Computational Genomics Research Articles

MicroRNA biomarkers of type 2 diabetes: A protocol for corroborating evidence by computational genomics and meta-analyses.

BackgroundFew microRNAs were found consistently dysregulated in type 2 diabetes (T2D) that would gain confidence from Big Pharma to develop diagnostic or therapeutic biomarkers. This study aimed to corroborate evidence from eligible microRNAs-T2D association studies according to stringent quality criteria covering both biological and statistical significance in T2D for biomarker development.Methods and analysesControlled microRNA expression profiling studies on human with T2D will be retrieved from PubMed, ScienceDirect, and Embase for selecting the statistically significant microRNAs according to pre-specified search strategies and inclusion criteria. Multiple meta-analyses with restricted maximum-likelihood estimation and empirical Bayes estimation under the random-effects model will be conducted by metafor package in R. Subgroup and sensitivity analyses further examine the microRNA candidates for their disease specificity, tissue specificity, blood fraction specificity, and statistical robustness of evidence. Biologically relevant microRNAs will then be selected through genomic database corroboration. Their association with T2D is further measured by area under the curve (AUC) of receive operating characteristic (ROC). Meta-analysis of AUC of potential biomarkers will also be conducted. Enrichment analysis on potential microRNA biomarkers and their target genes will be performed by iPathwayGuide and clusterProfiler, respectively. The corresponding reporting guidelines will be used to assess the quality of included studies according to their profiling methods (microarray, RT-PCR, and RNA-Seq).Ethics and disseminationNo ethics approval is required since this study does not include identifiable personal patient data.Protocol registration numberCRD42017081659.

A pilot trial of neoantigen DNA vaccine in combination with nivolumab/ipilimumab and prostvac in metastatic hormone-sensitive prostate cancer (mHSPC).

TPS192 Background: Despite robust advances in the use of immune checkpoint blockade (ICB) across multiple cancer types, responses in prostate cancer remain suboptimal. Overall response rates for single-agent ICB in prostate cancer are low, but recent modest gains have been made with ipilimumab and nivolumab combination therapy in metastatic castrate-resistant prostate cancer. Prostvac-VF Tricom is a therapeutic vaccine that incorporates DNA for the shared self-antigen PSA into the vaccinia (or fowlpox) virus strain. A large randomized phase III clinical trial recently showed no improvement in overall survival (OS) with Prostvac compared with placebo, suggesting that a combinatorial approach is warranted. Personalized neoantigen vaccines based on specific mutated epitopes may have the ability to overcome immunoresistance seen with self antigens. Even in low mutational burden tumors like prostate cancer, T cell responses against neoantigens are observed in patients with favorable clinical outcomes, supporting neoantigen vaccination as a promising therapeutic strategy. Improvements in computational genomics and predictive algorithms have allowed the incorporation of MHC class II neoepitopes and those from gene fusion events relevant in prostate cancer. We thus hypothesized that a strategy utilizing both shared antigen (Prostvac) and personalized MHC-I and MHC-II neoantigen vaccines combined with dual ICB would induce robust immune responses and improve clinical outcomes. To maximize tumor burden reduction and minimize tumor-mediated immunoresistence, we are evaluating this novel strategy in patients with metastatic hormone-sensitive prostate cancer (mHSPC) who have completed frontline docetaxel chemotherapy. Methods: This ongoing trial (NCT03532217) began accrual in September 2018. Eighteen of 20 planned patients have been enrolled to date. Eligible patients have histologically confirmed high risk/volume mHSPC defined as 4 or more sites of metastatic disease or visceral involvement. Patients receive continuous androgen deprivation therapy (ADT) and have completed upfront docetaxel chemotherapy. Patients begin treatment with Prostvac-VF in combination with ipilimumab (1 mg/kg every 3 weeks for 2 doses), and nivolumab (3 mg/kg every 3 weeks for 6 doses) within 60 days of the last dose of docetaxel. Subsequently, patients receive nivolumab 480 mg IV every 4 weeks in conjunction with a personalized neoantigen DNA vaccine administered monthly via intramuscular electroporation. The primary objective of this exploratory study is to assess the feasibility, safety/tolerability, and immune responses of this combination strategy. Key secondary objectives include failure-free survival and milestone survival at 2 years, PSA response, and radiographic progression-free survival. Clinical trial information: NCT03532217.

Metabolic Potential, Ecology and Presence of Associated Bacteria Is Reflected in Genomic Diversity of Mucoromycotina.

Mucoromycotina are often considered mainly in pathogenic context but their biology remains understudied. We describe the genomes of six Mucoromycotina fungi representing distant saprotrophic lineages within the subphylum (i.e., Umbelopsidales and Mucorales). We selected two Umbelopsis isolates from soil (i.e., U. isabellina, U. vinacea), two soil-derived Mucor isolates (i.e., M. circinatus, M. plumbeus), and two Mucorales representatives with extended proteolytic activity (i.e., Thamnidium elegans and Mucor saturninus). We complement computational genome annotation with experimental characteristics of their digestive capabilities, cell wall carbohydrate composition, and extensive total lipid profiles. These traits inferred from genome composition, e.g., in terms of identified encoded enzymes, are in accordance with experimental results. Finally, we link the presence of associated bacteria with observed characteristics. Thamnidium elegans genome harbors an additional, complete genome of an associated bacterium classified to Paenibacillus sp. This fungus displays multiple altered traits compared to the remaining isolates, regardless of their evolutionary distance. For instance, it has expanded carbon assimilation capabilities, e.g., efficiently degrades carboxylic acids, and has a higher diacylglycerol:triacylglycerol ratio and skewed phospholipid composition which suggests a more rigid cellular membrane. The bacterium can complement the host enzymatic capabilities, alter the fungal metabolism, cell membrane composition but does not change the composition of the cell wall of the fungus. Comparison of early-diverging Umbelopsidales with evolutionary younger Mucorales points at several subtle differences particularly in their carbon source preferences and encoded carbohydrate repertoire. Nevertheless, all tested Mucoromycotina share features including the ability to produce 18:3 gamma-linoleic acid, use TAG as the storage lipid and have fucose as a cell wall component.

Evolution of Microbial Genomics: Conceptual Shifts over a Quarter Century.

Prokaryote genomics started in earnest in 1995, with the complete sequences of two small bacterial genomes, those of Haemophilus influenzae and Mycoplasma genitalium. During the next quarter century, the prokaryote genome database has been growing exponentially, with no saturation in sight. For most of these 25 years, genome sequencing remained limited to cultivable microbes. Together with next-generation sequencing methods, advances in metagenomics and single-cell genomics have lifted this limitation, providing for an increasingly unbiased characterization of the global prokaryote diversity. Advances in computational genomics followed the progress of genome sequencing, even if occasionally lagging behind. Several major new branches of bacteria and archaea were discovered, including Asgard archaea, the apparent closest relatives of eukaryotes and expansive groups of bacteria and archaea with small genomes thought to be symbionts of other prokaryotes. Comparative analysis of numerous prokaryote genomes spanning a wide range of evolutionary distances changed the conceptual foundations of microbiology, supplanting the notion of species genomes with fixed gene sets with that of dynamic pangenomes and the notion of a single Tree of Life (ToL) with a statistical tree-like trend among individual gene trees. Strides were also made towards a theory and quantitative laws of prokaryote genome evolution.

Synthetic Biology: A Very Short Introduction by Jamie Davies

When Mary Shelley wrote Frankenstein in 1818, she probably did not see synthetic biology coming. Shelley’s unorthodox use of galvanism and vivisection may have become computational biology, protein engineering and genome editing, but – many scientists would argue – has lost none of its gothic charm. As a truly interdisciplinary field, synthetic biology has been around – at least in its modern molecular incarnation – since Stanley Cohen and Herbert Boyer genetically engineered the first plasmid in 1973. In Synthetic Biology: A Very Short Introduction, Jamie Davies starts us off on the ‘creation of new living systems by design’ with one of many predictable clashes – between the economic forces who swear by the promise of synthetic biology and the bioethicists whose reservations keep this field at arm’s length. As most books in the Very Short Introduction series, Davies’ pocket-size guide provides readers with a thorough and engaging introduction to the biology and methods of synthetic biology while touring its numerous applications, from saving our oceans to changing the way we produce drugs. Davies does a great job of sketching a field concerned with the deliberate redesigning of life. As with other great introductions, by the end of this book, one cannot help oneself, but wanting to know more.

Computing the Rearrangement Distance of Natural Genomes.

The computation of genomic distances has been a very active field of computational comparative genomics over the past 25 years. Substantial results include the polynomial-time computability of the inversion distance by Hannenhalli and Pevzner in 1995 and the introduction of the double cut and join distance by Yancopoulos et al. in 2005. Both results, however, rely on the assumption that the genomes under comparison contain the same set of unique markers (syntenic genomic regions, sometimes also referred to as genes). In 2015, Shao et al. relax this condition by allowing for duplicate markers in the analysis. This generalized version of the genomic distance problem is NP-hard, and they give an integer linear programming (ILP) solution that is efficient enough to be applied to real-world datasets. A restriction of their approach is that it can be applied only to balanced genomes that have equal numbers of duplicates of any marker. Therefore, it still needs a delicate preprocessing of the input data in which excessive copies of unbalanced markers have to be removed. In this article, we present an algorithm solving the genomic distance problem for natural genomes, in which any marker may occur an arbitrary number of times. Our method is based on a new graph data structure, the multi-relational diagram, that allows an elegant extension of the ILP by Shao et al. to count runs of markers that are under- or over-represented in one genome with respect to the other and need to be inserted or deleted, respectively. With this extension, previous restrictions on the genome configurations are lifted, for the first time enabling an uncompromising rearrangement analysis. Any marker sequence can directly be used for the distance calculation. The evaluation of our approach shows that it can be used to analyze genomes with up to a few 10,000 markers, which we demonstrate on simulated and real data.

Computational Genomics of RELA gene of Pig and its protein prescription

RELA gene is a pleiotropic transcription that is present in almost all living cells where several signal transduction events end points were initiated by many arrays of stimuli that were related to many biological processes such as inflammation, immunity, differentiation, cell growth, tumorigenesis and apoptosis in the cells. The nucleotide and amino acid sequence (AAS) of RELA gene of pig and other mammalian species were downloaded from the National Center for Biotechnology information (NCBI) data base, United State of America and Universal protein resources (Uniprot) database, United Kingdom. Data generated were analyzed by bioinformatics tools. The results showed a high variation of AAS among the mammals where only four mammals have the same AAS length (551) with pig while others were not. The RELA gene of pig shared very high percent identity with all mammals ranging from 95.5% (warthog) to 83.5% (guinea pig) and similarity percentage ranged from 85.1 to 99.5% where the warthog had the highest percentage value while the least mammal was Guinea pig. The tree showed RELA gene with two clades; the first for mammals and the second for non-mammals. The RELA gene of pig was in the same cluster with the other Suidae family, warthog and babirusa. For the protein structure obtained, only 43% can be meaningfully predicted from the 61% residues modelled at more than 90%. The domain architecture of RELA protein structure contained mainly RNA Recognition motif and region of low complexity. The domainstarting position of all the organisms ranged from 191 to 210 while the termination of all the mammals ranged from 102 to 306 amino acids.The region of low complexity starting point ranged from position 173 to 462while termination positions ranged from 184 to 474. The evolution relationship of RELA gene of pig and other mammalian species were highly related and conserved among the mammalian species; they have high comparability and they evolved from common ancestors.

Computational genomics of Torque teno sus virus and Porcine circovirus in swine samples from Canada

Emerging viral diseases include pathogens that can threaten the health of the Canadian swineherd. Anelloviruses and Circoviruses comprise of pathogens with veterinary significance. The aim of this study was to determine the genomic organization and phylogenetic relationships of Torque teno sus virus (TTsusV) and Porcine circovirus (PCV) from Canadian pig samples. Fecal and tissue specimens were collected during the winter, spring and summer of 2018. We utilized either virus- or genus-specific PCR assays to characterize the occurrence and genetic diversity of TTsusV and PCV in Canadian pigs. Pairwise comparison of all partial sequences and identity calculation was performed using MAFFT algorithm implemented in Sequence Demarcation Tool (SDT). The obtained full-length sequences were aligned using ClustalW, and phylogeny was inferred using a Maximum likelihood (ML) method by Geneious software. The PCR detection results revealed that the overall positive rate of TTsusV type-1 and type-2 was 45.6% and 32.6%, respectively. The TTsusV isolate MK990454 from Canada clustered in the subtype TTsusV1b, while the TTsusV isolate MK872392 fell in the subtype TTsusV2c, and all showed similarity to known American and Chinese isolates. In addition, our screening PCR showed that 2.7% of stool samples were positive for PCV1. Phylogenetic analysis using the full-length sequence demonstrated that PCV1 (MK872393) isolated from Quebec clustered with other Chinese PCV1 strains. Despite the far geographical distance between Canada and China, the close similarity between Canadian and Chinese TTsusV1 and 2, and PCV1 sequences may be explained by a considerable amount of pig trade between these two nations.

Algorithms meet sequencing technologies - 10th edition of the RECOMB-Seq workshop.

SummaryDNA and RNA sequencing is a core technology in biological and medical research. The high throughput of these technologies and the consistent development of new experimental assays and biotechnologies demand the continuous development of methods to analyze the resulting data. The RECOMB Satellite Workshop on Massively Parallel Sequencing brings together leading researchers in computational genomics to discuss emerging frontiers in algorithm development for massively parallel sequencing data. The 10th meeting in this series, RECOMB-Seq 2020, was scheduled to be held in Padua, Italy, but due to the ongoing COVID-19 pandemic, the meeting was carried out virtually instead. The online workshop featured keynote talks by Paola Bonizzoni and Zamin Iqbal, two highlight talks, ten regular talks, and three short talks. Seven of the works presented in the workshop are featured in this edition of iScience, and many of the talks are available online in the RECOMB-Seq 2020 YouTube channel.

Evolutionary Dynamics and Consequences of Parthenogenesis in Vertebrates

Parthenogenesis is asexual reproduction without any required participation from males and, as such, is a null model for sexual reproduction. In a comparative context, we can expand our understanding of the evolution and ecology of sex by investigating the consequences of parthenogenesis. In this review, we examine the theoretical predictions of and empirical results on the evolution of asexual reproduction in vertebrates, focusing on recent studies addressing the origins and geographic spread of parthenogenetic lineages and the genomic consequences of an asexual life history. With advances in computational methods and genome technologies, researchers are poised to make rapid and significant progress in studying the origin and evolution of parthenogenesis in vertebrates, thus providing an important perspective on understanding biodiversity patterns of both asexual and sexual populations.

A connectivity map-based drug repurposing study and integrative analysis of transcriptomic profiling of SARS-CoV-2 infection.

AimsThe recent outbreak of COVID-19 has become a global health concern. There are currently no effective treatment strategies and vaccines for the treatment or prevention of this fatal disease. The current study aims to determine promising treatment options for the COVID-19 through a computational drug repurposing approach.Materials and methodsIn this study, we focus on differentially expressed genes (DEGs), detected in SARS-CoV-2 infected cell lines including “the primary human lung epithelial cell line NHBE” and “the transformed lung alveolar cell line A549”. Next, the identified DEGs are used in the connectivity map (CMap) analysis to identify similarly acting therapeutic candidates. Furthermore, to interpret lists of DEGs, pathway enrichment and protein network analysis are performed. Genes are categorized into easily interpretable pathways based on their biological functions, and overrepresentation of each pathway is tested in comparison to what is expected randomly.Key findingsThe results suggest the effectiveness of lansoprazole, folic acid, sulfamonomethoxine, tolnaftate, diclofenamide, halcinonide, saquinavir, metronidazole, ebselen, lidocaine and benzocaine, histone deacetylase (HDAC) inhibitors, heat shock protein 90 (HSP90) inhibitors, and many other clinically approved drugs as potent drugs against COVID-19 outbreak.SignificanceMaking new drugs remain a lengthy process, so the drug repurposing approach provides an insight into the therapeutics that might be helpful in this pandemic. In this study, pathway enrichment and protein network analysis are also performed, and the effectiveness of some drugs obtained from the CMap analysis has been investigated according to previous researches.

The Nucleome Data Bank: web-based resources to simulate and analyze the three-dimensional genome.

We introduce the Nucleome Data Bank (NDB), a web-based platform to simulate and analyze the three-dimensional (3D) organization of genomes. The NDB enables physics-based simulation of chromosomal structural dynamics through the MEGABASE + MiChroM computational pipeline. The input of the pipeline consists of epigenetic information sourced from the Encode database; the output consists of the trajectories of chromosomal motions that accurately predict Hi-C and fluorescence insitu hybridization data, as well as multiple observations of chromosomal dynamics in vivo. As an intermediate step, users can also generate chromosomal sub-compartment annotations directly from the same epigenetic input, without the use of any DNA–DNA proximity ligation data. Additionally, the NDB freely hosts both experimental and computational structural genomics data. Besides being able to perform their own genome simulations and download the hosted data, users can also analyze and visualize the same data through custom-designed web-based tools. In particular, the one-dimensional genetic and epigenetic data can be overlaid onto accurate 3D structures of chromosomes, to study the spatial distribution of genetic and epigenetic features. The NDB aims to be a shared resource to biologists, biophysicists and all genome scientists. The NDB is available at https://ndb.rice.edu.

Review on the Computational Genome Annotation of Sequences Obtained by Next-Generation Sequencing.

Simple SummaryDue to the development of high-throughput sequencing technologies, computational genome annotation of sequences has become one of the principal research area in computational biology. First, we reviewed comparative annotation tools and pipelines for both annotations of structures and functions, which enable us to comprehend gene functions and their genome evolution. Second, we compared genome annotation tools that utilize homology-based and ab initio methods depending on the similarity of sequences or the lack of evidences. Third, we explored visualization tools that aid the annotation process and stressed the need for the quality control of annotations and re-annotations, because misannotations may happen due to experimental errors or missed genes by preceding technologies. Finally, we highlighted how emerging technologies can be used in future annotations.Next-Generation Sequencing (NGS) has made it easier to obtain genome-wide sequence data and it has shifted the research focus into genome annotation. The challenging tasks involved in annotation rely on the currently available tools and techniques to decode the information contained in nucleotide sequences. This information will improve our understanding of general aspects of life and evolution and improve our ability to diagnose genetic disorders. Here, we present a summary of both structural and functional annotations, as well as the associated comparative annotation tools and pipelines. We highlight visualization tools that immensely aid the annotation process and the contributions of the scientific community to the annotation. Further, we discuss quality-control practices and the need for re-annotation, and highlight the future of annotation.

Leveraging computational genomics to understand the molecular basis of metal homeostasis.

Genome-based data is helping to reveal the diverse strategies plants and algae use to maintain metal homeostasis. In addition to acquisition, distribution and storage of metals, acclimating to feast or famine can involve a wealth of genes that we are just now starting to understand. The fast-paced acquisition of genome-based data, however, is far outpacing our ability to experimentally characterize protein function. Computational genomic approaches are needed to fill the gap between what is known and unknown. To avoid misconstruing bioinformatically derived data, which is the root cause of the inaccurate functional annotations that plague databases, functional inferences from diverse sources and contextualization of that evidence with a robust understanding of protein family evolution is needed. Phylogenomic- and comparative-genomic-based studies can aid in the interpretation of experimental data or provide a spark for the discovery of a new function. These analyses not only lead to novel insight into a target protein's function but can generate thought-provoking insights across protein families.

Integrated transcriptomic and metabolomic analyses of glutamine metabolism genes unveil key players in Oryza sativa (L.) to ameliorate the unique and combined abiotic stress tolerance

Plants can be considered to biosynthesize the specialized metabolites to adapt to various environmental stressors mainly on abiotic stresses (AbS). Among specialized metabolites, glutamine (Gln) is an essential plant metabolite to achieve sustainable plant growth, yield and food security. In this pilot study, swe employed computational metabolomics genome wide association survey (cmGWAS) of Gln metabolite profiling in Oryza sativa, targeting at the identification of abiotic stress responsible (AbSR) – Gln metabolite producing genes (GlnMPG). Identified 5 AbSR-GlnMPG alter the metabolite levels and play a predominant role in delineating the physiological significance of rice. These genes were systematically analysed for their biological features via OryzaCyc. Spatio-temporal and plant hormonal expression pattern of AbSR-GlnMPG was analysed and their differential expression profiling were noted in 48 different tissues and hormones, respectively. Furthermore, comparative ideogram of these genes revealed the chromosomal synteny with C4 grass genomes. Molecular crosstalks of these proteins, unravelled the various metabolic interaction. The systems expression profiling of AbSR-GlnMPG will lead to unravel the metabolite signaling and putative responses in multiple AbS. On the whole, this holistic study provides deeper insights on biomolecular features of AbSR-GlnMPG, which could be analysed further to decipher their functional metabolisms in AbS dynamism.

Efficient dynamic variation graphs.

Pangenomics is a growing field within computational genomics. Many pangenomic analyses use bidirected sequence graphs as their core data model. However, implementing and correctly using this data model can be difficult, and the scale of pangenomic datasets can be challenging to work at. These challenges have impeded progress in this field. Here, we present a stack of two C++ libraries, libbdsg and libhandlegraph, which use a simple, field-proven interface, designed to expose elementary features of these graphs while preventing common graph manipulation mistakes. The libraries also provide a Python binding. Using a diverse collection of pangenome graphs, we demonstrate that these tools allow for efficient construction and manipulation of large genome graphs with dense variation. For instance, the speed and memory usage are up to an order of magnitude better than the prior graph implementation in the VG toolkit, which has now transitioned to using libbdsg's implementations. libhandlegraph and libbdsg are available under an MIT License from https://github.com/vgteam/libhandlegraph and https://github.com/vgteam/libbdsg.

The Predominant microRNAs in β-cell Clusters for Insulin Regulation and Diabetic Control.

micro (mi)-RNAs are vital regulators of multiple processes including insulin signaling pathways and glucose metabolism. Pancreatic β-cells function is dependent on some miRNAs and their target mRNA, which together form a complex regulative network. Several miRNAs are known to be directly involved in β-cells functions such as insulin expression and secretion. These small RNAs may also play significant roles in the fate of β-cells such as proliferation, differentiation, survival and apoptosis. Among the miRNAs, miR-7, miR-9, miR-375, miR-130 and miR-124 are of particular interest due to being highly expressed in these cells. Under diabetic conditions, although no specific miRNA profile has been noticed, the expression of some miRNAs and their target mRNAs are altered by posttranscriptional mechanisms, exerting diverse signs in the pathobiology of various diabetic complications. The aim of this review article is to discuss miRNAs involved in the process of stem cells differentiation into β-cells, resulting in enhanced β-cell functions with respect to diabetic disorders. This paper will also look into the impact of miRNA expression patterns on in vitro proliferation and differentiation of β-cells. The efficacy of the computational genomics and biochemical analysis to link the changes in miRNA expression profiles of stem cell-derived β-cells to therapeutically relevant outputs will be discussed as well.

A computational subtractive genome analysis for the characterization of novel drug targets in Klebsiella pneumonia strain PittNDM01

The emergence of carbapenem-resistant Klebsiella Pneumoniae had been reported previously, which needs rapid attention. Currently, Pittsburgh University Hospital reported a new strain of carbapenem-resistant Klebsiella pneumoniae that was co-producing OXA-232 and NDM-1 named as PittNDM01. This strain is resistant to almost all beta-lactam antibiotics such as Carbapenem as well as to fluoroquinolones and aminoglycosides. Globally, failure to the wide-spread pathogenic strains had been observed due to the increased and antibiotic resistance, which leads to less antimicrobial drug efficacy. Since last decades, computational genomic approaches have been introduced to fight against resistant pathogens, which is an advanced approach for novel drug targets investigation. The current study emphasizes the utilization of the available genomic and proteomic data of Klebsiella pneumoniae PittNDM01 for the identification of novel drug targets for future drug developments. Comparative genomic analysis and molecular biological tools were applied, results in observing 582 non-human homologous-essential proteins of Klebsiella pneumoniae. Among the total 582 proteins, 66 were closely related to the pathogen-specific pathway. Out of all 66-targeted proteins, ten non-homologous essential proteins were found to have druggability potential. The subcellular localization of these proteins revealed; 6 proteins in the cytoplasm, 2 in the inner membrane, and one each in periplasmic space and outer membrane. All the above 10 proteins were compared to the proteins sequences of gut flora to eliminate the homologous proteins. In total, 6-novel non-human and non-gut flora essential drug targets of Klebsiella pneumoniae PittNDM01 strain were identified. Further, the 3D structures of the identified drug target proteins were developed, and protein-protein interaction network analysis was performed to know the functional annotation of the desire proteins. Therefore, these non-homologous essential targets ensure the survival of the pathogen and hence can be targeted for drug discovery.

Imaging-AMARETTO: An Imaging Genomics Software Tool to Interrogate Multiomics Networks for Relevance to Radiography and Histopathology Imaging Biomarkers of Clinical Outcomes.

PURPOSEThe availability of increasing volumes of multiomics, imaging, and clinical data in complex diseases such as cancer opens opportunities for the formulation and development of computational imaging genomics methods that can link multiomics, imaging, and clinical data.METHODSHere, we present the Imaging-AMARETTO algorithms and software tools to systematically interrogate regulatory networks derived from multiomics data within and across related patient studies for their relevance to radiography and histopathology imaging features predicting clinical outcomes.RESULTSTo demonstrate its utility, we applied Imaging-AMARETTO to integrate three patient studies of brain tumors, specifically, multiomics with radiography imaging data from The Cancer Genome Atlas (TCGA) glioblastoma multiforme (GBM) and low-grade glioma (LGG) cohorts and transcriptomics with histopathology imaging data from the Ivy Glioblastoma Atlas Project (IvyGAP) GBM cohort. Our results show that Imaging-AMARETTO recapitulates known key drivers of tumor-associated microglia and macrophage mechanisms, mediated by STAT3, AHR, and CCR2, and neurodevelopmental and stemness mechanisms, mediated by OLIG2. Imaging-AMARETTO provides interpretation of their underlying molecular mechanisms in light of imaging biomarkers of clinical outcomes and uncovers novel master drivers, THBS1 and MAP2, that establish relationships across these distinct mechanisms.CONCLUSIONOur network-based imaging genomics tools serve as hypothesis generators that facilitate the interrogation of known and uncovering of novel hypotheses for follow-up with experimental validation studies. We anticipate that our Imaging-AMARETTO imaging genomics tools will be useful to the community of biomedical researchers for applications to similar studies of cancer and other complex diseases with available multiomics, imaging, and clinical data.

A New Paradigm for Identifying Reconciliation-Scenario Altering Mutations Conferring Environmental Adaptation.

An important goal in microbial computational genomics is to identify crucial events in the evolution of a gene that severely alter the duplication, loss, and mobilization patterns of the gene within the genomes in which it disseminates. In this article, we formalize this microbiological goal as a new pattern-matching problem in the domain of gene tree and species tree reconciliation, denoted "Reconciliation-Scenario Altering Mutation (RSAM) Discovery." We propose an [Formula: see text] time algorithm to solve this new problem, wheremandnare the number of vertices of the input gene tree and species tree, respectively, andkis a user-specified parameter that bounds from above the number of optimal solutions of interest. The algorithm first constructs a hypergraph representing thekhighest scoring reconciliation scenarios between the given gene tree and species tree, and then interrogates this hypergraph for subtrees matching a prespecified RSAM pattern. Our algorithm is optimal in the sense that the number of hypernodes in the hypergraph can be lower bounded by [Formula: see text]. We implement the new algorithm as a tool, called RSAM-finder, and demonstrate its application to the identification of RSAMs in toxins and drug resistance elements across a data set spanning hundreds of species.