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Single Cell Multiplex Reverse Transcription Polymerase Chain Reaction After Patch-clamp
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MACSE: Multiple Alignment of Coding SEquences accounting for frameshifts and stop codons.

Vincent Ranwez1, Sébastien Harispe, Frédéric Delsuc

  • 1Institut des Sciences de l'Evolution, UMR5554-CNRS, Université Montpellier II, Montpellier, France. Vincent.Ranwez@univ-montp2.fr

Plos One
|September 28, 2011
PubMed
Summary

This study introduces MACSE, a novel algorithm for aligning coding DNA sequences. MACSE accurately handles frameshifts and stop codons, improving gene sequence alignment for biological research.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Traditional nucleotide sequence alignment methods struggle with coding sequences due to reading frame shifts and stop codons.
  • Indirect alignment via amino acid translation is hampered by premature stop codons and single nucleotide errors, leading to aberrant alignments.

Purpose of the Study:

  • To develop a direct nucleotide sequence alignment algorithm that accommodates biological deviations in coding sequences.
  • To create a robust multiple sequence alignment (MSA) tool for protein-coding gene datasets, including pseudogenes.

Main Methods:

  • Developed a new algorithm with the same time and space complexity as Needleman-Wunsch.
  • Extended the pairwise algorithm to a multiple sequence alignment (MSA) program named MACSE (Multiple Alignment of Coding SEquences).
  • MACSE accounts for frameshifts and stop codons directly at the nucleotide level.

Main Results:

  • MACSE provides accurate pairwise and multiple sequence alignments for coding DNA sequences, even with frameshifts and stop codons.
  • The tool successfully aligns datasets containing pseudogenes without disrupting codon structure.
  • Demonstrated utility in detecting frameshifts in public databases and aligning next-generation sequencing data.

Conclusions:

  • MACSE offers the first automated solution for aligning protein-coding gene datasets with inherent biological complexities.
  • The algorithm enhances the accuracy of sequence alignment, particularly for non-functional sequences and in the presence of sequencing errors.
  • MACSE is a valuable tool for genomic research, pseudogene analysis, and processing next-generation sequencing data.