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https://github.com/nanoporetech/qcat

Software Python command-line tool for demultiplexing Oxford Nanopore reads from FASTQ files.

Proper citation: qcat (RRID:SCR_024195) Copy   

•   Source: SciCrunch Registry

https://github.com/gem-pasteur/macsyfinder

Software tool to mine genomes for molecular systems with Application to CRISPR-Cas Systems. Detection of macromolecular systems in protein datasets using systems modelling and similarity search.

Proper citation: MacSyFinder (RRID:SCR_024106) Copy   

•   Source: SciCrunch Registry

https://github.com/SciLifeLab/TIDDIT

Software tool as structural variant calling.

Proper citation: tiddit (RRID:SCR_024361) Copy   

•   Source: SciCrunch Registry

https://github.com/Adamtaranto/Yanagiba

Software tool to filter and slice Nanopore reads which have been basecalled with Albacore.

Proper citation: Yanagiba (RRID:SCR_024362) Copy   

•   Source: SciCrunch Registry

https://github.com/mateidavid/nanocall

Software basecaller for Oxford Nanopore Technologies sequencing data. Oxford Nanopore Basecaller.

Proper citation: Nanocall (RRID:SCR_024124) Copy   

•   Source: SciCrunch Registry

https://github.com/torognes/swarm

Software tool as clustering method for amplicon-based studies.

Proper citation: swarm (RRID:SCR_024358) Copy   

•   Source: SciCrunch Registry

http://segway.hoffmanlab.org/

The free Segway software package contains a novel method for analyzing multiple tracks of functional genomics data. The method uses a dynamic Bayesian network (DBN) model, which enables it to analyze the entire genome at 1-bp resolution even in the face of heterogeneous patterns of missing data. This method is the first application of DBN techniques to genome-scale data and the first genomic segmentation method designed for use with the maximum resolution data available from ChIP-seq experiments without downsampling. Segway uses the Graphical Models Toolkit (GMTK) for efficient DBN inference. The software has extensive documentation and was designed from the outset with external users in mind.

Proper citation: Segway - a way to segment the genome (RRID:SCR_004206) Copy   

•   Source: SciCrunch Registry

Ratings or validation data are available for this resource

http://broadinstitute.github.io/picard/

Java toolset for working with next generation sequencing data in the BAM format.

Proper citation: Picard (RRID:SCR_006525) Copy   

•   Source: SciCrunch Registry

http://www.biopieces.org

A collection of bioinformatics tools that can be pieced together in a very easy and flexible manner to perform both simple and complex tasks. The Biopieces work on a data stream in such a way that the data stream can be passed through several different Biopieces, each performing one specific task: modifying or adding records to the data stream, creating plots, or uploading data to databases and web services. The Biopieces are executed in a command line environment where the data stream is initialized by specific Biopieces which read data from files, databases, or web services, and output records to the data stream that is passed to downstream Biopieces until the data stream is terminated at the end of the analysis. The advantage of the Biopieces is that a user can easily solve simple and complex tasks without having any programming experience. Moreover, since the data format used to pass data between Biopieces is text based, different developers can quickly create new Biopieces in their favorite programming language - and all the Biopieces will maintain compatibility. Finally, templates exist for creating new Biopieces in Perl and Ruby. There are currently ~190 Biopieces (March 2014).

Proper citation: Biopieces (RRID:SCR_005783) Copy   

•   Source: SciCrunch Registry

http://www.biochem.ucl.ac.uk/bsm/virus_database/VIDA3/VIDA.html

VIDA contains a collection of homologous protein families derived from open reading frames from complete and partial virus genomes. For each family, users can get an alignment of the conserved regions, functional and taxonomy information, and links to DNA sequences and structures. * Search homologous protein families from particular virus families * Links to complete genome sequence: Arteriviridae, Coronaviridae, Herpesviridae, Poxviridae The Virus Database at University College London has been developed as a system to organize animal virus open reading frame sequences. All known and predicted protein sequences from complete and partial genomes of particular virus families are extracted from GenBank and filtered to remove 100% redundancy. On the basis of sequence similarity the sequences are then clustered into homologous protein families (HPFs). The families are enriched with annotations including function and functional classification, related protein structures, taxonomy, length of the proteins, boundaries of the conserved region/s, virus-specific gene name and links to EMBL entries and SWISSPROT., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: VIDA (RRID:SCR_007111) Copy   

•   Source: SciCrunch Registry

http://cudasw.sourceforge.net/

CUDASW++ is a bioinformatics software for Smith-Waterman protein database searches that takes advantage of the massively parallel CUDA architecture of NVIDIA Tesla GPUs to perform sequence searches 10x-50x faster than NCBI BLAST. In this algorithm, we deeply explore the SIMT (Single Instruction, Multiple Thread) and virtualized SIMD (Single Instruction, Multiple Data) abstractions to achieve fast speed. This algorithm has been fully tested on Tesla C1060, Tesla C2050, GeForce GTX 280 and GTX 295 graphics cards, and has been incorporated to NVIDIA Tesla Bio Workbench. * Operating System: Linux * Programming language: CUDA and C * Other requirements: CUDA SDK and Toolkits 2.0 or higher

Proper citation: CUDASW++ (RRID:SCR_008862) Copy   

•   Source: SciCrunch Registry

http://www-stat.stanford.edu/~tibs/SAM/

Software for genomic expression data mining using a statistical technique for finding significant genes in a set of microarray experiments.

Proper citation: SAM (RRID:SCR_010951) Copy   

•   Source: SciCrunch Registry

http://fantom.gsc.riken.jp/4/

The FANTOM consortium is an international collaborative research project initiated and organized by the RIKEN Omics Science Center. In earlier FANTOM efforts we cloned and annotated 103,000 full-length cDNAs from mouse and distributed them to researchers throughout the world. FANTOM1-3 focused on identifying the transcribed components of mammalian cells. This work improved estimates of the total number of genes and their alternative transcript isoforms in both human and mouse, expanded gene families, and revealed that a large fraction of the transcriptome is non-coding. In addition, with the development of Cap Analysis of Gene Expression (CAGE) FANTOM3 could map a large fraction of transcription start sites and revise our models of promoter structure. This updated web resource provides the previous FANTOM results mapped to current genome builds and presents the results of FANTOM4. In FANTOM4 the focus has changed to understanding how these components work together in the context of a biological network. Using deepCAGE (deep sequencing with CAGE) we monitored the dynamics of transcription start site (TSS) usage during a time course of monocytic differentiation in the acute myeloid leukemia cell line THP-1. This allowed us to identify active promoters, monitor their relative expression and define relevant regions for carrying out transcription factor binding site predictions. Computational methods were then used to build a network model of gene expression in this leukemia and the transcription factors key to its regulation. This work gives the first picture of the wiring between genes involved in acute myeloid leukemia and provides a strategy for identifying key factors that determine cell fates. In addition to the network, FANTOM4 data was used in two additional analyses. The first identified a novel class of short RNAs associated with transcription start sites and the second focused on the role of repetitive element expression in the transcriptome. TOOLS *Genome Browser: graphical display of genomic features, such as promoters, exon structures, H3K9 acetylation, transcription factors positioning on the genome, coupled with gene and promoter activities. *EdgeExpressDB: regulatory interactions, such as transcriptional regulation, post-transcriptional silencing with miRNA, and PPI, coupled with gene and promoter activities. *SwissRegulon: FANTOM4 TF regulation is predicted using Motif Activity Response Analysis (MARA) developed by Erik van Nimwegen at Biozentrum. Follow the link to carry out MARA on your own dataset. *Custom Tracks on the UCSC Genome Browser: FANTOM4 tracks on the UCSC Genome Browser Database. *The RIKEN integrated database of mammals: Integration of FANTOM4 data with other mammalian resources, in particular, produced by RIKEN.

Proper citation: FANTOM DB (RRID:SCR_002678) Copy   

•   Source: SciCrunch Registry

http://www.omicsexpress.com/sva.php

Software package to annotate, visualize, and analyze the genetic variants identified through next-generation sequencing studies, including whole-genome sequencing (WGS) and exome sequencing studies. SVA aims to provide the research community with a user-friendly and efficient tool to analyze large amount of genetic variants, and to facilitate the identification of the genetic causes of human diseases and related traits.

Proper citation: SVA (RRID:SCR_002155) Copy   

•   Source: SciCrunch Registry

https://github.com/eduardporta/e-Driver

Software tool to identify cancer driver genes based on linear annotations of biological regions such as protein domains.Uses information on three-dimensional structures of mutated proteins to identify specific structural features. Then algorithm analyzes whether these features are enriched in cancer somatic mutations and are candidate driver genes.

Proper citation: e-Driver (RRID:SCR_002674) Copy   

•   Source: SciCrunch Registry

http://www.ncbi.nlm.nih.gov/igblast/

THIS RESOURCE IS NO LONGER IN SERVICE.Documented on January 4,2023. IgBLAST was developed at NCBI to facilitate analysis of immunoglobulin V region sequences in GenBank. In addition to performing a regular BLAST search, IgBLAST has several additional functions: - Reports the germline V, D and J gene matches to the query sequence. - Annotates the immunoglobulin domains (FWR1 through FWR3). - Matches the returned hits (for databases other than germline genes) to the closest germline V genes, making it easier to identify related sequences. - Reveals the V(D)J junction details such as nucleotide homology between the ends of V(D)J segments and N nucleotide insertions. D and J gene reporting is only for nucleotide sequence search and requires a stretch of five or more nucleotide identity between the query and D or J genes. Sponsors: This resource is supported by the National Center for Biotechnology Information, a division of the U.S. National Library of Medicine.

Proper citation: IgBLAST (RRID:SCR_002873) Copy   

•   Source: SciCrunch Registry

http://systemsbio.ucsd.edu/GoSurfer/

GoSurfer uses Gene Ontology (GO) information to analyze gene sets obtained from genome-wide computations or microarray analyses. GoSurfer is a graphical interactive data mining tool. It associates user input genes with GO terms and visualizes such GO terms as a hierarchical tree. Users can manipulate the tree output by various means, like setting heuristic thresholds or using statistical tests. Significantly important GO terms resulted from a statistical test can be highlighted. All related information are exportable either as texts or as graphics. Platform: Windows compatible

Proper citation: GoSurfer (RRID:SCR_005789) Copy   

•   Source: SciCrunch Registry

http://www.bioinfo.no/tools/bomp

BOMP is a tool for prediction of beta-barrel integral outer membrane proteins. The user may submit a list of proteins, and receive a list of predicted BOMPs. The program, called the beta-barrel Outer Membrane protein Predictor (BOMP), is based on two separate components to recognize integral beta-barrel proteins. The first component is a C-terminal pattern typical of many integral beta-barrel proteins. The second component calculates an integral beta-barrel score of the sequence based on the extent to which the sequence contains stretches of amino acids typical of transmembrane -strands. To use the BOMP tool simply paste your fasta-formatted sequences into the text area, or choose a file which contains sequences. Then hit the submit button. It is possible to perform a BLAST search parallel with the predictions, which may be suitable in some cases. Using the BLAST search will however increase the running time substantially. Sponsors: This work was supported in part by grants from the Norwegian Research Council [SUP 140785/420 (GABI); FUGE/CBU151899/ISO], and the Meltzer Foundation, University of Bergen. Keywords: Beta-barrel, Membrane, Protein, Program, Software, Beta strand, Bacteria,

Proper citation: BOMP: beta-barrel Outer Membrane protein Predictor (RRID:SCR_007268) Copy   

•   Source: SciCrunch Registry

http://www.psychopy.org

Open source application to allow the presentation of stimuli and collection of data for a wide range of neuroscience, psychology and psychophysics experiments. It is intended as a free, powerful alternative to Presentation or e-Prime.

Proper citation: PsychoPy (RRID:SCR_006571) Copy   

•   Source: SciCrunch Registry

https://varscan.sourceforge.net/

Platform-independent, technology-independent software tool for identifying SNPs and indels in massively parallel sequencing of individual and pooled samples. Given data for a single sample, VarScan identifies and filters germline variants based on read counts, base quality, and allele frequency. Given data for a tumor-normal pair, VarScan also determines the somatic status of each variant (Germline, Somatic, or LOH) by comparing read counts between samples. (entry from Genetic Analysis Software).

Proper citation: VarScan (RRID:SCR_006849) Copy   

•   Source: SciCrunch Registry