FusionAI: Predicting fusion breakpoint from DNA sequence with deep learning.
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ABSTRACT:
SUBMITTER: Kim P
PROVIDER: S-EPMC8501764 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Predicting in vivo protein-DNA binding sites is a challenging but pressing task in a variety of fields like drug design and development. Most promoters contain a number of transcription factor (TF) binding sites, but only a small minority has been identified by biochemical experiments that are time-consuming and laborious. To tackle this challenge, many computational methods have been proposed to predict TF binding sites from DNA sequence. Although previous methods have achieved remarkable performance in the prediction of protein-DNA interactions, there is still considerable room for improvement. In this paper, we present a hybrid deep learning framework, termed DeepD2V, for transcription factor binding sites prediction. First, we construct the input matrix with an original DNA sequence and its three kinds of variant sequences, including its inverse, complementary, and complementary inverse sequence. A sliding window of size k with a specific stride is used to obtain its k-mer representation of input sequences. Next, we use word2vec to obtain a pre-trained k-mer word distributed representation model. Finally, the probability of protein-DNA binding is predicted by using the recurrent and convolutional neural network. The experiment results on 50 public ChIP-seq benchmark datasets demonstrate the superior performance and robustness of DeepD2V. Moreover, we verify that the performance of DeepD2V using word2vec-based k-mer distributed representation is better than one-hot encoding, and the integrated framework of both convolutional neural network (CNN) and bidirectional LSTM (bi-LSTM) outperforms CNN or the bi-LSTM model when used alone. The source code of DeepD2V is available at the github repository.
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2023-07-10 | GSE221870 | GEO
Project description:Identifying functional effects of noncoding variants is a major challenge in human genetics. To predict the noncoding-variant effects de novo from sequence, we developed a deep learning-based algorithmic framework, DeepSEA (http://deepsea.princeton.edu/), that directly learns a regulatory sequence code from large-scale chromatin-profiling data, enabling prediction of chromatin effects of sequence alterations with single-nucleotide sensitivity. We further used this capability to improve prioritization of functional variants including expression quantitative trait loci (eQTLs) and disease-associated variants.
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Project description:Chromatin contacts between regulatory elements are of crucial importance for the interpretation of transcriptional regulation and the understanding of disease mechanisms. However, existing computational methods mainly focus on the prediction of interactions between enhancers and promoters, leaving enhancer-enhancer (E-E) interactions not well explored. In this work, we develop a novel deep learning approach, named Enhancer-enhancer contacts prediction (EnContact), to predict E-E contacts using genomic sequences as input. We statistically demonstrated the predicting ability of EnContact using training sets and testing sets derived from HiChIP data of seven cell lines. We also show that our model significantly outperforms other baseline methods. Besides, our model identifies finer-mapping E-E interactions from region-based chromatin contacts, where each region contains several enhancers. In addition, we identify a class of hub enhancers using the predicted E-E interactions and find that hub enhancers tend to be active across cell lines. We summarize that our EnContact model is capable of predicting E-E interactions using features automatically learned from genomic sequences.
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Project description:Deoxyribonucleic acid (DNA) has shown great promise in enabling computational applications, most notably in the fields of DNA digital data storage and DNA computing. Information is encoded as DNA strands, which will naturally bind in solution, thus enabling search and pattern-matching capabilities. Being able to control and predict the process of DNA hybridisation is crucial for the ambitious future of Hybrid Molecular-Electronic Computing. Current tools are, however, limited in terms of throughput and applicability to large-scale problems. We present the first comprehensive study of machine learning methods applied to the task of predicting DNA hybridisation. For this purpose, we introduce an in silico-generated hybridisation dataset of over 2.5 million data points, enabling the use of deep learning. Depending on hardware, we achieve a reduction in inference time ranging from one to over two orders of magnitude compared to the state-of-the-art, while retaining high fidelity. We then discuss the integration of our methods in modern, scalable workflows.
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Project description:BackgroundProtein subcellular localization prediction plays an important role in biology research. Since traditional methods are laborious and time-consuming, many machine learning-based prediction methods have been proposed. However, most of the proposed methods ignore the evolution information of proteins. In order to improve the prediction accuracy, we present a deep learning-based method to predict protein subcellular locations.ResultsOur method utilizes not only amino acid compositions sequence but also evolution matrices of proteins. Our method uses a bidirectional long short-term memory network that processes the entire protein sequence and a convolutional neural network that extracts features from protein sequences. The position specific scoring matrix is used as a supplement to protein sequences. Our method was trained and tested on two benchmark datasets. The experiment results show that our method yields accurate results on the two datasets with an average precision of 0.7901, ranking loss of 0.0758 and coverage of 1.2848.ConclusionThe experiment results show that our method outperforms five methods currently available. According to those experiments, we can see that our method is an acceptable alternative to predict protein subcellular location.
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