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Gillian Chu

gillian.chu@princeton.edu

UPP2

In Submission

Gillian Chu

1 minute read

UPP2 is a computational improvement on UPP, performing highly accurate and fast multiple sequence alignment.

Motivation: Multiple sequence alignment (MSA) is a basic step in many bioinformatics pipelines. However, achieving highly accurate alignments on large datasets, especially those with sequence length heterogeneity, is a challenging task. UPP (Ultra-large multiple sequence alignment using Phylogeny-aware Profiles) is a method for MSA estimation that builds an ensemble of Hidden Markov Models (eHMM) to represent an estimated alignment on the full length sequences in the input, and then adds the remaining sequences into the alignment using selected HMMs in the ensemble. Although UPP provides good accuracy, it is computationally intensive on large datasets.

Results: We present UPP2, a direct improvement on UPP. The main advance is a fast technique for selecting HMMs in the ensemble that allows us to achieve the same accuracy as UPP but with greatly reduced runtime. We show UPP2 produces more accurate alignments compared to leading MSA methods on datasets exhibiting substantial sequence length heterogeneity, and is among the most accurate otherwise.

Tool is available here. The paper has been made available on biorXiv here.


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About

Hi, I'm Gillian. I'm a PhD student in Computer Science at Princeton University, specializing in computational biology. Right now, I'm thinking about graph theory, algorithm design, probabilistic models and evolution.