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TITAN is a tool for predicting subclonal copy number alterations (CNA) and loss of heterozygosity (LOH) from tumour whole genome sequencing data. TITAN infers the clonal cluster of events along with their estimates of cellular prevalence, which is proportion of tumour cells harbouring an event. TITAN also estimates the normal contamination and tumour ploidy. TITAN can also be run on whole exome sequencing data. The TITAN software is implemented as an R package available through Bioconductor. Additional utilities for parsing BAM files are provided through the 3rd party tools, SAMtoolspysam, and bcftools. We provide a Python ruffus pipeline for running all steps involved in the analysis.


Getting Help

If you have questions or bugs to report, please post in the TitanCNA Google user group. This is the fastest and most resourceful way to get your questions answered.

Check out the source code from the TitanCNA GitHub repository.

You can also contact the author Gavin Ha (gavin.ha [at] gmail [dot] com).

Citing TITAN

Ha, G., Roth, A., Khattra, J., Ho, J., Yap, D., Prentice, L. M., Melnyk, N., McPherson, A., Bashashati, A., Laks, E., Biele, J., Ding, J., Le, A., Rosner, J., Shumansky, K., Marra, M. A., Huntsman, D. G., McAlpine, J. N., Aparicio, S. A. J. R., and Shah, S. P. (2014). TITAN: Inference of copy number architectures in clonal cell populations from tumour whole genome sequence data. Genome Research, 2014. 24: 1881-1893.

Download the paper.

Download Supplementary methods and figures.

Download Supplementary tables.

Download the whole genome and single-cell sequencing data at EGA under study EGAS00001000547 and Dataset ID EGAD00001000974.
Please request permissions from the BC Cancer Agency (BCCA) Data Access Committee by contacting Sarah Jane Lee (TDOadmin@phsa.ca).



November 13, 2014

TitanCNA v1.5.5 makes improvements to the computation of S_Dbw model selection. It now computes the S_Dbw validity index based on both log ratios and allelic ratios. This affects the “outputModelParameters” function; the S_Dbw is output for each data type independently and the combination (sum) of both, all in the same parameter output file.

Also, there is a change in initial parameter settings that affects loadDefaultParameters. When using symmetric=TRUE (default) for the allelic ratios, the baseline parameter is now estimated from the data itself; previously, this was hard-coded.

NOTE: Users will need to make a small change to their existing R scripts to use this feature. Please see TitanCNA instructions (Step #2) for more details.

October 22, 2014

TitanCNA v1.5.2 is available on Bioconductor 3.1 (development). The getPositionOverlap() function is sped up by using RangedData internally. Look for this change in the nightly build or developer’s release. See Installation for more details on how to obtain this latest version.

August 15, 2014

TitanCNA v1.3.0 has been pushed to Bioconductor.  Look for this change in the nightly build or the developer’s version.  See Installation page for more details.
This version includes an option to use a variant of the S_Dbw (1) that selects the optimal number of clonal clusters by accounting for the number of datapoints in each state.

(1) Tong and Tan (2009) Cluster validity based on the improved S_Dbw index. (2009). Journal of Electronics (China), Volume 26, Issue 2, pp 258-264.

July 28, 2014

1) TitanCNA v1.2.1 has been pushed to Bioconductor SVN. This version patches a critical memory bug. Please do not use TitanCNA v1.2.0. See Installation page for more details.

2) TITANRunner-0.1.1 can be downloaded from the Downloads page. This version of the TITANRunner python ruffus pipeline is compatible with TitanCNA v1.2.1.  TITANRunner-0.0.3 can still be downloaded for use with TitanCNA v1.0.0.


Request for Download
Running the all-in-one pipeline
Details for running TitanCNA R package
Details of TitanCNA output
Frequently Asked Questions

TITANRunner pipeline

We have provided a Python ruffus pipeline as a convenient way for users to generate TITAN results by providing input BAM files. This pipeline processes the BAM files, prepares input files for TitanCNA, and executes functions in R to generate TITAN results and figures for the results. See the TITANRunner Pipeline page

Quick Demo of TitanCNA R package

TitanCNA R package requires R version 3.0.2. R users can read the vignette included with the R package to get a sense of how to run the R package.

~$ [R dir]/bin/R # Following commands in R
library(TitanCNA) example(TitanCNA)

For more advanced users who wish to learn more about using TitanCNA within R, further details of the functions can be found in Details for running TitanCNA R package.