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# ReMixT ReMixT is a tool for joint inference of clone specific segment and breakpoint copy number in whole genome sequencing data. The input for the tool is a set of segments, a set of breakpoints predicted from the sequencing data, and normal and tumour bam files. Where multiple tumour samples are available, they can be analyzed jointly for additional benefit. ## Installation Conda is a prerequisite, install [anaconda python](https://store.continuum.io/cshop/anaconda/) from the continuum website. ### Installing from pip The recommended method of installation for ReMixT is using `pip`. pip install remixt You will also need to `shapeit` and `samtools` on your path. They can be installed using conda: conda install samtools conda install -c dranew shapeit ### Installing from conda The conda distribution is now out of date. However, to use conda, add my channel, and the bioconda channel, and install ReMixT as follows. conda config --add channels https://conda.anaconda.org/dranew conda config --add channels 'bioconda' conda install remixt ### Installing from source #### Clone Source Code To install the code, first clone from bitbucket. A recursive clone is preferred to pull in all submodules. git clone --recursive git@bitbucket.org:dranew/remixt.git #### Dependencies To install from source you will need several dependencies. A list of dependencies can be found in the `conda` `yaml` file in the repo at `conda/remixt/meta.yaml`. #### Build executables and install To build executables and install the ReMixT code as a python package run the following command in the ReMixT repo: python setup.py install ## Setup ReMixT ### Reference genome Download and setup of the reference genome is automated. The default is hg19. Select a directory on your system that will contain the reference data, herein referred to as `$ref_data_dir`. The `$ref_data_dir` directory will be used in many of the subsequent scripts when running destruct. Download the reference data and build the required indexes: remixt create_ref_data $ref_data_dir ### Mappability file Additionally, ReMixT requires a mappability file to be generated. We have provided a workflow for generating a mappability file based on `bwa` alignments, for other aligners, you may want to create your own mappability workflow, see `remixt/mappability/bwa/workflow.py` as an example. To create a mappability file for `bwa`, run: remixt mappability_bwa $ref_data_dir Note that this workflow will take a considerable amount of time and it is recommended you run this part of ReMixT setup on a cluster or multicore machine. For parallelism options see the section [Parallelism using pypeliner](#markdown-header-parallelism-using-pypeliner). ## Running ReMixT ### Input Data ReMixT takes multiple bam files as input. Bam files should be multiple samples from the same patient, with one bam sequenced from a normal sample from that patient. Additionally, ReMixT takes a list of predicted breakpoints detected by paired end sequencing as an additional input. #### Breakpoint Prediction Input Format The predicted breakpoints should be provided in a tab separated file with the following columns: * `prediction_id` * `chromosome_1` * `strand_1` * `position_1` * `chromosome_2` * `strand_2` * `position_2` The first line should be the column names, which should be identical to the above list. Each subsequent line is a breakpoint prediction. The `prediction_id` should be unique to each breakpoint prediction. The `chromosome_`, `strand_` and `position_` columns give the position and orientation of each end of the breakpoint. The values for `strand_` should be either `+` or `-`. A value of `+` means that sequence to the right of `chromosome_`, `position_` is preserved in the tumour chromosome containing the breakpoint. Conversely, a value of `-` means that sequence to the left of `chromosome_`, `position_` is preserved in the tumour chromosome containing the breakpoint. The following table may assist in understanding the strand of a break-end. Note that an inversion event produces two breakpoints, the strand configurations for both are shown. Additionally, for inter-chromosomal events, any strand configuration is possible. | Structural Variation | Strand of Leftmost Break-End | Strand of Rightmost Break-End | | ------------------------ | ---------------------------- | ----------------------------- | | Deletion | + | - | | Duplication | - | + | | Inversion (Breakpoint A) | + | + | | Inversion (Breakpoint B) | - | - | ### ReMixT Command Line Running ReMixT involves invoking a single command, `remixt run`. The result of ReMixT is an [hdf5](https://www.hdfgroup.org) file storing [pandas](http://pandas.pydata.org) tables. Suppose we have the following list of inputs: * Normal sample with ID `123N` and bam filename `$normal_bam` * Tumour sample with ID `123A` and bam filename `$tumour_a_bam` * Tumour sample with ID `123B` and bam filename `$tumour_b_bam` * Breakpoint table in TSV format with filename `$breakpoints` Additionally, ReMixT will generate the following outputs: * Results as HDF5 file storing pandas tables with filename `$results_h5` * Temporary files and logs stored in directory `$remixt_tmp_dir` (directory created if it doesnt exist) Given the above inputs and outputs run ReMixT as follows: remixt run $ref_data_dir $raw_data_dir $breakpoints \ --normal_sample_id 123N \ --normal_bam_file $normal_bam \ --tumour_sample_ids 123A 123B \ --tumour_bam_files $tumour_a_bam $tumour_b_bam \ --results_files $results_h5 --tmpdir $remixt_tmp_dir Note that ReMixT creates multiple jobs and many parts of ReMixT are massively parallelizable, thus it is recommended you run ReMixT on a cluster or multicore machine. For parallelism options see the section [Parallelism using pypeliner](#markdown-header-parallelism-using-pypeliner). ### Output File Formats The main output file is an HDF5 store containing pandas dataframes. These can be extracted in python or viewed using the ReMixT viewer. Important tables include: * `stats`: statistics for each restart * `solutions/solution_{idx}/cn`: segment copy number table for solution `idx` * `solutions/solution_{idx}/brk_cn`: breakpoint copy number table for solution `idx` * `solutions/solution_{idx}/h`: haploid depths for solution `idx` #### Statistics ReMixT uses optimal restarts and model selection by BIC. The statistics table contains one row per restart, sorted by BIC. The table contains the following columns: * `idx`: the solution index, used to refer to `solutions/solution_{idx}/*` tables. * `bic`: the bic of this solution * `log_posterior`: log posterior of the HMM * `log_posterior_graph`: log posterior of the genome graph model * `num_clones`: number of clones including normal * `num_segments`: number of segments * `h_converged`: whether haploid depths estimation converged * `h_em_iter`: number of iterations for convergence of h * `graph_opt_iter`: number of iterations for convergence of genome graph copy number * `decreased_log_posterior`: whether the genome graph optimization stopped due to a move that decreased the log posterior #### Segment Copy Number The segment copy number table adds additional columns to the segment counts table described above, including but not limited to: * `major_1` * `minor_1` * `major_2` * `minor_2` The columns refer to the major and minor copy number in tumour clone 1 and 2. #### Breakpoints Copy Number The breakpoint copy number table contains the following columns: * `prediction_id` * `cn_1` * `cn_2` The `prediction_id` column matches the column of the same name