Using AIMS to Analyze Immune Repertoires

AIMS Graphic By Anna Borowska

AIMS Graphic created by Anna Borowska (see


This readthedocs site is still under development. Hopefully what is included thus far helps users, but more information is coming soon!

AIMS, an Automated Immune Molecule Separator, was originally developed to identify differences between two distinct antibody repertoires, but has since expanded to become a multi-purpose repertoire analysis tool. Currently the AIMS analytical tools can be applied to immunoglobulin (Ig) molecules such as T cell receptors and antibodies, major histocompatibility complex (MHC) and MHC-like molecules, immunopeptidomic data, and broadly to any data presented in a multi-sequence alignment format. This documentation will teach users how to use AIMS to:

  • Get started following the Installation Instructions.

  • Analyze repertoires with no programming experience required in a user-friendly format with the AIMS GUI Walkthrough.

  • Characterize the key features of immune repertoires using the AIMS biophysical characterization tools through the AIMS Basics section. This biophysical characterization is the central feature of AIMS, taking properly formatted files (see Input Formatting) and applying analytical tools from the Core Functionalities of AIMS for downstream manipulation.

  • Identify biophysically distinct repertoire clusters within single repertoires or comparing across multiple repertoires using the AIMS Cluster features.

  • Build off the AIMS analysis with your own custom features by taking advantage of the AIMS Jupyter Notebooks.

AIMS is a python package distributed in both a notebook and GUI format. Those wishing to use the GUI, particularly those relatively new to programming, can follow the installation instructions. Example data is provided in AIMS/app/ab_testData and AIMS/app/mhc_testData, and an example of an application of AIMS can be seen in this peer-reviewed article:


When publishing analysis from this software, please cite:

Boughter CT, Borowska MT, Guthmiller JJ, Bendelac A, Wilson PC, Roux B, Adams EJ. Biochemical Patterns of Antibody Polyreactivity Revealed Through a Bioinformatics-Based Analysis of CDR Loops. eLife. 2020. DOI: 10.7554/eLife.61393