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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://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

http://omicslab.genetics.ac.cn/GOEAST/

Gene Ontology Enrichment Analysis Software Toolkit (GOEAST) is a web based software toolkit providing easy to use, visualizable, comprehensive and unbiased Gene Ontology (GO) analysis for high-throughput experimental results, especially for results from microarray hybridization experiments. The main function of GOEAST is to identify significantly enriched GO terms among give lists of genes using accurate statistical methods. Compared with available GO analysis tools, GOEAST has the following unique features: * GOEAST supports analysis for data from various resources, such as expression data obtained using Affymetrix, illumina, Agilent or customized microarray platforms. GOEAST also supports non-microarray based experimental data. The web-based feature makes GOEAST very user friendly; users only have to provide a list of genes in correct formats. * GOEAST provides visualizable analysis results, by generating graphs exhibiting enriched GO terms as well as their relationships in the whole GO hierarchy. * Note that GOEAST generates separate graph for each of the three GO categories, namely biological process, molecular function and cellular component. * GOEAST allows comparison of results from multiple experiments (see Multi-GOEAST tool). The displayed color of each GO term node in graphs generated by Multi-GOEAST is the combination of different colors used in individual GOEAST analysis. Platform: Online tool

Proper citation: GOEAST - Gene Ontology Enrichment Analysis Software Toolkit (RRID:SCR_006580) 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://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

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://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

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://datf.cbi.pku.edu.cn/

Database that collects all arabidopsis transcription factors (totally 1922 Loci; 2290 Gene Models) and classifies them into 64 families. It uses not only locus (gene), but also gene model (transcript, protein) and the detail information is for each gene model not for locus. It adds multiple alignment of the DNA-binding domain of each family, Neighbor-Joining phylogenetic tree of each family, the GO annotation, homolog with the Database of Rice Transcription Factors (DRTF). It also keeps old information items such as the unique cloned and sequenced information of about 1200 transcription factors, protein domains, 3D structure information with BLAST hits against PDB, predicted Nuclear Location Signals, UniGene information, as well as links to literature reference.

Proper citation: Database of Arabidopsis Transcription Factors (RRID:SCR_007101) Copy   

•   Source: SciCrunch Registry

http://www.immunoinformatics.net/HLAsupE/

Database of HLA supertype-specific epitopes. It describes major histocompatibility complex (MHC) molecules that bind short peptides derived from endogenous or exogenous antigens and present them onto the surface of antigen-presenting cells (APCs) for T-cell receptor (TCR) recognition.

Proper citation: HLAsupE (RRID:SCR_016277) Copy   

•   Source: SciCrunch Registry