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PicXAA: A Probabilistic Scheme for Finding the Maximum Expected Accuracy Alignment of Multiple Biological Sequences

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Multiple Sequence Alignment Methods

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1079))

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Abstract

PicXAA is a probabilistic nonprogressive alignment algorithm that finds protein (or DNA) multiple sequence alignments with maximum expected accuracy. PicXAA greedily builds up the alignment from sequence regions with high local similarity, thereby yielding an accurate global alignment that effectively captures the local similarities across sequences. PicXAA constantly yields accurate alignment results on a wide range of reference sets that have different characteristics, with especially remarkable improvements over other leading algorithms on sequence sets with high local similarities. In this chapter, we describe the overall alignment strategy used in PicXAA and discuss several important considerations for effective deployment of the algorithm.

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Acknowledgment

This work was supported in part by the National Science Foundation through NSF Award CCF-1149544.

Author information

Authors and Affiliations

  1. Department of Plant and Microbial Biology, University of California, Berkeley, CA, USA

    Sayed Mohammad Ebrahim Sahraeian

  2. Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, USA

    Byung-Jun Yoon

Authors
  1. Sayed Mohammad Ebrahim Sahraeian
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  2. Byung-Jun Yoon
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Editor information

Editors and Affiliations

  1. Dept. of Electrical Engineering, University of Nebraska-Lincoln, Lincoln, Nebraska, USA

    David J Russell

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Sahraeian, S.M.E., Yoon, BJ. (2014). PicXAA: A Probabilistic Scheme for Finding the Maximum Expected Accuracy Alignment of Multiple Biological Sequences. In: Russell, D. (eds) Multiple Sequence Alignment Methods. Methods in Molecular Biology, vol 1079. Humana Press, Totowa, NJ. https://doi.org/10.1007/978-1-62703-646-7_13

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  • DOI: https://doi.org/10.1007/978-1-62703-646-7_13

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  • Publisher Name: Humana Press, Totowa, NJ

  • Print ISBN: 978-1-62703-645-0

  • Online ISBN: 978-1-62703-646-7

  • eBook Packages: Springer Protocols

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