# A Low-Complexity Deep Learning Model for Predicting Targeted Sequencing Depth from Probe Sequence
## 3 Results
### Predicted Results  
Figure 6A-C depict a comparison of the predicted and observed sequencing depth for the different datasets. The dark gray shading marks areas where the difference between the predicted sequencing depth and observed actual read depth was within a multiple of two, and the light gray indicates where the difference was within a multiple of three. Figure 6A shows the combined results of the SNP dataset after 20-fold cross validation. At a sequencing depth of 100 and above (i.e., \(\log_{10}(\text{Depth}_{obs})>2\)), the points combining predicted and observed depth were densely distributed in the gray area. Sequences with a lower observed sequencing depth in the SNP dataset had a \(\log_{10}(\text{Depth}_{pred})\) of between 1 and 3.5, while the majority of these probes had a lower GC content (average GC content of 0.31 for sequencing depth less than 100 and 0.45 for sequences depth greater than 100). An explanation for this phenomenon is that probes with a lower observed sequencing depth are more likely  
depth (such as a lower GC content) were more affected by random fluctuations in the experimental procedure, which could also affect the sequencing depth [9]. This also illustrates the importance of using GC content as an input to the dense module in the neural network. To prove the effectiveness of the model in practical application scenarios, such as using the existing model to optimize the new probe design, the lncRNA dataset was taken as a prediction set from the SNP dataset, and the read depth of lncRNA was predicted through the model trained on the SNP dataset. It should be noted that although the lncRNA dataset and the SNP dataset had similar library preparation methods in terms of experimental work flow, hybridization temperature, and other relevant aspects, they differed in terms of the experimental operator, instrumentation, and reagents. In this study, the model obtained from a single fold of the 20-fold cross-validation process on the SNP dataset was employed to directly predict the probe log sequencing depth for the lncRNA dataset. Figure 6B displays the results of the lncRNA dataset generated from the proposed model trained on the SNP dataset. The proportion of points within the light gray region was marginally lower in the lncRNA compared to the SNP, and this decline might have been caused by  
Figure 6: Results of 20-fold cross-validation training of the proposed model based on different datasets. (**A–C**) Comparison of predicted log sequencing depth with observed log sequencing depth for different datasets, where (**A–C**) are under the SNP dataset, lncRNA dataset, and SynDNA dataset, respectively. These plots are a summary of the sequencing depth prediction results for all validation sets in the 20-fold cross-validation training. (**D**) The deviation of sequencing depth between predicted and observed depth for different datasets. the experimental changes associated with library preparation methods and other factors. However, because the proposed model only used the model trained in one fold of the SNP training process to predict the lncRNA log sequencing depth, this significantly reduced the cost of the sequencing probe design and prediction. The results of the lncRNA dataset show that the proposed model can predict the sequencing depth of probes obtained by similar library preparation methods to enable the generalization of method availability. Figure 6C shows the SynDNA dataset prediction results of probes applied in DNA information storage. The points in the figure are mostly concentrated within the gray area. The generation of these sequences by the program led to a reduction in the probability of high or low GC content, hairpin structures, and homopolymers, which resulted in a more focused distribution of sequencing depth and enhanced prediction performance. Figure 6D shows the deviation between the predicted and observed log sequencing depth in different datasets.