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ATaRVa: Analysis of Tandem Repeat Variation from Long Read Sequencing data

Sivakumar, A. K.; Sudarsanam, S.; Sharma, A.; Avvaru, A. K.; Sowpati, D. T.

2025-05-16 genomics
10.1101/2025.05.13.653434 bioRxiv
Show abstract

Long-read sequencing propelled comprehensive analysis of tandem repeats (TRs) in genomes. Current long-read TR genotypers are either inaccurate, platform-specific, or computationally inefficient. Here we present ATaRVa, a sequencing technology-agnostic genotyper that outperforms existing tools while running an order of magnitude faster. ATaRVa also supports short-read data, multi-threading, consensus sequence derivation, and motif decomposition, making it an invaluable tool for population scale TR analyses. AvailabilityATaRVa is implemented in Python and is freely available on PyPI. The source code is deposited to GitHub at https://github.com/SowpatiLab/ATaRVa under an MIT license.

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"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.