FusionAI: Predicting fusion breakpoint from DNA sequence with deep learning

G-Quadruplex-Mediated Transcriptional Regulation of SYT7: Implications for Tumor Progression and Therapeutic Strategies

Synaptotagmin 7 (SYT7), a member of the synaptotagmin family, exhibits high expression in various tumors and is closely associated with patient prognosis. The tight regulation of SYT7 expression assumes paramount significance in the progression of tumorigenesis. In this study, we detected a high GC content in the first 1000 bp of the promoter region of SYT7, suggesting a potential role of the G-quadruplex in its transcriptional regulation. Circular dichroism spectroscopy results showed that -187 to -172 bp sequence can form a typical parallel G-quadruplex structure, and site mutation revealed the critical role of the ninth guanine in its formation. Then, treatment of two ligands of G-quadruplex (TMPyP4 and Pyridostatin) reduced both the expression of SYT7 and subsequent tumor proliferation, demonstrating the potential of the G-quadruplex as a targeted therapy for tumors. By shedding light on the pivotal role of the G-quadruplex in regulating SYT7 transcription, our study not only advances our comprehension of this intricate regulatory mechanism but also emphasizes the significance of SYT7 in tumor proliferation. These findings collectively contribute to a more comprehensive understanding of the interplay between G-quadruplex regulation and SYT7 function in tumor development.

SaPt-CNN-LSTM-AR-EA: a hybrid ensemble learning framework for time series-based multivariate DNA sequence prediction

Biological sequence data mining is hot spot in bioinformatics. A biological sequence can be regarded as a set of characters. Time series is similar to biological sequences in terms of both representation and mechanism. Therefore, in the article, biological sequences are represented with time series to obtain biological time sequence (BTS). Hybrid ensemble learning framework (SaPt-CNN-LSTM-AR-EA) for BTS is proposed. Single-sequence and multi-sequence models are respectively constructed with self-adaption pre-training one-dimensional convolutional recurrent neural network and autoregressive fractional integrated moving average fused evolutionary algorithm. In DNA sequence experiments with six viruses, SaPt-CNN-LSTM-AR-EA realized the good overall prediction performance and the prediction accuracy and correlation respectively reached 1.7073 and 0.9186. SaPt-CNN-LSTM-AR-EA was compared with other five benchmark models so as to verify its effectiveness and stability. SaPt-CNN-LSTM-AR-EA increased the average accuracy by about 30%. The framework proposed in this article is significant in biology, biomedicine, and computer science, and can be widely applied in sequence splicing, computational biology, bioinformation, and other fields.

Systematic investigation of the homology sequences around the human fusion gene breakpoints in pan-cancer - bioinformatics study for a potential link to MMEJ

Microhomology-mediated end joining (MMEJ), an error-prone DNA damage repair mechanism, frequently leads to chromosomal rearrangements due to its ability to engage in promiscuous end joining of genomic instability and also leads to increasing mutational load at the sequences flanking the breakpoints (BPs). In this study, we systematically investigated the homology sequences around the genomic breakpoint area of human fusion genes, which were formed by the chromosomal rearrangements initiated by DNA double-strand breakage. Since the RNA-seq data is the typical data set to check the fusion genes, for the known exon junction fusion breakpoints identified from RNA-seq data, we have to infer the high chance of genomic breakpoint regions. For this, we utilized the high feature importance score area calculated from our recently developed fusion BP prediction model, FusionAI and identified 151 K microhomologies among ~24 K fusion BPs in 20 K fusion genes. From our multiple bioinformatics studies, we found a relationship between sequence homologies and the immune system. This in-silico study will provide novel knowledge on the sequence homologies around the coded structural variants.

NTRK fusions in thyroid cancer: Pathology and clinical aspects

Thyroid cancer is the most common endocrine cancer. Neurotrophic tyrosine receptor kinase (NTRK) fusions are oncogenic drivers in multiple solid tumors, including thyroid cancer. NTRK fusion thyroid cancer has unique pathological features such as mixed structure, multiple nodes, lymph node metastasis, and a background of chronic lymphocytic thyroiditis. Currently, RNA-based next-generation sequencing is the gold standard for the detection of NTRK fusions. Tropomyosin receptor kinase inhibitors have shown promising efficacy in patients with NTRK fusion-positive thyroid cancer. Efforts to overcome acquired drug resistance are the focus of research concerning next-generation TRK inhibitors. However, there are no authoritative recommendations or standardized procedures for the diagnosis and treatment of NTRK fusions in thyroid cancer. This review discusses current research progress regarding NTRK fusion-positive thyroid cancer, summarizes the clinicopathological features of the disease, and outlines the current statuses of NTRK fusion detection and targeted therapeutic agents.

FusionAI, a DNA-sequence-based deep learning protocol reduces the false positives of human fusion gene prediction

Even though there were many tool developments of fusion gene prediction from NGS data, too many false positives are still an issue. Wise use of the genomic features around the fusion gene breakpoints will be helpful to identify reliable fusion genes efficiently. For this aim, we developed FusionAI, a deep learning pipeline predicting human fusion gene breakpoints from DNA sequence. FusionAI is freely available via https://compbio.uth.edu/FusionGDB2/FusionAI.

For complete details on the use and execution of this protocol, please refer to Kim et al. (2021b).

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FusionAI can predict the fusion breakpoints from the given DNA sequence

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FusionAI can reduce the false positives of the predicted fusion genes by other tools

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FusionAI can identify the genomic features related to the genomic breakage

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FusionAI creates a landscape image of 44 human genomic features around the breakpoints

FusionGDB 2.0: fusion gene annotation updates aided by deep learning

A knowledgebase of the systematic functional annotation of fusion genes is critical for understanding genomic breakage context and developing therapeutic strategies. FusionGDB is a unique functional annotation database of human fusion genes and has been widely used for studies with diverse aims. In this study, we report fusion gene annotation updates aided by deep learning (FusionGDB 2.0) available at https://compbio.uth.edu/FusionGDB2/. FusionGDB 2.0 has substantial updates of contents such as up-to-date human fusion genes, fusion gene breakage tendency score with FusionAI deep learning model based on 20 kb DNA sequence around BP, investigation of overlapping between fusion breakpoints with 44 human genomic features across five cellular role's categories, transcribed chimeric sequence and following open reading frame analysis with coding potential based on deep learning approach with Ribo-seq read features, and rigorous investigation of the protein feature retention of individual fusion partner genes in the protein level. Among ∼102k fusion genes, about 15k kept their ORF as In-frames, which is two times compared to the previous version, FusionGDB. FusionGDB 2.0 will be used as the reference knowledgebase of fusion gene annotations. FusionGDB 2.0 provides eight categories of annotations and it will be helpful for diverse human genomic studies.