Integration Of Multi-platform Data Research Articles

High-density resolution of the Kaposi's sarcoma associated herpesvirus transcriptome identifies novel transcript isoforms generated by long-range transcription and alternative splicing.

Kaposi's sarcoma-associated herpesvirus is the etiologic agent of Kaposi's sarcoma and two B-cell malignancies. Recent advancements in sequencing technologies have led to high resolution transcriptomes for several human herpesviruses that densely encode genes on both strands. However, for KSHV progress remained limited due to the overall low percentage of KSHV transcripts, even during lytic replication. To address this challenge, we have developed a target enrichment method to increase the KSHV-specific reads for both short- and long-read sequencing platforms. Furthermore, we combined this approach with the Transcriptome Resolution through Integration of Multi-platform Data (TRIMD) pipeline developed previously to annotate transcript structures. TRIMD first builds a scaffold based on long-read sequencing and validates each transcript feature with supporting evidence from Illumina RNA-Seq and deepCAGE sequencing data. Our stringent innovative approach identified 994 unique KSHV transcripts, thus providing the first high-density KSHV lytic transcriptome. We describe a plethora of novel coding and non-coding KSHV transcript isoforms with alternative untranslated regions, splice junctions and open-reading frames, thus providing deeper insights on gene expression regulation of KSHV. Interestingly, as described for Epstein-Barr virus, we identified transcription start sites that augment long-range transcription and may increase the number of latency-associated genes potentially expressed in KS tumors.

Comparison of PlanetScope, Sentinel-2, and landsat 8 data in soybean yield estimation within-field variability with random forest regression

Accurate timely and early-season crop yield estimation within the field variability is important for precision farming and sustainable management applications. Therefore, the ability to estimate the within-field variability of grain yield is crucial for ensuring food security worldwide, especially under climate change. Several Earth observation systems have thus been developed to monitor crops and predict yields. Despite this, new research is required to combine multiplatform data integration, advancements in satellite technologies, data processing, and the application of this discipline to agricultural practices. This study provides further developments in soybean yield estimation by comparing multisource satellite data from PlanetScope (PS), Sentinel-2 (S2), and Landsat 8 (L8) and introducing topographic and meteorological variables. Herein, a new method of combining soybean yield, global positioning systems, harvester data, climate, topographic variables, and remote sensing images has been demonstrated. Soybean yield shape points were obtained from a combine-harvester-installed GPS and yield monitoring system from seven fields over the 2021 season. The yield estimation models were trained and validated using random forest, and four vegetation indices were tested. The result showed that soybean yield can be accurately predicted at 3-, 10-, and 30-m resolutions with mean absolute error (MAE) value of 0.091 t/ha for PS, 0.118 t/ha for S2, and 0.120 t/ha for L8 data (root mean square error (RMSE) of 0.111, 0.076). The combination of the environmental data with the original bands provided further improvements and an accurate yield estimation model within the soybean yield variability with MAE of 0.082 t/ha for PS, 0.097 t/ha for S2, and 0.109 t/ha for L8 (RMSE of 0.094, 0.069, and 0.108 t/ha). The results showed that the optimal date to predict the soybean yield within the field scale was approximately 60 or 70 days before harvesting periods during the beginning bloom stage. The developed model can be applied for other crops and locations when suitable training yield data, which are critical for precision farming, are available.

Abstract P009: A shared nearest neighbors approach for integrated, multi-platform networks and its application to the exploration of multiomics data from early-stage non-small cell lung cancers

Abstract Background: The ImmunogenomiC prOfiling of Non-small cell lung cancer (NSCLC) Project (ICON) represents an ambitious undertaking to comprehensively characterize immuno-genomic diversity in NSCLC across diverse platforms. The depth and breadth of this cohort presented a unique opportunity to develop a specialized method for multi-platform data integration and exploration, which can be broadly applied to forthcoming large-scale patient profiling studies. Such a holistic approach can unlock insights for therapeutic targets, biomarkers, and treatment plans by providing a more complete view of phenomena driving disease pathogenesis and evolution. Purpose: We developed a novel shared nearest neighbors (SNN) approach to create an integrated network of ICON’s multi-platform data and identified collections of closely related measurements within the resulting network tied to noteworthy patient characteristics, including recurrence and oncogenotype. Methods: The ICON dataset is derived from tumor and normal lung tissue samples collected from 150 patients at time of resection as well as blood samples collected then and at intervals during the year following. Tissue samples underwent RNA-sequencing (RNA-seq), whole exome sequencing, T-cell receptor sequencing, multiplex immunofluorescence for immune cells, and reverse phase protein array profiling; flow cytometry for immune cells was performed on tissue and blood samples. From these data, the ICON data network was built using an integrative approach based on the SNN algorithm in which genes were linked on the basis of their shared top correlates in orthogonal datasets. Results: The ICON data network currently includes over 20,000 genes linked by over 500,000 connections derived from correlations between RNA-seq and orthogonal platforms. We captured established associations between cancer-related genes and examined these along with new ones in the network. To do so, we used the InfoMap algorithm to extract more interpretable sub-networks, termed modules, from the ICON data network. Single sample gene set enrichment scores for each module were used in multivariate analysis to highlight modules linked to clinical characteristics of interest. As an example, we found modules significantly tied to disease recurrence. The most notable of these was strongly associated with metabolic pathways, and other modules associated with platelets and ion channels were also identified. The metabolic pathway module is being explored as a prognostic biomarker, underscoring the opportunites enabled by mining the network. Conclusions: Through the framework developed, we identified modules in the ICON data network significantly associated with important patient characteristics like recurrence and oncogenotype. We are validating the gene sets identified as potential biomarkers and are developing an interactive application to facilitate further mining of the network. Taken together, our SNN network-building approach enables the integration and exploration of patient data from diverse platforms. Citation Format: Stephanie T. Schmidt, Neal Akhave, Alexandre Reuben, Tina Cascone, Jianhua Zhang, Jun Li, Junya Fujimoto, Lauren A. Byers, Beatriz Sanchez-Espiridion, Lixia Diao, Jing Wang, Lorenzo Federico, Marie-Andree Forget, Daniel J McGrail, Annikka Weissferdt, Shiaw-Yih Lin, Younghee Lee, Natalie Vokes, Carmen Behrens, Ignacio I. Wistuba, Andrew Futreal, Ara Vaporciyan, Boris Sepesi, John V. Heymach, Chantale Bernatchez, Cara Haymaker, Jianjun Zhang, Christopher A. Bristow, Timothy P. Heffernan, Marcelo V. Negrao, Don L. Gibbons. A shared nearest neighbors approach for integrated, multi-platform networks and its application to the exploration of multiomics data from early-stage non-small cell lung cancers [abstract]. In: Proceedings of the AACR-NCI-EORTC Virtual International Conference on Molecular Targets and Cancer Therapeutics; 2021 Oct 7-10. Philadelphia (PA): AACR; Mol Cancer Ther 2021;20(12 Suppl):Abstract nr P009.

Comprehensive Analysis of Differentially Expressed Genes in Clinically Diagnosed Irreversible Pulpitis by Multiplatform Data Integration Using a Robust Rank Aggregation Approach

IntroductionMolecular diagnosis may overcome the limitations of clinical and histologic diagnosis in pulpitis, thereby benefiting many treatment techniques, such as vital pulp therapies. In this study, integrated microarray data on pulpitis were used to obtain a list of normalized differentially expressed (DE) genes for analyzing the molecular mechanisms underlying pulpitis and identifying potential diagnostic biomarkers. MethodsA systematic search of public microarray and sequencing databases was performed to obtain expression data of pulpitis. Robust rank aggregation (RRA) was used to obtain DE gene lists (RRA_DEmRNAs and RRA_DElncRNAs) between inflamed pulp and normal samples. DE genes were evaluated by functional enrichment analyses, correlation analyses for inflammation-related RRA_DEmRNAs, and protein-protein interaction and competing endogenous RNA network construction. Quantitative real-time polymerase chain reaction validation was applied in snap-frozen pulp tissues. ResultsUsing the GSE77459 and GSE92681 data sets, 280 RRA_DEmRNAs and 90 RRA_DElncRNAs were identified. RRA_DEmRNAs were significantly enriched in inflammation-related biological processes and osteoclast differentiation and tumor necrosis factor, chemokine, and B-cell receptor signaling pathways. The molecular complex detection and cytoHubba methods identified 2 clusters and 10 hub genes in the protein-protein interaction network. The competing endogenous RNA network was composed of 2 long noncoding RNAs (ADAMTS9-AS2 and LINC00290), 2 microRNAs (hsa-miR-30a-5p and hsa-miR-128-3p), and 3 messenger RNAs (ABCA1, FBLN5, and SOCS3). The expression between most top inflammation-related RRA_DEmRNAs in pulpitis showed positive correlations. Quantitative real-time polymerase chain reacation validated the expression trends of selected genes, including ITGAX, TREM1, CD86, FCGR2A, ADAMTS9-AS2, LINC00290, hsa-miR-30a-5p, hsa-miR-128-3p, RASGRP3, IL3RA, CCDC178, CRISPLD1, LINC01857, AC007991.2, ARHGEF26-AS1, and AL021408.1. ConclusionsThe identified biomarkers provide insight into the pathology and may aid in the molecular diagnosis of pulpitis.

Monitoring recent changes of vegetation in Fildes Peninsula (King George Island, Antarctica) through satellite imagery guided by UAV surveys

Mapping accurately vegetation surfaces in space and time in the ice-free areas of Antarctica can provide important information to quantitatively describe the evolution of their ecosystems. Spaceborne remote sensing is the adequate way to map and evaluate multitemporal changes on the Antarctic vegetation at large but its nature of occurrence, in relatively small and sparse patches, makes the identification very challenging. The inclusion of an intermediate scale of observation between ground and satellite scales, provided by Unmanned Aerial Vehicles (UAV) imagery, is of great help not only for their effective classification, but also for discriminating their main communities (lichens and mosses). Thus, this paper quantifies accurately recent changes of the vegetated areas in Fildes Peninsula (King George Island, Antarctica) through a novel methodology based on the integration of multiplatform data (satellite and UAV). It consists of multiscale imagery (spatial resolution of 2 m and 2 cm) from the same period to create a robust classifier that, after intensive calibration, is adequately used in other dates, where field reference data is scarce or not available at all. The methodology is developed and tested with UAV and satellite data from 2017 showing overall accuracies of 96% and kappa equal to 0.94 with a SVM classifier. These high performances allow the extrapolation to a pair of previous dates, 2006 and 2013, when atmospherically clear very high-resolution satellite imagery are available. The classification allows verifying a loss of the total area of vegetation of 4.5% during the 11-year time period under analysis, which corresponds to a 10.3% reduction for Usnea sp. and 9.8% for moss formations. Nevertheless, the breakdown analysis by time period shows a distinct behaviour for each vegetation type which are evaluated and discussed, namely for Usnea sp. whose decline is likely to be related to changing snow conditions.

Systems Biology for Multiplatform Data Integration: An Overview.

In this chapter, we consider some of the concepts behind multiplatform data integration. First, we examine the types of inferences that can be made using methods that integrate data types. Next, we discuss some broad considerations about methodologies. We conclude with the example of joint analyses of germ line genetic variation, gene expression and complex phenotypes. This chapter draws heavily from analyses that integrate datasets for inference on hereditary aspects of cancer susceptibility. However, these concepts should apply more broadly to other domains.

HetEnc: a deep learning predictive model for multi-type biological dataset

BackgroundResearchers today are generating unprecedented amounts of biological data. One trend in current biological research is integrated analysis with multi-platform data. Effective integration of multi-platform data into the solution of a single or multi-task classification problem; however, is critical and challenging. In this study, we proposed HetEnc, a novel deep learning-based approach, for information domain separation.ResultsHetEnc includes both an unsupervised feature representation module and a supervised neural network module to handle multi-platform gene expression datasets. It first constructs three different encoding networks to represent the original gene expression data using high-level abstracted features. A six-layer fully-connected feed-forward neural network is then trained using these abstracted features for each targeted endpoint. We applied HetEnc to the SEQC neuroblastoma dataset to demonstrate that it outperforms other machine learning approaches. Although we used multi-platform data in feature abstraction and model training, HetEnc does not need multi-platform data for prediction, enabling a broader application of the trained model by reducing the cost of gene expression profiling for new samples to a single platform. Thus, HetEnc provides a new solution to integrated gene expression analysis, accelerating modern biological research.

Abstract 974: The molecular and genomic drivers of immunologically cold tumors: Novel discoveries from pan-cancer analysis

Abstract Immune-checkpoint blockade (ICB) is able to elicit remarkable immune response but currently the durable response is limited to a minority of cancer patients. Recent evidence suggests that a prerequisite for the efficacy of ICB, for example the PD-1 inhibitor, is T-cells infiltration in the tumor microenvironment (TME). The immunologically ‘quite’ and T-cell depleted (immune ‘desert’) tumors are less likely respond to ICB. However, we still lack a detailed understanding of the genomic and molecular determinants that drive the ‘cold’ immune phenotypes. We here performed a comprehensive pan-cancer analysis to define and characterize the immunologically ‘hot’ (inflamed) and ‘cold’ (quite or T-cell depleted) tumors by high-dimensional, multi-platform data integration of 9,887 tumors from The Cancer Genome Atlas (TCGA). This study provided an unprecedented catalog of the molecular and genomic drivers of immune ‘cold’ tumors by analyzing the immune cell composition, patterns of their spatial organization within the TME, the dysregulated chemokines networks, somatic mutations, DNA copy-number alterations, aneuploidy status, differentially expressed immune regulatory genes, and enriched signaling pathways. We showed that the immunologic status is highly heterogeneous within and across TCGA tumor types and subtypes. We integrated the information of T-cell spatial organization with our immune phenotypes and found that the ‘band-like’ spatial organization (T cells clustered at the invasive margin of the tumor and didn’t get into the tumor body) pattern is strongly associated with the ‘cold’ phenotype, particularly in colon and stomach cancers. Our analysis revealed a number of other factors that are associated with the ‘cold’ phenotype by either preventing T-cell infiltration or promoting T-cell dysfunction, and we performed functional validation on several targets of interest. This study provided an in-depth understanding of the genomic and molecular determinants of immunologically ‘cold’ tumors and suggested potential biomarkers that can be targeted for the development of novel combination therapy strategies to convert the ‘cold’ tumors into ‘hot’ ones and improve the odds of cancer immunotherapy success. Citation Format: Guangchun Han, Ruiping Wang, Jianfeng Shen, Pankaj Singh, Shaojun Zhang, Arvind Rao, Jianjun Gao, Alexander Lazar, Andrew Futreal, Guang Peng, Kunal Rai, Linghua Wang. The molecular and genomic drivers of immunologically cold tumors: Novel discoveries from pan-cancer analysis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 974.

Genome-wide Transcript Structure Resolution Reveals Abundant Alternate Isoform Usage from Murine Gammaherpesvirus 68

SUMMARYThe gammaherpesviruses, including Epstein-Barr virus (EBV), Kaposi’s sarcoma-associated herpesvirus (KSHV), and murine gammaherpesvirus 68 (MHV68, MuHV-4, γHV68), are etiologic agents of a wide range of lymphomas and non-hematological malignancies. These viruses possess large and highly dense dsDNA genomes that feature >80 bidirectionally positioned open reading frames (ORFs). The abundance of overlapping transcripts and extensive splicing throughout these genomes have until now prohibited high throughput-based resolution of transcript structures. Here, we integrate the capabilities of long-read sequencing with the accuracy of short-read platforms to globally resolve MHV68 transcript structures using the transcript resolution through integration of multi-platform data (TRIMD) pipeline. This approach reveals highly complex features, including: (1) pervasive overlapping transcript structures; (2) transcripts containing intra-gene or trans-gene splices that yield chimeric ORFs; (3) antisense and intergenic transcripts containing ORFs; and (4) noncoding transcripts. This work sheds light on the underappreciated complexity of gammaherpesvirus transcription and provides an extensively revised annotation of the MHV68 transcriptome.

Global transcript structure resolution of high gene density genomes through multi-platform data integration.

Annotation of herpesvirus genomes has traditionally been undertaken through the detection of open reading frames and other genomic motifs, supplemented with sequencing of individual cDNAs. Second generation sequencing and high-density microarray studies have revealed vastly greater herpesvirus transcriptome complexity than is captured by existing annotation. The pervasive nature of overlapping transcription throughout herpesvirus genomes, however, poses substantial problems in resolving transcript structures using these methods alone. We present an approach that combines the unique attributes of Pacific Biosciences Iso-Seq long-read, Illumina short-read and deepCAGE (Cap Analysis of Gene Expression) sequencing to globally resolve polyadenylated isoform structures in replicating Epstein-Barr virus (EBV). Our method, Transcriptome Resolution through Integration of Multi-platform Data (TRIMD), identifies nearly 300 novel EBV transcripts, quadrupling the size of the annotated viral transcriptome. These findings illustrate an array of mechanisms through which EBV achieves functional diversity in its relatively small, compact genome including programmed alternative splicing (e.g. across the IR1 repeats), alternative promoter usage by LMP2 and other latency-associated transcripts, intergenic splicing at the BZLF2 locus, and antisense transcription and pervasive readthrough transcription throughout the genome.

Honing in on phenotypes: comprehensive two-dimensional gas chromatography of herbivory-induced volatile emissions and novel opportunities for system-level analyses

Plant volatile organic compound (VOC) production requires a complex network of biochemical pathways, which, although well mapped from a biochemical point of view, remains only partly understood with regard to its physiological and genetic regulation. Additionally, although analytical procedures for plant VOC measurement have become increasingly faster and more sensitive in recent years, pinpointing relevant shifts in VOC production from the thousands of molecular fragments that are generated by modern mass spectrometer instruments remains challenging. Here we discuss novel opportunities for system-wide analysis provided by the implementation of non-targeted data processing and multivariate statistics in VOC analysis. We illustrate the value of implementing non-targeted data processing with examples of recent findings from our group on the interactive control exerted by salivary components of a lepidopteran herbivore, Manduca sexta, on herbivory-induced VOC emissions in the wild tobacco Nicotiana attenuata. Finally, we briefly discuss the use of multi-platform data integration for probing the nature of metabolic and regulatory systems underlying VOC emissions.

Bioanalysis Young Investigator: Jia Li

Supervisor’s supporting comments I supervised Jia Li during her PhD research on the metabolic characterization of host–parasite interactions. Jia excelled at the analytical chemistry components of her thesis and specialized in the integration of multiplatform data (NMR, UPLC–MS, CE–MS and 454 data). Since obtaining her PhD (2009), she has achieved a prestigious Imperial College Junior Research Fellowship and has focused on applying data integration techniques to investigate mechanisms of bariatric surgery. To date she has 16 publications in journals including Proceedings of the National Academy of Sciences, Molecular Systems Biology and Gut, many as first author. Her achievements include the characterization of multicompartmental changes in Schistosoma mansoni infected mice and the metabolic characterization of bariatric surgery in rats. She used NMR spectroscopic analysis, hierarchical principal component analysis and partial least square discriminant analysis to create systems level models of parasitic infection, and subsequently developed a CE method to validate the biomarkers. For the bariatric surgery model she applied a combination of UPLC–MS and NMR methods for profiling biofluids and related them to changes in the fecal microbiome profiled using 454 sequencing. She has presented her work at international conferences and has developed a network of external collaborations. Jia is an excellent role model; she is genuinely innovative and motivated, and has a flair for analytical technology.