'Rna secondary structure prediction' presentation slideshows

C omputational ncRNA gene finding (& nc RNA structure prediction)

C omputational ncRNA gene finding (& nc RNA structure prediction). nc RNA structure prediction (& computational ncRNA gene finding). Liming Cai. (BINF8210@UGA, Fall 2010). Non-coding RNAs. Functions other than coding proteins, e.g., structural, catalytic, and regulatory factors

1.18k views • 98 slides

RNA folding & ncRNA discovery

I519 Introduction to Bioinformatics, Fall, 2012. RNA folding & ncRNA discovery. Adapted from Haixu Tang. Contents. Non-coding RNAs and their functions RNA structures RNA folding Nussinov algorithm Energy minimization methods microRNA target identification. RNAs have diverse functions.

1.08k views • 97 slides

RNA folding & ncRNA discovery

I519 Introduction to Bioinformatics, Fall, 2012. RNA folding & ncRNA discovery. Adapted from Haixu Tang. Contents. Non-coding RNAs and their functions RNA structures RNA folding Nussinov algorithm Energy minimization methods microRNA target identification. RNAs have diverse functions.

974 views • 97 slides

RNA folding & ncRNA discovery

I519 Introduction to Bioinformatics, Fall, 2012. RNA folding & ncRNA discovery. Adapted from Haixu Tang. Contents. Non-coding RNAs and their functions RNA structures RNA folding Nussinov algorithm Energy minimization methods microRNA target identification. RNAs have diverse functions.

971 views • 97 slides

DNA2: Last week's take home lessons

DNA2: Last week's take home lessons. Comparing types of alignments & algorithms Dynamic programming (DP) Multi-sequence alignment Space-time-accuracy tradeoffs Finding genes -- motif profiles Hidden Markov Model (HMM) for CpG Islands. RNA1: Today's story & goals.

644 views • 54 slides

Facing the Challenge of Infectious Diseases in Africa: The Role of Mathematical Modeling

Predicting RNA Secondary Structures: A Lattice Walk Approach to Modeling Sequences Within the HIV-1 RNA Structure. Facing the Challenge of Infectious Diseases in Africa: The Role of Mathematical Modeling University of Witswatersrand Johannesburg, South Africa September 25-27, 2006

511 views • 51 slides

Magic Moments: Moment-based Approaches to Structured Output Prediction

The Analysis of Patterns. Magic Moments: Moment-based Approaches to Structured Output Prediction. Elisa Ricci joint work with Nobuhisa Ueda, Tijl De Bie, Nello Cristianini. Thursday, October 25 th. Learning in structured output spaces Z-score Experimental results and computational issues

566 views • 48 slides

Chou-Fasman propensities (partial table)

Chou-Fasman propensities (partial table). a b t. Algorithms and Thermodynamics for RNA Secondary Structure Prediction:. http://www.bioinfo.rpi.edu/applications/mfold/old/rna/form1.cgi http://www.bioinfo.rpi.edu/~zukerm/. http ://www.rcsb.org. Proportion of new folds.

442 views • 33 slides

RNA 2D and 3D Structure

RNA 2D and 3D Structure. Craig L. Zirbel October 7, 2010. RNA primary sequences. Laboratory techniques make it possible to extract specific RNA molecules and determine the sequence of nucleotides. Here are the sequences of the 5S ribosomal RNA molecule from different organisms:.

405 views • 30 slides

RNA 2D and 3D Structure

RNA 2D and 3D Structure. Craig L. Zirbel October 7, 2010. RNA primary sequences. Laboratory techniques make it possible to extract specific RNA molecules and determine the sequence of nucleotides. Here are the sequences of the 5S ribosomal RNA molecule from different organisms:.

400 views • 30 slides

RNA Secondary Structure Prediction with Error-Driven Transformation-Based Learning (TBL)

469 views • 27 slides

Computing with Strings

Computing with Strings. CSC 161: The Art of Programming Prof. Henry Kautz 9/16/2009. Some of My Favorite Things. Things made of strings Human language Web pages Databases Programs ... Things to do with strings Translate Compose Format Analyze . String Data Type.

257 views • 16 slides

Computational Biology and Bioinformatics stats.ox.ac.uk/research/genome/projects

Computational Biology and Bioinformatics http://www.stats.ox.ac.uk/research/genome/projects. 11.10 Models of substitution I : Basic Models 12.10 Models of substitution II : Complex Models 18.10 Phylogenies I: Combinatorics 19.10 Phylogenies II: Distance, Parsimony & Likelihood

258 views • 13 slides

Esposito, D., Heitsch, C. E., Poznanovik, S. and Swenson, M. S. Georgia Institute of Technology

Improved RNA Secondary Structure Prediction Using Stochastic Context Free Grammars. Esposito, D., Heitsch, C. E., Poznanovik, S. and Swenson, M. S. Georgia Institute of Technology. Conclusions. Abstract. Introduction.

78 views • 1 slides