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Fig. 9 | BMC Bioinformatics

Fig. 9

From: AfterQC: automatic filtering, trimming, error removing and quality control for fastq data

Fig. 9

Six sample data were examined in this evaluation experiment, all of them were downloaded from NCBI Sequence Read Archive (accession numbers: SRR2496699 SRR2496709, SRR2496731, SRR2496739, SRR2496749, SRR2496716) [18]. AfterQC preprocessed every sample data and produced clean data files. BWA + Samtools + VarScan2 pipeline was applied on both raw data (not preprocessed) and clean data (AfterQC preprocessed). The variants called from raw data, but not called from clean data were counted. In this figure, values in X-axis denote the mutation frequency and the values in Y-axis denote the number of raw data only mutations, with frequency in each of the windows. Mutations with frequency lower than 2% are categorized to the first window. From this figure, we can learn that AfterQC helps filtering out lots of low frequency mutations, while seeing no difference for relatively high frequency (10%+) mutations

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