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https://github.com/cbrueffer/tophat-recondition

Software tool as post-processor for TopHat unmapped reads that restores read information in the proper format.Enables downstream software to process plethora of BAM files written by TopHat.

Proper citation: TopHat-Recondition (RRID:SCR_024383) Copy   

•   Source: SciCrunch Registry

https://www.teuniz.net/edfbrowser/

Open source, multiplatform, universal viewer, annotator and toolbox intended for time-series storage files like EEG, EMG, ECG, BioImpedance, etc.

Proper citation: EDFbrowser (RRID:SCR_024021) Copy   

•   Source: SciCrunch Registry

http://www.danielwilson.me.uk/omegaMap.html

Software tool for detecting natural selection and recombination in DNA or RNA sequences.

Proper citation: omegaMap (RRID:SCR_024143) Copy   

•   Source: SciCrunch Registry

https://www.open-emr.org/

Open source software for electronic health records and medical practice management solution.

Proper citation: OpenEMR (RRID:SCR_024144) Copy   

•   Source: SciCrunch Registry

https://github.com/nanoporetech/tombo

Software suite of tools for identification of modified nucleotides from nanopore sequencing data.Used also for analysis and visualization of raw nanopore signal.

Proper citation: Tombo (RRID:SCR_024388) Copy   

•   Source: SciCrunch Registry

http://assemblytics.com/

Web analytics tool for detection of variants from assembly. Used to detect and analyze structural variants from genome assembly by comparing it to reference genome.

Proper citation: Assemblytics (RRID:SCR_023967) Copy   

•   Source: SciCrunch Registry

https://zhanggroup.org/NW-align/

Software tool as alignment program for protein sequence-to-sequence alignments based on the standard Needleman-Wunsch dynamic programming algorithm.

Proper citation: NW-align (RRID:SCR_024138) Copy   

•   Source: SciCrunch Registry

https://sourceforge.net/projects/microbegps/

Software tool for analysis of metagenomic sequencing data.Used to profile composition of metagenomic communities. Calculates quality metrics for estimated candidates and allows the user to identify false candidates.

Proper citation: MicrobeGPS (RRID:SCR_024112) Copy   

•   Source: SciCrunch Registry

https://pyscanfcs.readthedocs.io/en/stable/

Software application for perpendicular line scanning fluorescence correlation spectroscopy.

Proper citation: pyscanfcs (RRID:SCR_024190) Copy   

•   Source: SciCrunch Registry

https://github.com/pyranges/pyranges

Software application for efficient comparison of genomic intervals in Python.

Proper citation: pyranges (RRID:SCR_024191) Copy   

•   Source: SciCrunch Registry

http://gmod.org/wiki/Chado

Relational database schema that underlies many GMOD installations. It is capable of representing many of the general classes of data frequently encountered in modern biology such as sequence, sequence comparisons, phenotypes, genotypes, ontologies, publications, and phylogeny. It has been designed to handle complex representations of biological knowledge and should be considered one of the most sophisticated relational schemas currently available in molecular biology. The price of this capability is that the new user must spend some time becoming familiar with its fundamentals.

Proper citation: Chado (RRID:SCR_024073) Copy   

•   Source: SciCrunch Registry

https://lcb.infotech.monash.edu/mustang/

Software tool for structural alignment of multiple protein structures. Used to produce sequence alignment. Reports multiple sequence alignment and corresponding superposition of structures.

Proper citation: Mustang (RRID:SCR_024126) Copy   

•   Source: SciCrunch Registry

http://cran.r-project.org/web/packages/mlgt/index.html

Software for processing and analysis of high throughput (Roche 454) sequences generated from multiple loci and multiple biological samples. Sequences are assigned to their locus and sample of origin, aligned and trimmed. Where possible, genotypes are called and variants mapped to known alleles.

Proper citation: mlgt (RRID:SCR_001211) Copy   

•   Source: SciCrunch Registry

http://soap.genomics.org.cn/

Software package that provides full solution to next generation sequencing data analysis consisting of an alignment tool (SOAPaligner/soap2), a re-sequencing consensus sequence builder (SOAPsnp), an indel finder ( SOAPindel ), a structural variation scanner ( SOAPsv ), a de novo short reads assembler ( SOAPdenovo ), and a GPU-accelerated alignment tool for aligning short reads with a reference sequence. (SOAP3/GPU)., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: SOAP (RRID:SCR_000689) Copy   

•   Source: SciCrunch Registry

http://idash.ucsd.edu/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on January 6, 2023. National Center for Biomedical Computing (NCBC) that develops new algorithms, opensource tools, computational infrastructure, and services for biomedical and behavioral researchers nationwide to promote the secure sharing and consuming of biomedical and behavioral resources (software, data, and computing systems) with iDASH collaborators. The center addresses fundamental challenges to research progress by providing a secure, privacypreserving environment in which researchers can analyze genomic, transcriptomic, clinical, behavioral, and social data relevant to health. Three driving biological projects in iDASH (Molecular Phenotyping of Kawasaki Disease, Post-Marketing Surveillance of Hematologic Medications, and Individualized Intervention to Enhance Physical Activity) span the molecular-individualpopulation spectrum, and they will motivate, inform, and support tool development. iDASH will collaborate with other NCBCs and will disseminate tools via annual workshops, presentations at major conferences, and scientific publications.

Proper citation: iDASH (RRID:SCR_003524) Copy   

•   Source: SciCrunch Registry

https://github.com/gt1/biobambam

Software tools for read pair collation based algorithms on BAM files including * bamcollate2: reads BAM and writes BAM reordered such that alignment or collated by query name * bammarkduplicates: reads BAM and writes BAM with duplicate alignments marked using the BAM flags field * bammaskflags: reads BAM and writes BAM while masking (removing) bits from the flags column * bamrecompress: reads BAM and writes BAM with a defined compression setting. This tool is capable of multi-threading. * bamsort: reads BAM and writes BAM resorted by coordinates or query name * bamtofastq: reads BAM and writes FastQ; output can be collated or uncollated by query name

Proper citation: biobambam (RRID:SCR_003308) Copy   

•   Source: SciCrunch Registry

http://phenoscape.org/

Project to create a scalable infrastructure that enables linking phenotypes across different fields of biology by the semantic similarity of their descriptions.

Proper citation: Phenoscape (RRID:SCR_003799) Copy   

•   Source: SciCrunch Registry

http://www.btool.org/ADGO2

A web-based tool that provides composite interpretations for microarray data comparing two sample groups as well as lists of genes from diverse sources of biological information. It provides multiple gene set analysis methods for microarray inputs as well as enrichment analyses for lists of genes. It screens redundant composite annotations when generating and prioritizing them. It also incorporates union and subtracted sets as well as intersection sets. Users can upload their gene sets (e.g. predicted miRNA targets) to generate and analyze new composite sets.

Proper citation: ADGO (RRID:SCR_006343) Copy   

•   Source: SciCrunch Registry

http://bioinformatics.biol.uoa.gr/HMM-TM/

A web tool using the Hidden Markov Model method for the topology prediction of alpha-helical membrane proteins that incorporates experimentally derived topological information. Hidden Markov Models (HMMs) have been extensively used in computational molecular biology, for modelling protein and nucleic acid sequences. In many applications, such as transmembrane protein topology prediction, the incorporation of limited amount of information regarding the topology, arising from biochemical experiments, has been proved a very useful strategy that increased remarkably the performance of even the top-scoring methods. However, no clear and formal explanation of the algorithms that retains the probabilistic interpretation of the models has been presented so far in the literature. We present here, a simple method that allows incorporation of prior topological information concerning the sequences at hand, while at the same time the HMMs retain their full probabilistic interpretation in terms of conditional probabilities. We present modifications to the standard Forward and Backward algorithms of HMMs and we also show explicitly, how reliable predictions may arise by these modifications, using all the algorithms currently available for decoding HMMs. A similar procedure may be used in the training procedure, aiming at optimizing the labels of the HMM''s classes, especially in cases such as transmembrane proteins where the labels of the membrane-spanning segments are inherently misplaced. We present an application of this approach developing a method to predict the transmembrane regions of alpha-helical membrane proteins, trained on crystallographically solved data. We show that this method compares well against already established algorithms presented in the literature, and it is extremely useful in practical applications.

Proper citation: HMM-TM (RRID:SCR_006186) Copy   

•   Source: SciCrunch Registry

http://bioinformatics.biol.uoa.gr/PRED-LIPO/

A web tool using the Hidden Markov Model method for the prediction of lipoprotein signal peptides of Gram-positive bacteria, trained on a set of 67 experimentally verified lipoproteins. The method outperforms LipoP and the methods based on regular expression patterns, in various data sets containing experimentally characterized lipoproteins, secretory proteins, proteins with an N-terminal TM segment and cytoplasmic proteins. The method is also very sensitive and specific in the detection of secretory signal peptides and in terms of overall accuracy outperforms even SignalP, which is the top-scoring method for the prediction of signal peptides.

Proper citation: PRED-LIPO (RRID:SCR_006187) Copy   

•   Source: SciCrunch Registry