  High-throughput sequencing of T- and B-cell receptors makes it possible to
track immune repertoires across time, in different tissues, in acute and
chronic diseases and in healthy individuals. However quantitative comparison
between repertoires is confounded by variability in the read count of each
receptor clonotype due to sampling, library preparation, and expression noise.
We present an easy-to-use python package NoisET that implements and generalizes
a previously developed Bayesian method. It can be used to learn experimental
noise models for repertoire sequencing from replicates, and to detect
responding clones following a stimulus. The package was tested on different
repertoire sequencing technologies and datasets.
  Availability: NoisET is freely available to use with source code at
github.com/statbiophys/NoisET.
