Tools   Select Another Resource Report Type

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/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://github.com/bm2-lab/PanPep

Software framework constructed in three levels for predicting the peptide and TCR binding recognition. Used to recognize TCR–antigen binding, by combining the concepts of meta-learning and the neural Turing machine.

Proper citation: PanPep (RRID:SCR_028580) Copy   

•   Source: SciCrunch Registry

https://guolab.wchscu.cn/TCellSI/

Software R package and web server for T cell state assessment and its applications in immune environment prediction.

Proper citation: TCellSI (RRID:SCR_028753) Copy   

•   Source: SciCrunch Registry

https://github.com/shenlongchen/immuscope

Software tool to predict CD4+ T cell epitopes, model MHC-II antigen presentation, and assess immune responses. It helps scientists with vaccine design, cancer neoantigen discovery, and tracking viral mutations.

Proper citation: ImmuScope (RRID:SCR_028676) 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

https://github.com/PaulingLiu/scibet

Software tool as supervised cell type identifier that accurately predicts cell identity for newly sequenced single cells.

Proper citation: SciBet (RRID:SCR_024743) Copy   

•   Source: SciCrunch Registry

http://mlodis.phasep.pro/

Manually curated database of relations between phase separation and diseases.

Proper citation: PhaSeDis (RRID:SCR_024963) Copy   

•   Source: SciCrunch Registry

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/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

http://fcon_1000.projects.nitrc.org/indi/pro/BeijingShortTR.html

Dataset of resting state fMRI scans obtained using two different TR's in healthy college-aged volunteers. Specifically, for each participant, data is being obtained with a short TR (0.4 seconds) and a long TR (2.0 seconds). In addition this dataset contains a 64-direction DTI scan for every participant. The following data are released for every participant: * 8-minute resting-state fMRI scan (TR = 2 seconds, # repetitions = 240) * 8-minute resting-state fMRI scans (TR = 0.4 seconds, # repetitions = 1200) * MPRAGE anatomical scan, defaced to protect patient confidentiality * 64-direction diffusion tensor imaging scan (2mm isotropic) * Demographic information

Proper citation: Beijing: Short TR Study (RRID:SCR_003502) 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