SUMMARY: We present Alignoth, a lightweight command line application that generates self-contained portable HTML reports of DNA sequencing read alignment pileups and additionally supports export to static formats such as PNG, SVG, and PDF as well as a JSON based embeddable representation. The HTML reports feature read name search and mapping-quality-based read highlighting, and require only minimal storage, making them practical to share, inspect, or integrate into broader reporting systems. They can be created in headless (i.e. terminal only) environments while being interactively inspected afterwards. AVAILABILITY AND IMPLEMENTATION: Alignoth is freely available under the MIT license at https://github.com/alignoth/alignoth (doi: https://doi.org/10.5281/zenodo.15837719). It is implemented in Rust and can be installed via Cargo or Conda.

MOTIVATION: Clustering T-cell receptor repertoire (TCRR) sequences according to antigen specificity is challenging. The previously published tool GLIPH needs several days to weeks for clustering large repertoires, making its use impractical in larger studies. In addition, the methodology used in GLIPH suffers from shortcomings, including non-determinism, potential loss of significant antigen-specific sequences or inclusion of too many unspecific sequences. RESULTS: We present an algorithm for clustering TCRR sequences that scales efficiently to large repertoires. We clustered 36 real datasets with up to 62 000 unique CDR3β sequences using both an implementation of our method called ting, GLIPH and its successor GLIPH2. While GLIPH required multiple weeks, ting only needed about one minute for the same task. GLIPH2 is comparably fast, but uses a different grouping paradigm. In addition, we found that in naïve repertoires, where no or very few antigen-specific CDR3 sequences or clusters should exist, our method indeed selects much fewer motifs and produces smaller clusters. AVAILABILITY AND IMPLEMENTATION: Our method has been implemented in Python as a tool called ting. It is available from GitHub (https://github.com/FelixMoelder/ting) or PyPI under the MIT license. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.