Table 1 Summary of detailed characteristics of the six available web servers for DNA-binding sites prediction.

DISIS

Support Vector Machine (SVM)

Neural network

Evolutionary profile

Conservation

Predicted secondary structure

Predicted solvent accessibility

http://cubic.bioc.columbia.edu/services/disis

DNABindR

Naïve Bayes classifier

Relative solvent accessibility

Sequence entropy

Secondary structure

Electrostatic potential

Hydrophobicity

http://turing.cs.iastate.edu/PredDNA/predict.html

BindN

SVM

The side chain pKa value

Hydrophobicity index

Molecular mass

http://bioinfo.ggc.org/bindn/

BindN-rf

Random forest

The side chain pKa value

Hydrophobicity index

Molecular mass

Blast-based conservation

Biochemical feature

Position-specific scoring matrix (PSSM)

http://bioinfo.ggc.org/bindn-rf/

DP-Bind

SVM

Kernel logistic regression

Penalized logistic regression

Sequence-based BLOSUM62

PSSM-based

http://lcg.rit.albany.edu/dp-bind/

DBS-PRED

Neural network

Protein sequence information

Solvent accessibility

Secondary structure

http://www.netasa.org/dbs-pred