Challenges in exome analysis by LifeScope and its alternative computational pipelines

Abstract

BackgroundEvery 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.ResultsSufficient 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.ConclusionsWe 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.

A common pathomechanism in GMAP-210– and LBR-related diseases

“…For subsequent bioinformatics analysis, an in-house pipeline based on GATK best practice was used (65). Alignments were generated by the proprietary Lifescope software (66). Variant files were annotated using the variant effect predictor software (67) and processed using GEMINI (68), with filter parameters set for allele frequencies of ≤1% in the 1000 Genomes Project, an impact severity classification of medium or high, and a recessive inheritance pattern.…”

Predicting Species-Resolved Macronutrient Acquisition during Succession in a Model Phototrophic Biofilm Using an Integrated ‘Omics Approach

“…The 50-base read sequences produced by the 5500XL SOLiD sequencer were mapped in color space using SOLiD LifeScope software version 2.5 using the default parameters against an artificial chromosome, as previously described ( Hess et al, 2013 ). LifeScope has been previously determined to be the optimal method for mapping sequence data obtained via the SOLiD 5500 system ( Pranckevičiene et al, 2015 ), but it is optimized for use with eukaryotic genomes with multiple linear chromosomes. Consequently, we constructed an artificial chromosome as a reference by concatenating genomes of isolated UCC members and all contigs of remaining metagenome bins.…”

Genomo persitvarkymai, esant įgimtiems centrinės nervų sistemos raidos sutrikimams: kilmė, genominiai mechanizmai, funkcinės ir klinikinės pasekmės. Darbų apžvalga

“…IN le mian èiø va rian tø pa to ge nið ku mo ver ti ni mas vis dar yra di de lis ið ðû kis ðiuo lai ki në je me di ci ni në je ge no miko je. Vie nas ið bû dø ver tin ti nu sta ty tø ko pi jø skai èiaus poky èiø ar DNR se kos va rian tø ga li mà áta kà IN raið kos mecha niz mams yra ti ria mos po pu lia ci jos struk tû ros cha rak teri za vi mas [24,39]. Pa kan ka mas va ria ci jos api bû di ni mas lei dþia pa nau do ti gau na mus duo me nis ti ria mø jø fe no ti po kon teks te [40], ti riant pa vel di mus ir ágim tus CNS rai dos su tri ki mus.…”