Essentiality-specific pathogenicity prioritization gene score to improve filtering of disease sequence data

Comprehensive assessment of computational methods for cancer immunoediting

Cancer immunoediting reflects the role of the immune system in eliminating tumor cells and shaping tumor immunogenicity, which leaves marks in the genome. In this study, we systematically evaluate four methods for quantifying immunoediting. In colorectal cancer samples from The Cancer Genome Atlas, we found that these methods identified 78.41%, 46.17%, 36.61%, and 4.92% of immunoedited samples, respectively, covering 92.90% of all colorectal cancer samples. Comparison of 10 patient-derived xenografts (PDXs) with their original tumors showed that different methods identified reduced immune selection in PDXs ranging from 44.44% to 60.0%. The proportion of such PDX-tumor pairs increases to 77.78% when considering the union of results from multiple methods, indicating the complementarity of these methods. We find that observed-to-expected ratios highly rely on neoantigen selection criteria and reference datasets. In contrast, HLA-binding mutation ratio, immune dN/dS, and enrichment score of cancer cell fraction were less affected by these factors. Our findings suggest integration of multiple methods may benefit future immunoediting analyses.

Identification of variants in genes associated with hypertrophic cardiomyopathy in Mexican patients

The objective of this work was to identify genetic variants in Mexican patients diagnosed with hypertrophic cardiomyopathy (HCM). According to world literature, the genes mainly involved are MHY7 and MYBPC3, although variants have been found in more than 50 genes related to heart disease and sudden death, and to our knowledge there are no studies in the Mexican population. These variants are reported and classified in the ClinVar (PubMed) database and only some of them are recognized in the Online Mendelian Information in Men (OMIM). The present study included 37 patients, with 14 sporadic cases and 6 familial cases, with a total of 21 index cases. Next-generation sequencing was performed on a predesigned panel of 168 genes associated with heart disease and sudden death. The sequencing analysis revealed twelve (57%) pathogenic or probably pathogenic variants, 9 of them were familial cases, managing to identify pathogenic variants in relatives without symptoms of the disease. At the molecular level, nine of the 12 variants (75%) were single nucleotide changes, 2 (17%) deletions, and 1 (8%) splice site alteration. The genes involved were MYH7 (25%), MYBPC3 (25%) and ACADVL, KCNE1, TNNI3, TPM1, SLC22A5, TNNT2 (8%). In conclusion; we found five variants that were not previously reported in public databases. It is important to follow up on the reclassification of variants, especially those of uncertain significance in patients with symptoms of the condition. All patients included in the study and their relatives received family genetic counseling.

Predicting disease genes based on multi-head attention fusion

BACKGROUND

The identification of disease-related genes is of great significance for the diagnosis and treatment of human disease. Most studies have focused on developing efficient and accurate computational methods to predict disease-causing genes. Due to the sparsity and complexity of biomedical data, it is still a challenge to develop an effective multi-feature fusion model to identify disease genes.

RESULTS

This paper proposes an approach to predict the pathogenic gene based on multi-head attention fusion (MHAGP). Firstly, the heterogeneous biological information networks of disease genes are constructed by integrating multiple biomedical knowledge databases. Secondly, two graph representation learning algorithms are used to capture the feature vectors of gene-disease pairs from the network, and the features are fused by introducing multi-head attention. Finally, multi-layer perceptron model is used to predict the gene-disease association.

CONCLUSIONS

The MHAGP model outperforms all of other methods in comparative experiments. Case studies also show that MHAGP is able to predict genes potentially associated with diseases. In the future, more biological entity association data, such as gene-drug, disease phenotype-gene ontology and so on, can be added to expand the information in heterogeneous biological networks and achieve more accurate predictions. In addition, MHAGP with strong expansibility can be used for potential tasks such as gene-drug association and drug-disease association prediction.

SVPath: an accurate pipeline for predicting the pathogenicity of human exon structural variants

Although there are a large number of structural variations in the chromosomes of each individual, there is a lack of more accurate methods for identifying clinical pathogenic variants. Here, we proposed SVPath, a machine learning-based method to predict the pathogenicity of deletions, insertions and duplications structural variations that occur in exons. We constructed three types of annotation features for each structural variation event in the ClinVar database. First, we treated complex structural variations as multiple consecutive single nucleotide polymorphisms events, and annotated them with correlation scores based on single nucleic acid substitutions, such as the impact on protein function. Second, we determined which genes the variation occurred in, and constructed gene-based annotation features for each structural variation. Third, we also calculated related features based on the transcriptome, such as histone signal, the overlap ratio of variation and genomic element definitions, etc. Finally, we employed a gradient boosting decision tree machine learning method, and used the deletions, insertions and duplications in the ClinVar database to train a structural variation pathogenicity prediction model SVPath. These structural variations are clearly indicated as pathogenic or benign. Experimental results show that our SVPath has achieved excellent predictive performance and outperforms existing state-of-the-art tools. SVPath is very promising in evaluating the clinical pathogenicity of structural variants. SVPath can be used in clinical research to predict the clinical significance of unknown pathogenicity and new structural variation, so as to explore the relationship between diseases and structural variations in a computational way.