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http://www.ohsu.edu/xd/research/centers-institutes/onprc/

Center that aims to develop biomedical technologies using nonhuman primate (NHP) models. Its goal is to uncover the root causes of various disease and disorders, unlock secrets of the brain, and unleash new methods of diagnostics and treatment.

Proper citation: Oregon National Primate Research Center (RRID:SCR_008291) Copy   

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http://bioinfo-out.curie.fr/projects/vamp/

Software for visualization and Analysis of CGH arrays, transcriptome and other Molecular Profiles.

Proper citation: VAMP (RRID:SCR_008527) Copy   

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http://flowingdata.com

FlowingData explores how designers, statisticians, and computer scientists are using data to understand ourselves better - mainly through data visualization. Money spent, reps at the gym, time you waste, and personal information you enter online are all forms of data. How can we understand these data flows? Data visualization lets non-experts make sense of it all. About Nathan As for me, I''m a PhD candidate in statistics. I live and breathe data. I also have a background in computer science and design. I do some freelancing from time to time, but mostly I''m just trying to work on my dissertation. You can find more about me here. For the New Readers If you''re new here, welcome to FlowingData. A good place to start is the archives where you''ll find FlowingData''s most popular posts or take a look at the FlowingData beginner''s guide. If you like what you see, subscribe to the feed, follow on Twitter, or fan on Facebook More specifically, I''m most interested in Social data visualization Self-surveillance Data for non-professionals FlowingData readers are data-savvy people who are interested in visualization, statistics, design, analysis, and programming. If you have a product or service that is of interest to them, then FD sponsorship is a great way for you to connect with them. There are two exclusive ways for you or your group to connect with readers. The first is through a banner ad, which appears in the top right of every page. The second is via a bimonthly sponsorship of the FlowingData RSS feed. Both sponsorship types also include a mention in the monthly thank you post.

Proper citation: FlowingData (RRID:SCR_008521) Copy   

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http://elegans.bcgsc.bc.ca/perl/fosmid/CloneSearch

C. elegans Gene Knockout Project Containing information about the Gene Knockout consortium, an on-line version of targeted gene database, knockout submission form and oligo prediction software. C. elegans Gene Expression Project Members investigating C. elegans gene expression using promoter GFP fusions and serial analysis of gene expression (SAGE). Serial Analysis of Gene Expression View and download C. elegans SAGE data. Provided are tag to gene mappings, various filters (e.g. sequence quality, minimum tag count), and search by keyword or tag. Also available are supplemental data from publications. Microarray Data Download C. elegans expression data generated from the Affymetrix GeneChipTM or a NimbleGen custom array. These experiments were done in parallel with the SAGE experiments, and there is data available for the six major developmental stages. Expression Patterns for promoter GFP fusions View and search expression patterns of C. elegans promoter GFP fusions. Tools for automated PCR primer design PCR-based screening for deletions in C. elegans genes, including the ''poison primer'' strategy Construction of promoter GFP fusions using PCR stitching (fusion PCR) Fosmid Clone Search We have created a C. elegans fosmid library. Use this site to search for fosmid clones for your gene or region of interest, and request clones.

Proper citation: C. elegans Resources (RRID:SCR_008523) Copy   

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http://dbm.neuro.uni-jena.de/vbm/download/

This is the website of the Structural Brain Mapping Group at the Department of Psychiatry, University of Jena. Our principal research focuses on the development of methods for structural brain imaging and their application. Specific areas of interest include the investigation of structural brain plasticity and schizophrenia research. Regional structural brain changes are among the most robust biological findings in schizophrenia, yet the underlying pathophysiological changes remain poorly understood. Recent evidence suggests that abnormal neuronal/dendritic plasticity is related to alterations in membrane lipids. We examined whether serum activity of membrane lipid remodeling/repairing cytosolic phospholipase A2 (PLA2) were related to regional brain structure in magnetic resonance images (MRI). The study involved 24 schizophrenia patients, who were either drug-nave or off antipsychotic medication, and 25 healthy controls. Using voxel-based morphometry (VBM) analysis of T1-high-resolution MRI-images, we correlated both gray matter and white matter changes with serum PLA2-activity. PLA2 activity was increased in patients, consistent with previous findings. VBM group comparison of patients vs. controls showed abnormalities of frontal and medial temporal cortices/hippocampus, and left middle/superior temporal gyrus in first-episode patients. Group comparison of VBM/ PLA2-correlations revealed a distinct pattern of disease-related interactions between gray/white matter changes in patients and PLA2-activity: in first-episode patients (n = 13), PLA2-activity was associated with structural alterations in the left prefrontal cortex and the bilateral thalamus. Recurrent-episode patients (n = 11) showed a wide-spread pattern of associations between PLA2-activity and structural changes in the left (less right) prefrontal and inferior parietal cortex, the left (less right) thalamus and caudate nucleus, the left medial temporal and orbitofrontal cortex and anterior cingulum, and the cerebellum. Our findings demonstrate a potential association between membrane lipid biochemistry and focal brain structural abnormalities in schizophrenia. Differential patterns in first-episode vs. chronic patients might be related to PLA2-increase at disease-onset reflecting localized regenerative activity, whereas correlations in recurrent- episode patients might point to less specific neurodegenerative aspects of disease progression.

Proper citation: Structural Brain Mapping Group (RRID:SCR_008487) Copy   

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https://www.flowjo.com/solutions/flowjo

Software for single-cell flow cytometry analysis. Its functions include management, display, manipulation, analysis and publication of the data stream produced by flow and mass cytometers.

Proper citation: FlowJo (RRID:SCR_008520) Copy   

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http://www.broad.mit.edu/mammals/dog

The genome of the domesticated dog, a close evolutionary relation to human, is a powerful new tool for understanding the human genome. Comparison of the dog with human and other mammals reveals key information about the structure and evolution of genes and genomes. The unique breeding history of dogs, with their extraordinary behavioral and physical diversity, offers the opportunity to find important genes underlying diseases shared between dogs and humans, such as cancer, diabetes, and epilepsy. The Canine Genome Sequencing Project produced a high-quality draft sequence of a female boxer named Tasha. By comparing Tasha with many other breeds, the project also compiled a comprehensive set of SNPs (single nucleotide polymorphisms) useful in all dog breeds. These closely spaced genomic landmarks are critical for disease mapping. By comparing the dog, rodent, and human lineages, researchers at the Broad Institute uncovered exciting new information about human genes, their evolution, and the regulatory mechanisms governing their expression. Using SNPs, researchers describe the strikingly different haplotype structure in dog breeds compared with the entire dog population. In addition, they show that by understanding the patterns of variation in dog breeds, scientists can design powerful gene mapping experiments for complex diseases that are difficult to map in human populations. Contribute Although the astounding generosity of Eli and Edythe L. Broad and several other venture philanthropists empowers our scientists to tackle many of the most important problems at the cutting edge of genomic medicine, there are many other critical challenges that they cannot yet pursue because of limited resources. We need additional visionary partners to join the Broads and the Broad Institute in transforming medicine with the power of genomics.

Proper citation: Dog Genome Project (RRID:SCR_008486) Copy   

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http://www.bioconductor.org/packages/2.6/bioc/html/DEGseq.html

R package to identify differentially expressed genes from RNA-Seq data.

Proper citation: DEGseq (RRID:SCR_008480) Copy   

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http://www.fungenes.org/

This website is dedicated to the dissemination of information about the European research project FunGenES (Functional Genomics in Embryonic Stem Cells). FunGenES is a research programme carried out on mouse embryonic stem cells with the aim to improve our understanding of cellular self-renewal and differentiation processes into tissue-specific cells. The website contains general knowledge about stem cell research and related issues such as ethics in stem cell related research, and more detailed information about the specific scope and objectives of the FunGenES project. A brochure containing a general project description in PDF format is available for download in the section Publications. FunGenES - Functional Genomics in Embryonic Stem Cells brings together experts in stem cell research from 18 organisations from industry and academic research across Europe. This research initiative is set up as an Integrated Project with a budget of approximately 12 Million Euros, partially funded by the 6th Framework Programme of the European Union. Specialists from Germany, France, Italy, Portugal, Greece and the UK collaborate for a period of 3 years to investigate the functional genomics of mouse embryonic stem (ES) cells. FunGenES aims to achieve a detailed basic understanding of stem cell self-renewal and differentiation. FunGenES investigates the unique ability of mouse embryonic stem cells to develop into any cell of an organ (this ability is also known as pluripotency), creates new tools for functional genomic studies and will thus provide key knowledge to understand the commitment of cells to a particular cell type. This complex process occurs in several steps and controls the development of pluripotent cells into highly specialised cells of an organism. In particular, FunGenES aims to identify genes controlling the development of the pluripotent ES cells into heart cells (cardiomyocytes), nerve cells (neurons), smooth muscle cells, vascular endothelial cells, fat cells (adipocytes), liver cells (hepatocytes) and insulin-producing cells of the pancreas. FunGenES will deliver a gene expression atlas summarising the genetical pathways for cell differentiation. The project aims to contribute to future therapeutic strategies for degenerative diseases such as heart disease, diabetes and Parkinson''s. All these diseases are characterised by the irreversible loss of functional cells. FunGenES was selected from a large number of proposals to be funded as an advanced and promising project in the area of functional genomics in the Life-Science-Health Programme of the European Union. In addition to its ambitious scientific programme, FunGenES aims to inform the general public about stem cell research, its ethical aspects and future therapeutical applications The project is coordinated by Professor Jrgen Hescheler (University of Cologne) Jrgen Hescheler is excited about the potential of the project: By understanding how mammalian genomic information is selectively used in development, we will acquire an essential key to understanding ourselves and our health. Sponsor. The FunGenES Integrated Project was funded by a grant from the European Commission (6th Framework Programme, Thematic Priority: Life sciences, Genomics and Biotechnology for Health, Contract No. : FunGenES LSHG-CT-2003-503494; http://ec.europa.eu/grants/index_en.htm). H.B. and M.T. were also supported by the University Bordeaux 2 (http://www.u-bordeaux2.fr/index.jsp) and CNRS (http://www.cnrs.fr/); A.K.H. received NIH support, grant HL08395 (http://www.nih.gov/). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Proper citation: Functional Genomics in Embryonic Stem Cells (RRID:SCR_008518) Copy   

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http://www.broad.mit.edu/diabetes/

The Diabetes Genetics Initiative is a collaboration of the Broad Institute of MIT and Harvard, Lund University, and Novartis Institutes for BioMedical Research The Diabetes Genetics Initiative combines the resources and expertise of the Novartis Institutes for BioMedical Research, the Broad Institute of MIT and Harvard, and Lund University to identify the genetic determinants of type 2 diabetes. This unique collaboration aims to collect and analyze samples from type 2 diabetic patients from nations across the globe, performing whole genome scans to provide a comprehensive view of the DNA sequence variants associated with the disease. This partnership has been forged with the explicit goal of making this vast amount of crucial data available to researchers globally and free of cost, which should lead to a greater understanding of disease biology and speed the development of more effective therapies. Contribute Although the astounding generosity of Eli and Edythe L. Broad and several other venture philanthropists empowers our scientists to tackle many of the most important problems at the cutting edge of genomic medicine, there are many other critical challenges that they cannot yet pursue because of limited resources. We need additional visionary partners to join the Broads and the Broad Institute in transforming medicine with the power of genomics.

Proper citation: Diabetes Genetics Initiative (RRID:SCR_008478) Copy   

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http://www.infomus.org/eyesweb_ita.php

EyesWeb refers both to the research projects of InfoMus Lab on multimodal interactive systems and expressive gesture, and to the open software platform to support the development of real-time multimodal distributed interactive applications. The EyesWeb project started in 1997, as a natural evolution of the HARP Project (see www.infomus.org). The current release of the open software platform is EyesWeb XMI (eXtended Multimodal Interaction). The EyesWeb software platform has been developed in EU IST projects in the 5th (MEGA, www.megaproject.org) and 6th Framework Programme (TAI-CHI, Tangible Acoustic Interfaces for Computer Human Interaction). EyesWeb has been adopted in several other EU projects, has been licensed to more than 15,000 individual users, companies, and institutions. EyesWeb is also used in University courses and summer schools (e.g. the New York University Summer Program on Music, dance and new technologies). Software tools EyesWeb open software platform FreeFrame SDK Harp Petri Net Visual Editor and Simulation Software (Linux Version) Petri Net Visual Editor and Simulation Software (Win32 Version) Hardware tools Wireless On-Body-Sensors-to-Midi Box Long-distances MIDI tx/rx Video Multiplexer for connecting and synchronizing two videocameras to the same frame grabber Multimedia interfaces for robot-human interaction DanceWeb ultrasound sensor system

Proper citation: EyesWeb (RRID:SCR_008511) Copy   

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https://www.numenta.com/index.html

This demonstration application illustrates how Hierarchical Temporal Memory (HTM) can be applied to video recognition. The HTM network in this demo has been trained to locate people in surveillance video footage. You can see how the HTM operates by experimenting with the sample videos that are bundled with this application. For each video, the application will process it in real time on your machine and track the locations of people and other moving objects, while correctly discriminating between the two. This demonstration application, called Vision4, gives a sense of how Hierarchical Temporal Memory (HTM) performs in recognizing objects in static images. Sponsor. Numenta has fewer than twenty employees and has been privately funded by its founders, board members, and close associates.

Proper citation: Vision Demos (RRID:SCR_008477) Copy   

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http://www.solver.com/

Solvers, or optimizers, are software tools that help users find the best way to allocate scarce resources. The resources may be raw materials, machine time or people time, money, or anything else in limited supply. The best or optimal solution may mean maximizing profits, minimizing costs, or achieving the best possible quality. An almost infinite variety of problems can be tackled this way, but here are some typical examples: Finance and Investment Working capital management involves allocating cash to different purposes (accounts receivable, inventory, etc.) across multiple time periods, to maximize interest earnings. Capital budgeting involves allocating funds to projects that initially consume cash but later generate cash, to maximize a firm''s return on capital. Portfolio optimization -- creating efficient portfolios -- involves allocating funds to stocks or bonds to maximize return for a given level of risk, or to minimize risk for a target rate of return. Manufacturing Job shop scheduling involves allocating time for work orders on different types of production equipment, to minimize delivery time or maximize equipment utilization. Blending (of petroleum products, ores, animal feed, etc.) involves allocating and combining raw materials of different types and grades, to meet demand while minimizing costs. Cutting stock (for lumber, paper, etc.) involves allocating space on large sheets or timbers to be cut into smaller pieces, to meet demand while minimizing waste. Distribution and Networks Routing (of goods, natural gas, electricity, digital data, etc.) involves allocating something to different paths through which it can move to various destinations, to minimize costs or maximize throughput. Loading (of trucks, rail cars, etc.) involves allocating space in vehicles to items of different sizes so as to minimize wasted or unused space. Scheduling of everything from workers to vehicles and meeting rooms involves allocating capacity to various tasks in order to meet demand while minimizing overall costs.

Proper citation: Solver (RRID:SCR_008510) Copy   

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http://linkage.rockefeller.edu/ott/eh.htm

EH is a program to test and estimate linkage disequilibrium between different markers or between a disease locus and markers. This is an updated version in which the previous disease (case-control) option has been deleted (but see below how to work with case-control data). The program is written in Free Pascal, which is compatible with (but mch more flexible than) Turbo Pascal. Free Pascal is available for various platforms, e.g. Windows and Linux. The data are taken to consist of a number of individuals collected at random from a population. Based on these sample data, the EH program estimates allele frequencies for each marker. Haplotype frequencies are estimated with allelic association (H1) and without (H0). The EH program also provides log likelihood, chi-square and the number of degrees of freedom under hypotheses H0 and H1. For more information please refer to Terwilliger and Ott (1994). Notes: For sparse data (relatively few observations with large numbers of alleles), the chi-square approximation to the test statistics used by EH is unreliable. Then, more sophisticated programs are recommended. Another program for estimating haplotype frequencies is SNPHAP. It is very flexible although it is restricted to analyzing bi-allelic markers. Also, PHASE is very useful for estimating haplotype frequencies and for inferring haplotypes to individuals. See Marchini et al. (2006). Files in this package (Windows): EH.PAS: Source code of EH program. EH.EXE: Executable code of EH program, which is compiled with a maximum of 30 alleles per locus, 10 loci, 1000 haplotypes, and 3600 genotype patterns (product of numbers of genotypes at each locus). EH.DAT, EH.OUT, etc: Sample input and output files. Input file The EH program does not require additional programs although you need a Pascal compiler (Free Pascal) to recompile the program when you change program constants. There is one input file whose name the user can determine, for example, EH.DAT (this is the default name). It contains the numbers of alleles for each marker and the observations for each genotype. First line: Number of alleles at the first marker, number of alleles at the second marker, and so on. Assuming you have 2 markers, the first marker has 2 alleles and the second marker has 3, you write 2 3 in the first line. The order of markers in the remainder of the input file is determined by the order of markers you entered in the first line. Subsequent lines: Number of observations for given genotypes. These numbers must be arranged as follows: The number of columns is the number of the possible genotypes at the last locus. Let M be the number of alleles at the last locus, then the number of the possible genotypes equals M(M 1)/2. For example, if the last locus has two alleles, then there are 3 possible genotypes which are 1/1, 1/2 and 2/2. Therefore, in each row there are 3 columns corresponding to the genotypes 1/1, 1/2, 2/2. Similarly, if the last marker has three alleles, then there are 6 columns corresponding to 1/1, 1/2, 2/2, 1/3, 2/3, 3/3. The number of rows is the product of the number of the possible genotypes at the first (N - 1) markers, where N is the total number of markers. That is, no. of rows = L1(L1 1)/2 L2(L2 1)/2 ... Li(Li 1)/2 ..., where Li is the number of alleles at the i-th locus. For example, assume you have 3 loci and the first and the second locus each have 2 alleles, and the third locus has 3 alleles. Thus, there are 6 columns and 9 rows (see example 1 below). However, if the first locus has 3 alleles and the second and third have 2 alleles each, there are 18 rows and 3 columns. The output file from the EH program, EH.OUT by default, contains the estimated haplotype frequencies and their corresponding log likelihoods. Sponsor. supported by the Wellcome Trust, the National Institutes of Health (NIH), The SNP Consortium, the Wolfson Foundation, the Nuffield Trust, and the Engineering and Physical Sciences Research Council. M.S. is supported by NIH grant 1RO1HG/LM02585-01. N.P. is a recipient of a K-01 NIH career-transition award. G.R.A. is supported by NIH National Human Genome Research Institute grant HG02651. E.E. is supported by the California Institute for Telecommunications and Information Technology, Calit2. Computational resources for HAP were provided by Calit2 and National Biomedical Computational Resource grant P41 RR08605 (National Center for Research Resources, NIH).

Proper citation: Users Guide to the EH program (RRID:SCR_008473) Copy   

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https://github.com/cidvbi/PathogenPortal/wiki

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on April 4,2025. These centers support existing and newly developed techniques for bioinformatic analysis aimed at obtaining a deeper understanding of the fundamental biology of a specific set of pathogenic organisms, and efforts to counter the threats posed by these pathogens. The mission of the PathogenPortal is to showcase the collective efforts of the BRCs, and to make the efforts of each BRC more accessible. The PathogenPortal''s content is organized by BRC in both the menus at the top of each page, and the categories in the left column of most pages.. As part of the Pathogen Portal, we have established a Scientific Working Group (SWG) comprised of members with deep expertise in a broad range of domains of relevance to the Pathogen Portal. The SWG provides advice about the management and performance of the resource, and about the needs of the scientific community in relation to the resource. Sponsor. Pathogen Portal is a repository linking to four Bioinformatics Resource Centers (BRCs) sponsored by the National Institute of Allergy and Infectious Diseases (NIAID) and maintained by The Virginia Bioinformatics Institute. The BRCs are providing web-based resources to scientific community conducting basic and applied research on organisms considered potential agents of biowarfare or bioterrorism or causing emerging or re-emerging diseases

Proper citation: Pathogen Portal (RRID:SCR_008472) Copy   

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http://www.expasy.org/tools/peptident.html

THIS RESOURCE IS NO LONGER IN SERVICE documented on June 4, 2013. Aldente is a tool to identify proteins from peptide mass fingerprinting data. This fast and powerful tool takes advantage of the Hough transform for spectra recalibration and outlier exclusion. The Aldente search form can be used in two modes: for a global view of all the search parameters on one page: click on the section tab All. This global view is useful to have a quick overview before sending the query. to have search parameters grouped into smaller logical sections: click on the corresponding section tab in the tabs banner. Note! Moving from one section to another keeps search parameter selections. For your convenience, you may view / hide the help during your search parameters selection. Use the Help or No help section tab accordingly.

Proper citation: ExPASy Aldente Peptide Mass Fingerprinting tool (RRID:SCR_008508) Copy   

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http://www.regeneron.com

Founded on the principle that strong science would lead to important new medicines, Regeneron has become an integrated biopharmaceutical company that discovers, develops, and commercializes medicines for the treatment of serious medical conditions. Regeneron currently markets ARCALYST (rilonacept) Injection for Subcutaneous Use for the treatment of a rare, inherited, inflammatory condition. Regeneron has therapeutic candidates in Phase 3 clinical trials for the potential treatment of gout, age-related macular degeneration, central retinal vein occlusion, and certain cancers. Additional therapeutic candidates are in earlier stage development programs in rheumatoid arthritis and other inflammatory conditions, pain, cholesterol reduction, allergic conditions, and cancer. The Company''s ability to develop product candidates is enhanced by the application of several proprietary technologies that Regeneron has incorporated into a comprehensive drug discovery and development process. This process is designed to thoroughly understand the biology of specific diseases, discover potential therapeutic candidates, and evaluate these candidates in clinical trials. One specific area of Regeneron expertise is the rapid development of fully-human monoclonal antibodies. In November 2007, Regeneron and sanofi-aventis entered into a global, strategic collaboration to discover, develop, and commercialize fully-human therapeutic antibodies utilizing Regeneron''s proprietary VelociSuite of technologies, and we expanded the collaboration in November 2009. Five human antibodies developed under the sanofi-aventis collaboration are in clinical development today, with a goal of advancing an average of four to five new antibodies into clinical development each year through 2017. In addition to the Company''s corporate headquarters and research laboratories in Tarrytown, New York, Regeneron has a large-scale biologics manufacturing facility in Rensselaer, New York, where it produces commercial and investigational products for its clinical trials, and a satellite office in Bridgewater, New Jersey.

Proper citation: REGENERON (RRID:SCR_008547) Copy   

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http://www.random.org/nform.html

RANDOM.ORG is a true random number service that generates randomness via atmospheric noise. This page explains why it''s hard (and interesting) to get a computer to generate proper random numbers. Random numbers are useful for a variety of purposes, such as generating data encryption keys, simulating and modeling complex phenomena and for selecting random samples from larger data sets. They have also been used aesthetically, for example in literature and music, and are of course ever popular for games and gambling. When discussing single numbers, a random number is one that is drawn from a set of possible values, each of which is equally probable, i.e., a uniform distribution. When discussing a sequence of random numbers, each number drawn must be statistically independent of the others. With the advent of computers, programmers recognized the need for a means of introducing randomness into a computer program. However, surprising as it may seem, it is difficult to get a computer to do something by chance. A computer follows its instructions blindly and is therefore completely predictable. (A computer that doesn''t follow its instructions in this manner is broken.) There are two main approaches to generating random numbers using a computer: Pseudo-Random Number Generators (PRNGs) and True Random Number Generators (TRNGs). The approaches have quite different characteristics and each has its pros and cons.

Proper citation: Random.org (RRID:SCR_008544) Copy   

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http://mfold.rit.albany.edu/?q=mfold/download-mfold

The abbreviated name, mfold web server, describes a number of closely related software applications available on the World Wide Web (WWW) for the prediction of the secondary structure of single stranded nucleic acids. The objective of this web server is to provide easy access to RNA and DNA folding and hybridization software to the scientific community at large. By making use of universally available web GUIs (Graphical User Interfaces), the server circumvents the problem of portability of this software. Detailed output, in the form of structure plots with or without reliability information, single strand frequency plots and energy dot plots, are available for the folding of single sequences. A variety of bulk servers give less information, but in a shorter time and for up to hundreds of sequences at once. The portal for the mfold web server is http://www.bioinfo.rpi.edu/applications/mfold. This URL will be referred to as MFOLDROOT.

Proper citation: RNA (RRID:SCR_008543) Copy   

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http://tuna.tamu.edu

THIS RESOURCE IS NO LONGER IN SERVICE, documented August 23, 2016. Map improvement server that returns a bias minimized, 6-fold averaged map generated from a model and diffraction data (with optional preceding Molecular Replacement). It does not build or repair the model for you (yet). For automated model building, you need to install a local copy of CCP4 and ARP/wARP (aka wARP&Trace), RESOLVE, MAID, or TEXTAL.

Proper citation: TB Consortium Bias Removal Server (RRID:SCR_008425) Copy   

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