Data from: Motifier: an IgOme profiler based on peptide-motifs using machine learning

Antibodies provide a comprehensive record of the encounters with threats
and insults to the immune system. The ability to examine the repertoire of
antibodies in serum and discover those that best represent “discriminating
features” characteristic of various clinical situations, is potentially
very useful. Recently, phage display technologies combined with
Next-Generation Sequencing (NGS) produced a powerful experimental
methodology, coined “Deep-Panning”, in which the spectrum of serum
antibodies is probed. In order to extract meaningful biological insights
from the tens of millions of affinity-selected peptides generated by
Deep-Panning, advanced bioinformatics algorithms are a must. In this
study, we describe Motifier, a computational pipeline comprised
of a set of algorithms that systematically generates discriminatory
peptide motifs based on the affinity-selected peptides identified by
Deep-Panning. These motifs are shown to effectively characterize antibody
binding activities and through the implementation of machine-learning
protocols are shown to accurately classify complex antibody mixtures
representing various biological conditions.