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RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
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Sequences are fundamental mathematical objects consisting of ordered lists of numbers that follow a specific rule or pattern. Sequences are critical in various mathematical concepts, including calculus, series, and number theory. They can model real-world phenomena such as population growth, financial investments, and physical processes like the diminishing height of a bouncing ball.Each number in a sequence is referred to as a term. Typically, the terms are denoted as a1, a2, a3,…, where...
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Optimization for Sequencing and Analysis of Degraded FFPE-RNA Samples
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Optimization for Sequencing and Analysis of Degraded FFPE-RNA Samples

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Iterative pass optimization of sequence data.

Ward C Wheeler1

  • 1Division of Invertebrate Zoology, American Museum of Natural History, New York, NY 10024-5192, USA. wheeler@amnh.org

Cladistics : the International Journal of the Willi Hennig Society
|August 7, 2003
PubMed
Summary
This summary is machine-generated.

Determining ancestral sequences is NP-complete. A new heuristic combining direct optimization and iterative improvement offers a more efficient method for calculating cladogram costs, improving phylogenetic accuracy.

Keywords:
NASA Discipline Evolutionary BiologyNon-NASA Center

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Area of Science:

  • Computational Biology
  • Phylogenetics
  • Bioinformatics

Background:

  • The minimum-cost ancestral sequence problem for cladograms is NP-complete, driving research in multiple sequence alignment.
  • Heuristic methods like direct optimization (Wheeler, 1996) offer efficiency but have limitations.

Purpose of the Study:

  • To develop a more effective algorithm for calculating cladogram costs.
  • To improve upon existing heuristic methods for ancestral sequence reconstruction.

Main Methods:

  • A novel algorithm combining three-sequence direct optimization with iterative improvement.
  • Integration with a branch-length-based cladogram cost procedure.
  • Addressing computational and memory intensiveness through economies.

Main Results:

  • The combined algorithm frequently yields superior (lower) cladogram costs compared to existing methods.
  • Demonstrated effectiveness in arthropod systematics example.
  • The method is computationally and memory intensive but offers significant improvements.

Conclusions:

  • The proposed heuristic algorithm provides a powerful tool for phylogenetic analysis.
  • Offers a balance between computational efficiency and accuracy in ancestral sequence reconstruction.
  • Potential for broader application in systematic biology.