文章目錄

• 前言: CLIP-Seq 與 RIP-Seq
• 一、piranha 簡介
• 二、piranha 使用
• • 1. 安裝
  • 2. 說明書
  • 3. 使用方法

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前言: CLIP-Seq 與 RIP-Seq

簡單的說，是RNA版本的ChIP-seq，CLIP-Seq 全稱crosslinking-immunprecipitation and high-throughput sequencing，RIP-Seq 全稱 RNA Binding Protein Immunoprecipitation and high-throughput sequencing。名字里說得很明確了，前者有一個紫外交聯(lián)的步驟，后者沒有，靠RNA結(jié)合蛋白免疫沉淀富集RNA。CLIP-Seq 與 RIP-Seq 都是用來測蛋白與RNA互做的，所以peak caller 是數(shù)據(jù)分析不可缺少的一部分。

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一、piranha 簡介

piranha, 南加州大學的Philip J. Uren 大神作品。一句話概括 Piranha is a peak-caller for CLIP- and RIP-seq data。

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二、piranha 使用

1. 安裝

方便快捷的 conda https://anaconda.org/bioconda/piranha：

conda install -c bioconda piranha

2. 說明書

https://github.com/smithlabcode/piranha：

********************************* Copyright and License Information ******************************************************************************* Copyright (C) 2012 University of Southern California, Philip J. Uren, Andrew D. SmithAuthors: Philip J. Uren, Emad Bahrami-Samani, Andrew D. SmithThis program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.You should have received a copy of the GNU General Public License along with this program. If not, see <http://www.gnu.org/licenses/>.This software package contains Google Test and BAMTools -- see their respective directories for copyright and license information for those packages. ***************** Table of contents ******************************************************************************* 1. Building and Installing Piranha 2. Basic usage of Piranha 3. Command-line options of Piranha 4. Input and output formats of Piranha 5. Simulation of input data for Piranha 6. Contacts and bug reports********************************** 1. Building and Installing Piranha ******************************************************************************* Piranha has been designed to operate in a UNIX-like environment. It has been tested on MacOS X Snow Leopard, USCLinux, Ubuntu and SUSE. We've made every effort to ensure it's compatible with a wide range of systems, but we can't test on all possible platforms and configurations. If you find an incompatibility when building or using it, please let us know!Step 0 -- dependencies and requirements ---------------------------------------64-bit machine and GCC version > 4.1 (to support TR1). Piranha additionally requires a functioning installation of the GNU Scientific Library (GSL). If you don't already have this installed, you willneed to download and install it from http://www.gnu.org/software/gsl/This release has been tested with GSL version 1.14Your installation of GSL must be built for a 64 bit architecture.[OPTIONAL] TestsThe regression tests (which you don't need to run, but can to test that Piranha was built correctly) require Python. You can download Python from http://www.python.org/This release has been tested with Python 2.6.1 [OPTIONAL] BAM supportIf you want BAM support, you must download and build BAMTools.If Piranha cannot find the BAMTools development headers and library,it will be compiled without support for BAM. See below for details onhow to specify the location of BAMTools during the build process. BAMTools is available from https://github.com/pezmaster31/bamtoolsThis release has been tested with BAMTools version 2.1.1 Step 1 -- configuring ---------------------First configure the installation. To do this, where '>' is your prompt and the CWD is the root of the distribution, type:> ./configure [OPTIONAL] BAM supportconfigure will warn you if BAM support is not available. If you don't want BAM support, this is fine. If you do want it however, and the configure script did not find BAMTools, you will have to specify where the BAMTools development headers and libraries are located. You can do that as follows> ./configure --with-bam_tools_headers="/some/path/BAMTools/include/" \--with-bam_tools_library="/some/path/BAMTools/lib/"the command is split into two lines here for aesthetic reasons, but it need not be so. There are a few caveats:* If you subsequently move the BAMTools library, Piranha will not work.(this should be obvious really).* The BAMTools installer currently doesn't update the linker info after installation. That means you might install it, but Piranha willstill not find it when running ./configure -- in this case, I suggestmanually specifying the location as shown above.Step 2 -- building ------------------To build the binaries, type the following, where '>' is your prompt and theCWD is the root of the distribution > make all Step 3 -- installing --------------------To install the binaries, type the following, where '>' is your prompt and theCWD is the root of the distribution> make installThis will place the binaries in the bin directory under the package root.They can be used directly from there without any additional steps. You canadd that directory to your PATH environment variable to avoid having to specify their full paths, or you can copy the binaries to another directoryof your choice in your PATH.Step 4 -- testing [OPTIONAL] ----------------------------You can verify that Piranha was built correctly by running the includedunit and regression tests. At the command prompt (assuming your prompt is '>') type the following:> make test ************************* 2. Basic usage of Piranha ******************************************************************************* Piranha has two main modes of operation. In the first, a single regular distribution is fit to the input data and each region is assigned a p-value based on this distribution. The default distribution is the zero-truncated negative binomial. To run Piranha in this way, use the following, where > is your prompt > ./Piranha input.bedThe second mode of operation fits a regression model relating counts to covariates. The default is a zero-truncated negative binomial regression. To run Piranha in this way, do the following, where > is your prompt > ./Piranha input.bed covariate1.bed covariate2.bedNote that the first file is always assumed to be the counts, and the remaining files are assumed to be covariates. For further options, run Piranha without any arguments, or see the section 'Command Line Options' below.********************************** 3. Command-line options of Piranha ******************************************************************************* Usage: Piranha [OPTIONS] *.bedOptions:-o, -output Name of output file, STDOUT if omitted -s, -sort indicates that input is unsorted and Piranha should sort it for you-p, -p_threshold significance threshold for sites -a, -background_thresh indicates that this proportion of the lowest scores should be considered the background. Default is 0.99-b, -bin_size indicates that input is raw reads and should be binned into bins of this size -i, -bin_size_covars indicates that the covariates (all except first file) are raw reads and should be binned into bins of this size -z, -bin_size_both synonymous with -b x -i x for any x -u, -cluster_dist merge significant bins within this distance. Setting to 0 disables merging, default is 1 (merge adjacent) -r, -suppress_covars don't print covariate values in output-f, -fit Fit only, output model to file -d, -dist Distribution type. Currently supports Poisson, NegativeBinomial, ZeroTruncatedPoisson, ZeroTruncatedNegativeBinomial, PoissonRegression, NegativeBinomialRegression, ZeroTruncatedPoissonRegression, ZeroTruncatedNegativeBinomialRegression -t, -fitMethod component fitting method -m, -model Use the specified model file instead of fitting to input data -v, -VERBOSE output additional messages about run to stderr if set -x, -unstranded Don't preserve strand (puts all the peaks in positive strand) -n, -no_normalisation don't normalise covariates -l, -log_covars convert covariates to log scale Help options:-?, -help print this help message -about print about message ************************************** 4. Input and output formats of Piranha *******************************************************************************NOTE: BAM support is only available if Piranha was linked with BAMTools=======================================================================Piranha takes three possible inputs:1.) The response file (BED or BAM format) [REQUIRED]2.) The covariate files (BED format) [OPTIONAL] 3.) The model file (XML format) [OPTIONAL]The response file (always the first argument, and required) may contain:1.) Binned read counts. In this case the input can be provided in BED format, but not BAM format. The Score field of the BED format file will be treated as the count of the number of reads mapping into that bin. This is the default and is what is expected if no contrary options are specified.2.) Raw read locations, in which case Piranha will bin the reads for you. If you provide your input like this, you MUST set the bin size option, or Piranha will treat your input as being already binned. If Piranha is creating bins from raw reads, it will always start at the first index of each chromosome (i.e. index 1) and move at a step size equal to the bin size. Bins with no reads in them will not be retained. The file format will be determined by looking at the file extension (.bam or .bed). Case is not important; unknown extensions are treated as .bed files. The covariates files are optional. You can provide one or more of them. If none are provided, the program fits a regular distribution (zero-truncated negative binomial by default) to the read counts. If covariates are provided, the program performs regression (zero truncated negative binomial regression by default) using the provided covariates. Covariates must be provided in BED format, where the score field (the fifth) is taken as value of the covariate for the genomic region defined by the other fields. Every covariate file must contain genomic loci that match the response file.Piranha has a special option for handling the case where a single covariate is present and this covariate is the number of reads from another sequencing run (for example, a control IP in the case of RIP or an RNA-seq experiment in the case of CLIP). In this case, the covariate can be provided as a BED file that contains the raw read locations; the -i option should be used to indicate that the covariate file is raw reads -- Piranha will then bin these reads into bins that match the response.Piranha's output is a tab delimited file with the input regions from the response (in BED format) and a single additional column giving the p-value for each bin. If covariates were provided, additional columns are added containing the value of each of the covariates for the given locus.If the -f option is specified, Piranha will output the model instead of the scored bins. Models are given in XML format, and can be loaded back into the program later for scoring a new (or the same) input using the -m option. *************************************** 5. Simulation of input data for Piranha ******************************************************************************* We've also included a program for simulating input data - Simulate. After building and installing, it can be found in the bin directory. This program can produce simulated data from a range of distributions, both regular and regression based. Run it without any arguments to see its command line options. As an example, to generate 1000 simulated regions where the response is dependent on two covariates from a Zero-truncated negative binomial regression distribution, you would execute the program as follows, where > is your prompt (here the command is split over two lines for formatting reasons, but this need not be the case). > ./Simulate -d ZeroTruncatedNegativeBinomialRegression -n 1000 \ > -r response.bed -c "cov1.bed cov2.bed" ***************************** 6. Frequently asked questions *******************************************************************************Q. I get the error: "Failed to split responses, smallest response accounts for more than 99% of all responses. Try increasing the threshold". What doesthis mean? How do I fix it? A. Piranha assumes most bins in your input are background noise. The programmodels this background and then tests each read count to see if it significantly exceeds the background. This doesn't work if all or most ofyour bins contain the same number of reads though. That is what this message is telling you: more than 99% of the bins in your input containthe same number of reads as the smallest read count (usually 1 read, but not neccessarily). Most often this is because you forgot to use the -boption to bin your reads and so Piranha thinks each read is its own bin.Q. I am running Piranha with one or more covariates and I got the followingerror (or similiar): "ERROR: evaluating zero-truncated negative binomial regression log-likelihood function with response 1 and distribution parameters beta: 6000, --- alpha: 100 failed. Reason: result was non-finite" A. The model fitting algorithm failed to converge. This often happens whenthe covariate(s) contain large values, which is most often the case whenyou provide sequencing data like RIP control IP or RNA-seq data as acovariate. Try running the program again with the -l option, whichconverts the read counts from your covariate into log-space; this generally solves the problem.*************************** 7. Contacts and bug reports ******************************************************************************* Philip J. Uren uren@usc.eduAndrew D. Smith andrewds@usc.eduIf you found a bug in Piranha, we'd like to know about it. Before contacting us though, please check the following list:1.) Are you using the latest version? The bug you found may already have been fixed.2.) Check that your input is in the correct format and you have selectedthe correct options.3.) Please reduce your input to the smallest possible size that still produces the bug; we will need your input data to reproduce the problem.

3. 使用方法

Piranha input.bed Piranha TH-10_hg38.bed -s -o TH-10_hg38_peaks.bed

piranha 的核心功能就是Peak finding，用于位點確認的input文件是已經(jīng)mapped到基因組上的reads。軟件里提出的第一個概念，就是bin。所謂一個bin，代表了一個基因組區(qū)間（genomic interval）。Bin的理想長度取決于測序coverage的深度以及具體采用的測序技術(shù)。Bin的理想長度取決于測序coverage的深度以及具體采用的測序技術(shù)。yi 是從ith起始的reads的count數(shù)。每一個bin有一個相關(guān)的協(xié)變量向量xi 。這個協(xié)變量是描述在平行的免疫沉淀raw count中，產(chǎn)生變化（多樣性）的特征。Example代表bin的可比對性（mappability）。簡單的說輸入數(shù)據(jù)是mapping 后的 reads，轉(zhuǎn)換成 bin后，被認為是零截斷負二項式，peak calling 后，輸出一個 BED， 第七列輸出一個P-value反應(yīng)這個分布。

Piranha Input.bed covariate1.bed covariate2.bed

加一個協(xié)變量，這個協(xié)變量是描述在平行的免疫沉淀raw count中，產(chǎn)生變化（多樣性）的特征。沒有協(xié)變量，采用 ZTNB model。提供了協(xié)變量, 則采用 ZTNBR model.

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