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http://www.cbioc.com/en/

Commercial organization that discovers, validates & analyzes genomic biomarkers with a focus on body fluid samples. Take advantage of their proven expertise in biomarker signature development and speed up your biomarker studies.

Proper citation: Comprehensive Biomarker Center (RRID:SCR_003901) Copy   

•   Source: SciCrunch Registry

http://www.flexgen.nl/

A biotechnology company that has developed technology for synthesizing custom microarrays, the FlexArrayer. Its is a desk-top sized instrument which allows the researcher to generate, in their own laboratory, either a custom oligonucleotide array in a single day or oligonucleotide pool in a few days. Recent developments in synthesis chemistry allows many modifications to be incorporated or for alternative chemistries to be considered.

Proper citation: FlexGen (RRID:SCR_003902) Copy   

•   Source: SciCrunch Registry

http://www.cxrbiosciences.com/

Commercial organization that provides preclinical services and expertise, specializing in investigative toxicology, exploratory and discovery toxicology, metabolism and pharmacokinetics. CXR Biosciences is now Concept Life Sciences.

Proper citation: CXR Biosciences (RRID:SCR_003961) Copy   

•   Source: SciCrunch Registry

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

Software that establishes a bridge between GEO and BioConductor.

Proper citation: GEOquery (RRID:SCR_000146) Copy   

•   Source: SciCrunch Registry

http://www.scienceexchange.com/facilities/genomics-services-lab

A lab that offers genetic research tools such as RNA sequencing and a variety of arrays.

Proper citation: HudsonAlpha Genomics Services Lab (RRID:SCR_000353) Copy   

•   Source: SciCrunch Registry

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

Software package to infer gene regulatory networks from time-series measurements. The algorithm is expected to be useful in reconstruction of small biological directed networks from time course data.

Proper citation: TDARACNE (RRID:SCR_000498) Copy   

•   Source: SciCrunch Registry

http://mfuzz.sysbiolab.eu/

Software package for noise-robust soft clustering of gene expression time-series data (including a graphical user interface).

Proper citation: Mfuzz (RRID:SCR_000523) Copy   

•   Source: SciCrunch Registry

http://www.bioconductor.org/packages/devel/bioc/html/DMRforPairs.html

Software for identifying differentially methylated regions between unique samples using array based methylation profiles. It allows researchers to compare n greater than or equal to 2 unique samples with regard to their methylation profile. The (pairwise) comparison of n unique single samples distinguishesit from other existing pipelines as these often compare groups of samples in either single CpG locus or region based analysis. DMRforPairs defines regions of interest as genomic ranges with sufficient probes located in close proximity to each other. Probes in one region are optionally annotated to the same functional class(es). Differential methylation is evaluated by comparing the methylation values within each region between individual samples and (if the difference is sufficiently large), testing this difference formally for statistical significance.

Proper citation: DMRforPairs (RRID:SCR_005702) Copy   

•   Source: SciCrunch Registry

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

A tool set for pathway-based data integration and visualization. It maps and renders a wide variety of biological data on relevant pathway graphs. All users need is to supply their data and specify the target pathway. Pathview automatically downloads the pathway graph data, parses the data file, maps user data to the pathway, and render pathway graph with the mapped data. In addition, Pathview also seamlessly integrates with pathway and gene set (enrichment) analysis tools for large-scale and fully automated analysis.

Proper citation: Pathview (RRID:SCR_002732) Copy   

•   Source: SciCrunch Registry

http://medgene.med.harvard.edu/MEDGENE/

An algorithm that generates lists of genes associated with a gene or one or more disorders. The algorithm can be used in high-throughput screening experiments, can create disease-specific micro-arrays, and can sort the results of gene profiling data. Based on the co-citations of all Medline records, MedGene can retrieve the following relationships: 1. A list of human genes associated with a particular human disease in ranking order 2. A list of human genes associated with multiple human diseases in ranking order 3. A list of human diseases associated with a particular human gene in ranking order 4. A list of human genes associated with a particular human gene in ranking order 5. The sorted gene list from other disease related high-throughput experiments, such as micro-array 6. The sorted gene list from other gene related high-throughput experiments, such as micro-array

Proper citation: MedGene (RRID:SCR_008122) Copy   

•   Source: SciCrunch Registry

http://bioinfo.cipf.es/isacghtrac

Software to analyze CNV that will now normalize arrays CGH and it will visually integrate different genome annotations.

Proper citation: IsaCGH (RRID:SCR_008375) 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

http://www.bioconductor.org/packages/release/bioc/html/ADaCGH2.html

Software for analysis and plotting of array comparative genomic hybridization (CGH) data. It allows usage of Circular Binary Segementation, wavelet-based smoothing (both as in Liu et al., and HaarSeg as in Ben-Yaacov and Eldar), HMM, BioHMM, GLAD, CGHseg. Most computations are parallelized (either via forking or with clusters, including MPI and sockets clusters) and use ff for storing data.

Proper citation: ADaCGH2 (RRID:SCR_001981) Copy   

•   Source: SciCrunch Registry

https://www.wtccc.org.uk/

Consortium of 50 research groups across the UK to harness the power of newly-available genotyping technologies to improve our understanding of the aetiological basis of several major causes of global disease. The consortium has gathered genotype data for up to 500,000 sites of genome sequence variation (single nucleotide polymorphisms or SNPs) in samples ascertained for the disease phenotypes. Analysis of the genome-wide association data generated has lead to the identification of many SNPs and genes showing evidence of association with disease susceptibility, some of which will be followed up in future studies. In addition, the Consortium has gained important insights into the technical, analytical, methodological and biological aspects of genome-wide association analysis. The core of the study comprised an analysis of 2,000 samples from each of seven diseases (type 1 diabetes, type 2 diabetes, coronary heart disease, hypertension, bipolar disorder, rheumatoid arthritis and Crohn's disease). For each disease, the case samples have been ascertained from sites widely distributed across Great Britain, allowing us to obtain considerable efficiencies by comparing each of these case populations to a common set of 3,000 nationally-ascertained controls also from England, Scotland and Wales. These controls come from two sources: 1,500 are representative samples from the 1958 British Birth Cohort and 1,500 are blood donors recruited by the three national UK Blood Services. One of the questions that the WTCCC study has addressed relates to the relative merits of these alternative strategies for the generation of representative population cohorts. Genotyping for this main Case Control study was conducted by Affymetrix using the (commercial) Affymetrix 500K chip. As part of this study a total of 17,000 samples were typed for 500,000 SNPs. There are two additional components to the study. First, the WTCCC award is part-funding a study of host resistance to infectious diseases in African populations. The same approach has been used to type 2,000 cases of tuberculosis (TB) and 2,000 cases of malaria, as well as 2,000 shared controls. As well as addressing diseases of major global significance, and extending WTCCC coverage into the area of infectious disease, the inclusion of samples of African origin has obvious benefits with respect to methodological aspects of genome-wide association analysis. Second, the WTCCC has, for four additional diseases (autoimmune thyroid disease, breast cancer, ankylosing spondylitis, multiple sclerosis), completed an analysis of 15,000 SNPs designed to represent a large proportion of the known non-synonymous coding SNPs across the genome. This analysis has been performed at the WTSI using a custom Infinium chip (Illumina). Data release The genotypic data of the control samples (1958 British Birth Cohort and UK Blood Service) and from seven diseases analyzed in the main study are now available to qualified researchers. Summary genotype statistics for these collections are available directly from the website. Access to the individual-level genotype data and summary genotype statistics is by application to the Consortium Data Access Committee (CDAC) and approval subject to a Data Access Agreement. WTCCC2: A further round of GWA studies were funded in April 2008. These include 15 WTCCC-collaborative studies and 12 independent studies be supported totaling approximately 120,000 samples. Many of the studies represent major international collaborative networks that have together assembled large sample collections. WTCCC2 will perform genome-wide association studies in 13 disease conditions: Ankylosing spondylitis, Barrett's oesophagus and oesophageal adenocarcinoma, glaucoma, ischaemic stroke, multiple sclerosis, pre-eclampsia, Parkinson's disease, psychosis endophenotypes, psoriasis, schizophrenia, ulcerative colitis and visceral leishmaniasis. WTCCC2 will also investigate the genetics of reading and mathematics abilities in children and the pharmacogenomics of statin response. Over 60,000 samples will be analyzed using either the Affymetrix v6.0 chip or the Illumina 660K chip. The WTCCC2 will also genotype 3,000 controls each from the 1958 British Birth cohort and the UK Blood Service control group, and the 6,000 controls will be genotyped on both the Affymetrix v6.0 and Illumina 1.2M chips. WTCCC3: The Wellcome Trust has provided support for a further round of GWA studies in January 2009. These include 5 WTCCC-collaborative studies to be carried out in WTCCC3 and 5 independent studies, across a range of diseases. Many of the studies represent major international collaborative networks that have together assembled large sample collections. WTCCC3 will perform genome-wide association studies in the following 4 disease conditions: primary biliary cirrhosis, anorexia nervosa, pre-eclampsia in UK subjects, and the interactions between donor and recipient DNA related to early and late renal transplant dysfunction. The WTCCC3 will also carry out a pilot in a study of the genetics of host control of HIV-1 infection. Over 40,000 samples will be analyzed using the Illumina 660K chip. The WTCCC3 will utilize the 6,000 control genotypes generated by the WTCCC2.

Proper citation: Wellcome Trust Case Control Consortium (RRID:SCR_001973) Copy   

•   Source: SciCrunch Registry

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

An R/Bioconductor package for quality assessment of target enrichment experiments. This package provides functionalities for assessing and visualizing the quality of the target enrichment process, like specificity and sensitivity of the capture, per-target read coverage and so on.

Proper citation: TEQC (RRID:SCR_001943) Copy   

•   Source: SciCrunch Registry

http://bioconductor.org/packages/devel/bioc/html/massiR.html

Software that predicts the sex of samples in gene expression microarray datasets.

Proper citation: massiR (RRID:SCR_001157) Copy   

•   Source: SciCrunch Registry

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

Software package for quality control of Affymetrix GeneChip expression data and reproducibility analysis of human whole genome chips with the MAQC reference datasets.

Proper citation: yaqcaffy (RRID:SCR_001295) Copy   

•   Source: SciCrunch Registry

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

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 23,2022. Software package for DNA methylation data analysis and visualization. A new class is defined to keep the chromosome location information together with the data. The current version of the package mainly focuses on analyzing the Illumina Infinium methylation array data, but most methods can be generalized to other methylation array or sequencing data.

Proper citation: methyAnalysis (RRID:SCR_001290) Copy   

•   Source: SciCrunch Registry

http://julian-gehring.github.io/les/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 23,2022. Software package that estimates Loci of Enhanced Significance (LES) in tiling microarray data. These are regions of regulation such as found in differential transcription, CHiP-chip, or DNA modification analysis. The package provides a universal framework suitable for identifying differential effects in tiling microarray data sets, and is independent of the underlying statistics at the level of single probes.

Proper citation: les (RRID:SCR_001291) Copy   

•   Source: SciCrunch Registry

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

Software package for non-parametric bootstrap and permutation resampling-based multiple testing procedures (including empirical Bayes methods) for controlling the family-wise error rate (FWER), generalized family-wise error rate (gFWER), tail probability of the proportion of false positives (TPPFP), and false discovery rate (FDR). Several choices of bootstrap-based null distribution are implemented (centered, centered and scaled, quantile-transformed). Single-step and step-wise methods are available. Tests based on a variety of t- and F-statistics (including t-statistics based on regression parameters from linear and survival models as well as those based on correlation parameters) are included. When probing hypotheses with t-statistics, users may also select a potentially faster null distribution which is multivariate normal with mean zero and variance covariance matrix derived from the vector influence function. Results are reported in terms of adjusted p-values, confidence regions and test statistic cutoffs. The procedures are directly applicable to identifying differentially expressed genes in DNA microarray experiments.

Proper citation: multtest (RRID:SCR_001255) Copy   

•   Source: SciCrunch Registry