FDM statistics

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Citations per year

Number of citations per year for the bioinformatics software tool FDM

Tool usage distribution map

This map represents all the scientific publications referring to FDM per scientific context
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FDM specifications


Unique identifier OMICS_01332
Name FDM
Alternative name flow difference metric
Software type Application/Script, Package/Module
Interface Command line interface
Restrictions to use None
Input format SAM+GTF
Operating system Unix/Linux
Computer skills Advanced
Stability No
Maintained No


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Publication for flow difference metric

FDM citations


A survey of best practices for RNA seq data analysis

Genome Biol
PMCID: 4728800
PMID: 26813401
DOI: 10.1186/s13059-016-0881-8

[…] r differences. By integrating the two steps, the uncertainty in the first step is taken into consideration when performing the statistical analysis to look for differential isoform expression []. The flow difference metric (FDM) uses aligned cumulative transcript graphs from mapped exon reads and junction reads to infer isoforms and the Jensen-Shannon divergence to measure the difference []. Recen […]


Comparisons of computational methods for differential alternative splicing detection using RNA seq in plant systems

BMC Bioinformatics
PMCID: 4271460
PMID: 25511303
DOI: 10.1186/s12859-014-0364-4

[…] of programs is not exhaustive; however, we have selected a set of programs which represent a variety of approaches. Due to our limited human resources and computational power, the current versions of FDM [] and JuncBase [] met our criteria but were excluded from this study. FDM uses a splice graph representation of aligned RNA-seq data and Jensen Shannon Divergence (JSD) to measure the difference […]


Efficient experimental design and analysis strategies for the detection of differential expression using RNA Sequencing

BMC Genomics
PMCID: 3560154
PMID: 22985019
DOI: 10.1186/1471-2164-13-484
call_split See protocol

[…] ecifically to appropriately handle expected technical and biological variation arising from RNA-Seq experiments. A non-exhaustive list of these algorithms is: edgeR [], DESeq [], NBPSeq [], BBSeq [], FDM [], RSEM [], NOISeq [], Myrna [], Cuffdiff []. A thorough comparison of these packages’ performance with datasets of different properties falls beyond the scope of this study, however before consi […]

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FDM institution(s)
Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; Department of Computer Science, University of Kentucky, Lexington, KY, USA; Department of Biology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; Department of Statistics and Operations Research, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
FDM funding source(s)
Supported by National Science Foundation (ABI/EF grant number 0850237); National Institutes of Health: NCI TCGA (grant number CA143848); NCRR Idea (INBRE Grant P20RR016481); NCI GI SPORE Developmental Project Award (P50CA106991); Alfred P. Sloan Foundation fellowship.

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