Challenges in exome analysis by LifeScope and its alternative computational pipelines.
BACKGROUND
Every next generation sequencing (NGS) platform relies on proprietary and open source computational tools to analyze sequencing data. NGS tools for Illumina platforms are well documented which is not the case with AB SOLiD systems. We applied several computational and variant calling pipelines to analyse targeted exome sequencing data obtained using AB SOLiD 5500 system. Our investigated tools comprised proprietary LifeScope's pipeline in combination with open source color-space competent mapping programs and a variant caller. We present instrumental details of the pipelines that were used and quantitative comparative analysis of variant lists generated by LifeScope's pipeline versus open source tools.


RESULTS
Sufficient coverage of targeted regions was achieved by all investigated pipelines. High variability was observed in identities of variants across the mapping programs. We observed less than 50% concordance of variant lists produced by approaches based on different mapping algorithms. We summarized different approaches with regards to coverage (DP) and quality (QUAL) properties of the variants provided by GATK and found that LifeScope's computational pipeline is superior. Fusion of information on mapping profiles (pileup) at genomic positions of variants in several different alignments proved to be a useful strategy to assess questionable singleton variants.


CONCLUSIONS
We quantitatively supported a conclusion that Lifescope's pipeline is superior for processing sequencing data obtained by AB SOLiD 5500 system. Nevertheless the use of alternative pipelines is encouraged because aggregation of information from other mapping and variant calling approaches helps to resolve questionable calls and increases the confidence of the call. It was noted that a coverage threshold for variant to be considered for further analysis has to be chosen in data-driven way to prevent a loss of important information.
Department of Human and Medical Genetics, Faculty of Medicine, Vilnius University, Santariskiu str. 2, LT-08661, Vilnius, Lithuania. erinija.pranckeviciene@mf.vu.lt.
Erinija
E
Pranckevičiene
Department of Human and Medical Genetics, Faculty of Medicine, Vilnius University, Santariskiu str. 2, LT-08661, Vilnius, Lithuania. tautvydas.rancelis@mf.vu.lt.
Tautvydas
T
Rančelis
Department of Human and Medical Genetics, Faculty of Medicine, Vilnius University, Santariskiu str. 2, LT-08661, Vilnius, Lithuania. aidas.pranculis@mf.vu.lt.
Aidas
A
Pranculis
Department of Human and Medical Genetics, Faculty of Medicine, Vilnius University, Santariskiu str. 2, LT-08661, Vilnius, Lithuania. vaidutis.kucinskas@mf.vu.lt.
Vaidutis
V
Kučinskas
D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D059472:Exome / Q000235:genetics*; D015894:Genome, Human / Q000235:genetics; D023281:Genomics / Q000379:methods; D059014:High-Throughput Nucleotide Sequencing / Q000379:methods*; D006801:Humans; D020641:Polymorphism, Single Nucleotide; D015203:Reproducibility of Results; D017422:Sequence Analysis, DNA / Q000379:methods*; D012984:Software