- Bam file format gatk update#
- Bam file format gatk full#
- Bam file format gatk code#
- Bam file format gatk download#
For steps 5 and 7 you will also want a BED file that lists the regions you targeted when you did your exome capture. The running times I give below are for my full-size files, not for the shortened test version. I used head -40000 1_1.fq > subset/1_1.fq to create shorter FASTQs with just 10,000 reads each to use while debugging my pipeline.
Bam file format gatk full#
Also a general note: before you go ahead and actually run the following steps on your full FASTQs with millions of reads, you might want to try out the pipeline on much shorter files. end updateThe resource bundle does not include the bwa indices for hg19, so you’ll have to create those yourself: bwa index Haven’t tested it myself to see how much faster.
Bam file format gatk update#
update : I have heard you may get faster alignment results in step 4 if you use the regular human reference genome plus the decoy genome as your reference. In the second line above, I use sed to remove the absolute path information from each file. Broad uses absolute paths in its md5 files so md5sum -c will fail if you try to use them as is. Ls *.md5 | awk '' | bash # decompress all the gz filesĪ note about the above.
Bam file format gatk download#
txt extension. To get the resource bundle, visit Broad’s GSA public FTP server as of September 12, 2012, the latest version is 1.5 (I’m using hg19) and you can get it, checksum it ( thanks and decompress it via the unix command line as follows: wget -r # download all files in this directory after you download, rename to remove the. Because my pipeline depends on it, I am providing the version of DataProcessingPipeline.scala that worked with my pipeline: DataProcessingPipeline.scala I cannot guarantee compatibility with newer builds of GATK and Queue. update: as of : Broad has deprecated the DataProcessingPipeline. Download GATK and Queue here (you need to register for an account first) and find the text of the latest DataProcessingPipeline.scala here. prep steps Before you can do anything, make sure you’ve got GATK, Queue, DataProcessingPipeline and the hg19 resource bundle. If I end up doing a lot more exome sequencing I guess I’ll have to look into it.Ġ. This worked for me, but I’m told you can get much better performance by letting Queue manage LSF jobs for you so it can parallelize stuff within GATK. Instead I used a combination of calls to Queue using existing scripts along with direct calls to GATK and submitted all these things as separate jobs to my LSF scheduler. disclaimer: I didn’t want to learn Scala to do this, but doing so, and writing a pipeline for Queue, would clearly be the right way to pipeline GATK. update : Broad has since deprecated the DataProcessingPipeline and pulled it from their site below I provide the version of it that I had working successfully in this pipeline. Making use of the DataProcessingPipeline as a starting point I was finally able to run GATK on my data. I don’t know Scala, and was not super keen on learning a new programming language just to use GATK, but Broad provides some Queue pipelines, including a DataProcessingPipeline, that you can (almost) use out of the box. For this reason, I was attracted to Queue, a framework that allows you to create pipelines in Scala that stitch together various tasks. As I mentioned last week, GATK isn’t quite an end-to-end solution as it still relies on BWA and Picard for alignment and pre-processing, and it is all too easy to run into compatibility problems when calling BWA, Picard and GATK separately. But because GATK is such an industry standard tool and seems to have a stream of constant improvements to offer, I have also been interested in getting an exome pipeline up and running using GATK. Motivation: I recently developed an exome sequencing pipeline using bowtie2 and samtools. That’s helpful because then you know whether to kill them if they’ve been running too long, and you can set running time limits on your jobs (using bsub‘s -W option if you’re on an LSF scheduler) which will help keep your priority nice and high on shared resources. But IMHO, the details aren’t important– your running times will obviously be different than mine, but this will give you a sense of the order of magnitude of how long these things will take. For the record, I’ve got 50 samples with paired-end reads, so 100 FASTQ files, ranging from about 15 to 30 million reads each, and I am running this on an HP Red Hat Linux cluster with 8GB memory and 8 cores per node.
Bam file format gatk code#
Below I will include code and estimated running times for every step.
![bam file format gatk bam file format gatk](https://us.v-cdn.net/5019796/uploads/editor/za/kh3tsomcn4zq.png)
It assumes you have paired-end reads in the form of FASTQ files from your sequencing company and that your goal is to align these to the genome and then call variants for further analysis. This pipeline is for analyzing human exome data. The prefatory remarks from the bowtie2/samtools exome pipeline I’ve posted apply to this pipeline as well: Overview: This post documents a pipeline for human exome sequencing using GATK.