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Challenges in exome analysis by LifeScope and its alternative computational pipelines.

Pranckevičiene E, Rančelis T, Pranculis A, Kučinskas V - BMC Res Notes (2015)

Bottom Line: 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.We quantitatively supported a conclusion that Lifescope's pipeline is superior for processing sequencing data obtained by AB SOLiD 5500 system.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.

View Article: PubMed Central - PubMed

Affiliation: Department of Human and Medical Genetics, Faculty of Medicine, Vilnius University, Santariskiu str. 2, LT-08661, Vilnius, Lithuania. erinija.pranckeviciene@mf.vu.lt.

ABSTRACT

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.

No MeSH data available.


Related in: MedlinePlus

Schema comprising an investigated workflow of exome analysis by LifeScope and the alternative pipeline. This schema represents exome’s computational pipeline steps that were applied in the study. The LifeScope’s pipeline includes proprietary programs to perform alignment and variant calling. Alternatively we applied another four approaches based on combinations of LifeScope, SHRiMP, MAQ, BFAST mapping programs and GATK modules for variant calling, using the same exomes as with Lifescope program
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Fig1: Schema comprising an investigated workflow of exome analysis by LifeScope and the alternative pipeline. This schema represents exome’s computational pipeline steps that were applied in the study. The LifeScope’s pipeline includes proprietary programs to perform alignment and variant calling. Alternatively we applied another four approaches based on combinations of LifeScope, SHRiMP, MAQ, BFAST mapping programs and GATK modules for variant calling, using the same exomes as with Lifescope program

Mentions: Comparative analysis of the effects of mapping programs on the outcome of variant calling We analyzed color-space competent mapping programs LifeScope, MAQ, SHRiMP and BFAST using near default settings. The mapping programs produce aligned BAM files that are input to a variant calling procedure by GATK. The same variant calling algorithm was applied to all BAM files and produced lists of variants that were different from each other. We aimed to determine the best variant calling approach out of investigated LifeScope, LifeScope-GATK, MAQ-GATK, BFAST-GATK and SHRiMP-GATK combinations. Schema of our experimental setup is presented in Fig. 1. Quality (QUAL) and coverage depth (DP) of the variant reported by GATK were used as criteria to compare the approaches.


Challenges in exome analysis by LifeScope and its alternative computational pipelines.

Pranckevičiene E, Rančelis T, Pranculis A, Kučinskas V - BMC Res Notes (2015)

Schema comprising an investigated workflow of exome analysis by LifeScope and the alternative pipeline. This schema represents exome’s computational pipeline steps that were applied in the study. The LifeScope’s pipeline includes proprietary programs to perform alignment and variant calling. Alternatively we applied another four approaches based on combinations of LifeScope, SHRiMP, MAQ, BFAST mapping programs and GATK modules for variant calling, using the same exomes as with Lifescope program
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4562342&req=5

Fig1: Schema comprising an investigated workflow of exome analysis by LifeScope and the alternative pipeline. This schema represents exome’s computational pipeline steps that were applied in the study. The LifeScope’s pipeline includes proprietary programs to perform alignment and variant calling. Alternatively we applied another four approaches based on combinations of LifeScope, SHRiMP, MAQ, BFAST mapping programs and GATK modules for variant calling, using the same exomes as with Lifescope program
Mentions: Comparative analysis of the effects of mapping programs on the outcome of variant calling We analyzed color-space competent mapping programs LifeScope, MAQ, SHRiMP and BFAST using near default settings. The mapping programs produce aligned BAM files that are input to a variant calling procedure by GATK. The same variant calling algorithm was applied to all BAM files and produced lists of variants that were different from each other. We aimed to determine the best variant calling approach out of investigated LifeScope, LifeScope-GATK, MAQ-GATK, BFAST-GATK and SHRiMP-GATK combinations. Schema of our experimental setup is presented in Fig. 1. Quality (QUAL) and coverage depth (DP) of the variant reported by GATK were used as criteria to compare the approaches.

Bottom Line: 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.We quantitatively supported a conclusion that Lifescope's pipeline is superior for processing sequencing data obtained by AB SOLiD 5500 system.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.

View Article: PubMed Central - PubMed

Affiliation: Department of Human and Medical Genetics, Faculty of Medicine, Vilnius University, Santariskiu str. 2, LT-08661, Vilnius, Lithuania. erinija.pranckeviciene@mf.vu.lt.

ABSTRACT

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.

No MeSH data available.


Related in: MedlinePlus