scholarly journals Visual Analytics of Genomic and Cancer Data: A Systematic Review

2019 ◽  
Vol 18 ◽  
pp. 117693511983554 ◽  
Author(s):  
Zhonglin Qu ◽  
Chng Wei Lau ◽  
Quang Vinh Nguyen ◽  
Yi Zhou ◽  
Daniel R Catchpoole
Keyword(s):  
Systematic Review ◽  
Visual Analytics ◽  
Personalised Medicine ◽  
Genomic Data ◽  
Data Visualisation ◽  
Cancer Data ◽  
Sequencing Technologies ◽  
Scatter Plots ◽  
Speed Up ◽  
Traditional Approaches

Visual analytics and visualisation can leverage the human perceptual system to interpret and uncover hidden patterns in big data. The advent of next-generation sequencing technologies has allowed the rapid production of massive amounts of genomic data and created a corresponding need for new tools and methods for visualising and interpreting these data. Visualising genomic data requires not only simply plotting of data but should also offer a decision or a choice about what the message should be conveyed in the particular plot; which methodologies should be used to represent the results must provide an easy, clear, and accurate way to the clinicians, experts, or researchers to interact with the data. Genomic data visual analytics is rapidly evolving in parallel with advances in high-throughput technologies such as artificial intelligence (AI) and virtual reality (VR). Personalised medicine requires new genomic visualisation tools, which can efficiently extract knowledge from the genomic data and speed up expert decisions about the best treatment of individual patient’s needs. However, meaningful visual analytics of such large genomic data remains a serious challenge. This article provides a comprehensive systematic review and discussion on the tools, methods, and trends for visual analytics of cancer-related genomic data. We reviewed methods for genomic data visualisation including traditional approaches such as scatter plots, heatmaps, coordinates, and networks, as well as emerging technologies using AI and VR. We also demonstrate the development of genomic data visualisation tools over time and analyse the evolution of visualising genomic data.

scholarly journals Long Read Alignment with Parallel MapReduce Cloud Platform

2015 ◽  
Vol 2015 ◽  
pp. 1-13 ◽  
Author(s):  
Ahmed Abdulhakim Al-Absi ◽  
Dae-Ki Kang
Keyword(s):  
Sequence Alignment ◽  
Genomic Sequence ◽  
Genomic Data ◽  
Parallel Execution ◽  
Cloud Platform ◽  
Hadoop Mapreduce ◽  
Sequencing Technologies ◽  
Long Read ◽  
Execution Strategy ◽  
Speed Up

Genomic sequence alignment is an important technique to decode genome sequences in bioinformatics. Next-Generation Sequencing technologies produce genomic data of longer reads. Cloud platforms are adopted to address the problems arising from storage and analysis of large genomic data. Existing genes sequencing tools for cloud platforms predominantly consider short read gene sequences and adopt the Hadoop MapReduce framework for computation. However, serial execution of map and reduce phases is a problem in such systems. Therefore, in this paper, we introduce Burrows-Wheeler Aligner’s Smith-Waterman Alignment on Parallel MapReduce (BWASW-PMR) cloud platform for long sequence alignment. The proposed cloud platform adopts a widely accepted and accurate BWA-SW algorithm for long sequence alignment. A custom MapReduce platform is developed to overcome the drawbacks of the Hadoop framework. A parallel execution strategy of the MapReduce phases and optimization of Smith-Waterman algorithm are considered. Performance evaluation results exhibit an average speed-up of 6.7 considering BWASW-PMR compared with the state-of-the-art Bwasw-Cloud. An average reduction of 30% in the map phase makespan is reported across all experiments comparing BWASW-PMR with Bwasw-Cloud. Optimization of Smith-Waterman results in reducing the execution time by 91.8%. The experimental study proves the efficiency of BWASW-PMR for aligning long genomic sequences on cloud platforms.

scholarly journals Systematic Review of the Integrative Medicine Recommendations for Patients with Pancreatic Cancer

Surgeries ◽  
2021 ◽  
Vol 2 (2) ◽  
pp. 216-230
Author(s):  
Andrew A. Gumbs ◽  
Manana Gogol ◽  
Gaya Spolverato ◽  
Hebatallah Taher ◽  
Elie K. Chouillard
Keyword(s):  
Systematic Review ◽  
Pancreatic Cancer ◽  
Integrative Medicine ◽  
Lifestyle Changes ◽  
Randomized Controlled ◽  
Nutrition Supplementation ◽  
Traditional Therapies ◽  
Traditional Approaches

Introduction: Integrative medicine (IM) is a relatively new field where non-traditional therapies with peer-reviewed evidence are incorporated or integrated with more traditional approaches. Methods: A systematic review of the literature from the last 10 years was done by searching clinical trials and randomized-controlled trials on Pubmed that discuss nutrition, supplementation, and lifestyle changes associated with “Pancreatic Cancer.” Results: Only 50 articles ultimately met the inclusion criteria for this review. A total of 15 articles discussed the role of obesity and 10 discussed the influence of stress in increasing the risk of pancreatic cancer. Six discussed the potential beneficial role of Vitamins, 5 of cannabinoids, 4 an anti-inflammatory diet, 3 of nut consumption, 2 of green tea consumption, 2 of curcumin supplementation, 1 role of melatonin, and 1 of probiotics. One article each was found on the theoretical benefits of adhering to either a Mediterranean or ketogenic diet. Discussion: As more surgeons become interested in IM, it is hoped that more diseases where the curative treatment is mainly surgical can benefit from the all-encompassing principles of IM in an effort to improve quality of life and survival in patients with pancreatic cancer.

scholarly journals Ionizing Radiation Protein Biomarkers in Normal Tissue and Their Correlation to Radiosensitivity: A Systematic Review

2021 ◽  
Vol 11 (2) ◽  
pp. 140
Author(s):  
Prabal Subedi ◽  
Maria Gomolka ◽  
Simone Moertl ◽  
Anne Dietz
Keyword(s):  
Systematic Review ◽  
Ionizing Radiation ◽  
Normal Tissue ◽  
English Language ◽  
Kinase Inhibitor ◽  
Personalised Medicine ◽  
Model Systems ◽  
Second Phase ◽  
Limited Information ◽  
Eligibility Criteria

Background and objectives: Exposure to ionizing radiation (IR) has increased immensely over the past years, owing to diagnostic and therapeutic reasons. However, certain radiosensitive individuals show toxic enhanced reaction to IR, and it is necessary to specifically protect them from unwanted exposure. Although predicting radiosensitivity is the way forward in the field of personalised medicine, there is limited information on the potential biomarkers. The aim of this systematic review is to identify evidence from a range of literature in order to present the status quo of our knowledge of IR-induced changes in protein expression in normal tissues, which can be correlated to radiosensitivity. Methods: Studies were searched in NCBI Pubmed and in ISI Web of Science databases and field experts were consulted for relevant studies. Primary peer-reviewed studies in English language within the time-frame of 2011 to 2020 were considered. Human non-tumour tissues and human-derived non-tumour model systems that have been exposed to IR were considered if they reported changes in protein levels, which could be correlated to radiosensitivity. At least two reviewers screened the titles, keywords, and abstracts of the studies against the eligibility criteria at the first phase and full texts of potential studies at the second phase. Similarly, at least two reviewers manually extracted the data and accessed the risk of bias (National Toxicology Program/Office for Health Assessment and Translation—NTP/OHAT) for the included studies. Finally, the data were synthesised narratively in accordance to synthesis without meta analyses (SWiM) method. Results: In total, 28 studies were included in this review. Most of the records (16) demonstrated increased residual DNA damage in radiosensitive individuals compared to normo-sensitive individuals based on γH2AX and TP53BP1. Overall, 15 studies included proteins other than DNA repair foci, of which five proteins were selected, Vascular endothelial growth factor (VEGF), Caspase 3, p16INK4A (Cyclin-dependent kinase inhibitor 2A, CDKN2A), Interleukin-6, and Interleukin-1β, that were connected to radiosensitivity in normal tissue and were reported at least in two independent studies. Conclusions and implication of key findings: A majority of studies used repair foci as a tool to predict radiosensitivity. However, its correlation to outcome parameters such as repair deficient cell lines and patients, as well as an association to moderate and severe clinical radiation reactions, still remain contradictory. When IR-induced proteins reported in at least two studies were considered, a protein network was discovered, which provides a direction for further studies to elucidate the mechanisms of radiosensitivity. Although the identification of only a few of the commonly reported proteins might raise a concern, this could be because (i) our eligibility criteria were strict and (ii) radiosensitivity is influenced by multiple factors. Registration: PROSPERO (CRD42020220064).

scholarly journals Sequoia: an interactive visual analytics platform for interpretation and feature extraction from nanopore sequencing datasets

BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Ratanond Koonchanok ◽  
Swapna Vidhur Daulatabad ◽  
Quoseena Mir ◽  
Khairi Reda ◽  
Sarath Chandra Janga
Keyword(s):  
Single Molecule ◽  
Visual Analytics ◽  
Visual Analysis ◽  
Direct Sequencing ◽  
Visual Exploration ◽  
Nanopore Sequencing ◽  
Sequencing Data ◽  
Rna Sequences ◽  
Sequencing Technologies ◽  
Signal Features

Abstract Background Direct-sequencing technologies, such as Oxford Nanopore’s, are delivering long RNA reads with great efficacy and convenience. These technologies afford an ability to detect post-transcriptional modifications at a single-molecule resolution, promising new insights into the functional roles of RNA. However, realizing this potential requires new tools to analyze and explore this type of data. Result Here, we present Sequoia, a visual analytics tool that allows users to interactively explore nanopore sequences. Sequoia combines a Python-based backend with a multi-view visualization interface, enabling users to import raw nanopore sequencing data in a Fast5 format, cluster sequences based on electric-current similarities, and drill-down onto signals to identify properties of interest. We demonstrate the application of Sequoia by generating and analyzing ~ 500k reads from direct RNA sequencing data of human HeLa cell line. We focus on comparing signal features from m6A and m5C RNA modifications as the first step towards building automated classifiers. We show how, through iterative visual exploration and tuning of dimensionality reduction parameters, we can separate modified RNA sequences from their unmodified counterparts. We also document new, qualitative signal signatures that characterize these modifications from otherwise normal RNA bases, which we were able to discover from the visualization. Conclusions Sequoia’s interactive features complement existing computational approaches in nanopore-based RNA workflows. The insights gleaned through visual analysis should help users in developing rationales, hypotheses, and insights into the dynamic nature of RNA. Sequoia is available at https://github.com/dnonatar/Sequoia.

scholarly journals Foreign Language Learning Gamification Using Virtual Reality—A Systematic Review of Empirical Research

2021 ◽  
Vol 11 (5) ◽  
pp. 222
Author(s):  
Rafael Darque Pinto ◽  
Bruno Peixoto ◽  
Miguel Melo ◽  
Luciana Cabral ◽  
Maximino Bessa
Keyword(s):  
Systematic Review ◽  
Virtual Reality ◽  
Foreign Language ◽  
Language Learning ◽  
Empirical Research ◽  
Second Language Learning ◽  
English Language ◽  
New Technologies ◽  
Foreign Language Learning ◽  
Traditional Approaches

Virtual reality has shown to have great potential as an educational tool when it comes to new learning methods. With the growth and dissemination of this technology, there is a massive opportunity for teachers to add this technology to their methods of teaching a second/foreign language, since students keep showing a growing interest in new technologies. This systematic review of empirical research aims at understanding whether the use of gaming strategies in virtual reality is beneficial for the learning of a second/foreign language or not. Results show that more than half of the articles proved that virtual reality technologies with gaming strategies can be used to learn a foreign language. It was also found that “learning” was the most evaluated dependent variable among the chosen records, augmented reality was the leading technology used, primary education and lower secondary was the most researched school stages, and the most used language to evaluate the use of gamified technology was by far the English language. Given the lack of directed investigation, it is recommended to use these technologies to support second language learning and not entirely replace traditional approaches. A research agenda is also proposed by the authors.

scholarly journals Bridging the Gap between Vertebrate Cytogenetics and Genomics with Single-Chromosome Sequencing (ChromSeq)

Genes ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 124
Author(s):  
Alessio Iannucci ◽  
Alexey I. Makunin ◽  
Artem P. Lisachov ◽  
Claudio Ciofi ◽  
Roscoe Stanyon ◽  
...  
Keyword(s):  
Genome Evolution ◽  
Karyotype Evolution ◽  
Genomic Data ◽  
Anolis Carolinensis ◽  
Vertebrate Genome ◽  
Single Chromosome ◽  
Sequencing Technologies ◽  
Novel Approaches ◽  
Genome Assemblies ◽  
Generation Sequencing

The study of vertebrate genome evolution is currently facing a revolution, brought about by next generation sequencing technologies that allow researchers to produce nearly complete and error-free genome assemblies. Novel approaches however do not always provide a direct link with information on vertebrate genome evolution gained from cytogenetic approaches. It is useful to preserve and link cytogenetic data with novel genomic discoveries. Sequencing of DNA from single isolated chromosomes (ChromSeq) is an elegant approach to determine the chromosome content and assign genome assemblies to chromosomes, thus bridging the gap between cytogenetics and genomics. The aim of this paper is to describe how ChromSeq can support the study of vertebrate genome evolution and how it can help link cytogenetic and genomic data. We show key examples of ChromSeq application in the refinement of vertebrate genome assemblies and in the study of vertebrate chromosome and karyotype evolution. We also provide a general overview of the approach and a concrete example of genome refinement using this method in the species Anolis carolinensis.

Big Data Visualisation and Visual Analytics for Music Data Mining

2018 ◽  
Author(s):  
Katrina E. Barkwell ◽  
Alfredo Cuzzocrea ◽  
Carson K. Leung ◽  
Ashley A. Ocran ◽  
Jennifer M. Sanderson ◽  
...  
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Visual Analytics ◽  
Data Visualisation ◽  
Music Data Mining

Systematic review: predictive biomarkers of therapeutic response in inflammatory bowel disease-personalised medicine in its infancy

2018 ◽  
Vol 48 (11-12) ◽  
pp. 1213-1231 ◽  
Author(s):  
Toer W. Stevens ◽  
Mijntje Matheeuwsen ◽  
Maria H. Lönnkvist ◽  
Claire E. Parker ◽  
Manon E. Wildenberg ◽  
...  
Keyword(s):  
Systematic Review ◽  
Inflammatory Bowel Disease ◽  
Bowel Disease ◽  
Therapeutic Response ◽  
Personalised Medicine ◽  
Predictive Biomarkers ◽  
Inflammatory Bowel

scholarly journals Rapid advancement in cancer genomic big data in the pursuit of precision oncology

2021 ◽  
Author(s):  
Tiara Bunga Mayang Permata ◽  
Sri Mutya Sekarutami ◽  
Endang Nuryadi ◽  
Angela Giselvania ◽  
Soehartati Gondhowiardjo
Keyword(s):  
Big Data ◽  
Open Access ◽  
Cancer Cell ◽  
Cancer Cell Line ◽  
Genomic Data ◽  
The Cancer Genome Atlas ◽  
Clinical Samples ◽  
Precision Oncology ◽  
Cancer Data ◽  
User Friendly

In the current big data era, massive genomic cancer data are available for open access from anywhere in the world. They are obtained from popular platforms, such as The Cancer Genome Atlas, which provides genetic information from clinical samples, and Cancer Cell Line Encyclopedia, which offers genomic data of cancer cell lines. For convenient analysis, user-friendly tools, such as the Tumor Immune Estimation Resource (TIMER), which can be used to analyze tumor-infiltrating immune cells comprehensively, are also emerging. In clinical practice, clinical sequencing has been recommended for patients with cancer in many countries. Despite its many challenges, it enables the application of precision medicine, especially in medical oncology. In this review, several efforts devoted to accomplishing precision oncology and applying big data for use in Indonesia are discussed. Utilizing open access genomic data in writing research articles is also described.

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