# A Low-Complexity Deep Learning Model for Predicting Targeted Sequencing Depth from Probe Sequence
## 4 Discussion and Conclusions  
Targeted high-throughput sequencing of DNA has emerged as a superior technique in biomedical research. Although the cost of NGS has decreased exponentially over the years, the problem of poor sequencing uniformity remains a significant issue. Inefficient sequencing of high-depth targets and insufficient coverage of low-depth targets can result in a wasteful use of reads. Therefore, a low-complexity targeted sequencing depth prediction model was proposed in this paper to provide guidance for probe sequence analysis work. In this study, we implemented the prediction function of sequencing depth prediction using probe sequences. Our innovation can be summarized as follows. (1) A distributed representation of \(k\)-mer was trained using the word-embedding model, and a sequence-encoding model was constructed to realize the representation process from sequences to feature matrices. (2) The sequencing depth prediction model based on Bi-LSTM with ab attention mechanism was proposed to realize the sequencing depth prediction result of generating sequences from the feature matrix representation of probe sequences. Our model used a new sequence-encoding process that only took 4 min of CPU time for the SNP dataset. Compared with the complex biochemical calculations in DLM, our process  
Figure 8: Results of the robustness analysis of the proposed model and ablation experiments based on the SNP dataset. (**A**) Performance comparison of our model, our model (no GC feature), our model (no attention), DLM, and the linear model on the SNP dataset under the RMSE. (**B**) Performance comparison of different models on the SNP dataset under the F2 err and F3 err. (**C**) Comparison of sequencing depth predictions from two independent prediction rounds. (**D**) Summary of 105 pairwise comparative correlation coefficients for the results of 15 training process runs. was more efficient and convenient. The network architecture was optimized, and the parameters were reduced from 300 k to 70 k, which reduced the risk of overfitting. The prediction accuracy was improved based on the different datasets compared with the linear model and the DLM. The proposed model predicted the difference between predicted and observed sequencing depths with an F3 err of 5.7% for SNP and 0.3% for SynDNA. In conclusion, our model provides a practical solution for prediction of the sequencing depth of the probe sequence. In future work, the proposed model can be transferred and extended to other bioinformatics tasks. For example, the proposed model can be extended by constructing corresponding datasets and combining other types of features. The intermolecular interactions that occur between a large number of sequences in solution during target capture are modeled and analyzed to assess their impact on sequencing depth. Meanwhile, we can attempt to introduce a transformer [35] to optimize the existing sequencing depth prediction model to address the potential vanishing gradient problem of existing RNN models when sequentially processing long sequence information. [PERSON], [PERSON] and [PERSON] proposed the idea and revised the manuscript. [PERSON], [PERSON], [PERSON] and [PERSON] processed and analyzed the data. [PERSON] wrote the programs. [PERSON], [PERSON], [PERSON] and [PERSON] wrote the paper. All authors have read and agreed to the published version of the manuscript. This research is partly supported by the Seed Fund of Tianjin University (No. 0903061008). Not applicable. Data Availability Statement: Data are available upon request. The authors declare no conflict of interest. The following abbreviations are used in this manuscript: