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Related Experiment Video

Updated: Aug 12, 2025

Single Cell Multiplex Reverse Transcription Polymerase Chain Reaction After Patch-clamp
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Published on: June 20, 2018

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From pairwise to multiple spliced alignment.

Safa Jammali1,2, Abigaïl Djossou1, Wend-Yam D D Ouédraogo1

  • 1Département D'informatique, Faculté des Sciences, Université de Sherbrooke, 2500, boul. de l'Université, Sherbrooke (Québec) J1K 2R1, Canada.

Bioinformatics Advances
|January 26, 2023
PubMed
Summary
This summary is machine-generated.

We introduce multiple spliced alignments (MSAs) to study transcript evolution and improve gene models. Our SFAM method computes MSAs for gene families, enabling accurate superstructure and annotation predictions.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Evolutionary Biology

Background:

  • Alternative splicing generates diverse transcripts from a single gene, crucial for eukaryotic complexity.
  • Studying transcript evolution within gene families requires methods to compare splicing structures.
  • Current methods for comparing multiple transcripts with complex splicing patterns are limited.

Purpose of the Study:

  • To introduce the concept and computational methods for multiple spliced alignments (MSAs).
  • To develop a method for computing MSAs of gene families to facilitate transcript evolution studies.
  • To enhance gene model prediction and genome annotation accuracy.

Main Methods:

  • Generalizing pairwise spliced alignments (PSpAs) to multiple spliced alignments (MSAs).
  • Introducing the SplicedFamAlignMulti (SFAM) method for computing MSAs of gene families.
  • Combining PSpAs of coding DNA and gene sequences into a single MSA structure.

Main Results:

  • SFAM successfully computes accurate gene family superstructures and MSAs.
  • The method aids in inferring splicing orthologous groups.
  • SFAM improves gene model annotations using real and simulated data.

Conclusions:

  • MSAs are essential for studying transcript evolution and improving genome annotation.
  • The SFAM method provides a robust approach for generating MSAs of gene families.
  • SFAM is a valuable tool for genomic and evolutionary analyses.