Error correction in single-cell DNA sequencing: Finding that rare allele for MRD clone
Creators: name: Manimozhi Manivannan
name: Sombeet Sahu
name: Wang, Shu
name: Kim, Dong
name: Sciambi, Adam
name: Nianzhen Li
name: Viernes, Hannah
name: Beard, Nigel
Publisher: name: F1000 Research Limited
Year: 2019

description: Background<br>With the advancements in single-cell sequencing technologies, it is now possible to interrogate thousands of cells in a single experiment for studying genetic variability. Although Single-Cell DNA sequencing platforms like Tapestri has been readily available in the community, it is still susceptible to errors from polymerase incorporations, structure induced template switching, PCR mediated recombination in Tapestri workflow or DNA-damage. Errors from sequencing could propagate from cluster amplification, cycle sequencing or image analysis. All together these errors can be divided into substitutions, insertions and deletion errors and can range from 0.5% to 2% depending on the sequencer. This makes variant calling and minimal residual disease detection challenging. To address these challenges, we developed a novel consensus sequence-based method for correcting the errors and reduce false-positive rates.<br> <br>Methods<br>Here we present a model to correct base position errors in Tapestri single-cell DNA analytical workflow. The error correction method involves 2 steps. First, we train the sample set to generate base transition probabilities. Then we use the base transition probability to error correct reads per amplicon. We show that base substitution rates could have a significant coefficient of variation across different Tapestri runs/ sequencing runs and could have standard deviations as high as 73% of the mean error rate. Hence the first step of training on the fly per run becomes important.<br> <br>To train the transition probabilities we parse the reads from cell bam files having cell barcode as the read group identifier. A pileup is generated per amplicon and we calculate the probability of each base occurring along the amplicon. We also see that training across all the reads tends to flatten out and hence to improve compute efficiency we stop training after a few million bases.<br> <br>After we generate the multinomial transition probabilities we apply error correction on each read per cell. We calculate the frequency at which each non-reference base is observed per loci. We use the transition probability matrix and correct to reference based on a threshold of significance. Also, to ensure that we filter out the noisy reads before passing the data to our variant caller we suppress the quality scores of reads having very low coverage. Since error correcting on deeper sequencing runs is very computationally intensive, we used several methods to parallelize this process to make use of modern compute-architectures.<br> <br>Results<br>To validate this method, we used two different targeted panels on a Latin square model system with known truth mutations. We performed titrations experiments of 4 cell line mixtures with 98.4%, 1%, 0.5% and 0.1% dilutions. We processed the samples through Tapestri platform and sequenced over multiple Illumina sequencers (Hiseq 2500, Miseq). We ran the Tapestri analytical workflow with and without error correction. With the error correction pipeline, we were able to reduce our false positive rates by ~60-80% while maintaining our sensitivity.