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https://www.tau.ac.il/~itaymay/cp/rate4site.html

Software tool for detecting conserved amino-acid sites by computing relative evolutionary rate for each site in multiple sequence alignment. Used for identification of functional regions in proteins.

Proper citation: Rate4Site (RRID:SCR_024222) Copy   

•   Source: SciCrunch Registry

https://www.sofa-framework.org/

Open source software framework targeting at real-time simulation, with emphasis on medical simulation.

Proper citation: sofa-apps (RRID:SCR_024346) Copy   

•   Source: SciCrunch Registry

https://github.com/cbrueffer/tophat-recondition

Software tool as post-processor for TopHat unmapped reads that restores read information in the proper format.Enables downstream software to process plethora of BAM files written by TopHat.

Proper citation: TopHat-Recondition (RRID:SCR_024383) Copy   

•   Source: SciCrunch Registry

https://sourceforge.net/projects/surankco/

Machine learning based software to score and rank contigs from de novo assemblies of next generation sequencing data. It trains with alignments of contigs with known reference genomes and predicts scores and ranking for contigs which have no related reference genome yet.

Proper citation: surankco (RRID:SCR_024355) Copy   

•   Source: SciCrunch Registry

http://mgc.nci.nih.gov/

NIH initiative project to provide full-length open reading frame (FL-ORF) clones for human, mouse, and rat genes, cow. MGC cDNA clones were obtained by screening of cDNA libraries, by transcript-specific RT-PCR cloning, and by DNA synthesis of cDNA inserts. All MGC sequences are deposited in GenBank and clones can be purchased from distributors of IMAGE consortium. With conclusion of MGC project in March 2009, GenBank records of MGC sequences will be frozen, without further updates. Since definition of what constitutes full-length coding region for some of genes and transcripts for which they have MGC clones will likely change in future, users planning to order MGC clones will need to monitor for these changes. Users can make use of genome browsers and gene-specific databases, such as the UCSC Genome browser, NCBI's Map Viewer, and Entrez Gene, to view relevant regions of genome (browsers) or gene-related information (Entrez Gene).

Proper citation: Mammalian Gene Collection (RRID:SCR_007024) Copy   

•   Source: SciCrunch Registry

http://oufti.org/

Software designed for analysis of microscopy data. It performs sub-pixel precision detection, quantification of cells and fluorescence signals, as well as other image analysis functions.

Proper citation: Oufti (RRID:SCR_016244) Copy   

•   Source: SciCrunch Registry

http://www.genoscope.cns.fr/gmove

Software tool for genome annotation. Eukaryotic gene prediction tool focused on evidence supported by expressed sequences like transcripts and conserved proteins alignments. Can be used to reannotate genomes, to do comparative gene prediction and improve existing genome annotation. Can predict gene models with canonical and non-canonical splice sites.

Proper citation: Gmove (RRID:SCR_019132) Copy   

•   Source: SciCrunch Registry

https://github.com/Mangul-Lab-USC/telescope

Open source web application that tracks progress of jobs submitted to remote servers using Sun Grid Engine (SGE) on-demand scheduling system. Allows remote scheduling of pre-defined pipelines, as well as re-scheduling queued jobs. Telescope does not assume anything from the remote server, except for SSH connection. The connection is established using SSH key pairs that are stored after encrypted.

Proper citation: Telescope (RRID:SCR_017626) Copy   

•   Source: SciCrunch Registry

https://github.com/Gaius-Augustus/BRAKER

Software tool as pipeline for accurate and automated gene prediction in novel eukaryotic genomes. Automated gene prediction training and gene prediction pipeline.BRAKER1 is eukaryotic genome annotation pipeline. BRAKER2 is extension of BRAKER1 which allows for fully automated training of gene prediction tools GeneMark EX R14, R15, R17, F1 and AUGUSTUS from RNA Seq and/or protein homology information, and that integrates extrinsic evidence from RNA-Seq and protein homology information into prediction.

Proper citation: BRAKER (RRID:SCR_018964) Copy   

•   Source: SciCrunch Registry

http://blocks.fhcrc.org/codehop.html

THIS RESOURCE IS NO LONGER IN SERVICE, documented May 10, 2017. A pilot effort that has developed a centralized, web-based biospecimen locator that presents biospecimens collected and stored at participating Arizona hospitals and biospecimen banks, which are available for acquisition and use by researchers. Researchers may use this site to browse, search and request biospecimens to use in qualified studies. The development of the ABL was guided by the Arizona Biospecimen Consortium (ABC), a consortium of hospitals and medical centers in the Phoenix area, and is now being piloted by this Consortium under the direction of ABRC. You may browse by type (cells, fluid, molecular, tissue) or disease. Common data elements decided by the ABC Standards Committee, based on data elements on the National Cancer Institute''s (NCI''s) Common Biorepository Model (CBM), are displayed. These describe the minimum set of data elements that the NCI determined were most important for a researcher to see about a biospecimen. The ABL currently does not display information on whether or not clinical data is available to accompany the biospecimens. However, a requester has the ability to solicit clinical data in the request. Once a request is approved, the biospecimen provider will contact the requester to discuss the request (and the requester''s questions) before finalizing the invoice and shipment. The ABL is available to the public to browse. In order to request biospecimens from the ABL, the researcher will be required to submit the requested required information. Upon submission of the information, shipment of the requested biospecimen(s) will be dependent on the scientific and institutional review approval. Account required. Registration is open to everyone.Service to design PCR primers from protein multiple sequence alignments. NOTICE: This version of CODEHOP is no longer maintained.

Proper citation: CODEHOP (RRID:SCR_002898) Copy   

•   Source: SciCrunch Registry

https://services.healthtech.dtu.dk/services/DictyOGlyc-1.1/

Server that produces neural network predictions for GlcNAc O-glycosylation sites in Dictyostelium discoideum proteins.

Proper citation: DictyOGlyc (RRID:SCR_001600) Copy   

•   Source: SciCrunch Registry

http://www.glycosciences.de/modeling/glyprot/

Web-based tool that enables meaningful N-glycan conformations to be attached to all the spatially accessible potential N-glycosylation sites of a known three-dimensional (3D) protein structure. The 3D structure of protein is required as input. Potential N-glysylations site are automatically detected. The attached glycan are constructed with SWEET-II, http://www.glycosciences.de/modeling/sweet2/doc/index.php

Proper citation: GlyProt (RRID:SCR_001560) Copy   

•   Source: SciCrunch Registry

http://bibiserv.techfak.uni-bielefeld.de/genefisher2/

A web-based program for designing degenerate primers. The procedure leads to isolation of genes in a target organism using multiple alignments of related genes from different organisms. The term gene fishing refers to the technique where PCR is used to isolate a postulated but unknown target sequence from a pool of DNA.

Proper citation: GeneFisher (RRID:SCR_003060) Copy   

•   Source: SciCrunch Registry

http://www.ncbi.nlm.nih.gov/tools/epcr/

Web tool that identifies sequence tagged sites (STSs) within DNA sequences. Using e-PCR, you can search for sub-sequences that closely match the PCR primers and have the correct order, orientation, and spacing. The software may also be downloaded to run locally.

Proper citation: e-PCR (RRID:SCR_003082) Copy   

•   Source: SciCrunch Registry

http://hipipe.ncgm.sinica.edu.tw/

Tool that provides high performance NGS (next-generation sequencing) data analysis pipelines so that researchers with minimum IT or bioinformatics knowledge can perform common analyses on NGS data. 3 TB of storage space is reserved for each task.

Proper citation: HiPipe (RRID:SCR_001215) Copy   

•   Source: SciCrunch Registry

https://netbio.bgu.ac.il/labwebsite/software/responsenet/

WebServer that identifies high-probability signaling and regulatory paths that connect input data sets. The input includes two weighted lists of condition-related proteins and genes, such as a set of disease-associated proteins and a set of differentially expressed disease genes, and a molecular interaction network (i.e., interactome). The output is a sparse, high-probability interactome sub-network connecting the two sets that is biased toward signaling pathways. This sub-network exposes additional proteins that are potentially involved in the studied condition and their likely modes of action. Computationally, it is formulated as a minimum-cost flow optimization problem that is solved using linear programming.

Proper citation: ResponseNet (RRID:SCR_003176) Copy   

•   Source: SciCrunch Registry

http://ilyinlab.org/StSNP/

A web server for mapping and modeling nsSNPs on protein structures with linkage to metabolic pathways.

Proper citation: StSNP (RRID:SCR_005417) Copy   

•   Source: SciCrunch Registry

http://pilgrm.princeton.edu

PILGRM (the platform for interactive learning by genomics results mining) puts advanced supervised analysis techniques applied to enormous gene expression compendia into the hands of bench biologists. This flexible system empowers its users to answer diverse biological questions that are often outside of the scope of common databases in a data-driven manner. This capability allows domain experts to quickly and easily generate hypotheses about biological processes, tissues or diseases of interest. Specifically PILGRM helps biologists generate these hypotheses by analyzing the expression levels of known relevant genes in large compendia of microarray data. PILGRM is for the biologist with a set of proteins relevant to a disease, biological function or tissue of interest who wants to find additional players in that process. It uses a data driven method that provides added value for literature search results by mining compendia of publicly available gene expression datasets using lists of relevant and irrelevant genes (standards). PILGRM produces publication quality PDFs usable as supplementary material to describe the computational approach, standards and datasets. Each PILGRM analysis starts with an important biological question (e.g. What genes are relevant for breast cancer but not mammary tissue in general?). For PILGRM to discover relevant genes, it needs examples of both genes that you would (positive) and would not (negative) find interesting. Lists of these genes are what we call standards and in PILGRM you can build your own standards or you can use standards from common sources that we pre-load for your convenience. PILGRM lets you build your own literature-documented standards so that processes, disease, and tissues that are not well covered in databases of tissue expression, disease, or function can still be used for an analysis.

Proper citation: PILGRM (RRID:SCR_004749) Copy   

•   Source: SciCrunch Registry

http://spot.cgsmd.isi.edu

A web-based tool for using biological databases to prioritize single nucleotide polymorphisms (SNPs) after a genome-wide association study (GWAS). The site allows users to upload a list of SNPs and GWAS P-values and returns a prioritized list of SNPs using the GIN method. Users can specify candidate genes or genomic regions with custom levels of prioritization. The results can be downloaded or viewed in the browser where users can interactively explore the details of each SNP, including graphical representations of the genomic information network (GIN) method. For investigators interested in incorporating biological databases into a post-GWAS SNP selection strategy, the SPOT web tool is an easily implemented and flexible solution.

Proper citation: SPOT - Biological prioritization after a SNP association study (RRID:SCR_005193) Copy   

•   Source: SciCrunch Registry

http://metagenomics.anl.gov/

An automated analysis platform for metagenomes providing quantitative insights into microbial populations based on sequence data. The server primarily provides upload, quality control, automated annotation and analysis for prokaryotic metagenomic shotgun samples.

Proper citation: MG-RAST (RRID:SCR_004814) Copy   

•   Source: SciCrunch Registry