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http://db.phasep.pro/

Provides a collection of manually curated phase separation (PS) proteins and Membraneless organelles (MLOs) related proteins. Annotated phase separation-related proteins with droplet states, co-phase separation partners and other experimental information.

Proper citation: PhaSepDB (RRID:SCR_024964) Copy   

•   Source: SciCrunch Registry

https://rps.renlab.org/#/Home

Comprehensive database of RNAs involved in liquid-liquid phase separation.

Proper citation: RPS (RRID:SCR_024960) Copy   

•   Source: SciCrunch Registry

https://github.com/IOBR/IOBR

Software R package to perform comprehensive analysis of tumor microenvironment and signatures for immuno-oncology. Used for comprehensively interpreting multi-omics data.

Proper citation: IOBR (RRID:SCR_025619) Copy   

•   Source: SciCrunch Registry

https://appyters.maayanlab.cloud/#/hTFtarget_Harmonizome_ETL

Comprehensive database for regulations of Human Transcription Factors and their targets. Provides tools for visualization, interpretation, and analysis of pathway knowledge.

Proper citation: hTFtarget (RRID:SCR_025626) Copy   

•   Source: SciCrunch Registry

https://github.com/nayu0419/stMMR

Software tool for spatial domain identification from spatially resolved transcriptomics with multi-modal feature representation.

Proper citation: stMMR (RRID:SCR_025601) Copy   

•   Source: SciCrunch Registry

https://ngdc.cncb.ac.cn/gsa/

Public archive of raw sequence data in National Genomics Data Center as part of the China National Center for Bioinformation. GSA accepts worldwide data submissions, performs data curation and quality control for all submitted data. Provides data storage and sharing services.

Proper citation: Chinese Genome Sequence Archive (RRID:SCR_025826) Copy   

•   Source: SciCrunch Registry

http://biocc.hrbmu.edu.cn/CancerSEA/

Database that aims to comprehensively explore distinct functional states of cancer cells at the single-cell level. Provides functional state-associated PCG/lncRNA repertoires across all cancers, in specific cancers, and in individual cancer single-cell datasets. Provides interface for comprehensively searching, browsing, visualizing and downloading functional state activity profiles of cancer single cells and corresponding PCGs/lncRNAs expression profiles.

Proper citation: CancerSEA (RRID:SCR_026155) Copy   

•   Source: SciCrunch Registry

http://gepia2.cancer-pku.cn/#index

Enhanced web server for large-scale expression profiling and interactive analysis. GEPIA2 is updated and enhanced version of GEPIA, offering more functionalities, higher resolution data analysis, and additional features like ability to analyze specific cancer subtypes, quantify gene signatures based on single-cell sequencing studies, and allow users to upload their own RNA-seq data for comparison with the TCGA and GTEx datasets; essentially providing more comprehensive and advanced platform for gene expression analysis compared to the original GEPIA version.

Proper citation: Gene Expression Profiling Interactive Analysis 2 (RRID:SCR_026154) Copy   

•   Source: SciCrunch Registry

https://github.com/zengxiaofei/HapHiC

Software fast, reference-independent, allele-aware scaffolding tool based on Hi-C data. Allele-aware scaffolding tool that uses Hi-C data to scaffold haplotype-phased genome assemblies into chromosome-scale pseudomolecules.

Proper citation: HapHiC (RRID:SCR_026284) Copy   

•   Source: SciCrunch Registry

https://pmc.ncbi.nlm.nih.gov/articles/PMC3783192/

Software tool for utilizing sequence intrinsic composition to classify protein-coding and long non-coding transcripts.

Proper citation: Coding-Non-Coding Index (RRID:SCR_026554) Copy   

•   Source: SciCrunch Registry

https://github.com/lvrgb777/STPoseNet

Source code for pose recognition model for laboratory mice based on yolov8. Real-time spatiotemporal network model for robust mouse pose estimation.

Proper citation: STPoseNet (RRID:SCR_026834) Copy   

•   Source: SciCrunch Registry

https://github.com/BigDataBiology/SemiBin/

Software command tool for metagenomic binning with deep learning, handles both short and long reads. Used for metagenomic binning at contig level which uses deep contrastive learning.

Proper citation: SemiBin (RRID:SCR_026896) Copy   

•   Source: SciCrunch Registry

https://cran.r-project.org/web/packages/ggVennDiagram/readme/README.html

Software R package to generate Venn diagram.'ggplot2' implement of Venn Diagram.

Proper citation: ggVennDiagram (RRID:SCR_026950) Copy   

•   Source: SciCrunch Registry

https://github.com/Baohua-Chen/GFFx

Software Rust-Based suite of utilities for ultra-fast genomic feature extraction. Used for ultra-fast and scalable genome annotation access. Can be used both as a command-line tool and as a Rust library.

Proper citation: GFFx (RRID:SCR_027445) Copy   

•   Source: SciCrunch Registry

https://github.com/The-Zhou-Lab/SeedGerm-VIG

Software pipeline to quantify seed vigour in wheat and other cereal crops using deep learning powered dynamic phenotypic analysis.

Proper citation: SeedGerm-VIG (RRID:SCR_027483) Copy   

•   Source: SciCrunch Registry

https://guolab.wchscu.cn/ImmuCellAI/#!/

Software tool for comprehensive T‐Cell subsets abundance prediction and its application in cancer immunotherapy.

Proper citation: ImmuCellAI (RRID:SCR_027645) Copy   

•   Source: SciCrunch Registry

http://hapmap.ncbi.nlm.nih.gov/

THIS RESOURCE IS NO LONGER IN SERVICE, documented August 22, 2016. A multi-country collaboration among scientists and funding agencies to develop a public resource where genetic similarities and differences in human beings are identified and catalogued. Using this information, researchers will be able to find genes that affect health, disease, and individual responses to medications and environmental factors. All of the information generated by the Project will be released into the public domain. Their goal is to compare the genetic sequences of different individuals to identify chromosomal regions where genetic variants are shared. Public and private organizations in six countries are participating in the International HapMap Project. Data generated by the Project can be downloaded with minimal constraints. HapMap project related data, software, and documentation include: bulk data on genotypes, frequencies, LD data, phasing data, allocated SNPs, recombination rates and hotspots, SNP assays, Perlegen amplicons, raw data, inferred genotypes, and mitochondrial and chrY haplogroups; Generic Genome Browser software; protocols and information on assay design, genotyping and other protocols used in the project; and documentation of samples/individuals and the XML format used in the project.

Proper citation: International HapMap Project (RRID:SCR_002846) Copy   

•   Source: SciCrunch Registry

http://fcon_1000.projects.nitrc.org/indi/CoRR/html/

Consortium that has aggregated resting state fMRI (R-fMRI) and diffusion imaging data from laboratories around the world, creating an open science resource for the imaging community, that facilitates the assessment of test-retest reliability and reproducibility for functional and structural connectomics. Given that this was a retrospective data collection, they have focused on basic phenotypic measures that are relatively standard in the neuroimaging field, as well as fundamental for analyses and sample characterization. Their phenotypic key is organized to reflect three classifications of variables: 1) core (i.e., minimal variables required to characterize any dataset), 2) preferred (i.e., variables that were strongly suggested for inclusion due to their relative import and/or likelihood of being collected by most sites), and 3) optional (variables that are data-set specific or only shared by a few sites). CoRR includes 33 datasets consisting of: * 1629 Subjects * 3357 Anatomical Scans * 5093 Resting Functional Scans * 1302 Diffusion Scans * 300 CBF and ASL Scans

Proper citation: Consortium for Reliability and Reproducibility (RRID:SCR_003774) Copy   

•   Source: SciCrunch Registry

http://ahd.cbi.pku.edu.cn

Database providing a systematic and comprehensive view of morphological phenotypes regulated by plant hormones, as well as regulatory genes participating in numerous plant hormone responses. By integrating the data from mutant studies, transgenic analysis and gene ontology annotation, genes related to the stimulus of eight plant hormones were identified, including abscisic acid, auxin, brassinosteroid, cytokinin, ethylene, gibberellin, jasmonic acid and salicylic acid. Another pronounced characteristics of this database is that a phenotype ontology was developed to precisely describe all kinds of morphological processes regulated by plant hormones with standardized vocabularies. To increase the coverage of phytohormone related genes, the database has been updated from AHD to AHD2.0 adding and integrating several pronounced features: (1) added 291 newly published Arabidopsis hormone related genes as well as corrected information (e.g. the arguable ABA receptors) based on the recent 2-year literature; (2) integrated orthologues of sequenced plants in OrthoMCLDB into each gene in the database; (3) integrated predicted miRNA splicing site in each gene in the database; (4) provided genetic relationship of these phytohormone related genes mining from literature, which represents the first effort to construct a relatively comprehensive and complex network of hormone related genes as shown in the home page of our database; (5) In convenience to in-time bioinformatics analysis, they also provided links to a powerful online analysis platform Weblab that they have recently developed, which will allow users to readily perform various sequence analysis with these phytohormone related genes retrieved from AHD2.0; (6) provided links to other protein databases as well as more expression profiling information that would facilitate users for a more systematic analysis related to phytohormone research. Please help to improve the database with your contributions.

Proper citation: Arabidopsis Hormone Database (RRID:SCR_001792) Copy   

•   Source: SciCrunch Registry

http://www.megabionet.org/atpid/webfile/

Centralized platform to depict and integrate the information pertaining to protein-protein interaction networks, domain architecture, ortholog information and GO annotation in the Arabidopsis thaliana proteome. The Protein-protein interaction pairs are predicted by integrating several methods with the Naive Baysian Classifier. All other related information curated is manually extracted from published literature and other resources from some expert biologists. You are welcomed to upload your PPI or subcellular localization information or report data errors. Arabidopsis proteins is annotated with information (e.g. functional annotation, subcellular localization, tissue-specific expression, phosphorylation information, SNP phenotype and mutant phenotype, etc.) and interaction qualifications (e.g. transcriptional regulation, complex assembly, functional collaboration, etc.) via further literature text mining and integration of other resources. Meanwhile, the related information is vividly displayed to users through a comprehensive and newly developed display and analytical tools. The system allows the construction of tissue-specific interaction networks with display of canonical pathways.

Proper citation: Arabidopsis thaliana Protein Interactome Database (RRID:SCR_001896) Copy   

•   Source: SciCrunch Registry