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https://code.google.com/p/softsearch/

A sensitive structural variant (SV) detection software tool for Illumina paired-end next-generation sequencing data. It simultaneously utilizes soft-clipping and read-pair strategies for detecting SVs to increase sensitivity. Soft clips are proxies for split-reads that indicate part of the read maps to the reference genome, but the other part is not localized at the same place (e.g. breakpoint spanning reads). Discordant read-pairs refer to a read and its mate, where the insert size is greater (or less than) the expected distribution of the dataset ? or ? where the mapping orientation of the reads is unexpected (e.g. both on the same strand). SoftSearch looks for areas with soft-clipping in the genome that have discordant read pairs supporting the anomaly. Once areas with both these conditions are identified, the read and mate information is extracted directly from the BAM file containing the discordant reads, obviating the need for time-consuming and error-prone complex alignment strategies. Only a small number of soft-masked bases discordant read-pairs are necessary to identify an SV, which on their own would not be sufficient to make an SV call, thus highlighting SoftSearch?s improved sensitivity. SoftSearch is well suited to be ?plugged in? to most sequence analysis workflows, since it requires standard file inputs, such as a BAM file using almost any aligner and a reference genome FASTA file. Because SoftSearch requires soft-masked bases, the only requirement is that the aligner must have this functionality, which is usually turned on by default by many standard aligners (e.g. BWA, Novoalign, etc).

Proper citation: SoftSearch (RRID:SCR_006683) Copy   

•   Source: SciCrunch Registry

http://www.nactem.ac.uk/GREC/

A semantically annotated corpus of 240 MEDLINE abstracts (167 on the subject of E. coli species and 73 on the subject of the Human species) intended for training information extraction (IE) systems and/or resources which are used to extract events from biomedical literature. The corpus has been manually annotated with events relating to gene regulation by biologists. Each event is centered on either a verb (e.g. transcribe) or nominalized verb (e.g. transcription) and annotation consists of identifying, as exhaustively as possible, the structurally-related arguments of the verb or nominalized verb within the same sentence. Each event argument is then assigned the following information: * A semantic role from a fixed set of 13 roles which are tailored to the biomedical domain. * A biomedical concept type (where appropriate). The corpus in available for download in 2 formats: * A standoff format, based on the BioNLP'09 Shared Task format * An XML format, based on the GENIA event annotation format

Proper citation: GREC Corpus (RRID:SCR_006719) Copy   

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http://www.innatedb.com

Publicly available database of the genes, proteins, experimentally-verified interactions and signaling pathways involved in the innate immune response of humans, mice and bovines to microbial infection. The database captures coverage of the innate immunity interactome by integrating known interactions and pathways from major public databases together with manually-curated data into a centralized resource. The database can be mined as a knowledgebase or used with the integrated bioinformatics and visualization tools for the systems level analysis of the innate immune response. Although InnateDB curation focuses on innate immunity-relevant interactions and pathways, it also incorporates detailed annotation on the entire human, mouse and bovine interactomes by integrating data (178,000+ interactions & 3,900+ pathways) from several of the major public interaction and pathway databases. InnateDB also has integrated human, mouse and bovine orthology predictions generated using Ortholgue software. Ortholgue uses a phylogenetic distance-based method to identify possible paralogs in high-throughput orthology predictions. Integrated human and mouse conserved gene order and synteny information has also been determined to provide further support for orthology predictions. InnateDB Capabilities: * View statistics for manually-curated innate immunity relevant molecular interactions. New manually curated interactions are submitted weekly. * Search for genes and proteins of interest. * Search for experimentally-verified molecular interactions by gene/protein name, interaction type, cell type, etc. * Search genes/interactions belonging to 3,900 pathways. * Visualize interactions using an intuitive subcellular localization-based layout in Cerebral. * Upload your own list of genes along with associated gene expression data (from up to 10 experimental conditions) to interactively analyze this data in a molecular interaction network context. Once you have uploaded your data, you will be able to interactively visualize interaction networks with expression data overlaid; carry out Pathway, Gene Ontology and Transcription Factor Binding Site over-representation analyses; construct orthologous interaction networks in other species; and much more. * Access curated interaction data via a dedicated PSICQUIC webservice.

Proper citation: InnateDB (RRID:SCR_006714) Copy   

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http://www.chuq.qc.ca/fr/

Network of three teaching hospitals affiliated with the medical school of Université Laval and several specialized institutions in Quebec City.

Proper citation: University of Quebec Hospital Centre; Quebec; Canada (RRID:SCR_006740) Copy   

•   Source: SciCrunch Registry

http://www.nida.nih.gov/mediaguide/index.html

The latest findings on the science of drug abuse and addiction and commonly abused drugs, and lists resources for more information. They are committed to bringing timely, factual information on addiction and treatment to the press and public. NIDA''s Public Information and Liaison Branch (PILB) is part of NIDA''s Office of Science Policy and Communications. Linking scientists, the scientific community, and the media, PILB supports the rapid dissemination of research information to inform policy and to improve practice. NIDA''s goal is to ensure that science - not ideology or anecdote - forms the foundation of public information on drug abuse and addiction. NIDAs online MEDIA GUIDE provides answers on how to find what you need to know about drug abuse and addiction, including information on the basics (The Science of Drug Abuse and Addiction and Commonly Abused Drugs), resources (Where to Find Nationwide Trends and Statistics, NIDA Resources, and Other Government Web Sites for Health and Science Information), NIDAs history and background, a glossary and relevant contact information. NIDA is pleased to offer this guide to the important findings that are emerging as a result of research on addiction and its treatment. NIDA, part of the National Institutes of Health under the U.S. Department of Health and Human Services, supports most of the world''s research on drug abuse and addiction, including basic and behavioral science research that addresses fundamental and essential questions relevant to drug abuse, ranging from its causes and consequences to its treatment and prevention. The purpose of this guide is to give journalists fast and user-friendly access to the latest scientific information but it is useful for anyone interested in how to access accurate information about drug abuse and addiction. In more than three decades as a researcher, I have seen the impact that science and health journalists have had in bringing scientific research to the public. It is through information that Americans gain hope and understanding. I have come to know many of you over the years and remain committed to releasing scientific information as quickly as possible for rapid dissemination to the public. Please keep this guide nearby as a useful tool and let us know how NIDA''s public liaison staff can help you reach your information and deadline needs. A PDF version is available for download.

Proper citation: National Institute on Drug Abuse Media Guide (RRID:SCR_006850) Copy   

•   Source: SciCrunch Registry

http://diabetes.niddk.nih.gov/dm/pubs/america/

A compilation and assessment of epidemiologic, public health, and clinical data on diabetes and its complications in the United States. Published by the National Diabetes Data Group of the National Institute of Diabetes and Digestive and Kidney Diseases, the book contains 36 chapters organized in five areas: * the descriptive epidemiology of diabetes in the United States based on national surveys and community-based studies, including prevalence, incidence, sociodemographic and metabolic characteristics, risk factors for developing diabetes, and mortality * the myriad complications that affect patients with diabetes * characteristics of therapy and medical care for diabetes * economic aspects, including health insurance and health care costs * diabetes in special populations, including African Americans, Hispanics, Asian and Pacific Islanders, Native Americans, and pregnant women. Diabetes in America, 2nd Edition, has been designed to serve as a reliable scientific resource for assessing the scope and impact of diabetes and its complications, determining health policy and priorities in diabetes, and identifying areas of need in research. The intended audience includes health policy makers at the local and Federal levels who need a sound quantitative base of knowledge to use in decision making; clinicians who need to know the probability that their patients will develop diabetes and the prognosis of the disease for complications and premature mortality; persons with diabetes and their families who need sound information on which to make decisions about their life with diabetes; and the research community which needs to identify areas where important scientific knowledge is lacking.

Proper citation: Diabetes in America (RRID:SCR_006754) Copy   

•   Source: SciCrunch Registry

http://www.aids.gov/podcast/podcast-gallery/

Podcasts from AIDS.gov, featuring information from the Federal government about HIV/AIDS prevention, testing, research, treatment, and using new media in response to HIV/AIDS. Categories include: Basic HIV information, New Media, Federal Programs and Policies, HIV/AIDS Awareness Days, and Real Stories.

Proper citation: AIDS.gov Podcast (RRID:SCR_006750) Copy   

•   Source: SciCrunch Registry

http://watson.nci.nih.gov/bioc_mirror/packages/2.11/bioc/html/EDASeq.html

Software for numerical and graphical summaries of RNA-Seq read data. Within-lane normalization procedures to adjust for GC-content effect (or other gene-level effects) on read counts: loess robust local regression, global-scaling, and full-quantile normalization (Risso et al., 2011). Between-lane normalization procedures to adjust for distributional differences between lanes (e.g., sequencing depth): global-scaling and full-quantile normalization (Bullard et al., 2010)., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: EDASeq (RRID:SCR_006751) Copy   

•   Source: SciCrunch Registry

http://sourceforge.net/projects/polgui/

An interface between MATLAB and the Polhemus Fastrak digitizer used to digitize fiducial locations and scalp EEG electrode locations. There are 5 versions all of which work under MATLAB R14 (on both linux and windows platforms), # polgui_ver1_r14 : works with 1 receiver (stylus pen) # polgui_ver2_r14 : works with 2 receivers (including the pen) # polgui_ver3_r14 : works with 3 receivers(including the pen) # polgui_ver4_r14 : works with 4 receivers (including the pen) # polgui_ver5_r14 : Generic version which works with 1/2/3/4 receivers (WARNING: Ver 5 might be buggy; not fully tested) Requirements: MATLAB R14 (Linux/Windows)

Proper citation: POLGUI - Matlab Polhemus Interface (RRID:SCR_006752) Copy   

•   Source: SciCrunch Registry

https://rdrr.io/bioc/sRAP/

Software package that provides a pipeline for gene expression analysis (primarily for RNA-Seq data). The normalization function is specific for RNA-Seq analysis, but all other functions (Quality Control Figures, Differential Expression and Visualization, and Functional Enrichment via BD-Func) will work with any type of gene expression data.

Proper citation: sRAP (RRID:SCR_001297) Copy   

•   Source: SciCrunch Registry

http://www.bioconductor.org/packages/release/bioc/html/SCAN.UPC.html

A microarray normalization software (SCAN) to facilitate personalized-medicine workflows with an extension (UPC) that estimates whether a given gene/transcript is active above background levels in a given sample. Rather than processing microarray samples as groups, which can introduce biases and present logistical challenges, SCAN normalizes each sample individually by modeling and removing probe- and array-specific background noise using only data from within each array. SCAN can be applied to one-channel (e.g., Affymetrix) or two-channel (e.g., Agilent) microarrays. The UPC method can be applied to one-channel or two-channel microarrays as well as to RNA-Seq read counts. Because UPC values are represented on the same scale and have an identical interpretation for each platform, they can be used for cross-platform data integration. A

Proper citation: SCAN.UPC (RRID:SCR_001334) Copy   

•   Source: SciCrunch Registry

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

Software package that uses a modeling strategy especially designed for normalizing high-throughput genomic data. The premise is that your data is a function of study-specific variables which are either biological variables that represent the target of the statistical analysis, or adjustment variables that represent factors arising from the experimental or biological setting the data is drawn from. The SNM approach aims to simultaneously model all study-specific variables in order to more accurately characterize the biological or clinical variables of interest.

Proper citation: SNM (RRID:SCR_001299) Copy   

•   Source: SciCrunch Registry

https://rdrr.io/bioc/betr/man/betr.html#heading-0

Software package that implements the Bayesian Estimation of Temporal Regulation algorithm to identify differentially expressed genes in microarray time-course data.

Proper citation: betr (RRID:SCR_001332) Copy   

•   Source: SciCrunch Registry

http://www.roche.com/

A Swiss global health-care company that operates under two divisions: Pharmaceuticals and Diagnostics.

Proper citation: Roche (RRID:SCR_001326) Copy   

•   Source: SciCrunch Registry

https://bioconductor.org/packages//2.11/bioc/html/gprege.html

Software R package for Gaussian Process Ranking and Estimation of Gene Expression time-series. The software fits two Gaussian processes (GPs) with an radial basis function (RBF) (+ noise diagonal) kernel on each profile. One GP kernel is initialized wih a short lengthscale hyperparameter, signal variance as the observed variance and a zero noise variance. It is optimized via scaled conjugate gradients (netlab). A second GP has fixed hyperparameters: zero inverse-width, zero signal variance and noise variance as the observed variance. The log-ratio of marginal likelihoods of the two hypotheses acts as a score of differential expression for the profile. Comparison via receiver operating characteristic curves (ROC curves) is performed against Bayesian hierarchical model for the analysis of time-series (BATS) (Angelini et.al, 2007).

Proper citation: gprege (RRID:SCR_001324) Copy   

•   Source: SciCrunch Registry

https://www.bioconductor.org/packages//2.13/bioc/html/waveTiling.html

Software package to conduct transcriptome analysis for tiling arrays based on fast wavelet-based functional models.

Proper citation: waveTiling (RRID:SCR_001322) Copy   

•   Source: SciCrunch Registry

http://sourceforge.net/projects/kanalyze/

A Java toolkit designed to convert DNA and RNA sequences into k-mers.

Proper citation: KAnalyze (RRID:SCR_001323) Copy   

•   Source: SciCrunch Registry

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

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 23,2022. Software package for quality assessment and to identify differentially expressed genes in the Affymetrix gene expression data.

Proper citation: AffyExpress (RRID:SCR_001321) Copy   

•   Source: SciCrunch Registry

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

Software package to read bead-level data (raw TIFFs and text files) output by BeadScan as well as bead-summary data from BeadStudio. Methods for quality assessment and low-level analysis are provided.

Proper citation: beadarray (RRID:SCR_001314) Copy   

•   Source: SciCrunch Registry

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

Software package that supports the application of diverse quality metrics to AffyBatch instances, summarizing these metrics via PCA, and then performing parametric outlier detection on the PCs to identify aberrant arrays with a fixed Type I error rate.

Proper citation: arrayMvout (RRID:SCR_001317) Copy   

•   Source: SciCrunch Registry