Two Independent and Highly Efficient Open Source TKF91 Implementations

Abstract:

In the context of a master level programming practical at the computer science department of the Karlsruhe Institute of Technology, we developed and make available two independent and highly optimized open-source implementations for the pair-wise statistical alignment model, also known as TKF91, that was developed by Thorne, Kishino, and Felsenstein in 1991. This paper has two parts. In the educational part, we cover teaching issues regarding the setup of the course and the practical and summarize student and teacher experiences. In the scientific part, the two student teams (Team I: Nikolai, Sebastian, Daniel; Team II: Sarah, Pierre) present their solutions for implementing efficient and numerically stable implementations of the TKF91 algorithm. The two teams worked independently on implementing the same algorithm. Hence, since the implementations yield identical results -with slight numerical deviations- we are confident that the implementations are correct. We describe the optimizations applied and make them available as open-source codes in the hope that our findings and software will be useful to the community as well as for similar programming practicals at other universities.

SEEK ID: https://publications.h-its.org/publications/81

DOI: 10.1101/033191

Research Groups: Computational Molecular Evolution

Publication type: Journal

Journal: bioRxiv

Publisher: Cold Spring Harbor Labs Journals

Citation: biorxiv;033191v1,[Preprint]

Date Published: 2015

Registered Mode: imported from a bibtex file

Authors: Nikolai Baudis, Pierre Barbera, Sebastian Graf, Sarah Lutteropp, Daniel Opitz, Tomas Flouri, Alexandros Stamatakis

Citation
Baudis, N., Barbera, P., Graf, S., Lutteropp, S., Opitz, D., Flouri, T., & Stamatakis, A. (2015). Two Independent and Highly Efficient Open Source TKF91 Implementations. In []. Cold Spring Harbor Laboratory. https://doi.org/10.1101/033191
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Created: 7th Sep 2019 at 07:10

Last updated: 5th Mar 2024 at 21:23

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