Citation: X Fan, G Luo, YS Huang (2021) BMC Bioinformatics. Accucopy: Accurate and Fast Inference of Allele-specific Copy Number Alterations from Low-coverage Low-purity Tumor Sequencing Data.
2021/05/03 Fixed bugs in cases where the number of autosomes are NOT 22 (non-human).
2020/12/07 Manuscript accepted at BMC Bioinformatics.
2020/09/24 Improvement to the classification of the clonal vs. subclonal segments. Fixed a prior bug that tends to call subclonal as clonal CNAs. The results are more robust.
2020/08/01 Fixed a few bugs in reading bam files with a random-order of chromosomes in the bam header.
2019/10/22 First release.
Copy number alterations (CNAs), due to its large impact on the genome, have been an important contributing factor to oncogenesis and metastasis (Priestley, et al., 2019). Different approaches have been applied to infer CNAs from genomic sequencing data (Boeva, et al., 2012; Carter, et al., 2012; Chen, et al., 2015; Cun, et al., 2018; Favero, et al., 2015). However, detecting genomic alterations from the shallow-sequencing data of a low-purity tumor sample is still a challenging task.
Accucopy is a CNA-calling method that extends our previous Accurity model to predict both total (TCN) and allele-specific copy numbers (ASCN) for the tumor genome. Accucopy adopts a tiered Gaussian mixture model coupled with an innovative autocorrelation-guided EM algorithm to find the optimal solution quickly. The Accucopy model utilizes information from both total sequencing coverage and allelic sequencing coverage. Through comparative analyses in both simulation and real-sequencing samples, we demonstrate that Accucopy is more accurate than existing methods
The manuscript, titled "Accucopy: Accurate and Fast Inference of Allele-specific Copy Number Alterations from Low-coverage Low-purity Tumor Sequencing Data", is published in BMC Bioinformatics.
3 Get our software
The license follows our institute policy that you can use the program for free as long as you are using Accucopy strictly for non-profit research purposes. However, if you plan to use Accucopy for commercial purposes, a license is required and please contact firstname.lastname@example.org or email@example.com to obtain one.
The full-text of the license is included in the software package.
3.2 Register to receive updates.
Register site: https://www.yfish.org/accurity/
Please register to receive updates and the download link (a standalone Accucopy package without dependencies). If you have trouble installing packages described in 3.1, use the docker version instead.
NOTE Due to the difficulty (i.e. no root access to install required libraries or incompatible libraries) in running our binary software, we have made a docker image available at dockerhub, which contains the latest development version of our software and all dependent libraries. Accucopy inside the docker is ahead of what is downloadable from this website.
- Install Ubuntu package "docker.io" before you do anything below.
- Download the refData package from section refData.
- To run Accucopy on a HPC cluster, singularity is a good alternative to run a docker image.
yh@cichlet:~$ docker pull polyactis/accucopy Using default tag: latest latest: Pulling from polyactis/accucopy ... fd6992ef54e0: Pull complete Digest: sha256:a6f72af3114ba903f26b60265e10e6f13b8d943d25e740ab0a715d1a99000188 Status: Downloaded newer image for polyactis/accucopy:latest yh@cichlet:~$ docker images REPOSITORY TAG IMAGE ID CREATED SIZE polyactis/accucopy latest a11fdb62c5d4 5 months ago 1.04GB # Log into the docker image, without mounting. Useful just to look inside the docker. yh@cichlet:~$ docker run -i -t polyactis/accucopy /bin/bash # Download the reference genome folder (links on this page) into /home/mydata (or any folder) # Put your bam files into /home/mydata # Log into the docker image. # Mount /home/mydata to /mnt inside the docker yh@cichlet:~$ docker run -i -t -v /home/mydata:/mnt polyactis/accucopy /bin/bash root@cc7807445e40:/$ cd /usr/local/Accucopy/ /usr/local/Accucopy root@cc7807445e40:/usr/local/Accucopy$ ls GADA maestre main.py plot_autocor_diff.py plot_snp_maf_peak.py LICENSE configure plot.tre.autocor.R plot_coverage_after_normalization.py plot_tre.py __init__.py infer plotCPandMCP.py plot_snp_maf_exp.py root@cc7807445e40:/usr/local/Accucopy$ ./main.py usage: main.py [-h] [-v] -c CONFIGURE_FILEPATH -t TUMOR_BAM -n NORMAL_BAM -o OUTPUT_DIR [--snp_output_dir SNP_OUTPUT_DIR] [--clean CLEAN] [--segment_stddev_divider SEGMENT_STDDEV_DIVIDER] [--snp_coverage_min SNP_COVERAGE_MIN] [--snp_coverage_var_vs_mean_ratio SNP_COVERAGE_VAR_VS_MEAN_RATIO] [--max_no_of_peaks_for_logL MAX_NO_OF_PEAKS_FOR_LOGL] [--nCores NCORES] [-s STEP] [-l LAM] [-d DEBUG] [--auto AUTO] main.py: error: argument -c/--configure_filepath is required # modify file "configure" to reflect paths of input data and relevant binaries root@cc7807445e40:/usr/local/Accucopy$ cat configure read_length 101 window_size 500 reference_folder_path /mnt/hs37d5 samtools_path /usr/local/bin/samtools caller_path /usr/local/strelka binary_folder /usr/local/Accucopy root@cc7807445e40:/usr/local/Accucopy$ ls /usr/local/bin/ total 11640 -rwxrwxr-x 1 root root 4436160 Jul 7 2018 samtools*
3.4 Install piece by piece
- A computer with at least 32GB of memory (recommend 64GB).
- Strelka2. A variant caller that is used to call SNPs. https://github.com/Illumina/strelka
- Pyflow https://github.com/Illumina/pyflow
- samtools (A pre-compiled binary for Ubuntu 16.04)
- A high-quality block-sorting file compressor library, install it via "apt install libbz2-1.0" in Debian/Ubuntu.
- If your OS (like CentOS) has this library installed but Accucopy still fails to load it, you can do a symlink from the installed libarary file to "libbz2.so.1.0".
- liblzma5 (XZ-format compression library)
- (Only for compiling) libclang-dev, clang
- (Only for building from source) pkg-config: used by Rust compiler to find library paths. i.e. "pkg-config --libs --cflags openssl"
- (Optional) R packages ggplot2, grid, scales. Only needed if you obtain a development version of Accucopy. Required to make one R plot.
- But the R plot is NOT a must-have, one python plot has similar content as the R plot.
Running Accucopy requires a project-specific configure file, details below. configure according to your OS environment.
3.4.2 Install pyflow and other Python packages
git clone https://github.com/Illumina/pyflow.git pyflow cd pyflow/pyflow python setup.py build install
Other python packages can be installed through Python package system "pip install ..." or Ubuntu package system, dpkg/apt-get.
3.4.3 Register to download the Accucopy binary package and receive update emails.
Please register here to receive an email that contains a download link. After finishing download, unpack the package via this:
tar -xvzf Accucopy.tar.gz
The Accucopy package contains a few binary executables and R/Python scripts. All binary executables were compiled for the Linux platform (Ubuntu 18 tested). It also contains a sample configure file. Denote the full path of the Accucopy folder as accucopy_path in the configure file (described below).
- If you are having difficulty in getting Accucopy to work, please use the docker image instead.
- This binary package is behind the docker release.
3.5 Compile source code (for advanced users)
Source code is at https://github.com/polyactis/Accucopy.
WARNING: you may run into problems (missing packages, wrong paths, etc.) in compiling the C++/Rust code on non-Ubuntu platforms.
Compiling Accucopy requires those "lib..." packages in section 3.1 and their corresponding development packages (for example, libbz2-dev). In addition, it requires an installation of Rust, https://www.rust-lang.org/. We have compiled successfully on Ubuntu 16.04 and 18.04.
- The public source code on github is older than the most recent development version. We advise users to use the latest version that is encapsulated in the docker.
3.6 Download a reference genome folder
Accucopy requires a reference genome folder to run. The snp file contains coordinates of common (allele frequency >10%) SNPs from the 1000 Genomes project. The chromosome coordinates are denoted as "chr1", not "1". We advise users to re-align reads against the genome file included in the package, in order to minimize wrong alignments and more importantly, match the coordinates of the provided 1000Genomes SNP file. We provide two different versions of the human reference genome.
- hs38d1.7z (714MB, NCBI hs38/GRCh38 is equivalent to UCSC hg38)
- hs37d5.7z (718MB, NCBI hs37 is equivalent to UCSC hg19)
We use the 7z compressor (higher compression than gzip or zip). Run "7z x hs38d1.7z" to extract all files.
3.7 Make your own reference genome package
Accucopy can work on custom genomes, i.e. non-human genomes or a human genome different from above. Here are the instructions to make a custom reference genome folder. This folder should contain these files:
- genome.fa: the fasta file of the reference genome.
- genome.fa.fai: the index file of genome.fa by "
- genome.dict: the chromosome:length dictionary file generated by Picard
- snp_sites.gz: the common SNP file in the bed format, 3 columns:
chr1 14598 14599
chr1 14603 14604
chr1 14929 14930
- You can copy the snp_sites.gz from our pre-packaged file if your reference genome is of the same coordinate system as ours (i.e. your reference genome is hs38d5 or another variant of hs38).
- snp_sites.gz.tbi: the index file of snp_sites.gz generated by tabix.
Make sure the chromosome coordinates are denoted as "chr1", not "1". Non-numeric-labelled chromosomes (i.e. X, Y) are ignored in our modelling.
4 Run Accucopy
4.1 Input bam files
For an example, you have a pair of matched tumor and normal samples.
- *.bam.bai files (bam index) are not required. Accucopy will call samtools to generate them if they are found to be missing.
- Bam files should be sorted and duplicates marked.
- The chromosome coordinates are denoted as "chr1", not "1". We advise users to re-align reads against the genome file included in the package, in order to minimize wrong alignments and more importantly, match the coordinates of the provided 1000Genomes SNP file.
4.2 Setup the configure file (latest format, as in the docker version)
Copy the sample configure file (tab-delimited) from the Accucopy package into your project folder and modify it accordingly. An example looks like this:
read_length 101 window_size 500 reference_folder_path /mnt/hs38d1 samtools_path /usr/local/bin/samtools caller_path /usr/local/strelka accucopy_path /usr/local/Accucopy
All the fields in the configure file:
read length the length in base pair of the read.
window_size the window size in base pair for segmentation. The segmentation program (GADA) first calculates the number of reads for each window and then perform segmentation over the genome. A small window size often leads to a large number of small segments. The recommended window size is 500bp.
reference_folder_path the path of the genome folder. Two human versions are downloadable from this site. You can make one for your particular genome. Check this page.
samtools_path the path of the samtools binary
caller_path the path of the 3rd-party variant calling program. We use Strelka2. This is the path of the folder that contains all Strelka2 code/executables, i.e. /usr/local/strelka.
accucopy_path the path of the Accucopy software. See section 3.2
4.3 Example commandlines
Accucopy consists of several binary executables. To make everything easy, we have written a Python program main.py ( inside the "Accucopy" folder ) which wraps all binary executables in a workflow.
./main.py –h gives you an explanation of all the arguments:
yh@hello:~/Accucopy$ ./main.py -h usage: main.py [-h] -c CONFIGURE_FILEPATH -t TUMOR_BAM -n NORMAL_BAM -o OUTPUT_DIR [--snp_output_dir SNP_OUTPUT_DIR] [--clean CLEAN] [--segment_stddev_divider SEGMENT_STDDEV_DIVIDER] [--snp_coverage_min SNP_COVERAGE_MIN] [--snp_coverage_var_vs_mean_ratio SNP_COVERAGE_VAR_VS_MEAN_RATIO] [--max_no_of_peaks_for_logL MAX_NO_OF_PEAKS_FOR_LOGL] [--nCores NCORES] [-s STEP] [-l LAM] [-d DEBUG] [--auto AUTO] optional arguments: -h, --help show this help message and exit -c CONFIGURE_FILEPATH, --configure_filepath CONFIGURE_FILEPATH the path to the configure file. -t TUMOR_BAM, --tumor_bam TUMOR_BAM the path to the tumor bam file. If the bam is not indexed, an index file will be generated -n NORMAL_BAM, --normal_bam NORMAL_BAM the path to the normal bam file. If the bam is not indexed, an index file will be generated -o OUTPUT_DIR, --output_dir OUTPUT_DIR the output directory path. --snp_output_dir SNP_OUTPUT_DIR the directory to hold the SNP calling output. Default is the same folder as the bam file. --clean CLEAN whether to remove the existing output folders and files? 0 No, 1 Yes. Default is 0. --segment_stddev_divider SEGMENT_STDDEV_DIVIDER A factor that reduces the segment noise level. The default value is recommended. Default is 20. --snp_coverage_min SNP_COVERAGE_MIN the minimum SNP coverage in adjusting the expected SNP MAF. Default is 2. --snp_coverage_var_vs_mean_ratio SNP_COVERAGE_VAR_VS_MEAN_RATIO Instead of using the observed SNP coverage variance (not consistent), use coverage_mean X this-parameter as the variance for the negative binomial model which is used in adjusting the expected SNP MAF. Default is 10. --max_no_of_peaks_for_logL MAX_NO_OF_PEAKS_FOR_LOGL the maximum number of peaks used in the log likelihood calculation. The final logL is average over the number of peaks used. Default is 3 --nCores NCORES the max number of CPUs to use in parallel. Increase the number if you have many cores. Default is 2. -s STEP, --step STEP 0: start from the very begining (Default). 1: obtain the read positions and the major allele fractions. 2: normalization. 3: segmentation. 4: infer purity and ploidy only. -l LAM, --lam LAM lambda for the segmentation algorithm. Default is 4. -d DEBUG, --debug DEBUG Set debug value. Default is 0, which means no debug output. Anything >0 enables several plots being made. --auto AUTO The integer-valued argument that decides which method to use to detect the period in the read-count ratio histogram. 0: the simple auto-correlation method. 1: a GADA-based algorithm (recommended). Default is 1.
In the debug mode (-d 1), Accucopy will produce several intermediate plots, offering insights into how well it is handling the input data.
./main.py -c configure_file --nCores 30 -t sample_1_cancer.bam -n sample_1_normal.bam -o sample1_output -d 1
./main.py -c configure_file --nCores 20 -t sample_1_cancer.bam -n sample_1_normal.bam -o sample1_output --snp_output_dir sample1_output -d 1 --step 2
./main.py -c configure_file -t sample_1_cancer.bam -n sample_1_normal.bam -o sample1_output -d 1 --clean 1
5 Simulation data for testing
We generated in silico tumor and matching-normal WGS data using an EAGLE-based workflow. EAGLE is a software developed by Illumina to mimic their own high-throughput DNA sequencers and the simulated reads bear characteristics that are close to real-sequencing reads. We introduced twenty-one somatic copy number alterations (SCNAs), with length varying from 5MB to 135MB and copy number from 0 to 8, affecting about 28% of the genome, to each simulated tumor genome. The entire genome of its matching normal sample is of copy number two. A total of 1.8 million heterozygous single-nucleotide loci were introduced to each normal and its matching tumor sample. For each coverage setting, we first generated a pure tumor sample (purity=1.0) and its matching normal sample. We then generated nine different impure tumor samples (purity from 0.1 to 0.9) by data of the pure tumor sample with its matching normal data. The mixing proportion determines the tumor sample’s true purity.
Due to space constraints on our public server, we only provide simulation data of tumor samples that contain one subclone. If you need simulated data with more subclones or HCC1187 impure data, please contact us.
Here is the http list of one simulated normal sample (coverage=5X) and nine simulated tumor samples (coverage=2X), with purity from 0.1 to 0.9, for researchers to test their own method.
The true SCNA profile of all tumor samples is identical. And within each tumor sample, only one tumor clone exists (hence called singleclone). The truth SCNA profile is at https://www.yfish.org/data/singleclone_2x/CNA_truth.tsv. Here is what each column means.
- Column "cp" is the total copy number of the designated region.
- Column "major_allele_cp" is the copy number of the major allele. The copy number of the minor allele = "cp" - "major_allele_cp".
- Please ignore the "IsClonal" column.
- Regions, not in this file, are of copy number two.
chr start end cp major_allele_cp IsClonal chr1 200000000 240000000 3 2 T chr2 20000000 25010000 4 3 T chr2 40000000 45000000 1 1 T chr3 50000000 100000000 8 7 T chr4 40000000 80000000 1 1 T chr10 30000000 45000000 2 2 T chr20 3000000 45000000 3 3 T chr15 20000000 40000000 7 6 T chr17 10000000 60000000 5 4 T chr8 10000000 145000000 3 2 T chr1 135000000 185000000 5 3 T chr2 80000000 130000000 4 2 T chr5 30000000 75000000 6 4 T chr6 50000000 90000000 6 3 T chr7 90000000 130000000 7 5 T chr12 25000000 65000000 7 4 T chr11 80000000 130000000 4 4 T chr12 85000000 120000000 5 5 T chr13 25000000 65000000 6 6 T chr14 30000000 45000000 7 7 T chr15 50000000 90000000 8 8 T
6 Accucopy output
6.1 Key output files
A summary output that contains purity and ploidy estimates, and some other statistical measures. Probably the most important file to a user.
purity ploidy 0.66735 2.0612 logL period best_no_of_copy_nos_bf_1st_peak first_peak_int 9.9811e+06 327 2 980 no_of_segments no_of_segments_used no_of_snps no_of_snps_used 539 539 1333539 933576
This contains lots of internal model output, useful for developers.
This contains preliminary copy number alteration predictions.
The important columns are chr, start, end.
"cp" is the predicted copy number.
"copy_no_float" is the raw copy number outputted by our program, which will be converted to an integer (the "cp" column) if our model deems it a clonal (shared by all cancer cells) CNV. Some "cp" will stay as "float" because our model thinks they are subclonal (some cancer cells in one CNV state, some cancer cells in another).
Ignore column "cumu_start", "cumu_end".
chr start end cp major_allele_cp copy_no_float oneSegment.stddev maf_mean maf_stddev maf_expected cumu_start cumu_end 5 8215001 8363001 2 1 1.76147 0.00987896 0.622568 0.0685194 0.632948 895215001 895363001 3 16591001 16751001 2 1 1.76758 0.00891515 0.62856 0.063834 0.632948 511591001 511751001 ...
A genome-wide CNV plot.
A period plot. Check if the model fits data well.
A plot for developers.
An intermediate segmentation output file, NOT the final CNV output. For the final CNV calls, check "cnv.output.tsv". The format is tab-delimited:
chromosome_id chr_start_pos chr_stop_pos segment_mean segment_stddev segment_length
Column "segment_length" is the number of 500bp bins.
# Parameters: a=0.5,T=30,MinSegLen=50,sigma2=0.00607344,BaseAmp=0, convergenceDelta=1e-08, maxNoOfIterations=50000, convergenceMaxAlpha=1e+08, convergenceB=1e-20. # 447684 data points in input file # Overall mean 1.08811 # Sigma^2=0.00607344 # Convergence: delta=9.9872e-09 after 6560 EM iterations. # Found 59940 breakpoints after SBL # Kept 408 breakpoints after BE chr1 11001 144001 1.96458 0.285963 267 chr1 144501 359001 1.55429 0.283916 165 chr1 359501 3392501 0.946436 0.103084 5603 chr1 3393001 4385001 0.848227 0.091197 1681 chr1 4385501 4726001 1.11841 0.102009 682 chr1 4726501 5318501 0.923708 0.0856138 1185 chr1 5319001 5596001 1.12449 0.125894 555 chr1 5596501 6407501 0.948009 0.0875873 1616 chr1 6408001 6497001 1.21138 0.103352 179 ...
There is a gap of 500 between the previous chr_end_pos and current chr_start_pos because sequencing coverage data is aggregated in 500bp bins.
6.2 A clean-data example
All major results are stored in the output directory. File sample_1_infer/infer.out.tsv contains the purity and ploidy estimates. Here is an example. (Viewing in Excel is a lot nicer) :
purity ploidy purity_naive ploidy_naive rc_ratio_of_cp_2 rc_ratio_of_cp_2_corrected segment_stddev_divider snp_maf_stddev_divider snp_coverage_min snp_coverage_var_vs_mean_ratio period_discover_run_type 0.7246 2.2428 0.72078 2.2282 924 919.13 10 20 2 10 1 logL period best_no_of_copy_nos_bf_1st_peak first_peak_int 1.5204e+07 333 1 585 no_of_segments no_of_segments_used no_of_snps no_of_snps_used 697 697 1517851 1062619
In the output above, the column ‘purity’ is the final purity estimate, and ‘purity_naive’ is the pre-adjusted estimate which can be ignored. ‘logL’ is the maximum likelihood of the hierarchical Gaussian Mixture model. ‘period’ is the 1000 X period of the tumor-read-enrichment (TRE) histogram (=333 in this case), which is detected by auto-regression. 'no_of_snps' and ‘no_of_segments’ is the result of step 3 and step 4. Other columns are values of commandline arguments.
There are other important output files, such as all_segments.tsv.gz and het_snp.tsv.gz, which are output of step 4 and step 3 respectively. If the sample is abnormal, we can usually see an unreasonable number of segments and SNPs in these two files.
Besides the text output, Accucopy will produce some graphic output. One of the the most important plots is plot.tre.png (Figure 1), available only in debug mode ( -d 1):
Figure 1. Periodic pattern (plot.tre.png) for a clean-data example, on which Accucopy will succeed.
TRE stands for Tumor Read Enrichment. You can think of it as a normalized version of the read count ratio between the tumor and normal samples for one chromosome window. More details can be found in our paper. The Y axis in the two panels is the window count. The lower panel is in the log scale. A clean TRE histogram leads to a confident purity estimate.
In this clean-data example, the tumor read enrichment (TRE) histogram displays a beautiful periodic pattern. That means we can confidently infer the period (=0.333) from the TRE data and the ensuing maximum likelihood estimates will be more robust. The CNV estimates in plot.cnv.png (Figure 2), also demonstrates a clean copy number variation (CNV) profile.
Figure 2. Copy number alteration plot (plot.cnv.png) for a clean-data example, on which Accucopy will succeed.
This is the estimated CNV profile for the example. The top plot is the estimated absolute copy number for each segment. For a normal sample, the absolute copy number should be 2 throughout the genome. The lower plot shows the major allele copy number for each segment.
There are cases where purity and ploidy can not be inferred:
- The cancer genome contains too few somatic copy number alterations.
- The noise level is too high, or the noise level is moderate but the sample purity is very low (<0.05).
6.3 A noisy-data example
Occasionally, a user will encounter extremely noisy data. The user should learn to identify the noisy data from plots and do NOT use the estimates made by Accucopy. Here is a noisy-data example.
Content of infer.out.tsv for a noisy-data example. The high number of segments is a red flag.
purity ploidy purity_naive ploidy_naive rc_ratio_of_cp_2 rc_ratio_of_cp_2_corrected segment_stddev_divider snp_maf_stddev_divider snp_coverage_min snp_coverage_var_vs_mean_ratio period_discover_run_type 0.90938 1.9375 0.91675 1.9551 1021 1029.3 10 20 2 10 2 logL period best_no_of_copy_nos_bf_1st_peak first_peak_int 4.3681e+06 468 1 555 no_of_segments no_of_segments_used no_of_snps no_of_snps_used 19909 19909 1559676 1092048
Its tumor-read-enrichment (TRE) histogram (plot.tre.png) has one big and unclean peak (its landscape looks like being cut through by a lousy jigsaw). It makes it really difficult to accurately estimate its period. The period estimate (0.468, 468 in the 2nd cell of the 4th line is 1000Xperiod.) is probably far from the truth. All ensuing maximum likelihood estimates are questionable. The estimated CNV profile further confirms the great amount of noise in this data.
Figure 3. Periodic pattern (plot.tre.png) for a noisy-data example, on which Accucopy will fail. Another example (not shown) on which Accucopy will fail is when there is a single peak in this plot.
Figure 4. Copy number alteration plot (plot.cnv.png) for a noisy-data example, on which Accucopy will fail.
If you encounter any issues, please email firstname.lastname@example.org or file an issue at https://github.com/polyactis/Accucopy/issues (so that everyone can learn).
8.1 "At genomic regions of the first type, all cancer subclones have the same integral copy number....we call these regions clonal."
Why is the copy number integral?
Copy number estimate is an integer. like 1, 2, 3. sounds obvious, but some regions are of fractional (2.3, 3.5) copy numbers because 1) your sequencing data is a mixture of thousands or even millions of cells , which is called batch sequencing (not single-cell sequencing), 2) a tumor is usually not homogeneous, so some cancer cells differ from others in terms of copy numbers. For example, in one region, 50% cells is of copy number 2, the other 50% is of copy number 3. Then you'll see 2.5 as a whole. These regions are called subclonal.
8.2 "How does Accucopy deal with multiple subclones(>2)?"
We don't estimate the number (or fractions) of cancer subclones. Our software tells the user whether a region is clonal or subclonal, and estimate their copy numbers.
Please note. "subclone" and "subclonal" are referring to different things. "subclone" or "clone" refers to a lineage of cancer cells during the cancer cell evolution process. "subclonal" or "clonal" is referring to mutations. "Subclonal" mutations are the ones that lead to a type of cancer subclones on the evolutionary branch, and thus these are the mutations that not shared across all cancer cells in the tumor sample. "Clonal" mutations are the ones that are shared across all cancer cells.
I think lots of people get confused about it.
8.3 Accucopy's normalization output is empty for some chromosomes and thus failed in the segmentation (GADA) stage.
It is likely that your bam files are aligned to a reference genome different from ours (maybe the order of chromosomes are different in the bam header). You can make a custom ref genome folder by following instructions from 3.7.