# Tagged with #workflow 4 documentation articles | 0 announcements | 3 forum discussions

Created 2015-08-14 01:57:37 | Updated 2015-08-14 01:58:03 | Tags: best-practices workshop workflow presentations

Joel Thibault, Valentin Ruano-Rubio and Geraldine Van der Auwera presented this workshop in Edinburgh, Scotland, and Cambridge, England, upon invitation from the Universities of Edinburgh and Cambridge.

This workshop included two modules:

• Best Practices for Variant Calling with the GATK

The core steps involved in calling variants with the Broad’s Genome Analysis Toolkit, using the “Best Practices” developed by the GATK team. The presentation materials describe why each step is essential to the calling process, what are the key operations performed on the data at each step, and how to use the GATK tools to get the most accurate and reliable results out of your dataset.

• Beyond the Best Practices

Additional considerations such as calling variants in RNAseq data and calling cohorts efficiently, as well as dealing with non-human data, RNAseq data, whole-genome vs. exome, basic quality control, and performance.

This was complemented by a set of hands-on exercises aiming to teach basic GATK usage to new users.

The workshop materials are available at this link if you're viewing this post in the forum, or below if you are viewing the presentation page already.

Created 2015-08-14 01:50:16 | Updated 2015-08-14 01:56:56 | Tags: best-practices workshop workflow presentations

The full GATK team presented this workshop at the Broad Institute with support form the BroadE education program.

This workshop covered the core steps involved in calling variants with the Broad’s Genome Analysis Toolkit, using the “Best Practices” developed by the GATK team. The presentation materials describe why each step is essential to the calling process, what are the key operations performed on the data at each step, and how to use the GATK tools to get the most accurate and reliable results out of your dataset.

The workshop materials are available at this link if you're viewing this post in the forum, or below if you are viewing the presentation page already.

Created 2013-08-02 20:23:38 | Updated 2015-08-13 23:14:55 | Tags: best-practices workflow multiplexing pre-processing

Our Best Practices Pre-processing documentation assumes a simple experimental design in which you have one set of input sequence files (forward/reverse or interleaved FASTQ, or unmapped uBAM) per sample, and you run each step of the pre-processing workflow separately for each sample, resulting in one BAM file per sample at the end of this phase.

However, if you are generating multiple libraries for each sample, and/or multiplexing samples within and/or across sequencing lanes, the data must be de-multiplexed before pre-processing, typically resulting in multiple sets of FASTQ files per sample all of which should have distinct read group IDs. At that point there are several different valid strategies for implementing the pre-processing workflow. At the Broad Institute, we run the entire pre-processing workflow separately on each individual read group, then we merge the data to produce a single BAM file for each sample, then we re-run two steps (Mark Duplicates and Indel Realignment) on the per-sample BAM files. See the worked-out example below and this presentation for more details.

Note that there are many possible ways to achieve a similar result; here we present the way we think gives the best combination of efficiency and quality. This assumes that you are dealing with one or more samples, and each of them was sequenced on one or more lanes.

### Example

Let's say we have this example data:

• sample1_lane1.fq
• sample1_lane2.fq
• sample2_lane1.fq
• sample2_lane2.fq

#### 1. Run all core steps per-lane once

At the basic level, all pre-processing steps are meant to be performed per-lane. Assuming that you received one FASTQ file per lane of sequence data, just run each file through each pre-processing step individually: map & dedup -> realign -> recal.

The example data becomes:

• sample1_lane1.dedup.realn.recal.bam
• sample1_lane2.dedup.realn.recal.bam
• sample2_lane1.dedup.realn.recal.bam
• sample2_lane2.dedup.realn.recal.bam

#### 2. Merge lanes per sample

Once you have pre-processed each lane individually, you merge lanes belonging to the same sample into a single BAM file.

The example data becomes:

• sample1.merged.bam
• sample2.merged.bam

#### 3. Per-sample refinement

You can increase the quality of your results by performing an extra round of dedupping and realignment, this time at the sample level. It is not absolutely required and will increase your computational costs, so it's up to you to decide whether you want to do it on your data, but that's how we do it internally at Broad.

The example data becomes:

• sample1.merged.dedup.realn.bam
• sample2.merged.dedup.realn.bam

This gets you two big wins:

• Dedupping per-sample eliminates PCR duplicates across all lanes in addition to optical duplicates (which are by definition only per-lane)
• Realigning per-sample means that you will have consistent alignments across all lanes within a sample.

People often ask also if it's worth the trouble to try realigning across all samples in a cohort. The answer is almost always no, unless you have very shallow coverage. The problem is that while it would be lovely to ensure consistent alignments around indels across all samples, the computational cost gets too ridiculous too fast. That being said, for contrastive calling projects -- such as cancer tumor/normals -- we do recommend realigning both the tumor and the normal together in general to avoid slight alignment differences between the two tissue types.

Finally, why not do base recalibration across lanes or across samples? Well, by definition there is no sense in trying to recalibrate across lanes, since the purpose of this processing step is to compensate for the errors made by the machine during sequencing, and the lane is the base unit of the sequencing machine. That said, don't worry if you find yourself needing to recalibrate a BAM file with the lanes already merged -- the GATK's BaseRecalibrator is read group-aware, which means that it will identify separate lanes as such even if they are in the same BAM file, and it will always process them separately.

Note that BaseRecalibrator distinguishes read groups by RGID, or RGPU if it is available (PU takes precedence over ID).

Created 2012-07-23 17:05:10 | Updated 2013-03-25 22:18:53 | Tags: official analyst dataprocessingpipeline queue workflow pacbio qscript intermediate

### Introduction

Processing data originated in the Pacific Biosciences RS platform has been evaluated by the GSA and publicly presented in numerous occasions. The guidelines we describe in this document were the result of a systematic technology development experiment on some datasets (human, E. coli and Rhodobacter) from the Broad Institute. These guidelines produced better results than the ones obtained using alternative pipelines up to this date (september 2011) for the datasets tested, but there is no guarantee that it will be the best for every dataset and that other pipelines won't supersede it in the future.

The pipeline we propose here is illustrated in a Q script (PacbioProcessingPipeline.scala) distributed with the GATK as an example for educational purposes. This pipeline has not been extensively tested and is not supported by the GATK team. You are free to use it and modify it for your needs following the guidelines below.

### BWA alignment

First we take the filtered_subreads.fq file output by the Pacific Biosciences RS SMRT pipeline and align it using BWA. We use BWA with the bwasw algorithm and allow for relaxing the gap open penalty to account for the excess of insertions and deletions known to be typical error modes of the data. For an idea on what parameters to use check suggestions given by the BWA author in the BWA manual page that are specific to Pacbio. The goal is to account for Pacific Biosciences RS known error mode and benefit from the long reads for a high scoring overall match. (for older versions, you can use the filtered_subreads.fasta and combine the base quality scores extracted from the h5 files using Pacific Biosciences SMRT pipeline python tools)

To produce a BAM file that is sorted by coordinate with adequate read group information we use Picard tools: SortSam and AddOrReplaceReadGroups. These steps are necessary because all subsequent tools require that the BAM file follow these rules. It is also generally considered good practices to have your BAM file conform to these specifications.

### Best Practices for Variant Calling

Once we have a proper BAM file, it is important to estimate the empirical quality scores using statistics based on a known callset (e.g. latest dbSNP) and the following covariates: QualityScore, Dinucleotide and ReadGroup. You can follow the GATK's Best Practices for Variant Detection according the type of data you have, with the exception of indel realignment, because the tool has not been adapted for Pacific Biosciences RS data.

### Problems with Variant Calling with Pacific Biosciences

• Calling must be more permissive of indels in the data.

You will have to adjust your calling thresholds in the Unified Genotyper to allow sites with a higher indel rate to be analyzed.

• Base quality thresholds should be adjusted to the specifics of your data.

Be aware that the Unified Genotyper has cutoffs for base quality score and if your data is on average Q20 (a common occurrence with Pacific Biosciences RS data) you may need to adjust your quality thresholds to allow the GATK to analyze your data. There is no right answer here, you have to choose parameters consistent with your average base quality scores, evaluate the calls made with the selected threshold and modify as necessary.

• Reference bias

To account for the high insertion and deletion error rate of the Pacific Biosciences data instrument, we often have to set the gap open penalty to be lower than the base mismatch penalty in order to maximize alignment performance. Despite aligning most of the reads successfully, this creates the side effect that the aligner will sometimes prefer to "hide" a true SNP inside an insertion. The result is accurate mapping, albeit with a reference-biased alignment. It is important to note however, that reference bias is an artifact of the alignment process, not the data, and can be greatly reduced by locally realigning the reads based on the reference and the data. Presently, the available software for local realignment is not compatible with the length and the high indel rate of Pacific Bioscience data, but we expect new tools to handle this problem in the future. Ultimately reference bias will mask real calls and you will have to inspect these by hand.

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Created 2014-02-12 20:31:19 | Updated | Tags: variantrecalibrator workflow multi-sample

Hi there,

We are sequencing a set of regions that covers about 1.5 megabases in total. We're running into problems with VQSR -- VariantRecalibrator says there are too few variants to do recalibration. To give a sense of numbers, in one sample we have about 3000 SNVs and 600 indels.

We seem to have too few indels to do VQSR on them and have a couple of questions:

1. Can we combine multiple samples to increase the number of variants, or does VariantRecalibrator need to work on each sample individually?

2. If we do not use VQSR for indels, should we also avoid VQSR for the SNPs?

3. The other question is whether joint or batch variant calling across several samples would help us in this case?

Created 2013-10-30 08:31:12 | Updated | Tags: license workflow graphics permission

I have used GATK in my PhD project, and was wondering if I could get the permission to use the Best Practices workflow graphics [1] in my doctoral dissertation? How should I attribute your copyright?