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http://ww2.sanbi.ac.za/Dbases.html

THIS RESOURCE IS NO LONGER IN SERVICE, documented August 23, 2016. The STACKdb is knowledgebase generated by processing EST and mRNA sequences obtained from GenBank through a pipeline consisting of masking, clustering, alignment and variation analysis steps. The STACK project aims to generate a comprehensive representation of the sequence of each of the expressed genes in the human genome by extensive processing of gene fragments to make accurate alignments, highlight diversity and provide a carefully joined set of consensus sequences for each gene. The STACK project is comprised of the STACKdb human gene index, a database of virtual human transcripts, as well as stackPACK, the tools used to create the database. STACKdb is organized into 15 tissue-based categories and one disease category. STACK is a tool for detection and visualization of expressed transcript variation in the context of developmental and pathological states. The data system organizes and reconstructs human transcripts from available public data in the context of expression state. The expression state of a transcript can include developmental state, pathological association, site of expression and isoform of expressed transcript. STACK consensus transcripts are reconstructed from clusters that capture and reflect the growing evidence of transcript diversity. The comprehensive capture of transcript variants is achieved by the use of a novel clustering approach that is tolerant of sub-sequence diversity and does not rely on pairwise alignment. This is in contrast with other gene indexing projects. STACK is generated at least four times a year and represents the exhaustive processing of all publicly available human EST data extracted from GenBank. This processed information can be explored through 15 tissue-specific categories, a disease-related category and a whole-body index

Proper citation: Sequence Tag Alignment and Consensus Knowledgebase Database (RRID:SCR_002156) Copy   

•   Source: SciCrunch Registry

http://hcv.lanl.gov/

The Hepatitis C Virus (HCV) Database Project strives to present HCV-associated genetic and immunologic data in a user-friendly way, by providing access to the central database via web-accessible search interfaces and supplying a number of analysis tools.

Proper citation: HCV Databases (RRID:SCR_002863) Copy   

•   Source: SciCrunch Registry

http://soybase.org

Professionally curated repository for genetics, genomics and related data resources for soybean that contains the most current genetic, physical and genomic sequence maps integrated with qualitative and quantitative traits. SoyBase includes annotated Williams 82 genomic sequence and associated data mining tools. The genetic and sequence views of the soybean chromosomes and the extensive data on traits and phenotypes are extensively interlinked. This allows entry to the database using almost any kind of available information, such as genetic map symbols, soybean gene names or phenotypic traits. The repository maintains controlled vocabularies for soybean growth, development, and traits that are linked to more general plant ontologies. Contributions to SoyBase or the Breeder''s Toolbox are welcome.

Proper citation: SoyBase (RRID:SCR_005096) Copy   

•   Source: SciCrunch Registry

http://igs-server.cnrs-mrs.fr/mgdb/Rickettsia/

THIS RESOURCE IS NO LONGER IN SERVICE, documented August 18, 2016. Rickettsia are obligate intracellular bacteria living in arthropods. They occasionally cause diseases in humans. To understand their pathogenicity, physiologies and evolutionary mechanisms, RicBase is sequencing different species of Rickettsia. Up to now we have determined the genome sequences of R. conorii, R. felis, R. bellii, R. africae, and R. massiliae. The RicBase aims to organize the genomic data to assist followup studies of Rickettsia. This website contains information on R. conorii and R. prowazekii. A R. conorii and R. prowazekii comparative genome map is also available. Images of genome maps, dendrogram, and sequence alignment allow users to gain a visualization of the diagrams.

Proper citation: Rickettsia Genome Database (RRID:SCR_007102) Copy   

•   Source: SciCrunch Registry

http://wwwmgs.bionet.nsc.ru/mgs/programs/panalyst/

WebProAnalyst provides web-accessible analysis for scanning the quantitative structure-activity relationships in protein families. It searches for a sequence region, whose substitutions are correlated with variations in the activities of a homologous protein set, the so-called activity modulating sites. WebProAnalyst allows users to search for the key physicochemical characteristics of the sites that affect the changes in protein activities. It enables the building of multiple linear regression and neural networks models that relate these characteristics to protein activities. WebProAnalyst implements multiple linear regression analysis, back propagation neural networks and the Structure-Activity Correlation/Determination Coefficient (SACC/SADC). A back propagation neural network is implemented as a two-layered network, one layer as input, the other as output (Rumelhart et al, 1986). WebProAnalyst uses alignment of amino acid sequences and data on protein activity (pK, Km, ED50, among others). The input data are the numerical values for the physicochemical characteristics of a site in the multiple alignment given by a slide window. The output data are the predicted activity values. The current version of WebProAnalyst handles a single activity for a single protein. The SACC/SADC may be defined as an estimate of the strongest multiple correlation between the physicochemical characteristics of a site in a multiple alignment and protein activities. The SACC/SADC coefficient makes possible the calculation of the possible highest correlation achievable for the quantitative relationship between the physicochemical properties of sites and protein activities. The SACC/SADC is a convenient means for an arrangement of positions by their functional significance. WebProAnalyst outputs a list of multiple alignment positions, the respective correlation values, also regression analysis parameters for the relationships between the amino acid physicochemical characteristics at these positions and the protein activity values.

Proper citation: Webproanalyst (RRID:SCR_008348) Copy   

•   Source: SciCrunch Registry

http://phylopythias.bifo.helmholtz-hzi.de/index.php?phase=wait

Web Server for Taxonomic Assignment of Metagenome Sequences that is a fast and accurate sequence composition-based classifier that utilizes the hierarchical relationships between clades. Taxonomic assignments with the web server can be made with a generic model, or with sample-specific models that users can specify and create. Several interactive visualization modes and multiple download formats allow quick and convenient analysis and downstream processing of taxonomic assignments.

Proper citation: PhyloPythiaS (RRID:SCR_011923) Copy   

•   Source: SciCrunch Registry

http://viewer.shigen.info/cgi-bin/crispr/crispr.cgi

Web tool to show micro homology sequences striding over double strand break point created by CRISPR/Cas9 system. Used to search for CRISPR target site with micro-homology sequences. Used to predict deletion pattern.

Proper citation: NBRP Medaka CRISPR target site (RRID:SCR_018159) Copy   

•   Source: SciCrunch Registry

http://nucleobytes.com/index.php/4peaks

Software application for viewing and editing sequence trace files.

Proper citation: 4Peaks (RRID:SCR_000015) Copy   

•   Source: SciCrunch Registry

http://cisbp.ccbr.utoronto.ca

Software tool as catalog of inferred sequence binding preferences. Online library of transcription factors and their DNA binding motifs.

Proper citation: CIS-BP (RRID:SCR_017236) Copy   

•   Source: SciCrunch Registry

https://github.com/isovic/racon

Software tool as de novo genome assembly from long uncorrected reads. Used to correct raw contigs generated by rapid assembly methods which do not include consensus step. Supports data produced by Pacific Biosciences and Oxford Nanopore Technologies.

Proper citation: Racon (RRID:SCR_017642) Copy   

•   Source: SciCrunch Registry

https://github.com/TransDecoder/TransDecoder

Software tool to identify candidate coding regions within transcript sequences, such as those generated by de novo RNA-Seq transcript assembly using Trinity, or constructed based on RNA-Seq alignments to genome using Tophat and Cufflinks.Starts from FASTA or GFF file. Can scan and retain open reading frames (ORFs) for homology to known proteins by using BlastP or Pfam search and incorporate results into obtained selection. Predictions can then be visualized by using genome browser such as IGV.

Proper citation: TransDecoder (RRID:SCR_017647) Copy   

•   Source: SciCrunch Registry

https://www.sanger.ac.uk/science/tools/reapr

Software tool to identify errors in genome assemblies without need for reference sequence. Can be used in any stage of assembly pipeline to automatically break incorrect scaffolds and flag other errors in assembly for manual inspection. Reports mis-assemblies and other warnings, and produces new broken assembly based on error calls.

Proper citation: Recognition of Errors in Assemblies using Paired Reads (RRID:SCR_017625) Copy   

•   Source: SciCrunch Registry

https://github.com/lufuhao/ExonerateTransferAnnotation

Software tool as pipeline to make anntotations using cDNA and CDS sequences.

Proper citation: ExonerateTransferAnnotation (RRID:SCR_017557) Copy   

•   Source: SciCrunch Registry

https://github.com/nextstrain/augur

Software package to track evolution from sequence and serological data. Provides collection of commands which are designed to be composable into larger processing pipelines.

Proper citation: Augur (RRID:SCR_023964) Copy   

•   Source: SciCrunch Registry

http://cbcsrv.watson.ibm.com/phylopythia.html

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 1, 2023. Data analysis service for accurate phylogenetic classification of variable-length DNA fragments.

Proper citation: PhyloPythia (RRID:SCR_000540) Copy   

•   Source: SciCrunch Registry

http://www.scied.com/pr_cmbas.htm

A software system to assist with cloning simulation, enzyme operations, and graphic map drawing. Clone Manager can also be used as a way to view or edit sequence files, find open reading frames, translate genes, or find genes or text in files. Clone Manager Professional is an upgraded version of Clone Manager Basic., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: Clone Manager Software (RRID:SCR_014521) Copy   

•   Source: SciCrunch Registry

http://aem.asm.org/content/71/12/8228.full

THIS RESOURCE IS NO LONGER IN SERVICE, documented Setember 8, 2016. A suite of tools for the comparison of microbial communities using phylogenetic information. It takes as input a single phylogenetic tree that contains sequences derived from at least two different environmental samples and a file describing which sequences came from which sample.

Proper citation: Unifrac (RRID:SCR_014616) Copy   

•   Source: SciCrunch Registry

http://www.cbcb.umd.edu/software/metapath

A statistical package for comparing metagenomic data-sets at the pathway level. It relies on a combination of metagenomic sequence data and prior metabolic pathway knowledge, which is pulled from KEGG.

Proper citation: Metapath (RRID:SCR_014621) Copy   

•   Source: SciCrunch Registry

https://sourceforge.net/projects/soapdenovo2/files/GapCloser/

Module of SOAPdenovo2 commonly used independently to close gaps in genome assemblies.

Proper citation: GapCloser (RRID:SCR_015026) Copy   

•   Source: SciCrunch Registry

http://gladyshevlab.org/SelenoproteinPredictionServer/

Web server to predict eukaryotic selenoproteins and SECIS (SElenoCysteine Insertion Sequences) elements along nucleotide sequences. SECISearch3 replaces its predecessor SECISearch as a tool for prediction of eukaryotic SECIS elements. Seblastian is a method for selenoprotein gene detection that uses SECISearch3 and then predicts selenoprotein sequences encoded upstream of SECIS elements. Seblastian is able to both identify known selenoproteins and predict new selenoproteins.

Proper citation: SECISearch3 and Seblastian (RRID:SCR_003186) Copy   

•   Source: SciCrunch Registry