Git & Version Control With Tower or on the Command Line. PDF, ePub, Mobi for all devices; Start Now for Free. 20+ high quality videos one topic per video; highly visual way of learning real life workflows.This tutorial assumes you are using Py. Clone 0. 1. 2. 3. Most of this tutorial will apply to the older 0. This tutorial also assumes you are working in a Unix like environment. I have tested it on Linux, and it should work on Mac though I can't guarantee that. It almost certainly will not work for Windows and there will likely never be support in Py. Clone for Windows. Historically Py. Clone has referred to the proportion of cancer cells which contain a mutation as the cellular frequency of the mutation. Recent publications in the field have referred to the same quantity as the cellular prevlance of the mutation. In this document we will generally use prevalence, but some of the Py. Clone functions use frequency. Py. Clone requires that you generate prior information about the genotype of each mutation in the sample. In principle this depends on the algorithm(s) you use for copy number inference, tumour content prediction and your own beliefs. In practice specifying priors is challenging so Py.
Clone includes some functionality to automatically generate the required prior information based on simpler input information. Before you can use Py. Clone you will need the following information for each mutation in the sample. Allelic count data from a sequencing experiment. Py. Clone requires that you specify the number of reads overlapping the mutation which contain the reference allele and the number of reads which contain the variant allele. The copy number of the genomic region containing the mutation. Py. Clone can work with either predictions of total copy number or parental (allele specific) copy number. In general performance will be better if you can specify parental copy number. In addition to above data for each mutation, Py. Clone can also use an estimate of the tumour content of the sample. The tumour content is not strictly required, but if it is not passed performance will be worse. In addition the estimates of cellular prevalence of the mutations will need to be interpreted differently. If you specify the tumour content, then the cellular prevalence estimates represent the fraction of cancer cells which contain the mutation. If you do not specify the tumour content, which in practice means setting it to 1. This means that the highest estimated cellular prevalence will be less then or equal to the true tumour content. Note : If you have estimates of tumour content use them, otherwise the inferred cellular prevalences may exceed the predicted tumour content. The sequencing data can be obtained from any sequencing platform which provides digital allelic count information. For examples: Hi. Seq, Mi. Seq, 4. 54 and Ion Torrent sequencers would all provided the required data. The data will of course need to be aligned and the allelic counts extracted. In principle whole genomes shotgun sequencing (WGSS) or exome capture sequencing data could be used. In practice the depth of these approaches will be to low for an accurate Py. Clone analysis. The preferred approach is to use deep sequence data acquired by targeted amplicon sequencing or custom capture arrays. The copy number information can be elicited from either arrays such as the Affymetrix SNP6. WGSS data. Computational tools will need to be applied in either case to infer the copy number profiles of the genomes. Tools which can predict parental copy number are ideal, and even better are tools which also provide an estimate of tumour content. Assuming you have derived a copy number profile for your samples, you will need to extract the copy number of the segments which contain your mutations. For this tutorial we will use data from a mixture of normal tissue samples. There are four samples in this dataset which were generated by mixing tissues from four 1. The data was deeply sequenced using the Illumina Mi. Seq platform. Positions in which only one of the cases has a variant genotype (AB or BB) are included in this dataset. Conceptually this is equivalent to a sample with four populations of diploid clones, which share no mutations. Learn how to use Git with Code School’s interactive course, Try Git. Because we excluded sex chromosomes, the total copy number of all positions is 2. For this dataset we still need to get the parental copy number for mutation. This can be done since we have the predicted genotype of the variants (AB or BB). In the case the mutation is AB the major and minor copy numbers would both be 1. In the case the genotype was BB the major copy number would be 2 and the minor copy number would be one. Below is an example of the first two rows of one of the input files, SRR3. The input files are located under the tsv/ folder. The first row is the header, the subsequent rows correspond to mutations. There are 6 mandatory fields in for the input file. In general specifying the gene for the mutation is a bad idea in case a gene contains multiple mutations. Usually some combination of gene name and genomic coordinates is a good choice. In this case I have used the case with the variant, the genotype of mutation in the variant case and the genomic coordinates. In most cases this will be 2, with the following exceptions (there may be some others I haven't considered). If the sample is from a male and the mutation is on a sex chromosomes (X or Y) you would expect the normal cells to have copy number 1. If the normal tissue has a germline copy number variant you would need to set the copy number to the predicted value. The only way to get this is to run a copy number analysis on normal tissue from the same donor. The convention is that the major copy number is the larger of the two values. Note : If you only have total copy number for the tumour, not parental copy number, you can set the minor. When we use the Py. Clone build. By default the command assumes parental copy number information is being passed. This files also contains 3 additional fields. Py. Clone will ignore these fields during analyses, and they are only useful for your own annotations. Any fields beyond the 6 mandatory ones mentioned above will be ignored by Py. Clone. For reference the additional 3 fields in this file arevariant. There is one file and one folder in this directory. Py. Clone analysis. The first thing we will need to do is take the files in tsv/ and convert them to a format Py. Clone can work with. Specifically we need to specify the possible states of each mutation and their prior probabilities. The state of a mutation is combination of genotypes for the normal, reference and variant populations. Py. Clone includes a command build. There are some options for the build. The first few lines of the file yaml/SRR3. NA1. 21. 56: BB: chr. The first linespecifies we are listing out the mutations for this sample. The next line- id: NA1. BB: chr. 2: 1. 75. NA1. 21. 56: BB: chr. Note the - indicates were are starting a new mutation entry. The linesand specify the reference and variant counts for the mutations. The state priors are specified bystates: -. For each state we specify 4 values: g. Recall the reference sub- populations consists of all cancer cells which lack the mutation. Thus valid genotypes here should not contain a B allele. For examples A, AA, AAA, AAAAAAAA are all valid but B, AB, BB, AAAAB are invalid. Recall the variant sub- populations consists of all cancer cells which posses the mutation. Thus valid genotypes should contain at least one B allele. For example B, AB, BB, ABBB are all valid but A, AA, AAB are not valid. The relative prior belief you have in this state versus the others specified. This value will normalised so that the sum over the prior beliefs for all states for a mutation equals 1. Py. Clone won't warn you about invalid entries so you need to be careful if you create the yaml files by hand. This means that you could include germline mutations or LOH events. You can also re- purpose the model for other applications. Before Py. Clone can perform an analysis we need to create one more YAML format file. This file tells Py. Clone The directory structure on the system Where the file with the mutation informations reside. The model which we want to run. The tumour content and error rates for sequencing for each sample were are going to analyse. Various parameter settings for the model. Their is no helper function included with Py. Clone so this file will have to be created manually. The tutorial data includes a file config. The file looks likenum. More iterations will lead to a more accurate estimate of the posterior distribution at the cost of more computational effort. Note: One way to check if you have run a sufficient number of iterations is to perform the analysis twice from different initialisations (default if you don't set the - -seed flag with the analyse command). If the results are the same then it is likely that enough iterations have been run. If not you will need to re- run for more iterations. Note: In my experience 1. Burnin is set during the post- processing phase. The next linesbase. The default values are uniformly spread over . Most users will not need to change these default values. The next few linesconcentration: value: 1. Dirichlet Process (DP). Again most users will not need to edit these and the defaults should suffice. If the linesprior: shape: 1. The first thing we will do is add an entry to specify which density (model) we want to use. There are several options gaussian : This option will use an infinite Gaussian mixture model (IGMM) fit to the variant allelic frequencies. This option will use an infinite Binomial mixture model (IBMM) fit to allelic count data. This will be the choice most users will want. We will use the Py. Clone model with the Beta Binomial emission, to specify this add following line to the config. We will add the following lines to the file to do this. To keep the configuration file more readable Py. Clone specifies a working directory where all analyses will be performed, every other path in the config. To do this add the lineworking. To do this add the following linewhich will cause Py. Clone to place the output in /path/to/tutorial/directory/trace. Next we need to tell Py. Git - Documentation. Pro Git is written by Scott Chacon and Ben Straub and published by Apress.
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