Welcome to Galaxy based service in OSDDlinux Online
Galaxy is a open source web-based platform which can meet the needs of bioinformaticians analysing the bulk data. Users can use galaxy without command line since it is a graphical user interface.
We have integrated GPSR1.0, GPSR2.0 along with the webservers and the standalone in the OSDDlinux which can help the users and the scientists in drug discovery.
- Users can make workflows, manipulate workflows and use the pre-existing workflows for the pipeline used in the drug discovery.
- Heavy jobs are automatically saved in the history panel which can can be accessed later on.
- Available at: LiveGalaxy
Following is the list of the available LiveGalaxy packages integrated in the OSDDlinux.
| GPSR1.0 |
| Program | Decription |
| fasta2sfasta | Convert fasta format to single fasta format |
| pro2aac | To calculate amino acid composition of protein |
| pro2aac_nt | To calculate amino acid composition of N-terminal (nt) residues of a protein |
| pro2aac_ct | To calculate amino acid composition of C-terminal (ct) residues of a protein |
| pro2aac_rest.pl | To calculate amino acid composition of a protein after removing N-, and C-terminal residues |
| pro2aac_split | To calculate split amino acid composition (SSAC) of a protein |
| pro2dpc | To calculate dipeptide composition of protein |
| pro2dpc_nt | To calculate dipeptide composition of N-terminal (nt) residues of a protein |
| pro2dpc_ct | To calculate dipeptide composition of C-terminal (ct) residues of a protein |
| pro2tpc | To calculate tripeptide composition of protein |
| add_cols | To add columns of two files |
| col2svm | To generating SVM_light input format |
| col_mult | To multiplying each column of input file with a number |
| col_mult_sel | To multiplying selective columns with a number |
| col_rem | To remove selective columns from a file |
| col_ext | To extract selective columns from a file |
| col_corr | To compute correlation co-efficient between two column |
| col_avg | To calculate average column of two files |
| seq2pssm_imp | To calculate PSSM matrix in column format without any normalization |
| pssm_n1 | To normalize pssm profile based on 1/(1+e-x) formula |
| pssm_n2 | To normalize pssm profile based on (numb -min)/(max -min) formula |
| pssm_n3 | To normalize pssm profile based on (numb -min)*100/(max -min) formula |
| pssm_n4 | To normalize pssm profile based on 1/(1+e-(x/100) formula |
| pssm_comp | To compute PSSM composition (400 points) |
| col_sig | Significance of columns in two column files |
| pssm2pat | To generate patterns of given size from PSSM matrix |
| pssm_smooth | To designed smooth pssm profile for plot |
| seq2motif | To create motifs by sliding window of user defined length with option of adding terminal X |
| motif2bin | To make binary input from the multifasta motif file |
| blast_similarity | To perform blast |
| GPSR2.0 |
Tools for Chemo-informatics: Part A
| Program | Discription |
| desc_imp_a | Gives n most important descriptors for predicting positive and negative examples (n given by user) |
| desc_sel_a | Selects the final set of descriptors for prediction by removing very similar descriptors |
| desc_graph_a | Creates barplot of importance of descriptors (in terms of IDD) vs important Descriptors |
| desc_mod_a | Modifies the binary descriptors based on relative frequency in positive and negative datasets |
| desc_clust_a | Performs clustering of descriptors (i.e. column wise) with graphical representation |
| chem_clust_a | Performs clustering of chemicals (i.e. row wise) with graphical representation |
| sim_chem_a | Finds the most similar chemical from the database of chemicals based on distance between descriptors of chemicals |
Tools for Chemo-informatics: Part B
| Program | Discription |
| desc_imp_b | Gives n most important descriptors for predicting positive and negative examples (n given by user) |
| desc_sel_b | Selects the final set of descriptors for prediction by removing very similar descriptors |
| desc_graph_b | Creates barplot of importance of descriptors (in terms of IDD) vs important Descriptors |
| desc_clust_b | Performs clustering of descriptors (i.e. column wise) with graphical representation |
| chem_clust_b | Performs clustering of chemicals (i.e. row wise) with graphical representation |
| sim_chem_b | Finds the most similar chemical from the database of chemicals based on distance between descriptors of chemicals |
Tools for Chemo-informatics: Part C
| Program | Discription |
| desc_imp_c | Gives n most important descriptors for predicting positive and negative examples (n given by user) |
| desc_sel_c | Selects the final set of descriptors for prediction by removing very similar descriptors |
| desc_graph_c | Creates barplot of importance of descriptors (in terms of IDD) vs important Descriptors |
| desc_clust_c | Performs clustering of descriptors (i.e. column wise) with graphical representation |
| chem_clust_c | Performs clustering of chemicals (i.e. row wise) with graphical representation |
Tools for Chemo-informatics: Part D
| Program | Discription |
| desc_imp_d | Gives n most important descriptors based upon correlation with response. (n given by user). An additional file with all descriptors with correlation values is also given as output |
| desc_sel_d | Selects the final set of descriptors for prediction by removing very similar descriptors |
| desc_graph_d | Creates barplot of importance of descriptors (in terms of IDD) vs important Descriptors |
| desc_clust_d | Performs clustering of descriptors (i.e. column wise) with graphical representation |
| chem_clust_d | Performs clustering of chemicals (i.e. row wise) with graphical representation |
Miscellaneous
| Program | Discription |
| make_selectedfile | Extracts specific columns from input file and writes in output file |
| shiftcol | Shifts the 2 columns in a file and writes in an output file |
| rem_identicalcol | Removes identical columns in a file and writes unique columns in output file |
| matrix_optimization | For a given positive and negative dataset of protein sequences this program optimizes the substitution matrix which can be used in classification of positive and negative examples |
| randomizefile | shuffles the rows of a file randomly and writes in an output file. (can also extract user defined number of lines randomly from input file and write in output file) |
| mean | Calculates row wise or column wise mean of file in csv format |
| median | Calculates row wise or column wise median of file in csv format |
| stdev | Calculates row wise or column wise standard deviation of file in csv format |
| stderr | Calculates row wise or column wise standard error of file in csv format |
| correlation | Calculates correlation of all columns of a file or between 2 columns |
| barplot | Draws a barplot between 2 properties |
| roc | Draws a roc plot. |
| PSSM-pattern | Makes PSSM profile of positive and negative patterns for prediction at residue level (see gpsr_1.0 manual for residue level prediction) |
| Protein Structure |
| Program | Discription |
| PepStr | 3D structure prediction of bioactive peptides |
| AlphaPred | Prediction of Alpha turns in protein |
| APSSP2 | Advanced Protein Secondary Structure Prediction Server |
| AR_NHPred | Prediction of aromatic backbone NH interaction in proteins |
| TBBPred | Prediction of Transmembrane Beta Barrel in Proteins |
| BetatPred | Prediction of betaturns in proteins |
| BetatPred2 | Prediction of betaturns in proteins |
| BetaTurns | Prediction of betaturn types in protein |
| BhairPred | Prediction of Beta Hairpins |
| CHpredict | Prediction of CH..O and CH..Pi interactions |
| Gammapred | Prediction of Gamma turns in protein |
| SARpred | A neural network based method predicts the real value of surface acessibility (SA) by using multiple sequence alignment |
| Protein Function |
| Program | Discription |
| NRpred | A server for classification of nuclear receptors |
| ESLpred | SVM Based Method for Subcellular Localization of Eukaryotic Proteins |
| PSLpred | Prediction of subcellular localization of bacterial proteins |
| BTXpred | Prediction of Bacterial Toxins |
| GPCRsclass | Classification of amine type of G-protein-coupled receptors |
| SRTpred | SVM-based method for the classification of protein sequence as secretory or non-secretory protein |
| HSLpred | A server for the prediction of the subcellular localization of human protein |
| GSTpred | Prediciton of Glutathione S-transferase protein |
| Glycopp | A webserver for predicting potential N-and O-glycosites in prokaryotic protein sequence(s) |
| Glycoep | In silico platform for prediction of N-, O- and C-Glycosites in eukaryotic protein sequences |
| NTXpred | Prediction of Neurotoxins and its source and probable function from primary amino acid sequence |
| VICMpred | Prediction of Virulence factors, Information molecule, Cellular process and Metabolism molecule in the Bacterial proteins |
| ALGpred | Prediction of allergenic proteins and mapping of IgE epitopes |
| PseaPred | Prediction of proteins secreted by malarial parasite P.falciparum into infected-erythrocyte |
| RSL-Pred | A SVM based method for subcellular localization prediction of rice proteins |
| IssPred | Intein Splice Site Prediction |
| TBPred | Support Vector Machine-based method for predicting subcellular localization of mycobacterial proteins using evolutionary information and motifs |
| PROprint | Prediction of physical or functional interactions between protein molecules |
| Hivcopred | Server for prediction of HIV coreceptor usage |
| Molecular Interaction |
| Program | Discription |
| ATPint | Prediction of ATP interacting protein residues |
| GTPbinder | Prediction of GTP interacting residues, dipeptides and tripeptides in a protein from its evolutionary information |
| NADbinder | Prediction of NAD interacting residues in proteins |
| FADpred | A webserver for the prediction of FAD interacting residues |
| Pprint | A tool for predicting RNA-binding residues of a protein |
| PreMieR | A webserver for the prediction of Mannose Interacting residue |
| VitaPred | Prediction method for the vitamin-interacting residues in protein sequences |
| PROprint | Prediction of physical or functional interactions between protein molecules |
| nHLAPred | A neural network based MHC Class-I Binding Peptide Prediction Server |
| Therapeutic peptides |
| Program | Discription |
| CellPPD | CellPPD predicts the cell penetration efficiency of the peptides |
| TumorHPD | TumorHPD predicts the tumor homing property of the peptides |
| AntiBP | A server for the prediction of the antibacterial peptides |
| AntiBP2 | A server for the prediction of the antibacterial peptides |
| Subcellular localization |
| Program | Discription |
| ESLpred | SVM Based Method for Subcellular Localization of Eukaryotic Proteins |
| ESLpred2 | ESLpred2 is an improved version of ESLpred, for predicting eukaryotic sub cellular localization |
| RSLpred | A SVM based method for subcellular localization prediction of rice proteins |
| PSLpred | Prediction of subcellular localization of bacterial proteins |
| HSLpred | A server for the prediction of the subcellular localization of human protein |
| Annotation of Nucleotide Sequences |
| Program | Discription |
| Desirm | Designing of Highly Effective Complementary and Mismatch siRNAs for Silencing a Gene |
| EGpred | Eukaryotic Gene prediction finder |
| SRF | Spectural Repeat finder |
| PolApred | Prediction of polyadenylation signal |
| Marspred | Prediction of mitochondrial aminoacyl-tRNA synthetases |
| Icaars | Identification & classification of aminoacyl-tRNA synthetases |
| Vaccine Design |
| Program | Discription |
| CTLpred | Prediction of cytotoxic T cell epitopes |
| ABCpred | Prediction of B-cell epitopes in antigen sequence |
| BCEpred | Prediction of linear B-cell epitopes, using physico-chemical properties |
| LBtope | Prediction of linear B-cell epitopes |
| CHEMOpred | A server to predict chemokines and their receptors |
| CBtope | Conformational B-cell epitope prediction |
| Propred | Prediction of HLA-DR binding sites |
| Propred1 | Prediction of promiscuous MHC Class-I binding sites |
| PCleavage | Prediction method for proteasome cleavage |
| TAPpred | Predicting binding affinity of peptides toward the TAP transporter |
| HLADR4pred | SVM and ANN based HLA-DRB1*0401(MHC class II alleles) binding peptides prediction |
| Drug Design |
| Program | Discription |
| KiDoQ | Designing of inhibitors against Dihydrodipicolinate synthase (DHDPS) |
| Toxinpred | Desigining and prediction of toxic peptides |
| DrugMint | A method developed for predicting drug-likelihood of a compound |
| Metapred | Prediction of Cytochrome P450 isoform responsible for metabolizing a drug molecule |
| GDoQ | Prediction of GLMU inhibitors using QSAR and AutoDock |