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http://protein.bio.unipd.it/pasta2/

Online interface that utilizes an algorithm to predict the most aggregation-prone portions and the corresponding beta-strand inter-molecular pairing for a given input sequence. Users can paste the sequence into the interface and output the appropriate sequence.

Proper citation: Prediction of Amyloid Structure Aggregation (RRID:SCR_001768) Copy   

•   Source: SciCrunch Registry

https://www.uniklinik-freiburg.de/mr-en/research-groups/diffperf/fibertools.html

Implemented under MATLAB, this DTI image processing toolbox provides import-filters for several MR file standards, a processing unit to calculate the diffusion tensors; several GUI based tools to calculate fiber tracks and to evaluate the DTI dataset. The results can be filed as images with 3D impression or can be logged in formatted ASCII files. Tools and features: * DTI Processing Unit: Calculates the diffusion tensors and their eigenvalues and eigenvectors. Different file formats are supported (like DICOM, Bruker, binary files, Matlab structures). The standard SIEMENS and GE diffusion encoding schemes are supported; other schemes have to be defined in a separate text, .m or .mat file. * FiberTracking: ** Fiber tracking is realized by using the FACT algorithm (Mori et al., Annal. Neurol 1999). ** Probabilistic tracking realized by using the PiCo (Parker et al., JMRI 2003) approach but with DTI data as basis. It is possible to extract pathways between two seeds by combining two maps (Kreher et al., NeuroImage 2008). ** Global Fiber Tracking on basis of HARDI or DTI data. The method is based on the approach reported in (Marco Reisert et al: Global fiber reconstruction becomes practical. NeuroImage 54(2):955-62) * FiberViewer: ** Visualization and Navigation through different data modalities like DTI maps, fiber tracks, diffusion main directions. ** Supports different kinds of DTI maps (e.g. FA, Trace, lambda images ) ** Creation and manipulation of mask based ROIs. ** Selection of streamline fibers ** Visualization of probabilistic fiber tracking results ** Documentation by logging statistics of ROIs and fiber tracks into text files. ** Import/Export from/to ANALYZE or Nifti * 3D Visualizer: Visualization of map slices, ROIs, and fiber tracks with 3D impression. * Batch Editor: Automatic processing of high amounts of data. Possibility to link processing with SPM8 easily.

Proper citation: DTI and Fibertools Software Package (RRID:SCR_001641) Copy   

•   Source: SciCrunch Registry

http://www.bioit.org.cn/ao/aobase

THIS RESOURCE IS NO LONGER IN SERVICE, documented on July 15, 2013. AOBase is a database for antisense oligonucleotides (AOs) selection and design. AOBase is a database developed to facilitate Antisense Oligonucleotides (ODNs) selection for gene expression modulation and to provide a free data source for computer aided ODNs design. Information about valid and invalid ODNs reported in literature are collected and stored in the database, including oligo sequences, target sequences, secondary structures of the target sites, oligo activity measured, and the assay type used for activity measurement. The details on target RNA molecules and reference literature can be explored through the hyperlinks linked to GenBank and PubMed respectively. Each record can be searched for via two web retrieval interfaces: 1) TargetSearch interface, which allows users to query ODNs by name, accession number, or only imprecise descriptions of its target RNA; 2) AOSearch interface, which allows users to search ODNs with several parameters combined, such as oligo activity measured, oligo concentration applied, and motifs involved in oligo sequences. With these two retrieval interfaces, AOBase can be used to select effective ODNs for gene function exploration without expensive in vitro screening experiments, and contribute to mining rules for rational ODNs design. A user friendly interface to encourage data submission is provided.

Proper citation: Database for Antisense Oligonucleotides Selection and Design (RRID:SCR_001753) Copy   

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http://www.nitrc.org/projects/plink

Open source whole genome association analysis toolset, designed to perform range of basic, large scale analyses in computationally efficient manner. Used for analysis of genotype/phenotype data. Through integration with gPLINK and Haploview, there is some support for subsequent visualization, annotation and storage of results. PLINK 1.9 is improved and second generation of the software.

Proper citation: PLINK (RRID:SCR_001757) Copy   

•   Source: SciCrunch Registry

http://www.atcc.org/

Global nonprofit biological resource center (BRC) and research organization that provides biological products, technical services and educational programs to private industry, government and academic organizations. Its mission is to acquire, authenticate, preserve, develop and distribute biological materials, information, technology, intellectual property and standards for the advancement and application of scientific knowledge. The primary purpose of ATCC is to use its resources and experience as a BRC to become the world leader in standard biological reference materials management, intellectual property resource management and translational research as applied to biomaterial development, standardization and certification. ATCC characterizes cell lines, bacteria, viruses, fungi and protozoa, as well as develops and evaluates assays and techniques for validating research resources and preserving and distributing biological materials to the public and private sector research communities.

Proper citation: ATCC (RRID:SCR_001672) Copy   

•   Source: SciCrunch Registry

http://www.bioconductor.org/packages/release/bioc/html/RSVSim.html

A software package for the simulation of deletions, insertions, inversions, tandem duplications and translocations of various sizes in any genome available as FASTA-file or data package in R. SV breakpoints can be placed uniformly accross the whole genome, with a bias towards repeat regions and regions of high homology (for hg19) or at user-supplied coordinates.

Proper citation: RSVSim (RRID:SCR_001777) Copy   

•   Source: SciCrunch Registry

http://www.annualreviews.org/

Annual Reviews offers comprehensive, timely collections of critical reviews written by leading scientists. It publishes authoritative, analytic reviews in 37 focused disciplines within the Biomedical, Life, Physical, and Social Sciences. The mission of Annual Reviews is to provide systematic, periodic examinations of scholarly advances in a number of fields of science through critical authoritative reviews. The comprehensive critical review not only summarizes a topic but also roots out errors of fact or concept and provokes discussion that will lead to new research activity. The critical review is an essential part of the scientific method. Sponsors: Annual Reviews is a non-profit organization created and managed by scientists to serve science by publishing reviews in 40 different scientific fields.

Proper citation: Annual Reviews: A Nonprofit Scientific Publisher (RRID:SCR_001655) Copy   

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http://www.bioconductor.org/packages/release/bioc/html/TCC.html

An R package that provides a series of functions for differential expression analysis from RNA-seq count data using robust normalization strategy (called DEGES). The basic idea of DEGES is that potential differentially expressed genes or transcripts (DEGs) among compared samples should be removed before data normalization to obtain a well-ranked gene list where true DEGs are top-ranked and non-DEGs are bottom ranked. This can be done by performing a multi-step normalization strategy (called DEGES for DEG elimination strategy). A major characteristic of TCC is to provide the robust normalization methods for several kinds of count data (two-group with or without replicates, multi-group/multi-factor, and so on) by virtue of the use of combinations of functions in other sophisticated packages (especially edgeR, DESeq, and baySeq).

Proper citation: TCC (RRID:SCR_001779) Copy   

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http://cmb.gis.a-star.edu.sg/ChIPSeq/paperCCAT.htm

THIS RESOURCE IS OUT OF SERVICE, documented on April 5, 2017, A software package for the analysis of ChIP-seq data with negative control., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: CCAT (RRID:SCR_001843) Copy   

•   Source: SciCrunch Registry

http://www.genabel.org/packages/GenABEL

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on February 28,2023. R software library for genome-wide association analysis for quantitative, binary and time-till-event traits.

Proper citation: GenABEL (RRID:SCR_001842) Copy   

•   Source: SciCrunch Registry

http://surfer.nmr.mgh.harvard.edu/

Open source software suite for processing and analyzing human brain MRI images. Used for reconstruction of brain cortical surface from structural MRI data, and overlay of functional MRI data onto reconstructed surface. Contains automatic structural imaging stream for processing cross sectional and longitudinal data. Provides anatomical analysis tools, including: representation of cortical surface between white and gray matter, representation of the pial surface, segmentation of white matter from rest of brain, skull stripping, B1 bias field correction, nonlinear registration of cortical surface of individual with stereotaxic atlas, labeling of regions of cortical surface, statistical analysis of group morphometry differences, and labeling of subcortical brain structures.Operating System: Linux, macOS.

Proper citation: FreeSurfer (RRID:SCR_001847) Copy   

•   Source: SciCrunch Registry

http://silac.org/

Stable isotope labeling with amino acids in cell culture (SILAC) is a simple and straightforward approach for in vivo incorporation of a label into proteins for mass spectrometry (MS)-based quantitative proteomics. SILAC relies on metabolic incorporation of a given "light" or "heavy" form of the amino acid into the proteins. The method relies on the incorporation of amino acids with substituted stable isotopic nuclei (e.g. deuterium, 13C, 15N). In an experiment, two cell populations are grown in culture media that are identical except that one of them contains a "light" and the other a "heavy" form of a particular amino acid (e.g. 12C and 13C labeled L-lysine, respectively). When the labeled analog of an amino acid is supplied to cells in culture instead of the natural amino acid, it is incorporated into all newly synthesized proteins. After a number of cell divisions, each instance of this particular amino acid will be replaced by its isotope labeled analog. Since there is hardly any chemical difference between the labeled amino acid and the natural amino acid isotopes, the cells behave exactly like the control cell population grown in the presence of normal amino acid. It is efficient and reproducible as the incorporation of the isotope label is 100%. SILAC Applications: - Differential expression of proteins and identification of disease biomarkers - Cell signaling dynamics - Analysis of yeast pheromone signaling pathway - Identification of methylation sites - Identification of protease substrates - Study of protein complexes/protein interactions - Analysis of signaling pathways and effect of pharmacological inhibitors - Subcellular proteomics Sponsors: Supported in part by an NIH Roadmap grant Technology Center for Networks & Pathways of Lysine Modification.

Proper citation: Stable Isotope Labeling with Amino Acids in Cell Culture (RRID:SCR_001873) Copy   

•   Source: SciCrunch Registry

http://blog.expressionplot.com/

Software package consisting of a default back end, which prepares raw sequencing or Affymetrix microarray data, and a web-based front end, which offers a biologically centered interface to browse, visualize, and compare different data sets.

Proper citation: ExpressionPlot (RRID:SCR_001904) Copy   

•   Source: SciCrunch Registry

https://urgi.versailles.inra.fr/Tools/S-Mart

Software toolbox that manages your RNA-Seq and ChIP-Seq data and also produces many different plots to visualize your data. It performs several tasks that are usually required during the analysis of mapped RNA-Seq and ChIP-Seq reads, including data selection and data visualization. It includes the selection (or the exclusion) of the data that overlaps with a reference set, clustering and comparative analysis. It also provides many ways to visualize data: size of the reads, density on the genome, distance with respect to a reference set, and the correlation of two data sets (with cloud plots). A computer science background is not required to run it through a graphical interface and it can be run on any personal computer, yielding results within an hour for most queries.

Proper citation: S-MART (RRID:SCR_001908) Copy   

•   Source: SciCrunch Registry

http://www.dendrites.org/software

Dendritica is a program package for relating dendritic geometry and signal propagation. The programs are based on those used for the simulations described in the following paper: Vetter, P., Roth, A. & Husser, M. (2001). Action potential propagation in dendrites depends on dendritic morphology. Journal of Neurophysiology, 85: 926-937. Dendritica can functionally be divided into three main parts: - Interactive morphological analysis and electrophysiological simulation of single cells - Automated batch simulations across a set of morphologies using the same simulation parameters - Automated analysis of batch simulation runs Dendritica requires NEURON 4.1.1 with some modifications described in Appendix 1. It was tested for NEURON 4.1.1 on Linux and SGI IRIX. Some modifications to the Dendritica code may be necessary in order to run it on older or newer versions of NEURON. Sponsors: This work was supported by the Wellcome Trust, the European Community, the Max-Planck-Gesellschaft, the Wellcome Trust 4-year PhD Programme in Neuroscience.

Proper citation: Dendritica: Software Tools for Studying Dendritic Signaling (RRID:SCR_001865) Copy   

•   Source: SciCrunch Registry

http://dendrites.esam.northwestern.edu/

This database contains morphologies of hippocampal pyramidal cells and interneurons (in Neurolucida, NEURON, and pdf formats) as well as data recorded from those cells. Sponsors:This work was supported by grants from the NIH (T32-GM-08061 to T.J.M., F32-NS-10532 to N.L.G., and R01-NS35180 and R01-NS 46064 to N.S. and W.L.K.) and NSF (IGERT fellowship to Y.K.). NS46064 is part of the NSF/NIH Collaborative Research in Computational Neuroscience Program

Proper citation: SPRUSTON / KATH LAB: Neuraling Modeling Database NEURAL MODELING DATABASE (RRID:SCR_001869) Copy   

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http://www.kaist.edu/html/en/index.html

Institute dedicated to research in science and technology in South Korea modeled after a research university.

Proper citation: Korea Advanced Institute of Science and Technology; Daejeon; South Korea (RRID:SCR_001902) Copy   

•   Source: SciCrunch Registry

http://www.blki.hu/~szucs/OS3.html

Orbital Spike is a tool for time series analysis. It contains a wide range of methods to analyze data from point processes such as spike arrival times, heart beats or other behavioral episodes. It is optimized this program for spike trains but it works with other types of data, too. The program can analyze up to 8 channels recorded simultaneously each containing a maximum of 132,000 events (spikes). Assuming an average firing rate of 10 Hz for a neuron, you can then analyze a time series of approximately 3 and half hours long. There are up to 8 panels shown in the Orbital Spike desktop. The panels will contain the kind of data of interest. The graphs are associated with a bunch of parameters like window width, bin size, resolution, delay etc. All these parameters are listed in the parameter box, which appears on the right side of the desktop. It is pretty easy to change the parameters and what is nice, the corresponding graph(s) will be recalculated immediately. You can also use a dialog box to change parameters. There are a lot of functions, statistics, graphs and diagrams available. A few of them are: * Interspike interval sequences * ISI Poincar * maps or return maps Instantaneous firing rate * ISI histograms and probability densities * Joint ISI and MSI probability densitograms * Autocorrelation, crosscorrelation * Spike density functions using kernel estimators * Fourier-amplitude spectrum and spectogram * Symbolic maps, recurrence plots * Phase plots of spike density functions Sponsors: Support for this work came from the U.S. Department of Energy, Office of Basic Energy Sciences, Division of Engineering and Geosciences, under Grants DE-FG03-90ER14138 and DE-FG03-96ER14592; from the Office of Naval Research under Grant N00014-00-1-0181; from the National Science Foundation under Grant PHY0097134; from the National Institutes of Health under Grants R01 NS-40110-01A2 and 1RO1 NS-40110; and from the Army Research Office under Contract DAAD19-01-1-0026. R. D. Pinto was supported by the State of Sao Paulo Research Foundation (FAPESP).

Proper citation: Spike Train Analysis Software by Attila Szucs: Orbital Spike 4 (RRID:SCR_001868) Copy   

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https://github.com/raphael-group/THetA

An algorithm that estimates the tumor purity and clonal / subclonal copy number aberrations directly from high-throughput DNA sequencing data.

Proper citation: THetA (RRID:SCR_001860) Copy   

•   Source: SciCrunch Registry

http://www.ualberta.ca/

Public research university in Edmonton, Alberta, Canada that offers degree programs in a variety of fields including business, arts, education, engineering, nursing, and medicine.

Proper citation: University of Alberta; Alberta; Canada (RRID:SCR_001853) Copy   

•   Source: SciCrunch Registry