Feature-Response Curve (FRC): Assembly Metric and Analysis Tool
Inspired by the standard receiver operating characteristic (ROC) curve, the Feature-Response curve
characterizes the sensitivity (coverage) of the sequence assembler output (contigs) as a function of
its discrimination threshold (number of features/errors).
Each contig is assigned a number of features that correspond to doubtful regions of the sequence. Given any such set of features,
the response (quality) of the assembler output is then analyzed as a function of the maximum number of possible
errors (features) allowed in the contigs.
- The FRC can be used as a metric to compare the assembly quality of multiple assemblers.
- The FRC does not require any reference sequence (except an estimate of the genome size) to be used for validation,
thus making it a very useful tool in de novo sequencing projects.
- Separate FRCs can be generated for each feature type enabling to scrutinize the relative strengths and weaknesses of different assemblers.
Scoring-and-Unfolding Trimmed Tree Assembler
SUTTA is a new sequence assembly algorithm based on global search-methods (e.g. branch-and-bound
or beam search).
Some of its features are:
Technologically Agnostic: supports different set of technologies with minimal changes to its
architecture (currently long Sanger reads and short next-generation Illumina reads).
Search strategy: each contig is assembled independently and
dynamically without creating in advance the graph that describes the overlapping relations between all the reads;
Score-based: score functions are used to evaluate the DNA sequences concurrently while being assembled.
The functions combine different structural properties (e.g., transitivity, coverage, mated pairs, physical maps, etc).
- Project web-site
at NYU Bioinformatics Lab.
- Featured in the Bioinformatics for Next Generation Sequencing
Planning with Large Agent-Networks against Catastrophes
PLAN C is an innovative tool for emergency managers, urban planners and public health
officials to prepare and evaluate Pareto-optimal plans to respond to urban catastrophic situations.
PLAN C was designed and developed at the NYU Bioinformatics Group
for the Large-Scale Emergency Readiness project
as part of the NYU
Center for Catastrophe Preparedness & Response
Immune Pareto Archived Evolution Strategy
I-PAES is a modified version of the multi-objective evolutionary algorithm PAES
(Pareto Archived Evolution Strategy), proposed by Knowles and Corne in 1999, with a
different solution representation (polypeptide chain) and immune inspired operators
(cloning and hypermutation) for tackling the Protein Structure Prediction (PSP)
as a Multi-Objective Optimization Problem (MOOP).
A recent review paper published by the Journal of the Royal Society Interface,
G. Helles,(2008; 5(21): 387--396, DOI: 10.1098/rsif.2007.1278)
ranks I-PAES among the best state-of-the-art folding algorithm.
I-PAES code uses some external routines from the TINKER Molecular Modeling Package:
It also requires to use the force field parameter set of CHARMM (version 27) energy function (charmm27.prm).
These routines are avilable for download directly from the
("Readme" file in the I-PAES package contains informations about the
installation of these external files in the software and the compilation of the code).
- i-paes.zip - C code of I-PAES including scripts and input files for 1ZDD protein.
- TINKER Molecular Modeling Package.
Copyright © 2010 Giuseppe Narzisi