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http://sourceforge.net/projects/bio-rainbow/

Software developed to provide an ultra-fast and memory-efficient solution to clustering and assembling short reads produced by RAD-seq.

Proper citation: Rainbow (RRID:SCR_002724) Copy   

•   Source: SciCrunch Registry

http://www.gabipd.org/

Database that collects, integrates and links all relevant primary information from the GABI plant genome research projects and makes them accessible via internet. Its purpose is to support plant genome research in Germany, to yield information about commercial important plant genomes, and to establish a scientific network within plant genomic research.
GreenCards is the main interface for text based retrieval of sequence, SNP, mapping data etc. Sharing and interchange of data among collaborating research groups, industry and the patent- and licensing agency are facilitated.
* GreenCards: Text based search for sequence, mapping, SNP data etc. * Maps: Visualization of genetic or physical maps. * BLAST: Secure BLAST search against different public databases or non-public sequence data stored in GabiPD. * Proteomics: View interactive 2D-gels and view or download information for identified protein spots. Registered users can submit data via secure file upload.

Proper citation: Gabi Primary Database (RRID:SCR_002755) Copy   

•   Source: SciCrunch Registry

https://github.com/GiBacci/StreamingTrim/

A DNA reads trimming software, written in Java, with which researchers are able to analyse the quality of DNA sequences in fastq files and to search for low-quality zones in a very conservative way.

Proper citation: StreamingTrim (RRID:SCR_002922) Copy   

•   Source: SciCrunch Registry

http://www.ambystoma.org/

Portal that supports Ambystoma-related research and educational efforts. It is composed of several resources: Salamander Genome Project, Ambystoma EST Database, Ambystoma Gene Collection, Ambystoma Map and Marker Collection, Ambystoma Genetic Stock Center, and Ambystoma Research Coordination Network.

Proper citation: Sal-Site (RRID:SCR_002850) Copy   

•   Source: SciCrunch Registry

http://cbio.mskcc.org/

Computational biology research at Memorial Sloan-Kettering Cancer Center (MSKCC) pursues computational biology research projects and the development of bioinformatics resources in the areas of: sequence-structure analysis; gene regulation; molecular pathways and networks, and diagnostic and prognostic indicators. The mission of cBio is to move the theoretical methods and genome-scale data resources of computational biology into everyday laboratory practice and use, and is reflected in the organization of cBio into research and service components ~ the intention being that new computational methods created through the process of scientific inquiry should be generalized and supported as open-source and shared community resources. Faculty from cBio participate in graduate training provided through the following graduate programs: * Gerstner Sloan-Kettering Graduate School of Biomedical Sciences * Graduate Training Program in Computational Biology and Medicine Integral to much of the research and service work performed by cBio is the creation and use of software tools and data resources. The tools that we have created and utilize provide evidence of our involvement in the following areas: * Cancer Genomics * Data Repositories * iPhone & iPod Touch * microRNAs * Pathways * Protein Function * Text Analysis * Transcription Profiling

Proper citation: Computational Biology Center (RRID:SCR_002877) Copy   

•   Source: SciCrunch Registry

http://lab.rockefeller.edu/tuschl/

RNA is not only a carrier of genetic information, but also a catalyst and a guide for sequence-specific recognition and processing of other RNA molecules. This lab investigates the regulatory mechanisms of RNA interference, RNA-mediated translational control, and nuclear pre-mRNA splicing. Classical and combinatorial biochemical techniques are used to analyze the function of the RNA- and protein-components involved in those processes.

Proper citation: Tuschl Laboratory: RNA Molecular Biology (RRID:SCR_002866) Copy   

•   Source: SciCrunch Registry

http://www.ub.edu/dnasp/

A software package for the analysis of nucleotide polymorphism from aligned DNA sequence data. DnaSP can estimate several measures of DNA sequence variation within and between populations (in noncoding, synonymous or nonsynonymous sites, or in various sorts of codon positions), as well as linkage disequilibrium, recombination, gene flow and gene conversion parameters. DnaSP can also carry out several tests of neutrality: Hudson, Kreitman and Aguad (1987), Tajima (1989), McDonald and Kreitman (1991), Fu and Li (1993), and Fu (1997) tests. Additionally, DnaSP can estimate the confidence intervals of some test-statistics by the coalescent. The results of the analyses are displayed on tabular and graphic form.

Proper citation: DnaSP (RRID:SCR_003067) Copy   

•   Source: SciCrunch Registry

http://www.genepaint.org

Digital atlas of gene expression patterns in developing and adult mouse. Several reference atlases are also available through this site. Expression patterns are determined by non-radioactive in situ hybridization on serial tissue sections. Sections are available from several developmental ages: E10.5, E14.5 (whole embryos), E15.5, P7 and P56 (brains only). To retrieve expression patterns, search by gene name, site of expression, GenBank accession number or sequence homology. For viewing expression patterns, GenePaint.org features virtual microscope tool that enables zooming into images down to cellular resolution.

Proper citation: GenePaint (RRID:SCR_003015) Copy   

•   Source: SciCrunch Registry

https://code.google.com/p/gutentag/

An interactive, user-editable genetic sequence database tool, targeted at molecular biology research groups that can be browsed using tags. The tool is Web 2.0-flavoured, allowing users to do more than just retrieve information. Its focus on user-editability is supported by the use of tags (metadata) associated with genetic sequences. Several methods of retrieving stored data are available including tag-clouds, BLAST and keyword searches. Also, sequence tags related to HGNC gene names, conserved domains (CDD) and GO terms can be automatically generated given sequence data. The tool is constructed using the high-level Python web framework, Django, with a SQLite3 backend.

Proper citation: Gutentag (RRID:SCR_003051) Copy   

•   Source: SciCrunch Registry

http://bibiserv.techfak.uni-bielefeld.de/dialign/

Tool for multiple sequence alignment using various sources of external information that is particularly useful to detect local homologies in sequences with low overall similarity. While standard alignment methods rely on comparing single residues and imposing gap penalties, DIALIGN constructs pairwise and multiple alignments by comparing entire segments of the sequences. No gap penalty is used. This approach can be used for both global and local alignment, but it is particularly successful in situations where sequences share only local homologies. Several versions of DIALIGN are available online at GOBICS, http://dialign.gobics.de/

Proper citation: DIALIGN (RRID:SCR_003041) Copy   

•   Source: SciCrunch Registry

http://compbio.cs.sfu.ca/software-novelseq

Software pipeline to detect novel sequence insertions using high throughput paired-end whole genome sequencing data.

Proper citation: NovelSeq (RRID:SCR_003136) Copy   

•   Source: SciCrunch Registry

http://dip.doe-mbi.ucla.edu/

Database to catalog experimentally determined interactions between proteins combining information from a variety of sources to create a single, consistent set of protein-protein interactions that can be downloaded in a variety of formats. The data were curated, both, manually and also automatically using computational approaches that utilize the the knowledge about the protein-protein interaction networks extracted from the most reliable, core subset of the DIP data. Because the reliability of experimental evidence varies widely, methods of quality assessment have been developed and utilized to identify the most reliable subset of the interactions. This CORE set can be used as a reference when evaluating the reliability of high-throughput protein-protein interaction data sets, for development of prediction methods, as well as in the studies of the properties of protein interaction networks. Tools are available to analyze, visualize and integrate user's own experimental data with the information about protein-protein interactions available in the DIP database. The DIP database lists protein pairs that are known to interact with each other. By interact they mean that two amino acid chains were experimentally identified to bind to each other. The database lists such pairs to aid those studying a particular protein-protein interaction but also those investigating entire regulatory and signaling pathways as well as those studying the organization and complexity of the protein interaction network at the cellular level. Registration is required to gain access to most of the DIP features. Registration is free to the members of the academic community. Trial accounts for the commercial users are also available.

Proper citation: Database of Interacting Proteins (DIP) (RRID:SCR_003167) Copy   

•   Source: SciCrunch Registry

http://wiki.c2b2.columbia.edu/honiglab_public/index.php/Main_Page

Laboratory portal, including software, web-based tools, databases and data sets, related to their research that focuses on the development and application of biophysical and bioinformatics methods aimed at understanding the structural and energetic origins of protein-protein, protein-nucleic acid, and protein-membrane interactions. Their work includes fundamental theoretical research, the development of software tools, and applications to problems of biological importance. In this regard they maintain an active collaborative computational and experimental research program on the molecular basis of cell-cell adhesion. Other problems of current interest include protein structure prediction, the organization of protein sequence/structure space, the prediction of protein function based on protein structure, the structural origins of specificity in protein-DNA interactions, RNA function and, more generally, the electrostatic properties of biological macromolecules.

Proper citation: Honig Lab (RRID:SCR_003410) Copy   

•   Source: SciCrunch Registry

http://nematode.lab.nig.ac.jp/

Expression pattern map of the 100Mb genome of the nematode Caenorhabditis elegans through EST analysis and systematic whole mount in situ hybridization. NEXTDB is the database to integrate all information from their expression pattern project and to make the data available to the scientific community. Information available in the current version is as follows: * Map: Visual expression of the relationships among the cosmids, predicted genes and the cDNA clones. * Image: In situ hybridization images that are arranged by their developmental stages. * Sequence: Tag sequences of the cDNA clones are available. * Homology: Results of BLASTX search are available. Users of the data presented on our web pages should not publish the information without our permission and appropriate acknowledgment. Methods are available for: * In situ hybridization on whole mount embryos of C.elegans * Protocols for large scale in situ hybridization on C.elegans larvae

Proper citation: NEXTDB (RRID:SCR_004480) Copy   

•   Source: SciCrunch Registry

http://metagenomics.atc.tcs.com/binning/SOrt-ITEMS/

Sequence orthology based software for improved taxonomic estimation of metagenomic sequences., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: SOrt-ITEMS (RRID:SCR_004716) Copy   

•   Source: SciCrunch Registry

http://omics.informatics.indiana.edu/AbundanceBin/

An abundance-based software tool for binning metagenomic sequences, such that the reads classified in a bin belong to species of identical or very similar abundances. AbundanceBin also gives estimations of species abundances and their genome sizes -two important characteristic parameters for a microbial community.

Proper citation: AbundanceBin (RRID:SCR_004648) Copy   

•   Source: SciCrunch Registry

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

Software for Phylogenetic Classification of Metagenomic Data with Interpolated Markov Models to taxonomically classify DNA sequences and accurately classify reads as short as 100 bp. PhymmBL, the hybrid classifier included in this distribution which combines analysis from both Phymm and BLAST, produces even higher accuracy.

Proper citation: Phymm and PhymmBL (RRID:SCR_004751) Copy   

•   Source: SciCrunch Registry

http://www.genedb.org/Homepage/Tbruceibrucei927

Database of the most recent sequence updates and annotations for the T. brucei genome. New annotations are constantly being added to keep up with published manuscripts and feedback from the Trypanosomatid research community. You may search by Protein Length, Molecular Mass, Gene Type, Date, Location, Protein Targeting, Transmembrane Helices, Product, GO, EC, Pfam ID, Curation and Comments, and Dbxrefs. BLAST and other tools are available. T. brucei possesses a two-unit genome, a nuclear genome and a mitochondrial (kinetoplast) genome with a total estimated size of 35Mb/haploid genome. The nuclear genome is split into three classes of chromosomes according to their size on pulsed-field gel electrophoresis, 11 pairs of megabase chromosomes (0.9-5.7 Mb), intermediate (300-900 kb) and minichromosomes (50-100 kb). The T. brucei genome contains a ~0.5Mb segmental duplication affecting chromosomes 4 and 8, which is responsible for some 75 gene duplicates unique to this species. A comparative chromosome map of the duplicons can be accessed here (PubmedID 18036214). Protozoan parasites within the species Trypanosoma brucei are the etiological agent of human sleeping sickness and Nagana in animals. Infections are limited to patches of sub-Saharan Africa where insects vectors of the Glossina genus are endemic. The most recent estimates indicate between 50,000 - 70,000 human cases currently exist, with 17 000 new cases each year (WHO Factsheet, 2006). In collaboration with GeneDB, the EuPathDB genomic sequence data and annotations are regularly deposited on TriTrypDB where they can be integrated with other datasets and queried using customized queries.

Proper citation: GeneDB Tbrucei (RRID:SCR_004786) Copy   

•   Source: SciCrunch Registry

http://www.ncbi.nlm.nih.gov/sra

Repository of raw sequencing data from next generation of sequencing platforms including including Roche 454 GS System, Illumina Genome Analyzer, Applied Biosystems SOLiD System, Helicos Heliscope, Complete Genomics, and Pacific Biosciences SMRT. In addition to raw sequence data, SRA now stores alignment information in form of read placements on reference sequence. Data submissions are welcome. Archive of high throughput sequencing data,part of international partnership of archives (INSDC) at NCBI, European Bioinformatics Institute and DNA Database of Japan. Data submitted to any of this three organizations are shared among them.

Proper citation: NCBI Sequence Read Archive (SRA) (RRID:SCR_004891) Copy   

•   Source: SciCrunch Registry

http://clams.jgi-psf.org/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on May 2nd, 2023. Sequence composition based classifier for metagenomic sequences. It works by capturing signatures of each sequence based on the sequence composition. Each sequence is modeled as a walk in a de Bruijn graph with underlying Markov chain properties. ClaMS captures stationary parameters of the underlying Markov chain as well as structural parameters of the underlying de Bruijn graph to form this signature. In practice, for each sequence to binned, such a signature is computed and matched to similar signatures computed for the training sets. The best match that also qualifies the normalized distance cut-off wins. In the case that the best match does not qualify this cut-off, the sequence remains un-binned.

Proper citation: Classifier for Metagenomic Sequences (RRID:SCR_004929) Copy   

•   Source: SciCrunch Registry