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http://sourceforge.net/projects/mtoolbox/

Software for a highly automated bioinformatics pipeline to reconstruct and analyze human mitochondrial DNA from high throughput sequencing data.

Proper citation: MToolBox (RRID:SCR_012112) Copy   

•   Source: SciCrunch Registry

https://code.google.com/p/allim/

A user-friendly software tool to estimate allele-specific gene expression.

Proper citation: Allim (RRID:SCR_012114) Copy   

•   Source: SciCrunch Registry

http://sourceforge.net/projects/nailsystemsbiology/

A set of software tools to simplify the range of computational activities involved in regulatory network inference. It is technology-independent and includes an interface layer to allow easy integration of components into other applications. It is implemented in MATLAB and is available for all researchers to use.

Proper citation: NAIL (RRID:SCR_012134) Copy   

•   Source: SciCrunch Registry

http://sourceforge.net/projects/cohcap/

An algorithm to analyze single-nucleotide resolution methylation data (Illumina 450k methylation array, targeted BS-Seq, etc.). It provides QC metrics, differential methylation for CpG Sites, differential methylation for CpG Islands, integration with gene expression data, and visualization of methylation values.

Proper citation: COHCAP (RRID:SCR_006499) Copy   

•   Source: SciCrunch Registry

http://sammeth.net/confluence/display/FLUX/Home

Software to recontruct abundances of known transcript forms from RNAseq data. The algorithm works by distributing the reads mapping to a given exonic region (or splice junction) among the transcripts including the exon (or splice junction). The input is the annotation of a reference transcriptome and reads from RNAseq technologies aligned to the genome. From the reference annotation, splicing graphs are produced and reads are mapped to corresponding edges in these graphs according to the position where they align in the genomic sequence. The resulting graph with edges labelled by the number of reads can be interpreted as a flow network where each transcript representing a transportation path from its start to its end and consequently each edge a possibly shared segment of transportation along which a certain number of reads per nucleotide -- i.e., a flux -- is observed. Given a density function of reads along a transcript, the expected participation of each transcript in an edge under consideration can be estimated. The basic idea is to cast back from these latter participations and the observed number of reads - allowing for a certain amount of noise - to the original transcript abundancies. To do so, a linear constraint is formalized for each edge, and an optimal solution for the complete set of constraints is found by a standard linear program solver.

Proper citation: FLUX CAPACITOR (RRID:SCR_006651) Copy   

•   Source: SciCrunch Registry

http://www.niehs.nih.gov/research/resources/software/biostatistics/eagleview/

An information-rich viewer for next-generation genome assembles with data integration capability. EagleView can display a dozen different types of information including base qualities, machine specific trace signals, and genome feature annotations. It provides an easy way for inspecting visually the quality of a genome assembly and validating polymorphism candidate sites (e.g., SNPs) reported by polymorphism discovery tools. It can also facilitate data interpretation and hypothesis generation. EagleView is a multi-platform application developed with C++ and is available for all three major platforms: Windows, Linux, and Mac OS.

Proper citation: EagleView (RRID:SCR_006859) Copy   

•   Source: SciCrunch Registry

http://sourceforge.net/projects/bigpre/

A quality assessment software package for next-genomics sequencing data.

Proper citation: BIGpre (RRID:SCR_006781) Copy   

•   Source: SciCrunch Registry

http://bioconductor.org/packages/2.12/bioc/html/seqbias.html

Software package that implements a model of per-position sequencing bias in high-throughput sequencing data using a simple Bayesian network, the structure and parameters of which are trained on a set of aligned reads and a reference genome sequence.

Proper citation: seqbias (RRID:SCR_006832) Copy   

•   Source: SciCrunch Registry

https://sites.google.com/site/quantisnp/

THIS RESOURCE IS NO LONGER IN SERVICE, documented May 10, 2017. A pilot effort that has developed a centralized, web-based biospecimen locator that presents biospecimens collected and stored at participating Arizona hospitals and biospecimen banks, which are available for acquisition and use by researchers. Researchers may use this site to browse, search and request biospecimens to use in qualified studies. The development of the ABL was guided by the Arizona Biospecimen Consortium (ABC), a consortium of hospitals and medical centers in the Phoenix area, and is now being piloted by this Consortium under the direction of ABRC. You may browse by type (cells, fluid, molecular, tissue) or disease. Common data elements decided by the ABC Standards Committee, based on data elements on the National Cancer Institute''s (NCI''s) Common Biorepository Model (CBM), are displayed. These describe the minimum set of data elements that the NCI determined were most important for a researcher to see about a biospecimen. The ABL currently does not display information on whether or not clinical data is available to accompany the biospecimens. However, a requester has the ability to solicit clinical data in the request. Once a request is approved, the biospecimen provider will contact the requester to discuss the request (and the requester''s questions) before finalizing the invoice and shipment. The ABL is available to the public to browse. In order to request biospecimens from the ABL, the researcher will be required to submit the requested required information. Upon submission of the information, shipment of the requested biospecimen(s) will be dependent on the scientific and institutional review approval. Account required. Registration is open to everyone.Software to detect rare or de novo copy number alterations in normal DNA samples. Please note that QuantiSNP is no longer under active development.

Proper citation: QuantiSNP (RRID:SCR_013091) Copy   

•   Source: SciCrunch Registry

http://bioinfo.ctb.pku.edu.cn/MAP/

This resource is out of service. Documented on February 23,2021. Software for de novo metagenomic assembly program for shotgun DNA reads., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: MAP (RRID:SCR_013216) Copy   

•   Source: SciCrunch Registry

http://bowtie-bio.sourceforge.net/crossbow/index.shtml

A scalable software pipeline for whole genome resequencing analysis.

Proper citation: Crossbow (RRID:SCR_013306) Copy   

•   Source: SciCrunch Registry

http://tomcatbackup.esat.kuleuven.be/inclusive/

A suit of algorithms and tools for the analysis of gene expression data and the discovery of cis-regulatory sequence elements.

Proper citation: INCLUSive (RRID:SCR_013488) Copy   

•   Source: SciCrunch Registry

http://hannonlab.cshl.edu/Alta-Cyclic/main.html

An Illumina Genome-Analyzer (Solexa) base caller.

Proper citation: Alta-Cyclic (RRID:SCR_013373) Copy   

•   Source: SciCrunch Registry

http://swift.cmbi.ru.nl/gv/hssp/

HSSP (homology-derived structures of proteins) is a derived database merging structural (2-D and 3-D) and sequence information (1-D). For each protein of known 3D structure from the Protein Data Bank, the database has a file with all sequence homologues, properly aligned to the PDB protein. Homologues are very likely to have the same 3D structure as the PDB protein to which they have been aligned. As a result, the database is not only a database of sequence aligned sequence families, but it is also a database of implied secondary and tertiary structures. Likely secondary structure are carried over from the PDB protein to each homologous protein. Tertiary structure models can be built by fitting the sequence of the homologue as aligned into the 3D template of the protein of known structure. Special software is needed to construct 3D models by homology, such WHATIF by Gert Vriend or MaxSprout by Liisa Holm and Chris Sander. The command rsync can be used to obtain a local copy of the HSSP. We appreciate receiving an Email from people who do so, but there are no strings attached. Everybody can freely download the files, academia and industry alike. If your institute''s firewall doesn''t allow you to use the (preferred) rsync way of obtaining HSSP files, feel free to work with FTP. The files are in that case available from: ftp://ftp.cmbi.ru.nl//pub/molbio/data/hssp/

Proper citation: HSSP (RRID:SCR_004953) Copy   

•   Source: SciCrunch Registry

http://sms.cbi.cnptia.embrapa.br/SMS/STINGm/SMSReport/

Sting Report is a database of amino acid sequences, structures, functions, and parameters. It allows users to easily extract from the Blue Star Sting Database detailed but focused information about an individual amino acid, which belongs to a structure described in a PDB file. The extracted information is presented as a series of GIF images and a table, which are generated by Blue Star Sting modules and contain values of up to 125 sequence/structure/function descriptors/parameters. The HTML page resulting from a query on Sting Report, containing the GIF images and the table, is printable, and can also be composed and visualized at a computer platform with elementary configuration.

Proper citation: STING Report (RRID:SCR_005121) Copy   

•   Source: SciCrunch Registry

http://ahdb.ee.ncku.edu.tw/

Database of apo and holo structure pairs of proteins before and after binding. Various protein functions have been shown directly associated with conformational transitions triggered by binding other molecules. Tertiary structures determined in the unbound and bound state are usually named apo and holo structures, respectively. AH-DB is the largest database of apo-holo structure pairs and provides a sophisticated interface to search and view the collected data. It contains 746314 apo-holo pairs of 3638 proteins from 702 organisms.

Proper citation: Apo and Holo structures DataBase (RRID:SCR_004800) Copy   

•   Source: SciCrunch Registry

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

Database of biological data related to a single initiative, originating from a single organization or from a consortium. A BioProject record provides users a single place to find links to the diverse data types generated for that project. It is a searchable collection of complete and incomplete (in-progress) large-scale sequencing, assembly, annotation, and mapping projects for cellular organisms. Submissions are supported by a web-based Submission Portal. The database facilitates organization and classification of project data submitted to NCBI, EBI and DDBJ databases that captures descriptive information about research projects that result in high volume submissions to archival databases, ties together related data across multiple archives and serves as a central portal by which to inform users of data availability. BioProject records link to corresponding data stored in archival repositories. The BioProject resource is a redesigned, expanded, replacement of the NCBI Genome Project resource. The redesign adds tracking of several data elements including more precise information about a project''''s scope, material, and objectives. Genome Project identifiers are retained in the BioProject as the ID value for a record, and an Accession number has been added. Database content is exchanged with other members of the International Nucleotide Sequence Database Collaboration (INSDC). BioProject is accessible via FTP.

Proper citation: NCBI BioProject (RRID:SCR_004801) 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://bioinfo.iitk.ac.in/MIPModDB/

This is a database of comparative protein structure models of MIP (Major Intrinsic Protein) family of proteins. The nearly completed sets of MIPs have been identified from the completed genome sequence of organisms available at NCBI. The structural models of MIP proteins were created by defined protocol. The database aims to provide key information of MIPs in particular based on sequence as well as structures. This will further help to decipher the function of uncharacterized MIPs. For each MIP entry, this database contains information about the source, gene structure, sequence features, substitutions in the conserved NPA motifs, structural model, the residues forming the selectivity filter and channel radius profile. For selected set of MIPs, it is possible to derive structure-based sequence alignment and evolutionary relationship. Sequences and structures of selected MIPs can be downloaded from MIPModDB database.

Proper citation: MIPModDB (RRID:SCR_006058) Copy   

•   Source: SciCrunch Registry

http://www.yeastract.com

A curated repository of more than 206000 regulatory associations between transcription factors (TF) and target genes in Saccharomyces cerevisiae, based on more than 1300 bibliographic references. It also includes the description of 326 specific DNA binding sites shared among 113 characterized TFs. Further information about each Yeast gene has been extracted from the Saccharomyces Genome Database (SGD). For each gene the associated Gene Ontology (GO) terms and their hierarchy in GO was obtained from the GO consortium. Currently, YEASTRACT maintains a total of 7130 terms from GO. The nucleotide sequences of the promoter and coding regions for Yeast genes were obtained from Regulatory Sequence Analysis Tools (RSAT). All the information in YEASTRACT is updated regularly to match the latest data from SGD, GO consortium, RSA Tools and recent literature on yeast regulatory networks. YEASTRACT includes DISCOVERER, a set of tools that can be used to identify complex motifs found to be over-represented in the promoter regions of co-regulated genes. DISCOVERER is based on the MUSA algorithm. These algorithms take as input a list of genes and identify over-represented motifs, which can then be compared with transcription factor binding sites described in the YEASTRACT database.

Proper citation: Yeast Search for Transcriptional Regulators And Consensus Tracking (RRID:SCR_006076) Copy   

•   Source: SciCrunch Registry