq sequencing data.

<p>Normalized relative variable importance for all parameters characterizing indels, considering 454, Ion Torrent and Illumina NextSeq sequencing data.</p

Overview of the subjects sequenced on 454, Ion Torrent and Illumina NextSeq (comparison set marked with a <i>c</i>, re-sequencing set marked with an <i>r</i>).

<p>Overview of the subjects sequenced on 454, Ion Torrent and Illumina NextSeq (comparison set marked with a <i>c</i>, re-sequencing set marked with an <i>r</i>).</p

True- and false positive indel calls, sensitivity (sens) and PPV considering the training subset (454 and Illumina: <i>n</i> = 12, Ion Torrent: <i>n</i> = 7) and the test subset (<i>n</i> = 3), comparing the standard analysis pipleine (without GLM) and the optimized analysis pipleine (with GLM). Only those variants are considered that are covered by at least 20 reads.

<p>True- and false positive indel calls, sensitivity (sens) and PPV considering the training subset (454 and Illumina: <i>n</i> = 12, Ion Torrent: <i>n</i> = 7) and the test subset (<i>n</i> = 3), comparing the standard analysis pipleine (without GLM) and the optimized analysis pipleine (with GLM). Only those variants are considered that are covered by at least 20 reads.</p

Median coverage of the genes in the intersecting target region in the case of 454 (black), Ion Torrent (red) and Illumina (green) considering the comparison data set.

<p>Median coverage of the genes in the intersecting target region in the case of 454 (black), Ion Torrent (red) and Illumina (green) considering the comparison data set.</p

Overview of the parameters investigated for the variant calling pipeline with GLM.

<p>Overview of the parameters investigated for the variant calling pipeline with GLM.</p

True- and false positive SNV calls, sensitivity and PPV considering the comparison subset (<i>n</i> = 9), the re-sequencing subset (<i>n</i> = 5) and all data (454 and Illumina: <i>n</i> = 15, Ion Torrent: <i>n</i> = 10), using the standard analysis pipleine (without GLM). Only those variants are considered that are covered by at least 20 reads.

<p>True- and false positive SNV calls, sensitivity and PPV considering the comparison subset (<i>n</i> = 9), the re-sequencing subset (<i>n</i> = 5) and all data (454 and Illumina: <i>n</i> = 15, Ion Torrent: <i>n</i> = 10), using the standard analysis pipleine (without GLM). Only those variants are considered that are covered by at least 20 reads.</p

True- and false positive indel calls, sensitivity and PPV considering the comparison subset (<i>n</i> = 9), the re-sequencing subset (<i>n</i> = 5) and all data (454 and Illumina: <i>n</i> = 15, Ion Torrent: <i>n</i> = 10), using the standard analysis pipleine (without GLM). Only those variants are considered that are covered by at least 20 reads.

<p>True- and false positive indel calls, sensitivity and PPV considering the comparison subset (<i>n</i> = 9), the re-sequencing subset (<i>n</i> = 5) and all data (454 and Illumina: <i>n</i> = 15, Ion Torrent: <i>n</i> = 10), using the standard analysis pipleine (without GLM). Only those variants are considered that are covered by at least 20 reads.</p