AML-Varan – a generic approach to integrate a multi-tool-combination based NGS variant calling pipeline into a web-based diagnostics platform
Creators: name: Wünsch, Christian
name: Sandmann, Sarah
name: Windau, Sebastian
name: Dugas, Martin
Publisher: name: F1000Research
Year: 2017

description: <i>I. Introduction</i><br>The web-based clinical diagnostics platform AMLVaran supports physicians in the automated analysis of targeted Next-generation sequencing samples for patients with Acute Myeloid Leukemia (AML). This should now be equipped with a new (also in-house developed) variant-calling technology, which combines eight different variant calling tools and has already demonstrated sensitivities around 0.98 together with a PPV of up to 0.98 on several data sets.<br>However, the presented approach should be chosen as generical as possible, in order to enable custom adjustments in regard to the used calling tools, annotation databases and filter settings.<br><br><i>II. The workflow of our generic variant calling Pipeline:</i>


	Multi-tool Variant Calling:Several different caller tools will be launched to process the sample. The caller tools will be run in parallel. For adding another caller tool, just a simple shell script for the execution and a meta file, specifying the output format of the caller, has to be provided.We are using GATK, freebayes, lofreq, Platypus, samtools, SNVer, varscan and vardict.
	Normalization and Consolidation:The output of the individual callers has normalized and standardized to ensure that different representations of some variant are identified as the same. Then all the result files are integrated into one common vcf. VariantTools is used for this step.
	Annotation:Functional annotation is provided by SNPeff.Further annotations with lots of clinical databases (dbSNP, 1000 Genomes, ESP6500, ExAC, COSMIC, ClinVar and CiVIC) are added from an in-house annotation database.
	Quality analysis:In order to be independent of the caller’s form of output regarding read counts and base qualities, this information is separately retrieved from the bam-file via BAMReadCount.
	Hard filter:Less important variant types and poorly discovered variations can just be excluded in this step. So that the rather complex operations like variant effect prediction have to be performed only for reliable variants.
	Effect prediction:SIFT and Provean scores are calculated using the published precalculation data.
	Finally the variants alongside with functional effects and prediction is stored within a MySQL database.

<i>III. Interactive method for complex fitering:</i><br>In order to distinguish true mutations from sequencing errors (artifacts) and harmless polymorphisms, sophisticated filtering methods are essential.<br>For this purpose we developed a flexible, generic filtering framework that allows a score-based identification of (probably) true variants: A JSON API is used to specify filtering rules, that will then be graphically presented in the web interface and allow dynamic customization of several thresholds.<br>The framework allows single operations, that will contribute to the artifact or polymorphism scores and can be enabled or disabled via a checkbox, as well as simple combinations like AND and OR.<br><br>As a proof-of-concept, we implemented the also inhouse-developed variant identification score algorithm appreci8 using the proposed generic framework. Appreci8 evaluates 96 single features of a variant, combining them in several ways, and finally allows a validated and highly precise estimation of a variant being true, artifact or polymorphism.<br><br><i>IV. Results:</i><br>We processed a set of 120 targeted sequencing samples from AML patients with the proposed generic pipeline and the appreci8.<br>The number of detected variants was nearly identical to classic appreci8, but the runtime has been significantly improved. An average time of 9.9 minutes per sample (excluding the time for running the caller tools) could be achieved, which is a speedup of ~25x compared with classic appreci8 (~4 h / sample).<br><br>(Funded by Deutsche Krebshilfe, Grant Number 110495)