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http://www.jcvi.org/charprotdb/index.cgi/home

The Characterized Protein Database, CharProtDB, is designed and being developed as a resource of expertly curated, experimentally characterized proteins described in published literature. For each protein record in CharProtDB, storage of several data types is supported. It includes functional annotation (several instances of protein names and gene symbols) taxonomic classification, literature links, specific Gene Ontology (GO) terms and GO evidence codes, EC (Enzyme Commisssion) and TC (Transport Classification) numbers and protein sequence. Additionally, each protein record is associated with cross links to all public accessions in major protein databases as ��synonymous accessions��. Each of the above data types can be linked to as many literature references as possible. Every CharProtDB entry requires minimum data types to be furnished. They are protein name, GO terms and supporting reference(s) associated to GO evidence codes. Annotating using the GO system is of importance for several reasons; the GO system captures defined concepts (the GO terms) with unique ids, which can be attached to specific genes and the three controlled vocabularies of the GO allow for the capture of much more annotation information than is traditionally captured in protein common names, including, for example, not just the function of the protein, but its location as well. GO evidence codes implemented in CharProtDB directly correlate with the GO consortium definitions of experimental codes. CharProtDB tools link characterization data from multiple input streams through synonymous accessions or direct sequence identity. CharProtDB can represent multiple characterizations of the same protein, with proper attribution and links to database sources. Users can use a variety of search terms including protein name, gene symbol, EC number, organism name, accessions or any text to search the database. Following the search, a display page lists all the proteins that match the search term. Click on the protein name to view more detailed annotated information for each protein. Additionally, each protein record can be annotated.

Proper citation: CharProtDB: Characterized Protein Database (RRID:SCR_005872) Copy   

•   Source: SciCrunch Registry

http://www.neuroepigenomics.org/methylomedb/

A database containing genome-wide brain DNA methylation profiles for human and mouse brains. The DNA methylation profiles were generated by Methylation Mapping Analysis by Paired-end Sequencing (Methyl-MAPS) method and analyzed by Methyl-Analyzer software package. The methylation profiles cover over 80% CpG dinucleotides in human and mouse brains in single-CpG resolution. The integrated genome browser (modified from UCSC Genome Browser allows users to browse DNA methylation profiles in specific genomic loci, to search specific methylation patterns, and to compare methylation patterns between individual samples. Two species were included in the Brain Methylome Database: human and mouse. Human postmortem brain samples were obtained from three distinct cortical regions, i.e., dorsal lateral prefrontal cortex (dlPFC), ventral prefrontal cortex (vPFC), and auditory cortex (AC). Human samples were selected from our postmortem brain collection with extensive neuropathological and psychopathological data, as well as brain toxicology reports. The Department of Psychiatry of Columbia University and the New York State Psychiatric Institute have assembled this brain collection, where a validated psychological autopsy method is used to generate Axis I and II DSM IV diagnoses and data are obtained on developmental history, history of psychiatric illness and treatment, and family history for each subject. The mouse sample (strain 129S6/SvEv) DNA was collected from the entire left cerebral hemisphere. The three human brain regions were selected because they have been implicated in the neuropathology of depression and schizophrenia. Within each cortical region, both disease and non-psychiatric samples have been profiled (matching subjects by age and sex in each group). Such careful matching of subjects allows one to perform a wide range of queries with the ability to characterize methylation features in non-psychiatric controls, as well as detect differentially methylated domains or features between disease and non-psychiatric samples. A total of 14 non-psychiatric, 9 schizophrenic, and 6 depression methylation profiles are included in the database.

Proper citation: MethylomeDB (RRID:SCR_005583) Copy   

•   Source: SciCrunch Registry

http://www.broadinstitute.org/mammals/haploreg/haploreg.php

HaploReg is a tool for exploring annotations of the noncoding genome at variants on haplotype blocks, such as candidate regulatory SNPs at disease-associated loci. Using linkage disequilibrium (LD) information from the 1000 Genomes Project, linked SNPs and small indels can be visualized along with their predicted chromatin state in nine cell types, conservation across mammals, and their effect on regulatory motifs. HaploReg is designed for researchers developing mechanistic hypotheses of the impact of non-coding variants on clinical phenotypes and normal variation.

Proper citation: HaploReg (RRID:SCR_006796) Copy   

•   Source: SciCrunch Registry

http://www.uniprot.org/help/uniref

Databases which provide clustered sets of sequences from UniProt Knowledgebase and selected UniParc records, in order to obtain complete coverage of sequence space at several resolutions while hiding redundant sequences from view. The UniRef100 database combines identical sequences and sub-fragments with 11 or more residues (from any organism) into a single UniRef entry. The sequence of a representative protein, the accession numbers of all the merged entries, and links to the corresponding UniProtKB and UniParc records are all displayed in the entry. UniRef90 and UniRef50 are built by clustering UniRef100 sequences with 11 or more residues such that each cluster is composed of sequences that have at least 90% (UniRef90) or 50% (UniRef50) sequence identity to the longest sequence (UniRef seed sequence). All the sequences in each cluster are ranked to facilitate the selection of a representative sequence for the cluster.

Proper citation: UniRef (RRID:SCR_010646) Copy   

•   Source: SciCrunch Registry

http://www.informatics.jax.org/

Community model organism database for laboratory mouse and authoritative source for phenotype and functional annotations of mouse genes. MGD includes complete catalog of mouse genes and genome features with integrated access to genetic, genomic and phenotypic information, all serving to further the use of the mouse as a model system for studying human biology and disease. MGD is a major component of the Mouse Genome Informatics.Contains standardized descriptions of mouse phenotypes, associations between mouse models and human genetic diseases, extensive integration of DNA and protein sequence data, normalized representation of genome and genome variant information. Data are obtained and integrated via manual curation of the biomedical literature, direct contributions from individual investigators and downloads from major informatics resource centers. MGD collaborates with the bioinformatics community on the development and use of biomedical ontologies such as the Gene Ontology (GO) and the Mammalian Phenotype (MP) Ontology.

Proper citation: Mouse Genome Database (RRID:SCR_012953) Copy   

•   Source: SciCrunch Registry

http://hb.flatironinstitute.org/

Formerly known as GIANT (Genome-scale Integrated Analysis of gene Networks in Tissues), HumanBase applies machine learning algorithms to learn biological associations from massive genomic data collections. These integrative analyses reach beyond existing "biological knowledge" represented in the literature to identify novel, data-driven associations.

Proper citation: HumanBase (RRID:SCR_016145) Copy   

•   Source: SciCrunch Registry

http://thecellmap.org

Web accessible database for visualizing and mining global yeast genetic interaction network. Allows users to easily access, visualize, explore, and functionally annotate genetic interactions, or to extract and reorganize sub networks, using data driven network layouts in intuitive and interactive manner. Used for storing and visualizing genetic interactions in S. cerevisiae.

Proper citation: TheCellMap (RRID:SCR_018728) Copy   

•   Source: SciCrunch Registry

https://phenodb.org/

Database for phenotype genotype associations for humans. Used by clinical researchers to store standardized phenotypic information, diagnosis, and pedigree data and then run analyses on VCF files from individuals, families or cohorts with suspected Mendelian disease.

Proper citation: PhenoDB (RRID:SCR_016551) Copy   

•   Source: SciCrunch Registry

http://www.regulomedb.org/

Database that annotates SNPs with known and predicted regulatory elements in intergenic regions of H. sapiens genome. Known and predicted regulatory DNA elements include regions of DNAase hypersensitivity, binding sites of transcription factors, and promoter regions that have been biochemically characterized to regulation transcription. Source of these data include public datasets from GEO, ENCODE project, and published literature.

Proper citation: RegulomeDB (RRID:SCR_017905) Copy   

•   Source: SciCrunch Registry

https://dfam.org/home

Open collection of Transposable Element DNA sequence alignments, hidden Markov Models, consensus sequences, and genome annotations.Dfam 3.2 provides early access to uncurated, de novo generated families.

Proper citation: Dfam (RRID:SCR_021168) Copy   

•   Source: SciCrunch Registry

https://github.com/rondolab/MR-PRESSO

Software R package for performing Mendelian randomization pleiotropy residual sum and outlier method.Used to identify horizontal pleiotropic outliers in multi instrument summary level MR testing.

Proper citation: MR-PRESSO (RRID:SCR_023697) Copy   

•   Source: SciCrunch Registry

https://data.broadinstitute.org/alkesgroup/Eagle/

Software package for statistical estimation of haplotype phase either within a genotyped cohort or using a phased reference panel in large scale sequencing. The package includes Eagle1 (to harness identity-by-descent among distant relatives to rapidly call phase using a fast scoring approach) and Eagle2 (to analyze a full probabilistic model similar to the diploid Li-Stephens model used by previous HMM-based methods.

Proper citation: Eagle (RRID:SCR_015991) Copy   

•   Source: SciCrunch Registry

https://bioconductor.org/packages/rtracklayer/

Software R package for interfacing with genome browsers.Supports integration of existing genome browsers with experimental data analyses performed in R. R interface to genome annotation files and UCSC genome browser.

Proper citation: rtracklayer (RRID:SCR_021325) Copy   

•   Source: SciCrunch Registry

https://github.com/marbl/Mash

Software tool for genome and metagenome distance estimation using MinHash. Reduces large sequences and sequence sets to small, representative sketches, from which global mutation distances can be rapidly estimated.

Proper citation: Mash (RRID:SCR_019135) Copy   

•   Source: SciCrunch Registry

https://github.com/stephens999/ashr

Software R package for adaptive shrinkage. Implements Empirical Bayes approach for large scale hypothesis testing and false discovery rate estimation.

Proper citation: Adaptive Shrinkage in R (RRID:SCR_023486) Copy   

•   Source: SciCrunch Registry

https://pepatac.databio.org/en/latest/

Software standardized pipeline for ATAC-seq data analysis with serial alignments. Leverages unique features of ATAC-seq data to optimize for speed and accuracy, and provides several unique analytical approaches. Downstream analysis is simplified by standard definition format, modularity of components, and metadata APIs in R and Python. Restartable, fault-tolerant, and can be run on local hardware, using any cluster resource manager, or in provided Linux containers. We also emphasize the advantage of aligning to the mitochondrial genome serially, which improves alignment and quality control metrics. Includes quality control plots, summary statistics, and variety of data formats.

Proper citation: PEPATAC (RRID:SCR_024758) Copy   

•   Source: SciCrunch Registry

https://jackbibby1.github.io/SCPA/

Software R package for pathway analysis in scRNA-seq data. It’s a different approach to pathway analysis that defines pathway activity as a change in multivariate distribution of a given pathway across conditions, rather than enrichment or over representation of genes.

Proper citation: Single Cell Pathway Analysis (RRID:SCR_024909) Copy   

•   Source: SciCrunch Registry

https://www.bioconductor.org/packages/release/bioc/html/HiCDCPlus.html

Software package for Hi-C/HiChIP interaction calling and differential analysis using efficient implementation of HiC-DC statistical framework. Enables principled statistical analysis of Hi-C and HiChIP data sets. Enables systematic 3D interaction calls and differential analysis for Hi-C and HiChIP

Proper citation: HiCDCPlus (RRID:SCR_025317) Copy   

•   Source: SciCrunch Registry

https://github.com/pjedge/longshot

Software variant calling tool for diploid genomes using long error prone reads such as Pacific Biosciences (PacBio) SMRT and Oxford Nanopore Technologies (ONT). Enables accurate variant calling in diploid genomes from single-molecule long read sequencing. Takes as input aligned BAM/CRAM file and outputs phased VCF file with variants and haplotype information.

Proper citation: longshot (RRID:SCR_025318) Copy   

•   Source: SciCrunch Registry

https://trna.ucsc.edu/tRAX/

Software package built for in-depth analyses of tRNA-derived small RNAs (tDRs), mature tRNAs, and inference of RNA modifications from high-throughput small RNA sequencing data. Used for integrating analysis of tRNAs, tRNA-derived small RNAs, and tRNA modifications.

Proper citation: tRNA Analysis of eXpression (RRID:SCR_025486) Copy   

•   Source: SciCrunch Registry