AKSmooth 1.0 – Ajusted Local Kernel Smoothing for Bisulfite Sequencing data

AKSmooth 1.0

:: DESCRIPTION

AKSmooth is a statistical method that can accurately and efficiently reconstruct the single CpG methylation estimate across the entire methylome using low-coverage bisulfite sequencing (Bi-Seq) data.

::DEVELOPER

AKSmooth team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Linux
R

:: DOWNLOAD

AKSmooth

:: MORE INFORMATION

Citation

J Bioinform Comput Biol. 2014 Dec;12(6):1442005. doi: 10.1142/S0219720014420050.
AKSmooth: enhancing low-coverage bisulfite sequencing data via kernel-based smoothing.
Chen J1, Lutsik P, Akulenko R, Walter J, Helms V.

Kerfdr 2.0.1 – A Semi-parametric Kernel-based approach to local FDR Estimations

Kerfdr 2.0.1

:: DESCRIPTION

Kerfdr is a semi-parametric kernel-based approach to local false discovery rate estimation.

::DEVELOPER

SSB group.

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Linux / Windows / MacOsX
R package

:: DOWNLOAD

Kerfdr

:: MORE INFORMATION

Citation:

BMC Bioinformatics. 2009 Mar 16;10:84. doi: 10.1186/1471-2105-10-84.
Kerfdr: a semi-parametric kernel-based approach to local false discovery rate estimation.
Guedj M, Robin S, Celisse A, Nuel G.

GSKernel 1.1 – Generic String Kernel

GSKernel 1.1

:: DESCRIPTION

GSKernel is a specialized string kernel for small bio-molecules, peptides and pseudo-sequences of binding interfaces. The kernel incorporates physico-chemical properties of amino acids and elegantly generalizes eight kernels, comprised of the Oligo, the Weighted Degree, the Blended Spectrum, and the Radial Basis Function.

::DEVELOPER

the Group for Research in Artificial Intellience of Laval University (GRAIL)

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Linux

:: DOWNLOAD

GSKernel

:: MORE INFORMATION

Citation

Sébastien Giguère, Mario Marchand, François Laviolette, Alexandre Drouin, and Jacques Corbeil.
“Learning a peptide-protein binding affinity predictor with kernel ridge regression.”
BMC bioinformatics 14, no. 1 (2013): 82.

KeBABS 1.20.0 – R Package for Kernel-Based Analysis of Biological Sequences

KeBABS 1.20.0

:: DESCRIPTION

The KeBABS package provides functionality for kernel based analysis of biological sequences via Support Vector Machine (SVM) based methods. Biological sequences include DNA, RNA, and amino acid (AA) sequences. Sequence kernels define similarity measures between sequences.

::DEVELOPER

Institute of Bioinformatics, Johannes Kepler University Linz

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Windows/ Linux / MacOsX
R
BioConductor

:: DOWNLOAD

KeBABS

:: MORE INFORMATION

Citation

KeBABS: an R package for kernel-based analysis of biological sequences.
Palme J, Hochreiter S, Bodenhofer U.
Bioinformatics. 2015 Mar 25. pii: btv176.

Stem Kernels 193 – Directed acyclic graph kernels for structural RNA analysis

Stem Kernels 193

:: DESCRIPTION

The stem kernel is a kernel function for structural RNAs to measure a kind of similarity between a pair of RNA sequences from the viewpoint of secondary structures.

::DEVELOPER

Computational Biology Research Center (CBRC),

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Linux

:: DOWNLOAD

Stem Kernels

:: MORE INFORMATION

Citation:

Sato K., Mituyama, T., Asai, K. and Sakakibara, Y.:
Directed acyclic graph kernels for Structural RNA analysis,
BMC Bioinformatics, 9:318, 2008.

kernel-compute 1.0 – Profile-based Kernel Compute Package

kernel-compute 1.0

:: DESCRIPTION

kernel-compute is a package that computes pairwise profile-based similarity matrix. This scoring matrix has shown to be the best performing method for developing remote homology detection and fold recognition models.

::DEVELOPER

Huzefa Rangwala

:: SCREENSHOTS

Command Line

:: REQUIREMENTS

Linux
Matlab

:: DOWNLOAD

kernel-compute

:: MORE INFORMATION

Citation:

Huzefa Rangwala & George Karypis
Profile Based Direct Kernels for Remote Homology Detection and Fold Recognition
Bioinformatics 2005 21(23):4239-4247