MiRQC PGM data
Ontology highlight
ABSTRACT:
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| ERR325030.fastq.gz | Fastqsanger.gz | |||
| ERR325031.fastq.gz | Fastqsanger.gz | |||
| ERR325032.fastq.gz | Fastqsanger.gz | |||
| ERR325033.fastq.gz | Fastqsanger.gz | |||
| ERR325034.fastq.gz | Fastqsanger.gz |
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Project description:Cherax destructor isolate Yabby genome sequencing on two Ion Torrent PGM chips (200bp 316v1 and 400bp 316v2)
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Project description:The Ion Torrent Personal Genome Machine (PGM) is a new sequencing platform that substantially differs from other sequencing technologies by measuring pH rather than light to detect polymerisation events. Using re-sequencing datasets, we comprehensively characterise the biases and errors introduced by the PGM at both the base and flow level, across a combination of factors, including chip density, sequencing kit, template species and machine. We found two distinct insertion/deletion (indel) error types that accounted for the majority of errors introduced by the PGM. The main error source was inaccurate flow-calls, which introduced indels at a raw rate of 2.84% (1.38% after quality clipping) using the OneTouch 200 bp kit. Inaccurate flow-calls typically resulted in over-called short-homopolymers and under-called long-homopolymers. Flow-call accuracy decreased with consecutive flow cycles, but we also found significant periodic fluctuations in the flow error-rate, corresponding to specific positions within the flow-cycle pattern. Another less common PGM error, high frequency indel (HFI) errors, are indels that occur at very high frequency in the reads relative to a given base position in the reference genome, but in the majority of instances were not replicated consistently across separate runs. HFI errors occur approximately once every thousand bases in the reference, and correspond to 0.06% of bases in reads. Currently, the PGM does not achieve the accuracy of competing light-based technologies. However, flow-call inaccuracy is systematic and the statistical models of flow-values developed here will enable PGM-specific bioinformatics approaches to be developed, which will account for these errors. HFI errors may prove more challenging to address, especially for polymorphism and amplicon applications, but may be overcome by sequencing the same DNA template across multiple chips.
| S-EPMC3623719 | biostudies-literature
Project description:Ion Torrent Personal Genome Machine (PGM) technology is a mid-length read, low-cost and high-speed next-generation sequencing platform with a relatively high insertion and deletion (indel) error rate. A full systematic assessment of the effectiveness of various error correction algorithms in PGM viral datasets (e.g., hepatitis B virus (HBV)) has not been performed. We examined 19 quality-trimmed PGM datasets for the HBV reverse transcriptase (RT) region and found a total error rate of 0.48% ± 0.12%. Deletion errors were clearly present at the ends of homopolymer runs. Tests using both real and simulated data showed that the algorithms differed in their abilities to detect and correct errors and that the error rate and sequencing depth significantly affected the performance. Of the algorithms tested, Pollux showed a better overall performance but tended to over-correct 'genuine' substitution variants, whereas Fiona proved to be better at distinguishing these variants from sequencing errors. We found that the combined use of Pollux and Fiona gave the best results when error-correcting Ion Torrent PGM viral data.
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Project description:BackgroundThere are various next-generation sequencing techniques, all of them striving to replace Sanger sequencing as the gold standard. However, false positive calls of single nucleotide variants and especially indels are a widely known problem of basically all sequencing platforms.MethodsWe considered three common next-generation sequencers-Roche 454, Ion Torrent PGM and Illumina NextSeq-and applied standard as well as optimized variant calling pipelines. Optimization was achieved by combining information of 23 diverse parameters characterizing the reported variants and generating individually calibrated generalized linear models. Models were calibrated using amplicon-based targeted sequencing data (19 genes, 28,775 bp) from seven to 12 myelodysplastic syndrome patients. Evaluation of the optimized pipelines and platforms was performed using sequencing data from three additional myelodysplastic syndrome patients.ResultsUsing standard analysis methods, true mutations were missed and the obtained results contained many artifacts-no matter which platform was considered. Analysis of the parameters characterizing the true and false positive calls revealed significant platform- and variant specific differences. Application of optimized variant calling pipelines considerably improved results. 76% of all false positive single nucleotide variants and 97% of all false positive indels could be filtered out. Positive predictive values could be increased by factors of 1.07 to 1.27 in case of single nucleotide variant calling and by factors of 3.33 to 53.87 in case of indel calling. Application of the optimized variant calling pipelines leads to comparable results for all next-generation sequencing platforms analyzed. However, regarding clinical diagnostics it needs to be considered that even the optimized results still contained false positive as well as false negative calls.
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Project description:BackgroundMassively parallel sequencing (MPS) technologies have the capacity to sequence targeted regions or whole genomes of multiple nucleic acid samples with high coverage by sequencing millions of DNA fragments simultaneously. Compared with Sanger sequencing, MPS also can reduce labor and cost on a per nucleotide basis and indeed on a per sample basis. In this study, whole genomes of human mitochondria (mtGenome) were sequenced on the Personal Genome Machine (PGMTM) (Life Technologies, San Francisco, CA), the out data were assessed, and the results were compared with data previously generated on the MiSeqTM (Illumina, San Diego, CA). The objectives of this paper were to determine the feasibility, accuracy, and reliability of sequence data obtained from the PGM.Results24 samples were multiplexed (in groups of six) and sequenced on the at least 10 megabase throughput 314 chip. The depth of coverage pattern was similar among all 24 samples; however the coverage across the genome varied. For strand bias, the average ratio of coverage between the forward and reverse strands at each nucleotide position indicated that two-thirds of the positions of the genome had ratios that were greater than 0.5. A few sites had more extreme strand bias. Another observation was that 156 positions had a false deletion rate greater than 0.15 in one or more individuals. There were 31-98 (SNP) mtGenome variants observed per sample for the 24 samples analyzed. The total 1237 (SNP) variants were concordant between the results from the PGM and MiSeq. The quality scores for haplogroup assignment for all 24 samples ranged between 88.8%-100%.ConclusionsIn this study, mtDNA sequence data generated from the PGM were analyzed and the output evaluated. Depth of coverage variation and strand bias were identified but generally were infrequent and did not impact reliability of variant calls. Multiplexing of samples was demonstrated which can improve throughput and reduce cost per sample analyzed. Overall, the results of this study, based on orthogonal concordance testing and phylogenetic scrutiny, supported that whole mtGenome sequence data with high accuracy can be obtained using the PGM platform.
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