:: DESCRIPTION
RSEM (RNA-Seq expression estimation by Expectation-Maximization) estimates gene and isoform expression levels from RNA-Seq data with a statistical model that takes into account reads that map to multiple positions.
::DEVELOPER
:: SCREENSHOTS
N/A
:: REQUIREMENTS
:: DOWNLOAD
:: MORE INFORMATION
Citation
Li, B. and Dewey, C. N.
RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome.
BMC Bioinformatics 2011, 12:323
:: DESCRIPTION
SBML-PET-MPI is a parallel parameter estimation tool for Systems Biology Markup Language (SBML) based models. The tool allows the user to perform parameter estimation, parameter uncertainty and identifiability analysis by collectively fitting multiple experimental data sets.
::DEVELOPER
:: SCREENSHOTS
Command Line
:: REQUIREMENTS
:: DOWNLOAD
:: MORE INFORMATION
Citation:
SBML-PET-MPI: a parallel parameter estimation tool for Systems Biology Markup Language based models.
Zi Z. (2011)
Bioinformatics. 2011 Apr 1;27(7):1028-9.
:: DESCRIPTION
REDEMPTION is a MATLAB toolbox for the identification of parameters and parameter ensembles of ODE models from time-series data.
:: DEVELOPER
Chemical and Biological Systems Engineering Laboratory
:: SCREENSHOTS
N/A
:: REQUIREMENTS
:: DOWNLOAD
:: MORE INFORMATION
Citation
REDEMPTION: Reduced Dimension Ensemble Modeling and Parameter Estimation.
Liu Y, Manesso E, Gunawan R.
Bioinformatics. 2015 Jun 14. pii: btv365.
:: DESCRIPTION
BEGFE implements a Markov Chain Monte Carlo algorithm to estimate the posterior probability distribution of the birth and death rate parameters and the numbers of gene copies at the internodes of the phylogenetic tree. In addition, BEGFE can simulate gene family data under the birth and death model.
::DEVELOPER
:: SCREENSHOTS
N/A
:: REQUIREMENTS
:: DOWNLOAD
:: MORE INFORMATION
Citation
Liu, L., L. Yu, V. Kalavacharla, Z. Liu.
A Bayesian model for gene family evolution.
BMC Bioinformatics. 2011, 12:426
:: DESCRIPTION
FRST serves to validate the energy of a protein structure. The server computes both an overall and a per-residue energy profile of a protein structure. The energy profile can be visualized in different ways, including a per-residue plot and a colored structure representation, accessible as a series of dynamic web pages.
::DEVELOPER
:: SCREENSHOTS
N/A
:: REQUIREMENTS
:: DOWNLOAD
:: MORE INFORMATION
Citation:
Silvio C.E. Tosatto.
The Victor/FRST Function for Model Quality Estimation.
Journal of Computational Biology 2005; 12(10):1316-1327
:: DESCRIPTION
FUGUE is the program used to construct haplotypes for the chromosome and linkage disequilibrium maps.
::DEVELOPER
:: SCREENSHOTS
Command Line
:: REQUIREMENTS
:: DOWNLOAD
:: MORE INFORMATION
Citation:
Dawson E, Abecasis GR, Bumpstead S, Chen Y, Hunt S, Beare DM, Pabial J, Dibling T, Tinsley E, Kirby S, Carter D, Papaspyridonos M, Livingstone S, Ganske R, Lohmussaar E, Zernant J, Tonisson N, Remm M, Magi R, Puurand T, Vilo J, Kurg A, Rice K, Deloukas P, Mott R, Metspalu A, Bentley DR, Cardon LR and Dunham I
A first-generation linkage disequilibrium map of human chromosome
Nature (2002) 418:544-548
Phillips MS, Lawrence R, Sachidanandam R, Morris AP, Balding DJ, Donaldson MA, Studebaker JF, Ankener WM, Alfisi SV, Kuo FS, Camisa AL, Pazorov V, Scott KE, Carey BJ, Faith J, Katari G, Bhatti HA, Cyr JM, Derohannessian V, Elosua C, Forman AM, Grecco NM, Hock CR, Kuebler JM, Lathrop JA, Mockler MA, Nachtman EP, Restine SL, Varde SA, Hozza MJ, Gelfand CA, Broxholme J, Abecasis GR, Boyce-Jacino MT and Cardon LR
Chromosome-wide distribution of haplotype blocks and the role of recombination hot spots.
Nat Genet (2003) 33:382-7
:: DESCRIPTION
SLR (Sitewise Likehood Ratio) is a program to detect sites in coding DNA that are unusually conserved and/or unusually variable (that is, evolving under purify or positive selection) by analysing the pattern of changes for an alignment of sequences on an evolutionary tree. The strength of selection at each site is determined by comparing the rate of nonsynonymous (amino acid changing) substitutions to that of synonymous (silent) substituions, the latter assumed to be invisible to selection and so evolving in a strictly neutral fashion.
::DEVELOPER
:: SCREENSHOTS
N/A
:: REQUIREMENTS
:: DOWNLOAD
:: MORE INFORMATION
Citation:
Genetics. 2005 Mar;169(3):1753-62. Epub 2005 Jan 16.
Detecting amino acid sites under positive selection and purifying selection.
Massingham T, Goldman N.
:: DESCRIPTION
cn.FARMS is a latent variable model for detecting copy number variations in microarray data.
::DEVELOPER
Institute of Bioinformatics, Johannes Kepler University Linz
:: SCREENSHOTS
N/A
:: REQUIREMENTS
:: DOWNLOAD
:: MORE INFORMATION
Citation
Nucleic Acids Res. 2011 Jul;39(12):e79. doi: 10.1093/nar/gkr197. Epub 2011 Apr 12.
cn.FARMS: a latent variable model to detect copy number variations in microarray data with a low false discovery rate.
Clevert DA1, Mitterecker A, Mayr A, Klambauer G, Tuefferd M, De Bondt A, Talloen W, G?hlmann H, Hochreiter S.
:: DESCRIPTION
MetaPIGA is a robust implementation of several stochastic heuristics for large phylogeny inference (under maximum likelihood), including a random-restart hill climbing, a simulated annealing algorithm, a classical genetic algorithm, and the metapopulation genetic algorithm (metaGA) together with complex substitution models, discrete Gamma rate heterogeneity, and the possibility to partition data.
::DEVELOPER
Laboratory of Artificial & Natural Evolution
:: SCREENSHOTS
:: REQUIREMENTS
:: DOWNLOAD
:: MORE INFORMATION
Citation
MetaPIGA v2.0: maximum likelihood large phylogeny estimation using the metapopulation genetic algorithm and other stochastic heuristics
aphaël Helaers & Michel C. Milinkovitch
BMC Bioinformatics 2010, 11:379
:: DESCRIPTION
Ribomap is a package that generates isoform-level ribosome profiles from ribosome profiling data.
::DEVELOPER
:: SCREENSHOTS
N/A
:: REQUIREMENTS
:: DOWNLOAD
:: MORE INFORMATION
Citation
Isoform-level ribosome occupancy estimation guided by transcript abundance with Ribomap.
Wang H, McManus J, Kingsford C.
Bioinformatics. 2016 Jun 15;32(12):1880-2. doi: 10.1093/bioinformatics/btw085
