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http://csgr.pgml.uga.edu/

The objective of this project is to develop physical maps of the sorghum and rice genomes, based on BAC contigs that are cross-linked to each other and also to genetic maps and BAC islands for other large-genome crops and a library of ca. 50,000 expressed-sequence tags (EST''s) and corresponding cDNA clones, from diverse sorghum organs and developmental states. It also aims to improve understanding of genetic diversity and allelic richness that might be harbored ex situ (in gene banks) or in situ (in nature), and refine techniques for assesing allelic richness and Expedite data acquisition and utilization by a sound parnership between laboratory scientists and computational biologists. Specific goals of developing physical maps of sorghum and rice genomes include: -Enrich cross-links between sorghum and rice by mapping additional rice probes on sorghum. -Apply mapped DNA probes to macroarrays of sorghum, sugarcane, rice, and maize BACs. -Fingerprint 10x BAC libraries of Sorghum bicolor and S. propinquum. Libraries presently 3x and 6x respectively, to be expanded to 10x each. -Use fragment-matching (BAC-RF) method to determine locus-specificity in polyploids. - Contig assembly based on 1-3, plus rice BAC fingerprints generated under a separate Novartis project. -Evaluate methodology for rapid high-throughput assignment of new ESTs to BACs. -Conduct genomic sequencing in a region duplicated in both sorghum and arabidopsis. Selected BACs from sorghum(2), sugarcane, maize, rice, wheat. By improving the understanding of genetic diversity and allelic richness, the goal is to: -Sequence previously mapped sorghum DNA probes. -Discover & characterize 100 single nucleotide polymorphisms (SNPs) from cDNA markers. -Develop colorimetric high-throughput genotyping assays, and utilize to assess genetic diversity in geographically- and phenotypically-diverse sorghums. -Develop colorimetric high-throughput asssays for identifying phytochrome allelic variation, and apply these assays to a core collection representing a large set of genetic resources. -Support informatics group to streamline cataloging of DNA-level information relevant to large genetic resources collections. Lastly, the goals of expediting data acquisition and utilization include: -A new web-based resource for 3D-integration and visualization of structural and functional genomic data will be developed. -New sequence assembly and alignment software SABER (Sequence AssemBly in the presence of ERror), and PRIMAL(Practical RIgorous Multiple ALignment), will be evaluated with reference to existing standards (PHRED, PHRAP). -Specialized image processing and image analysis tools will be developed for acquistion and interpretation of qualitative and quantitative hybridization signals. To deal expeditiously with large volumes of data, parallel processing approaches will be investigated. Sponsors: * National Science Foundation (NSF) * National Sorghum Producers * University of Georgia Research Foundation (UGARF) * Georgia Research Alliance (GRA)

Proper citation: Comparative Saccharinae Genomics Resource (RRID:SCR_008153) Copy   

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http://www.loni.usc.edu/Software/jViewbox

A portable software framework for medical imaging research. jViewbox consists of a set of Java classes organized under a simple but extensive API that provides the core functionality of 2D image presentation needed by most imaging applications. It follows Java's Swing model closely to make it easy for application developers to build GUIs where end users can use various tools in a tool bar to manipulate the image displays. No optional add-ons or native code is used, which makes jViewBox compatible with any standard Java 2 Runtime Environment (version 1.3 or later).

Proper citation: jViewbox (RRID:SCR_008274) Copy   

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http://www.baderlab.org/Software/ActiveDriver

A statistical method for interpreting variations in protein sequence (e.g. coding SNPs in the population, SNVs in cancer genomes) in the context of protein post-translational signaling modifications.

Proper citation: ActiveDriver (RRID:SCR_008104) 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.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://www.uhnres.utoronto.ca/facilities/wcif/download.php

The ImageJ installations below correspond to the WCIF ImageJ manual. The manual is written for this particular installation of ImageJ. This ImageJ installation has, among other plugins, one that links to an online version of the manual. The online manual is more up-to-date than the PDF version. Windows users Download WCIF ImageJ bundle (~23Mb) v1.34i, 3rd March 2005 with J2SE 5.0 (formerly J2SE 1.5). For Windows: download and run program. Mac and Linux users Download your OS specific version of ImageJ from the ImageJ website then extract the following file to the plugins folder. Download WCIF ImageJ bundle plugins only (~2Mb) This contains only the plugins, IJ preferences, LUTs and plugin source code. Image Processing and Analysis Software ImageJ LSM Browser (*.lsm) Axiovision viewer (*.zvi) Manufacturers of our microscopes and related equipment Zeiss - Microscopes and imaging systems. P.A.L.M. Microlaser Technologies - Manufacturer of our laser capture system. Sutter Instruments - Micromanipulators. Uniblitz - Shutters. Ludl - Manufacturers of our motorised x-, y-stage Hamamatsu - Digital cameras. Molecular Probes - Dyes and reagents. Scanalytics - Image acquisition and processing software. MicroBrightField - Developers of the Neurolucida and Stereo Investigator software. DVC - Digital cameras. Bitplane - Developers of the Imaris suite of software. AutoQuant - Developers of the AutoDeblur deconvolution software

Proper citation: Wright Cell Imaging Facility (RRID:SCR_008488) Copy   

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http://tree.bio.ed.ac.uk/software/figtree

A graphical viewer of phylogenetic trees and a program for producing publication-ready figures. It is designed to display summarized and annotated trees produced by BEAST.

Proper citation: FigTree (RRID:SCR_008515) Copy   

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http://www.liden.cc/Visionary/

It is a dictionary for terminology used in the study of human and animal vision. It includes terms from the areas of biological and machine vision, visual psychophysics, visual neuroscience and other related fields. Sponsors: Visionary is sponsored by Educational Software for Autism.

Proper citation: Visionary: A Dictionary for the Study of Vision (RRID:SCR_008307) Copy   

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http://biosig.sourceforge.net/

Software library for processing of electroencephalogram (EEG) and other biomedical signals like electroencephalogram (EEG), electrocorticogram (ECoG), electrocardiogram (ECG), electrooculogram (EOG), electromyogram (EMG), respiration, and so on. Biosig contains tools for quality control, artifact processing, time series analysis, feature extraction, classification and machine learning, and tools for statistical analysis. Many tools are able to handle data with missing values (statistics, time series analysis, machine learning). Another feature is that more then 40 different data formats are supported, and a number of converters for EEG,, ECG and polysomnography are provided. Biosig has been widely used for scientific research on EEG-based BraiN-Computer Interfaces (BCI), sleep research, and ECG and HRV analysis. It provides software interfaces several programming languages (C, C++, Matlab/Octave, Python), and it provides also an interactive viewing and scoring software for adding, and editing of annotations, markers and events.

Proper citation: BioSig: An Imaging Bioinformatics System for Phenotypic Analysis (RRID:SCR_008428) Copy   

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http://biq-analyzer.bioinf.mpi-sb.mpg.de

BiQ Analyzer is a software tool for easy visualization and quality control of DNA methylation data from bisulfite sequencing. Highlights: - End-to-end support of the analysis process: from raw sequence files to a comprehensive documentation and visualization. - Automatically generate publication-quality lollipop diagrams (show example.) - Integrated 1-click multiple sequence alignment. - Automated CpG highlighting- never spend your time highlighting CpGs by hand anymore. - Open electropherogram files to check for sequencing problems (requires an electropherogram viewer such as Chromas LITE.) - Generate MethDB-compatible DNA methylation files for database submission. - Factor 5 speedup of sequence analysis while at the same time achieving better data quality. Intended users: - Anyone who works with DNA methylation data from bisulfite sequencing. - Occasional users as well as experts (the former will benefit from the help that the program gives in order to achieve a good quality management whereas the latter will save hours and days of tedious work.) Sponsors: This resource is supported by the Max Planck Institute. Keywords: Software, Visualization, DNA, Methylation, Data, Bisulfite, Sequencing, Electropherogram, Analysis,

Proper citation: BiQ Analyzer: A Software Tool for DNA Methylation Analysis (RRID:SCR_008423) Copy   

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http://bioinf.uni-greifswald.de/augustus/

Software for gene prediction in eukaryotic genomic sequences. Serves as a basis for further steps in the analysis of sequenced and assembled eukaryotic genomes.

Proper citation: Augustus (RRID:SCR_008417) Copy   

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http://www.jneurosci.org/supplemental/18/12/4570/

THIS RESOURCE IS NO LONGER IN SERVICE, documented on January 29, 2013. Supplemental data for the paper Changes in mitochondrial function resulting from synaptic activity in the rat hippocampal slice, by Vytautas P. Bindokas, Chong C. Lee, William F. Colmers, and Richard J. Miller that appears in the Journal of Neuroscience June 15, 1998. You can view digital movies of changes in fluorescence intensity by clicking on the title of interest.

Proper citation: Hippocampal Slice Wave Animations (RRID:SCR_008372) Copy   

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http://derisilab.ucsf.edu/

The DeRisi Lab focuses on genomic approaches to the study of infectious disease. Specifically, we are studying Plasmodium falciparum, the causative agent of the most deadly form of human malaria. We are also involved in a major effort for the discovery of new viral pathogens associated with diseases of unknown etiology. Software tools developed in the lab include: HMMSplicer discovers splice sites in high throughput sequencing datasets without using gene models. HMMSplicer can also be used to find non-canonical junctions as well. HMMSplicer was benchmarked on publickly available A. thaliana, H. sapiens, and P. falciparum datasets and performs well on all genomes. Information about the datasets tested, including the exact command parameters and the final results, is provided. HMMSplicer is implemented in Python and is freely available for all. VersaCount is a simple application to assist with the counting of cells by microscopy. When used with a numeric keypad, it can significantly increase counting efficiency when compared with a traditional clicker. Although it was designed for malaria work, it can be customized for a wide variety of cell counting applications. VersaCount was written by Charlie Kim. ExpressionNet is a program written by Jingchun Zhu that uses Bayesian network learning algorithms to explore relationships among random variables to generate network models. The software has been used to study the transcriptional response to environmental perturbations in budding yeast. Details of the program and the study of yeast transcription using Bayesian Networks was published in PLoS ONE. DNA microarrays may be used to identify microbial species present in environmental and clinical samples. However, automated tools for reliable species identification based on observed microarray hybridization patterns are lacking. We present an algorithm, E-Predict, for microarray-based species identification. ArrayOligoSelector (AOS) is an open source program developed by Jingchun Zhu for the purpose of systematically designing gene-specific long oligonucleotide probes for entire genomes. For each open reading frame, the program optimizes oligo selection based upon several parameters, including uniqueness, complexity, secondary structure, GC content, and 3'' end proximity. AOS also is hosted at SourceForge. This site contains documentation and a user-friendly how-to. ArrayMaker 2 provides high performance robotic control of microarrayer robots with an incredibly intuitive, easy to use interface. ArrayMaker 2 is optimized for use with the new generation of ultra fast linear servo driven arrayers, yet it is backwards compatible with the original MGuide style of ball-screw driven arrayers.

Proper citation: DeRisi Lab (RRID:SCR_008581) Copy   

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http://www.brainvoyager.de/BV2000OnlineHelp/BrainVoyagerWebHelp/Talairach_brain_atlas.htm

The Talairach brain atlas visualized via BrainVoyager (Commercial software) can be used to visualize Brodmann areas as they were defined for the Talairach brain (Talairach & Tournaux, 1988) and to compare regions of subjects with respect to the Brodmann areas. The demarcated areas are based on the Talairach demon, which is a digitized version of the Talairach atlas and which has been transferred into BrainVoyager VOI files by Matthias Ruf, Mannheim. Using the Brodman.voi file you may ask questions like the following: What is the signal time course of subject N in experiment A within Brodmann area X ?. Note, however, that the defined areal boundaries should be used only as a rough guideline for determining the location of activated regions: There is substantial variation of histologically defined areas between subjects. Since cytoarchitectonically defined Brodmann areas are not available in vivo, we advise to use the provided information with care. The TalairachBrain.vmr file is located in the same folder as your BrainVoyager executable file. It can be loaded as any VMR project by using the Open... item in the File menu (or the Open icon). The TalairachBrain.vmr file is also loaded automatically when using the glass brain visualization tool.

Proper citation: BrainVoyager: Talairach Brain Atlas (RRID:SCR_008800) Copy   

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http://go.princeton.edu/cgi-bin/GOTermFinder

The Generic GO Term Finder finds the significant GO terms shared among a list of genes from an organism, displaying the results in a table and as a graph (showing the terms and their ancestry). The user may optionally provide background information or a custom gene association file or filter evidence codes. This tool is capable of batch processing multiple queries at once. GO::TermFinder comprises a set of object-oriented Perl modules GO::TermFinder can be used on any system on which Perl can be run, either as a command line application, in single or batch mode, or as a web-based CGI script. This implementation, developed at the Lewis-Sigler Institute at Princeton, depends on the GO-TermFinder software written by Gavin Sherlock and Shuai Weng at Stanford University and the GO:View module written by Shuai Weng. It is made publicly available through the GMOD project. The full source code and documentation for GO:TermFinder are freely available from http://search.cpan.org/dist/GO-TermFinder/. Platform: Online tool, Windows compatible, Mac OS X compatible, Linux compatible, Unix compatible

Proper citation: Generic GO Term Finder (RRID:SCR_008870) Copy   

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http://www.bic.mni.mcgill.ca/users/crisco/jiv/

JIV is a Java software for visualization and side-by-side comparison of multiple 3D image datasets. While it was originally designed for remote data access, it can be used as a traditional local application as well. It features include being highly portable and platform-independent, the ability to run with a common Web browser and to cope with slow network links, an independent 3D image data format, and a display of gray-level (intensity) image data with a user-controlled color-mapping, among other features.

Proper citation: JIV: A 3D Image Data Visualization and Comparison Tool (RRID:SCR_008657) Copy   

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http://portal.ncibi.org/gateway/bcde.html

Biological Concept Diagram Editor (BCDE) is a conceptual relationship diagramming tool specifically designed for biomedical researchers. It allows for efficient knowledge and data capture, fast diagram creation, easy data retrieval, and flexible exporting. The BCDE application is the main diagramming tool in the system. Through it, users can create, modify, load, and save BCDE diagrams. The diagrams created with BCDE application are network oriented. Each BCDE figure can be annotated using fields from the BioPAX level II format. In addition, a user can add URL links and attachments to a BCDE figure. Diagrams generated in BCDE are stored in the BCDE XML format for better database integration and better data extraction.

Proper citation: Biological Concept Diagram Editor (RRID:SCR_008654) Copy   

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https://www.stat.auckland.ac.nz/~paul/plaudits/Iobion.htm

GeneTraffic is a web-based microarray data analysis and management software developed by Iobion Informatics that allows users to log onto a server, upload their microarray data and perform analysis and project management remotely. GeneTraffic was made by Iobion Informatics (now under Stratagene) and can be accessed thorough Internet Explorer 6.0 or greater on Windows XP.

Proper citation: GeneTraffic (RRID:SCR_008651) Copy   

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http://meme.nbcr.net/meme/cgi-bin/gomo.cgi

Gene Ontology for Motifs (GOMO) is an alignment- and threshold-free comparative genomics approach for assigning functional roles to DNA regulatory motifs from DNA sequence. The algorithm detects associations between a user-specified DNA regulatory motif (expressed as a position weight matrix; PWM) and Gene Ontology terms. The original method for predicting the roles of transcription factors (TFs starts with a PWM motif describing the DNA-binding affinity of the TF. GOMO uses the PWM to score the promoter region of each gene in the genome for its likelihood to be bound by the TF. The resulting ''''affinity'''' scores are then used to test each term in the Gene Ontology for association with high-scoring genes. The algorithm was subsequently extended to leverage conserved signals using multiple, related species in a comparative approach, which greatly improves the resulting annotations. Platform: Online tool, Windows compatible, Mac OS X compatible, Linux compatible, Unix compatible

Proper citation: GOMO - Gene Ontology for Motifs (RRID:SCR_008864) Copy   

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