Review of current methods, applications, and data management for the bioinformatics analysis of whole exome sequencing

PathVar: A Customisable NGS Variant Calling Algorithm Implicates Novel Candidate Genes and Pathways in Hemiplegic Migraine

The exponential growth of next-generation sequencing (NGS) data requires innovative bioinformatics approaches to unravel the genetic underpinnings of diseases. Hemiplegic migraine (HM), a debilitating neurological disorder with a genetic basis, is one such condition that warrants further investigation. Notably, the genetic heterogeneity of HM is underscored by the fact that approximately two-thirds of patients lack pathogenic variants in the known causal ion channel genes. In this context, we have developed PathVar, a novel bioinformatics algorithm that harnesses publicly available tools and software for pathogenic variant discovery in NGS data. PathVar integrates a suite of tools, including HaplotypeCaller from the Genome Analysis Toolkit (GATK) for variant calling, Variant Effect Predictor (VEP) and ANNOVAR for variant annotation, and TAPES for assigning the American College of Medical Genetics and Genomics (ACMG) pathogenicity labels. Applying PathVar to whole exome sequencing data from 184 HM patients, we detected 648 variants that are probably pathogenic in multiple patients. Moreover, we have identified several candidate genes for HM, many of which cluster around the Rho GTPases pathway. Future research can leverage PathVar to generate high quality, candidate pathogenic variants, which may enhance our understanding of HM and other complex diseases.

From Tradition to Innovation: Diverse Molecular Techniques in the Fight Against Infectious Diseases

Infectious diseases impose a significant burden on global health systems due to high morbidity and mortality rates. According to the World Health Organization, millions die from infectious diseases annually, often due to delays in accurate diagnosis. Traditional diagnostic methods in clinical microbiology, primarily culture-based techniques, are time-consuming and may fail with hard-to-culture pathogens. Molecular biology advancements, notably the polymerase chain reaction (PCR), have revolutionized infectious disease diagnostics by allowing rapid and sensitive detection of pathogens' genetic material. PCR has become the gold standard for many infections, particularly highlighted during the COVID-19 pandemic. Following PCR, next-generation sequencing (NGS) has emerged, enabling comprehensive genomic analysis of pathogens, thus facilitating the detection of new strains and antibiotic resistance tracking. Innovative approaches like CRISPR technology are also enhancing diagnostic precision by identifying specific DNA/RNA sequences. However, the implementation of these methods faces challenges, particularly in low- and middle-income countries due to infrastructural and financial constraints. This review will explore the role of molecular diagnostic methods in infectious disease diagnosis, comparing their advantages and limitations, with a focus on PCR and NGS technologies and their future potential.

Omics Biology in Diagnosis of Diseases: Towards Empowering Genomic Medicine from an Evolutionary Perspective

In this section, we reintroduce the original aims and scope of the Special Issue entitled "Omics Biology in Diagnosis of Diseases: Advances in Bioinformatics and Data Analyses", enabling readers to find an appropriate framing for the remainder of the present closing editorial [...].

Whole exome sequencing in energy deficiency inborn errors of metabolism: A systematic review

Broad biochemical complexity and frequent overlapping clinical symptoms of inborn errors of metabolism (IEM), especially in energy-deficient patients, make accurate diagnosis difficult. In recent years, whole exome sequencing (WES), a comprehensive protein coding genetic test, has been used to diagnose patients at the molecular level. This study aims to evaluate the potential of WES in diagnosing energy-deficient IEM patients with limited biochemical findings and to identify common symptoms patterns in reported cases. Articles were identified using a combination of search terms in online databases (Science Direct, PubMed Central and Wiley). English-language case reports citing WES in the diagnosis of energy-deficient IEM patients were reviewed. This systematic review was conducted and reported using the 'Preferred Reporting Items for Systematic Reviews and Meta-Analyses' checklist. The quality and risk of bias were assessed using Joanna Briggs Institute critical appraisal tool. A total of 37 studies comprising of 54 case reports were included in this review. The median age of the patients was 0.4 years, with 55.6% being male and 44.4% being female. A total of 33 mutant genes were reported and they related to either metabolism or mitochondrial function. WES was able to identify mutations in 53 of 54 cases reported. The diagnosis of energy-deficient IEM patients is crucial, particularly given the challenging range of diverse clinical symptoms they present. The high accuracy of the WES technique appears to improve the diagnostic process. Further research defining more detailed guidelines is needed to engage with this rare set of genetic diseases.

In silico bioprospecting of receptors associated with the mechanism of action of Rondonin, an antifungal peptide from spider Acanthoscurria rondoniae haemolymph

Multiple drug-resistant fungal species are associated with the development of diseases. Thus, more efficient drugs for the treatment of these aetiological agents are needed. Rondonin is a peptide isolated from the haemolymph of the spider Acanthoscurria rondoniae. Previous studies have shown that this peptide has antifungal activity against Candida sp. and Trichosporon sp. strains, acting on their genetic material. However, the molecular targets involved in its biological activity have not yet been described. Bioinformatics tools were used to determine the possible targets involved in the biological activity of Rondonin. The PharmMapper server was used to search for microorganismal targets of Rondonin. The PatchDock server was used to perform the molecular docking. UCSF Chimera software was used to evaluate these intermolecular interactions. In addition, the I-TASSER server was used to predict the target ligand sites. Then, these predictions were contrasted with the sites previously described in the literature. Molecular dynamics simulations were conducted for two promising complexes identified from the docking analysis. Rondonin demonstrated consistency with the ligand sites of the following targets: outer membrane proteins F (id: 1MPF) and A (id: 1QJP), which are responsible for facilitating the passage of small molecules through the plasma membrane; the subunit of the flavoprotein fumarate reductase (id: 1D4E), which is involved in the metabolism of nitrogenous bases; and the ATP-dependent Holliday DNA helicase junction (id: 1IN4), which is associated with histone proteins that package genetic material. Additionally, the molecular dynamics results indicated the stability of the interaction of Rondonin with 1MPF and 1IN4 during a 10 ns simulation. These interactions corroborate with previous in vitro studies on Rondonin, which acts on fungal genetic material without causing plasma membrane rupture. Therefore, the bioprospecting methods used in this research were considered satisfactory since they were consistent with previous results obtained via in vitro experimentation.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40203-024-00224-1.

Detection of common pathogenesis of rheumatoid arthritis and atherosclerosis via microarray data analysis

Despite extensive research reveal rheumatoid arthritis (RA) is related to atherosclerosis (AS), common pathogenesis between these two diseases still needs to be explored. In current study, we explored the common pathogenesis between rheumatoid arthritis (RA) and atherosclerosis (AS) by identifying 297 Differentially Expressed Genes (DEGs) associated with both diseases. Through KEGG and GO functional analysis, we highlighted the correlation of these DEGs with crucial biological processes such as the vesicle transport, immune system process, signaling receptor binding, chemokine signaling and many others. Employing Protein-Protein Interaction (PPI) network analysis, we elucidated the associations between DEGs, revealing three gene modules enriched in immune system process, vesicle, signaling receptor binding, Pertussis, and among others. Additionally, through CytoHubba analysis, we pinpointed 11 hub genes integral to intergrin-mediated signaling pathway, plasma membrane, phosphotyrosine binding, chemokine signaling pathway and so on. Further investigation via the TRRUST database identified two key Transcription Factors (TFs), SPI1 and RELA, closely linked with these hub genes, shedding light on their regulatory roles. Finally, leveraging the collective insights from hub genes and TFs, we proposed 10 potential drug candidates targeting the molecular mechanisms underlying RA and AS pathogenesis. Further investigation on xCell revealed that 14 types of cells were all different in both AS and RA. This study underscores the shared pathogenic mechanisms, pivotal genes, and potential therapeutic interventions bridging RA and AS, offering valuable insights for future research and clinical management strategies.

Multi‑dimensional analysis reveals NCKAP5L is a promising biomarker for the diagnosis and prognosis of human cancers, especially colorectal cancer

The Nck-associated protein 5-like (NCKAP5L) gene, also known as Cep169, is associated with certain cancers. However, the diagnosis and prognosis value of NCKAP5L in several types of human cancer, including colorectal cancer, is not fully understood. In the present study, a comprehensive pan-cancer analysis of NCKAP5L was performed using several approaches, including gene expression and alteration, protein phosphorylation, immune infiltration, survival prognosis analyses and gene enrichment using the following: The University of California Santa Cruz Genome Browser Human Dec. 2013 (GRCh38/hg38) Assembly, Tumor Immune Estimation Resource (version 2), Human Protein Atlas, Gene Expression Profiling Interactive Analysis (version 2), University of Alabama at Birmingham Cancer Data Analysis portal, the Kaplan-Meier Plotter, cBioportal, Search Tool for the Retrieval of Interacting Genes/Proteins, Jvenn and the Metascape server. The role of NCKAP5L in colorectal cancer was further assessed by reverse transcription-quantitative PCR. The results demonstrated that NCKAP5L was upregulated in the majority of cancer types, including colorectal cancer. The high expression of NCKAP5L was significantly correlated with patient survival prognosis and immune infiltration of cancer-associated fibroblasts in numerous types of cancer, including colorectal cancer. Furthermore, Gene Ontology analysis identified that NCKAP5L may serve an important role in metabolic and cellular processes in human cancers. In summary, the data from the present study demonstrate that NCKAP5L is a potential tumor biomarker for the diagnosis and prognosis of human cancers, especially colorectal cancer.

Biology-aware mutation-based deep learning for outcome prediction of cancer immunotherapy with immune checkpoint inhibitors

The response rate of cancer immune checkpoint inhibitors (ICI) varies among patients, making it challenging to pre-determine whether a particular patient will respond to immunotherapy. While gene mutation is critical to the treatment outcome, a framework capable of explicitly incorporating biology knowledge has yet to be established. Here we aim to propose and validate a mutation-based deep learning model for survival analysis on 1571 patients treated with ICI. Our model achieves an average concordance index of 0.59 ± 0.13 across nine types of cancer, compared to the gold standard Cox-PH model (0.52 ± 0.10). The "black box" nature of deep learning is a major concern in healthcare field. This model's interpretability, which results from incorporating the gene pathways and protein interaction (i.e., biology-aware) rather than relying on a 'black box' approach, helps patient stratification and provides insight into novel gene biomarkers, advancing our understanding of ICI treatment.

Accurate phenotypic classification and exome sequencing allow identification of novel genes and variants associated with adult-onset hearing loss

Adult-onset progressive hearing loss is a common, complex disease with a strong genetic component. Although to date over 150 genes have been identified as contributing to human hearing loss, many more remain to be discovered, as does most of the underlying genetic diversity. Many different variants have been found to underlie adult-onset hearing loss, but they tend to be rare variants with a high impact upon the gene product. It is likely that combinations of more common, lower impact variants also play a role in the prevalence of the disease. Here we present our exome study of hearing loss in a cohort of 532 older adult volunteers with extensive phenotypic data, including 99 older adults with normal hearing, an important control set. Firstly, we carried out an outlier analysis to identify genes with a high variant load in older adults with hearing loss compared to those with normal hearing. Secondly, we used audiometric threshold data to identify individual variants which appear to contribute to different threshold values. We followed up these analyses in a second cohort. Using these approaches, we identified genes and variants linked to better hearing as well as those linked to worse hearing. These analyses identified some known deafness genes, demonstrating proof of principle of our approach. However, most of the candidate genes are novel associations with hearing loss. While the results support the suggestion that genes responsible for severe deafness may also be involved in milder hearing loss, they also suggest that there are many more genes involved in hearing which remain to be identified. Our candidate gene lists may provide useful starting points for improved diagnosis and drug development.

A conditional knockout rat resource of mitochondrial protein-coding genes via a DdCBE-induced premature stop codon

Hundreds of pathogenic variants of mitochondrial DNA (mtDNA) have been reported to cause mitochondrial diseases, which still lack effective treatments. It is a huge challenge to install these mutations one by one. We repurposed the DddA-derived cytosine base editor to incorporate a premature stop codon in the mtProtein-coding genes to ablate mitochondrial proteins encoded in the mtDNA (mtProteins) instead of installing pathogenic variants and generated a library of both cell and rat resources with mtProtein depletion. In vitro, we depleted 12 of 13 mtProtein-coding genes with high efficiency and specificity, resulting in decreased mtProtein levels and impaired oxidative phosphorylation. Moreover, we generated six conditional knockout rat strains to ablate mtProteins using Cre/loxP system. Mitochondrially encoded ATP synthase membrane subunit 8 and NADH:ubiquinone oxidoreductase core subunit 1 were specifically depleted in heart cells or neurons, resulting in heart failure or abnormal brain development. Our work provides cell and rat resources for studying the function of mtProtein-coding genes and therapeutic strategies.

Characterization of driver mutations in Chinese non-small cell lung cancer patients using a novel targeted sequencing panel

Background

The identification of driver mutations has greatly promoted the precise diagnosis and treatment of non-small cell lung cancer (NSCLC), but there is lack of targeted sequencing panels specifically designed and applied to Chinese NSCLC patients. This study aimed to design and validate of a novel sequencing panel for comprehensive characterization of driver mutations in Chinese NSCLC patients, facilitating further exploration of downstream pathway alterations and therapeutic utility.

Methods

A novel target sequencing panel including 21 driver genes was designed and examined in a cohort of 260 Chinese NSCLC patients who underwent surgery in Peking Union Medical College Hospital (PUMCH). Genetic alterations were identified and further analyzed for driver mutations, downstream pathways and therapeutic utilities.

Results

The most frequently identified driver mutations in PUMCH NSCLC cohort were on genes TP53 (28%), EGFR (27%) and PIK3CA (19%) for lung adenocarcinoma (LUAD), and TP53 (41%), PIK3CA (14%) and CDKN2A (13%) for lung squamous cell carcinoma (LUSC), respectively. Downstream pathway analysis revealed common pathways like G1_AND_S1_PHASES pathway were shared not only between LUAD and LUSC patients, but also among three different NSCLC cohorts, while other pathways were subtype-specific, like the unique enrichment of SHC1_EVENT_IN_EGFR_SIGNALING pathway in LUAD patients, and P38_ALPHA_BETA_DOWNSTREAM pathway in LUSC patients, respectively. About 60% of both LUAD and LUSC patients harbored driver mutations as sensitive biomarkers for different targeted therapies, covering not only frequent mutations like EGFR L858R mutation, but also rare mutations like BRAF D594N mutation.

Conclusions

Our study provides a novel target sequencing panel suitable for Chinese NSCLC patients, which can effectively identify driver mutations, analyze downstream pathway alterations and predict therapeutic utility. Overall it is promising to further optimize and apply this panel in clinic with convenience and effectiveness.

Whole Exome Sequencing in South Africa: Stakeholder Views on Return of Individual Research Results and Incidental Findings

The use of whole exome sequencing (WES) in medical research is increasing in South Africa (SA), raising important questions about whether and which individual genetic research results, particularly incidental findings, should be returned to patients. Whilst some commentaries and opinions related to the topic have been published in SA, there is no qualitative data on the views of professional stakeholders on this topic. Seventeen participants including clinicians, genomics researchers, and genetic counsellors (GCs) were recruited from the Western Cape in SA. Semi-structured interviews were conducted, and the transcripts analysed using the framework approach for data analysis. Current roadblocks for the clinical adoption of WES in SA include a lack of standardised guidelines; complexities relating to variant interpretation due to lack of functional studies and underrepresentation of people of African ancestry in the reference genome, population and variant databases; lack of resources and skilled personnel for variant confirmation and follow-up. Suggestions to overcome these barriers include obtaining funding and buy-in from the private and public sectors and medical insurance companies; the generation of a locally relevant reference genome; training of health professionals in the field of genomics and bioinformatics; and multidisciplinary collaboration. Participants emphasised the importance of upscaling the accessibility to and training of GCs, as well as upskilling of clinicians and genetic nurses for return of genetic data in collaboration with GCs and medical geneticists. Future research could focus on exploring the development of stakeholder partnerships for increased access to trained specialists as well as community engagement and education, alongside the development of guidelines for result disclosure.

Gene Hunting Approaches through the Combination of Linkage Analysis with Whole-Exome Sequencing in Mendelian Diseases: From Darwin to the Present Day

BACKGROUND

In the context of medical genetics, gene hunting is the process of identifying and functionally characterizing genes or genetic variations that contribute to disease phenotypes. In this review, we would like to summarize gene hunting process in terms of historical aspects from Darwin to now. For this purpose, different approaches and recent developments will be detailed.

SUMMARY

Linkage analysis and association studies are the most common methods in use for explaining the genetic background of hereditary diseases and disorders. Although linkage analysis is a relatively old approach, it is still a powerful method to detect disease-causing rare variants using family-based data, particularly for consanguineous marriages. As is known that, consanguineous marriages or endogamy poses a social problem in developing countries, however, this same condition also provides a unique opportunity for scientists to identify and characterize pathogenic variants. The rapid advancements in sequencing technologies and their parallel implementation together with linkage analyses now allow us to identify the candidate variants related to diseases in a relatively short time. Furthermore, we can now go one step further and functionally characterize the causative variant through in vitro and in vivo studies and unveil the variant-phenotype relationships on a molecular level more robustly. Key Messages: Herein, we suggest that the combined analysis of linkage and exome analysis is a powerful and precise tool to diagnose clinically rare and recessively inherited conditions.

Fusion Gene Detection Using Whole-Exome Sequencing Data in Cancer Patients

Several fusion genes are directly involved in the initiation and progression of cancers. Numerous bioinformatics tools have been developed to detect fusion events, but they are mainly based on RNA-seq data. The whole-exome sequencing (WES) represents a powerful technology that is widely used for disease-related DNA variant detection. In this study, we build a novel analysis pipeline called Fuseq-WES to detect fusion genes at DNA level based on the WES data. The same method applies also for targeted panel sequencing data. We assess the method to real datasets of acute myeloid leukemia (AML) and prostate cancer patients. The result shows that two of the main AML fusion genes discovered in RNA-seq data, PML-RARA and CBFB-MYH11, are detected in the WES data in 36 and 63% of the available samples, respectively. For the targeted deep-sequencing of prostate cancer patients, detection of the TMPRSS2-ERG fusion, which is the most frequent chimeric alteration in prostate cancer, is 91% concordant with a manually curated procedure based on four other methods. In summary, the overall results indicate that it is challenging to detect fusion genes in WES data with a standard coverage of ∼ 15–30x, where fusion candidates discovered in the RNA-seq data are often not detected in the WES data and vice versa. A subsampling study of the prostate data suggests that a coverage of at least 75x is necessary to achieve high accuracy.

Genome-wide SNP identification in Fraxinus linking genetic characteristics to tolerance of Agrilus planipennis

Ash (Fraxinus spp.) is one of the most widely distributed tree genera in North America. Populations of ash in the United States and Canada have been decimated by the introduced pest Agrilus planipennis (Coleoptera: Buprestidae; emerald ash borer), having negative impacts on both forest ecosystems and economic interests. The majority of trees succumb to attack by A. planipennis, but some trees have been found to be tolerant to infestation despite years of exposure. Restriction site-associated DNA (RAD) sequencing was used to sequence ash individuals, both tolerant and susceptible to A. planipennis attack, in order to identify single nucleotide polymorphism (SNP) patterns related to tolerance and health declines. de novo SNPs were called using SAMtools and, after filtering criteria were implemented, a set of 17,807 SNPs were generated. Principal component analysis (PCA) of SNPs aligned individual trees into clusters related to geography; however, five tolerant trees clustered together despite geographic location. A subset of 32 outlier SNPs identified within this group, as well as a subset of 17 SNPs identified based on vigor rating, are potential candidates for the selection of host tolerance. Understanding the mechanisms of host tolerance through genome-wide association has the potential to restore populations with cultivars that are able to withstand A. planipennis infestation. This study was successful in using RAD-sequencing in order to identify SNPs that could contribute to tolerance of A. planipennis. This was a first step toward uncovering the genetic basis for host tolerance to A. planipennis. Future studies are needed to identify the functionality of the loci where these SNPs occur and how they may be related to tolerance of A. planipennis attack.

Genetic Survey of Autosomal Recessive Peripheral Neuropathy Cases Unravels High Genetic Heterogeneity in a Turkish Cohort

Background and Objectives

Inherited peripheral neuropathies (IPNs) are a group of genetic disorders of the peripheral nervous system in which neuropathy is the only or the most predominant clinical feature. The most common type of IPN is Charcot-Marie-Tooth (CMT) disease. Autosomal recessive CMT (ARCMT) is generally more severe than dominant CMT and its genetic basis is poorly understood due to high clinical and genetic diversity. Here, we report clinical and genetic findings from 56 consanguineous Turkish families initially diagnosed with CMT disease.

Methods

We initially screened the GDAP1 gene in our cohort as it is the most commonly mutated ARCMT gene. Next, whole-exome sequencing and homozygosity mapping based on whole-exome sequencing (HOMWES) analysis was performed. To understand the molecular impact of candidate causative genes, functional analyses were performed in patient primary fibroblasts.

Results

Biallelic recurrent mutations in the GDAP1 gene have been identified in 6 patients. Whole-exome sequencing and HOMWES analysis revealed 16 recurrent and 13 novel disease-causing alleles in known IPN-related genes and 2 novel candidate genes: 1 for a CMT-like disease and 1 for autosomal recessive cerebellar ataxia with axonal neuropathy. We have achieved a potential genetic diagnosis rate of 62.5% (35/56 families) in our cohort. Considering only the variants that meet the American College for Medical Genetics and Genomics (ACMG) classification as pathogenic or likely pathogenic, the definitive diagnosis rate was 55.35% (31/56 families).

Discussion

This study paints a genetic landscape of the Turkish ARCMT population and reports additional candidate genes that might help enlighten the mechanism of pathogenesis of the disease.

Tripterygium wilfordii derivative LLDT-8 targets CD2 in the treatment of rheumatoid arthritis

Rheumatoid arthritis (RA), a chronic inflammatory synovitis systemic disease, can lead to joint deformities, loss of function and even death. The pathogenesis of RA may be related to genetics, infection and/or sex hormones; however, detailed accounts of the molecular mechanisms underlying its pathogenesis are lacking. In the present study, the synovial tissues of patients with RA and healthy individuals were analyzed to identify the pathogenic signaling pathways and key candidate genes involved in RA. Gene Ontology (GO), pathway enrichment and protein-protein interaction analysis were further used to identify the differentially expressed genes (DEGs) and their potential roles in RA. Molecular docking was used to screen the potential candidate drugs for management of RA. Small interfering RNA was used for knockdown of the CD2 protein. A Cell Counting Kit-8 assay was used to detect the proliferation of cells. Changes in the levels of inflammatory cytokines were detected using ELISA. A total of 279 DEGs were identified in RA; amongst these genes, 166 and 113 were upregulated and downregulated, respectively. GO analysis revealed that the upregulated DEGs were primarily enriched in the activation of the immune and adaptive immune responses, as well as the inflammatory response. The T-cell surface antigen CD2 (CD2) was identified as the most important hub gene by selecting the most important module from the protein-protein interaction network. Knockout of CD2 reduced the damaging effects of TNF-α on synovial cells. Through in situ screening using computer-aided drug design, the triptolide derivative (5R)-5-hydroxytriptolide (LLDT-8) was determined to have the highest docking score based on the CD2 protein structure. Cell experiments showed that LLDT-8 could inhibit the expression of CD2. Cell proliferation and inflammatory cytokine assays confirmed that CD2 was the direct target of LLDT-8. Together, the results of the present study determined factors involved in the pathogenesis of RA and the important role of CD2 in this process by analyzing the DEGs in the RA process. LLDT-8 inhibited CD2 and may thus be used to treat RA. These candidate genes and signaling pathways may serve as potential targets for the clinical treatment of RA.

Identification of key molecules in recurrent miscarriage based on bioinformatics analysis

BACKGROUND

Recurrent miscarriage (RM) affects 1% to 5% of couples, and the mechanisms still stay unclear. In this study, we explored the underlying molecular mechanism and potential molecular biomarkers of RM as well as constructed a miRNA-mRNA regulation network.

METHODS

The microarray datasets GSE73025 and GSE22490, which represent mRNA and miRNA profiles, respectively, were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) with p-value < 0.05 and fold-change > 2 were identified while the miRNAs with p-value < 0.05 and fold-change > 1.3 were considered as significant differentially expressed miRNAs (DEMs).

RESULTS

A total of 373 DEGs, including 218 up-regulated genes and 155 down-regulated genes, were identified, while 138 up-regulated and 68 down-regulated DEMs were screened out. After functional enrichment analysis, we found GO biological process (BP) terms significantly enriched in the Fc-gamma receptor signaling pathway involved in phagocytosis. Moreover, signaling pathway analyses indicated that the neurotrophin signaling pathway (hsa04722) was the top KEGG enrichment. 6 hub genes (FPR1, C5AR1, CCR1, ADCY7, CXCR2, NPY) were screened out to construct a complex regulation network in RM because they had the highest degree of affecting the network. Besides, we constructed miRNA-mRNA network between DEMs target genes and DEGs in RM, including hsa-miR-1297- KLHL24 and hsa-miR-548a-5p-KLHL24 pairs.

CONCLUSIONS

In conclusion, the novel differentially expressed molecules in the present study could provide a new sight to explore the pathogenesis of RM as well as potential biomarkers and therapeutic targets for RM diagnosis and treatment.

OVarFlow: a resource optimized GATK 4 based Open source Variant calling workFlow

Background

The advent of next generation sequencing has opened new avenues for basic and applied research. One application is the discovery of sequence variants causative of a phenotypic trait or a disease pathology. The computational task of detecting and annotating sequence differences of a target dataset between a reference genome is known as "variant calling". Typically, this task is computationally involved, often combining a complex chain of linked software tools. A major player in this field is the Genome Analysis Toolkit (GATK). The "GATK Best Practices" is a commonly referred recipe for variant calling. However, current computational recommendations on variant calling predominantly focus on human sequencing data and ignore ever-changing demands of high-throughput sequencing developments. Furthermore, frequent updates to such recommendations are counterintuitive to the goal of offering a standard workflow and hamper reproducibility over time.

Results

A workflow for automated detection of single nucleotide polymorphisms and insertion-deletions offers a wide range of applications in sequence annotation of model and non-model organisms. The introduced workflow builds on the GATK Best Practices, while enabling reproducibility over time and offering an open, generalized computational architecture. The workflow achieves parallelized data evaluation and maximizes performance of individual computational tasks. Optimized Java garbage collection and heap size settings for the GATK applications SortSam, MarkDuplicates, HaplotypeCaller, and GatherVcfs effectively cut the overall analysis time in half.

Conclusions

The demand for variant calling, efficient computational processing, and standardized workflows is growing. The Open source Variant calling workFlow (OVarFlow) offers automation and reproducibility for a computationally optimized variant calling task. By reducing usage of computational resources, the workflow removes prior existing entry barriers to the variant calling field and enables standardized variant calling.

Bioinformatics Analysis Reveals the Potential Diagnostic Biomarkers for Abdominal Aortic Aneurysm

Objectives: Abdominal aortic aneurysms (AAAs) are associated with high mortality rates. The genes and pathways linked with AAA remain poorly understood. This study aimed to identify key differentially expressed genes (DEGs) linked to the progression of AAA using bioinformatics analysis.

Methods: Gene expression profiles of the GSE47472 and GSE57691 datasets were acquired from the Gene Expression Omnibus (GEO) database. These datasets were merged and normalized using the “sva” R package, and DEGs were identified using the limma package in R. The functions of these DEGs were assessed using Cytoscape software. We analyzed the DEGs using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis. Protein–protein interaction networks were assembled using Cytoscape, and crucial genes were identified using the Cytoscape plugin, molecular complex detection. Data from GSE15729 and GSE24342 were also extracted to verify our findings.

Results: We found that 120 genes were differentially expressed in AAA. Genes associated with inflammatory responses and nuclear-transcribed mRNA catabolic process were clustered in two gene modules in AAA. The hub genes of the two modules were IL6, RPL21, and RPL7A. The expression levels of IL6 correlated positively with RPL7A and negatively with RPL21. The expression of RPL21 and RPL7A was downregulated, whereas that of IL6 was upregulated in AAA.

Conclusions: The expression of RPL21 or RPL7A combined with IL6 has a diagnostic value for AAA. The novel DEGs and pathways identified herein might provide new insights into the underlying molecular mechanisms of AAA.

Understanding the Gastrointestinal Protective Effects of Polyphenols using Foodomics-Based Approaches

Plant polyphenols are rich sources of natural anti-oxidants and prebiotics. After ingestion, most polyphenols are absorbed in the intestine and interact with the gut microbiota and modulated metabolites produced by bacterial fermentation, such as short-chain fatty acids (SCFAs). Dietary polyphenols immunomodulatory role by regulating intestinal microorganisms, inhibiting the etiology and pathogenesis of various diseases including colon cancer, colorectal cancer, inflammatory bowel disease (IBD) and colitis. Foodomics is a novel high-throughput analysis approach widely applied in food and nutrition studies, incorporating genomics, transcriptomics, proteomics, metabolomics, and integrating multi-omics technologies. In this review, we present an overview of foodomics technologies for identifying active polyphenol components from natural foods, as well as a summary of the gastrointestinal protective effects of polyphenols based on foodomics approaches. Furthermore, we critically assess the limitations in applying foodomics technologies to investigate the protective effect of polyphenols on the gastrointestinal (GI) system. Finally, we outline future directions of foodomics techniques to investigate GI protective effects of polyphenols. Foodomics based on the combination of several analytical platforms and data processing for genomics, transcriptomics, proteomics and metabolomics studies, provides abundant data and a more comprehensive understanding of the interactions between polyphenols and the GI tract at the molecular level. This contribution provides a basis for further exploring the protective mechanisms of polyphenols on the GI system.

Toward Genome-Based Selection in Asian Seabass: What Can We Learn From Other Food Fishes and Farm Animals?

Due to the steadily increasing need for seafood and the plateauing output of fisheries, more fish need to be produced by aquaculture production. In parallel with the improvement of farming methods, elite food fish lines with superior traits for production must be generated by selection programs that utilize cutting-edge tools of genomics. The purpose of this review is to provide a historical overview and status report of a selection program performed on a catadromous predator, the Asian seabass (Lates calcarifer, Bloch 1790) that can change its sex during its lifetime. We describe the practices of wet lab, farm and lab in detail by focusing onto the foundations and achievements of the program. In addition to the approaches used for selection, our review also provides an inventory of genetic/genomic platforms and technologies developed to (i) provide current and future support for the selection process; and (ii) improve our understanding of the biology of the species. Approaches used for the improvement of terrestrial farm animals are used as examples and references, as those processes are far ahead of the ones used in aquaculture and thus they might help those working on fish to select the best possible options and avoid potential pitfalls.

Zika Virus Congenital Syndrome and MTOR gene variants: insights from a family of dizygotic twins

Congenital Zika virus syndrome (CZS) is associated with damage to neural progenitor cells by ZIKA virus infection. There are no accurate statistics on the percentage of pregnant mothers who have had babies affected by the syndrome. Few cases of discordant twins have been described in the literature and, therefore, we hypothesize that the genetic background of the progeny and/or mother may play a role in the fate of the syndrome. We performed a complete exome sequencing in a set of dizygotic individuals and their parents. After that, we selected discordant variants on the MTOR gene between the affected and unaffected twin and we observed a mutation (rs2295079), placed in a region restricted to proximal 5'-UTR, as a strong possible causal variant. In addition, in most brain tissues (including fetal brain) evaluated for expression quantitative trait loci (eQTL), this locus is strongly correlated with post-translational modifications of histones (promoter and enhancer marks) and hypersensitivity to DNAse I (open chromatin mark). Taken together, our data suggest that changes in the MTOR gene may be related to CZS. Additional functional studies should be carried out to prove how and why a MTOR mutation can predispose the fetus to the syndrome.

Simulation of African and non-African low and high coverage whole genome sequence data to assess variant calling approaches

Current variant calling (VC) approaches have been designed to leverage populations of long-range haplotypes and were benchmarked using populations of European descent, whereas most genetic diversity is found in non-European such as Africa populations. Working with these genetically diverse populations, VC tools may produce false positive and false negative results, which may produce misleading conclusions in prioritization of mutations, clinical relevancy and actionability of genes. The most prominent question is which tool or pipeline has a high rate of sensitivity and precision when analysing African data with either low or high sequence coverage, given the high genetic diversity and heterogeneity of this data. Here, a total of 100 synthetic Whole Genome Sequencing (WGS) samples, mimicking the genetics profile of African and European subjects for different specific coverage levels (high/low), have been generated to assess the performance of nine different VC tools on these contrasting datasets. The performances of these tools were assessed in false positive and false negative call rates by comparing the simulated golden variants to the variants identified by each VC tool. Combining our results on sensitivity and positive predictive value (PPV), VarDict [PPV = 0.999 and Matthews correlation coefficient (MCC) = 0.832] and BCFtools (PPV = 0.999 and MCC = 0.813) perform best when using African population data on high and low coverage data. Overall, current VC tools produce high false positive and false negative rates when analysing African compared with European data. This highlights the need for development of VC approaches with high sensitivity and precision tailored for populations characterized by high genetic variations and low linkage disequilibrium.

CopyDetective: Detection threshold-aware copy number variant calling in whole-exome sequencing data

BACKGROUND

Copy number variants (CNVs) are known to play an important role in the development and progression of several diseases. However, detection of CNVs with whole-exome sequencing (WES) experiments is challenging. Usually, additional experiments have to be performed.

FINDINGS

We developed a novel algorithm for somatic CNV calling in matched WES data called "CopyDetective". Different from other approaches, CNV calling with CopyDetective consists of a 2-step procedure: first, quality analysis is performed, determining individual detection thresholds for every sample. Second, actual CNV calling on the basis of the previously determined thresholds is performed. Our algorithm evaluates the change in variant allele frequency of polymorphisms and reports the fraction of affected cells for every CNV. Analyzing 4 WES data sets (n = 100) we observed superior performance of CopyDetective compared with ExomeCNV, VarScan2, ControlFREEC, ExomeDepth, and CNV-seq.

CONCLUSIONS

Individual detection thresholds reveal that not every WES data set is equally apt for CNV calling. Initial quality analyses, determining individual detection thresholds-as realized by CopyDetective-can and should be performed prior to actual variant calling.

Genomics-based immuno-oncology: bridging the gap between immunology and tumor biology

The first hypotheses about how the immune system affects cancers were proposed in the early 20th century. These early concepts about cancer immunosurveillance were further developed in the decades that followed, but a detailed understanding of cancer immunity remained elusive. It was only recently, through the advent of high-throughput technologies, that scientists gained the ability to profile tumors with a resolution that allowed for granular assessment of both tumor cells and the tumor microenvironment. The advent of immune checkpoint inhibitors (ICIs), which have proven to be effective cancer therapies in many malignancies, has spawned great interest in developing biomarkers for efficacy, an endeavor that highlighted the value of dissecting tumor immunity using large-scale methods. Response to ICI therapy has been shown to be a highly complex process, where the dynamics of tumor and immune cells is key to success. The need to understand the biologic mechanisms at the tumor-immune interface has given rise to the field of cancer immunogenomics, a discipline that aims to bridge the gap between cancer genomics and classical immunology. We provide a broad overview of this emerging branch of translational science, summarizing common platforms used and recent discoveries in the field, which are having direct clinical implications. Our discussion will be centered around the genetic foundations governing tumor immunity and molecular determinants associated with clinical benefit from ICI therapy. We emphasize the importance of molecular diversity as a driver of anti-tumor immunity and discuss how these factors can be probed using genomic approaches.

Annotation of Human Exome Gene Variants with Consensus Pathogenicity

A novel approach is developed to address the challenge of annotating with phenotypic effects those exome variants for which relevant empirical data are lacking or minimal. The predictive annotation method is implemented as a stacked ensemble of supervised base-learners, including distributed random forest and gradient boosting machines. Ensemble models were trained and cross-validated on evidence-based categorical variant effect annotations from the ClinVar database, and were applied to 84 million non-synonymous single nucleotide variants (SNVs). The consensus model combined 39 functional mutation impacts, cross-species conservation score, and gene indispensability score. The indispensability score, accounting for differences in variant pathogenicities including in essential and mutation-tolerant genes, considerably improved the predictions. The consensus combination is consistent with as many input scores as possible while minimizing false predictions. The input scores are ranked based on their ability to predict effects. The score rankings and categorical phenotypic variant effect predictions are aimed for direct use in clinical and biological applications to prioritize human exome variants and mutations.

GOTI, a method to identify genome-wide off-target effects of genome editing in mouse embryos

Genome editing holds great potential for correcting pathogenic mutations. We developed a method called GOTI (genome-wide off-target analysis by two-cell embryo injection) to detect off-target mutations by editing one blastomere of two-cell mouse embryos using either CRISPR-Cas9 or base editors. GOTI directly compares edited and non-edited cells without the interference of genetic background and thus could detect potential off-target variants with high sensitivity. Notably, the GOTI method was designed to detect potential off-target variants of any genome editing tools by the combination of experimental and computational approaches, which is critical for accurate evaluation of the safety of genome editing tools. Here we provide a detailed protocol for GOTI, including mice mating, two-cell embryo injection, embryonic day 14.5 embryo digestion, fluorescence-activated cell sorting, whole-genome sequencing and data analysis. To enhance the utility of GOTI, we also include a computational workflow called GOTI-seq (https://github.com/sydaileen/GOTI-seq) for the sequencing data analysis, which can generate the final genome-wide off-target variants from raw sequencing data directly. The protocol typically takes 20 d from the mice mating to sequencing and 7 d for sequencing data analysis.

CAPICE: a computational method for Consequence-Agnostic Pathogenicity Interpretation of Clinical Exome variations

Exome sequencing is now mainstream in clinical practice. However, identification of pathogenic Mendelian variants remains time-consuming, in part, because the limited accuracy of current computational prediction methods requires manual classification by experts. Here we introduce CAPICE, a new machine-learning-based method for prioritizing pathogenic variants, including SNVs and short InDels. CAPICE outperforms the best general (CADD, GAVIN) and consequence-type-specific (REVEL, ClinPred) computational prediction methods, for both rare and ultra-rare variants. CAPICE is easily added to diagnostic pipelines as pre-computed score file or command-line software, or using online MOLGENIS web service with API. Download CAPICE for free and open-source (LGPLv3) at https://github.com/molgenis/capice .

NGSEP3: accurate variant calling across species and sequencing protocols

MOTIVATION

Accurate detection, genotyping and downstream analysis of genomic variants from high-throughput sequencing data are fundamental features in modern production pipelines for genetic-based diagnosis in medicine or genomic selection in plant and animal breeding. Our research group maintains the Next-Generation Sequencing Experience Platform (NGSEP) as a precise, efficient and easy-to-use software solution for these features.

RESULTS

Understanding that incorrect alignments around short tandem repeats are an important source of genotyping errors, we implemented in NGSEP new algorithms for realignment and haplotype clustering of reads spanning indels and short tandem repeats. We performed extensive benchmark experiments comparing NGSEP to state-of-the-art software using real data from three sequencing protocols and four species with different distributions of repetitive elements. NGSEP consistently shows comparative accuracy and better efficiency compared to the existing solutions. We expect that this work will contribute to the continuous improvement of quality in variant calling needed for modern applications in medicine and agriculture.

AVAILABILITY AND IMPLEMENTATION

NGSEP is available as open source software at http://ngsep.sf.net.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Next-Generation Sequencing: An Emerging Tool for Drug Designing

BACKGROUND

With the outbreak of high throughput next-generation sequencing (NGS), the biological research of drug discovery has been directed towards the oncology and infectious disease therapeutic areas, with extensive use in biopharmaceutical development and vaccine production.

METHOD

In this review, an effort was made to address the basic background of NGS technologies, potential applications of NGS in drug designing. Our purpose is also to provide a brief introduction of various Nextgeneration sequencing techniques.

DISCUSSIONS

The high-throughput methods execute Large-scale Unbiased Sequencing (LUS) which comprises of Massively Parallel Sequencing (MPS) or NGS technologies. The Next geneinvolved necessarily executes Largescale Unbiased Sequencing (LUS) which comprises of MPS or NGS technologies. These are related terms that describe a DNA sequencing technology which has revolutionized genomic research. Using NGS, an entire human genome can be sequenced within a single day.

CONCLUSION

Analysis of NGS data unravels important clues in the quest for the treatment of various lifethreatening diseases and other related scientific problems related to human welfare.

Next-generation sequencing and its application in diagnosis of retinitis pigmentosa

Retinitis Pigmentosa (RP) is a major cause of heritable human blindness with a high genetic heterogeneity. It is characterized by the initial degeneration of rod photoreceptors followed by cone photoreceptors. RP is also a prominent reason of visual impairment, by a global prevalence of 1:4000. RP is usually specified with nyctalopia in puberty, followed by concentric visual field loss, that reflects the main impairment of rod photoreceptors; later in the life, as disease progresses, because of cone dysfunction, central vision loss also occurs. A precise molecular diagnosis is crucial for disease characterization and clinical prognosis. DNA sequencing is a powerful tool for deciphering various causes of different human diseases. The arrival of next-generation sequencing (NGS) technologies has diminished sequencing cost and considerably augmented the throughput, making whole-genome sequencing (WGS) a conceivable way for obtaining comprehensive genomic data and a more precise clinical decision. Nevertheless, the advantages gained from NGS technologies are among a number of challenges that must be sufficiently addressed before this technique can be altered from an investigation tools to a helpful method in routine clinical practices. This article aims to provide an overview about NGS technology and its related platforms. The challenges in the analysis and choosing an appropriate NGS method likewise their potential applications in clinical diagnosis are also discussed. The merit of such technique has been reflected in some recent studies where it is shown that using NGS and molecular information could help with clinical diagnosis, providing potential treatment options or changes, up-to-date family counseling and management.

SRPRISM (Single Read Paired Read Indel Substitution Minimizer): an efficient aligner for assemblies with explicit guarantees

BACKGROUND

Alignment of sequence reads generated by next-generation sequencing is an integral part of most pipelines analyzing next-generation sequencing data. A number of tools designed to quickly align a large volume of sequences are already available. However, most existing tools lack explicit guarantees about their output. They also do not support searching genome assemblies, such as the human genome assembly GRCh38, that include primary and alternate sequences and placement information for alternate sequences to primary sequences in the assembly.

FINDINGS

This paper describes SRPRISM (Single Read Paired Read Indel Substitution Minimizer), an alignment tool for aligning reads without splices. SRPRISM has features not available in most tools, such as (i) support for searching genome assemblies with alternate sequences, (ii) partial alignment of reads with a specified region of reads to be included in the alignment, (iii) choice of ranking schemes for alignments, and (iv) explicit criteria for search sensitivity. We compare the performance of SRPRISM to GEM, Kart, STAR, BWA-MEM, Bowtie2, Hobbes, and Yara using benchmark sets for paired and single reads of lengths 100 and 250 bp generated using DWGSIM. SRPRISM found the best results for most benchmark sets with error rate of up to ∼2.5% and GEM performed best for higher error rates. SRPRISM was also more sensitive than other tools even when sensitivity was reduced to improve run time performance.

CONCLUSIONS

We present SRPRISM as a flexible read mapping tool that provides explicit guarantees on results.

Parameterized Algorithms in Bioinformatics: An Overview

Bioinformatics regularly poses new challenges to algorithm engineers and theoretical computer scientists. This work surveys recent developments of parameterized algorithms and complexity for important NP-hard problems in bioinformatics. We cover sequence assembly and analysis, genome comparison and completion, and haplotyping and phylogenetics. Aside from reporting the state of the art, we give challenges and open problems for each topic.

Rare and common variant discovery in complex disease: the IBD case study

Complex diseases such as inflammatory bowel disease (IBD), which consists of ulcerative colitis and Crohn's disease, are a significant medical burden-70 000 new cases of IBD are diagnosed in the United States annually. In this review, we examine the history of genetic variant discovery in complex disease with a focus on IBD. We cover methods that have been applied to microsatellite, common variant, targeted resequencing and whole-exome and -genome data, specifically focusing on the progression of technologies towards rare-variant discovery. The inception of these methods combined with better availability of population level variation data has led to rapid discovery of IBD-causative and/or -associated variants at over 200 loci; over time, these methods have grown exponentially in both power and ascertainment to detect rare variation. We highlight rare-variant discoveries critical to the elucidation of the pathogenesis of IBD, including those in NOD2, IL23R, CARD9, RNF186 and ADCY7. We additionally identify the major areas of rare-variant discovery that will evolve in the coming years. A better understanding of the genetic basis of IBD and other complex diseases will lead to improved diagnosis, prognosis, treatment and surveillance.

A Nutrigenomics Approach Using RNA Sequencing Technology to Study Nutrient-Gene Interactions in Agricultural Animals

Thorough understanding of animal gene expression driven by dietary nutrients can be regarded as a bottom line of advanced animal nutrition research. Nutrigenomics (including transcriptomics) studies the effects of dietary nutrients on cellular gene expression and, ultimately, phenotypic changes in living organisms. Transcriptomics can be applied to investigate animal tissue transcriptomes at a defined nutritional state, which can provide a holistic view of intracellular RNA expression. As a novel transcriptomics approach, RNA sequencing (RNA-Seq) technology can monitor all gene expressions simultaneously in response to dietary intervention. The principle and history of RNA-Seq are briefly reviewed, and its 3 principal steps are described in this article. Application of RNA-Seq in different areas of animal nutrition research is summarized. Lastly, the application of RNA-Seq in swine science and nutrition is also reviewed. In short, RNA-Seq holds significant potential to be employed for better understanding the nutrient-gene interactions in agricultural animals.

A Multi-center Study on the Reproducibility of Drug-Response Assays in Mammalian Cell Lines

Evidence that some high-impact biomedical results cannot be repeated has stimulated interest in practices that generate findable, accessible, interoperable, and reusable (FAIR) data. Multiple papers have identified specific examples of irreproducibility, but practical ways to make data more reproducible have not been widely studied. Here, five research centers in the NIH LINCS Program Consortium investigate the reproducibility of a prototypical perturbational assay: quantifying the responsiveness of cultured cells to anti-cancer drugs. Such assays are important for drug development, studying cellular networks, and patient stratification. While many experimental and computational factors impact intra- and inter-center reproducibility, the factors most difficult to identify and control are those with a strong dependency on biological context. These factors often vary in magnitude with the drug being analyzed and with growth conditions. We provide ways to identify such context-sensitive factors, thereby improving both the theory and practice of reproducible cell-based assays.

Factors that impact the reproducibility of experimental data are poorly understood. Five NIH-LINCS centers performed the same set of drug-response measurements and compared results. Technical and biological variables that impact precision and reproducibility and are also sensitive to biological context were the most problematic.

Managing genomic variant calling workflows with Swift/T

Bioinformatics research is frequently performed using complex workflows with multiple steps, fans, merges, and conditionals. This complexity makes management of the workflow difficult on a computer cluster, especially when running in parallel on large batches of data: hundreds or thousands of samples at a time. Scientific workflow management systems could help with that. Many are now being proposed, but is there yet the “best” workflow management system for bioinformatics? Such a system would need to satisfy numerous, sometimes conflicting requirements: from ease of use, to seamless deployment at peta- and exa-scale, and portability to the cloud. We evaluated Swift/T as a candidate for such role by implementing a primary genomic variant calling workflow in the Swift/T language, focusing on workflow management, performance and scalability issues that arise from production-grade big data genomic analyses. In the process we introduced novel features into the language, which are now part of its open repository. Additionally, we formalized a set of design criteria for quality, robust, maintainable workflows that must function at-scale in a production setting, such as a large genomic sequencing facility or a major hospital system. The use of Swift/T conveys two key advantages. (1) It operates transparently in multiple cluster scheduling environments (PBS Torque, SLURM, Cray aprun environment, etc.), thus a single workflow is trivially portable across numerous clusters. (2) The leaf functions of Swift/T permit developers to easily swap executables in and out of the workflow, which makes it easy to maintain and to request resources optimal for each stage of the pipeline. While Swift/T’s data-level parallelism eliminates the need to code parallel analysis of multiple samples, it does make debugging more difficult, as is common for implicitly parallel code. Nonetheless, the language gives users a powerful and portable way to scale up analyses in many computing architectures. The code for our implementation of a variant calling workflow using Swift/T can be found on GitHub at https://github.com/ncsa/Swift-T-Variant-Calling, with full documentation provided at http://swift-t-variant-calling.readthedocs.io/en/latest/.

A novel knowledge-derived data potentizing method revealed unique liver cancer-associated genetic variants

Background

Next-generation sequencing (NGS) has been advancing the progress of detection of disease-associated genetic variants and genome-wide profiling of expressed sequences over the past decade. NGS enables the analyses of multiple regions of a genome in a single reaction format and has been shown to be a cost-effective and efficient tool for root-cause analysis of disease and optimization of treatment. NGS has been leading global efforts to device personalized and precision medicine (PM) in clinical practice. The effectiveness of NGS for the aforementioned applications has been proven unequivocal for multifactorial diseases like cancer. However, definitive prediction of cancer markers for all types of diseases and for global populations still remains highly rewarding because of the diversity of cancer types and genetic variants in human.

Results

We performed exome sequencing of four samples in quest of critical genetic factor/s associated with liver cancer. By imposing knowledge-based filter chains, we have revealed a panel of genetic variants, which are unrecognized by current major genomics data repositories. Total 20 MNV-induced, 5 INDEL-induced, and 31 SNV-induced neoplasm-exclusive genes were revealed through NGS data acquisition followed by data curing with the application of quality filter chains. Liver-specific expression profile of the identified gene pool is directed to the selection of 17 genes which could be the as likely causative genetic factors for liver cancer. Further study on expression level and relevant functional significance enables us to identify and conclude the following four novel variants, viz., c.416T>C (p.Phe139Ser) in SORD, c.1048_1049delGCinsCG (p.Ala350Arg) in KRT6A, c.1159G>T (p.Gly387Cys) in SVEP1, and c.430G>C (p.Gly144Arg) in MRPL38 as a critical genetic factor for liver cancer.

Conclusion

By applying a novel data prioritizing rationale, we explored a panel of previously unaddressed liver cancer-associated variants. These findings may have an opportunity for early prediction of neoplasm/cancer in liver and designing of relevant personalized/precision liver cancer therapeutics in clinical practice. Since NGS protocol is associated with tons of non-specific mutations due to the variation in background genetic makeup of subjects, therefore, our method of data curing could be applicable for more effective screening of global genetic variants related to disease onset, progression, and remission.

Electronic supplementary material

The online version of this article (10.1186/s40246-019-0213-7) contains supplementary material, which is available to authorized users.

Dissecting in silico Mutation Prediction of Variants in African Genomes: Challenges and Perspectives

Genomic medicine is set to drastically improve clinical care globally due to high throughput technologies which enable speedy in silico detection and analysis of clinically relevant mutations. However, the variability in the in silico prediction methods and categorization of functionally relevant genetic variants can pose specific challenges in some populations. In silico mutation prediction tools could lead to high rates of false positive/negative results, particularly in African genomes that harbor the highest genetic diversity and that are disproportionately underrepresented in public databases and reference panels. These issues are particularly relevant with the recent increase in initiatives, such as the Human Heredity and Health (H3Africa), that are generating huge amounts of genomic sequence data in the absence of policies to guide genomic researchers to return results of variants in so-called actionable genes to research participants. This report (i) provides an inventory of publicly available Whole Exome/Genome data from Africa which could help improve reference panels and explore the frequency of pathogenic variants in actionable genes and related challenges, (ii) reviews available in silico prediction mutation tools and the criteria for categorization of pathogenicity of novel variants, and (iii) proposes recommendations for analyzing pathogenic variants in African genomes for their use in research and clinical practice. In conclusion, this work proposes criteria to define mutation pathogenicity and actionability in human genetic research and clinical practice in Africa and recommends setting up an African expert panel to oversee the proposed criteria.

eDiVA-Classification and prioritization of pathogenic variants for clinical diagnostics

Mendelian diseases have shown to be an and efficient model for connecting genotypes to phenotypes and for elucidating the function of genes. Whole‐exome sequencing (WES) accelerated the study of rare Mendelian diseases in families, allowing for directly pinpointing rare causal mutations in genic regions without the need for linkage analysis. However, the low diagnostic rates of 20–30% reported for multiple WES disease studies point to the need for improved variant pathogenicity classification and causal variant prioritization methods. Here, we present the exome Disease Variant Analysis (eDiVA; http://ediva.crg.eu), an automated computational framework for identification of causal genetic variants (coding/splicing single‐nucleotide variants and small insertions and deletions) for rare diseases using WES of families or parent–child trios. eDiVA combines next‐generation sequencing data analysis, comprehensive functional annotation, and causal variant prioritization optimized for familial genetic disease studies. eDiVA features a machine learning‐based variant pathogenicity predictor combining various genomic and evolutionary signatures. Clinical information, such as disease phenotype or mode of inheritance, is incorporated to improve the precision of the prioritization algorithm. Benchmarking against state‐of‐the‐art competitors demonstrates that eDiVA consistently performed as a good or better than existing approach in terms of detection rate and precision. Moreover, we applied eDiVA to several familial disease cases to demonstrate its clinical applicability.

Clinical utility in infants with suspected monogenic conditions through next-generation sequencing

Background

Rare diseases are complex disorders with huge variability in clinical manifestations. Decreasing cost of next‐generation sequencing (NGS) tests in recent years made it affordable. We witnessed the diagnostic yield and clinical use of different NGS strategies on a myriad of monogenic disorders in a pediatric setting.

Methods

Next‐generation sequencing tests are performed for 98 unrelated Chinese patients within their first year of life, who were admitted to Xin Hua Hospital, affiliated with Shanghai Jiao Tong University School of Medicine, during a 2‐year period.

Results

Clinical indications for NGS tests included a range of medical concerns. The mean age was 4.4 ± 4.2 months of age for infants undergoing targeting specific (known) disease‐causing genes (TRS) analysis, and 4.4 ± 4.3 months of age for whole‐exome sequencing (WES) (p > 0.05). A molecular diagnosis is done in 72 infants (73.47%), which finds a relatively high yield with phenotypes of metabolism/homeostasis abnormality (HP: 0001939) (odds ratio, 1.83; 95% CI, 0.56–6.04; p = 0.32) and a significantly low yield with atypical symptoms (without a definite HPO term) (odds ratio, 0.08; 95% CI, 0.01–0.73; p = 0.03). TRS analysis provides molecular yields higher than WES (p = 0.01). Ninety‐eight different mutations are discovered in 72 patients. Twenty‐seven of them have not been reported previously. Nearly half (43.06%, 31/72) of the patients are found to carry 11 common disorders, mostly being inborn errors of metabolism (IEM) and neurogenetic disorders and all of them are observed through TRS analysis. Eight positive cases are identified through WES, and all of them are sporadic, of highly variable phenotypes and severity. There are 26 patients with negative findings in this study.

Conclusion

This study provides evidence that NGS can yield high success rates in a tertiary pediatric setting, but suggests that the scope of known Mendelian conditions may be considerably broader than currently recognized.

Similarities and differences between variants called with human reference genome HG19 or HG38

Background

Reference genome selection is a prerequisite for successful analysis of next generation sequencing (NGS) data. Current practice employs one of the two most recent human reference genome versions: HG19 or HG38. To date, the impact of genome version on SNV identification has not been rigorously assessed.

Methods

We conducted analysis comparing the SNVs identified based on HG19 vs HG38, leveraging whole genome sequencing (WGS) data from the genome-in-a-bottle (GIAB) project. First, SNVs were called using 26 different bioinformatics pipelines with either HG19 or HG38. Next, two tools were used to convert the called SNVs between HG19 and HG38. Lastly we calculated conversion rates, analyzed discordant rates between SNVs called with HG19 or HG38, and characterized the discordant SNVs.

Results

The conversion rates from HG38 to HG19 (average 95%) were lower than the conversion rates from HG19 to HG38 (average 99%). The conversion rates varied slightly among the various calling pipelines. Around 1.5% SNVs were discordantly converted between HG19 or HG38. The conversions from HG38 to HG19 had more SNVs which failed conversion and more discordant SNVs than the opposite conversion (HG19 to HG38). Most of the discordant SNVs had low read depth, were low confidence SNVs as defined by GIAB, and/or were predominated by G/C alleles (52% observed versus 42% expected).

Conclusion

A significant number of SNVs could not be converted between HG19 and HG38. Based on careful review of our comparisons, we recommend HG38 (the newer version) for NGS SNV analysis. To summarize, our findings suggest caution when translating identified SNVs between different versions of the human reference genome.

Electronic supplementary material

The online version of this article (10.1186/s12859-019-2620-0) contains supplementary material, which is available to authorized users.

Analyzing large scale genomic data on the cloud with Sparkhit

Motivation

The increasing amount of next-generation sequencing data poses a fundamental challenge on large scale genomic analytics. Existing tools use different distributed computational platforms to scale-out bioinformatics workloads. However, the scalability of these tools is not efficient. Moreover, they have heavy run time overheads when pre-processing large amounts of data. To address these limitations, we have developed Sparkhit: a distributed bioinformatics framework built on top of the Apache Spark platform.

Results

Sparkhit integrates a variety of analytical methods. It is implemented in the Spark extended MapReduce model. It runs 92-157 times faster than MetaSpark on metagenomic fragment recruitment and 18-32 times faster than Crossbow on data pre-processing. We analyzed 100 terabytes of data across four genomic projects in the cloud in 21 h, which includes the run times of cluster deployment and data downloading. Furthermore, our application on the entire Human Microbiome Project shotgun sequencing data was completed in 2 h, presenting an approach to easily associate large amounts of public datasets with reference data.

Availability and implementation

Sparkhit is freely available at: https://rhinempi.github.io/sparkhit/.

Contact

asczyrba@cebitec.uni-bielefeld.de.

Supplementary information

Supplementary data are available at Bioinformatics online.

Application of next-generation sequencing technology to precision medicine in cancer: joint consensus of the Tumor Biomarker Committee of the Chinese Society of Clinical Oncology

Next-generation sequencing (NGS) technology is capable of sequencing millions or billions of DNA molecules simultaneously. Therefore, it represents a promising tool for the analysis of molecular targets for the initial diagnosis of disease, monitoring of disease progression, and identifying the mechanism of drug resistance. On behalf of the Tumor Biomarker Committee of the Chinese Society of Clinical Oncology (CSCO) and the China Actionable Genome Consortium (CAGC), the present expert group hereby proposes advisory guidelines on clinical applications of NGS technology for the analysis of cancer driver genes for precision cancer therapy. This group comprises an assembly of laboratory cancer geneticists, clinical oncologists, bioinformaticians, pathologists, and other professionals. After multiple rounds of discussions and revisions, the expert group has reached a preliminary consensus on the need of NGS in clinical diagnosis, its regulation, and compliance standards in clinical sample collection. Moreover, it has prepared NGS criteria, the sequencing standard operation procedure (SOP), data analysis, report, and NGS platform certification and validation.

A Review of the Genomic Analysis of Children Presenting with Developmental Delay/Intellectual Disability and Associated Dysmorphic Features

This review describes the clinical criteria of developmental delay (DD)/intellectual disability (ID) as well as the various techniques that are currently implemented to diagnose neurodevelopmental disorders that typically present with associated dysmorphic features such as Angelman syndrome, Prader-Willi syndrome, and DiGeorge syndrome. These analyses include various forms of chromosomal microarray (CMA), which have proven to be superior to previously implemented techniques such as G-banded karyotyping and fluorescent in situ hybridization (FISH) analysis, as well as whole exome sequencing (WES), which is implemented as a secondary examination when CMA analysis is unrevealing. The clinical significance of identified variants and how it relates to facilitating the management of specific genetic disorders such as the above mentioned is also discussed. In addition, the importance of genomic databases and bioinformatics technologies as they relate to variant classification is also considered. Essentially, the discovery of pathogenic variants allows for enhanced management of a patient’s clinical phenotype, whereas the identification of variants of uncertain significance (VUS) has proven to have an increase in the number of associated conflicts as they typically generate more ambiguity in regard to the clinical manifestations present within the child. As a result, additional procedures need to be implemented to mitigate the issues that surround their identification. The concluding remarks are in regard to both the ethical and legal considerations of genetic testing as they relate to informed consent, testing of minors, how to handle secondary findings, as well as the anticipated future direction of genomic analysis.

Effects of solvent quality and non-equilibrium conformations on polymer translocation

The conformation and its relaxation of a single polymer depend on solvent quality in a polymer solution: a polymer collapses into a globule in a poor solvent, while the polymer swells in a good solvent. When one translocates a polymer through a narrow pore, a drastic conformational change occurs such that the kinetics of the translocation is expected to depend on the solvent quality. However, the effects of solvent quality on the translocation kinetics have been controversial. In this study, we employ a coarse-grained model for a polymer and perform Langevin dynamics simulations for the driven translocation of a polymer in various types of solvents. We estimate the free energy of polymer translocation using steered molecular dynamics simulations and Jarzynski's equality and find that the free energy barrier for the translocation increases as the solvent quality becomes poorer. The conformational entropy contributes most to the free energy barrier of the translocation in a good solvent, while a balance between entropy and energy matters in a poor solvent. Interestingly, contrary to what is expected from the free energy profile, the translocation kinetics is a non-monotonic function of the solvent quality. We find that for any type of solvent, the polymer conformation stays far away from the equilibrium conformation during translocation due to an external force and tension propagation. However, the degree of tension propagation differs depending on the solvent quality as well as the magnitude of the external force: the tension propagation is more significant in a good solvent than in a poor solvent. We illustrate that such differences in tension propagation and non-equilibrium conformations between good and poor solvents are responsible for the complicated non-monotonic effects of solvent quality on the translocation kinetics.

Blacklisting variants common in private cohorts but not in public databases optimizes human exome analysis

Computational analyses of human patient exomes aim to filter out as many nonpathogenic genetic variants (NPVs) as possible, without removing the true disease-causing mutations. This involves comparing the patient's exome with public databases to remove reported variants inconsistent with disease prevalence, mode of inheritance, or clinical penetrance. However, variants frequent in a given exome cohort, but absent or rare in public databases, have also been reported and treated as NPVs, without rigorous exploration. We report the generation of a blacklist of variants frequent within an in-house cohort of 3,104 exomes. This blacklist did not remove known pathogenic mutations from the exomes of 129 patients and decreased the number of NPVs remaining in the 3,104 individual exomes by a median of 62%. We validated this approach by testing three other independent cohorts of 400, 902, and 3,869 exomes. The blacklist generated from any given cohort removed a substantial proportion of NPVs (11-65%). We analyzed the blacklisted variants computationally and experimentally. Most of the blacklisted variants corresponded to false signals generated by incomplete reference genome assembly, location in low-complexity regions, bioinformatic misprocessing, or limitations inherent to cohort-specific private alleles (e.g., due to sequencing kits, and genetic ancestries). Finally, we provide our precalculated blacklists, together with ReFiNE, a program for generating customized blacklists from any medium-sized or large in-house cohort of exome (or other next-generation sequencing) data via a user-friendly public web server. This work demonstrates the power of extracting variant blacklists from private databases as a specific in-house but broadly applicable tool for optimizing exome analysis.

Allele balance bias identifies systematic genotyping errors and false disease associations

In recent years, next‐generation sequencing (NGS) has become a cornerstone of clinical genetics and diagnostics. Many clinical applications require high precision, especially if rare events such as somatic mutations in cancer or genetic variants causing rare diseases need to be identified. Although random sequencing errors can be modeled statistically and deep sequencing minimizes their impact, systematic errors remain a problem even at high depth of coverage. Understanding their source is crucial to increase precision of clinical NGS applications. In this work, we studied the relation between recurrent biases in allele balance (AB), systematic errors, and false positive variant calls across a large cohort of human samples analyzed by whole exome sequencing (WES). We have modeled the AB distribution for biallelic genotypes in 987 WES samples in order to identify positions recurrently deviating significantly from the expectation, a phenomenon we termed allele balance bias (ABB). Furthermore, we have developed a genotype callability score based on ABB for all positions of the human exome, which detects false positive variant calls that passed state‐of‐the‐art filters. Finally, we demonstrate the use of ABB for detection of false associations proposed by rare variant association studies. Availability: https://github.com/Francesc-Muyas/ABB.