find_circ specifications


Unique identifier OMICS_10928
Software type Application/Script
Interface Command line interface
Restrictions to use Academic or non-commercial use
Input data RNA-seq reads
Output data The detected linear and circular candidate splice sites.
Output format BED
Operating system Unix/Linux
Programming languages Python
License GNU General Public License version 3.0
Computer skills Advanced
Version 1.2
Stability Stable
Requirements Numpy, pysam
Maintained Yes


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  • person_outline Nikolaus Rajewsky <>

find_circ article

find_circ institution(s)
Systems Biology of Gene Regulatory Elements, Max-Delbruck-Center for Molecular Medicine, Berlin, Germany; Angiogenesis and Cardiovascular Pathology, Max Delbruck-Center for Molecular Medicine, Berlin, Germany; RNA Biology and Post-Transcriptional Regulation, Max-Delbruck-Center for Molecular Medicine, Berlin, Germany; Signaling Dynamics in Single Cells, Max-Delbruck-Center for Molecular Medicine, Berlin, Germany
find_circ funding source(s)
Supported by PhD program of the Max-Delbruck-Center (MDC); the MDC-NYU exchange program; BMBF project 1210182, ‘MiRNAs as therapeutic targets’; DFG for KFO218; Helmholtz Association for the ‘MDC Systems Biology Network’, MSBN; BMBF support for the DZHK; Center for Stroke Research Berlin and BMBF-funding for the Berlin Institute for Medical Systems Biology (0315362C).

find_circ review

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Rajnish Kumar

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find_circ was first Circular RNAs identification tools which was open source available. This is very simple to use and implement. The whole pipeline is written using python and and it uses python based dependency like NumPy. The code is very simply written which can be simply understood. The most interesting thing is that , it start with unmapped reads , which are considered to be unmapped because these reads supposed to cross the splice junctions. The tool make pseudo pair-end by taking the end fragments of unmapped reads of some size (e.g. 20). The newly constructed pair-end file are mapped using the Bowtie2 tool. Primary mapping of these pairs is discriminated based on their mapping order, means if the sub-reads are mapped following the canonicity principle then its may be potential start target for finding the linear splice sites and if their is any non- canonicity or non-linearity in the order , then it is considered as the starting point of back splice sites. The tool uses one level of filtering at these step by considering only the mapped pair which are on the same chromosome for the further step of identification.In the next step the alignment is extended till it satisfy the constraint given with the tools. Out of many hits best one is selected and reported as linear or back-splice sites based on the using the above mentioned staring target.Beautifully this tool report the average quality of the back-splice sub-reads and number of the reads support the particular sites. To reduce the false positive circular candidates it used the min and max intron size as one of the filtering criteria. The minimum reads supporting the back-splice site was taken as 2 , which can be high for better stringency criteria. Overall the tool is user friendly and useful for prediction study.

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