RNA secondary structure detection programs with an emphasis on covariance models

RNA secondary structure prediction requires a different approach from traditional alignment methods. Functional RNAs often have their secondary structure better conserved than their primary structure. Covariance models, probabilistic models that utilize stochastic-context-free grammars, are one approach. CMs allow for homology to be detected where purely sequence-based methods would fail. A background on CMs is given, as well as a background of the major classes of non-coding RNAs (ncRNAs). Comparisons are made between some CM-using tools (the Infernal suite and CMfinder) and some other RNA secondary structure tools (CARNAC, miRNAminer, Pfold, Mfold) as well as between Infernal and the primary alignment tool BLAT. CMfinder and Infernal are also compared against each other. RNA secondary structure databases, mainly Rfam and miRBase, are used to provide sequence and alignment data.

Name thesis:degree_name

Master of Science in Bioinformatics - (M.S.)

Discipline thesis:degree_discipline

Computer Science

Year

2009

Author dc:creator

• Slotman, Justin

Contributors dc:contributor

• Jason T. L. Wang
• Dimitri Theodoratos
• Guiling Wang

• RNA secondary structure prediction
• Covariance models
• Bioinformatics
• Computer Sciences

Repository record dc:identifier

https://digitalcommons.njit.edu/theses/304

OAI identifier oai:identifier

oai:digitalcommons.njit.edu:theses-1303