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http://ms-utils.org

Open source software suite for mass spectrometry based proteomics. Software repository and collection of free software for analysis of mass spectrometry data. Software and code snippets for visualization and analysis of mass spectrometry data with emphasis on automated methods for proteomics and protein analysis.

Proper citation: ms-utils.org (RRID:SCR_019810) Copy   

•   Source: SciCrunch Registry
  

https://urgi.versailles.inra.fr/Tools/PASTEClassifier

Software tool for automatic transposable element classification. Used for searching for structural features and similarity to classify transposable elements.

Proper citation: PASTEClassifier (RRID:SCR_017645) Copy   

•   Source: SciCrunch Registry
  

https://github.com/fritzsedlazeck/Sniffles

Software tool as structural variation caller using third generation sequencing (PacBio or Oxford Nanopore). It detects all types of SVs (10bp+) using evidence from split-read alignments, high-mismatch regions, and coverage analysis. Used to avoid single molecule long read sequencing high error rates.

Proper citation: Sniffles (RRID:SCR_017619) Copy   

•   Source: SciCrunch Registry
  

https://github.com/nch-igm/rna-stability

Software tool as parallel processing framework for large scale generation of secondary RNA structures and folding statistics for transcriptome of any species.

Proper citation: rna-stability (RRID:SCR_019259) Copy   

•   Source: SciCrunch Registry
  

https://github.com/BGI-Qingdao/TGS-GapCloser

Software tool that uses long reads to enhance genome assembly. Fast and accurate gap closing software tool that uses low coverage of error-prone long reads generated by third generation sequence techniques (Pacbio, Oxford Nanopore, etc.) or preassembled contigs for large genomes.

Proper citation: TGS-GapCloser (RRID:SCR_017633) Copy   

•   Source: SciCrunch Registry
  

https://github.com/AnacletoLAB/parSMURF

Open source software package as high performance computing imbalance aware machine learning tool for genome wide detection of pathogenic variants.

Proper citation: parSMURF (RRID:SCR_017560) Copy   

•   Source: SciCrunch Registry
  

https://github.com/hms-dbmi/EHRtemporalVariability

Software R package for delineating temporal dataset shifts in electronic health records. Functions to delineate temporal dataset shifts in electronic health records through projection and visualization of dissimilarities among data temporal batches.Enables exploration and identification of dataset shifts, contributing to broadly examine and repurpose large, longitudinal datasets. Used to help ensure reliable data reuse to biomedical data users.

Proper citation: EHRtemporalVariability (RRID:SCR_018663) Copy   

•   Source: SciCrunch Registry
  

https://github.com/pybel

Software Python package for parsing, validating, compiling, and converting networks encoded in Biological Expression Language.Package consists of network data container, parser and validator, network database manager, data converter and network visualizer. Computational framework for Biological Expression Language. Used to pars BEL documents, validate their semantics, and facilitate data interchange between common formats and database systems like JSON, CSV, Excel, SQL, CX, and Neo4J.

Proper citation: PyBEL (RRID:SCR_017660) Copy   

•   Source: SciCrunch Registry
  

https://github.com/voutcn/megahit

Software tool as Next Generation Sequencing assembler. Optimized for metagenomes, but also works well on generic single genome assembly (small or mammalian size) and single cell assembly. Can assemble genome sequences from metagenomic datasets of hundreds of Giga base-pairs in time and memory efficient manner on single server.

Proper citation: MEGAHIT (RRID:SCR_018551) Copy   

•   Source: SciCrunch Registry
  

https://github.com/brentp/mosdepth

Software command line tool for rapidly calculating genome wide sequencing coverage. Measures depth from BAM or CRAM files at either each nucleotide position in genome or for sets of genomic regions. Used for fast BAM/CRAM depth calculation for WGS, exome, or targeted sequencing quick coverage calculation for genomes and exomes.

Proper citation: mosdepth (RRID:SCR_018929) Copy   

•   Source: SciCrunch Registry
  

https://radar-base.org/index.php/home/about-us/

Open source mobile health platform for collecting, monitoring, and analyzing data using sensors, wearables, and mobile devices. Enables study design and set up, active and passive remote data collection, secure data transmission via Wifi and/or Bluetooth and scalable solutions for data storage, management and access. Allows study participants to share their health data with clinicians and researchers in secure way.

Proper citation: RADAR-base (RRID:SCR_019233) Copy   

•   Source: SciCrunch Registry
  

https://services.healthtech.dtu.dk/services/DictyOGlyc-1.1/

Server that produces neural network predictions for GlcNAc O-glycosylation sites in Dictyostelium discoideum proteins.

Proper citation: DictyOGlyc (RRID:SCR_001600) Copy   

•   Source: SciCrunch Registry
  

http://www-personal.umich.edu/~jianghui/rseq/

A software toolkit for RNA sequence data analysis. It contains programs that cover several aspects of RNA-Seq data analysis such as read quality assessment, reference sequence generation, sequence mapping, and gene and isoform expressions estimations.

Proper citation: rSeq (RRID:SCR_000562) Copy   

•   Source: SciCrunch Registry
  

http://aclame.ulb.ac.be/

A database dedicated to the collection and classification of mobile genetic elements (MGEs) from various sources, comprising all known phage genomes, plasmids and transposons. In addition to provide information on the full genomes and genetic entities, it aims at building a comprehensive classification of the functional modules of MGE's at the protein, gene, and higher levels. Prophinder, a tool dedicated to the detection of prophages in sequenced bacterial genomes, is available on ACLAME.

Proper citation: A Classification of Mobile genetic Elements (RRID:SCR_001694) Copy   

•   Source: SciCrunch Registry
  

http://www.glycosciences.de/modeling/glyprot/

Web-based tool that enables meaningful N-glycan conformations to be attached to all the spatially accessible potential N-glycosylation sites of a known three-dimensional (3D) protein structure. The 3D structure of protein is required as input. Potential N-glysylations site are automatically detected. The attached glycan are constructed with SWEET-II, http://www.glycosciences.de/modeling/sweet2/doc/index.php

Proper citation: GlyProt (RRID:SCR_001560) Copy   

•   Source: SciCrunch Registry
  

http://phospho.elm.eu.org/

Database of experimentally verified phosphorylation sites in eukaryotic proteins. Entries are manually curated with links to literature references, information about structure, interaction partners and sub-cellular compartment tissues, and sequences from the UniProt database.

Proper citation: Phospho.ELM (RRID:SCR_001109) Copy   

•   Source: SciCrunch Registry
  

http://hipipe.ncgm.sinica.edu.tw/

Tool that provides high performance NGS (next-generation sequencing) data analysis pipelines so that researchers with minimum IT or bioinformatics knowledge can perform common analyses on NGS data. 3 TB of storage space is reserved for each task.

Proper citation: HiPipe (RRID:SCR_001215) Copy   

•   Source: SciCrunch Registry
  

http://www.coryneregnet.de

Reference database and analysis platform for corynebacterial transcription factors and gene regulatory networks. It generates links to genome annotations, to identified transcription factors and to the corresponding cis-regulatory elements. CoryneRegNet is based on a multi-layered, hierarchical and modular concept of transcriptional regulation and was implemented by using the relational database management system MySQL and an ontology-based data structure.

Proper citation: CoryneRegNet (RRID:SCR_002255) Copy   

•   Source: SciCrunch Registry
  

http://www.germonline.org/

Cross-species microarray expression database focusing on high-throughput expression data relevant for germline development, meiosis and gametogenesis as well as the mitotic cell cycle. The database contains a unique combination of information: 1) High-throughput expression data obtained with whole-genome high-density oligonucleotide microarrays (GeneChips). 2) Sample annotation (mouse over the sample name and click on it) using the Multiomics Information Management and Annotation System (MIMAS 3.0). 3) In vivo protein-DNA binding data and protein-protein interaction data (available for selected species). 4) Genome annotation information from Ensembl version 50. 5) Orthologs are identified using data from Ensembl and OMA and linked to each other via a section in the report pages. The portal provides access to the Saccharomyces Genomics Viewer (SGV) which facilitates online interpretation of complex data from experiments with high-density oligonucleotide tiling microarrays that cover the entire yeast genome. The database displays only expression data obtained with high-density oligonucleotide microarrays (GeneChips)., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on January 15,2026.

Proper citation: GermOnline (RRID:SCR_002807) Copy   

•   Source: SciCrunch Registry
  

http://cubic.bioc.columbia.edu/db/LOC3d/

THIS RESOURCE IS NO LONGER IN SERVICE, documented on July 16, 2013. LOC3d is a database of predicted subcellular localization for eukaryotic proteins of known 3-D structure taken from the Protein Databank. Subcellular localization is currently predicted using four different methods: predictNLS (nuclear localization signal), LOChom (using homology), LOCkey (using keywords) and LOC3d (neural network based prediction). The reported localization is based on the method which predicts localization of a given protein with the highest confidence. LOCtree is a novel system of support vector machines (SVMs) that predict the subcellular localization of proteins, and DNA-binding propensity for nuclear proteins, by incorporating a hierarchical ontology of localization classes modeled onto biological processing pathways. Biological similarities are incorporated from the description of cellular components provided by the gene ontology consortium (GO). GO definitions have been simplified and tailored to the problem of protein sorting. Technically the ontology has been implemented using a decision tree with SVMs as the nodes. LOCtree, was extremely successful at learning evolutionary similarities among subcellular localization classes and was significantly more accurate than other traditional networks at predicting subcellular localization. Whenever available, LOCtree also reports predictions based on the following: 1) Nuclear localization signals found by PredictNLS, 2) Localization inferred using Prosite motifs and Pfam domains found in the protein, and 3) SWISS-PROT keywords associated with a protein. Localization is inferred in the last two cases using the entropy-based LOCkey algorithm. Additional information can be found in the LOCtree manuscript and associated PredictNLS and LOCkey publications.

Proper citation: Database oDatabase of Predicted Subcellular Localization for Eukaryotic PDB Chainsf Predicted Subcellular Localization for Eukaryotic PDB Chains (RRID:SCR_002831) Copy   

•   Source: SciCrunch Registry