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http://cns.hkbu.edu.hk/RIDE.htm

Software Matlab based toolbox for temporal decomposition of EEG signal. Used for decomposition, reconstruction, and single trial analysis of event related potentials.

Proper citation: Residue Iteration Decomposition (RRID:SCR_022174) Copy   

•   Source: SciCrunch Registry

https://github.com/Kinggerm/GetOrganelle

Software toolkit to assembly of organelle genome from genomic skimming data. Used for accurate de novo assembly of organelle genomes.

Proper citation: GetOrganelle (RRID:SCR_022963) Copy   

•   Source: SciCrunch Registry

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

Database provides cell markers for various cell types in tissues of human and mouse. Manually curated resource of cell markers in human and mouse. Provides user-friendly interface for browsing, searching and downloading markers of diverse cell types of different tissues. Summarized marker prevalence in each cell type is graphically presented., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: CellMarker (RRID:SCR_018503) 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

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

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://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/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://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

https://sourceforge.net/projects/metabarcoding/

Software for metabarcoding of DNA. SOAPBarcode takes advantage of high throughput capacity of next-generation-sequencing (NGS) platforms and can characterize the biodiversity of large volumes of eukaryote samples.

Proper citation: SOAPBarcode (RRID:SCR_015776) Copy   

•   Source: SciCrunch Registry

http://medblast.sibsnet.org/

THIS RESOURCE IS NO LONGER IN SERVICE, documented August 29, 2016. An algorithm that finds articles most relevant to a genetic sequence. In the genomic era, researchers often want to know more information about a biological sequence by retrieving its related articles. However, there is no available tool yet to achieve conveniently this goal. Here, a new literature-mining tool MedBlast is developed, which uses natural language processing techniques, to retrieve the related articles of a given sequence. An online server of this program is also provided. The genome sequencing projects generate such a large amount of data every day that many molecular biologists often encounter some sequences that they know nothing about. Literature is usually the principal resource of such information. It is relatively easy to mine the articles cited by the sequence annotation; however, it is a difficult task to retrieve those relevant articles without direct citation relationship. The related articles are those described in the given sequence (gene/protein), or its redundant sequences, or the close homologs in various species. They can be divided into two classes: direct references, which include those either cited by the sequence annotation or citing the sequence in its text; indirect references, those which contain gene symbols of the given sequence. A few additional issues make the task even more complicated: (1) symbols may have aliases; and (2) one sequence may have a couple of relatives that we want to take into account too, which include redundant (e.g. protein and gene sequences) and close homologs. Here the issues are addressed by the development of the software MedBlast, which can retrieve the related articles of the given sequence automatically. MedBlast uses BLAST to extend homology relationships, precompiled species-specific thesauruses, a useful semantics technique in natural language processing (NLP), to extend alias relationship, and EUtilities toolset to search and retrieve corresponding articles of each sequence from PubMed. MedBlast take a sequence in FASTA format as input. The program first uses BLAST to search the GenBank nucleic acid and protein non-redundant (nr) databases, to extend to those homologous and corresponding nucleic acid and protein sequences. Users can input the BLAST results directly, but it is recommended to input the result of both protein and nucleic acid nr databases. The hits with low e-values are chosen as the relatives because the low similarity hits often do not contain specific information. Very long sequences, e.g. 100k, which are usually genomic sequences, are discarded too, for they do not contain specific direct references. User can adjust these parameters to meet their own needs.

Proper citation: MedBlast (RRID:SCR_008202) Copy   

•   Source: SciCrunch Registry

http://idm.fudan.edu.cn/PBmice/

Database for storing, retrieving, and displaying the information derived from piggyBac (PB) insertions (Insert) and their characterizations in the mouse genome with piggyBac transposon system. Quick Search and Advanced Search tools have been provided to find information in the PBmice database, the result is centered on Inserts and provides information related to the Inserts. A mapping database is linked to PBmice too. This mapping database allows row experiment data to be inputted in. All the mature data can be allowed publish to PBmice. PBmice Source Code is available with a License Agreement.

Proper citation: PBmice (RRID:SCR_006978) 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