Integrated Analysis Pipeline Research Articles

Abstract C107: Use of spatial transcriptomics to identify molecular features associated with African American heritage in pancreatic cancer

Abstract Pancreatic cancer is the third leading cause of cancer-related deaths in the US, with a 5-year survival rate of 12%. Black or African American patients (BAA) have a 20% higher incidence of pancreatic cancer, and recent studies suggest that there are biological factors that may influence this disparity, in addition to complex socioeconomic factors. Pancreatic tumors are highly enriched with stroma and exhibit significant inter- and intra-patient morphological and molecular heterogeneity. To improve the care for all patients, we need to better understand the disease mechanisms by comprehensively mapping different spatial locations within individual patients’ tumors and determining whether there are common patterns of pathogenesis across diverse patient populations. In this study, we utilized the 10X Visium spatial transcriptomics platform on eight treatment-naïve primary pancreatic tumors. We tested multiple published bioinformatic packages, including SpatialDE, SPARK-X, Spatial PCA, and Cottrazm, and optimized a multi-sample integrated analysis pipeline that allows us to automatically segment tumor lesions, infer major cell types in regions of interest on tissues, detect spatially variant genes, and assess associations between tissue morphological phenotypes and transcriptomic features. Importantly, our study cohort includes four White patients and four BAA patients. Through the comparison of spatial transcriptomes between White and BAA patients, we identified several genes displaying differential expression in tumor lesions of distinct racial groups, including Trefoil Factor 1 (TFF1) and CYP3A5. These genes present potential candidates for further investigation to elucidate the biological mechanisms underlying racial disparities in pancreatic cancer. Citation Format: Yuesong Wu, Aoqi Xie, Ian Loveless, Madison George, Kendyll Gartrelle, Julie Clark, Daniel Salas-Escabillas, Rupen Shah, David Kwon, Ralph Francescone, Débora B. Vendramini Costa, Howard Crawford, Brian Theisen, Yuehua Cui, Ling Huang, Nina G. Steele. Use of spatial transcriptomics to identify molecular features associated with African American heritage in pancreatic cancer [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Pancreatic Cancer; 2023 Sep 27-30; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(2 Suppl):Abstract nr C107.

P02.14.B SPATIAL TRANSCRIPTOMIC ARCHITECTURE OF TUMOURS OF THE PERIPHERAL NERVOUS SYSTEM

Abstract BACKGROUND Studies into the genetics of peripheral nerve tumors have been focused on their association with known gene mutations and syndromes as well as on the tumor microenvironment of malignant peripheral nerve sheath tumors. Still, not many advances have been made into clarifying the structure and diverse pathophysiology of these tumors. Here we present spatially resolved transcriptomic profiling of benign and malignant entities and describe the topographical architecture of the neoplastic and microenvironmental diversity. MATERIAL AND METHODS We employed spatially resolved transcriptomics using the 10X Visium technology from paraffine embedded specimens. We included hybrid peripheral nerve sheath tumors (HPNSTs) plexiform neurofibromas and malignant peripheral nerve sheath tumors (MPNST). Computational analysis was employed by the SPATA2 toolbox. Transcriptomic niches were identified by graph-based spatial clustering. Correlation of histomorphological features and transcriptomic programs was performed by a co-expression network-based analysis. RESULTS We identified hub genes associated with neurofibroma and schwannoma histopathology across primary pathologies and discovered that neurofibroma regions of HPNSTs exhibited unique transcriptional programs marked by a significant higher local accumulation of bone-derived macrophages and lymphoid cells compared to primary neurofibromas. A novel marker, Apolipoprotein D (APOD), was found to accurately identify neurofibroma subregions in HPNSTs, which was confirmed by immunostaining in a control cohort. In MPNSTs, we observed a high grade of inter- and intrapatient heterogeneity driven by unique transcriptional niches and genetic variability. Subsequent pathway analysis revealed distinct oncogenic signaling driver across regional niches along with variance in immune infiltration. Our findings highlight the disbalance between histological annotation and actual tumor cell abundance in MPNSTs indicated by inferred copy number alterations. CONCLUSION Our integrated analysis pipeline revealed distinct transcriptional signatures and cellular interactions within benign PNSTs, including neurofibromas and hybrid peripheral nerve sheath tumors (HPNSTs) and sought to expose the tumor heterogeneity of MPNST and identify various cancer niches. Our study underscores the potential of spatially resolved transcriptomics as a powerful tool for advancing our understanding of PNST biology, and ultimately improving diagnosis, prognosis, and treatment strategies.

ARGs-OAP v3.0: Antibiotic-Resistance Gene Database Curation and Analysis Pipeline Optimization

Antibiotic resistance, which is encoded by antibiotic-resistance genes (ARGs), has proliferated to become a growing threat to public health around the world. With technical advances, especially in the popularization of metagenomic sequencing, scientists have gained the ability to decipher the profiles of ARGs in diverse samples with high accuracy at an accelerated speed. To analyze thousands of ARGs in a high-throughput way, standardized and integrated pipelines are needed. The new version (v3.0) of the widely used ARGs online analysis pipeline (ARGs-OAP) has made significant improvements to both the reference database—the structured ARG (SARG) database—and the integrated analysis pipeline. SARG has been enhanced with sequence curation to improve annotation reliability, incorporate emerging resistance genotypes, and determine rigorous mechanism classification. The database has been further organized and visualized online in the format of a tree-like structure with a dictionary. It has also been divided into sub-databases for different application scenarios. In addition, the ARGs-OAP has been improved with adjusted quantification methods, simplified tool implementation, and multiple functions with user-defined reference databases. Moreover, the online platform now provides a diverse biostatistical analysis workflow with visualization packages for the efficient interpretation of ARG profiles. The ARGs-OAP v3.0 with an improved database and analysis pipeline will benefit academia, governmental management, and consultation regarding risk assessment of the environmental prevalence of ARGs.

MeRIPseqPipe: an integrated analysis pipeline for MeRIP-seq data based on Nextflow.

MeRIPseqPipe is an integrated and automatic pipeline that can provide users a friendly solution to perform in-depth mining of MeRIP-seq data. It integrates many functional analysis modules, range from basic processing to downstream analysis. All the processes are embedded in Nextflow with Docker support, which ensures high reproducibility and scalability of the analysis. MeRIPseqPipe is particularly suitable for analyzing a large number of samples at once with a simple command. The final output directory is structured based on each step and tool. And visualization reports containing various tables and plots are provided as HTML files. MeRIPseqPipe is freely available at https://github.com/canceromics/MeRIPseqPipe. Supplementary data are available at Bioinformatics online.

ChIP-AP: an integrated analysis pipeline for unbiased ChIP-seq analysis.

Chromatin immunoprecipitation coupled with sequencing (ChIP-seq) is a technique used to identify protein–DNA interaction sites through antibody pull-down, sequencing and analysis; with enrichment ‘peak’ calling being the most critical analytical step. Benchmarking studies have consistently shown that peak callers have distinct selectivity and specificity characteristics that are not additive and seldom completely overlap in many scenarios, even after parameter optimization. We therefore developed ChIP-AP, an integrated ChIP-seq analysis pipeline utilizing four independent peak callers, which seamlessly processes raw sequencing files to final result. This approach enables (1) better gauging of peak confidence through detection by multiple algorithms, and (2) more thoroughly surveys the binding landscape by capturing peaks not detected by individual callers. Final analysis results are then integrated into a single output table, enabling users to explore their data by applying selectivity and sensitivity thresholds that best address their biological questions, without needing any additional reprocessing. ChIP-AP therefore presents investigators with a more comprehensive coverage of the binding landscape without requiring additional wet-lab observations.

Modern simulation utilities for genetic analysis

BackgroundStatistical geneticists employ simulation to estimate the power of proposed studies, test new analysis tools, and evaluate properties of causal models. Although there are existing trait simulators, there is ample room for modernization. For example, most phenotype simulators are limited to Gaussian traits or traits transformable to normality, while ignoring qualitative traits and realistic, non-normal trait distributions. Also, modern computer languages, such as Julia, that accommodate parallelization and cloud-based computing are now mainstream but rarely used in older applications. To meet the challenges of contemporary big studies, it is important for geneticists to adopt new computational tools.ResultsWe present TraitSimulation, an open-source Julia package that makes it trivial to quickly simulate phenotypes under a variety of genetic architectures. This package is integrated into our OpenMendel suite for easy downstream analyses. Julia was purpose-built for scientific programming and provides tremendous speed and memory efficiency, easy access to multi-CPU and GPU hardware, and to distributed and cloud-based parallelization. TraitSimulation is designed to encourage flexible trait simulation, including via the standard devices of applied statistics, generalized linear models (GLMs) and generalized linear mixed models (GLMMs). TraitSimulation also accommodates many study designs: unrelateds, sibships, pedigrees, or a mixture of all three. (Of course, for data with pedigrees or cryptic relationships, the simulation process must include the genetic dependencies among the individuals.) We consider an assortment of trait models and study designs to illustrate integrated simulation and analysis pipelines. Step-by-step instructions for these analyses are available in our electronic Jupyter notebooks on Github. These interactive notebooks are ideal for reproducible research.ConclusionThe TraitSimulation package has three main advantages. (1) It leverages the computational efficiency and ease of use of Julia to provide extremely fast, straightforward simulation of even the most complex genetic models, including GLMs and GLMMs. (2) It can be operated entirely within, but is not limited to, the integrated analysis pipeline of OpenMendel. And finally (3), by allowing a wider range of more realistic phenotype models, TraitSimulation brings power calculations and diagnostic tools closer to what investigators might see in real-world analyses.

Abstract 170: Development and analytical validation of a novel next-generation DNA sequencing assay, the oncomine lymphoma panel, to detect SNV, insertion, deletion and copy number variants in 25 Lymphoma genes in FFPE samples

Abstract Introduction: Lymphomas, including diffuse large B-cell lymphoma (DLBCL), Hodgkin's lymphoma, and other lymphomas, are clinically heterogeneous: some respond well to therapy, but many fail to respond. Much of this variability in response is reported to reflect molecular heterogeneity of the tumor. Identifying somatic variants including SNVs, insertions, deletions, and copy number variations (CNVs) is important in characterizing these samples. Robust detection of variants in multiple genes using fine needle aspirate (FNA) samples, low abundance DNA, and FFPE samples is needed. Methods: We describe a next-generation sequencing assay with 25 genes, the Oncomine Lymphoma Panel, including ARID1A, ATM, B2M, BCL2, BCL6, BRAF, BTK, CARD11, CD79B, CDKN2A, CREBBP, EZH2, GNA13, HIST1H1E, KMT2D, MTOR, MYC, MYD88, PIM1, SF3B1, SOCS1, TNFAIP3, TNFRSF14, TP53, and XPO1. This panel comprises 976 amplicons in total. The assays for these genes have been optimized, and performance has been tested on control samples and on representative clinical research samples. Another 300 genes, with optimized and verified performance, can be added to customize the panel. This panel is designed to work with 20 ng input DNA from FFPE samples and can also be used with samples having non-degraded DNA. A comprehensive bioinformatics analysis solution was developed to detect SNPs, indels, and CNVs; to perform filtering for the most relevant variants; to annotate these variants with a wide variety of bioinformatics databases; and to report on the interpretation of the selected variants. Results: We tested this panel on various types of input material, including control samples and FFPE samples. Uniformity, coverage, on target mapping, reproducibility, and sensitivity to detect variants were high in all cases and above established quality criteria (>90% or > 95%). Finally, a coordinated analysis solution uses information about the panel and provides an integrated analysis pipeline with a simple and powerful visual interface, including variant calling, CNV detection, functional annotation, population MAF, predicted protein effect, and annotations including ClinVar, COSMIC, etc. Filtering tools utilizing this information facilitate variant prioritization. Conclusions: An NGS lymphoma assay with a comprehensive data analysis approach was developed and analytically validated. The system is capable of detecting both small mutations and CNVs simultaneously with high sensitivity in FFPE samples for lymphoma translational research. For research use only. Not for use in diagnostic procedures. Citation Format: Fiona Hyland, Charles Scafe, Yun Zhu, Chenchen Yang, Yu-Ting Tseng, Brooke McKnight, Fernando Farfan, Santhoshi Bandla, Seth Sadis, Steve Roman. Development and analytical validation of a novel next-generation DNA sequencing assay, the oncomine lymphoma panel, to detect SNV, insertion, deletion and copy number variants in 25 Lymphoma genes in FFPE samples [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 170.

BioMedR: an R/CRAN package for integrated data analysis pipeline in biomedical study.

With the increasing development of biotechnology and information technology, publicly available data in chemistry and biology are undergoing explosive growth. Such wealthy information in these resources needs to be extracted and then transformed to useful knowledge by various data mining methods. However, a main computational challenge is how to effectively represent or encode molecular objects under investigation such as chemicals, proteins, DNAs and even complicated interactions when data mining methods are employed. To further explore these complicated data, an integrated toolkit to represent different types of molecular objects and support various data mining algorithms is urgently needed. We developed a freely available R/CRAN package, called BioMedR, for molecular representations of chemicals, proteins, DNAs and pairwise samples of their interactions. The current version of BioMedR could calculate 293 molecular descriptors and 13 kinds of molecular fingerprints for small molecules, 9920 protein descriptors based on protein sequences and six types of generalized scale-based descriptors for proteochemometric modeling, more than 6000 DNA descriptors from nucleotide sequences and six types of interaction descriptors using three different combining strategies. Moreover, this package realized five similarity calculation methods and four powerful clustering algorithms as well as several useful auxiliary tools, which aims at building an integrated analysis pipeline for data acquisition, data checking, descriptor calculation and data modeling. BioMedR provides a comprehensive and uniform R package to link up different representations of molecular objects with each other and will benefit cheminformatics/bioinformatics and other biomedical users. It is available at: https://CRAN.R-project.org/package=BioMedR and https://github.com/wind22zhu/BioMedR/.

MinOmics, an Integrative and Immersive Tool for Multi-Omics Analysis.

Proteomic and transcriptomic technologies resulted in massive biological datasets, their interpretation requiring sophisticated computational strategies. Efficient and intuitive real-time analysis remains challenging. We use proteomic data on 1417 proteins of the green microalga Chlamydomonas reinhardtii to investigate physicochemical parameters governing selectivity of three cysteine-based redox post translational modifications (PTM): glutathionylation (SSG), nitrosylation (SNO) and disulphide bonds (SS) reduced by thioredoxins. We aim to understand underlying molecular mechanisms and structural determinants through integration of redox proteome data from gene- to structural level. Our interactive visual analytics approach on an 8.3 m2 display wall of 25 MPixel resolution features stereoscopic three dimensions (3D) representation performed by UnityMol WebGL. Virtual reality headsets complement the range of usage configurations for fully immersive tasks. Our experiments confirm that fast access to a rich cross-linked database is necessary for immersive analysis of structural data. We emphasize the possibility to display complex data structures and relationships in 3D, intrinsic to molecular structure visualization, but less common for omics-network analysis. Our setup is powered by MinOmics, an integrated analysis pipeline and visualization framework dedicated to multi-omics analysis. MinOmics integrates data from various sources into a materialized physical repository. We evaluate its performance, a design criterion for the framework.

IDEA: Integrated Drug Expression Analysis-Integration of Gene Expression and Clinical Data for the Identification of Therapeutic Candidates.

Cancer drug discovery is an involved process spanning efforts from several fields of study and typically requires years of research and development. However, the advent of high-throughput genomic technologies has allowed for the use of in silico, genomics-based methods to screen drug libraries and accelerate drug discovery. Here we present a novel approach to computationally identify drug candidates for the treatment of breast cancer. In particular, we developed a Drug Regulatory Score similarity metric to evaluate gene expression profile similarity, in the context of drug treatment, and incorporated time-to-event patient survival information to develop an integrated analysis pipeline: Integrated Drug Expression Analysis (IDEA). We were able to predict drug candidates that have been known and those that have not been known in the literature to exhibit anticancer effects. Overall, our method enables quick preclinical screening of drug candidates for breast cancer and other diseases by using the most important indicator of drug efficacy: survival.

ChAMP: 450k Chip Analysis Methylation Pipeline.

The Illumina Infinium HumanMethylation450 BeadChip is a new platform for high-throughput DNA methylation analysis. Several methods for normalization and processing of these data have been published recently. Here we present an integrated analysis pipeline offering a choice of the most popular normalization methods while also introducing new methods for calling differentially methylated regions and detecting copy number aberrations. ChAMP is implemented as a Bioconductor package in R. The package and the vignette can be downloaded at bioconductor.org

High-resolution imaging of young M-type stars of the solar neighbourhood: probing for companions down to the mass of Jupiter

Context. High contrast imaging is a powerful technique to search for gas giant planets and brown dwarfs orbiting at separation larger than several AU. Around solar-type stars, giant planets are expected to form by core accretion or by gravitational instability, but since core accretion is increasingly difficult as the primary star becomes lighter, gravitational instability would be the a probable formation scenario for yet-to-be-found distant giant planets around a low-mass star. A systematic survey for such planets around M dwarfs would therefore provide a direct test of the efficiency of gravitational instability. Aims. We search for gas giant planets orbiting around late-type stars and brown dwarfs of the solar neighborhood. Methods. We obtained deep high resolution images of 16 targets with the adaptive optic system of VLT-NACO in the Lp band, using direct imaging and angular differential imaging. This is currently the largest and deepest survey for Jupiter-mass planets around Mdwarfs. We developed and used an integrated reduction and analysis pipeline to reduce the images and derive our 2D detection limits for each target. The typical contrast achieved is about 9 magnitudes at 0.5" and 11 magnitudes beyond 1". For each target we also determine the probability of detecting a planet of a given mass at a given separation in our images. Results. We derived accurate detection probabilities for planetary companions, taking into account orbital projection effects, with in average more than 50% probability to detect a 3MJup companion at 10AU and a 1.5MJup companion at 20AU, bringing strong constraints on the existence of Jupiter-mass planets around this sample of young M-dwarfs.

MediPlEx - a tool to combine in silico & experimental gene expression profiles of the model legume Medicago truncatula.

BackgroundExpressed Sequence Tags (ESTs) are in general used to gain a first insight into gene activities from a species of interest. Subsequently, and typically based on a combination of EST and genome sequences, microarray-based expression analyses are performed for a variety of conditions. In some cases, a multitude of EST and microarray experiments are conducted for one species, covering different tissues, cell states, and cell types. Under these circumstances, the challenge arises to combine results derived from the different expression profiling strategies, with the goal to uncover novel information on the basis of the integrated datasets.FindingsUsing our new analysis tool, MediPlEx (MEDIcago truncatula multiPLe EXpression analysis), expression data from EST experiments, oligonucleotide microarrays and Affymetrix GeneChips® can be combined and analyzed, leading to a novel approach to integrated transcriptome analysis. We have validated our tool via the identification of a set of well-characterized AM-specific and AM-induced marker genes, identified by MediPlEx on the basis of in silico and experimental gene expression profiles from roots colonized with AM fungi.ConclusionsMediPlEx offers an integrated analysis pipeline for different sets of expression data generated for the model legume Medicago truncatula. As expected, in silico and experimental gene expression data that cover the same biological condition correlate well. The collection of differentially expressed genes identified via MediPlEx provides a starting point for functional studies in plant mutants. MediPlEx can freely be used at http://www.cebitec.uni-bielefeld.de/mediplex.