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Copy-number Haplotype Inference in Single-cell by Evolutionary Links

CHISEL is an algorithm to infer allele- and haplotype-specific copy numbers in individual cells from low-coverage single-cell DNA sequencing data (e.g., those generated by Direct Library Preparation+ (DLP+), 10x Genomics CNV Solution, DOP-PCR, etc.). The full description of the algorithm and its application on published cancer datasets are described in

Simone Zaccaria and Ben Raphael, Nature Biotechnology, 2021

The results of CHISEL on the published low-coverage single-cell DNA sequencing data of cancer patients and all the related analyses are available at


This repository includes detailed instructions for installation and requirements, demos with related tutorial of different CHISEL applications, a list of current issues, and contacts. Please frequently read Development and keep software up to date since CHISEL is being updated and improved frequently at this stage.


  1. Overview
  2. Quick start
  3. Setup
  4. Usage
  5. Development
  6. Contacts



The CHISEL algorithm. (A) CHISEL computes RDRs and BAFs in low-coverage (<0.05X per cell) single-cell DNA sequencing data (top left). Read counts from 2000 individual cells (rows) in 5Mb genomic bins (columns) across three chromosomes (grey rectangles in first row) are shown. For each bin in each cell, CHISEL computes the RDR (top) by normalizing the observed read counts. CHISEL computes the BAF in each bin and cell (bottom) by first performing referenced-based phasing of germline SNPs in 50kb haplotype blocks (magenta and green) and then phasing all these blocks jointly across all cells. (B) CHISEL clusters RDRs and BAFs globally along the genome and jointly across all cells resulting here in 5 clusters of genomic bins (red, blue, purple, yellow, and grey) with distinct copy-number states. (C) CHISEL infers a pair of allele-specific copy numbers for each cluster by determining whether the allele-specific copy numbers of the largest balanced (BAF~0.5) cluster are equal to {1, 1} (diploid), {2, 2} (tetraploid), or are higher ploidy. (D) CHISEL infers haplotype-specific copy numbers (a, b) by phasing the allele-specific copy numbers consistently across all cells. (E) CHISEL clusters tumor cells into clones according to their haplotype-specific copy numbers. Here, a diploid clone (light gray) and two tumor clones (red and blue) are obtained. A phylogenetic tree describes the evolution of these clones. Somatic single-nucleotide variants (SNVs) are derived from pseudo-bulk samples and placed on the branches of the tree.


The current implementation of CHISEL is written in python2.7 and CHISEL is distributed as a bioconda package. CHISEL provides different commands (in bin) to automatically execute the different features/pipelines of CHISEL. See details in Usage.

Quick start

To get started quickly, you can install CHISEL and test the complete CHISEL pipeline on example data using the 4 following commands.

git clone && cd chisel # Clone CHISEL and enters the directory
bash # Run the automatic installation, which installs conda and CHISEL on it
source conda/bin/activate chisel # Activate CHISEL, remember to run this command before every new session of CHISEL
cd demos/complete/ && bash # Enter the directory of the complete demo and run it

A successful execution of these commands will generate the final plots of the demo as PNG images in chisel/demos/complete/plots/, the inferred allele- and haplotype-specific copy numbers in chisel/demos/complete/calls/calls.tsv, and the clones in chisel/demos/complete/clones/mapping.tsv. Further details of the temporary files and final outputs are described here.


CHISEL is distributed as a bioconda package, and can be installed either from conda or directly from source.
Depending on your interest in using conda, there are three possible installation approaches for CHISEL.

  1. Full automatic installation: creates a new conda installation with Miniconda and installs CHISEL on it;
  2. Standard installation: uses an existing conda installation and installs CHISEL on it;
  3. Custom installation: installs CHISEL from source without conda.

Additional notes and comments are reported at the end of this section.

Full automatic installation

An automatic BASH script that fully installs conda through Miniconda and installs chisel on it is provided in Simply run the following command from the CHISEL's home ${CHISEL_HOME}.


Note that this procedures installs conda in ${CHISEL_HOME}/conda and the following command (output at the end of the installation) must be executed before using chisel in every new session:

source ${CHISEL_HOME}/conda/bin/activate chisel

Standard installation

CHISEL can be installed using an existing installation of conda (e.g. either the compact Miniconda or the complete Anaconda) in two steps. First, bioconda requires to set the following channels in this exact order:

conda config --add channels defaults
conda config --add channels bioconda
conda config --add channels conda-forge

Next, chisel can be installed with the following one-time command:

conda install chisel

This command installs chisel in the base conda environment, however best practices generally recommend, especially in computing servers, to keep distinct packages independent. Therefore, it is preferable to use the following command to install chisel:

conda create -n chisel chisel

In this case, chisel must be activated before every session with the following command

source activate chisel

Note that all the conda and activate commands above are located in ${CONDA_HOME}/bin/.

Custom installation

CHISEL is written in python2.7 and requires few standard python packages and two additional standard tools. Once all the requirements have been successfully installed, chisel can be manually installed from ${CHISEL_HOME} as

python install

> Python packages

CHISEL depends on the following standard python packages, which must be available in the python environment where the user runs CHISEL.

Package Tested version Comments
numpy 1.16.1 Efficient scientific computations
scipy 1.2.1 Efficient mathematical functions and methods
pandas 0.20.1 Dataframe management
matplotlib 2.0.2 Basic plotting utilities
seaborn 0.7.1 Advanced plotting utilities
statsmodels 0.10.1 Statistical functions

> Additional software

CHISEL also requires few standard additional tools, which must be included in PATH (e.g. by executing export PATH=${SAMTOOLS_HOME}/bin/:${PATH})

Software Tested version Comments
AWK GNU Awk 4.0.2 Scripting language available by default on most Unix-like OSs and with specific implementation available for any other OS
SAMtools and BCFtools 1.9 Suite of programs for interacting with high-throughput sequencing data
bwa 0.7.17 Alignment of DNA sequencing reads
art 2016-06-05 Simulation of Illumina DNA sequencing reads

Additional notes:

  • To be to run the conda commands conda and activate without prefix, add their path full path to PATH by
export PATH=${CONDA_HOME}/bin/:${PATH}


  1. Required data
  2. System requirements
  3. Commands
  4. Demos
  5. Reccomendations and quality control

Required data

CHISEL requires 4 input data:

  1. A single-cell barcoded BAM file containing aligned DNA sequencing reads from single cells of a single patient in a barcoded format. CHISEL includes a command chisel_prep to automatically generate such barcoded BAM file (aligned and sorted) starting from standard formats for single-cell DNA sequencing data, including single-cell FASTQs, single-cell BAMs, etc. The barcoded BAM file must be indexed and sorted (see SAMtools and GATK Best Practices). In a barcoded BAM file, reads must be labelled by a barcode, which is a string or substring PREFIX[A,C,G,T]+ composed of two parts: (1) a prefix PREFIX; and (2) the actual barcode [A,C,G,T]+ which is an arbitrary-long string of letters A,C,G,T uniquely identifiying a single cell. By default, CHISEL implementation defines the PREFIX as CB:Z: according to the 10X Genomics format, however different barcodes PREFIX can be used and should be specified with the appropriate CHISEL flag --cellprefix. Similarly, a barcode SUFFIX can also be specified when the barcode is defined as PREFIX[A,C,G,T]+SUFFIX. Note that the barcode PREFIX:[A,C,G,T]+ can be either an independent tab-separated field of the SAM alignments, part of the RG flags, or can be incorporated in the name of the sequencing reads.

  2. The reference human genome used to align the sequencing reads in the single-cell barcoded BAM. The most-used human reference genome are available at GRC or UCSC. Moreover, the reference genome ${REF}.fa must be index by SAMtools (i.e. samtools faidx ${REF}.fa) and a dictionary must be created (i.e. samtools dict ${REF}.fa > ${REF}.dict) in the same folder as ${REF}.

  3. A VCF file with phased germline SNPs present in the matched-normal sample. CHISEL only requires a VCF with the phase 0|1 or 1|0 within the record of heterozygous phased SNPs and any reference-based phasing method can be used. Here, we recommend a very easy two step procedure: (1) use BCFtools to call germline SNPs from the matched-normal sample (as described here); (2) use Eagle2 through the Michigan Imputation Server to phase germline SNPs with respect to the reference panel HRC. Alternatively, Eagle2 can be used locally, and other methods as SHAPEIT or other panels as 1000 Genomes Phase 3 can be applied, too. Note that HRC currently supports only reference hg19, therefore user can use LiftoverVcf from Picard tools or similar methods to convert genomic coordinates to the required hg19 build, when a different reference genome has been used to align sequencing reads. Note that phased germline SNPs can be also provided in POS format, i.e. tab-separated dataframe with field CHROMOSOME POSITION PHASE where phase is either 0|1 or 1|0. Please note that SNPs can also be directly identified from the single-cell barcoded BAM file when it contains a sufficiently high number of normal diploid cells.

  4. [Optional in nonormal mode] A matched-normal BAM file containing sequencing reads from a matched-normal sample from the same patient is required in the default mode of CHISEL, i.e., the one executed by the main command chisel. When this sample is not available or it has been obtained from a sequencing assay substantially different from that of cancer cells, users can use the new "no-normal" version of CHISEL through the new command chisel_nonormal. Importantly, even when the user decides to no use a matched-normal sample because from a different sequencing assay, SNP phasing should always be performed from SNPs identified from a matched normal sample when possible. Lastly, user can use the corresponding CHISEL command chisel_pseudonormal to form a pseudo matched-normal sample by extracting diploid normal cells (note that this approach is successful only when normal diploid cells are present in the barcoded BAM file).

System requirements

CHISEL is highly parallelized in order to make efficient the extensive computations needed for counting sequencing reads from BAM files, often splitting independent computations to parallel processes. We recommend executing CHISEL on multi-processing computing machines. The minimum system requirements that we have tested for running the demos or running every CHISEL command after BAM file processing are:

  • CPU with at least 2 virtual cores
  • 4GB of RAM

However, when the complete CHISEL pipeline is applied to huge BAM files (>200GB) for millions of germline SNPs across thousands of single cells, CHISEL requirements are limited by the existing requirements of SAMtools and BCFtools. We recommend a machine with larger RAM (>100GB of RAM) and a larger number of cores (e.g. >10 cores). In general, the higher the number of cores is with a sufficient amount of RAM, the faster the execution will be.

CHISEL Commands

CHISEL offers different commands to run either the entire pipeline with all the steps or only some specific steps. In particular, the latter commands are useful when user wants to re-run some specific steps by varying some of the default parameters or for quality control (see below). Every command can be run directly when CHISEL has been correctly installed.

Command Description Required input Output
chisel Running the complete CHISEL pipeline All 4 required input data Final results and plots
chisel_nonormal Running the complete CHISEL pipeline without matched normal sample The first 3 required input data Final results and plots
chisel_calling Running from the inference of copy numbers Computed RDRs and BAFs Final results and plots
chisel_cloning Running from the identification of clones Inferred copy numbers Final results and plots
chisel_plotting Running plot generation Inferred copy numbers and clones Final plots
chisel_pseudonormal Extracting diploid cells to form a pseudo matched-normal sample A barcoded BAM file containing diploid cells and the corresponding reference genome A BAM file to be used as a matched-normal sample
chisel_prep Generating a barcoded BAM Single-cell FASTQs (or FASTQ.gz), single-cell BAMs, and RG:-barcoded BAM barcoded BAM file (see Required data)
chisel_bedding Generating a BED file per cell with CHISEL results CHISEL-inferred allele- and haplotype-specific copy numbers (e.g. file) A BED file per cell with CHROM START END HAP-COPY-NUMBERS

Click on the name of each command to obtain a description of all the available parameters.


Each demo is an exemplary and guided execution of a CHISEL command on available data. The demos are meant to illustrate the complete CHISEL pipeline and the usage of each specific command. In particular, the specific commands allow the user to explore the results when varying the main parameters (without the need to re-execute the entire pipeline), allowing to deal with different features of distinct datasets and to obtain the best-quality results (see recommendations below). Each demo is simultaneously a guided description of the entire example and a BASH script which can be directly executed to run the complete demo. As such, the user can both read the guided description as a web page and run the same script to execute the demo. At this time the following demos are available (more demos will be available soon):

Demo Description
complete Demo of the complete CHISEL pipeline
complete-nonormal Demo of the complete CHISEL pipeline in "nonormal" mode
callingE Demo of command to re-run pipeline from the inference of copy numbers on tumor section E of breast cancer patient S0
cloningE Demo of command to re-run pipeline from the identification of clones on tumor section E of breast cancer patient S0
plottingE Demo of command to re-run plot generation on tumor section E of breast cancer patient S0
pseudonormal Demo of command to generate a pseudo matched-normal sample by extracting diploid cells from a barcoded BAM file

Recommendations and quality control

The following recommendations guide the user in the process of quality control for the final results. In order to deal with datasets with different features, these recommendations help the user in investigating solutions obtained with different parameters. In particular, some of these guides are especially helpful when analyzing datasets with non-standard features and with noisy or high-variance sequencing data, e.g. due to low number of cells, different sequencing coverages, low number of phased germline SNPs.

Recommendation Description
Ploidy selection Adjusting model-selection parameters to accurately infer ploidy in noisy and high-variance datasets
Identification of clones Interpreting the identified clones and explore alternative solutions
Global clustering Interpreting the global clustering of RDRs and BAFs, and exploring alternative solutions


Recent and important updates

  • [26-Jan-2022] CHISEL v1.1 introduces the new command chisel_nonormal to run the complete CHISEL pipeline without a matched-normal sample. In addition, the new version introduces the possibility to add additional GC correction in the default mode by adding the flag --addgccorr and it introduces a statistical test which attempts to correct phasing errors (which can disable by the flag --nophasecorr in case of high quality phasing).
  • [12-Jan-2021] Introducing two new CHISEL commands: chisel_prep to generate input barcoded BAM file from standard formats, and chisel_bedding to generate BED files with CHISEL results.
  • [20-Jul-2020] CHISEL has been deposited and approved in Bioconda; check the new streamlined installation procedure.
  • [22-Jan-2020] This version introduced two important updates:
    1. CHISEL now use the inferred clones to correct the inferred allele- and haplotype-specific copy numbers, removing noise and outlying errors. As such, CHISEL generates plots with both uncorrected and corrected copy numbers;
    2. CHISEL now has a sensitivity, which can be varied through the command to enable the accurate inference of cell ploidy even in the case of noisy or high-variance data (e.g. low number of cells with low coverage).
  • [20-Jan-2020] This version introduced the previously missing command This command can be used to extract sequencing reads from diploid cells to form a pseudo matched-normal sample, which is required by the full CHISEL pipeline. A corresponding demo has also been introduced.

Current issues

CHISEL is in active development, please report any issue or question as this could help the development and improvement of CHISEL. Known issues with current version are reported here below.


CHISEL's repository is actively maintained by Simone Zaccaria, previously a Postdoctoral Research Associate at Princeton University in the research group of prof. Ben Raphael and currently group leader of the Computational Cancer Genomics lab at the UCL Cancer Institute.


CHISEL -- Copy-number Haplotype Inference in Single-cell by Evolutionary Links







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