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https://ecog-acrin.org/resources/ecog-performance-status/

ECOG Performance Scale describes patient’s level of functioning in terms of their ability to care for themself, daily activity, and physical ability (walking, working, etc.). Standard criteria for measuring how the disease impacts patient’s daily living abilities. Used to assess the functional status of patient.

Proper citation: Eastern Cooperative Oncology Group Performance Status Scale (RRID:SCR_026432) Copy   

•   Source: SciCrunch Registry

https://github.com/compgenomics/MeTPeak

Software package for finding the location of m6A sites in MeRIP-seq data.

Proper citation: MeTPeak (RRID:SCR_026533) Copy   

•   Source: SciCrunch Registry

https://github.com/higlass/higlass

Web-based visual exploration and analysis of genome interaction maps.

Proper citation: HiGlass (RRID:SCR_026687) Copy   

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https://github.com/AMICI-dev/AMICI/

Software toolbox implemented in C++/Python/MATLAB that provides efficient simulation and sensitivity analysis routines tailored for scalable, gradient-based parameter estimation and uncertainty quantification. Used for high-performance sensitivity analysis for large ordinary differential equation models.

Proper citation: AMICI (RRID:SCR_026913) Copy   

•   Source: SciCrunch Registry

https://github.com/OpenTOPAS/OpenTOPAS

Software Monte Carlo tool for particle simulation. Used for simulation of medical applications of ionizing radiation with the Monte Carlo method. Allows to assemble and control library of simulation objects (geometry components, particle sources, scorers, etc.) with no need to write C++ code and without knowledge of underlying Geant4 Simulation Toolkit.

Proper citation: OpenTOPAS (RRID:SCR_026927) Copy   

•   Source: SciCrunch Registry

https://github.com/dpeerlab/Palantir/

Algorithm to align cells along differentiation trajectories. Models trajectories of differentiating cells by treating cell fate as probabilistic process and leverages entropy to measure cell plasticity along the trajectory. Generates high-resolution pseudo-time ordering of cells and, for each cell state, assigns probability of differentiating into each terminal state.

Proper citation: Palantir (RRID:SCR_027194) Copy   

•   Source: SciCrunch Registry

https://weghornlab.org/software.html

Software tool which derives gene-specific probabilistic estimates of the strength of negative and positive selection in cancer.

Proper citation: CBaSE (RRID:SCR_027765) Copy   

•   Source: SciCrunch Registry

https://github.com/McGranahanLab/TcellExTRECT

Software R package to calculate T cell fractions from WES data from hg19 or hg38 aligned genomes.

Proper citation: T Cell ExTRECT (RRID:SCR_027742) Copy   

•   Source: SciCrunch Registry

https://github.com/vanallenlab/comut

Software Python library for creating comutation plots to visualize genomic and phenotypic information. Used for visualizing genomic and phenotypic information via comutation plots.

Proper citation: CoMUT (RRID:SCR_027745) Copy   

•   Source: SciCrunch Registry

https://github.com/SalasLab/HiTIMED

Software DNA methylation-based algorithm, to estimate cell proportions in tumor microenvironment. Profiles tumor, immune, and angiogenic components, allowing researchers to study tumor composition and its clinical implications using archival biospecimens.

Proper citation: HiTIMED (RRID:SCR_028180) Copy   

•   Source: SciCrunch Registry

https://oncodb.org/

Database offers integrated multi-omic data for patients across 33 cancer types. It encompasses gene expression, DNA methylation, somatic mutations, proteomic profiles, and chromatin accessibility, drawing from TCGA, GTEx, and CPTAC projects. Users can compare gene expression, DNA methylation, and protein levels between tumor and normal tissues, identifying differentially expressed genes and proteins, and examining gene-to-gene correlations. Provides oncogene mutation profiles and allows for survival analysis based on gene expression and methylation, linked to clinical parameters. Facilitates exploration of multi-omic correlations, such as gene expression with DNA methylation, and their variations with mutation status. Extends its analytical capabilities to include six major oncoviruses, offering insights into their impact on gene expression, methylation, and patient survival.

Proper citation: OncoDB (RRID:SCR_028340) Copy   

•   Source: SciCrunch Registry

https://github.com/andygxzeng/BoneMarrowMap

Software R package to enable rapid reference mapping and annotation of new scRNA-seq data across the spectrum of normal and malignant hematopoietic contexts. Single cell RNA-seq reference map of human hematopoietic development in the bone marrow, with balanced representation of hematopoietic stem and progenitor cells and differentiated populations.

Proper citation: BoneMarrowMap (RRID:SCR_028324) Copy   

•   Source: SciCrunch Registry

https://github.com/KarchinLab/mhcnuggets

Software tool that predicts how protein pieces bind to Major Histocompatibility Complex (MHC) molecules. It uses deep learning to process peptide sequences, handle variable lengths, and evaluate both common and rare alleles. Used to predicts peptide-MHC binding. Can predict binding for common or rare alleles of MHC class I or II with a single neural network architecture.

Proper citation: MHCnuggets (RRID:SCR_028674) Copy   

•   Source: SciCrunch Registry

https://www.nature.com/articles/s41467-018-03367-w

Nanodroplet processing platform for deep and quantitative proteome profiling of 10 to 100 mammalian cells. It enhances efficiency and recovery of sample processing by downscaling processing volumes.

Proper citation: nanoPOTS (RRID:SCR_017129) Copy   

•   Source: SciCrunch Registry

https://bitbucket.org/wanding/duprecover/overview

Software that facilitates accurate estimation for sampling-induced read duplication in deep sequencing experiments.

Proper citation: DupRecover (RRID:SCR_006410) Copy   

•   Source: SciCrunch Registry

http://cancergenome.nih.gov/

Project exploring the spectrum of genomic changes involved in more than 20 types of human cancer that provides a platform for researchers to search, download, and analyze data sets generated. As a pilot project it confirmed that an atlas of changes could be created for specific cancer types. It also showed that a national network of research and technology teams working on distinct but related projects could pool the results of their efforts, create an economy of scale and develop an infrastructure for making the data publicly accessible. Its success committed resources to collect and characterize more than 20 additional tumor types. Components of the TCGA Research Network: * Biospecimen Core Resource (BCR); Tissue samples are carefully cataloged, processed, checked for quality and stored, complete with important medical information about the patient. * Genome Characterization Centers (GCCs); Several technologies will be used to analyze genomic changes involved in cancer. The genomic changes that are identified will be further studied by the Genome Sequencing Centers. * Genome Sequencing Centers (GSCs); High-throughput Genome Sequencing Centers will identify the changes in DNA sequences that are associated with specific types of cancer. * Proteome Characterization Centers (PCCs); The centers, a component of NCI's Clinical Proteomic Tumor Analysis Consortium, will ascertain and analyze the total proteomic content of a subset of TCGA samples. * Data Coordinating Center (DCC); The information that is generated by TCGA will be centrally managed at the DCC and entered into the TCGA Data Portal and Cancer Genomics Hub as it becomes available. Centralization of data facilitates data transfer between the network and the research community, and makes data analysis more efficient. The DCC manages the TCGA Data Portal. * Cancer Genomics Hub (CGHub); Lower level sequence data will be deposited into a secure repository. This database stores cancer genome sequences and alignments. * Genome Data Analysis Centers (GDACs) - Immense amounts of data from array and second-generation sequencing technologies must be integrated across thousands of samples. These centers will provide novel informatics tools to the entire research community to facilitate broader use of TCGA data. TCGA is actively developing a network of collaborators who are able to provide samples that are collected retrospectively (tissues that had already been collected and stored) or prospectively (tissues that will be collected in the future).

Proper citation: The Cancer Genome Atlas (RRID:SCR_003193) Copy   

•   Source: SciCrunch Registry

http://www.nihpromis.org/

Repository of person centered measures that evaluates and monitors physical, mental, and social health in adults and children.

Proper citation: Patient-Reported Outcomes Measurement Information System (RRID:SCR_004718) Copy   

•   Source: SciCrunch Registry

http://ki.se/ki/jsp/polopoly.jsp?d=29332&a=23686&l=en

THIS RESOURCE IS NO LONGER IN SERVICE, documented August 22, 2016. The original aim of this study was to increase our understanding of the etiology of malignant lymphomas, especially in view of the increasing trend in incidence. Malignant lymphoma (including non-Hodgkin lymphoma, NHL, Hodgkin lymphoma, HL, and chronic lymphocytic leukemia, CLL) constitute a heterogeneous group of malignancies with regard to histology, molecular characteristics and clinical course. Etiological factors may also vary by lymphoma subtype. The incidence of NHL, the most common lymphoma group, has increased dramatically during the past decades in Sweden and in many other Western countries. The reasons for this increase as well as for the majority of all new cases is not well understood. Well established risk factors for lymphoma overall include hereditary and acquired disorders of strong immune dysfunction such as HIV/AIDS and organ transplantation, but they explain few new cases in the population. Approach: Population-based case-control study in Sweden and Denmark. The study includes in total 3740 patients and 3187 controls in both countries recruited during the period October 1999 to October 2002. Through a rapid case ascertainment system, the cases were identified shortly after diagnosis. The controls were randomly selected from national population registers and frequency-matched to the expected number of cases by sex and age group. Both cases and controls were interviewed by telephone based on a standardized questionnaire to obtain detailed information on potential risk factors for lymphoma such as medical history including infectious diseases, drug use and blood transfusions, socio-economic factors and life-style. Blood samples were also collected and stored as serum, plasma, DNA and live lymphocytes. In addition, written questionnaires about dietary habits or work exposures were sent out in Sweden. Tumor material from the cases was re-examined and uniformly classified according to the REAL classification. Status The data collection ended in 2002 and data analysis has been ongoing since then. We have primarily analyzed a range of environmental factors in relation risk of malignant lymphoma subgroups including sun exposure, body mass index, family history of hematopoietic cancer, allergy, autoimmune disorders and mononucleosis. We have also assessed specific genetic determinants in a subgroups of patients with follicular lymphoma and controls. Study results have so far been presented in 14 publications in peer-reviewed journals. In addition to new analyses on other environmental factors, we now also work to understand genetic susceptibility and gene-environmental interaction and risk of lymphoma. Also, prognostic studies have been initiated in collaboration with other research groups with regard to in CLL, HL and T-cell lymphoma.

Proper citation: SCALE - Scandinavian lymphoma etiology (RRID:SCR_006041) Copy   

•   Source: SciCrunch Registry

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

Software tool to infer allele and haplotype specific copy numbers in individual cells from low coverage single cell DNA sequencing data. Integrates weak allelic signals across individual cells, powering strength of single cell sequencing technologies to overcome weakness. Includes global clustering of RDRs and BAFs, and rigorous model selection procedure for inferring genome ploidy that improves both inference of allele specific and total copy numbers.

Proper citation: CHISEL (RRID:SCR_023220) Copy   

•   Source: SciCrunch Registry

http://www.chernobyltissuebank.com/

The CTB (Chernobyl Tissue Bank) is an international cooperation that collects, stores and disseminates biological samples from tumors and normal tissues from patients for whom the aetiology of their disease is known - exposure to radioiodine in childhood following the accident at the Chernobyl power plant. The main objective of this project is to provide a research resource for both ongoing and future studies of the health consequences of the Chernobyl accident. It seeks to maximize the amount of information obtained from small pieces of tumor by providing multiple aliquots of RNA and DNA extracted from well documented pathological specimens to a number of researchers world-wide and to conserve this valuable material for future generations of scientists. It exists to promote collaborative, rather than competitive, research on a limited biological resource. Tissue is collected to an approved standard operating procedure (SOP) and is snap frozen; the presence or absence of tumor is verified by frozen section. A representative paraffin block is also obtained for each case. Where appropriate, we also collect fresh and paraffin-embedded tissue from loco-regional metastases. Currently we do not issue tissue but provide extracted nucleic acid, paraffin sections and sections from tissue microarrays from this material. The project is coordinated from Imperial College, London and works with Institutes in the Russian Federation (the Medical Radiological Research Centre in Obninsk) and Ukraine (the Institute of Endocrinology and Metabolism in Kiev) to support local scientists and clinicians to manage and run a tissue bank for those patients who have developed thyroid tumors following exposure to radiation from the Chernobyl accident. Belarus was also initially included in the project, but is currently suspended for political reasons.

Proper citation: Chernobyl Tissue Bank (RRID:SCR_010662) Copy   

•   Source: SciCrunch Registry