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http://www.umd.be/HSF3/

Software tool to help study pre-mRNA splicing and to better understand intronic and exonic mutations leading to splicing defects. To calculate the consensus values of potential splice sites and search for branch points, new algorithms were developed. Furthermore, they have integrated all available matrices to identify exonic and intronic motifs, as well as new matrices to identify hnRNP A1, Tra2-? and 9G8.

Proper citation: Human Splicing Finder (RRID:SCR_005181) Copy   

•   Source: SciCrunch Registry

http://www.btool.org/ADGO2

A web-based tool that provides composite interpretations for microarray data comparing two sample groups as well as lists of genes from diverse sources of biological information. It provides multiple gene set analysis methods for microarray inputs as well as enrichment analyses for lists of genes. It screens redundant composite annotations when generating and prioritizing them. It also incorporates union and subtracted sets as well as intersection sets. Users can upload their gene sets (e.g. predicted miRNA targets) to generate and analyze new composite sets.

Proper citation: ADGO (RRID:SCR_006343) Copy   

•   Source: SciCrunch Registry

http://bioinformatics.biol.uoa.gr/HMM-TM/

A web tool using the Hidden Markov Model method for the topology prediction of alpha-helical membrane proteins that incorporates experimentally derived topological information. Hidden Markov Models (HMMs) have been extensively used in computational molecular biology, for modelling protein and nucleic acid sequences. In many applications, such as transmembrane protein topology prediction, the incorporation of limited amount of information regarding the topology, arising from biochemical experiments, has been proved a very useful strategy that increased remarkably the performance of even the top-scoring methods. However, no clear and formal explanation of the algorithms that retains the probabilistic interpretation of the models has been presented so far in the literature. We present here, a simple method that allows incorporation of prior topological information concerning the sequences at hand, while at the same time the HMMs retain their full probabilistic interpretation in terms of conditional probabilities. We present modifications to the standard Forward and Backward algorithms of HMMs and we also show explicitly, how reliable predictions may arise by these modifications, using all the algorithms currently available for decoding HMMs. A similar procedure may be used in the training procedure, aiming at optimizing the labels of the HMM''s classes, especially in cases such as transmembrane proteins where the labels of the membrane-spanning segments are inherently misplaced. We present an application of this approach developing a method to predict the transmembrane regions of alpha-helical membrane proteins, trained on crystallographically solved data. We show that this method compares well against already established algorithms presented in the literature, and it is extremely useful in practical applications.

Proper citation: HMM-TM (RRID:SCR_006186) Copy   

•   Source: SciCrunch Registry

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

A web tool using the Hidden Markov Model method for the prediction of lipoprotein signal peptides of Gram-positive bacteria, trained on a set of 67 experimentally verified lipoproteins. The method outperforms LipoP and the methods based on regular expression patterns, in various data sets containing experimentally characterized lipoproteins, secretory proteins, proteins with an N-terminal TM segment and cytoplasmic proteins. The method is also very sensitive and specific in the detection of secretory signal peptides and in terms of overall accuracy outperforms even SignalP, which is the top-scoring method for the prediction of signal peptides.

Proper citation: PRED-LIPO (RRID:SCR_006187) Copy   

•   Source: SciCrunch Registry

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

A web tool for prediction of signal peptides in archaea. Computational prediction of signal peptides (SPs) and their cleavage sites is of great importance in computational biology; however, currently there is no available method capable of predicting reliably the SPs of archaea, due to the limited amount of experimentally verified proteins with SPs. We performed an extensive literature search in order to identify archaeal proteins having experimentally verified SP and managed to find 69 such proteins, the largest number ever reported. A detailed analysis of these sequences revealed some unique features of the SPs of archaea, such as the unique amino acid composition of the hydrophobic region with a higher than expected occurrence of isoleucine, and a cleavage site resembling more the sequences of gram-positives with almost equal amounts of alanine and valine at the position-3 before the cleavage site and a dominant alanine at position-1, followed in abundance by serine and glycine. Using these proteins as a training set, we trained a hidden Markov model method that predicts the presence of the SPs and their cleavage sites and also discriminates such proteins from cytoplasmic and transmembrane ones.

Proper citation: PRED-SIGNAL (RRID:SCR_006181) Copy   

•   Source: SciCrunch Registry

http://llama.mshri.on.ca/funcassociate/

A web-based tool that accepts as input a list of genes, and returns a list of GO attributes that are over- (or under-) represented among the genes in the input list. Only those over- (or under-) representations that are statistically significant, after correcting for multiple hypotheses testing, are reported. Currently 37 organisms are supported. In addition to the input list of genes, users may specify a) whether this list should be regarded as ordered or unordered; b) the universe of genes to be considered by FuncAssociate; c) whether to report over-, or under-represented attributes, or both; and d) the p-value cutoff. A new version of FuncAssociate supports a wider range of naming schemes for input genes, and uses more frequently updated GO associations. However, some features of the original version, such as sorting by LOD or the option to see the gene-attribute table, are not yet implemented. Platform: Online tool

Proper citation: FuncAssociate: The Gene Set Functionator (RRID:SCR_005768) Copy   

•   Source: SciCrunch Registry

http://wego.genomics.org.cn/cgi-bin/wego/index.pl

Web Gene Ontology Annotation Plot (WEGO) is a simple but useful tool for plotting Gene Ontology (GO) annotation results. Different from other commercial software for chart creating, WEGO is designed to deal with the directed acyclic graph (DAG) structure of GO to facilitate histogram creation of GO annotation results. WEGO has been widely used in many important biological research projects, such as the rice genome project and the silkworm genome project. It has become one of the useful tools for downstream gene annotation analysis, especially when performing comparative genomics tasks. Platform: Online tool

Proper citation: WEGO - Web Gene Ontology Annotation Plot (RRID:SCR_005827) Copy   

•   Source: SciCrunch Registry

http://snps-and-go.biocomp.unibo.it/snps-and-go/

A server for the prediction of single point protein mutations likely to be involved in the insurgence of diseases in humans.

Proper citation: SNPsandGO (RRID:SCR_005788) Copy   

•   Source: SciCrunch Registry

http://edwards.sdsu.edu/cgi-bin/prinseq/prinseq.cgi

A publicly available tool that is able to filter, reformat and trim your genomic and metagenomic sequence data and provide you summary statistics for your sequence data. The interactive web interface facilitates visualizations of the results and export functionality for subsequent data processing. The standalone lite version is written in Perl and does not require any non-core Perl modules. The lite version is primarily designed for data preprocessing and does not generate summary statistics in graphical form., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: PRINSEQ (RRID:SCR_005454) Copy   

•   Source: SciCrunch Registry

http://www.tmanavigator.org/

A free web-based service open to all users for analysis of tissue microarray (TMA) data and related information, accommodating categorical, semi-continuous and continuous expression scores. There is no login requirement.

Proper citation: TMA Navigator (RRID:SCR_005599) Copy   

•   Source: SciCrunch Registry

http://array.mbb.yale.edu/analysis/

A fully integrated platform for processing microarray data.

Proper citation: ExpressYourself (RRID:SCR_008881) Copy   

•   Source: SciCrunch Registry

http://wavi.bioinfo.cnio.es/

A versatile web-server application for the analysis and visualization of array-CGH data.

Proper citation: waviCGH (RRID:SCR_006662) Copy   

•   Source: SciCrunch Registry

http://gaa.mpi-bn.mpg.de/

Data analysis service that allows to process CEL files from Affymetrix, Inc. GeneChip Gene 1.0 ST Arrays to identify alternative splicing.

Proper citation: Gene Array Analyzer (RRID:SCR_008323) Copy   

•   Source: SciCrunch Registry

http://www.jstacs.de/index.php/GeMoMa

Software tool as homology based gene prediction program that predicts gene models in target species based on gene models in evolutionary related reference species. Utilizes amino acid sequence conservation, intron position conservation, and RNA-seq data to accurately predict protein-coding transcripts. Supports combination of predictions based on several reference species allowing to transfer high quality annotation of different reference species to target species.

Proper citation: GeMoMa (RRID:SCR_017646) Copy   

•   Source: SciCrunch Registry

https://github.com/zhanxw/MB-GAN

Software tool as deep learning simulation framework for simulating realistic microbiome data. Can automatically learn from given microbial abundances and compute simulated abundances that are indistinguishable from it.

Proper citation: MB-GAN (RRID:SCR_019289) Copy   

•   Source: SciCrunch Registry

http://www.cbs.dtu.dk/services/ProP/

Web application which predicts arginine and lysine propeptide cleavage sites in eukaryotic protein sequences using an ensemble of neural networks. Furin-specific prediction is the default. It is also possible to perform a general proprotein convertase prediction.

Proper citation: ProP Server (RRID:SCR_014936) Copy   

•   Source: SciCrunch Registry

http://www.cprofiler.org/

Web tool for discovery and visualization of differences in amino acid composition. Two samples of amino acid sequences serve as input and a bar chart composed of twenty data points is output.

Proper citation: Composition Profiler (RRID:SCR_014630) Copy   

•   Source: SciCrunch Registry

http://www.ebi.ac.uk/Tools/psa/genewise/

Gene alignment tool from the EBI which predicts gene structure using similar protein sequences. See also the associated GenomeWise tool.

Proper citation: GeneWise (RRID:SCR_015054) Copy   

•   Source: SciCrunch Registry

http://www.biolchem.ucla.edu/labs/ernst/ChromImpute/

Software tool for large scale systematic epigenome imputation. ChromImpute takes existing compendium of epigenomic data and uses it to predict signal tracks for mark-sample combinations not experimentally mapped or to generate a potentially more robust version of data sets that have been mapped experimentally.

Proper citation: ChromImpute (RRID:SCR_023990) Copy   

•   Source: SciCrunch Registry

https://github.com/HadrienG/InSilicoSeq

Software tool as sequencing simulator producing realistic Illumina reads. Primarily intended for simulating metagenomic samples, it can also be used to produce sequencing data from a single genome.

Proper citation: InSilicoSeq (RRID:SCR_024041) Copy   

•   Source: SciCrunch Registry