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Informatic Analysis of Sequence Data from Batch Yeast 2-Hybrid Screens
Published on: June 28, 2018
Comprehensive and relaxed search for oligonucleotide signatures in hierarchically clustered sequence datasets
Kai Christian Bader1, Christian Grothoff, Harald Meier
1Services Department of Informatics, Technische Universität München, Boltzmannstrasse 3, 85748 Garching, Germany.
Bioinformatics (Oxford, England)
|April 8, 2011
Summary
We developed CaSSiS, a computational method to efficiently find oligonucleotide signatures for molecular diagnostics. This tool aids in designing primers and probes for DNA sequencing and PCR by analyzing large sequence datasets.
Area of Science:
- Bioinformatics
- Molecular Biology
- Genomics
Background:
- Molecular diagnostics utilize oligonucleotide primers and probes for PCR, hybridization, and DNA sequencing.
- Designing these requires identifying specific oligonucleotide signatures within large genomic or gene sequence datasets.
- Existing databases lack comprehensive signature collections, and searching them is computationally intensive, especially with growing data.
Purpose of the Study:
- To develop an efficient and scalable method for computing oligonucleotide signatures from large, hierarchically clustered nucleic acid sequence data.
- To enable the identification of both sequence-specific and sequence group-specific signatures.
- To provide a tool that handles complex search requirements, including relaxed matching criteria for signature discovery.
Main Methods:
- Developed CaSSiS (Computational Analysis of Sequence Signatures), a novel method for signature computation.
- Utilized the ARB Positional Tree (PT-)Server and a new BGRT data structure.
- Implemented algorithms for finding exact and near-perfect group-covering signatures, allowing user-defined specificity and sensitivity.
Main Results:
- CaSSiS demonstrates good runtime and memory performance on large phylogenetic gene sequence datasets.
- The method successfully identifies sequence-specific signatures and perfect group-covering signatures.
- It also finds signatures with maximal group coverage (sensitivity) within specified non-target hit ranges (specificity), accommodating imperfect matches.
Conclusions:
- CaSSiS provides a fast and scalable solution for discovering oligonucleotide signatures from extensive sequence data.
- The identified signatures serve as effective blueprints for designing group-specific oligonucleotide probes for molecular diagnostics.
- The software is available online, facilitating its application in research and diagnostics.
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