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The detection of somatic point mutations is a key component of cancer genomic research, which has been rapidly developing since next-generation sequencing (NGS) technology revealed its potential for describing genetic alterations in cancer. We present MuSE, a novel approach to mutation calling based on the F81 Markov substitution model for molecular evolution [1] , which models the evolution of the reference allele to the allelic composition of the matched tumor and normal tissue at each genomic locus. To improve overall accuracy, we further adopt a sample-specific error model to identify cutoffs, reflecting the variation in tumor heterogeneity among samples.

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Linux Binary File: MuSEv1.0rc_b MuSEv1.0rc_c

After downloading the source file, for Unix-like operating systems please type the following commands sequentially in the command line to generate the executable:

For Windows, please install Cygwin ( nike grey roshe one print premium
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MuSE is comprised of two steps, which requires

The first step, ‘MuSE call’, takes as input (1) and (2). The BAM files require aligning all the sequence reads against the reference genome using the Burrows-Wheeler alignment tool (BWA), with either the backtrack or the maximal exact matches (MEM) algorithm air jordan nyc stats on sole
. In addition, the BAM files need to be processed by following the Genome Analysis Toolkit (GATK) Best Practices [3-5] that include marking duplicates, realigning the paired tumor-normal BAMs jointly and recalibrating base quality scores.

To speed up ‘MuSE call’, we recommend splitting the WGS data into small blocks (<50Mb) by using the provided option either ‘-r’ or ‘-l’, and concatenating all the output files by the Linux command CAT.

The second step, ‘MuSE sump’, takes as input the output file from ‘MuSE call’ and (3). We provide two options for building the sample-specific error model. One is applicable to WES data (option ‘-E’), and the other to WGS data (option ‘-G’).

The following commands briefly illustrate how to use MuSE. As to the preparation of BAM files, please refer to the first part, PRE-PROCESSING, of the Genome Analysis Toolkit (GATK) Best Practices ( http://www.broadinstitute.org/gatk/guide/best-practices ).

Publishing : Once you are ready to expose your uploaded files, you can publish them and make them visible and available to all Bintray users. Files can be published via the Bintray UI, or via the REST API, both as part of the upload , or separately . In some cases, uploading the files publishes them automatically, so you can skip this step.

Publishing

You can resolve artifacts from and deploy them to Bintray directly using the Maven client.

You can configure your Maven settings.xml file to resolve artifacts through Bintray. The example below shows a settings file that is generated by Bintray.

To deploy artifacts using the Maven client, you first need to declare your Bintray credentials in the settings.xml file using your API key as your password (not your Bintray login password).

Then, add the the following Distribution Management section to your project’s pom.xml file to tell Maven to deploy into this package using the credentials you configured in the previous step.

You can resolve artifacts from and deploy them to Bintray directly using Gradle.

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To upload a file directly use the following command:

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