Bcl2fastq2 conversion software v2

The original base call sequencing of the BCL files of Illumina (CA, United States) were demultiplexed into FASTQ files using the Illumina bcl2fastq2 Conversion Software v2.20. The quality of the obtained raw reads was examined using FastQC software v0.11.9. All low-quality reads, including reads containing adapters, with more than 5% N (base unknown) bases, and with Q ≤ 19 bases account for 50% of the total bases, were removed using Trimmomatic software v0.32 (Williams et al., 2016 (link)). Read pairs were dropped even if one read did not pass the quality matrices. Obtained clean reads were used for further analysis.

NGS raw data (BCL files) generated from the NextSeq 500 were demultiplexed by the sample index reads using bcl2fastq2 Conversion Software v2.20 (Illumina) and ran through an in-house pipeline written in Java programming language to count the total number of scFv-specific tags for each sample. For our analysis we used only reads that passed the sequencer chastity filter and had base call quality for each base over Q30 (Phred Quality score)^{32 (link),33 (link)}.
Next, the counts were median normalized and log2 transformed before two-group classification using SVM LOO cross-validation to generate ROC curves and AUC values. The SVM analysis is a supervised machine learning algorithm and was performed with the R package “e1071” and a linear kernel. No prior data filtration was done before the SVM, i.e., all scFv antibodies used in the assay were also included in the analysis. The SVM finds an optimal hyperplane that separates the two groups and the classification performance is measured by the ROC–AUC value, where the value 1 would mean a perfect classifier and 0.5 a random classifier.
Data were also analyzed using PCA in Qlucore Omics Explorer 3.5 (Qlucore AB, Lund, Sweden). PCA was used as an unsupervised method to reduce the dimensionality and allow visual interpretation of the data in a 3D-plot.