Phred quality score

Abstract: Semiconductor high-throughput sequencing, represented by Ion Torrent PGM/Proton, proves to be feasible in the noninvasive prenatal diagnosis of fetal aneuploidies. It is commendable that, with less data and relevant cost also, an accurate result can be achieved owing to the high sensitivity and specificity of such kind of technology. We conducted a comparative analysis of the performance of four different Ion chips in detecting fetal chromosomal aneuploidies. Eight maternal plasma DNA samples, including four pregnancies with normal fetuses and four with trisomy 21 fetuses, were sequenced on Ion Torrent 314/316/318/PI chips, respectively. Results such as read mapped ratio, correlation coefficient and phred quality score were calculated and parallelly compared. All samples were correctly classified even with low-throughput chip, and, among the four chips, the 316 chip had the highest read mapped ratio, correlation coefficient, mean read length and phred quality score. All chips were well consistent with each other. Our results showed that all Ion chips are applicable in noninvasive prenatal fetal aneuploidy diagnosis. We recommend researchers or clinicians to use the appropriate chip with barcoding technology on the basis of the sample number.

Abstract: Choroid plexus papilloma (CPP) is a rare benign tumor of the central nervous system that is usually confined to the cerebral ventricles. According to the World Health Organization, CPP corresponds to a grade I atypical CPP (a-CPP); however, it can become more aggressive and reach grade II, which can rarely undergo malignant transformation into a choroid plexus carcinoma (grade III). To the best of our knowledge, identification of these tumors mutations by next generation DNA sequencing (NGS) has not been yet reported. In the present study, NGS analysis of an a-CPP case was performed. Data were analyzed using Advaita Bioinformatics i-VariantGuide and Ion Reporter 5.6 programs. The results from NGS identified 12 novel missense mutations in the following genes: NOTCH1, ATM, STK36, MAGI1, DST, RECQL4, NUMA1, THBS1, MYH11, MALT1, SMARCA4 and CDH20. The PolyPhen score of six variants viz., DST, RECQL4, NUMA1, THBS1, MYHI1 and SMARCA4 were high, which suggested these variants represents pathogenic variants. Two novel insertions that caused frameshift were also found. Furthermore, two novel nonsense mutations and 14 novel intronic variants were identified in this tumor. The novel missense mutation detected in ATM gene was situated in c.5808A>T; p. (Leu1936Phe) in exon 39, and a known ATM mutation was in c.5948A>G; p. (Asn1983Ser). These novel mutations had not been reported in previous database. Subsequently, the quality statistics of these variants, including allele coverage, allele ratio, P-value, Phred quality score, sequencing coverage, PolyPhen score and alleles frequency was performed. For all variants, P-value was highly significant and the Phred quality score was high. In addition, the results from sequencing coverage demonstrated that 97.02% reads were on target and that 97.88% amplicons had at least 500 reads. These findings may serve at determining new strategies to distinguish the types of choroid plexus tumor, and at developing novel targeted therapies. Development of NGS technologies in the Kingdom of Saudi Arabia may be used in molecular pathology laboratories.

Abstract: 3615Background: Location and somatic gene signature of CRCs may impact prognosis and therapy response. The aim was to characterize colon Microbiota in CRC patients regarding location, gene markers and outcome. Methods: Patients (N = 173) signed consent for whole metagenome (shot gun sequencing on Illumina HiSeq2500) analysis of stool DNA: 72 CRC (53 sporadic-S, 19 Lynch-L), 87 asymptomatic subjects (normal colonoscopy), 14 first degree healthy relatives from Lynch families. "MOCAT" pipeline was used, library sorted (Phred quality score i20 Alientrimmer v0.4.0) after exclusion of < 35 nt, human genes or phage sequences. Quality sequences were aligned (REFMG.V13) and most abundant genes constructed (MBMA program v0.1). The Shaman program (shaman.c3bi.pasteur.fr) was used. The number of bacteria was estimated (REFMG program). The linear model (GLM) was implemented in the DESeq2 R kit. Dfferences between Control (N = 87) and CRCs (N = 69), between L (N = 19) and S CRCs(N = 50), and between LCRC (N = 19) and H...

Abstract: The Phred quality score can measure the sequence quality, and quality scores are normally stored together with the nucleotide sequence in the widely accepted FASTQ format. For sequence raw reads with various FASTQ formats, the range of scores will depend on the technology and the base caller used. With the different technology, the range of scores is different. For learning the comparing and converting method of sequence Phred quality score, we do an experiment, and analyze the different between various FASTQ quality formats. Introduction A Phred quality score is a measure of the quality of the identification of the nucleobases generated by automated DNA sequencing [1]. It was originally developed for Phred base calling to help in the automation of DNA sequencing in the Human Genome Project. Phred quality scores are assigned to each nucleotide base call in automated sequencer traces [2]. FASTQ format is a text-based format for storing both a biological sequence and its corresponding quality scores. Both the sequence letter and quality score are each encoded with a single ASCII character for brevity. For sequence raw reads with various FASTQ formats, the range of scores will depend on the technology and the base caller used. In this paper, we study Phred quality score and FASTQ format. Then, we make a RNA sequence as the experiment resource and do an experiment to analyze the different between various FASTQ quality formats for learning the comparing and converting method of sequence Phred quality score. Phred Quality Score Phred quality scores have become widely accepted to characterize the quality of DNA sequences, and can be used to compare the efficacy of different sequencing methods. Perhaps the most important use of Phred quality scores is the automatic determination of accurate, quality-based consensus sequences. Phred quality scores are used for assessment of sequence quality, recognition and removal of low-quality sequence (end clipping), and determination of accurate consensus sequences. Originally, Phred quality scores were primarily used by the sequence assembly program Phrap. Phrap was routinely used in some of the largest sequencing projects in the Human Genome Sequencing Project and is currently one of the most widely used DNA sequence assembly programs in the biotech industry. Phrap uses Phred quality scores to determine highly accurate consensus sequences and to estimate the quality of the consensus sequences. Phrap also uses Phred quality scores to estimate whether discrepancies between two overlapping sequences are more likely to arise from random errors, or from different copies of a repeated sequence. Within the Human Genome Project, the most important use of Phred quality scores was for automatic determination of consensus sequences. Before Phred and Phrap, scientists had to carefully look at discrepancies between overlapping DNA fragments; often, this involved manual determination 6th International Conference on Management, Education, Information and Control (MEICI 2016) © 2016. The authors – Published by Atlantis Press 0260 of the highest-quality sequence, and manual editing of any errors. Phrap's use of Phred quality scores effectively automated finding the highest-quality consensus sequence; in most cases, this completely circumvents the need for any manual editing. As a result, the estimated error rate in assemblies that were created automatically with Phred and Phrap is typically substantially lower than the error rate of manually edited sequence. In 2009, many commonly used software packages make use of Phred quality scores, albeit to a different extent. Programs like Sequencher use quality scores for display, end clipping, and consensus determination; other programs like CodonCode Aligner also implement quality-based consensus methods. Phred quality scores Q are defined as a property which is logarithmically related to the base-calling error probabilities P [2]. -Q 10 P = 10 (1) Phred quality scores are logarithmically linked to error probabilities as Table 1. Table 1 Phred quality scores, probability of incorrect base call and base call accuracy Phred Quality Score Probability of incorrect base call Base call accuracy 10 1 in 10 90% 20 1 in 100 99% 30 1 in 1000 99.9% 40 1 in 10,000 99.99% 50 1 in 100,000 99.999% 60 1 in 1,000,000 99.9999% Quality scores are normally stored together with the nucleotide sequence in the widely accepted FASTQ format. They account for about half of the required disk space in the FASTQ format (before compression), and therefore the compression of the quality values can significantly reduce storage requirements and speed up analysis and transmission of sequencing data. Both lossless and lossy compression are recently being considered in the literature. For example, the algorithm QualComp [3] performs lossy compression with a rate (number of bits per quality value) specified by the user. Based on rate-distortion theory results, it allocates the number of bits so as to minimize the MSE (mean squared error) between the original (uncompressed) and the reconstructed (after compression) quality values. Other algorithms for compression of quality values include SCALCE [4] and Fastqz.[5] Both are lossless compression algorithms that provide an optional controlled lossy transformation approach. For example, SCALCE reduces the alphabet size based on the observation that “neighboring” quality values are similar in general. FASTQ Format FASTQ format is a text-based format for storing both a biological sequence and its corresponding quality scores. Both the sequence letter and quality score are each encoded with a single ASCII character for brevity. It was originally developed at the Wellcome Trust Sanger Institute to bundle a FASTA sequence and its quality data, but has recently become the de facto standard for storing the output of high-throughput sequencing instruments such as the Illumina Genome Analyzer [6]. A FASTQ file normally uses four lines per sequence.  Line 1 begins with a '@' character and is followed by a sequence identifier and an optional description (like a FASTA title line).  Line 2 is the raw sequence letters.  Line 3 begins with a '+' character and is optionally followed by the same sequence identifier (and any description) again. 6th International Conference on Management, Education, Information and Control (MEICI 2016) © 2016. The authors – Published by Atlantis Press 0261  Line 4 encodes the quality values for the sequence in Line 2, and must contain the same number of symbols as letters in the sequence. Here are the quality value characters in left-to-right increasing order of quality (ASCII): !"#$%&'()*+,-./0123456789:;<=>?@ABCDEFGHIJKLMNOPQRSTUVWXYZ[\]^_`abcdefghij klmnopqrstuvwxyz{|}~ The range of scores of various FASTQ formats will depend on the technology and the base caller used. Fig. 1 is the comparing of different range of scores for Sanger, Illumina and Solexa. Figure 1. The different range of scores for Sanger, Illumina and Solexa Experiment Results FASTQ read simulation has been approached by several tools [7] [8]. A comparison of those tools can be seen in [9]. In this paper, we do an experiment with the above tools to analyze the different between various FASTQ quality formats for learning the comparing and converting method of sequence Phred quality score. The following is an Illumina 1.8+-assigned [10] identifier example with foure lines per sequence. @HWI-ST1268:95:D1MY0ACXX:4:1101:2314:2086 1:N:0:AGTTCC NCGATTGAATGGTCCGGTTGGAATTCTCGGGTGCCAAGGAACTCCAGTCA + #1=DDF?EHHGHFGIGIAFHIHEGIIEIHGI8@?BFG;BFHGGIFEF;>EGFFHE;>EGE Summary Phred quality scores are assigned to each nucleotide base call in automated sequencer traces. The range of scores of sequence raw reads with various FASTQ formats is different, and will depend on the technology and the base caller used. For learning the comparing and converting method of sequence Phred quality score, we do the converting from Illumina 1.8+-assigned to Sanger-assigned and Solexa-assigned experiments in this paper, and compare and analyze the different between various FASTQ quality formats. With this paper for the comparing and converting method of sequence Phred quality score, we can covert and compare sequence Phred quality score between FASTQ quality formats better, and make a basic for learning other bioinformatics analysis method more easy. Acknowledgement Corresponding author is Shi Henghua. The authors would like to acknowledge the supports provided by 2016 General Scientific Research Project of Beijing Municipal Education Commission (PXM2016_014207_000008). References [1] B. Ewing, L. Hillier, M. C. Wendl, P. Green: Base-calling of automated sequencer traces using phred. I. Accuracy assessment. Genome Res. 8(3): p.175-185. [2] B. Ewing, P. Green: Base-calling of automated sequencer traces using phred. II. Error probabilities. Genome Res. 8(3):p.186-194. [3] I. Ochoa, et al. QualComp: a new lossy compressor for quality scores based on rate distortion theory. BMC Bioinformatics 14.1 (2013): p.187. [4] F. Hach, I. Numanagi ́c, C. Alkan, S. C. Sahinalp: SCALCE: boosting sequence compression algorithms using locally consistent encoding. Bioinformatics2012, 28(23): p. 3051-3057. [5] Information on http://mattmahoney.net/dc/fastqz [6] P. J. A. Cock, C. J. Fields, N. Goto, M. L. Heuer, P. M. Rice: The Sanger FASTQ file format for sequences with quality scores, and the Solexa/Illumina FASTQ variants. Nucleic Acids Research. 38 (6): p. 1767-1771. [7] W. Huang, L. Li, J. R. Myers, G. T. Marth: ART: a next-generation sequencing read simulator. Bioinformatics 28, p.593-594. [8] D. Pratas, A. J. Pinho, O. S. Rodrigues, J. M. XS: a FASTQ read simulator. BMC Res. Notes 7, p. 40. [9] M. Escalona, S. Roch, D. Posada: A comparison of tools for the simulation of genomic next-generation sequencing data. Nature Reviews Genetics, 17, p.459-469. [10] Information on http://www.illumina.com/ 6th Internati