Basecalling is an essential step in nanopore sequencing analysis where the raw signals of nanopore sequencers are converted into nucleotide sequences, i.e., reads. State-of-the-art basecallers employ complex deep learning models to achieve high basecalling accuracy. This makes basecalling computationally-inefficient and memory-hungry; bottlenecking the entire genome analysis pipeline. However, for many applications, the majority of reads do no match the reference genome of interest (i.e., target reference) and thus are discarded in later steps in the genomics pipeline, wasting the basecalling computation. To overcome this issue, we propose TargetCall, the first pre-basecalling filter to eliminate the wasted computation in basecalling. TargetCall's key idea is to discard reads that will not match the target reference (i.e., off-target reads) prior to basecalling. TargetCall consists of two main components: (1) LightCall, a lightweight neural network basecaller that produces noisy reads; and (2) Similarity Check, which labels each of these noisy reads as on-target or off-target by matching them to the target reference. TargetCall aims to filter out all off-target reads before basecalling. The highly-accurate but slow basecalling is performed only on the raw signals whose noisy reads are labeled as on-target. Our thorough experimental evaluations using both real and simulated data show that TargetCall 1) improves the end-to-end basecalling performance while maintaining high sensitivity in keeping on-target reads, 2) maintains high accuracy in downstream analysis, 3) precisely filters out up to 94.71% of off-target reads, and 4) achieves better performance, throughput, sensitivity, precision, and generality compared to prior works. We open-source TargetCall at https://github.com/CMU-SAFARI/TargetCall

翻译：碱基判定是纳米孔测序分析中的关键步骤，该过程将纳米孔测序仪的原始信号转化为核苷酸序列（即读段）。现有最先进的碱基判定器采用复杂的深度学习模型以实现高精度碱基判定，导致计算效率低下且内存消耗巨大，成为整个基因组分析流程的瓶颈。然而在许多应用中，大多数读段与目标参考基因组不匹配，这些读段在基因组分析流程的后续步骤中被丢弃，造成碱基判定计算的浪费。为解决这一问题，我们提出TargetCall——首个在碱基判定前消除无效计算的预过滤方法。TargetCall的核心思想是在碱基判定前剔除与目标参考不匹配的读段（即脱靶读段）。TargetCall由两个主要组件构成：（1）LightCall——产生噪声读段的轻量级神经网络碱基判定器；（2）相似性检查模块——通过将噪声读段与目标参考进行比对，将其标记为靶向或脱靶读段。TargetCall旨在碱基判定前过滤所有脱靶读段，仅对噪声读段被标记为靶向的原始信号执行高精度但缓慢的碱基判定。基于真实及模拟数据的全面实验评估表明，TargetCall能够：1）在维持高灵敏度保留靶向读段的同时提升端到端碱基判定性能；2）保持下游分析的高准确性；3）精准过滤高达94.71%的脱靶读段；4）在性能、通量、灵敏度、精确度和泛化能力方面均优于现有方法。我们已在https://github.com/CMU-SAFARI/TargetCall 开源TargetCall。