Tools   Select Another Resource Report Type

http://www.yandell-lab.org/software/maker.html

Software genome annotation pipeline. Portable and easily configurable genome annotation pipeline. Used to allow smaller eukaryotic and prokaryotic genomeprojects to independently annotate their genomes and to create genome databases. MAKER identifies repeats, aligns ESTs and proteins to genome, produces ab-initio gene predictions and automatically synthesizes these data into gene annotations having evidence based quality values.

Proper citation: MAKER (RRID:SCR_005309) Copy   

•   Source: SciCrunch Registry

http://ugene.unipro.ru/

A multiplatform open-source software to assist molecular biologists without much expertise in bioinformatics to manage, analyze and visualize their data. UGENE integrates widely used bioinformatics tools within a common user interface. The toolkit supports multiple biological data formats and allows the retrieval of data from remote data sources. It provides visualization modules for biological objects such as annotated genome sequences, Next Generation Sequencing (NGS) assembly data, multiple sequence alignments, phylogenetic trees and 3D structures. Most of the integrated algorithms are tuned for maximum performance by the usage of multithreading and special processor instructions. UGENE includes a visual environment for creating reusable workflows that can be launched on local resources or in a High Performance Computing (HPC) environment. UGENE is written in C++ using the Qt framework. The built-in plugin system and structured UGENE API make it possible to extend the toolkit with new functionality.

Proper citation: Unipro UGENE (RRID:SCR_005579) Copy   

•   Source: SciCrunch Registry

https://github.com/bionitio-team/bionitio

Open source software tool to provide template for command line bioinformatics tools in various programming languages. Program reads one or more input FASTA files, computes variety of statistics on each file, and prints tabulated output. Used as basis for learning and as foundation for starting new projects.

Proper citation: Bionitio (RRID:SCR_017259) Copy   

•   Source: SciCrunch Registry

http://prospector.ucsf.edu

A package of over twenty mass spectrometry-based tools primarily geared toward proteomic data analysis and database mining. It can be run from the command line, but is primarily used through a web browser, and there is a public website that allows anyone to use the software without local installation. Tandem mass spectrometry analysis tools are used for database searching and identification of peptides, including post-translationally modified peptides and cross-linked peptides. Support for isotope and label-free quantification from this type of data is provided. MS-Viewer software allows sharing and displaying of annotated spectra from many different tandem mass spectrometry data analysis packages. Other tools include software for analyzing peptide mass fingerprinting data (MS-Fit); prediction of theoretical fragmentation of peptides (MS-Product); theoretical chemical or enzymatic digestion of proteins (MS-Digest); and theoretical modeling of the isotope distribution of any chemical, including peptides (MS-Isotope). Searches using amino acid sequence can be used to identify homologous peptides in a database (MS-Pattern); the use of the combination of amino acid sequence and masses can be used for homologous peptide and protein identification using MS-Homology. Tandem mass spectrometry peak list files can be filtered for the presence of certain peaks or neutral losses using MS-Filter. Given a list of proteins, MS-Bridge can report all potential cross-linked peptide combinations of a specified mass. Given a precursor peptide mass and information about known amino acid presence, absence, or modifications, MS-Comp can report all amino acid combinations that could lead to the observed mass.

Proper citation: Protein Prospector (RRID:SCR_014558) Copy   

•   Source: SciCrunch Registry

https://bioconductor.org/packages/EpiDISH/

Software R package provides tools to infer proportions of priori known cell-types present in sample representing mixture of such cell-types. Comparison of reference based algorithms for correcting cell-type heterogeneity in Epigenome-Wide Association Studies.

Proper citation: EpiDISH R package (RRID:SCR_018004) Copy   

•   Source: SciCrunch Registry

https://github.com/smajidian/phaseme

Software tool set to assess quality of per read phasing information and help to reduce errors during this process.

Proper citation: PhaseME (RRID:SCR_018739) Copy   

•   Source: SciCrunch Registry

https://github.com/smithlabcode/riborex

Software R package for identification of differential translation from Ribo-seq data. Computational tool for mapping genome wide differences in translation efficiency.

Proper citation: riborex (RRID:SCR_019104) Copy   

•   Source: SciCrunch Registry

https://github.com/anuprulez/galaxy_tool_recommendation

Software developed by analyzing workflows composed by researchers on European Galaxy server, using deep learning approach. Used to recommend tools in Galaxy. Gated recurrent units neural network.

Proper citation: Tool recommender system in Galaxy (RRID:SCR_018491) Copy   

•   Source: SciCrunch Registry

https://github.com/DReichLab/AdmixTools

Software package that supports formal tests of whether admixture occurred, and makes it possible to infer admixture proportions and dates.

Proper citation: ADMIXTOOLS (RRID:SCR_018495) Copy   

•   Source: SciCrunch Registry

https://github.com/cosanlab/facesync

Open source software Python toolbox which automatically synchronizes recorded facial expressions to videos and events such as social interactions.Framework for recording facial expressions with head mounted cameras.

Proper citation: FaceSync (RRID:SCR_021396) Copy   

•   Source: SciCrunch Registry

https://hoohm.github.io/CITE-seq-Count/

Software python package that allows to count antibody TAGS from CITE-seq and/or cell hashing experiment. Software tool that allows to get UMI counts from single cell protein assay. Used to count (UMI counts) antibody-derived-tags (ADTs) or Cell Hashing tags (HTOs) in raw sequencing reads and build count matrix.

Proper citation: CITE-seq-Count (RRID:SCR_019239) Copy   

•   Source: SciCrunch Registry

http://decgpu.sourceforge.net/homepage.htm

Software tool as parallel and distributed error correction algorithm for high-throughput short reads using CUDA and MPI parallel programming models.

Proper citation: DecGPU (RRID:SCR_000585) Copy   

•   Source: SciCrunch Registry

http://img.jgi.doe.gov/cgi-bin/m/main.cgi

Resource for analysis and annotation of genome and metagenome datasets in comprehensive comparative context. IMG provides users with tools for analyzing publicly available genome datasets and metagenome datasets.

Proper citation: IMG System (RRID:SCR_002965) Copy   

•   Source: SciCrunch Registry

https://nephele.niaid.nih.gov

Cloud based platform for simplified, standardized and reproducible microbiome data analysis. Allows users to process microbiome datasets through pipelines of existing software tools.

Proper citation: Nephele (RRID:SCR_016595) Copy   

•   Source: SciCrunch Registry

http://sideeffects.embl.de/

Database containing information on marketed medicines and their recorded adverse drug reactions. The information is extracted from public documents and package inserts. The available information include side effect frequency, drug and side effect classifications as well as links to further information, for example drug-target relations. The SIDER Side Effect Resource represents an effort to aggregate dispersed public information on side effects. To our knowledge, no such resource exist in machine-readable form despite the importance of research on drugs and their effects. The creation of this resource was motivated by the many requests for data that we received related to our paper (Campillos, Kuhn et al., Science, 2008, 321(5886):263-6.) on the utilization of side effects for drug target prediction. Inclusion of side effects as readouts for drug treatment should have many applications and we hope to be able to enhance the respective research with this resource. You may browse the drugs by name, browse the side effects by name, download the current version of SIDER, or use the search interface.

Proper citation: SIDER (RRID:SCR_004321) Copy   

•   Source: SciCrunch Registry

http://www.ebi.ac.uk/biosamples/

Database that aggregates sample information for reference samples (e.g. Coriell Cell lines) and samples for which data exist in one of the EBI''''s assay databases such as ArrayExpress, the European Nucleotide Archive or PRoteomics Identificates DatabasE. It provides links to assays for specific samples, and accepts direct submissions of sample information. The goals of the BioSample Database include: # recording and linking of sample information consistently within EBI databases such as ENA, ArrayExpress and PRIDE; # minimizing data entry efforts for EBI database submitters by enabling submitting sample descriptions once and referencing them later in data submissions to assay databases and # supporting cross database queries by sample characteristics. The database includes a growing set of reference samples, such as cell lines, which are repeatedly used in experiments and can be easily referenced from any database by their accession numbers. Accession numbers for the reference samples will be exchanged with a similar database at NCBI. The samples in the database can be queried by their attributes, such as sample types, disease names or sample providers. A simple tab-delimited format facilitates submissions of sample information to the database, initially via email to biosamples (at) ebi.ac.uk. Current data sources: * European Nucleotide Archive (424,811 samples) * PRIDE (17,001 samples) * ArrayExpress (1,187,884 samples) * ENCODE cell lines (119 samples) * CORIELL cell lines (27,002 samples) * Thousand Genome (2,628 samples) * HapMap (1,417 samples) * IMSR (248,660 samples)

Proper citation: BioSample Database at EBI (RRID:SCR_004856) Copy   

•   Source: SciCrunch Registry

http://www.yeastgenome.org/

A curated database that provides comprehensive integrated biological information for Saccharomyces cerevisiae along with search and analysis tools to explore these data. SGD allows researchers to discover functional relationships between sequence and gene products in fungi and higher organisms. The SGD also maintains the S. cerevisiae Gene Name Registry, a complete list of all gene names used in S. cerevisiae which includes a set of general guidelines to gene naming. Protein Page provides basic protein information calculated from the predicted sequence and contains links to a variety of secondary structure and tertiary structure resources. Yeast Biochemical Pathways allows users to view and search for biochemical reactions and pathways that occur in S. cerevisiae as well as map expression data onto the biochemical pathways. Literature citations are provided where available.

Proper citation: SGD (RRID:SCR_004694) Copy   

•   Source: SciCrunch Registry

http://pfam.xfam.org/

A database of protein families, each represented by multiple sequence alignments and hidden Markov models (HMMs). Users can analyze protein sequences for Pfam matches, view Pfam family annotation and alignments, see groups of related families, look at the domain organization of a protein sequence, find the domains on a PDB structure, and query Pfam by keywords. There are two components to Pfam: Pfam-A and Pfam-B. Pfam-A entries are high quality, manually curated families that may automatically generate a supplement using the ADDA database. These automatically generated entries are called Pfam-B. Although of lower quality, Pfam-B families can be useful for identifying functionally conserved regions when no Pfam-A entries are found. Pfam also generates higher-level groupings of related families, known as clans (collections of Pfam-A entries which are related by similarity of sequence, structure or profile-HMM).

Proper citation: Pfam (RRID:SCR_004726) Copy   

•   Source: SciCrunch Registry

http://www.uniprot.org/taxonomy/

NEWT is the taxonomy database maintained by the UniProt group. It integrates taxonomy data compiled in the NCBI database and data specific to the UniProt Knowledgebase. Browse by hierarchy, List all, or Complete proteomes. Organisms are classified in a hierarchical tree structure. Our taxonomy database contains every node (taxon) of the tree. UniProtKB taxonomy data is manually curated: next to manually verified organism names, we provide a selection of external links, organism strains and viral host information. Species with protein sequences stored in the UniProt Knowledgebase are named according to UniProt nomenclature. We endeavour to maintain a list of manually curated species names for which protein sequence data is available. In particular, we have adopted a systematic convention for naming viral and bacterial strains and isolates. Links to external sites are chosen by the UniProt taxonomy team and show pictures and various scientific data of interest (taxonomy, biology, physiology,...).

Proper citation: NEWT (RRID:SCR_004477) Copy   

•   Source: SciCrunch Registry

http://selectome.unil.ch/

Database of positive selection based on a rigorous branch-site specific likelihood test. Positive selection is detected using CODEML on all branches of animal gene trees.

Proper citation: Selectome: a Database of Positive Selection (RRID:SCR_004542) Copy   

•   Source: SciCrunch Registry