# A Low-Complexity Deep Learning Model for Predicting Targeted Sequencing Depth from Probe Sequence
## 1 Introduction  
With the development of next-generation sequencing (NGS) technology, its massively parallel sequencing ability and high analytical sensitivity have made it an increasingly prevalent tool in diverse fields, such as population genomics, cancer or disease genetics, and DNA data storage [1; 2; 3; 4]. In the realm of genome ecology, there are three main types of NGS sequencing, encompassing targeted sequencing, whole-exome sequencing, and whole-genome sequencing. Targeted sequencing uses probes to enable the process of enriching and sequencing specific regions of DNA [5], with the work flow shown in Figure 1A. In next-generation sequencing, probe sequences enable researchers to focus their sequencing efforts on areas of interest by targeting specific genomic regions [6], thereby reducing the sequencing time and cost associated with analyzing the entire genome. However, the hybridization kinetic properties of the probe structure lead to uneven enrichment efficiency, which results in increased sequencing costs. Therefore, modeling the impact of probe character on enrichment efficiency can aid in the design of probe sequences that ensure a uniform coverage of sequencing depth. In recent years, some traditional methods based on experience with DNA structure and biochemistry have been presented to optimize probe sequences for specific hybridization between probe and target sequences [7; 8]. Several studies have shown that the traditionalmethods are labor-intensive and poorly generalized [9]. In recent years, deep learning approaches have leveraged diverse neural network architectures to autonomously extract weakly correlated features from large datasets, leading to significant breakthroughs in various scientific domains, including natural language processing (NLP) and computer vision (CV) [10; 11; 12; 13]. The latest research indicates that deep learning has been successfully applied in the field of bioinformatics for emerging tasks (e.g., protein structure prediction and medical image analysis) [14; 15; 16; 17]. In the prediction of sequencing depth for targeted sequencing, the short-range and long-range interactions in the sequence can lead to the formation of different secondary structures, affecting the kinetic nature of the hybridization reaction and the efficiency of probe capture. This interaction phenomenon between different regions can be trapped by the recurrent neural network (RNN). The large NGS dataset also offers the possibility of using deep learning to solve this problem. Figure 1: Overview of targeted sequencing methods and different sequencing depth prediction models. (**A**) The specific process of targeted sequencing includes probe sequence design, sample library construction, probe and sample library hybridization capture, elution, and sequencing. (**B**) The proposed model is compared with the existing sequencing depth prediction model, DLM, and the proposed model differs from the sequence encoding model and the sequencing depth prediction model. A deep learning model (DLM) was introduced to predict sequencing depth [9]. The unpaired probability of each nucleotide in all possible secondary structures of the probe sequence was calculated using Nupack as the local feature [18]; then, the standard free energy of the probe sequence, the target sequence, and the molecule after hybridization of the probe sequence were calculated as global features. The local feature of each nucleotide with the nucleotide chemical identity (NCP) was used to complete the process of encoding the sequence into the matrix. Four gate-recurrent units (GRUs) were utilized to process the probe and target sequences separately. The processing results were combined with global features and fed into the fully connected neural network to accomplish sequencing depth prediction. Although the existing deep learning model is effective at predicting the sequencing depth of probe sequences, it suffers from several limitations.