Tools   Select Another Resource Report Type

http://liweilab.genetics.ac.cn/tm/

Web-based tool used to mine human protein-protein interactions (PPIs) from PubMed abstracts based on their co-occurrences and interaction words, followed by evidencs in human PPI databases and shared terms in GO database.

Proper citation: Human Protein-Protein Interaction Mining Tool (RRID:SCR_008040) Copy   

•   Source: SciCrunch Registry

https://awi.cuhk.edu.cn/KinasePhos/download.html

Software tool for redesign and expansion of prediction on kinase specific phosphorylation sites. Machine learning based kinase specific phosphorylation site prediction tool.

Proper citation: KinasePhos 3.0 (RRID:SCR_023595) Copy   

•   Source: SciCrunch Registry

https://github.com/esctrionsit/snphub

Web Shiny-based server framework for retrieving, analyzing and visualizing large genomic variations data.

Proper citation: SnpHub (RRID:SCR_018177) Copy   

•   Source: SciCrunch Registry

http://bio-bigdata.hrbmu.edu.cn/diseasemeth/

Human disease methylation database. DiseaseMeth version 2.0 is focused on aberrant methylomes of human diseases. Used for understanding of DNA methylation driven human diseases.

Proper citation: DiseaseMeth (RRID:SCR_005942) Copy   

•   Source: SciCrunch Registry

http://202.38.126.151:8080/SDisease/

Curated database of experimentally supported data of RNA Splicing mutation and disease. The RNA Splicing mutations include cis-acting mutations that disrupt splicing and trans-acting mutations that affecting RNA-dependent functions that cause disease. Information such as EntrezGeneID, gene genomic sequence, mutation (nucleotide substitutions, deletions and insertions), mutation location within the gene, organism, detailed description of the splicing mutation and references are also given. Users are able to submit new entries to the database. This database integrating RNA splicing and disease associations would be helpful for understanding not only the RNA splicing but also its contribution to disease. In SpliceDisease database, they manually curated 2337 splicing mutation disease entries involving 303 genes and 370 diseases, which have been supported experimentally in 898 publications. The SpliceDisease database provides information including the change of the nucleotide in the sequence, the location of the mutation on the gene, the reference PubMed ID and detailed description for the relationship among gene mutations, splicing defects and diseases. They standardized the names of the diseases and genes and provided links for these genes to NCBI and UCSC genome browser for further annotation and genomic sequences. For the location of the mutation, they give direct links of the entry to the respective position/region in the genome browser.

Proper citation: SpliceDisease (RRID:SCR_006130) Copy   

•   Source: SciCrunch Registry

https://funricegenes.github.io/

Dataset of functionally characterized rice genes and members of different gene families. The dataset was created by integrating data from available databases and reviewing publications of rice functional genomic studies.

Proper citation: funRiceGenes (RRID:SCR_015778) Copy   

•   Source: SciCrunch Registry

http://m6asnp.renlab.org

Web server implemented in JAVA and PHP for annotating genetic variants by m6A function. It predicts and annotates N6-methyladenosine (m6A) alterations from genetic variants data such as germline SNPs or cancer somatic mutations. It employs two accurate prediction models for human and mouse using Random Forest algorithm. It conducts a statistical analysis for all the predicted m6A alterations. Provides statistical diagrams and a genome browser to visualize the topology characteristics of predicted m6A alterations.

Proper citation: m6ASNP: Annotation of genetic variants by m6A function (RRID:SCR_016048) Copy   

•   Source: SciCrunch Registry

http://www.restfmri.net

A user-friendly convenient toolkit to calculate Functional Connectivity (FC), Regional Homogeneity (ReHo), Amplitude of Low-Frequency Fluctuation (ALFF), Fractional ALFF (fALFF), Gragner causality and perform statistical analysis. You also can use REST to view your data, perform Monte Carlo simulation similar to AlphaSim in AFNI, calculate your images, regress out covariates, extract Region of Interest (ROI) time courses, reslice images, and sort DICOM files.

Proper citation: REST: a toolkit for resting-state fMRI (RRID:SCR_009641) Copy   

•   Source: SciCrunch Registry

http://bioconductor.org/packages/release/bioc/html/clusterProfiler.html

Software R package for statistical analysis and visualization of functional profiles for genes and gene clusters.

Proper citation: clusterProfiler (RRID:SCR_016884) Copy   

•   Source: SciCrunch Registry

https://github.com/macmanes-lab/BinPacker/blob/master/README

Software tool as de novo trascriptome assembler for RNA-Seq data. Used to assemble full length transcripts by remodeling problem as tracking set of trajectories of items over splicing graph. Input RNA-Seq reads in fasta or fastq format, and ouput all assembled candidate transcripts in fasta format. Operating system Unix/Linux.

Proper citation: BinPacker (RRID:SCR_017038) Copy   

•   Source: SciCrunch Registry

http://gepia.cancer-pku.cn

Web server for cancer and normal gene expression profiling and interactive analyses. Interactive web server for analyzing RNA sequencing expression data of tumors and normal samples from TCGA and GTEx projects, using standard processing pipeline. Provides customizable functions such as tumor or normal differential expression analysis, profiling according to cancer types or pathological stages, patient survival analysis, similar gene detection, correlation analysis and dimensionality reduction analysis.

Proper citation: Gene Expression Profiling Interactive Analysis (RRID:SCR_018294) Copy   

•   Source: SciCrunch Registry

http://sumosp.biocuckoo.org.

Web service for prediction of SUMOylation sites and SUMO-interaction motifs in proteins by CUCKOO Workgroup.

Proper citation: GPS-SUMO (RRID:SCR_018261) Copy   

•   Source: SciCrunch Registry

http://code.google.com/p/panda-tool/

Software matlab toolbox for pipeline processing of diffusion MRI images. For each subject, PANDA can provide outputs in 2 types: i) diffusion parameter data that is ready for statistical analysis; ii) brain anatomical networks constructed by using diffusion tractography. Particularly, there are 3 types of resultant diffusion parameter data: WM atlas-level, voxel-level and TBSS-level. The brain network generated by PANDA has various edge definitions, e.g. fiber number, length, or FA-weighted. The key advantages of PANDA are as follows: # fully-automatic processing from raw DICOM/NIFTI to final outputs; # Supporting both sequential and parallel computation. The parallel environment can be a single desktop with multiple-cores or a computing cluster with a SGE system; # A very friendly GUI (graphical user interface).

Proper citation: PANDA (RRID:SCR_002511) Copy   

•   Source: SciCrunch Registry

http://indel.bioinfo.sdu.edu.cn/gridsphere/gridsphere

THIS RESOURCE IS NO LONGER IN SERVCE, documented September 2, 2016. Indel Flanking Region Database is an online resource for indels and the flanking regions of proteins in SCOP superfamilies, including amino acid sequences, lengths, locations, secondary structure constitutions, hydrophilicity / hydrophobicity, domain information, 3D structures and so on. It aims at providing a comprehensive dataset for analyzing the qualities of amino acid insertion/deletions(indels), substitutions and the relationship between them. The indels were obtained through the pairwise alignment of homologous structures in SCOP superfamilies. The IndelFR database contains 2,925,017 indels with flanking regions extracted from 373,402 structural alignment pairs of 12,573 non-redundant domains from 1053 superfamilies. IndelFR has already been used for molecular evolution studies and may help to promote future functional studies of indels and their flanking regions.

Proper citation: IndelFR - Indel Flanking Region Database (RRID:SCR_006050) Copy   

•   Source: SciCrunch Registry

http://202.120.189.88/drvis/

Dr.VIS collects and locates human disease-related viral integration sites. So far, about 600 sites covering 5 virus organisms and 11 human diseases are available. Integration sites in Dr.VIS are located against chromosome, cytoband, gene and refseq position as specific as possible. Viral-cellular junction sequences are extracted from papers and nucleotide databases, and linked to corresponding integration sites Graphic views summarizing distribution of viral integration sites are generated according to chromosome maps. Dr.VIS is built with a hope to facilitate research of human diseases and viruses. Dr.VIS provides curated knowledge of integration sites from chromosome region narrow to genomic position, as well as junction sequences if available. Dr.VIS is an open resource for free.

Proper citation: Dr.VIS - Human Disease-Related Viral Integration Sites (RRID:SCR_005965) Copy   

•   Source: SciCrunch Registry

http://omicslab.genetics.ac.cn/GOEAST/

Gene Ontology Enrichment Analysis Software Toolkit (GOEAST) is a web based software toolkit providing easy to use, visualizable, comprehensive and unbiased Gene Ontology (GO) analysis for high-throughput experimental results, especially for results from microarray hybridization experiments. The main function of GOEAST is to identify significantly enriched GO terms among give lists of genes using accurate statistical methods. Compared with available GO analysis tools, GOEAST has the following unique features: * GOEAST supports analysis for data from various resources, such as expression data obtained using Affymetrix, illumina, Agilent or customized microarray platforms. GOEAST also supports non-microarray based experimental data. The web-based feature makes GOEAST very user friendly; users only have to provide a list of genes in correct formats. * GOEAST provides visualizable analysis results, by generating graphs exhibiting enriched GO terms as well as their relationships in the whole GO hierarchy. * Note that GOEAST generates separate graph for each of the three GO categories, namely biological process, molecular function and cellular component. * GOEAST allows comparison of results from multiple experiments (see Multi-GOEAST tool). The displayed color of each GO term node in graphs generated by Multi-GOEAST is the combination of different colors used in individual GOEAST analysis. Platform: Online tool

Proper citation: GOEAST - Gene Ontology Enrichment Analysis Software Toolkit (RRID:SCR_006580) Copy   

•   Source: SciCrunch Registry

https://yanglab.nankai.edu.cn/trRosetta/

Software tool for fast and accurate protein structure prediction. Builds protein structure based on direct energy minimizations with restrained Rosetta. Restraints include inter-residue distance and orientation distributions, predicted by deep residual neural network. Homologous templates are included in network prediction to improve accuracy for easy targets.

Proper citation: trRosetta (RRID:SCR_021181) Copy   

•   Source: SciCrunch Registry

https://github.com/bioinfo-biols/CIRIquant

Software Python package for accurate circRNA quantification and differential expression analysis. Comprehensive analysis pipeline for circRNA detection and quantification in RNA-Seq data. Accurate quantification of circular RNAs identifies extensive circular isoform switching events.

Proper citation: CIRIquant (RRID:SCR_021661) Copy   

•   Source: SciCrunch Registry

https://circexplorer2.readthedocs.io/en/latest/

Software package for comprehensive and integrative circular RNA analysis. It is the successor of CIRCexplorer with plenty of new features to facilitate circular RNA identification and characterization. Used to annotate circRNAs, de novo assemble novel circular RNA transcripts and chracterize various of alternative (back-)splicing events of circular RNAs.

Proper citation: CIRCexplorer2 (RRID:SCR_021664) Copy   

•   Source: SciCrunch Registry

http://bioinfo.jialab-ucr.org/CancerMIRNome/

Web server for cancer miRNome interactive analysis and visualization based on human miRNome data of cancer types from The Cancer Genome Atlas, and public cancer circulating miRNome profiling datasets from NCBI Gene Expression Omnibus and ArrayExpress. Comprehensive database for interactive analysis and visualization of miRNA expression profiles.

Proper citation: CancerMIRNome (RRID:SCR_022092) Copy   

•   Source: SciCrunch Registry