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https://github.com/stamatak/ExaML

Source code for large-scale phylogenetic analyses on whole-transcriptome and whole-genome alignments using supercomputers.

Proper citation: Examl (RRID:SCR_016087) Copy   

•   Source: SciCrunch Registry

http://www.lncrnadb.org/

Searchable database of comprehensive annotations of eukaryotic long non-coding RNAs. Entries are manually curated from referenced literature.

Proper citation: lncRNAdb (RRID:SCR_015491) Copy   

•   Source: SciCrunch Registry

http://smithlabresearch.org/software/methbase/

Central reference methylome database created from public BS-seq datasets. Provides methylation level at individual sites, regions of allele specific methylation, hypo- or hyper-methylated regions, partially methylated regions, and detailed meta data and summary statistics.

Proper citation: MethBase (RRID:SCR_017487) Copy   

•   Source: SciCrunch Registry

https://signalingpathways.org

Web multi omics knowledgebase based upon public, manually curated transcriptomic and cistromic datasets involving genetic and small molecule manipulations of cellular receptors, enzymes and transcription factors. Integrated omics knowledgebase for mammalian cellular signaling pathways. Web browser interface was designed to accommodate numerous routine data mining strategies. Datasets are biocurated versions of publically archived datasets and are formatted according to recommendations of the FORCE11 Joint Declaration on Data Citation Principles73, and are made available under Creative Commons CC 3.0 BY license. Original datasets are available.

Proper citation: Signaling Pathways Project (RRID:SCR_018412) Copy   

•   Source: SciCrunch Registry

https://github.com/citiususc/veryfasttree

Software tool for speeding up estimation of phylogenetic trees for large alignments through parallelization and vectorization strategies.

Proper citation: VeryFastTree (RRID:SCR_023594) Copy   

•   Source: SciCrunch Registry

http://www.bioconductor.org/packages/release/bioc/html/ReadqPCR.html

A software package that provides functions to read raw RT-qPCR data of different platforms.

Proper citation: ReadqPCR (RRID:SCR_000030) Copy   

•   Source: SciCrunch Registry

http://sourceforge.net/projects/metabnorm/

Software tool as mixed model normalization method for metabolomics data.Uses normalization approach based on mixed model, with simultaneous estimation of correlation matrix.

Proper citation: metabnorm (RRID:SCR_001266) 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

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.cs.ucr.edu/~yyang027/mrfseq.htm

Algorithm based on a Markov random field (MRF) model that uses additional gene coexpression data to enhance differential gene expression prediction power. It is able to call differentially expressed (DE) genes but also assign confidence scores to each inferred DE gene.

Proper citation: MRFSEQ (RRID:SCR_002972) Copy   

•   Source: SciCrunch Registry

http://zfishbook.org/

Collection of revertible protein trap gene-breaking transposon (GBT) insertional mutants in zebrafish with active or cryopreserved lines from initially identified lines. Open to community-wide contributions including expression and functional annotation and represents world-wide central hub for information on how to obtain these lines from diverse members of International Zebrafish Protein Trap Consortium (IZPTC) and integration within other zebrafish community databases including Zebrafish Information Network (ZFIN), Ensembl and National Center for Biotechnology Information. Registration allows users to save their favorite lines for easy access, request lines from Mayo Clinic catalog, contribute to line annotation with appropriate credit, and puts them on optional mailing list for future zfishbook newletters and updates.

Proper citation: zfishbook (RRID:SCR_006896) Copy   

•   Source: SciCrunch Registry

http://www.atgc-montpellier.fr/phyml/

Web phylogeny server based on the maximum-likelihood principle.

Proper citation: PhyML (RRID:SCR_014629) Copy   

•   Source: SciCrunch Registry

http://rloop.bii.a-star.edu.sg/?pg=qmrlfs-finder

A software which predicts R-loop Forming Sequences (RLFSs) in nucleic acid sequences based on the experimentally supported structural models of RLFSs. The tool identifies and visualizes RLFS coordinates from natural or artificial DNA or RNA input sequences and creates standard-compliant output files for later annotation and analysis.

Proper citation: QmRLFS-finder (RRID:SCR_014584) Copy   

•   Source: SciCrunch Registry

http://www.funrich.org/

A software tool used for functional enrichment and interaction network analysis of genes and proteins. Users can search against a default background database or load customized database. The results can be depicted as venn, bar, column, pie and doughnut charts.

Proper citation: FunRich: Functional Enrichment analysis tool (RRID:SCR_014467) Copy   

•   Source: SciCrunch Registry

https://cibersort.stanford.edu/

Software tool to provide an estimation of the abundances of member cell types in a mixed cell population, using gene expression data. Used for characterizing cell composition of complex tissues from their gene expression profiles, large scale analysis of RNA mixtures for cellular biomarkers and therapeutic targets.

Proper citation: CIBERSORT (RRID:SCR_016955) Copy   

•   Source: SciCrunch Registry

http://myriads.webs.uvigo.es/

Software package for p value based multiple testing that also implements dependence test and p-value simulation.

Proper citation: Myriads (RRID:SCR_017447) Copy   

•   Source: SciCrunch Registry

http://www.pyrosetta.org/

Interactive Python based interface to Rosetta molecular modeling suite. Stand alone Python based implementation of Rosetta molecular modeling package that allows users to write custom structure prediction and design algorithms using major Rosetta sampling and scoring functions.

Proper citation: PyRosetta (RRID:SCR_018541) 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

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