Plasmids in the human gut reveal neutral dispersal and recombination that is overpowered by inflammatory diseases

Plasmids are pivotal in driving bacterial evolution through horizontal gene transfer. Here, we investigated 3467 human gut microbiome samples across continents and disease states, analyzing 11,086 plasmids. Our analyses reveal that plasmid dispersal is predominantly stochastic, indicating neutral processes as the primary driver of their wide distribution. We find that only 20-25% of plasmid DNA is being selected in various disease states, constraining its distribution across hosts. Selective pressures shape specific plasmid segments with distinct ecological functions, influenced by plasmid mobilization lifestyle, antibiotic usage, and inflammatory gut diseases. Notably, these elements are more commonly shared within groups of individuals with similar health conditions, such as Inflammatory Bowel Disease (IBD), regardless of geographic location across continents. These segments contain essential genes such as iron transport mechanisms- a distinctive gut signature of IBD that impacts the severity of inflammation. Our findings shed light on mechanisms driving plasmid dispersal and selection in the human gut, highlighting their role as carriers of vital gene pools impacting bacterial hosts and ecosystem dynamics.

Circadian clock gene disruption in white blood cells of patients with celiac disease

Clock gene disruption has been reported in inflammatory and autoimmune diseases. Specifically, it has been shown that clock gene expression is down-regulated in intestinal tissue and peripheral blood mononuclear cells of patients with inflammatory bowel disease (IBD). We aimed to determine the systemic expression of the circadian clock genes in newly diagnosed untreated, young patients with celiac disease (CeD). We prospectively enrolled patients younger than 20 years old who underwent diagnostic endoscopic procedures either for CeD diagnosis or due to other gastrointestinal complaints, at the pediatric and adult gastroenterology units, the Tel Aviv Sourasky Medical Center from 8/2016-8/2022. Demographic data, anthropometric parameters, and endoscopic macroscopic and microscopic findings were obtained. Blood samples were obtained to determine tissue transglutaminase (tTG) and core clock gene (CLOCK, BMAL1, PER1, PER2, CRY1, CRY2) expression in white blood cells (WBC). Thirty individuals were analyzed (18 with newly diagnosed CeD and 12 controls). Expression of the clock genes CLOCK, BMAL1, CRY2, PER1 and PER2 was significantly reduced in CeD patients compared to controls, while CRY1 did not differ between the groups. In conclusion, newly diagnosed, untreated, young patients with CeD have reduced clock gene expression in WBC compared to controls. These results suggest that, in CeD, the inflammatory response is associated with systemic disruption of clock gene expression, as is manifested in other inflammatory and autoimmune diseases. CLINICALTRIALS.GOV IDENTIFIER: NCT03662646.

Clustering the cortical laminae: in vivo parcellation

The laminar microstructure of the cerebral cortex has distinct anatomical characteristics of the development, function, connectivity, and even various pathologies of the brain. In recent years, multiple neuroimaging studies have utilized magnetic resonance imaging (MRI) relaxometry to visualize and explore this intricate microstructure, successfully delineating the cortical laminar components. Despite this progress, T1 is still primarily considered a direct measure of myeloarchitecture (myelin content), rather than a probe of tissue cytoarchitecture (cellular composition). This study aims to offer a robust, whole-brain validation of T1 imaging as a practical and effective tool for exploring the laminar composition of the cortex. To do so, we cluster complex microstructural cortical datasets of both human (N = 30) and macaque (N = 1) brains using an adaptation of an algorithm for clustering cell omics profiles. The resulting cluster patterns are then compared to established atlases of cytoarchitectonic features, exhibiting significant correspondence in both species. Lastly, we demonstrate the expanded applicability of T1 imaging by exploring some of the cytoarchitectonic features behind various unique skillsets, such as musicality and athleticism.

The predictive capacity of polygenic risk scores for disease risk is only moderately influenced by imputation panels tailored to the target population

MOTIVATION

Polygenic risk scores (PRSs) predict individuals' genetic risk of developing complex diseases. They summarize the effect of many variants discovered in genome-wide association studies (GWASs). However, to date, large GWASs exist primarily for the European population and the quality of PRS prediction declines when applied to other ethnicities. Genetic profiling of individuals in the discovery set (on which the GWAS was performed) and target set (on which the PRS is applied) is typically done by SNP arrays that genotype a fraction of common SNPs. Therefore, a key step in GWAS analysis and PRS calculation is imputing untyped SNPs using a panel of fully sequenced individuals. The imputation results depend on the ethnic composition of the imputation panel. Imputing genotypes with a panel of individuals of the same ethnicity as the genotyped individuals typically improves imputation accuracy. However, there has been no systematic investigation into the influence of the ethnic composition of imputation panels on the accuracy of PRS predictions when applied to ethnic groups that differ from the population used in the GWAS.

RESULTS

We estimated the effect of imputation of the target set on prediction accuracy of PRS when the discovery and the target sets come from different ethnic groups. We analyzed binary phenotypes on ethnically distinct sets from the UK Biobank and other resources. We generated ethnically homogenous panels, imputed the target sets, and generated PRSs. Then, we assessed the prediction accuracy obtained from each imputation panel. Our analysis indicates that using an imputation panel matched to the ethnicity of the target population yields only a marginal improvement and only under specific conditions.

AVAILABILITY AND IMPLEMENTATION

The source code used for executing the analyses is this paper is available at https://github.com/Shamir-Lab/PRS-imputation-panels.

Evaluation of European-based polygenic risk score for breast cancer in Ashkenazi Jewish women in Israel

BACKGROUND

Polygenic risk score (PRS), calculated based on genome-wide association studies (GWASs), can improve breast cancer (BC) risk assessment. To date, most BC GWASs have been performed in individuals of European (EUR) ancestry, and the generalisation of EUR-based PRS to other populations is a major challenge. In this study, we examined the performance of EUR-based BC PRS models in Ashkenazi Jewish (AJ) women.

METHODS

We generated PRSs based on data on EUR women from the Breast Cancer Association Consortium (BCAC). We tested the performance of the PRSs in a cohort of 2161 AJ women from Israel (1437 cases and 724 controls) from BCAC (BCAC cohort from Israel (BCAC-IL)). In addition, we tested the performance of these EUR-based BC PRSs, as well as the established 313-SNP EUR BC PRS, in an independent cohort of 181 AJ women from Hadassah Medical Center (HMC) in Israel.

RESULTS

In the BCAC-IL cohort, the highest OR per 1 SD was 1.56 (±0.09). The OR for AJ women at the top 10% of the PRS distribution compared with the middle quintile was 2.10 (±0.24). In the HMC cohort, the OR per 1 SD of the EUR-based PRS that performed best in the BCAC-IL cohort was 1.58±0.27. The OR per 1 SD of the commonly used 313-SNP BC PRS was 1.64 (±0.28).

CONCLUSIONS

Extant EUR GWAS data can be used for generating PRSs that identify AJ women with markedly elevated risk of BC and therefore hold promise for improving BC risk assessment in AJ women.

Patient-Specific Segmentation-Based Treatment Planning vs. NovoTAL for TTFields Therapy in Glioblastoma

PURPOSE/OBJECTIVE(S)

Patients treated with Tumor Treating Fields (TTFields) therapy for glioblastoma (GBM) have array layouts planned by NovoTAL. NovoTAL requires morphometric inputs and maximizes field intensity at the tumor. Patient-specific segmentation-based treatment planning (SBTP) software uses segmentation-based plans to maximize power density at defined regions of interest (ROIs). This technical analysis compared expected local minimum power density (LMiPD; mW/cm3) and local minimum field intensity (LMiFI; V/cm) delivered to ROIs with array layouts planned with SBTP vs NovoTAL. We hypothesized that SBTP has the potential to increase LMiPD and LMiFI to ROIs vs NovoTal.

MATERIALS/METHODS

37 patients from 5 sites who received TTFields therapy for GBM using NovoTAL were included. Treatment plans using the prescribed/treated NovoTAL layouts were created with SBTP. De novo SBTP layouts were also created. Three ROIs representing the original treated GBM (CTV), high risk margin around the GBM (CTV-2), and recurrent GBM (CTV-R) were created. Plans were optimized to CTV. SBTP vs NovoTAL LMiPD and LMiFI volumetrics to ROIs were evaluated. LMiPD and LMiFI were normalized with the delivered current from the treated NovoTAL layout. Layout rankings based on LMiPD and LMiFI, average LMiPD and LMiFI, D95, D5, DVHs, and voxel-by-voxel LMiPD and LMiFI for SBTP derived from NovoTAL layouts were compared to de novo SBTP layouts (paired t-tests).

RESULTS

Average LMiPD (1.551 vs 1.194) and LMiFI (1.115 vs 0.978) to CTV were significantly higher with SBTP vs NovoTAL (P < 0.0001 for each). Average LMiPD (1.445 vs 1.164) and LMiFI (1.197 vs 1.077) to CTV-2 were also higher (P < 0.0001 for each). There was a positive trend to higher average LMiPD (1.203 vs 1.157; P = 0.212) and LMiFI (1.103 vs 1.090; P = 0.311) to CTV-R. Top ranked overall layouts by LMiPD to CTV were SBTP layouts (97%; n = 36). Percent ratio ([SBTP-NovoTAL]/NovoTAL*100) D95 for LMiPD was 34% (to CTV), 24% (to CTV-2), and 5% (to CTV-R) and for LMiFI was 16%, 12%, and 2% respectively. Percent ratio D5 for LMiPD was 31%, 24%, and 3% and for LMiFI was 14%, 9%, and 0%, respectively. For a given percent CTV volume, minimum LMiPD and LMiFI were higher with SBTP (95%, n = 35; DVH curves shifted to right). SBTP yielded higher LMiPD and LMiFI to the majority of voxels within the CTV (95%, n = 35). With SBTP, LMiPD to CTV was significantly higher than to CTV-R (P < 0.001).

CONCLUSION

Overall, these data demonstrate that SBTP compared to NovoTAL yielded higher expected average LMiPD and LMiFI, D95, D5, and percent voxel LMiPD and LMiFI to defined ROIs. Higher LMiPD and LMiFI delivered to CTV vs CTV-R with SBTP suggests a benefit to re-planning if the GBM recurs. Given previous reports showing that higher LMiPD and LMiFI are positively correlated with improved overall and progression free survival, patient-specific SBTP may lead to improved clinical outcomes for GBM patients vs NovoTAL.

Integration of gene expression and DNA methylation data across different experiments

Integrative analysis of multi-omic datasets has proven to be extremely valuable in cancer research and precision medicine. However, obtaining multimodal data from the same samples is often difficult. Integrating multiple datasets of different omics remains a challenge, with only a few available algorithms developed to solve it. Here, we present INTEND (IntegratioN of Transcriptomic and EpigeNomic Data), a novel algorithm for integrating gene expression and DNA methylation datasets covering disjoint sets of samples. To enable integration, INTEND learns a predictive model between the two omics by training on multi-omic data measured on the same set of samples. In comprehensive testing on 11 TCGA (The Cancer Genome Atlas) cancer datasets spanning 4329 patients, INTEND achieves significantly superior results compared with four state-of-the-art integration algorithms. We also demonstrate INTEND's ability to uncover connections between DNA methylation and the regulation of gene expression in the joint analysis of two lung adenocarcinoma single-omic datasets from different sources. INTEND's data-driven approach makes it a valuable multi-omic data integration tool. The code for INTEND is available at https://github.com/Shamir-Lab/INTEND.

Drugst.One - A plug-and-play solution for online systems medicine and network-based drug repurposing

In recent decades, the development of new drugs has become increasingly expensive and inefficient, and the molecular mechanisms of most pharmaceuticals remain poorly understood. In response, computational systems and network medicine tools have emerged to identify potential drug repurposing candidates. However, these tools often require complex installation and lack intuitive visual network mining capabilities. To tackle these challenges, we introduce Drugst.One, a platform that assists specialized computational medicine tools in becoming user-friendly, web-based utilities for drug repurposing. With just three lines of code, Drugst.One turns any systems biology software into an interactive web tool for modeling and analyzing complex protein-drug-disease networks. Demonstrating its broad adaptability, Drugst.One has been successfully integrated with 21 computational systems medicine tools. Available at https://drugst.one, Drugst.One has significant potential for streamlining the drug discovery process, allowing researchers to focus on essential aspects of pharmaceutical treatment research.

Efficient minimizer orders for large values of k using minimum decycling sets

Minimizers are ubiquitously used in data structures and algorithms for efficient searching, mapping, and indexing of high-throughput DNA sequencing data. Minimizer schemes select a minimum k-mer in every L-long subsequence of the target sequence, where minimality is with respect to a predefined k-mer order. Commonly used minimizer orders select more k-mers than necessary and therefore provide limited improvement in runtime and memory usage of downstream analysis tasks. The recently introduced universal k-mer hitting sets produce minimizer orders with fewer selected k-mers. Generating compact universal k-mer hitting sets is currently infeasible for k > 13, and thus, they cannot help in the many applications that require minimizer orders for larger k Here, we close the gap of efficient minimizer orders for large values of k by introducing decycling-set-based minimizer orders: new minimizer orders based on minimum decycling sets. We show that in practice these new minimizer orders select a number of k-mers comparable to that of minimizer orders based on universal k-mer hitting sets and can also scale to a larger k Furthermore, we developed a method that computes the minimizers in a sequence on the fly without keeping the k-mers of a decycling set in memory. This enables the use of these minimizer orders for any value of k We expect the new orders to improve the runtime and memory usage of algorithms and data structures in high-throughput DNA sequencing analysis.

Single cell Hi-C identifies plastic chromosome conformations underlying the gastrulation enhancer landscape

Embryonic development involves massive proliferation and differentiation of cell lineages. This must be supported by chromosome replication and epigenetic reprogramming, but how proliferation and cell fate acquisition are balanced in this process is not well understood. Here we use single cell Hi-C to map chromosomal conformations in post-gastrulation mouse embryo cells and study their distributions and correlations with matching embryonic transcriptional atlases. We find that embryonic chromosomes show a remarkably strong cell cycle signature. Despite that, replication timing, chromosome compartment structure, topological associated domains (TADs) and promoter-enhancer contacts are shown to be variable between distinct epigenetic states. About 10% of the nuclei are identified as primitive erythrocytes, showing exceptionally compact and organized compartment structure. The remaining cells are broadly associated with ectoderm and mesoderm identities, showing only mild differentiation of TADs and compartment structures, but more specific localized contacts in hundreds of ectoderm and mesoderm promoter-enhancer pairs. The data suggest that while fully committed embryonic lineages can rapidly acquire specific chromosomal conformations, most embryonic cells are showing plastic signatures driven by complex and intermixed enhancer landscapes.

Clustering of clinical and echocardiographic phenotypes of covid-19 patients

We sought to divide COVID-19 patients into distinct phenotypical subgroups using echocardiography and clinical markers to elucidate the pathogenesis of the disease and its heterogeneous cardiac involvement. A total of 506 consecutive patients hospitalized with COVID-19 infection underwent complete evaluation, including echocardiography, at admission. A k-prototypes algorithm applied to patients' clinical and imaging data at admission partitioned the patients into four phenotypical clusters: Clusters 0 and 1 were younger and healthier, 2 and 3 were older with worse cardiac indexes, and clusters 1 and 3 had a stronger inflammatory response. The clusters manifested very distinct survival patterns (C-index for the Cox proportional hazard model 0.77), with survival best for cluster 0, intermediate for 1-2 and worst for 3. Interestingly, cluster 1 showed a harsher disease course than cluster 2 but with similar survival. Clusters obtained with echocardiography were more predictive of mortality than clusters obtained without echocardiography. Additionally, several echocardiography variables (E' lat, E' sept, E/e average) showed high discriminative power among the clusters. The results suggested that older infected males have a higher chance to deteriorate than older infected females. In conclusion, COVID-19 manifests differently for distinctive clusters of patients. These clusters reflect different disease manifestations and prognoses. Although including echocardiography improved the predictive power, its marginal contribution over clustering using clinical parameters only does not justify the burden of echocardiography data collection.

Decreasing albumin within normal range is associated with increased likelihood of ischemic heart disease

Background

Albumin (ALB) is a known biomarker of frailty, and cardiovascular disease and frailty are interdependent. Epidemiological evidence demonstrates that low serum albumin levels are linked to events of ischemic heart disease (IHD), venous thromboembolism, heart failure, atrial fibrillation, and stroke.

Purpose

We aimed to investigate the association of variations in ALB levels that are within normal range with IHD events among apparently healthy adults.

Methods

A case-control retrospective study of self-referred adults participating in an executive screening program between 2002 and 2017. All subjects were free of IHD and diabetes at baseline and had their ALB documented in each visit. Only subjects with at least two ALB measurements and whose ALB levels were within the normal range at all visits were included. Relationships between ALB trend and occurrence of IHD (acute coronary syndrome or percutaneous coronary intervention) within 2 years from the last visit were investigated.

Results

The final study cohort included 16,386 subjects. Median age was 53 (IQR 45–60), 11,461 (70%) were men. Analysis included a total of 99,127 visits. Median number of visits per subject was 5 (IQR 3–9, median inter-visit time 1.02 years) and median ALB level was 4.4 (IQR 4.2–4.6). IHD within 2 years was diagnosed in 545 (3%) subjects. Of those, only 36 were female and they tended to have lower variations in ALB throughout the years. Hence, we conducted an analysis of the 509 males only, and created an equal-size age-matched cohort of IHD-free subjects. Our analysis demonstrated a progressive and significant decrease in ALB levels among IHD cases, but not among controls (mean decrease of 0.021 g/DL vs. 0.004 g/DL per year, p&lt;0.01; OR [CI] = 0.82 [0.72–0.93]; Figure 1). Similar results were found among subjects with at least 3 or 4 visits (0.015 g/DL vs. 0.006 g/DL per year, p=0.027, and 0.009 g/DL vs. 0.003 g/DL per year, p=0.045, respectively).

Conclusions

Kinetics of ALB within the normal range can identify men at risk for IHD in preventive healthcare screening programs.

Funding Acknowledgement

Type of funding sources: None.

3CAC: improving the classification of phages and plasmids in metagenomic assemblies using assembly graphs

MOTIVATION

Bacteriophages and plasmids usually coexist with their host bacteria in microbial communities and play important roles in microbial evolution. Accurately identifying sequence contigs as phages, plasmids and bacterial chromosomes in mixed metagenomic assemblies is critical for further unraveling their functions. Many classification tools have been developed for identifying either phages or plasmids in metagenomic assemblies. However, only two classifiers, PPR-Meta and viralVerify, were proposed to simultaneously identify phages and plasmids in mixed metagenomic assemblies. Due to the very high fraction of chromosome contigs in the assemblies, both tools achieve high precision in the classification of chromosomes but perform poorly in classifying phages and plasmids. Short contigs in these assemblies are often wrongly classified or classified as uncertain.

RESULTS

Here we present 3CAC, a new three-class classifier that improves the precision of phage and plasmid classification. 3CAC starts with an initial three-class classification generated by existing classifiers and improves the classification of short contigs and contigs with low confidence classification by using proximity in the assembly graph. Evaluation on simulated metagenomes and on real human gut microbiome samples showed that 3CAC outperformed PPR-Meta and viralVerify in both precision and recall, and increased F1-score by 10-60 percentage points.

AVAILABILITY AND IMPLEMENTATION

The 3CAC software is available on https://github.com/Shamir-Lab/3CAC.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

The DOMINO web-server for active module identification analysis

MOTIVATION

Active module identification (AMI) is an essential step in many omics analyses. Such algorithms receive a gene network and a gene activity profile as input and report subnetworks that show significant over-representation of accrued activity signal ('active modules'). Such modules can point out key molecular processes in the analyzed biological conditions.

RESULTS

We recently introduced a novel AMI algorithm called DOMINO and demonstrated that it detects active modules that capture biological signals with markedly improved rate of empirical validation. Here, we provide an online server that executes DOMINO, making it more accessible and user-friendly. To help the interpretation of solutions, the server provides GO enrichment analysis, module visualizations and accessible output formats for customized downstream analysis. It also enables running DOMINO with various gene identifiers of different organisms.

AVAILABILITY AND IMPLEMENTATION

The server is available at http://domino.cs.tau.ac.il. Its codebase is available at https://github.com/Shamir-Lab.

CT-FOCS: a novel method for inferring cell type-specific enhancer–promoter maps

Spatiotemporal gene expression patterns are governed to a large extent by the activity of enhancer elements, which engage in physical contacts with their target genes. Identification of enhancer–promoter (EP) links that are functional only in a specific subset of cell types is a key challenge in understanding gene regulation. We introduce CT-FOCS (cell type FOCS), a statistical inference method that uses linear mixed effect models to infer EP links that show marked activity only in a single or a small subset of cell types out of a large panel of probed cell types. Analyzing 808 samples from FANTOM5, covering 472 cell lines, primary cells and tissues, CT-FOCS inferred such EP links more accurately than recent state-of-the-art methods. Furthermore, we show that strictly cell type-specific EP links are very uncommon in the human genome.

SCAPP: an algorithm for improved plasmid assembly in metagenomes

BACKGROUND

Metagenomic sequencing has led to the identification and assembly of many new bacterial genome sequences. These bacteria often contain plasmids: usually small, circular double-stranded DNA molecules that may transfer across bacterial species and confer antibiotic resistance. These plasmids are generally less studied and understood than their bacterial hosts. Part of the reason for this is insufficient computational tools enabling the analysis of plasmids in metagenomic samples.

RESULTS

We developed SCAPP (Sequence Contents-Aware Plasmid Peeler)-an algorithm and tool to assemble plasmid sequences from metagenomic sequencing. SCAPP builds on some key ideas from the Recycler algorithm while improving plasmid assemblies by integrating biological knowledge about plasmids. We compared the performance of SCAPP to Recycler and metaplasmidSPAdes on simulated metagenomes, real human gut microbiome samples, and a human gut plasmidome dataset that we generated. We also created plasmidome and metagenome data from the same cow rumen sample and used the parallel sequencing data to create a novel assessment procedure. Overall, SCAPP outperformed Recycler and metaplasmidSPAdes across this wide range of datasets.

CONCLUSIONS

SCAPP is an easy to use Python package that enables the assembly of full plasmid sequences from metagenomic samples. It outperformed existing metagenomic plasmid assemblers in most cases and assembled novel and clinically relevant plasmids in samples we generated such as a human gut plasmidome. SCAPP is open-source software available from: https://github.com/Shamir-Lab/SCAPP . Video abstract.

Classification of node-positive melanomas into prognostic subgroups using keratin, immune, and melanogenesis expression patterns

Cutaneous melanoma tumors are heterogeneous and show diverse responses to treatment. Identification of robust molecular biomarkers for classifying melanoma tumors into clinically distinct and homogenous subtypes is crucial for improving the diagnosis and treatment of the disease. In this study, we present a classification of melanoma tumors into four subtypes with different survival profiles based on three distinct gene expression signatures: keratin, immune, and melanogenesis. The melanogenesis expression pattern includes several genes that are characteristic of the melanosome organelle and correlates with worse survival, suggesting the involvement of melanosomes in melanoma aggression. We experimentally validated the secretion of melanosomes into surrounding tissues by melanoma tumors, which potentially affects the lethality of metastasis. We propose a simple molecular decision tree classifier for predicting a tumor's subtype based on representative genes from the three identified signatures. Key predictor genes were experimentally validated on melanoma samples taken from patients with varying survival outcomes. Our three-pattern approach for classifying melanoma tumors can contribute to advancing the understanding of melanoma variability and promote accurate diagnosis, prognostication, and treatment.

DOMINO: a network-based active module identification algorithm with reduced rate of false calls

Algorithms for active module identification (AMI) are central to analysis of omics data. Such algorithms receive a gene network and nodes' activity scores as input and report subnetworks that show significant over‐representation of accrued activity signal (“active modules”), thus representing biological processes that presumably play key roles in the analyzed conditions. Here, we systematically evaluated six popular AMI methods on gene expression and GWAS data. We observed that GO terms enriched in modules detected on the real data were often also enriched on modules found on randomly permuted data. This indicated that AMI methods frequently report modules that are not specific to the biological context measured by the analyzed omics dataset. To tackle this bias, we designed a permutation‐based method that empirically evaluates GO terms reported by AMI methods. We used the method to fashion five novel AMI performance criteria. Last, we developed DOMINO, a novel AMI algorithm, that outperformed the other six algorithms in extensive testing on GE and GWAS data. Software is available at https://github.com/Shamir‐Lab.

MONET: Multi-omic module discovery by omic selection

Recent advances in experimental biology allow creation of datasets where several genome-wide data types (called omics) are measured per sample. Integrative analysis of multi-omic datasets in general, and clustering of samples in such datasets specifically, can improve our understanding of biological processes and discover different disease subtypes. In this work we present MONET (Multi Omic clustering by Non-Exhaustive Types), which presents a unique approach to multi-omic clustering. MONET discovers modules of similar samples, such that each module is allowed to have a clustering structure for only a subset of the omics. This approach differs from most existent multi-omic clustering algorithms, which assume a common structure across all omics, and from several recent algorithms that model distinct cluster structures. We tested MONET extensively on simulated data, on an image dataset, and on ten multi-omic cancer datasets from TCGA. Our analysis shows that MONET compares favorably with other multi-omic clustering methods. We demonstrate MONET's biological and clinical relevance by analyzing its results for Ovarian Serous Cystadenocarcinoma. We also show that MONET is robust to missing data, can cluster genes in multi-omic dataset, and reveal modules of cell types in single-cell multi-omic data. Our work shows that MONET is a valuable tool that can provide complementary results to those provided by existent algorithms for multi-omic analysis.

NEMO: cancer subtyping by integration of partial multi-omic data

Motivation

Cancer subtypes were usually defined based on molecular characterization of single omic data. Increasingly, measurements of multiple omic profiles for the same cohort are available. Defining cancer subtypes using multi-omic data may improve our understanding of cancer, and suggest more precise treatment for patients.

Results

We present NEMO (NEighborhood based Multi-Omics clustering), a novel algorithm for multi-omics clustering. Importantly, NEMO can be applied to partial datasets in which some patients have data for only a subset of the omics, without performing data imputation. In extensive testing on ten cancer datasets spanning 3168 patients, NEMO achieved results comparable to the best of nine state-of-the-art multi-omics clustering algorithms on full data and showed an improvement on partial data. On some of the partial data tests, PVC, a multi-view algorithm, performed better, but it is limited to two omics and to positive partial data. Finally, we demonstrate the advantage of NEMO in detailed analysis of partial data of AML patients. NEMO is fast and much simpler than existing multi-omics clustering algorithms, and avoids iterative optimization.

Availability and implementation

Code for NEMO and for reproducing all NEMO results in this paper is in github: https://github.com/Shamir-Lab/NEMO.

Supplementary information

Supplementary data are available at Bioinformatics online.

Melanoma-Secreted Lysosomes Trigger Monocyte-Derived Dendritic Cell Apoptosis and Limit Cancer Immunotherapy

The recent success of checkpoint blockade therapies has established immunotherapy as one of the most promising treatments for melanoma. Nonetheless, a complete curative response following immunotherapy is observed only in a fraction of patients. To identify what factors limit the efficacy of immunotherapies, we established mouse models that cease to respond to immunotherapies once their tumors exceed a certain stage. Analysis of the immune systems of the organisms revealed that the numbers of tumor-infiltrating dendritic cells (TIDC) drastically decreased with time. Further, in contrast to the current paradigm, once melanoma was established, TIDC did not migrate into sentinel lymph nodes. Instead, they underwent local cell death due to excessive phagocytosis of lysosomes. Importantly, TIDC were required to license the cytotoxic activity of tumor CD8⁺ T cells, and in their absence, T cells did not lyse melanoma cells. Our results offer a paradigm shift regarding the role of TIDC and a framework to increase the efficacy of immunotherapies. SIGNIFICANCE: This work redefines the role of monocyte-derived dendritic cells in melanoma and provides a novel strategy to increase the efficacy of T-cell-based immunotherapies in nonresponding individuals. GRAPHICAL ABSTRACT: http://cancerres.aacrjournals.org/content/canres/80/10/1942/F1.large.jpg.

PRODIGY: personalized prioritization of driver genes

MOTIVATION

Evolution of cancer is driven by few somatic mutations that disrupt cellular processes, causing abnormal proliferation and tumor development, whereas most somatic mutations have no impact on progression. Distinguishing those mutated genes that drive tumorigenesis in a patient is a primary goal in cancer therapy: Knowledge of these genes and the pathways on which they operate can illuminate disease mechanisms and indicate potential therapies and drug targets. Current research focuses mainly on cohort-level driver gene identification but patient-specific driver gene identification remains a challenge.

METHODS

We developed a new algorithm for patient-specific ranking of driver genes. The algorithm, called PRODIGY, analyzes the expression and mutation profiles of the patient along with data on known pathways and protein-protein interactions. Prodigy quantifies the impact of each mutated gene on every deregulated pathway using the prize-collecting Steiner tree model. Mutated genes are ranked by their aggregated impact on all deregulated pathways.

RESULTS

In testing on five TCGA cancer cohorts spanning >2500 patients and comparison to validated driver genes, Prodigy outperformed extant methods and ranking based on network centrality measures. Our results pinpoint the pleiotropic effect of driver genes and show that Prodigy is capable of identifying even very rare drivers. Hence, Prodigy takes a step further toward personalized medicine and treatment.

AVAILABILITY AND IMPLEMENTATION

The Prodigy R package is available at: https://github.com/Shamir-Lab/PRODIGY.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

PROMO: an interactive tool for analyzing clinically-labeled multi-omic cancer datasets

BACKGROUND

Analysis of large genomic datasets along with their accompanying clinical information has shown great promise in cancer research over the last decade. Such datasets typically include thousands of samples, each measured by one or several high-throughput technologies ('omics') and annotated with extensive clinical information. While instrumental for fulfilling the promise of personalized medicine, the analysis and visualization of such large datasets is challenging and necessitates programming skills and familiarity with a large array of software tools to be used for the various steps of the analysis.

RESULTS

We developed PROMO (Profiler of Multi-Omic data), a friendly, fully interactive stand-alone software for analyzing large genomic cancer datasets together with their associated clinical information. The tool provides an array of built-in methods and algorithms for importing, preprocessing, visualizing, clustering, clinical label enrichment testing, and survival analysis that can be performed on a single or multi-omic dataset. The tool can be used for quick exploration and stratification of tumor samples taken from patients into clinically significant molecular subtypes. Identification of prognostic biomarkers and generation of simple subtype classifiers are additional important features. We review PROMO's main features and demonstrate its analysis capabilities on a breast cancer cohort from TCGA.

CONCLUSIONS

PROMO provides a single integrated solution for swiftly performing a complete analysis of cancer genomic data for subtype discovery and biomarker identification without writing a single line of code, and can, therefore, make the analysis of these data much easier for cancer biologists and biomedical researchers. PROMO is freely available for download at http://acgt.cs.tau.ac.il/promo/.

Inaccuracy of the log-rank approximation in cancer data analysis

The log-rank test statistic is very broadly used in biology. Unfortunately, P-values based on the popular chi-square approximation are often inaccurate and can be misleading.

ADEPTUS: a discovery tool for disease prediction, enrichment and network analysis based on profiles from many diseases

Motivation

Large-scale publicly available genomic data on many disease phenotypes could improve our understanding of the molecular basis of disease. Tools that undertake this challenge by jointly analyzing multiple phenotypes are needed.

Results

ADEPTUS is a web-tool that enables various functional genomics analyses based on a high-quality curated database spanning >38, 000 gene expression profiles and >100 diseases. It offers four types of analysis. (i) For a gene list provided by the user it computes disease ontology (DO), pathway, and gene ontology (GO) enrichment and displays the genes as a network. (ii) For a given disease, it enables exploration of drug repurposing by creating a gene network summarizing the genomic events in it. (iii) For a gene of interest, it generates a report summarizing its behavior across several studies. (iv) It can predict the tissue of origin and the disease of a sample based on its gene expression or its somatic mutation profile. Such analyses open novel ways to understand new datasets and to predict primary site of cancer.

Availability and implementation

Data and tool: http://adeptus.cs.tau.ac.il/home Analyses: Supplementary Material.

Contact

rshamir@tau.ac.il.

Supplementary information

Supplementary data are available at Bioinformatics online.

Multi-omic and multi-view clustering algorithms: review and cancer benchmark

Recent high throughput experimental methods have been used to collect large biomedical omics datasets. Clustering of single omic datasets has proven invaluable for biological and medical research. The decreasing cost and development of additional high throughput methods now enable measurement of multi-omic data. Clustering multi-omic data has the potential to reveal further systems-level insights, but raises computational and biological challenges. Here, we review algorithms for multi-omics clustering, and discuss key issues in applying these algorithms. Our review covers methods developed specifically for omic data as well as generic multi-view methods developed in the machine learning community for joint clustering of multiple data types. In addition, using cancer data from TCGA, we perform an extensive benchmark spanning ten different cancer types, providing the first systematic comparison of leading multi-omics and multi-view clustering algorithms. The results highlight key issues regarding the use of single- versus multi-omics, the choice of clustering strategy, the power of generic multi-view methods and the use of approximated p-values for gauging solution quality. Due to the growing use of multi-omics data, we expect these issues to be important for future progress in the field.

Bonnie Berger named ISCB 2019 ISCB Accomplishments by a Senior Scientist Award recipient

The International Society for Computational Biology (ISCB) honors a leader in the fields of computational biology and bioinformatics each year with the Accomplishments by a Senior Scientist Award. This award is the highest honor conferred by ISCB to a scientist who is recognized for significant research, education, and service contributions. Bonnie Berger, Simons Professor of Mathematics and Professor of Electrical Engineering and Computer Science at the Massachusetts Institute of Technology (MIT) is the 2019 recipient of the Accomplishments by a Senior Scientist Award. She is receiving her award and presenting a keynote address at the 2019 Joint International Conference on Intelligent Systems for Molecular Biology/European Conference on Computational Biology in Basel, Switzerland on July 21-25, 2019.

2019 ISCB Overton Prize: Christophe Dessimoz

Each year the International Society for Computational Biology (ISCB) honors the achievements of an early- to mid-career scientist with the Overton Prize. This prize was instituted in 2001 to honor the untimely loss of Dr. G. Christian Overton, a respected computational biologist and founding member of the ISCB Board of Directors. The Overton Prize recognizes early or mid-career phase scientists who have made significant contributions to computational biology or bioinformatics through their research, teaching, and service. In 2019, ISCB recognized Dr. Christophe Dessimoz, Swiss National Science Foundation (SNSF) Professor at the University of Lausanne, Associate Professor at the University College London, and Group Leader at the Swiss Institute for Bioinformatics. Dessimoz receives his award and is presenting a keynote address at the 2019 Joint Intelligent Systems for Molecular Biology/European Conference on Computational Biology held in Basel, Switzerland on July 21-25, 2019.

2019 Outstanding Contributions to ISCB awarded to Barb Bryant

The Outstanding Contributions to the International Society of Computational Biology (ISCB) Award recognizes outstanding service contributions to the Society by any member through exemplary leadership, education, service, or a combination of these three elements. Barbara (Barb) Bryant, Senior Director at Constellation Pharmaceuticals, is the 2019 ISCB winner of the Outstanding Contributions to ISCB Award and will be recognized at the 2019 Joint Intelligent Systems for Molecular Biology/European Conference on Computational Biology (ISMB/ECCB) in Basel, Switzerland on July 21-25, 2019.

2019 ISCB Innovator Award recognizes William Stafford Noble

The International Society for Computational Biology (ISCB) Innovator Award honors an ISCB scientist who is within two decades of having completed his or her graduate degree and has made outstanding contributions to the field of computational biology. The 2019 winner is Dr. William Stafford Noble, Professor in the Department of Genome Science, University of Washington. Noble will receive his award and deliver a keynote presentation at the 2019 Joint International Conference on Intelligent Systems for Molecular Biology/European Conference on Computational Biology in Basel, Switzerland being held on July 21-25, 2019.

Faucet: streaming de novo assembly graph construction

Motivation

We present Faucet, a two-pass streaming algorithm for assembly graph construction. Faucet builds an assembly graph incrementally as each read is processed. Thus, reads need not be stored locally, as they can be processed while downloading data and then discarded. We demonstrate this functionality by performing streaming graph assembly of publicly available data, and observe that the ratio of disk use to raw data size decreases as coverage is increased.

Results

Faucet pairs the de Bruijn graph obtained from the reads with additional meta-data derived from them. We show these metadata-coverage counts collected at junction k-mers and connections bridging between junction pairs-contain most salient information needed for assembly, and demonstrate they enable cleaning of metagenome assembly graphs, greatly improving contiguity while maintaining accuracy. We compared Fauceted resource use and assembly quality to state of the art metagenome assemblers, as well as leading resource-efficient genome assemblers. Faucet used orders of magnitude less time and disk space than the specialized metagenome assemblers MetaSPAdes and Megahit, while also improving on their memory use; this broadly matched performance of other assemblers optimizing resource efficiency-namely, Minia and LightAssembler. However, on metagenomes tested, Faucet,o outputs had 14-110% higher mean NGA50 lengths compared with Minia, and 2- to 11-fold higher mean NGA50 lengths compared with LightAssembler, the only other streaming assembler available.

Availability and implementation

Faucet is available at https://github.com/Shamir-Lab/Faucet.

Contact

rshamir@tau.ac.il or eranhalperin@gmail.com.

Supplementary information

Supplementary data are available at Bioinformatics online.

GePMI: A statistical model for personal intestinal microbiome identification

Human gut microbiomes consist of a large number of microbial genomes, which vary by diet and health conditions and from individual to individual. In the present work, we asked whether such variation or similarity could be measured and, if so, whether the results could be used for personal microbiome identification (PMI). To address this question, we herein propose a method to estimate the significance of similarity among human gut metagenomic samples based on reference-free, long k-mer features. Using these features, we find that pairwise similarities between the metagenomes of any two individuals obey a beta distribution and that a p value derived accordingly well characterizes whether two samples are from the same individual or not. We develop a computational framework called GePMI (Generating inter-individual similarity distribution for Personal Microbiome Identification) and apply it to several human gut metagenomic datasets (>300 individuals and >600 samples in total). From the results of GePMI, most of the human gut microbiomes can be identified (auROC = 0.9470, auPRC = 0.8702). Even after antibiotic treatment or fecal microbiota transplantation, the individual k-mer signature still maintains a certain specificity.

Genomic meta-analysis of the interplay between 3D chromatin organization and gene expression programs under basal and stress conditions

Background

Our appreciation of the critical role of the genome’s 3D organization in gene regulation is steadily increasing. Recent 3C-based deep sequencing techniques elucidated a hierarchy of structures that underlie the spatial organization of the genome in the nucleus. At the top of this hierarchical organization are chromosomal territories and the megabase-scale A/B compartments that correlate with transcriptional activity within cells. Below them are the relatively cell-type-invariant topologically associated domains (TADs), characterized by high frequency of physical contacts between loci within the same TAD, and are assumed to function as regulatory units. Within TADs, chromatin loops bring enhancers and target promoters to close spatial proximity. Yet, we still have only rudimentary understanding how differences in chromatin organization between different cell types affect cell-type-specific gene expression programs that are executed under basal and challenged conditions.

Results

Here, we carried out a large-scale meta-analysis that integrated Hi–C data from thirteen different cell lines and dozens of ChIP-seq and RNA-seq datasets measured on these cells, either under basal conditions or after treatment. Pairwise comparisons between cell lines demonstrate a strong association between modulation of A/B compartmentalization, differential gene expression and transcription factor (TF) binding events. Furthermore, integrating the analysis of transcriptomes of different cell lines in response to various challenges, we show that A/B compartmentalization of cells under basal conditions significantly correlates not only with gene expression programs and TF binding profiles that are active under the basal condition but also with those induced in response to treatment. Yet, in pairwise comparisons between different cell lines, we find that a large portion of differential TF binding and gene induction events occur in genomic loci assigned to A compartment in both cell types, underscoring the role of additional critical factors in determining cell-type-specific transcriptional programs.

Conclusions

Our results further indicate the role of dynamic genome organization in regulation of differential gene expression between different cell types and the impact of intra-TAD enhancer–promoter interactions that are established under basal conditions on both the basal and treatment-induced gene expression programs.

Electronic supplementary material

The online version of this article (10.1186/s13072-018-0220-2) contains supplementary material, which is available to authorized users.