An open access pilot freely sharing cancer genomic data from participants in Texas

O Uso do Sequenciamento Total do Exoma no Diagnóstico do Adenocarcinoma Ductal Pancreático

  Introdução: O adenocarcinoma ductal pancreático (PDAC) é uma doença agressiva responsável no Brasil por 2% das neoplasias e 5% das mortes por câncer. A análise do exoma – parte do DNA que codifica as proteínas – permite identificar as variantes somáticas do tumor e as germinativas do paciente. Essa informação é necessária para implementar a terapia-alvo para o PDAC, pois fornece evidência para selecionar, ou excluir, tratamentos para a doença. Objetivo: Identificar as variantes de interesse clínico e farmacológico presentes no PDAC de quatro pacientes, por meio da técnica de sequenciamento total do exoma (WES). Método: Foram utilizados dados públicos de quatro amostras de pares tumor-normal de PDAC, localizados na cabeça do pâncreas de pacientes caucasianos, estádio T3N1M0, sequenciadas e publicizadas pelo Texas Cancer Research Biobank. Para identificar as variações somáticas e germinativas, utilizou-se o software GATK. As consequências clínicas e farmacológicas dessas variações foram anotadas por meio do software VEP e analisadas mediante o software estatístico R. Resultados: Dos quatro tumores, um possui variante estrutural com duplicação do gene AKT2; outro, variantes nos genes da via das ciclinas CDK14 e CDKN2C, o que altera o regime quimioterápico; na linhagem germinativa, um paciente tem variantes no gene XRCC1, que sugere aumento da resposta à platina. Conclusão: Embora a patologia classifique todos os tumores como PDAC, cada paciente – bem como o respectivo tumor – apresenta especificidades que afetam o diagnóstico e as possibilidades terapêuticas. O WES permite identificá-las a um custo baixo, o que amplia as possibilidades de tratamento do PDAC.

Brain Tumor Characterization Using Radiogenomics in Artificial Intelligence Framework

Brain tumor characterization (BTC) is the process of knowing the underlying cause of brain tumors and their characteristics through various approaches such as tumor segmentation, classification, detection, and risk analysis. The substantial brain tumor characterization includes the identification of the molecular signature of various useful genomes whose alteration causes the brain tumor. The radiomics approach uses the radiological image for disease characterization by extracting quantitative radiomics features in the artificial intelligence (AI) environment. However, when considering a higher level of disease characteristics such as genetic information and mutation status, the combined study of "radiomics and genomics" has been considered under the umbrella of "radiogenomics". Furthermore, AI in a radiogenomics' environment offers benefits/advantages such as the finalized outcome of personalized treatment and individualized medicine. The proposed study summarizes the brain tumor's characterization in the prospect of an emerging field of research, i.e., radiomics and radiogenomics in an AI environment, with the help of statistical observation and risk-of-bias (RoB) analysis. The PRISMA search approach was used to find 121 relevant studies for the proposed review using IEEE, Google Scholar, PubMed, MDPI, and Scopus. Our findings indicate that both radiomics and radiogenomics have been successfully applied aggressively to several oncology applications with numerous advantages. Furthermore, under the AI paradigm, both the conventional and deep radiomics features have made an impact on the favorable outcomes of the radiogenomics approach of BTC. Furthermore, risk-of-bias (RoB) analysis offers a better understanding of the architectures with stronger benefits of AI by providing the bias involved in them.

Simplifying the development of portable, scalable, and reproducible workflows

Command-line software plays a critical role in biology research. However, processes for installing and executing software differ widely. The Common Workflow Language (CWL) is a community standard that addresses this problem. Using CWL, tool developers can formally describe a tool's inputs, outputs, and other execution details. CWL documents can include instructions for executing tools inside software containers. Accordingly, CWL tools are portable-they can be executed on diverse computers-including personal workstations, high-performance clusters, or the cloud. CWL also supports workflows, which describe dependencies among tools and using outputs from one tool as inputs to others. To date, CWL has been used primarily for batch processing of large datasets, especially in genomics. But it can also be used for analytical steps of a study. This article explains key concepts about CWL and software containers and provides examples for using CWL in biology research. CWL documents are text-based, so they can be created manually, without computer programming. However, ensuring that these documents conform to the CWL specification may prevent some users from adopting it. To address this gap, we created ToolJig, a Web application that enables researchers to create CWL documents interactively. ToolJig validates information provided by the user to ensure it is complete and valid. After creating a CWL tool or workflow, the user can create 'input-object' files, which store values for a particular invocation of a tool or workflow. In addition, ToolJig provides examples of how to execute the tool or workflow via a workflow engine. ToolJig and our examples are available at https://github.com/srp33/ToolJig.

Hummingbird: efficient performance prediction for executing genomic applications in the cloud

MOTIVATION

A major drawback of executing genomic applications on cloud computing facilities is the lack of tools to predict which instance type is the most appropriate, often resulting in an over- or under- matching of resources. Determining the right configuration before actually running the applications will save money and time. Here, we introduce Hummingbird, a tool for predicting performance of computing instances with varying memory and CPU on multiple cloud platforms.

RESULTS

Our experiments on three major genomic data pipelines, including GATK HaplotypeCaller, GATK Mutect2 and ENCODE ATAC-seq, showed that Hummingbird was able to address applications in command line specified in JSON format or workflow description language (WDL) format, and accurately predicted the fastest, the cheapest and the most cost-efficient compute instances in an economic manner.

AVAILABILITY AND IMPLEMENTATION

Hummingbird is available as an open source tool at: https://github.com/StanfordBioinformatics/Hummingbird.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Responsible, practical genomic data sharing that accelerates research

Data sharing anchors reproducible science, but expectations and best practices are often nebulous. Communities of funders, researchers and publishers continue to grapple with what should be required or encouraged. To illuminate the rationales for sharing data, the technical challenges and the social and cultural challenges, we consider the stakeholders in the scientific enterprise. In biomedical research, participants are key among those stakeholders. Ethical sharing requires considering both the value of research efforts and the privacy costs for participants. We discuss current best practices for various types of genomic data, as well as opportunities to promote ethical data sharing that accelerates science by aligning incentives.

TarPan: an easily adaptable targeted sequencing panel viewer for research and clinical use

BACKGROUND

The study of cancer genomics continually matures as the number of patient samples sequenced increases. As more data is generated, oncogenic drivers for specific cancer types are discovered along with their associated risks. This in turn leads to potential treatment strategies that pave the way to precision medicine. However, significant financial and analytical barriers make it infeasible to sequence the entire genome of every patient. In contrast, targeted sequencing panels give reliable information on relevant portions of the genome at a fiscally responsible cost. Therefore, we have created the Targeted Panel (TarPan) Viewer, a software tool, to investigate this type of data.

RESULTS

TarPan Viewer helps investigators understand data from targeted sequencing data by displaying the information through a web browser interface. Through this interface, investigators can easily observe copy number changes, mutations, and structural events in cancer samples. The viewer runs in R Shiny with a robust SQLite backend and its input is generated from bioinformatic algorithms reliably described in the literature. Here we show the results from using TarPan Viewer on publicly available follicular lymphoma, breast cancer, and multiple myeloma data. In addition, we have tested and utilized the viewer internally, and this data has been used in high-impact peer-reviewed publications.

CONCLUSIONS

We have designed a flexible, simple to setup viewer that is easily adaptable to any type of cancer targeted sequencing, and has already proven its use in a research laboratory environment. Further, we believe with deeper sequencing and/or more targeted application it could be of use in the clinic in conjunction with an appropriate targeted sequencing panel as a cost-effective diagnostic test, especially in cancers such as acute leukemia or diffuse large B-cell lymphoma that require rapid interventions.

Polymorphic edge detection (PED): two efficient methods of polymorphism detection from next-generation sequencing data

Background

Accurate detection of polymorphisms with a next generation sequencer data is an important element of current genetic analysis. However, there is still no detection pipeline that is completely reliable.

Result

We demonstrate two new detection methods of polymorphisms focusing on the Polymorphic Edge (PED). In the matching between two homologous sequences, the first mismatched base to appear is the SNP, or the edge of the structural variation. The first method is based on k-mers from short reads and detects polymorphic edges with k-mers for which there is no match between target and control, making it possible to detect SNPs by direct comparison of short-reads in two datasets (target and control) without a reference genome sequence. The second method is based on bidirectional alignment to detect polymorphic edges, not only SNPs but also insertions, deletions, inversions and translocations. Using these two methods, we succeed in making a high-quality comparison map between rice cultivars showing good match to the theoretical value of introgression, and in detecting specific large deletions across cultivars.

Conclusions

Using Polymorphic Edge Detection (PED), the k-mer method is able to detect SNPs by direct comparison of short-reads in two datasets without genomic alignment step, and the bidirectional alignment method is able to detect SNPs and structural variations from even single-end short-reads. The PED is an efficient tool to obtain accurate data for both SNPs and structural variations.

Availability

The PED software is available at: https://github.com/akiomiyao/ped.

Electronic supplementary material

The online version of this article (10.1186/s12859-019-2955-6) contains supplementary material, which is available to authorized users.

Personal Genome Project UK (PGP-UK): a research and citizen science hybrid project in support of personalized medicine

BACKGROUND

Molecular analyses such as whole-genome sequencing have become routine and are expected to be transformational for future healthcare and lifestyle decisions. Population-wide implementation of such analyses is, however, not without challenges, and multiple studies are ongoing to identify what these are and explore how they can be addressed.

METHODS

Defined as a research project, the Personal Genome Project UK (PGP-UK) is part of the global PGP network and focuses on open data sharing and citizen science to advance and accelerate personalized genomics and medicine.

RESULTS

Here we report our findings on using an open consent recruitment protocol, active participant involvement, open access release of personal genome, methylome and transcriptome data and associated analyses, including 47 new variants predicted to affect gene function and innovative reports based on the analysis of genetic and epigenetic variants. For this pilot study, we recruited 10 participants willing to actively engage as citizen scientists with the project. In addition, we introduce Genome Donation as a novel mechanism for openly sharing previously restricted data and discuss the first three donations received. Lastly, we present GenoME, a free, open-source educational app suitable for the lay public to allow exploration of personal genomes.

CONCLUSIONS

Our findings demonstrate that citizen science-based approaches like PGP-UK have an important role to play in the public awareness, acceptance and implementation of genomics and personalized medicine.

Chromatic: WebAssembly-Based Cancer Genome Viewer

Chromatic is a novel web-browser tool that enables researchers to visually inspect genomic variations identified through next-generation sequencing of cancer data sets to determine whether such calls are, in fact, valid. It is the first cancer bioinformatics tool developed using WebAssembly technology, which comprises a portable, low-level byte code format that provides for the rapid execution of programs within supported web browsers. It has been designed expressly for ease of use by scientists without extensive expertise in bioinformatics.

DNAp: A Pipeline for DNA-seq Data Analysis

Next-generation sequencing is empowering genetic disease research. However, it also brings significant challenges for efficient and effective sequencing data analysis. We built a pipeline, called DNAp, for analyzing whole exome sequencing (WES) and whole genome sequencing (WGS) data, to detect mutations from disease samples. The pipeline is containerized, convenient to use and can run under any system, since it is a fully automatic process in Docker container form. It is also open, and can be easily customized with user intervention points, such as for updating reference files and different software or versions. The pipeline has been tested with both human and mouse sequencing datasets, and it has generated mutations results, comparable to published results from these datasets, and reproducible across heterogeneous hardware platforms. The pipeline DNAp, funded by the US Food and Drug Administration (FDA), was developed for analyzing DNA sequencing data of FDA. Here we make DNAp an open source, with the software and documentation available to the public at http://bioinformatics.astate.edu/dna-pipeline/ .

Regulatory-accepted drug development tools are needed to accelerate innovative CNS disease treatments

Central Nervous System (CNS) diseases represent one of the most challenging therapeutic areas for successful drug approvals. Developing quantitative biomarkers as Drug Development Tools (DDTs) can catalyze the path to innovative treatments, and improve the chances of drug approvals. Drug development and healthcare management requires sensitive, reliable, validated, and regulatory accepted biomarkers and endpoints. This review highlights the regulatory paths and considerations for developing DDTs required to advance biomarker and endpoint use in clinical development (e.g., consensus CDISC [Clinical Data Interchange Standards Consortium] data standards, precompetitive sharing of anonymized patient-level data, and continual alignment with regulators). Summarized is the current landscape of biomarkers in a range of CNS diseases including Alzheimer disease, Parkinson Disease, Amyotrophic Lateral Sclerosis, Autism Spectrum Disorders, Depression, Huntington's disease, Multiple Sclerosis and Traumatic Brain Injury. Advancing DDTs for these devastating diseases that are both validated and qualified will require an integrated, cross-consortium approach to accelerate the delivery of innovative CNS therapeutics.

Using the Seven Bridges Cancer Genomics Cloud to Access and Analyze Petabytes of Cancer Data

Next-generation sequencing has produced petabytes of data, but accessing and analyzing these data remain challenging. Traditionally, researchers investigating public datasets like The Cancer Genome Atlas (TCGA) would download the data to a high-performance cluster, which could take several weeks even with a highly optimized network connection. The National Cancer Institute (NCI) initiated the Cancer Genomics Cloud Pilots program to provide researchers with the resources to process data with cloud computational resources. We present protocols using one of these Cloud Pilots, the Seven Bridges Cancer Genomics Cloud (CGC), to find and query public datasets, bring your own data to the CGC, analyze data using standard or custom workflows, and benchmark tools for accuracy with interactive analysis features. These protocols demonstrate that the CGC is a data-analysis ecosystem that fully empowers researchers with a variety of areas of expertise and interests to collaborate in the analysis of petabytes of data. © 2017 by John Wiley & Sons, Inc.

Can free open access resources strengthen knowledge-based emerging public health priorities, policies and programs in Africa?

Tackling emerging epidemics and infectious diseases burden in Africa requires increasing unrestricted open access and free use or reuse of regional and global policies reforms as well as timely communication capabilities and strategies. Promoting, scaling up data and information sharing between African researchers and international partners are of vital importance in accelerating open access at no cost. Free Open Access (FOA) health data and information acceptability, uptake tactics and sustainable mechanisms are urgently needed. These are critical in establishing real time and effective knowledge or evidence-based translation, proven and validated approaches, strategies and tools to strengthen and revamp health systems.  As such, early and timely access to needed emerging public health information is meant to be instrumental and valuable for policy-makers, implementers, care providers, researchers, health-related institutions and stakeholders including populations when guiding health financing, and planning contextual programs.