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Identification of errors introduced during high throughput sequencing of the T cell receptor repertoire.

Nguyen P, Ma J, Pei D, Obert C, Cheng C, Geiger TL - BMC Genomics (2011)

Bottom Line: Filtering for lower quality sequences diminished but did not eliminate sequence errors, which occurred within 1-6% of sequences.Caution is needed in interpreting repertoire data due to potential contamination with mis-sequence reads.However, a high association of errors with phred score, high relatedness of erroneous sequences with the parental sequence, dominance of specific nt substitutions, and skewed ratio of forward to reverse reads among erroneous sequences indicate approaches to filter erroneous sequences from repertoire data sets.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Pathology, St, Jude Children's Research Hospital, 262 Danny Thomas Pl., Memphis, TN 38105, USA.

ABSTRACT

Background: Recent advances in massively parallel sequencing have increased the depth at which T cell receptor (TCR) repertoires can be probed by >3log10, allowing for saturation sequencing of immune repertoires. The resolution of this sequencing is dependent on its accuracy, and direct assessments of the errors formed during high throughput repertoire analyses are limited.

Results: We analyzed 3 monoclonal TCR from TCR transgenic, Rag-/- mice using Illumina® sequencing. A total of 27 sequencing reactions were performed for each TCR using a trifurcating design in which samples were divided into 3 at significant processing junctures. More than 20 million complementarity determining region (CDR) 3 sequences were analyzed. Filtering for lower quality sequences diminished but did not eliminate sequence errors, which occurred within 1-6% of sequences. Erroneous sequences were pre-dominantly of correct length and contained single nucleotide substitutions. Rates of specific substitutions varied dramatically in a position-dependent manner. Four substitutions, all purine-pyrimidine transversions, predominated. Solid phase amplification and sequencing rather than liquid sample amplification and preparation appeared to be the primary sources of error. Analysis of polyclonal repertoires demonstrated the impact of error accumulation on data parameters.

Conclusions: Caution is needed in interpreting repertoire data due to potential contamination with mis-sequence reads. However, a high association of errors with phred score, high relatedness of erroneous sequences with the parental sequence, dominance of specific nt substitutions, and skewed ratio of forward to reverse reads among erroneous sequences indicate approaches to filter erroneous sequences from repertoire data sets.

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Error rate of sequencing reactions. Box and whisker plots show median, 25-75 percentile, and range for erroneous sequences for the 5C.C7 (A), OT-1 (B), and DO11.10 (C) CDR3 sequences expressed as a percent of total sequence events that met initial criteria. Phred values were used to further constrain sequence sets and the minimal phred cutoff score for any nt in a sequence is indicated.
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Figure 1: Error rate of sequencing reactions. Box and whisker plots show median, 25-75 percentile, and range for erroneous sequences for the 5C.C7 (A), OT-1 (B), and DO11.10 (C) CDR3 sequences expressed as a percent of total sequence events that met initial criteria. Phred values were used to further constrain sequence sets and the minimal phred cutoff score for any nt in a sequence is indicated.

Mentions: The overall rate of erroneous CDR3β sequences for the 3 TCR was similar, 5.23 ± 0.21%, 5.24 ± 0.12%, and 6.00 ± 0.34% for the 5C.C7, OT-1, and DO11.10 TCR respectively. We analyzed whether additional filtering of sequence based on quality scores could selectively reduce the percent of erroneous sequences. Increasing minimal nt phred (q) values from 0 - 30 led to a progressive reduction in errors (Figure 1a-c)[24]. At a q = 30, net error rate was reduced to 1.05 ± 0.12%, 2.25 ± 0.12%, and 3.19 ± 0.14% for the 5C.C7, OT-1, and DO11.10 sequences, a 47-80% reduction in the different TCR (Figure 1a-c). This was accompanied by a reduction in the total number of evaluable sequences by between 24.3 ± 3.6% (5C.C7) to 35.3 ± 4.3% (OT-1). However, the exclusion of erroneous sequences exceeded that of correct sequences, compensating for this loss in total sequence numbers (see Additional file 1, Supp. Figure S1a, b).


Identification of errors introduced during high throughput sequencing of the T cell receptor repertoire.

Nguyen P, Ma J, Pei D, Obert C, Cheng C, Geiger TL - BMC Genomics (2011)

Error rate of sequencing reactions. Box and whisker plots show median, 25-75 percentile, and range for erroneous sequences for the 5C.C7 (A), OT-1 (B), and DO11.10 (C) CDR3 sequences expressed as a percent of total sequence events that met initial criteria. Phred values were used to further constrain sequence sets and the minimal phred cutoff score for any nt in a sequence is indicated.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3045962&req=5

Figure 1: Error rate of sequencing reactions. Box and whisker plots show median, 25-75 percentile, and range for erroneous sequences for the 5C.C7 (A), OT-1 (B), and DO11.10 (C) CDR3 sequences expressed as a percent of total sequence events that met initial criteria. Phred values were used to further constrain sequence sets and the minimal phred cutoff score for any nt in a sequence is indicated.
Mentions: The overall rate of erroneous CDR3β sequences for the 3 TCR was similar, 5.23 ± 0.21%, 5.24 ± 0.12%, and 6.00 ± 0.34% for the 5C.C7, OT-1, and DO11.10 TCR respectively. We analyzed whether additional filtering of sequence based on quality scores could selectively reduce the percent of erroneous sequences. Increasing minimal nt phred (q) values from 0 - 30 led to a progressive reduction in errors (Figure 1a-c)[24]. At a q = 30, net error rate was reduced to 1.05 ± 0.12%, 2.25 ± 0.12%, and 3.19 ± 0.14% for the 5C.C7, OT-1, and DO11.10 sequences, a 47-80% reduction in the different TCR (Figure 1a-c). This was accompanied by a reduction in the total number of evaluable sequences by between 24.3 ± 3.6% (5C.C7) to 35.3 ± 4.3% (OT-1). However, the exclusion of erroneous sequences exceeded that of correct sequences, compensating for this loss in total sequence numbers (see Additional file 1, Supp. Figure S1a, b).

Bottom Line: Filtering for lower quality sequences diminished but did not eliminate sequence errors, which occurred within 1-6% of sequences.Caution is needed in interpreting repertoire data due to potential contamination with mis-sequence reads.However, a high association of errors with phred score, high relatedness of erroneous sequences with the parental sequence, dominance of specific nt substitutions, and skewed ratio of forward to reverse reads among erroneous sequences indicate approaches to filter erroneous sequences from repertoire data sets.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Pathology, St, Jude Children's Research Hospital, 262 Danny Thomas Pl., Memphis, TN 38105, USA.

ABSTRACT

Background: Recent advances in massively parallel sequencing have increased the depth at which T cell receptor (TCR) repertoires can be probed by >3log10, allowing for saturation sequencing of immune repertoires. The resolution of this sequencing is dependent on its accuracy, and direct assessments of the errors formed during high throughput repertoire analyses are limited.

Results: We analyzed 3 monoclonal TCR from TCR transgenic, Rag-/- mice using Illumina® sequencing. A total of 27 sequencing reactions were performed for each TCR using a trifurcating design in which samples were divided into 3 at significant processing junctures. More than 20 million complementarity determining region (CDR) 3 sequences were analyzed. Filtering for lower quality sequences diminished but did not eliminate sequence errors, which occurred within 1-6% of sequences. Erroneous sequences were pre-dominantly of correct length and contained single nucleotide substitutions. Rates of specific substitutions varied dramatically in a position-dependent manner. Four substitutions, all purine-pyrimidine transversions, predominated. Solid phase amplification and sequencing rather than liquid sample amplification and preparation appeared to be the primary sources of error. Analysis of polyclonal repertoires demonstrated the impact of error accumulation on data parameters.

Conclusions: Caution is needed in interpreting repertoire data due to potential contamination with mis-sequence reads. However, a high association of errors with phred score, high relatedness of erroneous sequences with the parental sequence, dominance of specific nt substitutions, and skewed ratio of forward to reverse reads among erroneous sequences indicate approaches to filter erroneous sequences from repertoire data sets.

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