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http://avis.princeton.edu/pixie/index.php

bioPIXIE is a general system for discovery of biological networks through integration of diverse genome-wide functional data. This novel system for biological data integration and visualization, allows you to discover interaction networks and pathways in which your gene(s) (e.g. BNI1, YFL039C) of interest participate. The system is based on a Bayesian algorithm for identification of biological networks based on integrated diverse genomic data. To start using bioPIXIE, enter your genes of interest into the search box. You can use ORF names or aliases. If you enter multiple genes, they can be separated by commas or returns. Press ''submit''. bioPIXIE uses a probabilistic Bayesian algorithm to identify genes that are most likely to be in the same pathway/functional neighborhood as your genes of interest. It then displays biological network for the resulting genes as a graph. The nodes in the graph are genes (clicking on each node will bring up SGD page for that gene) and edges are interactions (clicking on each edge will show evidence used to predict this interaction). Most likely, the first results to load on the results page will be a list of significant Gene Ontology terms. This list is calculated for the genes in the biological network created by the bioPIXIE algorithm. If a gene ontology term appears on this list with a low p-value, it is statistically significantly overrepresented in this biological network. As you move the mouse over genes in the network, interactions involving these genes are highlighted. If you click on any of the highlighted interactions graph, evidence pop-up window will appear. The Evidence pop-up lists all evidence for this interaction, with links to the papers that produced this evidence - clicking these links will bring up the relevant source citation(s) in PubMed. You may need to download the Adobe Scalable Vector Graphic (SVG) plugin to utilize the visualization tool (you will be prompted if you need it).

Proper citation: bioPIXIE (RRID:SCR_004182) Copy   

•   Source: SciCrunch Registry

http://life.ccs.miami.edu/life/

LIFE search engine contains data generated from LINCS Pilot Phase, to integrate LINCS content leveraging semantic knowledge model and common LINCS metadata standards. LIFE makes LINCS content discoverable and includes aggregate results linked to Harvard Medical School and Broad Institute and other LINCS centers, who provide more information including experimental conditions and raw data. Please visit LINCS Data Portal.

Proper citation: LINCS Information Framework (RRID:SCR_003937) Copy   

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http://www.t-profiler.org

One of the key challenges in the analysis of gene expression data is how to relate the expression level of individual genes to the underlying transcriptional programs and cellular state. The T-profiler tool hosted on this website uses the t-test to score changes in the average activity of pre-defined groups of genes. The gene groups are defined based on Gene Ontology categorization, ChIP-chip experiments, upstream matches to a consensus transcription factor binding motif, and location on the same chromosome, respectively. If desired, an iterative procedure can be used to select a single, optimal representative from sets of overlapping gene groups. A jack-knife procedure is used to make calculations more robust against outliers. T-profiler makes it possible to interpret microarray data in a way that is both intuitive and statistically rigorous, without the need to combine experiments or choose parameters. Currently, gene expression data from Saccharomyces cerevisiae and Candida albicans are supported. Users can submit their microarray data for analysis by clicking on one of the two organism-specific tabs above. Platform: Online tool

Proper citation: T-profiler (RRID:SCR_003452) Copy   

•   Source: SciCrunch Registry

http://www.hgsc.bcm.tmc.edu/content/hapmap-3-and-encode-3

Draft release 3 for genome-wide SNP genotyping and targeted sequencing in DNA samples from a variety of human populations (sometimes referred to as the HapMap 3 samples). This release contains the following data: * SNP genotype data generated from 1184 samples, collected using two platforms: the Illumina Human1M (by the Wellcome Trust Sanger Institute) and the Affymetrix SNP 6.0 (by the Broad Institute). Data from the two platforms have been merged for this release. * PCR-based resequencing data (by Baylor College of Medicine Human Genome Sequencing Center) across ten 100-kb regions (collectively referred to as ENCODE 3) in 712 samples. Since this is a draft release, please check this site regularly for updates and new releases. The HapMap 3 sample collection comprises 1,301 samples (including the original 270 samples used in Phase I and II of the International HapMap Project) from 11 populations, listed below alphabetically by their 3-letter labels. Five of the ten ENCODE 3 regions overlap with the HapMap-ENCODE regions; the other five are regions selected at random from the ENCODE target regions (excluding the 10 HapMap-ENCODE regions). All ENCODE 3 regions are 100-kb in size, and are centered within each respective ENCODE region. The HapMap 3 and ENCORE 3 data are downloadable from the ftp site.

Proper citation: HapMap 3 and ENCODE 3 (RRID:SCR_004563) Copy   

•   Source: SciCrunch Registry

http://www.yeastgenome.org/

A curated database that provides comprehensive integrated biological information for Saccharomyces cerevisiae along with search and analysis tools to explore these data. SGD allows researchers to discover functional relationships between sequence and gene products in fungi and higher organisms. The SGD also maintains the S. cerevisiae Gene Name Registry, a complete list of all gene names used in S. cerevisiae which includes a set of general guidelines to gene naming. Protein Page provides basic protein information calculated from the predicted sequence and contains links to a variety of secondary structure and tertiary structure resources. Yeast Biochemical Pathways allows users to view and search for biochemical reactions and pathways that occur in S. cerevisiae as well as map expression data onto the biochemical pathways. Literature citations are provided where available.

Proper citation: SGD (RRID:SCR_004694) Copy   

•   Source: SciCrunch Registry

http://mousesnp.roche.com/

THIS RESOURCE IS NO LONGER IN SERVICE, documented on July 17, 2013. This website contains a database of the mouse SNP. DNA sequencing was performed along with genotyping. There is information on genotyping, mouse strain, and haplotype map.

Proper citation: Mouse Single Nucleotide Polymorphism Database (RRID:SCR_000033) Copy   

•   Source: SciCrunch Registry

http://www.uniprot.org/

Collection of data of protein sequence and functional information. Resource for protein sequence and annotation data. Consortium for preservation of the UniProt databases: UniProt Knowledgebase (UniProtKB), UniProt Reference Clusters (UniRef), and UniProt Archive (UniParc), UniProt Proteomes. Collaboration between European Bioinformatics Institute (EMBL-EBI), SIB Swiss Institute of Bioinformatics and Protein Information Resource. Swiss-Prot is a curated subset of UniProtKB.

Proper citation: UniProt (RRID:SCR_002380) Copy   

•   Source: SciCrunch Registry

https://pephub.databio.org

Web biological metadata server to view, store, and share your sample metadata in form of Portable Encapsulated Projects. PEPhub takes advantage of PEP biological metadata standard to store, edit, and access your PEPs in one place. Components include database where PEPs are stored; API to programmatically read and write PEPs in database; web based user interface to view and manage these PEPs via front end.

Proper citation: PEPhub (RRID:SCR_024892) Copy   

•   Source: SciCrunch Registry

https://github.com/christopher-vollmers/C3POa

Software to detect DNA splint sequence raw reads. Computational pipeline for calling consensi on R2C2 nanopore data.

Proper citation: C3Poa (RRID:SCR_025484) Copy   

•   Source: SciCrunch Registry

https://github.com/KrishnaswamyLab/PHATE

Software tool for visualizing high dimensional data using novel conceptual framework for learning and visualizing manifold to preserve both local and global distances.

Proper citation: PHATE (RRID:SCR_027119) Copy   

•   Source: SciCrunch Registry

http://www.genome.gov/12514286

Current Topics in Genome Analysis lecture series consists of 13 lectures on successive Wednesdays, with a mixture of local and outside speakers covering the major areas of genomics. In this tenth edition of the series, rather than splitting the lectures into laboratory-based and computationally-based blocks, we have intermingled the lectures by general subject area. We hope that this approach conveys the idea that both laboratory- and computationally-based approaches are necessary in order to do cutting-edge biological research in the future. The lectures are geared at the level of first year graduate students, are practical in nature, and are intended for a diverse audience. Handouts will be provided for each lecture, and time will be available at the end of each lecture for questions and discussion. All lectures are held on Wednesday mornings from 9:30 a.m. to 11:00 a.m. in the Lipsett Amphitheatre of the National Institutes of Health Clinical Center (Building 10). Course Directors: Andy Baxevanis, Ph.D., Eric Green, M.D., Ph.D., Tyra Wolfsberg, Ph.D. Lectures in this series will be available on the GenomeTV channel of YouTube viewing shortly after the live lecture and also includes all of the handouts. Lectures will not be Webcast live. The lecture series archives (available from 2005-) covers important milestones in genetics. CME Credits: This activity has been approved for AMA PRA Category 1 Credits. The intended audience includes clinicians, clinical geneticists, social and behavioral scientists, genetic counselors, those involved with genetics and public policy, health educators, and other biomedical and clinical scientists with an interest in genetics, genomics and personalized medicine. No prior expertise on the part of the audience will be required and the lecturers will be instructed to provide any relevant background as part of their lectures.

Proper citation: Current Topics in Genome Analysis (RRID:SCR_006475) Copy   

•   Source: SciCrunch Registry

http://www.phrap.org/consed/consed.html

A graphical tool for sequence finishing (BAM File Viewer, Assembly Editor, Autofinish, Autoreport, Autoedit, and Align Reads To Reference Sequence)

Proper citation: Consed (RRID:SCR_005650) Copy   

•   Source: SciCrunch Registry

http://ccb.jhu.edu/software/ASprofile/

A suite of programs for extracting, quantifying and comparing alternative splicing (AS) events from RNA-seq data.

Proper citation: ASprofile (RRID:SCR_001833) Copy   

•   Source: SciCrunch Registry

http://jbrowse.org/

A high-performance visualization tool for interactive exploration of large, integrated genomic datasets written primarily in JavaScript. It supports a wide variety of data types, including array-based and next-generation sequence data, and genomic annotations.

Proper citation: JBrowse (RRID:SCR_001004) Copy   

•   Source: SciCrunch Registry

http://compgen.bscb.cornell.edu/phast/

A freely available software package for comparative and evolutionary genomics that consists of about half a dozen major programs, plus more than a dozen utilities for manipulating sequence alignments, phylogenetic trees, and genomic annotations. For the most part, PHAST focuses on two kinds of applications: the identification of novel functional elements, including protein-coding exons and evolutionarily conserved sequences; and statistical phylogenetic modeling, including estimation of model parameters, detection of signatures of selection, and reconstruction of ancestral sequences. It consists of over 60,000 lines of C code.

Proper citation: PHAST (RRID:SCR_003204) Copy   

•   Source: SciCrunch Registry

http://mendel.stanford.edu/SidowLab/downloads/MAPP/

Java program that predicts the impact of all possible amino acid substitutions on the function of the protein., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: MAPP (RRID:SCR_010775) Copy   

•   Source: SciCrunch Registry

https://www.phenx.org/Default.aspx?tabid=56

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on 05 01 2025. PhenX is a project to prioritize Phenotype and eXposure measures for Genome-wide Association Studies (GWAS). Leaders of the scientific community will assess and prioritize a broad range of domains relevant to genomics research and public health. The PhenX Steering Committee (SC), chaired by Dr. Jonathan Haines, provides leadership in the selection of domains and domain experts. Members of the SC include outstanding scientists from the research community and liaisons from the Institutes and Centers of the National Institutes of Health. Consensus measures for GWAS will have a direct impact on biomedical research and ultimately on public health. During the course of this project, up to 20 research domains will be examined, with up to 15 measures being recommended for use in future GWAS and other large-scale genomic research efforts. The goal is to maximize the benefits of future research by having comparable measures so that studies can be integrated. Each selected domain will be reviewed by a Working Group (WG) of scientists who are experts in the research area. A systematic review of the literature will guide the WGs selection of up to 15 high priority measures with standardized approaches for measurement. Selection criteria for the measures include factors such as validity, reproducibility, cost, feasibility, and burden to both investigators and participants. The scientific community will be asked to provide input on proposed measures. Consensus development is a key component of the project.

Proper citation: Consensus Measures for Phenotype and Exposure (RRID:SCR_006688) Copy   

•   Source: SciCrunch Registry

https://pepatac.databio.org/en/latest/

Software standardized pipeline for ATAC-seq data analysis with serial alignments. Leverages unique features of ATAC-seq data to optimize for speed and accuracy, and provides several unique analytical approaches. Downstream analysis is simplified by standard definition format, modularity of components, and metadata APIs in R and Python. Restartable, fault-tolerant, and can be run on local hardware, using any cluster resource manager, or in provided Linux containers. We also emphasize the advantage of aligning to the mitochondrial genome serially, which improves alignment and quality control metrics. Includes quality control plots, summary statistics, and variety of data formats.

Proper citation: PEPATAC (RRID:SCR_024758) Copy   

•   Source: SciCrunch Registry

https://www.bioconductor.org/packages/release/bioc/html/HiCDCPlus.html

Software package for Hi-C/HiChIP interaction calling and differential analysis using efficient implementation of HiC-DC statistical framework. Enables principled statistical analysis of Hi-C and HiChIP data sets. Enables systematic 3D interaction calls and differential analysis for Hi-C and HiChIP

Proper citation: HiCDCPlus (RRID:SCR_025317) Copy   

•   Source: SciCrunch Registry

http://hlathena.tools/

Web tool and predictive model used by researchers to identify which small protein fragments (peptides) will be presented by human leukocyte antigen (HLA) proteins on the surface of cells. It is heavily used in the development of cancer immunotherapies and personalized

Proper citation: HLAthena (RRID:SCR_028691) Copy   

•   Source: SciCrunch Registry