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http://edge.oncology.wisc.edu/edge.php

THIS RESOURCE IS NO LONGER IN SERVICE, documented on July 15, 2013. EDGE is a scientific resource for toxicology-related gene expression information. The site contains databases and analyses of gene expression studies following exposure to a variety of chemicals or physiological changes. The ultimate goal of the EDGE is to map transcriptional changes from chemical exposure that will someday be used as a diagnostic fingerprint to predict toxicity as well as provide valuable insights into the basic molecular changes responsible. EDGE gives you the ability to easily answer the following fundamental questions about your data 1. Can I compare transcriptional profiles across treatments? 2. What genes respond to my treatment? 3. What influences my favorite gene(s)? One of the major objectives of toxicology is to understand the adverse health effects that result from exposure to foreign chemicals. The traditional method for assessing the toxicity of a test chemical is very resource intensive; requiring the commitment of large amounts of money, time, and animals. According to the National Toxicology Program (NTP), each chemical study requires between 2 and 4 million dollars and several years to complete. Due to the cost and labor intensive nature of these studies, the number of chemicals currently tested by the NTP stands at less than 500. Given these statistics and the fact that there are approximately 70,000 chemicals in commerce today, it is increasingly apparent that alternative methods for assessing toxic potential must be explored if a significant portion of the remaining chemicals is to be tested. One potential solution is to develop a comprehensive database that describes alterations in gene expression resulting from chemical exposure. The pattern of transcriptional activity will not only be highly sensitive indicator of chemical exposure, but that this pattern will be diagnostic for mechanistically linked toxicants. In our laboratory, we have chosen to address this problem through a combination of high throughput sequencing of expressed sequence tags (ESTs) and construction of custom toxicology-related cDNA microarrays derived from the unique ESTs identified in the sequencing effort. By using this approach, we can simultaneously develop a quantitative gene expression profile using ESTs and the reagents for further analyzing these changes in a rapid, highly parallel manner. In addition, the expression profiles are not biased for preselected favorite genes. The resulting gene expression pattern can then be used as diagnostic fingerprint to predict toxicity and/or carcinogenicity as well as provide valuable insight into the basic biochemical and molecular changes responsible for toxicity. Submission of total RNA for Bradfield Lab Microarray Microarray comparisons are made between untreated, control animals and animals treated with ONE treatment. Please make sure the RNA submitted adheres to this experimental design. Necessary information is available on the site.

Proper citation: EDGE: Environment, Drugs and Gene Expression (RRID:SCR_008187) Copy   

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http://inc.ucsd.edu/

The Institute for Neural Computation (INC) is an organized research unit of the University of California at San Diego with 44 members representing 14 research disciplines, devoted to the research and development of a new generation of massively parallel computers through a coherent and cohesive plan of research spanning the areas of neuroscience, visual science, cognitive science, artificial intelligence, mathematics, economics and social science, and computer engineering. INC is a leading center in the field of neural computation, initiating joint research projects, providing special facilities for carrying out research, coordinating the training of young investigators, and offering special activities through its Industrial Affiliates Program. The INC also supports training programs for graduate students and postdoctoral fellows in Cognitive Neuroscience (NIH) and Computational Neurobiology (NFS).

Proper citation: Institute for Neural Computation (RRID:SCR_008068) Copy   

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http://sig.biostr.washington.edu/projects/fm/FME/index.html

The Foundational Model Explorer (FME) is an internet based software application developed for viewing the content and organization of the Foundational Model of Anatomy Ontology (FMA). The initial purpose of the FME was to provide a simple and intuitive interface to the FMA for domain experts, in the field of anatomy, participating in the evaluation of the FMA. The FME also provides an easily available method of exploring the FMA to individuals or groups considering the adoption of the Foundational Model of Anatomy knowledge base. The FME display consists of two panes: a hierarchical tree may be opened up in the pane on the left side; if a class is selected in the hierarchical tree, the pane on the right side displays the information that has been entered in the FMA for that class. The information associated with a given class is organized in so-called slots. Each slot has a name (e.g., Definition, Parts) and some content, which is that particular slots value (e.g., the English definition and the names of parts of the selected class, respectively). For an explanation of the interactive features of the FME, see the Knowledge Navigation Section. For a guided tutorial check out the Conducted Tour. In the left pane, the default tree is a subclass hierarchy, based on the -is a- or -kind of- relationship; it is the instantiation of the Anatomy taxonomy (At) component in the high level scheme of the Foundational Model of Anatomy. Apart from the slots Preferred Name and Synonyms, other slots relate to the Anatomical Structural Abstraction (ASA) component in the FMAs high level scheme. Hierarchies based on various part-whole relationships can also be opened up in the left pane. Once a class has been highlighted in the subclass hierarchy, you can choose a relationship from a drop down list labeled Select navigation tree type. Some other transitive relationships (e.g., -branch of- and -tributary of-) are also available. The Search facility matches a search term to the preferred name, as well as to the Latin name, or synonym of an FMA class (if such exist). The tree is expanded to reveal the matching class and the information about this class is displayed. The wildcard * is allowed in the search term and will match to any sequence of characters. For example the search term h*d matches the class names Head and Hepatic cord (amongst others). The search function is not case sensitive. If more than one class name matches with the search term, a list of matching terms is presented for the user to choose between.

Proper citation: Foundational Model Explorer (RRID:SCR_008189) Copy   

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http://genewindow.nci.nih.gov/

Software tool for pre- and post-genetic bioinformatics and analytical work, developed and used at the Core Genotyping Facility (CGF) at the National Cancer Institute. While Genewindow is implemented for the human genome and integrated with the CGF laboratory data, it stands as a useful tool to assist investigators in the selection of variants for study in vitro, or in novel genetic association studies. The Genewindow application and source code is publicly available for use in other genomes, and can be integrated with the analysis, storage, and archiving of data generated in any laboratory setting. This can assist laboratories in the choice and tracking of information related to genetic annotations, including variations and genomic positions. Features of GeneWindow include: -Intuitive representation of genomic variation using advanced web-based graphics (SVG) -Search by HUGO gene symbol, dbSNP ID, internal CGF polymorphism ID, or chromosome coordinates -Gene-centric display (only when a gene of interest is in view) oriented 5 to 3 regardless of the reference strand and adjacent genes -Two views, a Locus Overview, which varies in size depending on the gene or genomic region being viewed and, below it, a Sequence View displaying 2000 base pairs within the overview -Navigate the genome by clicking along the gene in the Locus Overview to change the Sequence View, expand or contract the genomic interval, or shift the view in the 5 or 3 direction (relative to the current gene) -Lists of available genomic features -Search for sequence matches in the Locus Overview -Genomic features are represented by shape, color and opacity with contextual information visible when the user moves over or clicks on a feature -Administrators can insert newly-discovered polymorphisms into the Genewindow database by entering annotations directly through the GUI -Integration with a Laboratory Information Management System (LIMS) or other databases is possible

Proper citation: GeneWindow (RRID:SCR_008183) Copy   

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http://www.utsa.edu/claibornelab/

The long-term goals of my research are to understand the relationship between neuronal structure and function, and to elucidate the factors that affect neuronal morphology and function over the lifespan of the mammal. Currently we are examining 1) the effects of synaptic activity on neuronal development; 2) the effects of estrogen on neuronal morphology and on learning and memory; and, 3) the effects of aging on neuronal structure and function. We have focused our efforts on single neurons in the hippocampal formation, a region that is critical for certain forms of learning and memory in rodents and humans. From the portal, you may click on a cell in your region of interest to see the complete database of cells from that region. You may also explore the Neuron Database: * Comparative Electrotonic Analysis of Three Classes of Rat Hippocampal Neurons. (Raw data available) * Quantitative, three-dimensional analysis of granule cell dendrites in the rat dentate gyrus. * Dendritic Growth and Regression in Rat Dentate Granule Cells During Late Postnatal Development.(Raw data available) * A light and electron microscopic analysis of the mossy fibers of the rat dentate gyrus.

Proper citation: University of Texas at San Antonio Laboratory of Professor Brenda Claiborne (RRID:SCR_008064) Copy   

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http://www.cambridgesoft.com/databases/login/?serviceid=128

THIS RESOURCE IS NO LONGER IN SERVICE,documented on January,18, 2022. ChemBioFinder.com is an online chemistry and biology reference database. With more than 500,000 compounds indexed and linked to other web sites, it provides a wealth of chemical information for professional chemists and students alike. ChemBioFinder.com is the gateway to all databases available from CambridgeSoft.
At ChemBioFinder.com, a subscriber can search for compounds by name, CAS Registry Number, molecular formula or weight, or by structure (exact and substructure). Successful searches return a basic profile of molecules indexed by this site. The profile contains the name, molecular formula and weight, CAS Registry Number, SMILES and InChI strings for each located compound, and lists the databases which contain entries for the located compound(s). Free trials to any of these databases are available, as are annual subscriptions for continuous use of the contents.
Users of ChemBioFinder.com are allowed 5 free searches before we request them to register with us as a cambridgesoft.com website user. The CambridgeSoft user account is free and will give you access to a growing list of products and services which includes, our quarterly print publication Chem & Bio News, frequent webinars, white papers and articles on all our offerings. Set up is fast & easy.
For ChemFinder.com users:
ChemFinder.Com has become ChemBioFinder.Com and has a whole new look and layout. This is part of a gradual redesign of the entire CambridgeSoft website. Here are some of the changes that were made to improve the vital information presented here to the scientific community:
1. Search results show the ChemBioFinder databases which have entries for the compound(s), and indicate the databases to which the logged in user has active subscriptions.
2. There are hyperlinks to the detailed records in the databases with active subscriptions.
3. Search results provide the name, molecular formula and weight, CAS Registry Number, SMILES and InChI strings for the compound.
4. Physical properties are no longer provided unless the user has a subscription to ChemIndex or other CambridgeSoft online databases that provide this information. Many of our products come with one year subscriptions to ChemIndex as part of the package. So you may actually be entitled to a subscription and dont realize it.

Proper citation: ChemBioFinder (RRID:SCR_008180) Copy   

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https://www.univ-rennes1.fr/

The University of Rennes 1 is one of the two main universities in the city of Rennes, France. It is under the Academy of Rennes. It specializes in science, technology, law, economy, management and philosophy.

Proper citation: University of Rennes 1; Rennes; France (RRID:SCR_007649) Copy   

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http://sys-bio.org/downloads/

An open source framework for systems biology connecting heterogeneous software applications written in diverse programming languages and running on different platforms-to communicate and use each others'' capabilities via a fast binary encoded-message system. It uses a broker-based, distributed, message-passing architecture, supports many languages including Java, C++, Perl & Python, and runs under Linux,OSX & Win32. Many biological modeling and simulation tools are a part of SBW, including JDesigner and Jarnac.

Proper citation: Systems Biology Workbench (RRID:SCR_008059) Copy   

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http://patricbrc.vbi.vt.edu/portal/portal/patric/IncumbentBRCs?page=eric

ERIC is a resource of annotated enterobacterial genomes. Information is available and accessed through a open web portal uniting biological data and analysis tools. ERIC contains information on Escherichia, Shigella, Salmonella, Yersinia, and other microorgansims. ERIC has recently been moved over to PATRIC: The PATRIC BRC is now responsible for all bacterial species in the NIAID Category A-C Priority Pathogen lists for biodefense research, and pathogens causing emerging/reemerging infectious diseases. For ERIC users, we understand that the resource was valuable to your work. As such, we will be doing our very best to create a useful PATRIC resource to continue supporting your work. We realize that the transition will cause disruptions. However, it is a priority for us to work with established BRC users and communities to identify and prioritize our transition efforts. We have concentrated on the transfer of genomic data for this initial release. We anticipate adding new data, tools, and website features over the next several months. We look forward to working with you during the next 5 years., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: ERIC (RRID:SCR_007644) Copy   

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http://www.sheffield.ac.uk/

Founded in 1905, the University of Sheffield is one of the UK''s leading Russell Group universities with an outstanding record in both teaching and research.

Proper citation: University of Sheffield; South Yorkshire; United Kingdom (RRID:SCR_008056) Copy   

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http://www.snl.salk.edu/~jude/neuron_exchange/

This resource contains to MATLAB code to make and show videos that can be acquired for free. Data for the movies came from a Macaque attention task. Data on this page came from the multiple-object tracking attention task in a Macaque: The monkeys fixated the white dot at the center of the computer monitor, and four striped stimuli appeared. Their eye position was monitored using an IR camera. The red cross shows where the eyes were pointing throughout each trial. The circle shows the location of the receptive field of the neuron under study during the recording. At the beginning of each trial, either one or two of the stimuli were highlighted, indicating to the monkey that they were the targets of attention. The stimuli then moved to new locations and paused, with one stimulus in the receptive field. After a brief pause, they moved to new locations and the fixation point disappeared. The monkey was rewarded with juice if it then looked at the cued targets. Attention Dask Demo (Avi File) contains Matlab code to make and show movies. Publication from this Dataset: * Differential attention-dependent response modulation across cell classes in macaque visual area V4. JF Mitchell, KA Sundberg, JH Reynolds. Neuron, 2007, 55. 131-141. * Supplemental Material, Neuron, 2007, 55. 131-141. :A Subset of data can be downloaded with analysis routines (easiest to download whole set with full subdirectory structure). Additionally, neuron data files can also be downloaded. :* Routines for Fano Factor, Autocorrelation, and Power Spectra (poster above): :o Plots Spike Waveform and Tests if Significant Visual Response: basic_info.m :o Firing Rate and Fano Factor Analysis (Mitchell et. al, 2007): rate_fano_psth.m :* Routines for Spike-LFP Coherence: :o Spike-LFP Coherence with Rate Normalization (attempting Womelsdorf & Fries, Cosyne, 2008): rate_normalized_coherence.m Sponsors: This work was supported by a grant from the National Eye Institute (EY016161, J.F.M. and J.H.R.), a National Institutes of Health Training Fellowship (J.F.M.), and a National Science Foundation Graduate Research Fellowship (K.A.S.).

Proper citation: Salk Institute for Biological Studies: Jude Mitchells Neuron Exchange and Matlab Analysis (RRID:SCR_008055) Copy   

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http://drive5.com/usearch/manual/uchime_algo.html

An algorithm for detecting chimeric sequences.

Proper citation: UCHIME (RRID:SCR_008057) Copy   

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http://www.nimh.nih.gov/funding/clinical-trials-for-researchers/practical/stard/index.shtml

A nationwide public health clinical trial conducted to determine the effectiveness of different treatments for people with major depression, in both primary and specialty care settings, who have not responded to initial treatment with an antidepressant. This is the largest and longest study ever done to evaluate depression treatment. The study is completed and no longer recruiting participants. Each of the four levels of the study tested a different medication or medication combination. The primary goal of each level was to determine if the treatment used during that level could adequately treat participants����?? major depressive disorder (MDD). Those who did not become symptom-free could proceed to the next level of treatment. The design of the STAR*D study reflects what is done in clinical practice because it allowed study participants to choose certain treatment strategies most acceptable to them and limited the randomization of each participant only to his/her range of acceptable treatment strategies. No prior studies have evaluated the different treatment strategies in broadly defined participant groups treated in diverse care settings. Over a seven-year period, the study enrolled 4,041 outpatients, ages 18-75 years, from 41 clinical sites around the country, which included both specialty care settings and primary medical care settings. Participants represented a broad range of ethnic and socioeconomic groups. All participants were diagnosed with MDD, were already seeking care at one of these sites, and were referred to the trial by their doctors. * STAR*D Study Medications: Citalopram (Celexa), Sertraline (Zoloft), Bupropion SR (Wellbutrin SR), Venlafaxine XR (Effexor XR), Buspirone (BuSpar), Mirtazapine (Remeron), Triiodothyronine (T3) (Cytomel), Nortriptyline (Pamelor, Aventyl), Tranylcypromine (Parnate), Lithium (Eskalith, Lithobid) *STAR*D Talk Therapy:Cognitive Therapy

Proper citation: Sequenced Treatment Alternatives to Relieve Depression Study (RRID:SCR_008051) Copy   

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http://openwetware.org/wiki/Main_Page

OpenWetWare is an effort to promote the sharing of information, know-how, and wisdom among researchers and groups who are working in biology & biological engineering. OWW provides a place for labs, individuals, and groups to organize their own information and collaborate with others easily and efficiently. In the process, the hope is that OWW will not only lead to greater collaboration between member groups, but also provide a useful information portal to our colleagues, and ultimately the rest of the world. OWW''s approaches to achieve their goals: # Lower the technical barriers to sharing and dissemination of knowledge in biological research # Build a community of researchers in biology and biological engineering that values, practices, and innovates the open sharing of information # Integrate OpenWetWare into existing and future reward structures in research

Proper citation: OpenWetWare (RRID:SCR_008053) Copy   

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http://zmf.umm.uni-heidelberg.de/apps/zmf/argonaute/single.php

A database is a of mammalian miRNAs and their known or predicted regulatory targets. It provides information on origin of miRNAs, tissue specificity of their expressions and their known or proposed functions, their potential target genes as well as data on miRNA families based on their co-expression and proteins known to be involved in miRNA processing. This database also contains three other navigation tools that can be used to find information relating to miRNA: 1.) Gene Annotations is an information retrieval system for miRNA target genes. It provides comprehensive information from sequence databases and allows to simultaneously search PubMed with all synonyms of a given gene. 2.) miRNA Motif Finder - Argonaute predicts miRNA motifs binding to the gene sequence of the user. The miRNA mature sequences are taken from Agronaute 2 database. miRNA Motif Finder - Custom predicts miRNA motifs binding to the gene sequence, both the gene sequence and miRNA mature sequences provided by the user. 3.) miRNA Statistics provides statistics for the mature miRNA sequences from Argonaute 2 as well as for the miRNA sequences uploaded by the user. It provides statitics on the individual nucleotide as well as pattern of nucleotides apperaing in the sequence.

Proper citation: ARGONAUTE 2 - A database on mammalian microRNAs and their function in gene and pathway regulation (RRID:SCR_007553) Copy   

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http://healthresearchfunding.org/

Health Research Funding is designed to bring researchers with peer-reviewed, worthwhile, unfunded projects together with patient advocacy organizations and other funding sources. Working together, we hope to foster the funding of new research that will provide hope to millions of people in this country with chronic diseases and disabilities. * We invite researchers with promising projects that have been scored but not funded by the NIH to submit their abstracts. By registering, you will be able to search for information about organizations that fund research and their requests for abstracts. * Researchers with proposals that have been peer-reviewed but not funded by a NHC member patient advocacy organization may also register. The National Health Council (NHC) developed this site with input from the National Institutes of Health (NIH), the nation''s medical research agency.

Proper citation: Health Research Funding (RRID:SCR_007790) Copy   

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http://www.nibb.ac.jp/brish/indexE.html

Database of detailed protocols for single and double in situ hybridization (ISH) method, probes used by Yamamori lab and others useful for studies of brain, and many photos of mammalian (mostly mouse and monkey) brains stained with various gene probes. Also includes a brain atlas of gene expression. Currently, the atlas comprises a series of un-annotated images showing the localization of a particular probe or molecule, e.g., AChE.

Proper citation: BraInSitu: A homepage for molecular neuroanatomy (RRID:SCR_008081) Copy   

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http://ekhidna.biocenter.helsinki.fi/sqgraph/pairsdb

This is a web interface for ADDA, an automatic algorithm for domain decomposition and clustering of all protein domain families. We use alignments derived from an all-on-all sequence comparison to define domains within protein sequences based on a global maximum likelihood model. ADDA is downloadable. There are three ways in which you can retrieve a protein sequence and its domains from ADDA. Sequences can be located using sequence identifiers and/or accession numbers, using a identical fragment lookup, or by running BLAST against all sequences in ADDA. ADDA is a protein sequence clustering algorithm. It takes a set of sequences and returns domain families. ADDA has two steps corresponding to the two aspects of the protein sequence clustering domain. First, ADDA splits protein sequences into domains. The idea behind ADDA is in principle the application of Occam''s razor; the goal is to describe the diversity of protein sequences with a minimal set of protein domains. The algorithm behind ADDA approximates this minimal set. In practice ADDA works by looking at where BLAST alignments are located on the sequence and splits the sequences, so that as few as possible alignments are cut by domain boundaries and that as many alignments as possible stretch over complete domains. Secondly, ADDA takes all the domains and then arranges them in a minimum spanning tree, where the similarity between two domains is determined by their relative overlap given a BLAST alignment. Each link in the tree is then checked by a pairwise profile-profile comparison and links below a threshold are removed. The remaining connected components are then taken to represent protein domain families.

Proper citation: ADDA - Automatic Domain Decomposition Algorithm (RRID:SCR_007546) Copy   

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http://www.pharmacy.wsu.edu/prospectivestudents/graduateprograms.html

The research-oriented program in pharmacology and toxicology prepares students for careers in independent research and teaching in pharmacology, toxicology and related areas.The research interests of the faculty are very broad and active areas of research include cancer biology, pharmacogenomics, pharmacokinetics, immuno-pharmacology and -toxicology and neuroscience. The diversity in faculty research interests provides students with a solid foundation in many areas of molecular and cellular pharmacology and toxicology and gives them a wide variety of research programs from which a dissertation proposal may be selected.
The curriculum provides exposure of students to virtually all areas of current research in molecular and cellular biochemistry, immunology, molecular biology, pharmacology and toxicology and formal course requirements are flexible to tailor programs to individual needs.Our graduates have been successfully placed in careers in universities and colleges, the pharmaceutical and biotech industries, and in federal and state agencies. The program awards Ph.D. and M.S. degrees.

Proper citation: Washington State University Pullman WA. Pharmacology and Toxicology (RRID:SCR_007543) Copy   

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http://www.gene-regulation.com/pub/programs.html

In an effort to strongly support the collaborative nature of scientific research, BIOBASE offers access to their tools. Programs that are available through this portal are: * AliBaba 2.1: AliBaba2 is a program for predicting binding sites of transcription factor binding sites in an unknown DNA sequence. Therefore it uses the binding sites collected in TRANSFAC. AliBaba2 is currently the most specific tool for predicting sites. * Boxshade 3.3.1: Pretty Printing and Shading of Multiple-Alignment files. * ClustalW 1.8: ClustalW Multiple Sequence Alignment Program. * Dialign2.0: Multiple Sequence Alignment Program. * F-Match 1.0: F-MATCH is a program for identifying statistically overrepresented Transcription Factor Binding Sites (TFBS) in a set of sequences compared against a control set, assuming a binomial distribution of TFBS frequency. The program reads MATCH output files for the query and control sets. F-Match uses a library of mononucleotide weight matrices from TRANSFAC 6.0 * Match 1.0 Public: Match is designed for searching potential binding sites for transcription factors (TF binding sites) nucleotide sequences. MatchTM uses a library of mononucleotide weight matrices from TRANSFAC 6.0 * molwSearch 1.0: Search for transcription factors with a certain molecular weight. * P-Match 1.0: P-Match is a new tool for identifying transcription factor binding sites (TF binding sites) in DNA sequences. It combines pattern matching and weight matrix approaches thus providing higher accuracy of recognition than each of the methods alone. P-Match uses a library of mononucleotide weight matrices from TRANSFAC 6.0 along with the site alignments associated with these matrices. * Patch 1.0: Search for potential transcription factor binding sites in your own sequences with the pattern search program using TRANSFAC 6.0 public sites. * m2transfac 1.0: m2transfac is a PWM-PWM alignment interface for the TRANSFAC(R) database. For given user motifs, m2transfac reports all non-overlapping pairwise alignments to a TRANSFAC(R) matrix which satisfy a specified threshold. * MatrixCatch 2.7: The MatrixCatch tool is designed for searching potential composite elements (CEs) for transcription factors (TFs) in any DNA sequence, which may be of interest. MatrixCatch uses a library of CE matrix models, which were compiled on a basis of experimentally identified CEs collected in TRANSCOMPEL database and mononucleotide weight matrices for single TF-binding sites collected in TRANSFAC 6.0 public database. * Composite Module Analyst (CMA) 1.0: CMA reads output of Match program and applies a genetic algorithm in order to define promoter models based on the composition of transcription factor binding sites and their pairs. * PolyA Scan 0.000707: Scanning a Sequence for potential Polyadenylation Sites. * ReadSeq 2.0: ReadSeq reads and writes nucleic/protein sequences in various formats. * SignalScan: Analysis of DNA Sequences for known Eukaryotic Signals * SbBlast 1.0: Search Tool for Sequence Search in the S/MARt Binder Database. SbBlast makes use of the BLAST Sequence Similarity Search Tool - Version 2.0.13 (May-26-2000). * SnpFind 0.3: SNPFIND is a tool for searches in the Database of Single Nucleotide Polymorphisms. The search algorithm used for the database search is the BLAST algorithm. * TfBlast 0.1: Search Tool for Sequence Search in the TRANSFAC Factor Table. SbBlast makes use of the BLAST Sequence Similarity Search Tool - Version 2.0.13 (May-26-2000).

Proper citation: Gene Regulation Programs (RRID:SCR_007787) Copy   

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