A deep learning-based algorithm, DeepSEA, was developed to predict the effects of noncoding variants by learning regulatory sequence codes from large-scale chromatin-profiling data. This approach enables the prediction of chromatin effects of sequence alterations with single-nucleotide sensitivity. DeepSEA integrates evolutionary conservation and chromatin annotations to improve the prioritization of functional variants, including expression quantitative trait loci (eQTLs) and disease-associated variants. The model was trained on a diverse compendium of genome-wide chromatin profiles from the Encyclopedia of DNA Elements (ENCODE) and Roadmap Epigenomics projects, including 690 TF binding profiles for 160 different TFs, 125 DHS profiles, and 104 histone-mark profiles. DeepSEA uses a deep convolutional network to extract sequence features at different spatial scales and predict chromatin features such as TF binding, DNase I sensitivity, and histone marks. The model was evaluated on holdout genomic sequences and demonstrated high accuracy in predicting chromatin features, including TF binding sites, with a median area under the curve (AUC) of 0.958. DeepSEA also accurately predicted the effects of individual SNPs on TF binding and was able to prioritize functional SNPs based on predicted chromatin effect signals. The model outperformed previous methods in prioritizing HGMD regulatory mutations, eQTLs, and GWAS phenotype-associated SNPs. DeepSEA was capable of delivering high-confidence predictions of chromatin effects of genomic variants based on genomic sequence alone. The model was also able to predict the effects of insertions or deletions (indels) and was evaluated on HGMD indels, showing high accuracy in prioritizing HGMD indels against nearby control 1000 Genomes indels. DeepSEA's approach can be readily adapted and likely further improved as knowledge of functional variants increases, providing additional training data. The model's ability to make de novo predictions based on exact sequence-change information allows for the prediction of the effects of noncoding variants. The DeepSEA model was validated using allelic imbalance information from digital genomic footprinting (DGF) DNase-seq data on ENCODE cell lines, demonstrating its ability to accurately predict the effect of single-nucleotide changes. The model's performance was further evaluated against other methods, showing that it outperformed previous approaches in predicting chromatin features and prioritizing functional variants. The model's ability to integrate sequence-based chromatin effect predictions with evolutionary conservation information enabled it to accurately predict the effects of noncoding variants. The DeepSEA model was also able to predict the effects of noncoding variants on transcription factor (TF) binding, DNA accessibility, and histone marks with high accuracy. The model's performance was evaluated using a variety of metrics, including area under the receiver operating characteristic curve (AUC), and it demonstrated superior accuracy in predicting chromatin features and prioritizing functional variants. The modelA deep learning-based algorithm, DeepSEA, was developed to predict the effects of noncoding variants by learning regulatory sequence codes from large-scale chromatin-profiling data. This approach enables the prediction of chromatin effects of sequence alterations with single-nucleotide sensitivity. DeepSEA integrates evolutionary conservation and chromatin annotations to improve the prioritization of functional variants, including expression quantitative trait loci (eQTLs) and disease-associated variants. The model was trained on a diverse compendium of genome-wide chromatin profiles from the Encyclopedia of DNA Elements (ENCODE) and Roadmap Epigenomics projects, including 690 TF binding profiles for 160 different TFs, 125 DHS profiles, and 104 histone-mark profiles. DeepSEA uses a deep convolutional network to extract sequence features at different spatial scales and predict chromatin features such as TF binding, DNase I sensitivity, and histone marks. The model was evaluated on holdout genomic sequences and demonstrated high accuracy in predicting chromatin features, including TF binding sites, with a median area under the curve (AUC) of 0.958. DeepSEA also accurately predicted the effects of individual SNPs on TF binding and was able to prioritize functional SNPs based on predicted chromatin effect signals. The model outperformed previous methods in prioritizing HGMD regulatory mutations, eQTLs, and GWAS phenotype-associated SNPs. DeepSEA was capable of delivering high-confidence predictions of chromatin effects of genomic variants based on genomic sequence alone. The model was also able to predict the effects of insertions or deletions (indels) and was evaluated on HGMD indels, showing high accuracy in prioritizing HGMD indels against nearby control 1000 Genomes indels. DeepSEA's approach can be readily adapted and likely further improved as knowledge of functional variants increases, providing additional training data. The model's ability to make de novo predictions based on exact sequence-change information allows for the prediction of the effects of noncoding variants. The DeepSEA model was validated using allelic imbalance information from digital genomic footprinting (DGF) DNase-seq data on ENCODE cell lines, demonstrating its ability to accurately predict the effect of single-nucleotide changes. The model's performance was further evaluated against other methods, showing that it outperformed previous approaches in predicting chromatin features and prioritizing functional variants. The model's ability to integrate sequence-based chromatin effect predictions with evolutionary conservation information enabled it to accurately predict the effects of noncoding variants. The DeepSEA model was also able to predict the effects of noncoding variants on transcription factor (TF) binding, DNA accessibility, and histone marks with high accuracy. The model's performance was evaluated using a variety of metrics, including area under the receiver operating characteristic curve (AUC), and it demonstrated superior accuracy in predicting chromatin features and prioritizing functional variants. The model