Scalable Pathogen Pipeline Platform (SP^3): Enabling Unified Genomic Data Analysis with Elastic Cloud Computing

Genomic data analysis across multiple cloud platforms is an ongoing challenge, especially when large amounts of data are involved. Here, we present Swarm, a framework for federated computation that promotes minimal data motion and facilitates crosstalk between genomic datasets stored on various cloud platforms. We demonstrate its utility via common inquiries of genomic variants across BigQuery in the Google Cloud Platform (GCP), Athena in the Amazon Web Services (AWS), Apache Presto and MySQL. Compared to single-cloud platforms, the Swarm framework significantly reduced computational costs, run-time delays and risks of security breach and privacy violation.

Genomic data analysis across multiple cloud platforms is an ongoing challenge, especially when large amounts of data are involved. Here, we present Swarm, a framework for federated computation that promotes minimal data motion and facilitates crosstalk between genomic datasets stored on various cloud platforms. We demonstrate its utility via common inquiries of genomic variants across BigQuery in the Google Cloud Platform (GCP), Athena in the Amazon Web Services (AWS), Apache Presto and MySQL. Compared to single-cloud platforms, the Swarm framework significantly reduced computational costs, run-time delays and risks of security breach and privacy violation.

Post-sequencing genomic data analysis becomes a major challenge while next-generation sequencing technologies evolve by leaps and bounds. The data-intensive and compute-intensive nature of genome analysis makes cluster computing an attractive choice for building efficient solutions. This paper presents HiGene, a high-performance genome analysis platform that exploits big data technology to revolutionize genomics data crunching power. HiGene reconstructs the genome analysis pipeline by exploiting both multi-core and multi-node parallelization using Apache Spark, and employs two key techniques to further boost the performance. First, a dynamic computing resource re-allocator is implemented, which allows flexible on-demand resource allocation for operations inside tasks. Second, an efficient skew mitigation approach is proposed, which automatically identifies and resolves data skew and computation skew through task repartitioning and resource reallocating respectively. HiGene has been evaluated with a whole human genome dataset on a 10-node Huawei 5885 cluster. Experimental results show that HiGene achieves remarkable high performance that reduces the total running time on a whole genome sequence dataset from days to nearly one hour. Furthermore, it is two times faster than state-of-the-art cluster based approaches.

Signal Track Analytical Research Tool (START), is a parallel system for analyzing large-scale genomic data. Currently, genomic data analyses are usually performed by using custom scripts developed by individual research groups, and/or by the integrated use of multiple existing tools (such as BEDTools and Galaxy). The goals of START are 1) to provide a single tool that supports a wide spectrum of genomic data analyses that are commonly done by analysts; and 2) to greatly simplify these analysis tasks by means of a simple declarative language (STQL) with which users only need to specify what they want to do, rather than the detailed computational steps as to how the analysis task should be performed.
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\nSTART consists of four major components: 1) A declarative language called Signal Track Query Language (STQL), which is a SQL-like language we specifically designed to suit the needs for analyzing genomic signal tracks. 2) A STQL processing system built on top of a large-scale distributed architecture. The system is based on the Hadoop distributed storage and the MapReduce Big Data processing framework. It processes each user query using multiple machines in parallel. 3) A simple and user-friendly web site that helps users construct and execute queries, upload/download compressed data files in various formats, man-age stored data, queries and analysis results, and share queries with other users.
\nIt also provides a complete help system, detailed specification of STQL, and a large number of sample queries for users to learn STQL and try START easily. Private files and queries are not accessible by other users. 4) A repository of public data popularly used for large-scale genomic data analysis, including data from ENCODE and Roadmap Epigenomics, that users can use in their analyses.

The interpretation of variants in cancer is frequently focused on direct protein coding alterations. However, most somatic mutations are in noncoding regions of the genome, and even exonic mutations may have unidentified noncoding consequences. Here we present Regtools, a software package designed to efficiently identify variants that may cause aberrant splicing in tumors. Our tool integrates variant calls from genomic data with junctions extracted from transcriptomic data in order to examine potential cis alterations to splicing near a somatic variant. Based on user-defined parameters and position relative to known exons, variants are first annotated as splicing relevant or not. Splicing junctions are inferred from transcriptomic sequencing data, and comparison of junctions to a reference transcriptome allows for identification and annotation of novel junctions and nearby regulatory or splicing motifs. From there, mutations are associated with junctions that overlap with a flanking region. In order to evaluate Regtools, we used it to analyze the transcriptional output of tumor-sequencing data from two cohorts of cancer patients, one of hepatocellular carcinoma and one of small cell lung cancer with 28 and 21 samples, respectively. We performed whole-exome and RNA sequencing on each sample. Somatic variants were called on whole-exome alignment data. For each cohort, we compared the junctional profiles between tumors and identified numerous examples of variants for which there are elevated levels of proximal novel or known junctions. Moreover, out of 754 (153 in HCC; 601 in SCLC) variants identified as splicing relevant by our approach, only 165 (20 in HCC; 145 in SCLC) were annotated as splicing relevant by Ensembl's Variant Effect Predictor, using the recommended “per_gene” option. This preliminary analysis illustrates the importance of an efficient, user-friendly computational tool for identifying important noncoding variants that would otherwise be undervalued or perhaps even completely ignored by traditional methods and annotators. Regtools is freely available and open source (https://github.com/griffithlab/regtools). Citation Format: Yang-Yang Feng, Avinash Ramu, Zachary L. Skidmore, Jason Kunisaki, Kelsy C. Cotto, Obi L. Griffith, Malachi Griffith. Regtools: Integrated analysis of genomic and transcriptomic data for discovery of mutations associated with aberrant splicing in cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 2285.

Purpose:HER2-targeted therapies in ERBB2-amplified metastatic colorectal cancer (mCRC) are effective; however, a notable portion of patients do not respond to treatment, and secondary resistance occurs in most patients receiving these treatments. The purpose of this study was to investigate determinants of treatment efficacy and resistance in patients with ERBB2-amplified mCRC who received HER2-targeted therapy by analyzing multiomics data.Experimental Design:We investigated genomic data from a nationwide large cancer genomic screening project, the SCRUM-Japan project. We analyzed paired genome and transcriptome data of tissue and genomic data of ctDNA collected pre- and postprogression in patients enrolled in the related trial, TRIUMPH, in ERBB2-amplified mCRC.Results:In 155 patients with ERBB2-amplified solid tumors who received HER2-targeted therapy based on the SCRUM-Japan project, the objective response rate was 50%, 51%, and 35% in ERBB2 wild-type, variant of unknown significance, and pathogenic variant groups, respectively. In the paired genome and transcriptome data analyses in TRIUMPH, we identified the novel splicing-associated variant c.644-66_-2del in one of the 11 patients with paired whole-exome sequencing and whole-transcriptome sequencing data sets, which lacks the binding domain of pertuzumab, in progressed metastatic tumor as a variant with potential pathogenicity. The time-course ctDNA analysis detected c.644-66_-2del as an acquired variant.Conclusions:This study highlighted the importance of ERBB2 genomic status when evaluating the efficacy of HER2-targeted therapies in ERBB2-amplified mCRC. The identification of a novel splicing-associated variant may provide insights into potential mechanisms of treatment resistance. Furthermore, we demonstrated the utility of ctDNA to follow the acquired genomic status of mCRC tumors.

The importance of genomic predictors for clinical outcome of hematological malignancies

Ancient DNA provides a unique frame for directly studying human population genetics in time and space. Still, since most of the ancient genomic data is low coverage, analysis is confronted with a low number of SNPs, genotype uncertainties, and reference-bias. Here, we for the first time benchmark the two distinct versions of Glimpse tools on 120 ancient human genomes from Eurasia including those largely from previously under-evaluated regions and compare the performance of genotype imputation with de facto analysis approaches for low coverage genomic data analysis. We further investigate the impact of two distinct reference panels on imputation accuracy for low coverage genomic data. We compute accuracy statistics and perform PCA and f4-statistics to explore the behaviour of genotype imputation on low coverage data regarding (i)two versions of Glimpse, (ii)two reference panels, (iii)four post-imputation filters and coverages, as well as (iv)data type and geographical origin of the samples on the analyses. Our results reveal that even for 0.1X coverage ancient human genomes, genotype imputation using Glimpse-v2 is suitable. Additionally, using the 1000 Genomes merged with Human Genome Diversity Panel improves the accuracy of imputation for the rare variants with low MAF, which might be important not only for ancient genomics but also for modern human genomic studies based on low coverage data and for haplotype-based analysis. Most importantly, we reveal that genotype imputation of low coverage ancient human genomes reduces the genetic affinity of the samples towards human reference genome. Through solving one of the most challenging biases in data analysis, so-called reference bias, genotype imputation using Glimpse v2 is promising for low coverage ancient human genomic data analysis and for rare-variant-based and haplotype-based analysis.

Inverting the model of genomics data sharing with the NHGRI Genomic Data Science Analysis, Visualization, and Informatics Lab-space

The content of the 13th Mutation Detection meeting (Leiden, April 2015) is summarized in this report. Topics discussed at the meeting included current challenges of clinical NGS, advances in bioinformatics, data quality control, single cell analysis and RNA sequencing, among others. Social, ethical and regulatory challenges of genomic data handling and data sharing were the focus of an expert panel debate. The 14th International Symposium on Variants in the Genome will take place in Santiago de Compostela, June 5-8, 2017. http://isv.variome.org.

This project will formulate a machine learning based model using human genome data and forecast the chance for patients developing to severe dengue, a severe form of dengue fever and a particular condition that could undoubtedly end up being life hazardous. The research seeks to improve treatment by identifying genetic factors that predispose some people to a severe form of dengue and thus reduce mortality. Genomic data analysis and training of machine learning techniques such as feature selection, model training, and interpretability are incorporated in this study. Predicting severe dengue prognosis using ML models (SVM, random forest, deep learning models) for their training. Some tools like SHAP and LIME—create a predictive model for severe dengue prognosis. Collecting and preprocessing of genomic data, selection of the important genetic markers and validation of the performance of the model with accuracy and sensitivity metrics can be key steps. The final aim is to create a clinically handy product with a risk score for severe dengue and all other important genetic markers that can be used by health professionals in the future. Exploiting the existence of other cases and the past history to predict future cases, aim the proposed model to be more tailored and efficient in dengue cases management. By using genetic data t o understand better the risks of severe dengue, it will help to improve public health efforts and patient care.

PIC-SURE is an open-source platform for integrating and analyzing large-scale clinical and genomic data. Part of the NIH NHLBI BioData Catalyst® ecosystem, PIC-SURE enables real-time cohort building and analysis across 1.4 M participants within 273 studies (May 2025). Through a graphical interface and API, researchers can explore and analyze complex datasets in real time. This flexibility supports scalable and reproducible research, lowering the barrier to integrated clinical and genomic data analysis.

The rapid growth of genomic data presents new challenges and opportunities in the field of bioinformatics. Deep learning has emerged as a powerful tool for analyzing large and complex genomic datasets, offering unprecedented capabilities for identifying patterns, predicting gene functions, and understanding genetic variations. This article explores the latest advancements in deep learning techniques applied to genomic data analysis. It discusses key approaches such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and autoencoders, and their applications in various aspects of genomics, including gene expression analysis, variant calling, and drug discovery. The article also highlights the integration of deep learning with other emerging technologies, such as CRISPR, and its potential to revolutionize personalized medicine and genetic research..

Resistance to the last-line antibiotics against invasive Gram-negative bacterial infection is a rising concern in public health. Multidrug resistant (MDR) Acinetobacter baumannii Aci46 can resist colistin and carbapenems with a minimum inhibitory concentration of 512 µg/mL as determined by microdilution method and shows no zone of inhibition by disk diffusion method. These phenotypic characteristics prompted us to further investigate the genotypic characteristics of Aci46. Next generation sequencing was applied in this study to obtain whole genome data. We determined that Aci46 belongs to Pasture ST2 and is phylogenetically clustered with international clone (IC) II as the predominant strain in Thailand. Interestingly, Aci46 is identical to Oxford ST1962 that previously has never been isolated in Thailand. Two plasmids were identified (pAci46a and pAci46b), neither of which harbors any antibiotic resistance genes but pAci46a carries a conjugational system (type 4 secretion system or T4SS). Comparative genomics with other polymyxin and carbapenem-resistant A. baumannii strains (AC30 and R14) identified shared features such as CzcCBA, encoding a cobalt/zinc/cadmium efflux RND transporter, as well as a drug transporter with a possible role in colistin and/or carbapenem resistance in A. baumannii. Single nucleotide polymorphism (SNP) analyses against MDR ACICU strain showed three novel mutations i.e., Glu229Asp, Pro200Leu, and Ala138Thr, in the polymyxin resistance component, PmrB. Overall, this study focused on Aci46 whole genome data analysis, its correlation with antibiotic resistance phenotypes, and the presence of potential virulence associated factors.

The article investigates the impact of modern cloud technologies on the efficiency of DevOps processes, which are key to the automation, flexibility and reliability of software development. With the rapid development of information technology, cloud platforms play an important role in accelerating software product development, testing and deployment. Implementing DevOps in the cloud environment reduces the time to market, increases the stability of software systems and optimizes the use of computing resources, which is critical for modern companies. This paper discusses DevOps teams' main challenges, including the need to scale infrastructure rapidly, ensure continuous integration and delivery (CI/CD), automate testing, monitor performance, and optimize costs. Particular attention is paid to security problems, configuration management and the human factor minimization when implementing program code changes. A comparative analysis of the three leading cloud platforms - Amazon Web Services, Google Cloud Platform, and Microsoft Azure – is carried out in the context of their impact on DevOps processes. The possibilities of automation, support for container technologies, infrastructure scalability, monitoring tools, and integration with CI/CD pipelines are evaluated. AWS was found to offer the broadest set of DevOps tools with a high level of automation, making it an attractive choice for organizations focused on complex enterprise solutions. Google Cloud Platform demonstrates the best support for Kubernetes and containerization, an essential factor for teams working with microservices architecture. Microsoft Azure provides the most profound integration with the Microsoft ecosystem and is the best choice for companies that use Windows and related products. An experimental study has shown that the choice of a cloud platform significantly impacts the speed of release cycles, the stability of the deployed infrastructure, the ability to respond quickly to changes in load, and the productivity of DevOps teams in general. Recommendations for choosing the optimal cloud platform are proposed depending on the specifics of the project, the scale of the organization, the level of system load, and automation requirements.