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http://fcon_1000.projects.nitrc.org/indi/retro/BeijingEOEC.html

Data set of 48 healthy controls from a community (student) sample from Beijing Normal University in China with 3 resting state fMRI scans each. During the first scan participants were instructed to rest with their eyes closed. The second and third resting state scan were randomized between resting with eyes open versus eyes closed. In addition this dataset contains a 64-direction DTI scan for every participant. The following data are released for every participant: * 6-minute resting state fMRI scan (R-fMRI) * MPRAGE anatomical scan, defaced to protect patient confidentiality * 64-direction diffusion tensor imaging scan (2mm isotropic) * Demographic information and information on the counterbalancing of eyes open versus eyes closed.

Proper citation: Beijing: Eyes Open Eyes Closed Study (RRID:SCR_001507) Copy   

•   Source: SciCrunch Registry

http://www.bioinfo.org.cn/hptaa/

To accelerate the process of tumor antigen discovery, we generated a publicly available Human Potential Tumor Associated Antigen database (HPtaa) with pTAAs identified by insilico computing. 3518 potential targets have been included in the database, which is freely available to academic users. It successfully screened out 41 of 82 known Cancer-Testis antigens, 6 of 18 differentiation antigen, 2 of 2 oncofetal antigen, and 7 of 12 FDA approved cancer markers that have Gene ID, therefore will provide a good platform for identification of cancer target genes. This database utilizes expression data from various expression platforms, including carefully chosen publicly available microarray expression data, GEO SAGE data, Unigene expression data. In addition, other relevant databases required for TAA discovery such as CGAP, CCDS, gene ontology database etc, were also incorporated. In order to integrate different expression platforms together, various strategies and algorithms have been developed. Known tumor antigens are gathered from literature and serve as training sets. A total tumor specificity penalty was computed from positive clue penalty for differential expression in human cancers, the corresponding differential ratio, and normal tissue restriction penalty for each gene. We hope this database will help with the process of cancer immunome identification, thus help with improving the diagnosis and treatment of human carcinomas.

Proper citation: Human Potential Tumor Associated Antigen database (RRID:SCR_002938) Copy   

•   Source: SciCrunch Registry

http://hdock.phys.hust.edu.cn/

Web server for protein-protein and protein-DNA/RNA docking based on hybrid strategy. With input information for receptor and ligand molecules either amino acid sequences or Protein Data Bank structures, the server automatically predicts their interaction through hybrid algorithm of template-based and template-free docking.

Proper citation: HDOCK server (RRID:SCR_024799) Copy   

•   Source: SciCrunch Registry

http://bio-comp.org.cn/llpsdb/home.html

Database of proteins undergoing liquid–liquid phase separation in vitro. Contains LLPS related proteins together with the corresponding phase separation conditions validated by experiments.

Proper citation: LLPSDB (RRID:SCR_024966) Copy   

•   Source: SciCrunch Registry

http://predict.phasep.pro/

Web server as meta-predictor for phase-separating proteins. Displays proteome-level quantiles of different features, thus profiling PS propensity and providing crucial information for identification of candidate proteins.

Proper citation: PhaSePred (RRID:SCR_024969) Copy   

•   Source: SciCrunch Registry

http://www.rnaphasep.cn/#/Home

Database that collects phase separation related RNAs manually curated from publication and public databases.

Proper citation: RNAPhaSep (RRID:SCR_024958) Copy   

•   Source: SciCrunch Registry

http://www.atcgn.com:8080/quarTeT/home.html

Web toolkit for studies of large scale T2T genomes. Collection of tools designed for T2T genome assembly and characterization, including reference guided genome assembly, ultra long sequence based gap filling, telomere identification, and de novo centromere prediction. Includes four modules: AssemblyMapper, GapFiller, TeloExplorer, and CentroMiner. Modules can be used alone or in combination with each other for T2T genome assembly and characterization.

Proper citation: quarTeT (RRID:SCR_025258) Copy   

•   Source: SciCrunch Registry

https://github.com/xiaochuanle/NECAT

Software error correction and de-novo assembly tool for Nanopore long noisy reads. Nanopore data assembler.

Proper citation: NECAT (RRID:SCR_025350) Copy   

•   Source: SciCrunch Registry

https://www.uii-ai.com/research.html

AI-powered integrated research platform for one-stop analysis of medical images. Provides advanced functionality such as automatic segmentation, registration, and classification for variety of application domains. Has major merits including Advanced built-in algorithms applicable to multiple imaging modalities (i.e., CT, MR, PET, DR), diseases (i.e., tumor, neurodegenerative disease, pneumonia), and applications (i.e., diagnosis, treatment planning, follow-up); Iterative deep learning-based training strategy for fast delineation of ROIs of large-scale datasets, thereby saving clinicians' time and obtaining novel and more robust models; Modular architecture with customization and extensibility, where plugins can be designed for specific purposes.

Proper citation: uAI Research Portal (RRID:SCR_025870) Copy   

•   Source: SciCrunch Registry

http://spatialomics.org/SpatialDB/

Database for spatially resolved transcriptomes. Provides curated spatially resolved transcriptomic data from published papers, aiming to provide comprehensive and accurate resource of spatial gene expression profiles in tissues. Allows users to browse spatial gene expression profile and compare spatial gene expression profile of any two datasets generated by same or different techniques side by side.

Proper citation: Spatial DB (RRID:SCR_026135) Copy   

•   Source: SciCrunch Registry

https://cadd.labshare.cn/cb-dock2/php/index.php

Web server for protein-ligand blind docking, integrating cavity detection, docking and homologous template fitting. Given the three-dimensional structure of protein and ligand, can predict their binding sites and affinity for computer-aided drug discovery.

Proper citation: CB-dock2 (RRID:SCR_026134) Copy   

•   Source: SciCrunch Registry

http://herb.ac.cn/

High-throughput experiment- and reference-guided database of traditional Chinese medicine.

Proper citation: HERB (RRID:SCR_026468) Copy   

•   Source: SciCrunch Registry

https://github.com/PaulingLiu/ROGUE

Software tool as entropy-based metric for assessing purity of single cell populations. Used to accurately quantify purity of identified cell clusters.

Proper citation: ROGUE (RRID:SCR_026568) Copy   

•   Source: SciCrunch Registry

http://www.zhounan.org/ferrdb/current/

Manually curated database of ferroptosis regulators and ferroptosis-disease associations. There are two secondary categories of ferroptosis regulators: (1) genes and (2) substances. Gene regulators include driver, suppressor, marker, and unclassified regulator. Substances cover range of chemical entities, including pure substances (e.g., iron, erastin) and mixtures (e.g., herbal extracts). Substance regulators include inducers and inhibitors. FerrDb V2 is updated database.

Proper citation: FerrDb (RRID:SCR_026852) Copy   

•   Source: SciCrunch Registry

https://bioconductor.org/packages/release/bioc/html/DOSE.html

Software R package for disease ontology semantic and enrichment analysis.

Proper citation: DOSE (RRID:SCR_027408) Copy   

•   Source: SciCrunch Registry

https://github.com/zhongguojie1998/CSOmap

Software tool for reconstruction of cell spatial organization from single-cell RNA sequencing data based on ligand-receptor mediated self-assembly. Infers cellular spatial organization from scRNA-seq by modeling ligand–receptor-mediated self-assembly. It constructs 3D pseudo-space and quantifies cell–cell interactions for downstream visualization and hypothesis testing.

Proper citation: CSOmap (RRID:SCR_027636) Copy   

•   Source: SciCrunch Registry

http://ctrdb.ncpsb.org.cn/

Data resource for clinical transcriptomes with cancer treatment response, and meanwhile supports various data analysis functions, providing insights into the molecular determinants of drug resistance. CTR-DB 2.0 is updated cancer clinical transcriptome resource, expanding primary drug resistance and newly adding acquired resistance datasets and enhancing the discovery and validation of predictive biomarkers.

Proper citation: Cancer Treatment Response gene signature DataBase (RRID:SCR_027950) Copy   

•   Source: SciCrunch Registry