LitVar: a semantic search engine for linking genomic variant data in PubMed and PMC

Human genetic variants in SLC39A8 impact uptake and steady-state metal levels within the cell

The human SLC39A8 (hSLC39A8) gene encodes a plasma membrane protein SLC39A8 (ZIP8) that mediates the specific uptake of the metals Cd2+, Mn2+, Zn2+, Fe2+, Co2+, and Se4+ Pathogenic variants within hSLC39A8 are associated with congenital disorder of glycosylation type 2 (CDG type II) or Leigh-like syndrome. However, numerous mutations of uncertain significance are also linked to different conditions or benign traits. Our study characterized 21 hSLC39A8 variants and measured their impact on protein localization and intracellular levels of Cd2+, Zn2+, and Mn2+ We identified four variants that disrupt protein expression, five variants with high retention in the endoplasmic reticulum, and 12 variants with localization to the plasma membrane. From the 12 variants with plasma membrane localization, we identified three with complete loss of detectable ion uptake by the cell and five with differential uptake between metal ions. Further in silico analysis on protein stability identified variants that may affect the stability of homodimer interfaces. This study elucidates the variety of effects of hSLC39A8 variants on ZIP8 and on diseases involving disrupted metal ion homeostasis.

Genome-first determination of the prevalence and penetrance of eight germline myeloid malignancy predisposition genes: a study of two population-based cohorts

It is estimated that 10% of individuals with a myeloid malignancy carry a germline susceptibility. Using the genome-first approach, in which individuals were ascertained on genotype alone, rather than clinical phenotype, we quantified the prevalence and penetrance of pathogenic germline variants in eight myeloid malignancy predisposition (gMMP) genes. ANKRD26, CEBPA, DDX41, MECOM, SRP72, ETV6, RUNX1 and GATA2, were analyzed from the Geisinger MyCode DiscovEHR (n = 170,503) and the United Kingdom Biobank (UKBB, n = 469,595). We identified a high risk of myeloid malignancies (MM) (odds ratio[OR] all genes: DiscovEHR, 4.6 [95% confidential interval (CI) 2.1-9.7], p < 0.0001; UKBB, 6.0 [95% CI 4.3-8.2], p = 3.1 × 10-27), and decreased overall survival (hazard ratio [HR] DiscovEHR, 1.8 [95% CI 1.3-2.6], p = 0.00049; UKBB, 1.4 [95% CI 1.2-1.8], p = 8.4 × 10-5) amongst heterozygotes. Pathogenic DDX41 variants were the most commonly identified, and in UKBB showed a significantly increased risk of MM (OR 5.7 [95% CI 3.9-8.3], p = 6.0 × 10-20) and increased all-cause mortality (HR 1.35 [95% CI 1.1-1.7], p = 0.0063). Through a genome-first approach, this study genetically ascertained individuals with a gMMP and determined their MM risk and survival.

The evolution of dbSNP: 25 years of impact in genomic research

The Single Nucleotide Polymorphism Database (dbSNP), established in 1998 by the National Center for Biotechnology Information (NCBI), has been a critical resource in genomics for cataloging small genetic variations. Originally focused on single nucleotide polymorphisms (SNPs), dbSNP has since expanded to include a variety of genetic variants, playing a key role in genome-wide association studies (GWAS), population genetics, pharmacogenomics, and cancer research. Over 25 years, dbSNP has grown to include more than 4.4 billion submitted SNPs and 1.1 billion unique reference SNPs, providing essential data for identifying disease-related genetic variants and studying human diversity. Integrating large-scale projects like 1000 Genomes, gnomAD, TOPMed, and ALFA has expanded dbSNP's catalog of human genetic variation, increasing its usefulness for research and clinical applications. Keeping up with advancements such as next-generation sequencing and cloud-based infrastructure, dbSNP remains a cornerstone of genetic research supporting continued discoveries in precision medicine and population genomics. DATABASE URL: https://www.ncbi.nlm.nih.gov/snp.

Screening of Inherited Retinal Disease Patients in a Low-Resource Setting Using an Augmented Next-Generation Sequencing Panel

BACKGROUND

Inherited retinal diseases (IRDs) are a clinically and genetically heterogeneous group of disorders affecting millions worldwide. Despite the widespread adoption of next-generation sequencing (NGS) panels, there remains a critical gap in the genetically diverse and understudied African populations.

METHODS

One hundred and thirty-five South African patients affected by various IRDs underwent NGS using a custom-targeted panel sequencing over 100 known genes. The panel was supplemented by in silico screening for a MAK-Alu insertion and screening of seven functionally established deep intronic variants.

RESULTS

Through our combined screening strategy, we obtained a probable genetic diagnosis for 56% of the cohort. We identified 83 unique variants in 29 IRD genes underlying the disease, including 16 putative novel variants. Molecular findings prompted recommendations for clinical re-examination in ten patients. Resolution rates varied across clinical classifications and population groups.

CONCLUSIONS

This study reports the first use of a targeted NGS panel for IRDs in southern Africa, demonstrating a cost-effective, customisable approach that optimises both diagnostic yield and resource efficiency, making it a valuable tool for IRD molecular characterisation in resource-limited settings. Augmenting the panel by screening for variants relevant to South African patients allowed us to achieve a resolution rate in line with international studies. Our study underscores the importance of investigating diverse populations to bridge disparities in genomic research and improve diagnostic outcomes for underrepresented population groups.

Interpretation knowledge extraction for genetic testing via question-answer model

BACKGROUND

Sequencing-based genetic testing is widely used in biomedical research, including pathogenic microorganism detection with metagenomic next-generation sequencing (mNGS). The application of sequencing results to clinical diagnosis and treatment relies on various interpretation knowledge bases. Currently, the existing knowledge bases are primarily built through manual knowledge extraction. This method requires professionals to read extensive literature and extract relevant knowledge from it, which is time-consuming and costly. Furthermore, manual extraction unavoidably introduces subjective biases. In this study, we aimed to automatically extract knowledge for interpreting mNGS results.

METHOD

We propose a novel approach to automatically extract pathogenic microorganism knowledge based on the question-answer (QA) model. First, we construct a MicrobeDB dataset since there is no available pathogenic microorganism QA dataset for training the model. The created dataset contains 3,161 samples from 618 published papers covering 224 pathogenic microorganisms. Then, we fine-tune the selected baseline model based on MicrobeDB. Finally, we utilize ChatGPT to enhance the diversity of training data, and employ data expansion to increase training data volume.

RESULTS

Our method achieves an Exact Match (EM) and F1 score of 88.39% and 93.18%, respectively, on the MicrobeDB test set. We also conduct ablation studies on the proposed data augmentation method. In addition, we perform comparative experiments with the ChatPDF tool based on the ChatGPT API to demonstrate the effectiveness of the proposed method.

CONCLUSIONS

Our method is effective and valuable for extracting pathogenic microorganism knowledge.

Variant Impact Predictor database (VIPdb), version 2: trends from three decades of genetic variant impact predictors

BACKGROUND

Variant interpretation is essential for identifying patients' disease-causing genetic variants amongst the millions detected in their genomes. Hundreds of Variant Impact Predictors (VIPs), also known as Variant Effect Predictors (VEPs), have been developed for this purpose, with a variety of methodologies and goals. To facilitate the exploration of available VIP options, we have created the Variant Impact Predictor database (VIPdb).

RESULTS

The Variant Impact Predictor database (VIPdb) version 2 presents a collection of VIPs developed over the past three decades, summarizing their characteristics, ClinGen calibrated scores, CAGI assessment results, publication details, access information, and citation patterns. We previously summarized 217 VIPs and their features in VIPdb in 2019. Building upon this foundation, we identified and categorized an additional 190 VIPs, resulting in a total of 407 VIPs in VIPdb version 2. The majority of the VIPs have the capacity to predict the impacts of single nucleotide variants and nonsynonymous variants. More VIPs tailored to predict the impacts of insertions and deletions have been developed since the 2010s. In contrast, relatively few VIPs are dedicated to the prediction of splicing, structural, synonymous, and regulatory variants. The increasing rate of citations to VIPs reflects the ongoing growth in their use, and the evolving trends in citations reveal development in the field and individual methods.

CONCLUSIONS

VIPdb version 2 summarizes 407 VIPs and their features, potentially facilitating VIP exploration for various variant interpretation applications. VIPdb is available at  https://genomeinterpretation.org/vipdb.

Regional-specific calibration enables application of computational evidence for clinical classification of 5' cis-regulatory variants in Mendelian disease

To date, clinical genetic testing for Mendelian disease variants has focused heavily on exonic coding and intronic gene regions. This multi-step study was undertaken to provide an evidence base for selecting and applying computational approaches for use in clinical classification of 5' cis-regulatory region variants. Curated datasets of clinically reported disease-causing 5' cis-regulatory region variants and variants from matched genomic regions in population controls were used to calibrate six bioinformatic tools as predictors of variant pathogenicity. Likelihood ratio estimates were aligned to code weights following ClinGen recommendations for application of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology (ACMG/AMP) classification scheme. Considering code assignment across all reference dataset variants, performance was best for CADD (81.2%) and REMM (81.5%). Optimized thresholds provided moderate evidence toward pathogenicity (CADD, REMM) and moderate (CADD) or supporting (REMM) evidence against pathogenicity. Both sensitivity and specificity of prediction were improved when further categorizing variants based on location in an EPDnew-defined promoter region. Combining predictions (CADD, REMM, and location in a promoter region) increased specificity at the expense of sensitivity. Importantly, the optimal CADD thresholds for assigning ACMG/AMP codes PP3 (≥10) and BP4 (≤8) were vastly different from recommendations for protein-coding variants (PP3 ≥25.3; BP4 ≤22.7); CADD <22.7 would incorrectly assign BP4 for >90% of reported disease-causing cis-regulatory region variants. Our results demonstrate the need to consider a tiered approach and tailored score thresholds to optimize bioinformatic impact prediction for clinical classification of 5' cis-regulatory region variants.

Variant Impact Predictor database (VIPdb), version 2: Trends from 25 years of genetic variant impact predictors

Background

Variant interpretation is essential for identifying patients' disease-causing genetic variants amongst the millions detected in their genomes. Hundreds of Variant Impact Predictors (VIPs), also known as Variant Effect Predictors (VEPs), have been developed for this purpose, with a variety of methodologies and goals. To facilitate the exploration of available VIP options, we have created the Variant Impact Predictor database (VIPdb).

Results

The Variant Impact Predictor database (VIPdb) version 2 presents a collection of VIPs developed over the past 25 years, summarizing their characteristics, ClinGen calibrated scores, CAGI assessment results, publication details, access information, and citation patterns. We previously summarized 217 VIPs and their features in VIPdb in 2019. Building upon this foundation, we identified and categorized an additional 186 VIPs, resulting in a total of 403 VIPs in VIPdb version 2. The majority of the VIPs have the capacity to predict the impacts of single nucleotide variants and nonsynonymous variants. More VIPs tailored to predict the impacts of insertions and deletions have been developed since the 2010s. In contrast, relatively few VIPs are dedicated to the prediction of splicing, structural, synonymous, and regulatory variants. The increasing rate of citations to VIPs reflects the ongoing growth in their use, and the evolving trends in citations reveal development in the field and individual methods.

Conclusions

VIPdb version 2 summarizes 403 VIPs and their features, potentially facilitating VIP exploration for various variant interpretation applications.

Availability

VIPdb version 2 is available at https://genomeinterpretation.org/vipdb.

A Combined Manual Annotation and Deep-Learning Natural Language Processing Study on Accurate Entity Extraction in Hereditary Disease Related Biomedical Literature

We report a combined manual annotation and deep-learning natural language processing study to make accurate entity extraction in hereditary disease related biomedical literature. A total of 400 full articles were manually annotated based on published guidelines by experienced genetic interpreters at Beijing Genomics Institute (BGI). The performance of our manual annotations was assessed by comparing our re-annotated results with those publicly available. The overall Jaccard index was calculated to be 0.866 for the four entity types-gene, variant, disease and species. Both a BERT-based large name entity recognition (NER) model and a DistilBERT-based simplified NER model were trained, validated and tested, respectively. Due to the limited manually annotated corpus, Such NER models were fine-tuned with two phases. The F1-scores of BERT-based NER for gene, variant, disease and species are 97.28%, 93.52%, 92.54% and 95.76%, respectively, while those of DistilBERT-based NER are 95.14%, 86.26%, 91.37% and 89.92%, respectively. Most importantly, the entity type of variant has been extracted by a large language model for the first time and a comparable F1-score with the state-of-the-art variant extraction model tmVar has been achieved.

ProteoMutaMetrics: machine learning approaches for solute carrier family 6 mutation pathogenicity prediction

The solute carrier transporter family 6 (SLC6) is of key interest for their critical role in the transport of small amino acids or amino acid-like molecules. Their dysfunction is strongly associated with human diseases such as including schizophrenia, depression, and Parkinson's disease. Linking single point mutations to disease may support insights into the structure-function relationship of these transporters. This work aimed to develop a computational model for predicting the potential pathogenic effect of single point mutations in the SLC6 family. Missense mutation data was retrieved from UniProt, LitVar, and ClinVar, covering multiple protein-coding transcripts. As encoding approach, amino acid descriptors were used to calculate the average sequence properties for both original and mutated sequences. In addition to the full-sequence calculation, the sequences were cut into twelve domains. The domains are defined according to the transmembrane domains of the SLC6 transporters to analyse the regions' contributions to the pathogenicity prediction. Subsequently, several classification models, namely Support Vector Machine (SVM), Logistic Regression (LR), Random Forest (RF), and Extreme Gradient Boosting (XGBoost) with the hyperparameters optimized through grid search were built. For estimation of model performance, repeated stratified k-fold cross-validation was used. The accuracy values of the generated models are in the range of 0.72 to 0.80. Analysis of feature importance indicates that mutations in distinct regions of SLC6 transporters are associated with an increased risk for pathogenicity. When applying the model on an independent validation set, the performance in accuracy dropped to averagely 0.6 with high precision but low sensitivity scores.

Comparison of literature mining tools for variant classification: Through the lens of 50 RYR1 variants

PURPOSE

The American College of Medical Genetics and Genomics and the Association for Molecular Pathology have outlined a schema that allows for systematic classification of variant pathogenicity. Although gnomAD is generally accepted as a reliable source of population frequency data and ClinGen has provided guidance on the utility of specific bioinformatic predictors, there is no consensus source for identifying publications relevant to a variant. Multiple tools are available to aid in the identification of relevant variant literature, including manually curated databases and literature search engines. We set out to determine the utility of 4 literature mining tools used for ascertainment to inform the discussion of the use of these tools.

METHODS

Four literature mining tools including the Human Gene Mutation Database, Mastermind, ClinVar, and LitVar 2.0 were used to identify relevant variant literature for 50 RYR1 variants. Sensitivity and precision were determined for each tool.

RESULTS

Sensitivity among the 4 tools ranged from 0.332 to 0.687. Precision ranged from 0.389 to 0.906. No single tool retrieved all relevant publications.

CONCLUSION

At the current time, the use of multiple tools is necessary to completely identify the literature relevant to curate a variant.

VarChat: the generative AI assistant for the interpretation of human genomic variations

MOTIVATION

In the modern era of genomic research, the scientific community is witnessing an explosive growth in the volume of published findings. While this abundance of data offers invaluable insights, it also places a pressing responsibility on genetic professionals and researchers to stay informed about the latest findings and their clinical significance. Genomic variant interpretation is currently facing a challenge in identifying the most up-to-date and relevant scientific papers, while also extracting meaningful information to accelerate the process from clinical assessment to reporting. Computer-aided literature search and summarization can play a pivotal role in this context. By synthesizing complex genomic findings into concise, interpretable summaries, this approach facilitates the translation of extensive genomic datasets into clinically relevant insights.

RESULTS

To bridge this gap, we present VarChat (varchat.engenome.com), an innovative tool based on generative AI, developed to find and summarize the fragmented scientific literature associated with genomic variants into brief yet informative texts. VarChat provides users with a concise description of specific genetic variants, detailing their impact on related proteins and possible effects on human health. In addition, VarChat offers direct links to related scientific trustable sources, and encourages deeper research.

AVAILABILITY AND IMPLEMENTATION

varchat.engenome.com.

Unexpected identification of obesity-associated mutations in LEP and MC4R genes in patients with anorexia nervosa

Mutations leading to a reduced or loss of function in genes of the leptin-melanocortin system confer a risk for monogenic forms of obesity. Yet, gain of function variants in the melanocortin-4-receptor (MC4R) gene predispose to a lower BMI. In individuals with reduced body weight, we thus expected mutations leading to an enhanced function in the respective genes, like leptin (LEP) and MC4R. Therefore, we have Sanger sequenced the coding regions of LEP and MC4R in 462 female patients with anorexia nervosa (AN), and 445 healthy-lean controls. In total, we have observed four and eight variants in LEP and MC4R, respectively. Previous studies showed different functional in vitro effects for the detected frameshift and non-synonymous variants: (1) LEP: reduced/loss of function (p.Val94Met), (2) MC4R: gain of function (p.Val103Ile, p.Ile251Leu), reduced or loss of function (p.Thr112Met, p.Ser127Leu, p.Leu211fsX) and without functional in vitro data (p.Val50Leut). In LEP, the variant p.Val94Met was detected in one patient with AN. For MC4R variants, one patient with AN carried the frameshift variant p.Leu211fsX. One patient with AN was heterozygous for two variants at the MC4R (p.Val103Ile and p.Ser127Leu). All other functionally relevant variants were detected in similar frequencies in patients with AN and lean individuals.

Coexistence of temporal lobe epilepsy and idiopathic generalized epilepsy

OBJECTIVE

We investigated the frequency of coexistence of temporal lobe epilepsy (TLE) and idiopathic generalized epilepsy (IGE) in a retrospective database study. We also explored the underlying pathomechanisms of the coexistence of TLE and IGE based on the available information, using bioinformatics tools.

METHODS

The first phase of the investigation was a retrospective study. All patients with an electro-clinical diagnosis of epilepsy were studied at the outpatient epilepsy clinic at Shiraz University of Medical Sciences, Shiraz, Iran, from 2008 until 2023. In the second phase, we searched the following databases for genetic variations (epilepsy-associated genetic polymorphisms) that are associated with TLE or syndromes of IGE: DisGeNET, genome-wide association study (GWAS) Catalog, epilepsy genetic association database (epiGAD), and UniProt. We also did a separate literature search using PubMed.

RESULTS

In total, 3760 patients with epilepsy were registered at our clinic; four patients with definitely mixed TLE and IGE were identified; 0.1% of all epilepsies. We could identify that rs1883415 of ALDH5A1, rs137852779 of EFHC1, rs211037 of GABRG2, rs1130183 of KCNJ10, and rs1045642 of ABCB1 genes are shared between TLE and syndromes of IGE.

CONCLUSION

While coexistence of TLE and IGE is a rare phenomenon, this could be explained by shared genetic variations.

PubMed and beyond: biomedical literature search in the age of artificial intelligence

Biomedical research yields vast information, much of which is only accessible through the literature. Consequently, literature search is crucial for healthcare and biomedicine. Recent improvements in artificial intelligence (AI) have expanded functionality beyond keywords, but they might be unfamiliar to clinicians and researchers. In response, we present an overview of over 30 literature search tools tailored to common biomedical use cases, aiming at helping readers efficiently fulfill their information needs. We first discuss recent improvements and continued challenges of the widely used PubMed. Then, we describe AI-based literature search tools catering to five specific information needs: 1. Evidence-based medicine. 2. Precision medicine and genomics. 3. Searching by meaning, including questions. 4. Finding related articles with literature recommendation. 5. Discovering hidden associations through literature mining. Finally, we discuss the impacts of recent developments of large language models such as ChatGPT on biomedical information seeking.

IDPpub: Illuminating the Dark Phosphoproteome Through PubMed Mining

Global phosphoproteomics experiments quantify tens of thousands of phosphorylation sites. However, data interpretation is hampered by our limited knowledge on functions, biological contexts, or precipitating enzymes of the phosphosites. This study establishes a repository of phosphosites with associated evidence in biomedical abstracts, using deep learning-based natural language processing techniques. Our model for illuminating the dark phosphoproteome through PubMed mining (IDPpub) was generated by fine-tuning BioBERT, a deep learning tool for biomedical text mining. Trained using sentences containing protein substrates and phosphorylation site positions from 3000 abstracts, the IDPpub model was then used to extract phosphorylation sites from all MEDLINE abstracts. The extracted proteins were normalized to gene symbols using the National Center for Biotechnology Information gene query, and sites were mapped to human UniProt sequences using ProtMapper and mouse UniProt sequences by direct match. Precision and recall were calculated using 150 curated abstracts, and utility was assessed by analyzing the CPTAC (Clinical Proteomics Tumor Analysis Consortium) pan-cancer phosphoproteomics datasets and the PhosphoSitePlus database. Using 10-fold cross validation, pairs of correct substrates and phosphosite positions were extracted with an average precision of 0.93 and recall of 0.94. After entity normalization and site mapping to human reference sequences, an independent validation achieved a precision of 0.91 and recall of 0.77. The IDPpub repository contains 18,458 unique human phosphorylation sites with evidence sentences from 58,227 abstracts and 5918 mouse sites in 14,610 abstracts. This included evidence sentences for 1803 sites identified in CPTAC studies that are not covered by manually curated functional information in PhosphoSitePlus. Evaluation results demonstrate the potential of IDPpub as an effective biomedical text mining tool for collecting phosphosites. Moreover, the repository (http://idppub.ptmax.org), which can be automatically updated, can serve as a powerful complement to existing resources.

An in silico toolbox for the prediction of the potential pathogenic effects of missense mutations in the dimeric region of hRPE65

hRPE65 is a fundamental enzyme of the retinoid visual cycle, and many missense mutations affecting its expression or function are associated with a wide range of diseases. Many hRPE65 missense mutations lack a clear pathogenicity classification or are labelled as VUS. In this context, we recently developed a protocol based on µs-long molecular dynamics simulations to study the potential pathogenic effect of hRPE65 missense mutations. In the present work, the structure-based protocol was integrated with a hRPE65-tailored consensus bioinformatics strategy, named ConPath, that showed high performance in predicting known pathogenic/benign hRPE65 missense mutations. The combined strategy was used to perform a multi-level evaluation of the potential pathogenicity of 13 different hRPE65 VUS, which were classified based on their likelihood of pathogenic effect. The obtained results provide information that may support the reclassification of these VUS and help clinicians evaluate the eligibility for gene therapy of patients diagnosed with such variants.

Protein-Protein Interaction Network Extraction Using Text Mining Methods Adds Insight into Autism Spectrum Disorder

Text mining methods are being developed to assimilate the volume of biomedical textual materials that are continually expanding. Understanding protein-protein interaction (PPI) deficits would assist in explaining the genesis of diseases. In this study, we designed an automated system to extract PPIs from the biomedical literature that uses a deep learning sentence classification model, a pretrained word embedding, and a BiLSTM recurrent neural network with additional layers, a conditional random field (CRF) named entity recognition (NER) model, and shortest-dependency path (SDP) model using the SpaCy library in Python. The automated system ensures that it targets sentences that contain PPIs and not just these proteins mentioned in the framework of disease discovery or other context. Our first model achieved 13% greater precision on the Aimed/BioInfr benchmark corpus than the previous state-of-the-art BiLSTM neural network models. The NER model presented in this study achieved 98% precision on the Aimed/BioInfr corpus over previous models. In order to facilitate the production of an accurate representation of the PPI network, the processes were developed to systematically map the protein interactions in the texts. Overall, evaluating our system through the use of 6027 abstracts pertaining to seven proteins associated with Autism Spectrum Disorder completed the manually curated PPI network for these proteins. When it comes to complicated diseases, these networks would assist in understanding how PPI deficits contribute to disease development while also emphasizing the influence of interactions on protein function and biological processes.

Comprehensive analysis predicting effects of deleterious SNPs of human progesterone receptor gene on its structure and functions: a computational approach

Progesterone receptor plays a crucial role in the development of the mammary gland and breast cancer. Single nucleotide polymorphisms (SNPs) within its gene, PGR, are associated with the risk of miscarriages and preterm birth as well as many cancers across different populations. The main aim of this work is to investigate the most deleterious SNPs in the PGR gene to identify potential biomarkers for various disease susceptibility and treatments. Both sequence and structure-based computational approaches were adopted and in total 11 nsSNPs have been filtered out of 674 nsSNPs along with seven non-coding SNPs. R740Q, I744T and D746E belonged to a mutation cluster. R740Q, D746E along with S865L altered H-bond interactions within the receptor. The same mutations have been found to be associated with several cancers including uterine and breast cancer among others. It is, therefore, possible that the high-risk SNPs associated with cancers may exert their effect by causing changes in the protein structure, particularly in its bonding patterns, and thus affecting its function. In addition, seven non-coding SNPs that were located in the UTR region created a new miRNA site while three SNPs disrupted a conserved miRNA site. These high-risk SNPs can play an instrumental role in generating a dataset of the PGR gene's SNPs. Thus, the present study may pave the way to design and develop novel therapeutics for overcoming the challenges associated with certain cancers and pregnancy that result from a change in the protein structure and function due to the SNP mutations in the PGR gene.Communicated by Ramaswamy H. Sarma.

Challenges in periodic revision of genetic testing results: Comparison of the main classification guidelines and report of a retrospective analysis involving BRCA1/BRCA2 variants of uncertain significance

In the context of cancer predisposition syndromes, it is widely known that the correct interpretation of germline variants identified in multigene panel testing is essential for adequate genetic counseling and clinical decision making, in which variants of uncertain significance (VUS) are not considered actionable findings. Thus, their periodic re-evaluation using appropriate guidelines is notably important. In the present study, we compared the performance of the main variant classification guidelines (ACMG, Sherloc and ENIGMA) in variant reassessment, using as input a BRCA1/2 VUS case series (retrospective analysis) from Brazil, an ethnically diverse and admixed country with substantial challenges in VUS reclassification. As main findings, two of the 15 VUS analyzed were reclassified as likely pathogenic by the 3 guidelines, BRCA1 c.4987-3C > G (rs397509213) and BRCA2 c.7868A > G (rs80359012). Moreover, challenges in variant classification and reassessment are described and additional in silico data about structural impact of the variant BRCA2 c.7868A > G are provided. We hypothesize that the establishment of a framework to reassess VUS could improve this process in health centers that have not yet implemented this practice. Results of this study underscore that periodic monitoring of the functional, clinical, and bioinformatics data of a VUS by a multidisciplinary team are of utmost importance in clinical practice. When there is a specific guideline for a given gene, such as ENIGMA for BRCA1/2, it should be considered the first option for variant assessment. Finally, recruitment of VUS carriers and their relatives to participate in variant segregation studies and publication of VUS reclassification results in the international scientific literature should be encouraged.

Analysis of SLC26A4 Gene in Individuals with Non Syndromic Hearing Impairment in Relation with GJB2 Associated Mutations

Background

Hearing Loss (HL) is the most common sensory disorder. HL commonly ranges from mild to severe. Persons with HL face difficulty in hearing conversations or sounds through one ear or both ears, which impacts one's ability to interact with others. Hence it is a communicable disorder that makes people socially isolated, lonely, and frustrated. HL in children severely affects language development. The people who are referred to as 'Deaf' with very little or no hearing capabilities, are considered as having profound hearing loss. More than 124 genes are causative for Non-Syndromic HL (NSHL) with varying inheritance, among which the SLC26A4 mutations are the second commonest cause of hereditary HL across the globe.

Methods

Samples from 70 NSHL patients were analyzed through Next-Generation Sequencing (NGS) and generated five pathogenic variants [N246fs (rs918684449), K564fs (rs746427774), F122fs, V239D (rs111033256), T721M (rs121908363)] each with frequency of 1.42%. Three missense variants [S399P (rs747431002), L597S (rs55638457), and G6V (rs111033423)] were reported under the "uncertain" category. All the collected samples were further genotyped to look for the possibility of having GJB2 and HL-associated mutations.

Results

Out of five SLC26A4 pathogenic mutations N246fs (rs918684449) and K564fs (rs746427774) were observed in samples which were positive for GJB2-HL associated candidate mutations [W24X (rs104894396), Q124X (rs397516874) and W77X (rs80338944)]. Similarly, pathogenic variants F122fs, V239D (rs111033256) and T721M (rs121908363) were observed in patient samples which were negative for GJB2-HL associated mutations.

Conclusion

Our data will expand the list of variants underlying NSHL and encourage further genotype SLC26A4 gene concerning the south Indian population with a large sample size.

The genetic link between systemic autoimmune disorders and temporal lobe epilepsy: A bioinformatics study

OBJECTIVE

We aimed to explore the underlying pathomechanisms of the comorbidity between three common systemic autoimmune disorders (SADs) [i.e., insulin-dependent diabetes mellitus (IDDM), systemic lupus erythematosus (SLE), and rheumatoid arthritis (RA)] and temporal lobe epilepsy (TLE), using bioinformatics tools. We hypothesized that there are shared genetic variations among these four conditions.

METHODS

Different databases (DisGeNET, Harmonizome, and Enrichr) were searched to find TLE-associated genes with variants; their single nucleotide polymorphisms (SNPs) were gathered from the literature. We also did a separate literature search using PubMed with the following keywords for original articles: "TLE" or "Temporal lobe epilepsy" AND "genetic variation," "single nucleotide polymorphism," "SNP," or "genetic polymorphism." In the next step, the SNPs associated with TLE were searched in the LitVar database to find the shared gene variations with RA, SLE, and IDDM.

RESULTS

Ninety unique SNPs were identified to be associated with TLE. LitVar search identified two SNPs that were shared between TLE and all three SADs (i.e., IDDM, SLE, and RA). The first SNP was rs16944 on the Interleukin-1β (IL-1β) gene. The second genetic variation was ε4 variation of apolipoprotein E (APOE) gene.

SIGNIFICANCE

The shared genetic variations (i.e., rs16944 on the IL-1β gene and ε4 variation of the APOE gene) may explain the underlying pathomechanisms of the comorbidity between three common SADs (i.e., IDDM, SLE, and RA) and TLE. Exploring such shared genetic variations may help find targeted therapies for patients with TLE, especially those with drug-resistant seizures who also have comorbid SADs.

A Boolean algebra for genetic variants

MOTIVATION

Beyond identifying genetic variants, we introduce a set of Boolean relations, which allows for a comprehensive classification of the relations of every pair of variants by taking all minimal alignments into account. We present an efficient algorithm to compute these relations, including a novel way of efficiently computing all minimal alignments within the best theoretical complexity bounds.

RESULTS

We show that these relations are common, and many non-trivial, for variants of the CFTR gene in dbSNP. Ultimately, we present an approach for the storing and indexing of variants in the context of a database that enables efficient querying for all these relations.

AVAILABILITY AND IMPLEMENTATION

A Python implementation is available at https://github.com/mutalyzer/algebra/tree/v0.2.0 as well as an interface at https://mutalyzer.nl/algebra.

DRDs and Brain-Derived Neurotrophic Factor Share a Common Therapeutic Ground: A Novel Bioinformatic Approach Sheds New Light Toward Pharmacological Treatment of Cognitive and Behavioral Disorders

Cognitive and behavioral disorders are subgroups of mental health disorders. Both cognitive and behavioral disorders can occur in people of different ages, genders, and social backgrounds, and they can cause serious physical, mental, or social problems. The risk factors for these diseases are numerous, with a range from genetic and epigenetic factors to physical factors. In most cases, the appearance of such a disorder in an individual is a combination of his genetic profile and environmental stimuli. To date, researchers have not been able to identify the specific causes of these disorders, and as such, there is urgent need for innovative study approaches. The aim of the present study was to identify the genetic factors which seem to be more directly responsible for the occurrence of a cognitive and/or behavioral disorder. More specifically, through bioinformatics tools and software as well as analytical methods such as systemic data and text mining, semantic analysis, and scoring functions, we extracted the most relevant single nucleotide polymorphisms (SNPs) and genes connected to these disorders. All the extracted SNPs were filtered, annotated, classified, and evaluated in order to create the "genomic grammar" of these diseases. The identified SNPs guided the search for top suspected genetic factors, dopamine receptors D and neurotrophic factor BDNF, for which regulatory networks were built. The identification of the "genomic grammar" and underlying factors connected to cognitive and behavioral disorders can aid in the successful disease profiling and the establishment of novel pharmacological targets and provide the basis for personalized medicine, which takes into account the patient's genetic background as well as epigenetic factors.

Semantic and Population Analysis of the Genetic Targets Related to COVID-19 and Its Association with Genes and Diseases

SARS-CoV-2 is a coronavirus responsible for one of the most serious, modern worldwide pandemics, with lasting and multifaceted effects. By late 2021, SARS-CoV-2 has infected more than 180 million people and has killed more than 3 million. The virus gains entrance to human cells through binding to ACE2 via its surface spike protein and causes a complex disease of the respiratory system, termed COVID-19. Vaccination efforts are being made to hinder the viral spread, and therapeutics are currently under development. Toward this goal, scientific attention is shifting toward variants and SNPs that affect factors of the disease such as susceptibility and severity. This genomic grammar, tightly related to the dark part of our genome, can be explored through the use of modern methods such as natural language processing. We present a semantic analysis of SARS-CoV-2-related publications, which yielded a repertoire of SNPs, genes, and disease ontologies. Population data from the 1000 Genomes Project were subsequently integrated into the pipeline. Data mining approaches of this scale have the potential to elucidate the complex interaction between COVID-19 pathogenesis and host genetic variation; the resulting knowledge can facilitate the management of high-risk groups and aid the efforts toward precision medicine.

A Genomic Study of the Japanese Population Focusing on the Glucocorticoid Receptor Interactome Highlights Distinct Genetic Characteristics Associated with Stress Response

All living organisms have been programmed to maintain a complex inner equilibrium called homeostasis, despite numerous adversities during their lifespan. Any threatening or perceived as such stimuli for homeostasis is termed a stressor, and a highly conserved response system called the stress response system has been developed to cope with these stimuli and maintain or reinstate homeostasis. The glucocorticoid receptor, a transcription factor belonging to the nuclear receptors protein superfamily, has a major role in the stress response system, and research on its interactome may provide novel information regarding the mechanisms underlying homeostasis maintenance. A list of 149 autosomal genes that have an essential role in GR function or are prime examples of GRE-containing genes was composed in order to gain a comprehensive view of the GR interactome. A search for SNPs on those particular genes was conducted on a dataset of 3554 Japanese individuals, with mentioned polymorphisms being annotated with relevant information from the ClinVar, LitVar, and dbSNP databases. Forty-two SNPs of interest and their genomic locations were identified. These SNPs have been associated with drug metabolism and neuropsychiatric, metabolic, and immune system disorders, while most of them were located in intronic regions. The frequencies of those SNPs were later compared with a dataset consisting of 1465 Korean individuals in order to find population-specific characteristics based on some of the identified SNPs of interest. The results highlighted.that rs1043618 frequencies were different in the two populations, with mentioned polymorphism having a potential role in chronic obstructive pulmonary disease in response to environmental stressors. This SNP is located in the HSPA1A gene, which codes for an essential GR co-chaperone, and such information showcases that similar gene may be novel genomic targets for managing or combatting stress-related pathologies.

TogoVar: A comprehensive Japanese genetic variation database

TogoVar ( https://togovar.org ) is a database that integrates allele frequencies derived from Japanese populations and provides annotations for variant interpretation. First, a scheme to reanalyze individual-level genome sequence data deposited in the Japanese Genotype-phenotype Archive (JGA), a controlled-access database, was established to make allele frequencies publicly available. As more Japanese individual-level genome sequence data are deposited in JGA, the sample size employed in TogoVar is expected to increase, contributing to genetic study as reference data for Japanese populations. Second, public datasets of Japanese and non-Japanese populations were integrated into TogoVar to easily compare allele frequencies in Japanese and other populations. Each variant detected in Japanese populations was assigned a TogoVar ID as a permanent identifier. Third, these variants were annotated with molecular consequence, pathogenicity, and literature information for interpreting and prioritizing variants. Here, we introduce the newly developed TogoVar database that compares allele frequencies among Japanese and non-Japanese populations and describes the integrated annotations.

Semantic Interpretation for Convolutional Neural Networks: What Makes a Cat a Cat?

The interpretability of deep neural networks has attracted increasing attention in recent years, and several methods have been created to interpret the "black box" model. Fundamental limitations remain, however, that impede the pace of understanding the networks, especially the extraction of understandable semantic space. In this work, the framework of semantic explainable artificial intelligence (S-XAI) is introduced, which utilizes a sample compression method based on the distinctive row-centered principal component analysis (PCA) that is different from the conventional column-centered PCA to obtain common traits of samples from the convolutional neural network (CNN), and extracts understandable semantic spaces on the basis of discovered semantically sensitive neurons and visualization techniques. Statistical interpretation of the semantic space is also provided, and the concept of semantic probability is proposed. The experimental results demonstrate that S-XAI is effective in providing a semantic interpretation for the CNN, and offers broad usage, including trustworthiness assessment and semantic sample searching.

Data-driven support to decision-making in molecular tumour boards for lymphoma: A design science approach

Background

The increasing amount of molecular data and knowledge about genomic alterations from next-generation sequencing processes together allow for a greater understanding of individual patients, thereby advancing precision medicine. Molecular tumour boards feature multidisciplinary teams of clinical experts who meet to discuss complex individual cancer cases. Preparing the meetings is a manual and time-consuming process.

Purpose

To design a clinical decision support system to improve the multimodal data interpretation in molecular tumour board meetings for lymphoma patients at Karolinska University Hospital, Stockholm, Sweden. We investigated user needs and system requirements, explored the employment of artificial intelligence, and evaluated the proposed design with primary stakeholders.

Methods

Design science methodology was used to form and evaluate the proposed artefact. Requirements elicitation was done through a scoping review followed by five semi-structured interviews. We used UML Use Case diagrams to model user interaction and UML Activity diagrams to inform the proposed flow of control in the system. Additionally, we modelled the current and future workflow for MTB meetings and its proposed machine learning pipeline. Interactive sessions with end-users validated the initial requirements based on a fictive patient scenario which helped further refine the system.

Results

The analysis showed that an interactive secure Web-based information system supporting the preparation of the meeting, multidisciplinary discussions, and clinical decision-making could address the identified requirements. Integrating artificial intelligence via continual learning and multimodal data fusion were identified as crucial elements that could provide accurate diagnosis and treatment recommendations.

Impact

Our work is of methodological importance in that using artificial intelligence for molecular tumour boards is novel. We provide a consolidated proof-of-concept system that could support the end-to-end clinical decision-making process and positively and immediately impact patients.

Conclusion

Augmenting a digital decision support system for molecular tumour boards with retrospective patient material is promising. This generates realistic and constructive material for human learning, and also digital data for continual learning by data-driven artificial intelligence approaches. The latter makes the future system adaptable to human bias, improving adequacy and decision quality over time and over tasks, while building and maintaining a digital log.

ViMRT: a text-mining tool and search engine for automated virus mutation recognition

MOTIVATION

Virus mutation is one of the most important research issues which plays a critical role in disease progression and has prompted substantial scientific publications. Mutation extraction from published literature has become an increasingly important task, benefiting many downstream applications such as vaccine design and drug usage. However, most existing approaches have low performances in extracting virus mutation due to both lack of precise virus mutation information and their development based on human gene mutations.

RESULTS

We developed ViMRT, a text-mining tool and search engine for automated virus mutation recognition using natural language processing. ViMRT mainly developed 8 optimized rules and 12 regular expressions based on a development dataset comprising 830 papers of 5 human severe disease-related viruses. It achieved higher performance than other tools in a test dataset (1662 papers, 99.17% in F1-score) and has been applied well to two other viruses, influenza virus and severe acute respiratory syndrome coronavirus-2 (212 papers, 96.99% in F1-score). These results indicate that ViMRT is a high-performance method for the extraction of virus mutation from the biomedical literature. Besides, we present a search engine for researchers to quickly find and accurately search virus mutation-related information including virus genes and related diseases.

AVAILABILITY AND IMPLEMENTATION

ViMRT software is freely available at http://bmtongji.cn:1225/mutation/index.

Autophagopathies: from autophagy gene polymorphisms to precision medicine for human diseases

At a time when complex diseases affect globally 280 million people and claim 14 million lives every year, there is an urgent need to rapidly increase our knowledge into their underlying etiologies. Though critical in identifying the people at risk, the causal environmental factors (microbiome and/or pollutants) and the affected pathophysiological mechanisms are not well understood. Herein, we consider the variations of autophagy-related (ATG) genes at the heart of mechanisms of increased susceptibility to environmental stress. A comprehensive autophagy genomic resource is presented with 263 single nucleotide polymorphisms (SNPs) for 69 autophagy-related genes associated with 117 autoimmune, inflammatory, infectious, cardiovascular, neurological, respiratory, and endocrine diseases. We thus propose the term 'autophagopathies' to group together a class of complex human diseases the etiology of which lies in a genetic defect of the autophagy machinery, whether directly related or not to an abnormal flux in autophagy, LC3-associated phagocytosis, or any associated trafficking. The future of precision medicine for common diseases will lie in our ability to exploit these ATG SNP x environment relationships to develop new polygenetic risk scores, new management guidelines, and optimal therapies for afflicted patients.Abbreviations: ATG, autophagy-related; ALS-FTD, amyotrophic lateral sclerosis-frontotemporal dementia; ccRCC, clear cell renal cell carcinoma; CD, Crohn disease; COPD, chronic obstructive pulmonary disease; eQTL, expression quantitative trait loci; HCC, hepatocellular carcinoma; HNSCC, head and neck squamous cell carcinoma; GTEx, genotype-tissue expression; GWAS, genome-wide association studies; LAP, LC3-associated phagocytosis; LC3-II, phosphatidylethanolamine conjugated form of LC3; LD, linkage disequilibrium; LUAD, lung adenocarcinoma; MAF, minor allele frequency; MAP1LC3/LC3: microtubule associated protein 1 light chain 3; NSCLC, non-small cell lung cancer; OS, overall survival; PtdIns3K CIII, class III phosphatidylinositol 3 kinase; PtdIns3P, phosphatidylinositol-3-phosphate; SLE, systemic lupus erythematosus; SNPs, single-nucleotide polymorphisms; mQTL, methylation quantitative trait loci; ULK, unc-51 like autophagy activating kinase; UTRs, untranslated regions; WHO, World Health Organization.

Assigning species information to corresponding genes by a sequence labeling framework

The automatic assignment of species information to the corresponding genes in a research article is a critically important step in the gene normalization task, whereby a gene mention is normalized and linked to a database record or an identifier by a text-mining algorithm. Existing methods typically rely on heuristic rules based on gene and species co-occurrence in the article, but their accuracy is suboptimal. We therefore developed a high-performance method, using a novel deep learning-based framework, to identify whether there is a relation between a gene and a species. Instead of the traditional binary classification framework in which all possible pairs of genes and species in the same article are evaluated, we treat the problem as a sequence labeling task such that only a fraction of the pairs needs to be considered. Our benchmarking results show that our approach obtains significantly higher performance compared to that of the rule-based baseline method for the species assignment task (from 65.8-81.3% in accuracy). The source code and data for species assignment are freely available. Database URL https://github.com/ncbi/SpeciesAssignment.

Alanyl-tRNA Synthetase 1 Gene Variants in Hereditary Neuropathy

Background and Objectives

Our objective was to report 2 novel variants and to reclassify previously reported alanyl-tRNA synthetase 1 (AARS1) variants associated with hereditary neuropathy and to summarize the clinical features of a previously published cohort of patients.

Methods

We performed detailed neurologic and electrophysiologic assessments and segregation analysis of 2 unrelated families with Charcot-Marie-Tooth (CMT) disease with novel variants in the AARS1 gene. Via literature search, we found studies that included neuropathy cases with AARS1 variants; we then reviewed and reclassified these variants.

Results

We identified 2 CMT families harboring previously unreported likely pathogenic AARS1 variants: c.1823C>A p.(Thr608Lys) and c.1815C>G p.(His605Gln). In addition, we reinterpreted a total of 35 different AARS1 variants reported in cases with neuropathy from the literature: 9 variants fulfilled the current criteria for being (likely) pathogenic. We compiled and summarized standardized clinical and genotypic information for 90 affected individuals from 32 families with (likely) pathogenic AARS1 variants. Most experienced motor weakness and sensory loss in the lower limbs.

Discussion

In total, 11 AARS1 variants can currently be classified as pathogenic or likely pathogenic and are associated with sensorimotor axonal or intermediate, slowly progressive polyneuropathy with common asymmetry and variable age of symptom onset with no apparent involvement of other organ systems.

In silico analysis of upstream variants in Brazilian patients with Familial Hypercholesterolemia

Familial hypercholesterolemia (FH) is a prevalent autosomal genetic disease associated with increased risk of early cardiovascular events and death due to chronic exposure to very high levels of low-density lipoprotein cholesterol (LDL-c). Pathogenic variants in the coding regions of LDLR, APOB and PCSK9 account for most FH cases, and variants in non-coding regions maybe involved in FH as well. Variants in the upstream region of LDLR, APOB and PCSK9 were screened by targeted next-generation sequencing and their effects were explored using in silico tools. Twenty-five patients without pathogenic variants in FH-related genes were selected. 3 kb upstream regions of LDLR, APOB and PCSK9 were sequenced using the AmpliSeq (Illumina) and Miseq Reagent Nano Kit v2 (Illumina). Sequencing data were analyzed using variant discovery and functional annotation tools. Potentially regulatory variants were selected by integrating data from public databases, published data and context-dependent regulatory prediction score. Thirty-four single nucleotide variants (SNVs) in upstream regions were identified (6 in LDLR, 15 in APOB, and 13 in PCSK9). Five SNVs were prioritized as potentially regulatory variants (rs934197, rs9282606, rs36218923, rs538300761, g.55038486A>G). APOB rs934197 was previously associated with increased rate of transcription, which in silico analysis suggests that could be due to reducing binding affinity of a transcriptional repressor. Our findings highlight the importance of variant screening outside of coding regions of all relevant genes. Further functional studies are necessary to confirm that prioritized variants could impact gene regulation and contribute to the FH phenotype.

BioRED: a rich biomedical relation extraction dataset

Automated relation extraction (RE) from biomedical literature is critical for many downstream text mining applications in both research and real-world settings. However, most existing benchmarking datasets for biomedical RE only focus on relations of a single type (e.g. protein-protein interactions) at the sentence level, greatly limiting the development of RE systems in biomedicine. In this work, we first review commonly used named entity recognition (NER) and RE datasets. Then, we present a first-of-its-kind biomedical relation extraction dataset (BioRED) with multiple entity types (e.g. gene/protein, disease, chemical) and relation pairs (e.g. gene-disease; chemical-chemical) at the document level, on a set of 600 PubMed abstracts. Furthermore, we label each relation as describing either a novel finding or previously known background knowledge, enabling automated algorithms to differentiate between novel and background information. We assess the utility of BioRED by benchmarking several existing state-of-the-art methods, including Bidirectional Encoder Representations from Transformers (BERT)-based models, on the NER and RE tasks. Our results show that while existing approaches can reach high performance on the NER task (F-score of 89.3%), there is much room for improvement for the RE task, especially when extracting novel relations (F-score of 47.7%). Our experiments also demonstrate that such a rich dataset can successfully facilitate the development of more accurate, efficient and robust RE systems for biomedicine. Availability: The BioRED dataset and annotation guidelines are freely available at https://ftp.ncbi.nlm.nih.gov/pub/lu/BioRED/.

tmVar 3.0: an improved variant concept recognition and normalization tool

MOTIVATION

Previous studies have shown that automated text-mining tools are becoming increasingly important for successfully unlocking variant information in scientific literature at large scale. Despite multiple attempts in the past, existing tools are still of limited recognition scope and precision.

RESULT

We propose tmVar 3.0: an improved variant recognition and normalization system. Compared to its predecessors, tmVar 3.0 recognizes a wider spectrum of variant-related entities (e.g. allele and copy number variants), and groups together different variant mentions belonging to the same genomic sequence position in an article for improved accuracy. Moreover, tmVar 3.0 provides advanced variant normalization options such as allele-specific identifiers from the ClinGen Allele Registry. tmVar 3.0 exhibits state-of-the-art performance with over 90% in F-measure for variant recognition and normalization, when evaluated on three independent benchmarking datasets. tmVar 3.0 as well as annotations for the entire PubMed and PMC datasets are freely available for download.

AVAILABILITY AND IMPLEMENTATION

https://github.com/ncbi/tmVar3.

OncoPubMiner: a platform for mining oncology publications

Updated and expert-quality knowledge bases are fundamental to biomedical research. A knowledge base established with human participation and subject to multiple inspections is needed to support clinical decision making, especially in the growing field of precision oncology. The number of original publications in this field has risen dramatically with the advances in technology and the evolution of in-depth research. Consequently, the issue of how to gather and mine these articles accurately and efficiently now requires close consideration. In this study, we present OncoPubMiner (https://oncopubminer.chosenmedinfo.com), a free and powerful system that combines text mining, data structure customisation, publication search with online reading and project-centred and team-based data collection to form a one-stop 'keyword in-knowledge out' oncology publication mining platform. The platform was constructed by integrating all open-access abstracts from PubMed and full-text articles from PubMed Central, and it is updated daily. OncoPubMiner makes obtaining precision oncology knowledge from scientific articles straightforward and will assist researchers in efficiently developing structured knowledge base systems and bring us closer to achieving precision oncology goals.

SNPMap—An integrated visual SNP interpretation tool

New technologies, such as next-generation sequencing, have advanced the ability to diagnose diseases and improve prognosis but require the identification of thousands of variants in each report based on several databases scattered across places. Curating an integrated interpretation database is time-consuming, costly, and needs regular update. On the other hand, the automatic curation of knowledge sources always results in overloaded information. In this study, an automated pipeline was proposed to create an integrated visual single-nucleotide polymorphism (SNP) interpretation tool called SNPMap. SNPMap pipelines periodically obtained SNP-related information from LitVar, PubTator, and GWAS Catalog API tools and presented it to the user after extraction, integration, and visualization. Keywords and their semantic relations to each SNP are rendered into two graphs, with their significance represented by the size/width of circles/lines. Moreover, the most related SNPs for each keyword that appeared in SNPMap were calculated and sorted. SNPMap retains the advantage of an automatic process while assisting users in accessing more lucid and detailed information through visualization and integration with other materials.

Measuring competitive exclusion in non-small cell lung cancer

In this study, we experimentally measure the frequency-dependent interactions between a gefitinib-resistant non-small cell lung cancer population and its sensitive ancestor via the evolutionary game assay. We show that cost of resistance is insufficient to accurately predict competitive exclusion and that frequency-dependent growth rate measurements are required. Using frequency-dependent growth rate data, we then show that gefitinib treatment results in competitive exclusion of the ancestor, while the absence of treatment results in a likely, but not guaranteed, exclusion of the resistant strain. Then, using simulations, we demonstrate that incorporating ecological growth effects can influence the predicted extinction time. In addition, we show that higher drug concentrations may not lead to the optimal reduction in tumor burden. Together, these results highlight the potential importance of frequency-dependent growth rate data for understanding competing populations, both in the laboratory and as we translate adaptive therapy regimens to the clinic.

pubmedKB: an interactive web server for exploring biomedical entity relations in the biomedical literature

With the proliferation of genomic sequence data for biomedical research, the exploration of human genetic information by domain experts requires a comprehensive interrogation of large numbers of scientific publications in PubMed. However, a query in PubMed essentially provides search results sorted only by the date of publication. A search engine for retrieving and interpreting complex relations between biomedical concepts in scientific publications remains lacking. Here, we present pubmedKB, a web server designed to extract and visualize semantic relationships between four biomedical entity types: variants, genes, diseases, and chemicals. pubmedKB uses state-of-the-art natural language processing techniques to extract semantic relations from the large number of PubMed abstracts. Currently, over 2 million semantic relations between biomedical entity pairs are extracted from over 33 million PubMed abstracts in pubmedKB. pubmedKB has a user-friendly interface with an interactive semantic graph, enabling the user to easily query entities and explore entity relations. Supporting sentences with the highlighted snippets allow to easily navigate the publications. Combined with a new explorative approach to literature mining and an interactive interface for researchers, pubmedKB thus enables rapid, intelligent searching of the large biomedical literature to provide useful knowledge and insights. pubmedKB is available at https://www.pubmedkb.cc/.

Causative Variants for Inherited Cardiac Conditions in a Southeast Asian Population Cohort

Background:

Variable penetrance and late-onset phenotypes are key challenges for classifying causal as well as incidental findings in inherited cardiac conditions. Allele frequencies of variants in ancestry-specific populations, along with clinical variant analysis and interpretation, are critical to determine their true significance.

Methods:

Here, we carefully reviewed and classified variants in genes associated with inherited cardiac conditions based on a population whole-genome sequencing cohort of 4810 Singaporeans representing Southeast Asian ancestries.

Results:

Eighty-nine (1.85%) individuals carried either pathogenic or likely pathogenic variants across 25 genes. Forty-six (51.7%) had variants in causal genes for familial hyperlipidemia, but there were also recurrent variants in SCN5A and MYBPC3 , causal genes for inherited arrhythmia and cardiomyopathy, which, despite previous reports, we determined to lack criteria for pathogenicity.

Conclusions:

Our findings highlight the incidence of disease-related variants in inherited cardiac conditions and emphasize the value of large-scale sequencing in specific ancestries. Follow-up detailed phenotyping and analysis of pedigrees are crucial because assigning pathogenicity will significantly affect clinical management for individuals and their family members.

Re-evaluation of Missense Variant Classifications in NF2

Missense variants in the NF2 gene result in variable NF2 disease presentation. Clinical classification of missense variants often represents a challenge, due to lack of evidence for pathogenicity and function. This study provides a summary of NF2 missense variants, with variant classifications based on currently available evidence. NF2 missense variants were collated from pathology‐associated databases and existing literature. Association for Clinical Genomic Sciences Best Practice Guidelines (2020) were followed in the application of evidence for variant interpretation and classification. The majority of NF2 missense variants remain classified as variants of uncertain significance. However, NF2 missense variants identified in gnomAD occurred at a consistent rate across the gene, while variants compiled from pathology‐associated databases displayed differing rates of variation by exon of NF2. The highest rate of NF2 disease‐associated variants was observed in exon 7, while lower rates were observed toward the C‐terminus of the NF2 protein, merlin. Further phenotypic information associated with variants, alongside variant‐specific functional analysis, is necessary for more definitive variant interpretation. Our data identified differences in frequency of NF2 missense variants by exon between gnomAD population data and NF2 disease‐associated variants, suggesting a potential genotype‐phenotype correlation; further work is necessary to substantiate this.

Variomes: a high recall search engine to support the curation of genomic variants

Motivation

Identification and interpretation of clinically actionable variants is a critical bottleneck. Searching for evidence in the literature is mandatory according to ASCO/AMP/CAP practice guidelines; however, it is both labor-intensive and error-prone. We developed a system to perform triage of publications relevant to support an evidence-based decision. The system is also able to prioritize variants. Our system searches within pre-annotated collections such as MEDLINE and PubMed Central.

Results

We assess the search effectiveness of the system using three different experimental settings: literature triage; variant prioritization and comparison of Variomes with LitVar. Almost two-thirds of the publications returned in the top-5 are relevant for clinical decision-support. Our approach enabled identifying 81.8% of clinically actionable variants in the top-3. Variomes retrieves on average +21.3% more articles than LitVar and returns the same number of results or more results than LitVar for 90% of the queries when tested on a set of 803 queries; thus, establishing a new baseline for searching the literature about variants.

Availability and implementation

Variomes is publicly available at https://candy.hesge.ch/Variomes. Source code is freely available at https://github.com/variomes/sibtm-variomes. SynVar is publicly available at https://goldorak.hesge.ch/synvar.

Supplementary information

Supplementary data are available at Bioinformatics online.

A neurodegenerative disease landscape of rare mutations in Colombia due to founder effects

BACKGROUND

The Colombian population, as well as those in other Latin American regions, arose from a recent tri-continental admixture among Native Americans, Spanish invaders, and enslaved Africans, all of whom passed through a population bottleneck due to widespread infectious diseases that left small isolated local settlements. As a result, the current population reflects multiple founder effects derived from diverse ancestries.

METHODS

We characterized the role of admixture and founder effects on the origination of the mutational landscape that led to neurodegenerative disorders under these historical circumstances. Genomes from 900 Colombian individuals with Alzheimer's disease (AD) [n = 376], frontotemporal lobar degeneration-motor neuron disease continuum (FTLD-MND) [n = 197], early-onset dementia not otherwise specified (EOD) [n = 73], and healthy participants [n = 254] were analyzed. We examined their global and local ancestry proportions and screened this cohort for deleterious variants in disease-causing and risk-conferring genes.

RESULTS

We identified 21 pathogenic variants in AD-FTLD related genes, and PSEN1 harbored the majority (11 pathogenic variants). Variants were identified from all three continental ancestries. TREM2 heterozygous and homozygous variants were the most common among AD risk genes (102 carriers), a point of interest because the disease risk conferred by these variants differed according to ancestry. Several gene variants that have a known association with MND in European populations had FTLD phenotypes on a Native American haplotype. Consistent with founder effects, identity by descent among carriers of the same variant was frequent.

CONCLUSIONS

Colombian demography with multiple mini-bottlenecks probably enhanced the detection of founder events and left a proportionally higher frequency of rare variants derived from the ancestral populations. These findings demonstrate the role of genomically defined ancestry in phenotypic disease expression, a phenotypic range of different rare mutations in the same gene, and further emphasize the importance of inclusiveness in genetic studies.

Controversial Trials First: Identifying Disagreement Between Clinical Guidelines and New Evidence

Clinical guidelines integrate latest evidence to support clinical decision-making. As new research findings are published at an increasing rate, it would be helpful to detect when such results disagree with current guideline recommendations. In this work, we describe a software system for the automatic identification of disagreement between clinical guidelines and published research. A critical feature of the system is the extraction and cross-lingual normalization of information through natural language processing. The initial version focuses on the detection of cancer treatments in clinical trial reports that are not addressed in oncology guidelines. We evaluate the relevance of trials retrieved by our system retrospectively by comparison with historic guideline updates and also prospectively through manual evaluation by guideline experts. The system improves precision over state-of-the-art literature research strategies while maintaining near-total recall. Detailed error analysis highlights challenges for fine-grained clinical information extraction, in particular when extracting population definitions for tumor-agnostic therapies.

Human intelectin-2 (ITLN2) is selectively expressed by secretory Paneth cells

Intelectins (intestinal lectins) are highly conserved across chordate evolution and have been implicated in various human diseases, including Crohn's disease (CD). The human genome encodes two intelectin genes, intelectin-1 (ITLN1) and intelectin-2 (ITLN2). Other than its high sequence similarity with ITLN1, little is known about ITLN2. To address this void in knowledge, we report that ITLN2 exhibits discrete, yet notable differences from ITLN1 in primary structure, including a unique amino terminus, as well as changes in amino acid residues associated with the glycan-binding activity of ITLN1. We identified that ITLN2 is a highly abundant Paneth cell-specific product, which localizes to secretory granules, and is expressed as a multimeric protein in the small intestine. In surgical specimens of ileal CD, ITLN2 mRNA levels were reduced approximately five-fold compared to control specimens. The ileal expression of ITLN2 was unaffected by previously reported disease-associated variants in ITLN2 and CD-associated variants in neighboring ITLN1 as well as NOD2 and ATG16L1. ITLN2 mRNA expression was undetectable in control colon tissue; however, in both ulcerative colitis (UC) and colonic CD, metaplastic Paneth cells were found to express ITLN2. Together, the data reported establish the groundwork for understanding ITLN2 function(s) in the intestine, including its possible role in CD.

AGER gene variant as a risk factor for juvenile idiopathic arthritis

BACKGROUND

The AGER gene encodes a cell surface multiligand receptor of advanced glycation end-products that is also capable of binding other molecules and is involved in numerous pathways related to inflammation, apoptosis, immunity and so on. In the present study, we aimed to investigate whether the AGER rs1035798 (G>A) intronic polymorphism, showing an association with multiple sclerosis and rheumatoid arthritis in adults, is related to juvenile idiopathic arthritis (JIA).

METHODS

Caucasian children from the Belarusian population were enrolled in the study. In total, there were 201 cases with JIA, 37 with juvenile systemic lupus erythematosus, 222 children with the articular syndrome of non-autoimmune etiology (positive control for JIA) and 365 negative controls (children without any autoimmune or inflammatory diseases). Genomic DNA samples from the patients and controls were genotyped by a real-time polymerase chain reaction.

RESULTS

A marked association of the homozygous AA rs1035798 genotype with JIA (p = 5 × 10^-4 ) was found. Allele A was also associated with JIA (p = 0.0058), as well as with the articular syndrome of non-autoimmune etiology (p = 0.0264). The highest frequencies of the AA genotype were found in the subgroups of JIA patients with polyarthritis or severe oligoarthritis. The AA genotype patients also had the smallest mean age of the JIA onset.

CONCLUSIONS

Our results demonstrate that the AGER rs1035798 AA genotype is a risk factor for JIA in Belarusian children. They also suggest a link between the AGER AA genotype and the risk of JIA early onset and severity. However, the functional relevance of the rs1035798 polymorphism is still unclear.

Epione application: An integrated web‑toolkit of clinical genomics and personalized medicine in systemic lupus erythematosus

Genome wide association studies (GWAS) have identified autoimmune disease-associated loci, a number of which are involved in numerous disease-associated pathways. However, much of the underlying genetic and pathophysiological mechanisms remain to be elucidated. Systemic lupus erythematosus (SLE) is a chronic, highly heterogeneous auto-immune disease, characterized by differences in autoantibody profile, serum cytokines and a multi-system involvement. This study presents the Epione application, an integrated bioinformatics web-toolkit, designed to assist medical experts and researchers in more accurately diagnosing SLE. The application aims to identify the most credible gene variants and single nucleotide polymorphisms (SNPs) associated with SLE susceptibility, by using patient's genomic data to aid the medical expert in SLE diagnosis. The application contains useful knowledge of >70,000 SLE-related publications that have been analyzed, using data mining and semantic techniques, towards extracting the SLE-related genes and the corresponding SNPs. Probable genes associated with the patient's genomic profile are visualized with several graphs, including chromosome ideograms, statistic bars and regulatory networks through data mining studies with relative publications, to obtain a representative number of the most credible candidate genes and biological pathways associated with the SLE. Furthermore, an evaluation study was performed on a patient diagnosed with SLE and is presented herein. Epione has also been expanded in family-related candidate patients to evaluate its predictive power. All the recognized gene variants that were previously considered to be associated with SLE were accurately identified in the output profile of the patient, and by comparing the results, novel findings have emerged. The Epione application may assist and facilitate in early stage diagnosis by using the patients' genomic profile to compare against the list of the most predictable candidate gene variants related to SLE. Its diagnosis-oriented output presents the user with a structured set of results on variant association, position in genome and links to specific bibliography and gene network associations. The overall aim of the present study was to provide a reliable tool for the most effective study of SLE. This novel and accessible webserver tool of SLE is available at http://geneticslab.aua.gr/epione/.

Knowledge bases and software support for variant interpretation in precision oncology

Precision oncology is a rapidly evolving interdisciplinary medical specialty. Comprehensive cancer panels are becoming increasingly available at pathology departments worldwide, creating the urgent need for scalable cancer variant annotation and molecularly informed treatment recommendations. A wealth of mainly academia-driven knowledge bases calls for software tools supporting the multi-step diagnostic process. We derive a comprehensive list of knowledge bases relevant for variant interpretation by a review of existing literature followed by a survey among medical experts from university hospitals in Germany. In addition, we review cancer variant interpretation tools, which integrate multiple knowledge bases. We categorize the knowledge bases along the diagnostic process in precision oncology and analyze programmatic access options as well as the integration of knowledge bases into software tools. The most commonly used knowledge bases provide good programmatic access options and have been integrated into a range of software tools. For the wider set of knowledge bases, access options vary across different parts of the diagnostic process. Programmatic access is limited for information regarding clinical classifications of variants and for therapy recommendations. The main issue for databases used for biological classification of pathogenic variants and pathway context information is the lack of standardized interfaces. There is no single cancer variant interpretation tool that integrates all identified knowledge bases. Specialized tools are available and need to be further developed for different steps in the diagnostic process.