RCPA: An Open-Source R Package for Data Processing, Differential Analysis, Consensus Pathway Analysis, and Visualization.

Identifying impacted pathways is important because it provides insights into the biology underlying conditions beyond the detection of differentially expressed genes. Because of the importance of such analysis, more than 100 pathway analysis methods have been developed thus far. Despite the availability of many methods, it is challenging for biomedical researchers to learn and properly perform pathway analysis. First, the sheer number of methods makes it challenging to learn and choose the correct method for a given experiment. Second, computational methods require users to be savvy with coding syntax, and comfortable with command-line environments, areas that are unfamiliar to most life scientists. Third, as learning tools and computational methods are typically implemented only for a few species (i.e., human and some model organisms), it is difficult to perform pathway analysis on other species that are not included in many of the current pathway analysis tools. Finally, existing pathway tools do not allow researchers to combine, compare, and contrast the results of different methods and experiments for both hypothesis testing and analysis purposes. To address these challenges, we developed an open-source R package for Consensus Pathway Analysis (RCPA) that allows researchers to conveniently: (1) download and process data from NCBI GEO; (2) perform differential analysis using established techniques developed for both microarray and sequencing data; (3) perform both gene set enrichment, as well as topology-based pathway analysis using different methods that seek to answer different research hypotheses; (4) combine methods and datasets to find consensus results; and (5) visualize analysis results and explore significantly impacted pathways across multiple analyses. This protocol provides many example code snippets with detailed explanations and supports the analysis of more than 1000 species, two pathway databases, three differential analysis techniques, eight pathway analysis tools, six meta-analysis methods, and two consensus analysis techniques. The package is freely available on the CRAN repository. © 2024 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Processing Affymetrix microarrays Basic Protocol 2: Processing Agilent microarrays Support Protocol: Processing RNA sequencing (RNA-Seq) data Basic Protocol 3: Differential analysis of microarray data (Affymetrix and Agilent) Basic Protocol 4: Differential analysis of RNA-Seq data Basic Protocol 5: Gene set enrichment analysis Basic Protocol 6: Topology-based (TB) pathway analysis Basic Protocol 7: Data integration and visualization.

Fourteen years of cellular deconvolution: methodology, applications, technical evaluation and outstanding challenges.

Single-cell RNA sequencing (scRNA-Seq) is a recent technology that allows for the measurement of the expression of all genes in each individual cell contained in a sample. Information at the single-cell level has been shown to be extremely useful in many areas. However, performing single-cell experiments is expensive. Although cellular deconvolution cannot provide the same comprehensive information as single-cell experiments, it can extract cell-type information from bulk RNA data, and therefore it allows researchers to conduct studies at cell-type resolution from existing bulk datasets. For these reasons, a great effort has been made to develop such methods for cellular deconvolution. The large number of methods available, the requirement of coding skills, inadequate documentation, and lack of performance assessment all make it extremely difficult for life scientists to choose a suitable method for their experiment. This paper aims to fill this gap by providing a comprehensive review of 53 deconvolution methods regarding their methodology, applications, performance, and outstanding challenges. More importantly, the article presents a benchmarking of all these 53 methods using 283 cell types from 30 tissues of 63 individuals. We also provide an R package named DeconBenchmark that allows readers to execute and benchmark the reviewed methods (https://github.com/tinnlab/DeconBenchmark).

A novel approach for predicting upstream regulators (PURE) that affect gene expression.

External factors such as exposure to a chemical, drug, or toxicant (CDT), or conversely, the lack of certain chemicals can cause many diseases. The ability to identify such causal CDTs based on changes in the gene expression profile is extremely important in many studies. Furthermore, the ability to correctly infer CDTs that can revert the gene expression changes induced by a given disease phenotype is a crucial step in drug repurposing. We present an approach for Predicting Upstream REgulators (PURE) designed to tackle this challenge. PURE can correctly infer a CDT from the measured expression changes in a given phenotype, as well as correctly identify drugs that could revert disease-induced gene expression changes. We compared the proposed approach with four classical approaches as well as with the causal analysis used in Ingenuity Pathway Analysis (IPA) on 16 data sets (1 rat, 5 mouse, and 10 human data sets), involving 8 chemicals or drugs. We assessed the results based on the ability to correctly identify the CDT as indicated by its rank. We also considered the number of false positives, i.e. CDTs other than the correct CDT that were reported to be significant by each method. The proposed approach performed best in 11 out of the 16 experiments, reporting the correct CDT at the very top 7 times. IPA was the second best, reporting the correct CDT at the top 5 times, but was unable to identify the correct CDT at all in 5 out of the 16 experiments. The validation results showed that our approach, PURE, outperformed some of the most popular methods in the field. PURE could effectively infer the true CDTs responsible for the observed gene expression changes and could also be useful in drug repurposing applications.

Immunological modifications following chemotherapy are associated with delayed recurrence of ovarian cancer.

Introduction: Ovarian cancer recurs in most High Grade Serous Ovarian Cancer (HGSOC) patients, including initial responders, after standard of care. To improve patient survival, we need to identify and understand the factors contributing to early or late recurrence and therapeutically target these mechanisms. We hypothesized that in HGSOC, the response to chemotherapy is associated with a specific gene expression signature determined by the tumor microenvironment. In this study, we sought to determine the differences in gene expression and the tumor immune microenvironment between patients who show early recurrence (within 6 months) compared to those who show late recurrence following chemotherapy. Methods: Paired tumor samples were obtained before and after Carboplatin and Taxol chemotherapy from 24 patients with HGSOC. Bioinformatic transcriptomic analysis was performed on the tumor samples to determine the gene expression signature associated with differences in recurrence pattern. Gene Ontology and Pathway analysis was performed using AdvaitaBio's iPathwayGuide software. Tumor immune cell fractions were imputed using CIBERSORTx. Results were compared between late recurrence and early recurrence patients, and between paired pre-chemotherapy and post-chemotherapy samples. Results: There was no statistically significant difference between early recurrence or late recurrence ovarian tumors pre-chemotherapy. However, chemotherapy induced significant immunological changes in tumors from late recurrence patients but had no impact on tumors from early recurrence patients. The key immunological change induced by chemotherapy in late recurrence patients was the reversal of pro-tumor immune signature. Discussion: We report for the first time, the association between immunological modifications in response to chemotherapy and the time of recurrence. Our findings provide novel opportunities to ultimately improve ovarian cancer patient survival.

Plasma proteome of Long-COVID patients indicates HIF-mediated vasculo-proliferative disease with impact on brain and heart function.

AIMS: Long-COVID occurs after SARS-CoV-2 infection and results in diverse, prolonged symptoms. The present study aimed to unveil potential mechanisms, and to inform prognosis and treatment. METHODS: Plasma proteome from Long-COVID outpatients was analyzed in comparison to matched acutely ill COVID-19 (mild and severe) inpatients and healthy control subjects. The expression of 3072 protein biomarkers was determined with proximity extension assays and then deconvoluted with multiple bioinformatics tools into both cell types and signaling mechanisms, as well as organ specificity. RESULTS: Compared to age- and sex-matched acutely ill COVID-19 inpatients and healthy control subjects, Long-COVID outpatients showed natural killer cell redistribution with a dominant resting phenotype, as opposed to active, and neutrophils that formed extracellular traps. This potential resetting of cell phenotypes was reflected in prospective vascular events mediated by both angiopoietin-1 (ANGPT1) and vascular-endothelial growth factor-A (VEGFA). Several markers (ANGPT1, VEGFA, CCR7, CD56, citrullinated histone 3, elastase) were validated by serological methods in additional patient cohorts. Signaling of transforming growth factor-ß1 with probable connections to elevated EP/p300 suggested vascular inflammation and tumor necrosis factor-α driven pathways. In addition, a vascular proliferative state associated with hypoxia inducible factor 1 pathway suggested progression from acute COVID-19 to Long-COVID. The vasculo-proliferative process predicted in Long-COVID might contribute to changes in the organ-specific proteome reflective of neurologic and cardiometabolic dysfunction. CONCLUSIONS: Taken together, our findings point to a vasculo-proliferative process in Long-COVID that is likely initiated either prior hypoxia (localized or systemic) and/or stimulatory factors (i.e., cytokines, chemokines, growth factors, angiotensin, etc). Analyses of the plasma proteome, used as a surrogate for cellular signaling, unveiled potential organ-specific prognostic biomarkers and therapeutic targets.

Discovery of Novel Transketolase Epitopes and the Development of IgG-Based Tuberculosis Serodiagnostics.

Despite advances in rapid molecular techniques for tuberculosis (TB) diagnostics, there is an unmet need for a point-of-care, nonsputum-based test. Previously, through a T7 phage antigen display platform and immunoscreening, we identified that the serum IgGs of active TB patients differentially bind to several antigen-clones and that this immunoreactivity discriminates TB from other respiratory diseases. One of these high-performance clones has some homology to the transketolase of Mycobacterium tuberculosis (M.tb TKT). In this study, we developed a direct enzyme-linked immunosorbent assay (ELISA) detecting IgG against the TKT antigen-clone (TKTµ). Through sequence alignment and in silico analysis, we designed two more peptides with potential antigenicity that correspond to M.tb-specific transketolase (M.tb TKT1 and M.tb TKT3) epitopes. After the development and standardization of a direct peptide ELISA for three peptides, we tested 292 subjects, including TB (n = 101), latent tuberculosis infection (LTBI) (n = 49), healthy controls (n = 66), and sarcoidosis (n = 76). We randomly assigned 60% of the subjects to a training set to create optimal models to distinguish positive TB samples, and the remaining 40% were used to validate the diagnostic power of the IgG-based assays that were developed in the training set. Antibodies against M.tb TKT3 yielded the highest sensitivity (0.845), and these were followed by TKTµ (0.817) and M.tb TKT1 (0.732). The specificities obtained by TKTµ, M.tb TKT3, and M.tb TKT1 on the test sets were 1, 0.95, and 0.875, respectively. The model using TKTµ obtained a perfect positive predictive value (PPV) of 1, and this was followed by M.tb TKT3 (0.968) and M.tb TKT1 (0.912). These results show that IgG antibodies against transketolase can discriminate active TB against LTBI, sarcoidosis, and controls. IMPORTANCE There is an unmet need for a point-of-care, nonsputum-based TB test. Through the immunoscreening of a novel T7 phage library, we identified classifiers that specifically bind to IgGs in active TB sera. We discovered that one of these clones is aligned with Mycobacterium tuberculosis transketolase (TKT). TKT is an essential enzyme for Mycobacterium tuberculosis growth. We designed three TKT epitopes (TKTµ, TKT1, and TKT3) to detect TKT-specific IgGs. After the development and standardization of three different ELISA-utilizing TKT peptides, we tested 292 subjects, including active TB, LTBI, healthy controls, and sarcoidosis. Rigorous statistical analyses using training and validation sets showed that ELISA-based detections of specific IgGs against TKT3 and TKTµ have the greatest sensitivity, specificity, and accuracy to distinguish active TB subjects from others, even LTBI. Our work provides a novel scientific platform from which to further develop a point-of-care test, thereby aiding in faster TB diagnoses.

Integration of Multimodal Data from Disparate Sources for Identifying Disease Subtypes.

Studies over the past decade have generated a wealth of molecular data that can be leveraged to better understand cancer risk, progression, and outcomes. However, understanding the progression risk and differentiating long- and short-term survivors cannot be achieved by analyzing data from a single modality due to the heterogeneity of disease. Using a scientifically developed and tested deep-learning approach that leverages aggregate information collected from multiple repositories with multiple modalities (e.g., mRNA, DNA Methylation, miRNA) could lead to a more accurate and robust prediction of disease progression. Here, we propose an autoencoder based multimodal data fusion system, in which a fusion encoder flexibly integrates collective information available through multiple studies with partially coupled data. Our results on a fully controlled simulation-based study have shown that inferring the missing data through the proposed data fusion pipeline allows a predictor that is superior to other baseline predictors with missing modalities. Results have further shown that short- and long-term survivors of glioblastoma multiforme, acute myeloid leukemia, and pancreatic adenocarcinoma can be successfully differentiated with an AUC of 0.94, 0.75, and 0.96, respectively.

Autoantibodies against cytoskeletons and lysosomal trafficking discriminate sarcoidosis from healthy controls, tuberculosis and lung cancers.

Sarcoidosis is a systemic granulomatous disease of unknown etiology. Hypergammaglobulinemia and the presence of autoantibodies in sarcoidosis suggest active humoral immunity to unknown antigen(s). We developed a complex cDNA library derived from tissues of sarcoidosis patients. Using a high throughput method, we constructed a microarray platform from this cDNA library containing large numbers of sarcoidosis clones. After selective biopanning, 1070 sarcoidosis-specifc clones were arrayed and immunoscreend with 152 sera from patients with sarcoidosis and other pulmonary diseases. To identify the sarcoidosis classifiers two statistical approaches were conducted: First, we identified significant biomarkers between sarcoidosis and healthy controls, and second identified markers comparing sarcoidosis to all other groups. At the threshold of an False Discovery Rate (FDR) < 0.01, we identified 14 clones in the first approach and 12 clones in the second approach discriminating sarcoidosis from other groups. We used the classifiers to build a naïve Bayes model on the training-set and validated it on an independent test-set. The first approach yielded an AUC of 0.947 using 14 significant clones with a sensitivity of 0.93 and specificity of 0.88, whereas the AUC of the second option was 0.92 with a sensitivity of 0.96 and specificity of 0.83. These results suggest robust classifier performance. Furthermore, we characterized the informative phage clones by sequencing and homology searches. Large numbers of classifier-clones were peptides involved in cellular trafficking and cytoskeletons. These results show that sarcoidosis is associated with a specific pattern of immunoreactivity that can discriminate it from other diseases.

Molecular Markers for Long-term Survival in Stage IIIA (N2) NSCLC Patients.

BACKGROUND: Survival rates among non-small cell lung cancer (NSCLC) stage IIIA (N2) patients are generally low and depend on the treatment. PATIENTS AND METHODS: We aimed to identify predictive markers for long term survival in responders and non-responders to chemotherapy, analyzing tumour and non-tumour samples by microarray (n=35) and whole exome sequencing (WES, n=25). RESULTS: WES data showed correlation of overall survival of all patients with rs9905892 in the SLFN12L gene. High frequency of mutations (4/6, 66.7%) was identified in members of SWI/SNF complex in responder patients and in patients that were alive after seven years. Microarray data for immune components showed that VISTA (VSIR) was down-regulated in tumoral tissue. CONCLUSION: Our research suggests that mutations in SWI/SNF complex associate with long term survival after multimodal treatment, while down-regulation of VISTA might indicate its immunomodulatory role in NSCLC stage III (N2) patients.

SMRT: Randomized Data Transformation for Cancer Subtyping and Big Data Analysis.

Cancer is an umbrella term that includes a range of disorders, from those that are fast-growing and lethal to indolent lesions with low or delayed potential for progression to death. The treatment options, as well as treatment success, are highly dependent on the correct subtyping of individual patients. With the advancement of high-throughput platforms, we have the opportunity to differentiate among cancer subtypes from a holistic perspective that takes into consideration phenomena at different molecular levels (mRNA, methylation, etc.). This demands powerful integrative methods to leverage large multi-omics datasets for a better subtyping. Here we introduce Subtyping Multi-omics using a Randomized Transformation (SMRT), a new method for multi-omics integration and cancer subtyping. SMRT offers the following advantages over existing approaches: (i) the scalable analysis pipeline allows researchers to integrate multi-omics data and analyze hundreds of thousands of samples in minutes, (ii) the ability to integrate data types with different numbers of patients, (iii) the ability to analyze un-matched data of different types, and (iv) the ability to offer users a convenient data analysis pipeline through a web application. We also improve the efficiency of our ensemble-based, perturbation clustering to support analysis on machines with memory constraints. In an extensive analysis, we compare SMRT with eight state-of-the-art subtyping methods using 37 TCGA and two METABRIC datasets comprising a total of almost 12,000 patient samples from 28 different types of cancer. We also performed a number of simulation studies. We demonstrate that SMRT outperforms other methods in identifying subtypes with significantly different survival profiles. In addition, SMRT is extremely fast, being able to analyze hundreds of thousands of samples in minutes. The web application is available at http://SMRT.tinnguyen-lab.com. The R package will be deposited to CRAN as part of our PINSPlus software suite.

Evaluation of Dispersion Methods and Mechanical Behaviour of Glass Fibre Composites with Embedded Self-Healing Systems.

The present paper is focused on evaluating the most suitable dispersion method in the epoxy matrix of two self-healing systems containing dicyclopentadiene (DCPD) and 5-ethylidene-2-norbornene (ENB) monomers encapsulated in a urea-formaldehyde (UF) shell, prior to integration, fabrication and impact testing of specimens. Both microstructural analysis and three-point bending tests were performed to evaluate and assess the optimum dispersion method. It was found that ultrasonication damages the microcapsules of both healing systems, thus magnetic stirring was used for the dispersion of both healing systems in the epoxy matrix. Using magnetic dispersion, 5%, 7%, 10%, 12% and 15% volumes of microcapsules were embedded in glass fibre composites. Some of the samples were subjected to thermal cycling between -20 °C and +100 °C for 8 h, to evaluate the behaviour of both healing systems after temperature variation. Impact test results showed that the mechanical behaviour decreases with increasing microcapsule volume, while for specimens subjected to thermal cycling, the impact strength increases with microcapsule volume up to 10%, after which a severe drop in impact strength follows. Retesting after 48 h shows a major drop in mechanical properties in specimens containing 15% MUF-ENB microcapsules, up to total penetration of the specimen.

CPA: a web-based platform for consensus pathway analysis and interactive visualization.

In molecular biology and genetics, there is a large gap between the ease of data collection and our ability to extract knowledge from these data. Contributing to this gap is the fact that living organisms are complex systems whose emerging phenotypes are the results of multiple complex interactions taking place on various pathways. This demands powerful yet user-friendly pathway analysis tools to translate the now abundant high-throughput data into a better understanding of the underlying biological phenomena. Here we introduce Consensus Pathway Analysis (CPA), a web-based platform that allows researchers to (i) perform pathway analysis using eight established methods (GSEA, GSA, FGSEA, PADOG, Impact Analysis, ORA/Webgestalt, KS-test, Wilcox-test), (ii) perform meta-analysis of multiple datasets, (iii) combine methods and datasets to accurately identify the impacted pathways underlying the studied condition and (iv) interactively explore impacted pathways, and browse relationships between pathways and genes. The platform supports three types of input: (i) a list of differentially expressed genes, (ii) genes and fold changes and (iii) an expression matrix. It also allows users to import data from NCBI GEO. The CPA platform currently supports the analysis of multiple organisms using KEGG and Gene Ontology, and it is freely available at http://cpa.tinnguyen-lab.com.

Discovery of primary prostate cancer biomarkers using cross cancer learning.

Prostate cancer (PCa), the second leading cause of cancer death in American men, is a relatively slow-growing malignancy with multiple early treatment options. Yet, a significant number of low-risk PCa patients are over-diagnosed and over-treated with significant and long-term quality of life effects. Further, there is ever increasing evidence of metastasis and higher mortality when hormone-sensitive or castration-resistant PCa tumors are treated indistinctively. Hence, the critical need is to discover clinically-relevant and actionable PCa biomarkers by better understanding the biology of PCa. In this paper, we have discovered novel biomarkers of PCa tumors through cross-cancer learning by leveraging the pathological and molecular similarities in the DNA repair pathways of ovarian, prostate, and breast cancer tumors. Cross-cancer disease learning enriches the study population and identifies genetic/phenotypic commonalities that are important across diseases with pathological and molecular similarities. Our results show that ADIRF, SLC2A5, C3orf86, HSPA1B are among the most significant PCa biomarkers, while MTRNR2L1, EEPD1, TEPP and VN1R2 are jointly important biomarkers across prostate, breast and ovarian cancers. Our validation results have further shown that the discovered biomarkers can predict the disease state better than any randomly selected subset of differentially expressed prostate cancer genes.

COVID-19: disease pathways and gene expression changes predict methylprednisolone can improve outcome in severe cases.

MOTIVATION: COVID-19 has several distinct clinical phases: a viral replication phase, an inflammatory phase and in some patients, a hyper-inflammatory phase. High mortality is associated with patients developing cytokine storm syndrome. Treatment of hyper-inflammation in these patients using existing approved therapies with proven safety profiles could address the immediate need to reduce mortality. RESULTS: We analyzed the changes in the gene expression, pathways and putative mechanisms induced by SARS-CoV2 in NHBE, and A549 cells, as well as COVID-19 lung versus their respective controls. We used these changes to identify FDA approved drugs that could be repurposed to help COVID-19 patients with severe symptoms related to hyper-inflammation. We identified methylprednisolone (MP) as a potential leading therapy. The results were then confirmed in five independent validation datasets including Vero E6 cells, lung and intestinal organoids, as well as additional patient lung sample versus their respective controls. Finally, the efficacy of MP was validated in an independent clinical study. Thirty-day all-cause mortality occurred at a significantly lower rate in the MP-treated group compared to control group (29.6% versus 16.6%, P = 0.027). Clinical results confirmed the in silico prediction that MP could improve outcomes in severe cases of COVID-19. A low number needed to treat (NNT = 5) suggests MP may be more efficacious than dexamethasone or hydrocortisone. AVAILABILITY AND IMPLEMENTATION: iPathwayGuide is available at https://advaitabio.com/ipathwayguide/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Automated Assay of a Four-Protein Biomarker Panel for Improved Detection of Ovarian Cancer.

Background: Mortality from ovarian cancer remains high due to the lack of methods for early detection. The difficulty lies in the low prevalence of the disease necessitating a significantly high specificity and positive-predictive value (PPV) to avoid unneeded and invasive intervention. Currently, cancer antigen- 125 (CA-125) is the most commonly used biomarker for the early detection of ovarian cancer. In this study we determine the value of combining macrophage migration inhibitory factor (MIF), osteopontin (OPN), and prolactin (PROL) with CA-125 in the detection of ovarian cancer serum samples from healthy controls. Materials and Methods: A total of 432 serum samples were included in this study. 153 samples were from ovarian cancer patients and 279 samples were from age-matched healthy controls. The four proteins were quantified using a fully automated, multi-analyte immunoassay. The serum samples were divided into training and testing datasets and analyzed using four classification models to calculate accuracy, sensitivity, specificity, PPV, negative predictive value (NPV), and area under the receiver operating characteristic curve (AUC). Results: The four-protein biomarker panel yielded an average accuracy of 91% compared to 85% using CA-125 alone across four classification models (p = 3.224 × 10-9). Further, in our cohort, the four-protein biomarker panel demonstrated a higher sensitivity (median of 76%), specificity (median of 98%), PPV (median of 91.5%), and NPV (median of 92%), compared to CA-125 alone. The performance of the four-protein biomarker remained better than CA-125 alone even in experiments comparing early stage (Stage I and Stage II) ovarian cancer to healthy controls. Conclusions: Combining MIF, OPN, PROL, and CA-125 can better differentiate ovarian cancer from healthy controls compared to CA-125 alone.

Platelets and renal failure in the SARS-CoV-2 syndrome.

The coronavirus disease 19 (COVID-19) is a highly transmittable viral infection caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). SARS-CoV-2 utilizes metallocarboxyl peptidase angiotensin receptor (ACE) 2 to gain entry into human cells. Activation of several proteases facilitates the interaction of viral spike proteins (S1) and ACE2 receptor. This leads to cleavage of host ACE2 receptors. ACE2 activity counterbalances the angiotensin II effect, its loss may lead to elevated angiotensin II levels with modulation of platelet function, size and activity. COVID-19 disease encompasses a spectrum of systemic involvement far beyond respiratory failure alone. Several features of this disease, including the etiology of acute kidney injury (AKI) and the hypercoagulable state, remain poorly understood. Here, we show that there is a high incidence of AKI (81%) in the critically ill adults with COVID-19 in the setting of elevated D-dimer, elevated ferritin, C reactive protein (CRP) and lactate dehydrogenase (LDH) levels. Strikingly, there were unique features of platelets in these patients, including larger, more granular platelets and a higher mean platelet volume (MPV). There was a significant correlation between measured D-dimer levels and MVP; but a negative correlation between MPV and glomerular filtration rates (GFR) in critically ill cohort. Our data suggest that activated platelets may play a role in renal failure and possibly hypercoagulability status in COVID19 patients.

Identification of key signaling pathways induced by SARS-CoV2 that underlie thrombosis and vascular injury in COVID-19 patients.

The SARS-CoV-2 pandemic has led to hundreds of thousands of deaths and billions of dollars in economic damage. The immune response elicited from this virus is poorly understood. An alarming number of cases have arisen where COVID-19 patients develop complications on top of the symptoms already associated with SARS, such as thrombosis, injuries of vascular system, kidney, and liver, as well as Kawasaki disease. In this review, a bioinformatics approach was used to elucidate the immune response triggered by SARS-CoV-2 infection in primary human lung epithelial and transformed human lung alveolar. Additionally, examined the potential mechanism behind several complications that have been associated with COVID-19 and determined that a specific cytokine storm is leading to excessive neutrophil recruitment. These neutrophils are directly leading to thrombosis, organ damage, and complement activation via neutrophil extracellular trap release.

Identification of cell types from single cell data using stable clustering.

Single-cell RNA-seq (scRNASeq) has become a powerful technique for measuring the transcriptome of individual cells. Unlike the bulk measurements that average the gene expressions over the individual cells, gene measurements at individual cells can be used to study several different tissues and organs at different stages. Identifying the cell types present in the sample from the single cell transcriptome data is a common goal in many single-cell experiments. Several methods have been developed to do this. However, correctly identifying the true cell types remains a challenge. We present a framework that addresses this problem. Our hypothesis is that the meaningful characteristics of the data will remain despite small perturbations of data. We validate the performance of the proposed method on eight publicly available scRNA-seq datasets with known cell types as well as five simulation datasets with different degrees of the cluster separability. We compare the proposed method with five other existing methods: RaceID, SNN-Cliq, SINCERA, SEURAT, and SC3. The results show that the proposed method performs better than the existing methods.

MAGPEL: an autoMated pipeline for inferring vAriant-driven Gene PanEls from the full-length biomedical literature.

In spite of the efforts in developing and maintaining accurate variant databases, a large number of disease-associated variants are still hidden in the biomedical literature. Curation of the biomedical literature in an effort to extract this information is a challenging task due to: (i) the complexity of natural language processing, (ii) inconsistent use of standard recommendations for variant description, and (iii) the lack of clarity and consistency in describing the variant-genotype-phenotype associations in the biomedical literature. In this article, we employ text mining and word cloud analysis techniques to address these challenges. The proposed framework extracts the variant-gene-disease associations from the full-length biomedical literature and designs an evidence-based variant-driven gene panel for a given condition. We validate the identified genes by showing their diagnostic abilities to predict the patients' clinical outcome on several independent validation cohorts. As representative examples, we present our results for acute myeloid leukemia (AML), breast cancer and prostate cancer. We compare these panels with other variant-driven gene panels obtained from Clinvar, Mastermind and others from literature, as well as with a panel identified with a classical differentially expressed genes (DEGs) approach. The results show that the panels obtained by the proposed framework yield better results than the other gene panels currently available in the literature.