SiFit statistics

To access cutting-edge analytics on consensus tools, life science contexts and associated fields, you will need to subscribe to our premium service.


Citations per year

Citations chart

Popular tool citations

chevron_left Single-cell assemblers chevron_right
Popular tools chart

Tool usage distribution map

Tool usage distribution map

Associated diseases

Associated diseases

SiFit specifications


Unique identifier OMICS_14101
Name SiFit
Software type Package/Module
Interface Graphical user interface
Restrictions to use None
Input data A genotype matrix with the mutational profile of single cells, a file with the name of the single cells and the true tree
Input format Newick
Output data A inferred tree
Output format Newick
Operating system Unix/Linux
Programming languages Java
Computer skills Medium
Stability Stable
Maintained Yes



Add your version


  • person_outline Luay Nakhleh <>

Publication for SiFit

SiFit in publications

PMCID: 5901877
PMID: 29661213
DOI: 10.1186/s13073-018-0537-2

[…] distance was used to measure the consistency in tree reconstruction across the different sequencing depths. furthermore, maximum-likelihood single-cell phylogenies were estimated from the snvs using sifit []. in this case, phylogenetic recall across sequencing depth was measured using the standard robinson-foulds tree distance []. in addition, we also calculated the homoplasy index (hi), […]

PMCID: 5599901
PMID: 28924377
DOI: 10.7150/ijbs.19627

[…] of the evolutionary history of a tumor by analyzing single cells, which is able to calculate the maximum-likelihood mutation history by using a flexible markov chain monte carlo sampling scheme . sifit is another novel method for tumor phylogenies from noisy single cell data by using a finite-sites model, which could improve inference of tumor phylogenies (biorxiv: […]

To access a full list of publications, you will need to upgrade to our premium service.

SiFit institution(s)
Department of Computer Science, Rice University, Houston, TX, USA; Department of Bioinformatics and Computational Biology, the University of Texas M.D. Anderson Cancer Center, Houston, TX, USA; Department of Genetics, the University of Texas M.D. Anderson Cancer Center, Houston, TX, USA
SiFit funding source(s)
The study was supported by the National Cancer Institute (grant n°R01 CA172652), the cancer center support (grant n°P30 CA016672) and Andrew Sabin Family Foundation.

SiFit reviews

star_border star_border star_border star_border star_border
star star star star star

Be the first to review SiFit