Tools   Select Another Resource Report Type

https://swissmodel.expasy.org/

Software tool as fully automated protein structure homology modeling server, accessible via ExPASy web server, or from program DeepView Swiss Pdb-Viewer. Structural bioinformatics web-server dedicated to homology modeling of 3D protein structures. Used to make protein modelling accessible to all biochemists and molecular biologists.

Proper citation: SWISS-MODEL (RRID:SCR_018123) Copy   

•   Source: SciCrunch Registry

https://github.com/lkmklsmn/DrivAER

Software tool as method for identification of driving transcriptional programs based on AutoEncoder derived Relevance scores. Infers relevance scores for transcriptional programs with respect to specified outcomes of interest in single-cell RNA sequencing data, such as psuedotemporal ordering or disease status.Used for manifold interpretation in scRNA-seq data.

Proper citation: DrivAER (RRID:SCR_019076) Copy   

•   Source: SciCrunch Registry

http://www.imgt.org/HighV-QUEST/home.action

Next generation B and T cell sequence alignment and characterization online surface by IMGT. Web portal for immunoglobulin (IG) or antibody and T cell receptor (TR) analysis from NGS high throughput and deep sequencing.

Proper citation: IMGT HighV-QUEST (RRID:SCR_018196) Copy   

•   Source: SciCrunch Registry

https://pm4ngs.readthedocs.io/

Software tool to generate standard organizational structure for Next Generation Sequencing data analysis. Includes directory structure for project, several Jupyter notebooks for data management and CWL workflows for pipeline execution.

Proper citation: PM4NGS (RRID:SCR_019164) Copy   

•   Source: SciCrunch Registry

https://signor.uniroma2.it/

Software application to organize and store in structured format signaling information published in scientific literature. Information is stored as binary causative relationships between biological entities and can be represented graphically as activity flow. Each relationship is linked to literature reporting experimental evidence. Each node is annotated with chemical inhibitors that modulate its activity. Signaling information is mapped to human proteome. SIGNOR 2.0 stores manually annotated causal relationships between proteins and other biologically relevant entities including chemicals, phenotypes, complexes, etc with compliance to FAIR data principles.

Proper citation: SIGNOR (RRID:SCR_018485) Copy   

•   Source: SciCrunch Registry

https://bioconductor.org/packages/fgsea/

Software R package for fast preranked gene set enrichment analysis. Allows to make more permutations and get more fine grained p-values, which allows to use accurate stantard approaches to multiple hypothesis correction.

Proper citation: fgsea (RRID:SCR_020938) Copy   

•   Source: SciCrunch Registry

https://guoweilong.github.io/BS_Seeker2/index.html

Software tool as versatile aligning pipeline for bisulfite sequencing data. Used for mapping bisulfite sequencing data and generating DNA methylomes. Improves mappability over existing aligners by using local alignment. Maps reads from RRBS library by building special indexes with improved efficiency and accuracy. Provides additional function for filtering out reads with incomplete bisulfite conversion, which is useful in minimizing overestimation of DNA methylation levels.

Proper citation: Bs-Seeker2 (RRID:SCR_020948) Copy   

•   Source: SciCrunch Registry

http://www.ccmb.med.umich.edu/ccdu/SNPAAMapper

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on August 19,2025. A downstream variant annotation program that can effectively classify variants by region (e.g. exon, intron, etc), predict amino acid change type (e.g. synonymous, non-synonymous mutation, etc), and prioritize mutation effects (e.g. CDS versus 5?UTR, etc). Major features: * The pipeline accepts the VCF (Variant Call Format) input file in tab-delimited format and processes the vcf input file containing all cases (G5, lowFreq, and novel) * The variant mapping step has the option of letting users select whether they want to report the bp distance between each identified intron variant and its nearby exon * The pipeline can deal with VCF files called by different SAMTools versions (0.1.18 and older ones) and also offers flexibility in dealing with vcf input files generated using SAMTools with two or three samples * The spreadsheet result file contains full protein sequences for both ref and alt alleles, which makes it easier for downstream protein structure/function analysis tools to take

Proper citation: SNPAAMapper (RRID:SCR_002012) Copy   

•   Source: SciCrunch Registry

http://sourceforge.net/projects/skewer/

Software program for adapter trimming that is specially designed for processing Illumina paired-end sequences.

Proper citation: skewer (RRID:SCR_001151) Copy   

•   Source: SciCrunch Registry

https://github.com/princelab/mspire-simulator

A free, open-source shotgun proteomic simulator that goes beyond previous simulation attempts by generating LC-MS features with realistic m/z and intensity variance along with other noise components.

Proper citation: Mspire-Simulator (RRID:SCR_001431) Copy   

•   Source: SciCrunch Registry

http://www.bioconductor.org/packages/release/bioc/html/HTqPCR.html

Software package for the analysis of Ct values from high throughput quantitative real-time PCR (qPCR) assays across multiple conditions or replicates. The input data can be from spatially-defined formats such ABI TaqMan Low Density Arrays or OpenArray; LightCycler from Roche Applied Science; the CFX plates from Bio-Rad Laboratories; conventional 96- or 384-well plates; or microfluidic devices such as the Dynamic Arrays from Fluidigm Corporation. HTqPCR handles data loading, quality assessment, normalization, visualization and parametric or non-parametric testing for statistical significance in Ct values between features (e.g. genes, microRNAs).

Proper citation: HTqPCR (RRID:SCR_003375) Copy   

•   Source: SciCrunch Registry

http://cran.r-project.org/web/packages/NanoStringNorm/

Software package for normalizing, diagnostics and visualization of NanoString nCounter data. Key features include an extensible environment for method comparison and new algorithm development, integrated gene and sample diagnostics, and facilitated downstream statistical analysis.

Proper citation: NanoStringNorm (RRID:SCR_003382) Copy   

•   Source: SciCrunch Registry

http://img.jgi.doe.gov/cgi-bin/m/main.cgi

Resource for analysis and annotation of genome and metagenome datasets in comprehensive comparative context. IMG provides users with tools for analyzing publicly available genome datasets and metagenome datasets.

Proper citation: IMG System (RRID:SCR_002965) 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

http://bioinformatics.biol.uoa.gr/PRED-TMBB/

A web tool, based on a Hidden Markov Model, capable of predicting the transmembrane beta-strands of the gram-negative bacteria outer membrane proteins, and of discriminating such proteins from water-soluble ones when screening large datasets. The model is trained in a discriminative manner, aiming at maximizing the probability of the correct prediction rather than the likelihood of the sequences. The training is performed on a non-redundant database consisting of 16 outer membrane proteins (OMP''s) with their structures known at atomic resolution. We show that we can achieve predictions at least as good comparing with other existing methods, using as input only the amino-acid sequence, without the need of evolutionary information included in multiple alignments. The method is also powerful when used for discrimination purposes, as it can discriminate with a high accuracy the outer membrane proteins from water soluble in large datasets, making it a quite reliable solution for screening entire genomes. This web-server can help you run a discriminating process on any amino-acid sequence and thereafter localize the transmembrane strands and find the topology of the loops.

Proper citation: PRED-TMBB (RRID:SCR_006190) Copy   

•   Source: SciCrunch Registry

http://bioapps.rit.albany.edu/MITOPRED/

THIS RESOURCE IS NO LONGER IN SERVICE, documented on July 16, 2013. It predicts nuclear-encoded mitochondrial proteins from all eukaryotic species including plants. Prediction is based on the occurrence patterns of Pfam domains (version 16.0) in different cellular locations, amino acid composition and pI value differences between mitochondrial and non-mitochondrial locations. Additionally, you may download MITOPRED predictions for complete proteomes. Re-calculated predictions are instantly accessible for proteomes of Saccharomyces cerevisiae, Caenorhabditis elegans, Drosophila, Homo sapiens, Mus musculus and Arabidopsis species as well as all the eukaryotic sequences in the Swiss-Prot and TrEMBL databases. Queries, at different confidence levels, can be made through four distinct options: (i) entering Swiss-Prot/TrEMBL accession numbers; (ii) uploading a local file with such accession numbers; (iii) entering protein sequences; (iv) uploading a local file containing protein sequences in FASTA format. The Mitopred algorithm works based on the differences in the Pfam domain occurrence patters and amino acid composition differences in different cellular compartments. Location specific Pfam domains have been determined from the entire eukaryotic set of Swissprot database. Similarly, differences in the amino acid composition between mitochondrial and non-mitochondrial sequences were pre-calculated. This information is used to calculate location-specific amino acid weights that are used to calculate amino acid score. Similarly, pI average values of the N-terminal 25 residues in different cellular location were also determined. This knowledge-base is accessed by the program during execution.

Proper citation: mitopred (RRID:SCR_006135) Copy   

•   Source: SciCrunch Registry

http://genetrail.bioinf.uni-sb.de/

A web-based application that analyzes gene sets for statistically significant accumulations of genes that belong to some functional category. Considered category types are: KEGG Pathways, TRANSPATH Pathways, TRANSFAC Transcription Factor, GeneOntology Categories, Genomic Localization, Protein-Protein Interactions, Coiled-coil domains, Granzyme-B clevage sites, and ELR/RGD motifs. The web server provides two statistical approaches, "Over-Representation Analysis" (ORA) comparing a reference set of genes to a test set, and "Gene Set Enrichment Analysis" (GSEA) scoring sorted lists of genes., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: GeneTrail (RRID:SCR_006250) Copy   

•   Source: SciCrunch Registry

http://bioinformatics.intec.ugent.be/magic/

Web based interface for exploring and analyzing a comprehensive maize-specific cross-platform expression compendium. This compendium was constructed by collecting, homogenizing and formally annotating publicly available microarrays from Gene Expression Omnibus (GEO), and ArrayExpress.

Proper citation: Magic (RRID:SCR_006406) Copy   

•   Source: SciCrunch Registry

http://www.bioinsilico.org/cgi-bin/CAPSDB/staticHTML/home

It is a structural classification of helix-cappings or caps compiled from protein structures. Caps extracted from protein structures have been structurally classified based on geometry and conformation and organized in a tree-like hierarchical classification where the different levels correspond to different properties of the caps. CASP-DB is fully browsable and searchable and is regularly updated. The regions of the polypeptide chain immediately preceding or following a helix are known as Nt- and Ct cappings, respectively. Cappings play a central role stabilizing helices due to lack of intrahelical hydrogen bonds in the first and last turn. Sequence patterns of amino acid type preferences have been derived for cappings but the structural motifs associated to them are still unclassified. CAPS-DB is a database of clusters of structural patterns of different capping types. The clustering algorithm is based in the geometry and the space conformation of these regions. CAPS-DB is a relational database that allows the user to search, browse, inspect and retrieve structural data associated to cappings. The contents of CAPS-DB might be of interest to a wide range of scientist covering different areas such as protein design and engineering, structural biology and bioinformatics. CapsDB v4.0 * PDB structures: 4591 * Number of clusters: 859 * Number of caps: 31452

Proper citation: CAPS Database (RRID:SCR_006862) Copy   

•   Source: SciCrunch Registry

http://scop.mrc-lmb.cam.ac.uk/scop/

The Structural Classification of Proteins (SCOP) database is a comprehensive ordering of all proteins of known structure, according to their evolutionary and structural relationships. Protein domains in SCOP are hierarchically classified into families, superfamilies, folds and classes. The continual accumulation of sequence and structural data allows more rigorous analysis and provides important information for understanding the protein world and its evolutionary repertoire. SCOP participates in a project that aims to rationalize and integrate the data on proteins held in several sequence and structure databases. As part of this project, starting with release 1.63, we have initiated a refinement of the SCOP classification, which introduces a number of changes mostly at the levels below superfamily. The pending SCOP reclassification will be carried out gradually through a number of future releases. In addition to the expanded set of static links to external resources, available at the level of domain entries, we have started modernization of the interface capabilities of SCOP allowing more dynamic links with other databases.

Proper citation: SCOP: Structural Classification of Proteins (RRID:SCR_007039) Copy   

•   Source: SciCrunch Registry