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Updated: Dec 27, 2025

Using In Vitro and In-cell SHAPE to Investigate Small Molecule Induced Pre-mRNA Structural Changes

Using In Vitro and In-cell SHAPE to Investigate Small Molecule Induced Pre-mRNA Structural Changes

Published on: January 30, 2019

Shape-IT: new rapid and accurate algorithm for haplotype inference.

Olivier Delaneau¹, Cédric Coulonges, Jean-François Zagury

¹Chaire de Bioinformatique, Conservatoire National des Arts et Métiers, 292 rue Saint-Martin, 75003 Paris, France.

BMC Bioinformatics

|December 18, 2008

Summary

This summary is machine-generated.

A new computational algorithm, Shape-IT, significantly accelerates haplotype inference, offering comparable accuracy to existing methods. This advancement utilizes binary trees for efficient processing, making it suitable for large-scale genetic studies.

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Last Updated: Dec 27, 2025

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Published on: January 30, 2019

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RNA Secondary Structure Prediction Using High-throughput SHAPE

RNA Secondary Structure Prediction Using High-throughput SHAPE

Published on: May 31, 2013

Area of Science:

Computational biology
Genetics
Bioinformatics

Background:

Haplotype inference is crucial for genetic studies.
Existing methods like Phase v2.1 face computational challenges with large datasets.
The coalescence with recombination model is a standard for haplotype inference.

Purpose of the Study:

To develop a faster and accurate haplotype inference algorithm.
To improve computational efficiency for large-scale genetic data analysis.
To leverage binary tree representations for algorithmic enhancement.

Main Methods:

Developed Shape-IT, a novel computational algorithm.
Employed binary trees to represent sets of candidate haplotypes.
Optimized posterior probability computations by avoiding redundant operations.
Implemented smart exploration of plausible pathways within binary trees.

Main Results:

Shape-IT demonstrates orders of magnitude speed improvement over Phase v2.1.
Maintained accuracy comparable to Phase v2.1 in tests.
Outperformed other software (Gerbil, PL-EM, Fastphase, 2SNP, Ishape) in accuracy.
Shape-IT showed superior speed for datasets under 100 SNPs compared to Ishape and Fastphase.

Conclusions:

Shape-IT is highly suitable for routine haplotype inference and high-throughput genotyping.
The algorithm efficiently fits the Phase v2.1 genetic model on large datasets.
The tree-based approach has potential applications in other HMM-based software and fields.