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Статті в журналах з теми "Computational Genomic"

Автор: Grafiati

Опубліковано: 11 березня 2023

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1

Nalbantoglu, Ozkan Ufuk, and Khalid Sayood. "Computational Genomic Signatures." Synthesis Lectures on Biomedical Engineering 6, no. 2 (May 31, 2011): 1–129. http://dx.doi.org/10.2200/s00360ed1v01y201105bme041.

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2

Yelick, Katherine, Aydın Buluç, Muaaz Awan, Ariful Azad, Benjamin Brock, Rob Egan, Saliya Ekanayake, et al. "The parallelism motifs of genomic data analysis." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 378, no. 2166 (January 20, 2020): 20190394. http://dx.doi.org/10.1098/rsta.2019.0394.

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Анотація:

Genomic datasets are growing dramatically as the cost of sequencing continues to decline and small sequencing devices become available. Enormous community databases store and share these data with the research community, but some of these genomic data analysis problems require large-scale computational platforms to meet both the memory and computational requirements. These applications differ from scientific simulations that dominate the workload on high-end parallel systems today and place different requirements on programming support, software libraries and parallel architectural design. For example, they involve irregular communication patterns such as asynchronous updates to shared data structures. We consider several problems in high-performance genomics analysis, including alignment, profiling, clustering and assembly for both single genomes and metagenomes. We identify some of the common computational patterns or ‘motifs’ that help inform parallelization strategies and compare our motifs to some of the established lists, arguing that at least two key patterns, sorting and hashing, are missing. This article is part of a discussion meeting issue ‘Numerical algorithms for high-performance computational science’.

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3

Hien, Le Thi Thu, Nguyen Tuong Van, Kim Thi Phuong Oanh, Nguyen Dang Ton, Huynh Thi Thu Hue, Nguyen Thuy Duong, Pham Le Bich Hang, and Nguyen Hai Ha. "Genomics and big data: Research, development and applications." Vietnam Journal of Biotechnology 19, no. 3 (October 13, 2021): 393–410. http://dx.doi.org/10.15625/1811-4989/16158.

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Recent years, genomics and big data analytics have been widely applied and have significant impacts in various important areas of social life worldwide. The development of the next-generation sequencing (NGS) technologies, such as whole-genome sequencing (WGS), whole-exome sequencing (WES), transcriptome, and/or targeted sequencing, has enabled quickly generating the genomes of interested living organisms. Around the world many nations have invested in and promoted the development of genomics and big data analytics. A number of well-established projects on sequencing of human, animal, plant, and microorganism genomes to generate vast amounts of genomic data have been conducted independently or as collaborative efforts by national or international research networks of scientists specializing in different technical fields of genomics, bioinformatics, computational and statistical biology, automation, artificial intelligence, etc. Complicated and large genomic datasets have been effectively established, storage, managed, and used. Vietnam supports this new field of study through setting up governmental authorized institutions and conducting genomic research projects of human and other endemic organisms. In this paper, the research, development, and applications of genomic big data are reviewed with focusing on: (i) Available sequencing technologies for generating genomic datasets; (ii) Genomics and big data initiatives worldwide; (iii) Genomics and big data analytics in selected countries and Vietnam; (iv) Genomic data applications in key areas including medicine for human health care, agriculture - forestry, food safety, and environment.

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4

Lu, Bingxin, and Hon Wai Leong. "Computational methods for predicting genomic islands in microbial genomes." Computational and Structural Biotechnology Journal 14 (2016): 200–206. http://dx.doi.org/10.1016/j.csbj.2016.05.001.

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5

Salamon, Hugh, Midori Kato-Maeda, Peter M. Small, Jorg Drenkow, and Thomas R. Gingeras. "Detection of Deleted Genomic DNA Using a Semiautomated Computational Analysis of GeneChip Data." Genome Research 10, no. 12 (November 21, 2000): 2044–54. http://dx.doi.org/10.1101/gr.152900.

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Genomic diversity within and between populations is caused by single nucleotide mutations, changes in repetitive DNA systems, recombination mechanisms, and insertion and deletion events. The contribution of these sources to diversity, whether purely genetic or of phenotypic consequence, can only be investigated if we have the means to quantitate and characterize diversity in many samples. With the advent of complete sequence characterization of representative genomes of different species, the possibility of developing protocols to screen for genetic polymorphism across entire genomes is actively being pursued. The large numbers of measurements such approaches yield demand that we pay careful attention to the numerical analysis of data. In this paper we present a novel application of an Affymetrix GeneChip to perform genome-wide screens for deletion polymorphism. A high-density oligonucleotide array formatted for mRNA expression and targeted at a fully sequenced 4.4-million–base pair Mycobacterium tuberculosis standard strain genome was adapted to compare genomic DNA. Hybridization intensities to 111,000 probe pairs (perfect complement and mismatch complement) were measured for genomic DNA from a clinical strain and from a vaccine organism. Because individual probe-pair hybridization intensities exhibit limited sensitivity/specificity characteristics to detect deletions, data-analytical methodology to exploit measurements from multiple probes in tandem locations across the genome was developed. The TSTEP (Tandem Set Terminal Extreme Probability) algorithm designed specifically to analyze the tandem hybridization measurements data was applied and shown to discover genomic deletions with high sensitivity. The TSTEP algorithm provides a foundation for similar efforts to characterize deletions in many hybridization measures in similar-sized and larger genomes. Issues relating to the design of genome content screening experiments and the implications of these methods for studying population genomics and the evolution of genomes are discussed.

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6

ZUO, GuangHong, and BaiLin HAO. "Computational microbiology in genomic era." SCIENTIA SINICA Vitae 47, no. 2 (January 22, 2017): 159–70. http://dx.doi.org/10.1360/n052016-00312.

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7

Le, Vinh. "A computational framework to analyze human genomes." Journal of Computer Science and Cybernetics 35, no. 2 (June 3, 2019): 105–18. http://dx.doi.org/10.15625/1813-9663/35/2/13827.

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The advent of genomic technologies has led to the current genomic era. Large-scale human genome projects have resulted in a huge amount of genomic data. Analyzing human genomes is a challenging task including a number of key steps from short read alignment, variant calling, and variant annotating. In this paper, the state-of-the-art computational methods and databases for each step will be analyzed to suggest a practical and efficient guideline for whole human genome analyses. This paper also discusses frameworks to combine variants from various genome analysis pipelines to obtain reliable variants. Finally, we will address advantages as well as discordances of widely-used variant annotation methods to evaluate the clinical significance of variants. The review will empower bioinformaticians to efficiently perform human genome analyses, and more importantly, help genetic consultants understand and properly interpret mutations for clinical purposes.

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8

Cui, Zhe, Jayaram Kancherla, Kyle W. Chang, Niklas Elmqvist, and Héctor Corrada Bravo. "Proactive visual and statistical analysis of genomic data in Epiviz." Bioinformatics 36, no. 7 (November 29, 2019): 2195–201. http://dx.doi.org/10.1093/bioinformatics/btz883.

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Abstract Motivation Integrative analysis of genomic data that includes statistical methods in combination with visual exploration has gained widespread adoption. Many existing methods involve a combination of tools and resources: user interfaces that provide visualization of large genomic datasets, and computational environments that focus on data analyses over various subsets of a given dataset. Over the last few years, we have developed Epiviz as an integrative and interactive genomic data analysis tool that incorporates visualization tightly with state-of-the-art statistical analysis framework. Results In this article, we present Epiviz Feed, a proactive and automatic visual analytics system integrated with Epiviz that alleviates the burden of manually executing data analysis required to test biologically meaningful hypotheses. Results of interest that are proactively identified by server-side computations are listed as notifications in a feed. The feed turns genomic data analysis into a collaborative work between the analyst and the computational environment, which shortens the analysis time and allows the analyst to explore results efficiently. We discuss three ways where the proposed system advances the field of genomic data analysis: (i) takes the first step of proactive data analysis by utilizing available CPU power from the server to automate the analysis process; (ii) summarizes hypothesis test results in a way that analysts can easily understand and investigate; (iii) enables filtering and grouping of analysis results for quick search. This effort provides initial work on systems that substantially expand how computational and visualization frameworks can be tightly integrated to facilitate interactive genomic data analysis. Availability and implementation The source code for Epiviz Feed application is available at http://github.com/epiviz/epiviz_feed_polymer. The Epiviz Computational Server is available at http://github.com/epiviz/epiviz-feed-computation. Please refer to Epiviz documentation site for details: http://epiviz.github.io/.

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9

Wan, Peng, and Dongsheng Che. "A Computational Framework for Tracing the Origins of Genomic Islands in Prokaryotes." International Scholarly Research Notices 2014 (October 28, 2014): 1–9. http://dx.doi.org/10.1155/2014/732857.

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Genomic islands (GIs) are chunks of genomic fragments that are acquired from nongenealogical organisms through horizontal gene transfer (HGT). Current researches on studying donor-recipient relationships for HGT are limited at a gene level. As more GIs have been identified and verified, the way of studying donor-recipient relationships can be better modeled by using GIs rather than individual genes. In this paper, we report the development of a computational framework for detecting origins of GIs. The main idea of our computational framework is to identify GIs in a query genome, search candidate genomes that contain genomic regions similar to those GIs in the query genome by BLAST search, and then filter out some candidate genomes if those similar genomic regions are also alien (detected by GI detection tools). We have applied our framework in finding the GI origins for Mycobacterium tuberculosis H37Rv, Herminiimonas arsenicoxydans, and three Thermoanaerobacter species. The predicted results were used to establish the donor-recipient network relationships and visualized by Gephi. Our studies have shown that donor genomes detected by our computational approach were mainly consistent with previous studies. Our framework was implemented with Perl and executed on Windows operating system.

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10

Chorbadjiev, Lubomir, Jude Kendall, Joan Alexander, Viacheslav Zhygulin, Junyan Song, Michael Wigler, and Alexander Krasnitz. "Integrated Computational Pipeline for Single-Cell Genomic Profiling." JCO Clinical Cancer Informatics, no. 4 (September 2020): 464–71. http://dx.doi.org/10.1200/cci.19.00171.

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PURPOSE Copy-number profiling of multiple individual cells from sparse sequencing may be used to reveal a detailed picture of genomic heterogeneity and clonal organization in a tissue biopsy specimen. We sought to provide a comprehensive computational pipeline for single-cell genomics, to facilitate adoption of this molecular technology for basic and translational research. MATERIALS AND METHODS The pipeline comprises software tools programmed in Python and in R and depends on Bowtie, HISAT2, Matplotlib, and Qt. It is installed and used with Anaconda. RESULTS Here we describe a complete pipeline for sparse single-cell genomic data, encompassing all steps of single-nucleus DNA copy-number profiling, from raw sequence processing to clonal structure analysis and visualization. For the latter, a specialized graphical user interface termed the single-cell genome viewer (SCGV) is provided. With applications to cancer diagnostics in mind, the SCGV allows for zooming and linkage to the University of California at Santa Cruz Genome Browser from each of the multiple integrated views of single-cell copy-number profiles. The latter can be organized by clonal substructure or by any of the associated metadata such as anatomic location and histologic characterization. CONCLUSION The pipeline is available as open-source software for Linux and OS X. Its modular structure, extensive documentation, and ease of deployment using Anaconda facilitate its adoption by researchers and practitioners of single-cell genomics. With open-source availability and Massachusetts Institute of Technology licensing, it provides a basis for additional development by the cancer bioinformatics community.

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11

Flanagan, Keith, Robert Stevens, Matthew Pocock, Pete Lee, and Anil Wipat. "Ontology for Genome Comparison and Genomic Rearrangements." Comparative and Functional Genomics 5, no. 6-7 (2004): 537–44. http://dx.doi.org/10.1002/cfg.436.

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We present an ontology for describing genomes, genome comparisons, their evolution and biological function. This ontology will support the development of novel genome comparison algorithms and aid the community in discussing genomic evolution. It provides a framework for communication about comparative genomics, and a basis upon which further automated analysis can be built. The nomenclature defined by the ontology will foster clearer communication between biologists, and also standardize terms used by data publishers in the results of analysis programs. The overriding aim of this ontology is the facilitation of consistent annotation of genomes through computational methods, rather than human annotators. To this end, the ontology includes definitions that support computer analysis and automated transfer of annotations between genomes, rather than relying upon human mediation.

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12

Chari, Raj, William W. Lockwood, and Wan L. Lam. "Computational Methods for the Analysis of Array Comparative Genomic Hybridization." Cancer Informatics 2 (January 2006): 117693510600200. http://dx.doi.org/10.1177/117693510600200007.

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Array comparative genomic hybridization (array CGH) is a technique for assaying the copy number status of cancer genomes. The widespread use of this technology has lead to a rapid accumulation of high throughput data, which in turn has prompted the development of computational strategies for the analysis of array CGH data. Here we explain the principles behind array image processing, data visualization and genomic profile analysis, review currently available software packages, and raise considerations for future software development.

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13

Osipowski, Paweł, Magdalena Pawełkowicz, Michał Wojcieszek, Agnieszka Skarzyńska, Zbigniew Przybecki, and Wojciech Pląder. "A high-quality cucumber genome assembly enhances computational comparative genomics." Molecular Genetics and Genomics 295, no. 1 (October 16, 2019): 177–93. http://dx.doi.org/10.1007/s00438-019-01614-3.

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Abstract Genetic variation is expressed by the presence of polymorphisms in compared genomes of individuals that can be transferred to next generations. The aim of this work was to reveal genome dynamics by predicting polymorphisms among the genomes of three individuals of the highly inbred B10 cucumber (Cucumis sativus L.) line. In this study, bioinformatic comparative genomics was used to uncover cucumber genome dynamics (also called real-time evolution). We obtained a new genome draft assembly from long single molecule real-time (SMRT) sequencing reads and used short paired-end read data from three individuals to analyse the polymorphisms. Using this approach, we uncovered differentiation aspects in the genomes of the inbred B10 line. The newly assembled genome sequence (B10v3) has the highest contiguity and quality characteristics among the currently available cucumber genome draft sequences. Standard and newly designed approaches were used to predict single nucleotide and structural variants that were unique among the three individual genomes. Some of the variant predictions spanned protein-coding genes and their promoters, and some were in the neighbourhood of annotated interspersed repetitive elements, indicating that the highly inbred homozygous plants remained genetically dynamic. This is the first bioinformatic comparative genomics study of a single highly inbred plant line. For this project, we developed a polymorphism prediction method with optimized precision parameters, which allowed the effective detection of small nucleotide variants (SNVs). This methodology could significantly improve bioinformatic pipelines for comparative genomics and thus has great practical potential in genomic metadata handling.

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14

Carpentieri, Bruno. "Compression of Next-Generation Sequencing Data and of DNA Digital Files." Algorithms 13, no. 6 (June 24, 2020): 151. http://dx.doi.org/10.3390/a13060151.

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The increase in memory and in network traffic used and caused by new sequenced biological data has recently deeply grown. Genomic projects such as HapMap and 1000 Genomes have contributed to the very large rise of databases and network traffic related to genomic data and to the development of new efficient technologies. The large-scale sequencing of samples of DNA has brought new attention and produced new research, and thus the interest in the scientific community for genomic data has greatly increased. In a very short time, researchers have developed hardware tools, analysis software, algorithms, private databases, and infrastructures to support the research in genomics. In this paper, we analyze different approaches for compressing digital files generated by Next-Generation Sequencing tools containing nucleotide sequences, and we discuss and evaluate the compression performance of generic compression algorithms by confronting them with a specific system designed by Jones et al. specifically for genomic file compression: Quip. Moreover, we present a simple but effective technique for the compression of DNA sequences in which we only consider the relevant DNA data and experimentally evaluate its performances.

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15

Bertelli, Claire, Keith E. Tilley, and Fiona S. L. Brinkman. "Microbial genomic island discovery, visualization and analysis." Briefings in Bioinformatics 20, no. 5 (June 3, 2018): 1685–98. http://dx.doi.org/10.1093/bib/bby042.

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Abstract Horizontal gene transfer (also called lateral gene transfer) is a major mechanism for microbial genome evolution, enabling rapid adaptation and survival in specific niches. Genomic islands (GIs), commonly defined as clusters of bacterial or archaeal genes of probable horizontal origin, are of particular medical, environmental and/or industrial interest, as they disproportionately encode virulence factors and some antimicrobial resistance genes and may harbor entire metabolic pathways that confer a specific adaptation (solvent resistance, symbiosis properties, etc). As large-scale analyses of microbial genomes increases, such as for genomic epidemiology investigations of infectious disease outbreaks in public health, there is increased appreciation of the need to accurately predict and track GIs. Over the past decade, numerous computational tools have been developed to tackle the challenges inherent in accurate GI prediction. We review here the main types of GI prediction methods and discuss their advantages and limitations for a routine analysis of microbial genomes in this era of rapid whole-genome sequencing. An assessment is provided of 20 GI prediction software methods that use sequence-composition bias to identify the GIs, using a reference GI data set from 104 genomes obtained using an independent comparative genomics approach. Finally, we present guidelines to assist researchers in effectively identifying these key genomic regions.

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16

Martinez, Manuel. "Computational Tools for Genomic Studies in Plants." Current Genomics 17, no. 6 (October 13, 2016): 509–14. http://dx.doi.org/10.2174/1389202917666160520103447.

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17

Ramanathan, Chandra Sekar, and Ethan Will Taylor. "Computational genomic analysis of hemorrhagic fever viruses." Biological Trace Element Research 56, no. 1 (January 1997): 93–106. http://dx.doi.org/10.1007/bf02778985.

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18

Deuber, Dominic, Christoph Egger, Katharina Fech, Giulio Malavolta, Dominique Schröder, Sri Aravinda Krishnan Thyagarajan, Florian Battke, and Claudia Durand. "My Genome Belongs to Me: Controlling Third Party Computation on Genomic Data." Proceedings on Privacy Enhancing Technologies 2019, no. 1 (January 1, 2019): 108–32. http://dx.doi.org/10.2478/popets-2019-0007.

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Abstract An individual’s genetic information is possibly the most valuable personal information. While knowledge of a person’s DNA sequence can facilitate the diagnosis of several heritable diseases and allow personalized treatment, its exposure comes with significant threats to the patient’s privacy. Currently known solutions for privacy-respecting computation require the owner of the DNA to either be heavily involved in the execution of a cryptographic protocol or to completely outsource the access control to a third party. This motivates the demand for cryptographic protocols which enable computation over encrypted genomic data while keeping the owner of the genome in full control. We envision a scenario where data owners can exercise arbitrary and dynamic access policies, depending on the intended use of the analysis results and on the credentials of who is conducting the analysis. At the same time, data owners are not required to maintain a local copy of their entire genetic data and do not need to exhaust their computational resources in an expensive cryptographic protocol. In this work, we present METIS, a system that assists the computation over encrypted data stored in the cloud while leaving the decision on admissible computations to the data owner. It is based on garbled circuits and supports any polynomially-computable function. A critical feature of our system is that the data owner is free from computational overload and her communication complexity is independent of the size of the input data and only linear in the size of the circuit’s output. We demonstrate the practicality of our approach with an implementation and an evaluation of several functions over real datasets.

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19

Tian, Long, Reza Mazloom, Lenwood S. Heath, and Boris A. Vinatzer. "LINflow: a computational pipeline that combines an alignment-free with an alignment-based method to accelerate generation of similarity matrices for prokaryotic genomes." PeerJ 9 (March 24, 2021): e10906. http://dx.doi.org/10.7717/peerj.10906.

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Background Computing genomic similarity between strains is a prerequisite for genome-based prokaryotic classification and identification. Genomic similarity was first computed as Average Nucleotide Identity (ANI) values based on the alignment of genomic fragments. Since this is computationally expensive, faster and computationally cheaper alignment-free methods have been developed to estimate ANI. However, these methods do not reach the level of accuracy of alignment-based methods. Methods Here we introduce LINflow, a computational pipeline that infers pairwise genomic similarity in a set of genomes. LINflow takes advantage of the speed of the alignment-free sourmash tool to identify the genome in a dataset that is most similar to a query genome and the precision of the alignment-based pyani software to precisely compute ANI between the query genome and the most similar genome identified by sourmash. This is repeated for each new genome that is added to a dataset. The sequentially computed ANI values are stored as Life Identification Numbers (LINs), which are then used to infer all other pairwise ANI values in the set. We tested LINflow on four sets, 484 genomes in total, and compared the needed time and the generated similarity matrices with other tools. Results LINflow is up to 150 times faster than pyani and pairwise ANI values generated by LINflow are highly correlated with those computed by pyani. However, because LINflow infers most pairwise ANI values instead of computing them directly, ANI values occasionally depart from the ANI values computed by pyani. In conclusion, LINflow is a fast and memory-efficient pipeline to infer similarity among a large set of prokaryotic genomes. Its ability to quickly add new genome sequences to an already computed similarity matrix makes LINflow particularly useful for projects when new genome sequences need to be regularly added to an existing dataset.

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20

Sankoff, David, and Lani Haque. "The Distribution of Genomic Distance between Random Genomes." Journal of Computational Biology 13, no. 5 (June 2006): 1005–12. http://dx.doi.org/10.1089/cmb.2006.13.1005.

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21

Pradhan, Manaswini. "Computational Machine Learning Application on Microarray Genomic Data." International Journal of Bioinformatics and Biological Science 5, no. 2 (2017): 51. http://dx.doi.org/10.5958/2321-7111.2017.00007.5.

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22

Handl, J., J. Knowles, and D. B. Kell. "Computational cluster validation in post-genomic data analysis." Bioinformatics 21, no. 15 (May 24, 2005): 3201–12. http://dx.doi.org/10.1093/bioinformatics/bti517.

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23

Xu, Ying. "Computational Challenges in Deciphering Genomic Structures of Bacteria." Journal of Computer Science and Technology 25, no. 1 (January 2010): 53–70. http://dx.doi.org/10.1007/s11390-010-9305-5.

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24

Rasool, Rabia, Inam Ullah, Bismillah Mubeen, Sultan Alshehri, Syed Sarim Imam, Mohammed M. Ghoneim, Sami I. Alzarea, et al. "Theranostic Interpolation of Genomic Instability in Breast Cancer." International Journal of Molecular Sciences 23, no. 3 (February 7, 2022): 1861. http://dx.doi.org/10.3390/ijms23031861.

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Breast cancer is a diverse disease caused by mutations in multiple genes accompanying epigenetic aberrations of hazardous genes and protein pathways, which distress tumor-suppressor genes and the expression of oncogenes. Alteration in any of the several physiological mechanisms such as cell cycle checkpoints, DNA repair machinery, mitotic checkpoints, and telomere maintenance results in genomic instability. Theranostic has the potential to foretell and estimate therapy response, contributing a valuable opportunity to modify the ongoing treatments and has developed new treatment strategies in a personalized manner. “Omics” technologies play a key role while studying genomic instability in breast cancer, and broadly include various aspects of proteomics, genomics, metabolomics, and tumor grading. Certain computational techniques have been designed to facilitate the early diagnosis of cancer and predict disease-specific therapies, which can produce many effective results. Several diverse tools are used to investigate genomic instability and underlying mechanisms. The current review aimed to explore the genomic landscape, tumor heterogeneity, and possible mechanisms of genomic instability involved in initiating breast cancer. We also discuss the implications of computational biology regarding mutational and pathway analyses, identification of prognostic markers, and the development of strategies for precision medicine. We also review different technologies required for the investigation of genomic instability in breast cancer cells, including recent therapeutic and preventive advances in breast cancer.

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Papanicolaou, Alexie. "The life cycle of a genome project: perspectives and guidelines inspired by insect genome projects." F1000Research 5 (January 5, 2016): 18. http://dx.doi.org/10.12688/f1000research.7559.1.

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Many research programs on non-model species biology have been empowered by genomics. In turn, genomics is underpinned by a reference sequence and ancillary information created by so-called “genome projects”. The most reliable genome projects are the ones created as part of an active research program and designed to address specific questions but their life extends past publication. In this opinion paper I outline four key insights that have facilitated maintaining genomic communities: the key role of computational capability, the iterative process of building genomic resources, the value of community participation and the importance of manual curation. Taken together, these ideas can and do ensure the longevity of genome projects and the growing non-model species community can use them to focus a discussion with regards to its future genomic infrastructure.

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26

Mohammed Yakubu, Abukari, and Yi-Ping Phoebe Chen. "Ensuring privacy and security of genomic data and functionalities." Briefings in Bioinformatics 21, no. 2 (February 12, 2019): 511–26. http://dx.doi.org/10.1093/bib/bbz013.

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Abstract In recent times, the reduced cost of DNA sequencing has resulted in a plethora of genomic data that is being used to advance biomedical research and improve clinical procedures and healthcare delivery. These advances are revolutionizing areas in genome-wide association studies (GWASs), diagnostic testing, personalized medicine and drug discovery. This, however, comes with security and privacy challenges as the human genome is sensitive in nature and uniquely identifies an individual. In this article, we discuss the genome privacy problem and review relevant privacy attacks, classified into identity tracing, attribute disclosure and completion attacks, which have been used to breach the privacy of an individual. We then classify state-of-the-art genomic privacy-preserving solutions based on their application and computational domains (genomic aggregation, GWASs and statistical analysis, sequence comparison and genetic testing) that have been proposed to mitigate these attacks and compare them in terms of their underlining cryptographic primitives, security goals and complexities—computation and transmission overheads. Finally, we identify and discuss the open issues, research challenges and future directions in the field of genomic privacy. We believe this article will provide researchers with the current trends and insights on the importance and challenges of privacy and security issues in the area of genomics.

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Bahmani, Amir, Kyle Ferriter, Vandhana Krishnan, Arash Alavi, Amir Alavi, Philip S. Tsao, Michael P. Snyder, and Cuiping Pan. "Swarm: A federated cloud framework for large-scale variant analysis." PLOS Computational Biology 17, no. 5 (May 12, 2021): e1008977. http://dx.doi.org/10.1371/journal.pcbi.1008977.

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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.

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Tahir Ul Qamar, Muhammad, Xitong Zhu, Feng Xing, and Ling-Ling Chen. "ppsPCP: a plant presence/absence variants scanner and pan-genome construction pipeline." Bioinformatics 35, no. 20 (March 9, 2019): 4156–58. http://dx.doi.org/10.1093/bioinformatics/btz168.

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Abstract Summary Since the idea of pan-genomics emerged several tools and pipelines have been introduced for prokaryotic pan-genomics. However, not a single comprehensive pipeline has been reported which could overcome multiple challenges associated with eukaryotic pan-genomics. To aid the eukaryotic pan-genomic studies, here we present ppsPCP pipeline which is designed for eukaryotes especially for plants. It is capable of scanning presence/absence variants (PAVs) and constructing a fully annotated pan-genome. We believe with these unique features of PAV scanning and building a pan-genome together with its annotation, ppsPCP will be useful for plant pan-genomic studies and aid researchers to study genetic/phenotypic variations and genomic diversity. Availability and implementation The ppsPCP is freely available at github DOI: https://doi.org/10.5281/zenodo.2567390 and webpage http://cbi.hzau.edu.cn/ppsPCP/. Supplementary information Supplementary data are available at Bioinformatics online.

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29

Hong, Seungpyo, and Dongsup Kim. "Computational characterization of chromatin domain boundary-associated genomic elements." Nucleic Acids Research 45, no. 18 (August 23, 2017): 10403–14. http://dx.doi.org/10.1093/nar/gkx738.

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30

Dubchak, Inna, Sandhya Balasubramanian, Sheng Wang, Cem Meyden, Dinanath Sulakhe, Alexander Poliakov, Daniela Börnigen, et al. "An Integrative Computational Approach for Prioritization of Genomic Variants." PLoS ONE 9, no. 12 (December 15, 2014): e114903. http://dx.doi.org/10.1371/journal.pone.0114903.

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31

Deichmann, Ute. "Special Issue: Genomic Regulation: Experiments, Computational Modeling, and Philosophy." Journal of Computational Biology 26, no. 7 (July 2019): 625–28. http://dx.doi.org/10.1089/cmb.2019.29021.ud.

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32

Das, R., N. Dimitrova, Z. Xuan, R. A. Rollins, F. Haghighi, J. R. Edwards, J. Ju, T. H. Bestor, and M. Q. Zhang. "Computational prediction of methylation status in human genomic sequences." Proceedings of the National Academy of Sciences 103, no. 28 (July 3, 2006): 10713–16. http://dx.doi.org/10.1073/pnas.0602949103.

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33

Tanaka, H. "Computational approach towards challenges in the post-genomic era." Yearbook of Medical Informatics 12, no. 01 (August 2003): 621–24. http://dx.doi.org/10.1055/s-0038-1638167.

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34

Simillion, Cedric, Klaas Vandepoele, and Yves Van de Peer. "Recent developments in computational approaches for uncovering genomic homology." BioEssays 26, no. 11 (2004): 1225–35. http://dx.doi.org/10.1002/bies.20127.

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Hou, M., P. Berman, C. H. Hsu, and R. S. Harris. "HomologMiner: looking for homologous genomic groups in whole genomes." Bioinformatics 23, no. 8 (February 18, 2007): 917–25. http://dx.doi.org/10.1093/bioinformatics/btm048.

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36

Robinson, Tony, Jim Harkin, and Priyank Shukla. "Hardware acceleration of genomics data analysis: challenges and opportunities." Bioinformatics 37, no. 13 (May 25, 2021): 1785–95. http://dx.doi.org/10.1093/bioinformatics/btab017.

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Summary The significant decline in the cost of genome sequencing has dramatically changed the typical bioinformatics pipeline for analysing sequencing data. Where traditionally, the computational challenge of sequencing is now secondary to genomic data analysis. Short read alignment (SRA) is a ubiquitous process within every modern bioinformatics pipeline in the field of genomics and is often regarded as the principal computational bottleneck. Many hardware and software approaches have been provided to solve the challenge of acceleration. However, previous attempts to increase throughput using many-core processing strategies have enjoyed limited success, mainly due to a dependence on global memory for each computational block. The limited scalability and high energy costs of many-core SRA implementations pose a significant constraint in maintaining acceleration. The Networks-On-Chip (NoC) hardware interconnect mechanism has advanced the scalability of many-core computing systems and, more recently, has demonstrated potential in SRA implementations by integrating multiple computational blocks such as pre-alignment filtering and sequence alignment efficiently, while minimizing memory latency and global memory access. This article provides a state of the art review on current hardware acceleration strategies for genomic data analysis, and it establishes the challenges and opportunities of utilizing NoCs as a critical building block in next-generation sequencing (NGS) technologies for advancing the speed of analysis.

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37

Pinheiro, M., V. Afreixo, G. Moura, A. Freitas, M. A. S. Santos, and J. L. Oliveira. "Statistical, Computational and Visualization Methodologies to Unveil Gene Primary Structure Features." Methods of Information in Medicine 45, no. 02 (2006): 163–68. http://dx.doi.org/10.1055/s-0038-1634061.

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Summary Objectives: Gene sequence features such as codon bias, codon context, and codon expansion (e.g. tri-nucleotide repeats) can be better understood at the genomic scale level by combining statistical methodologies with advanced computer algorithms and data visualization through sophisticated graphical interfaces. This paper presents the ANACONDA system, a bioinformatics application for gene primary structure analysis. Methods: Codon usage tables using absolute metrics and software for multivariate analysis of codon and amino acid usage are available in public databases. However, they do not provide easy computational and statistical tools to carry out detailed gene primary structure analysis on a genomic scale. We propose the usage of several statistical methods – contingency table analysis, residual analysis, multivariate analysis (cluster analysis) – to analyze the codon bias under various aspects (degree of association, contexts and clustering). Results: The developed solution is a software application that provides a user-guided analysis of codon sequences considering several contexts and codon usage on a genomic scale. The utilization of this tool in our molecular biology laboratory is focused on particular genomes, especially those from Saccharomyces cerevisiae, Candida albicansand Escherichia coli. In order to illustrate the applicability and output layouts of the software these species are herein used as examples. Conclusions: The statistical tools incorporated in the system are allowing to obtain global views of important sequence features. It is expected that the results obtained will permit identification of general rules that govern codon context and codon usage in any genome. Additionally, identification of genes containing expanded codons that arise as a consequence of erroneous DNA replication events will permit uncovering new genes associated with human disease.

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Sridhar, Jayavel, and Paramasamy Gunasekaran. "Computational Small RNA Prediction in Bacteria." Bioinformatics and Biology Insights 7 (January 2013): BBI.S11213. http://dx.doi.org/10.4137/bbi.s11213.

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Bacterial, small RNAs were once regarded as potent regulators of gene expression and are now being considered as essential for their diversified roles. Many small RNAs are now reported to have a wide array of regulatory functions, ranging from environmental sensing to pathogenesis. Traditionally, noncoding transcripts were rarely detected by means of genetic screens. However, the availability of approximately 2200 prokaryotic genome sequences in public databases facilitates the efficient computational search of those molecules, followed by experimental validation. In principle, the following four major computational methods were applied for the prediction of sRNA locations from bacterial genome sequences: (1) comparative genomics, (2) secondary structure and thermodynamic stability, (3) ‘Orphan’ transcriptional signals and (4) ab initio methods regardless of sequence or structure similarity; most of these tools were applied to locate the putative genomic sRNA locations followed by experimental validation of those transcripts. Therefore, computational screening has simplified the sRNA identification process in bacteria. In this review, a plethora of small RNA prediction methods and tools that have been reported in the past decade are discussed comprehensively and assessed based on their attributes, compatibility, and their prediction accuracy.

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Rodriguez, Oscar L., Anna Ritz, Andrew J. Sharp, and Ali Bashir. "MsPAC: a tool for haplotype-phased structural variant detection." Bioinformatics 36, no. 3 (August 9, 2019): 922–24. http://dx.doi.org/10.1093/bioinformatics/btz618.

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Abstract Summary While next-generation sequencing (NGS) has dramatically increased the availability of genomic data, phased genome assembly and structural variant (SV) analyses are limited by NGS read lengths. Long-read sequencing from Pacific Biosciences and NGS barcoding from 10x Genomics hold the potential for far more comprehensive views of individual genomes. Here, we present MsPAC, a tool that combines both technologies to partition reads, assemble haplotypes (via existing software) and convert assemblies into high-quality, phased SV predictions. MsPAC represents a framework for haplotype-resolved SV calls that moves one step closer to fully resolved, diploid genomes. Availability and implementation https://github.com/oscarlr/MsPAC. Supplementary information Supplementary data are available at Bioinformatics online.

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Aji, Oktira Roka. "COMPARATIVE GENOME OF TWO STRAIN MORAXELLA CATARRHALIS USING IN SILICO ANALYSIS." Journal of Islamic Pharmacy 2, no. 2 (November 18, 2017): 1. http://dx.doi.org/10.18860/jip.v2i2.4504.

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<p>Moraxella catarrhalis can cause otitis media and exacerbations of chronic obstructive pulmonary disease in human. Here we describe the comparison between two publicly available genomes of two strain of M.catarrhalis using computational analysis to obtain genomic features between them. Comparative genomic analysis were carried out using available tools in public domain websites. The aim of this study was to investigate the differences and similarities between two strains by comparing their genomic sequences. The results indicated that may be used to offer better understanding M.catarrhalis lifestyle.</p><p> </p><p><strong>Keywords:</strong> <em>Moraxella catarrhalis; In Silico; Comparative genome analysis</em></p>

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van den Broek, Evert, Stef van Lieshout, Christian Rausch, Bauke Ylstra, Mark A. van de Wiel, Gerrit A. Meijer, Remond J. A. Fijneman, and Sanne Abeln. "GeneBreak: detection of recurrent DNA copy number aberration-associated chromosomal breakpoints within genes." F1000Research 5 (September 19, 2016): 2340. http://dx.doi.org/10.12688/f1000research.9259.1.

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Development of cancer is driven by somatic alterations, including numerical and structural chromosomal aberrations. Currently, several computational methods are available and are widely applied to detect numerical copy number aberrations (CNAs) of chromosomal segments in tumor genomes. However, there is lack of computational methods that systematically detect structural chromosomal aberrations by virtue of the genomic location of CNA-associated chromosomal breaks and identify genes that appear non-randomly affected by chromosomal breakpoints across (large) series of tumor samples. ‘GeneBreak’ is developed to systematically identify genes recurrently affected by the genomic location of chromosomal CNA-associated breaks by a genome-wide approach, which can be applied to DNA copy number data obtained by array-Comparative Genomic Hybridization (CGH) or by (low-pass) whole genome sequencing (WGS). First, ‘GeneBreak’ collects the genomic locations of chromosomal CNA-associated breaks that were previously pinpointed by the segmentation algorithm that was applied to obtain CNA profiles. Next, a tailored annotation approach for breakpoint-to-gene mapping is implemented. Finally, dedicated cohort-based statistics is incorporated with correction for covariates that influence the probability to be a breakpoint gene. In addition, multiple testing correction is integrated to reveal recurrent breakpoint events. This easy-to-use algorithm, ‘GeneBreak’, is implemented in R (www.cran.r-project.org) and is available from Bioconductor (www.bioconductor.org/packages/release/bioc/html/GeneBreak.html).

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42

van den Broek, Evert, Stef van Lieshout, Christian Rausch, Bauke Ylstra, Mark A. van de Wiel, Gerrit A. Meijer, Remond J. A. Fijneman, and Sanne Abeln. "GeneBreak: detection of recurrent DNA copy number aberration-associated chromosomal breakpoints within genes." F1000Research 5 (July 6, 2017): 2340. http://dx.doi.org/10.12688/f1000research.9259.2.

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Development of cancer is driven by somatic alterations, including numerical and structural chromosomal aberrations. Currently, several computational methods are available and are widely applied to detect numerical copy number aberrations (CNAs) of chromosomal segments in tumor genomes. However, there is lack of computational methods that systematically detect structural chromosomal aberrations by virtue of the genomic location of CNA-associated chromosomal breaks and identify genes that appear non-randomly affected by chromosomal breakpoints across (large) series of tumor samples. ‘GeneBreak’ is developed to systematically identify genes recurrently affected by the genomic location of chromosomal CNA-associated breaks by a genome-wide approach, which can be applied to DNA copy number data obtained by array-Comparative Genomic Hybridization (CGH) or by (low-pass) whole genome sequencing (WGS). First, ‘GeneBreak’ collects the genomic locations of chromosomal CNA-associated breaks that were previously pinpointed by the segmentation algorithm that was applied to obtain CNA profiles. Next, a tailored annotation approach for breakpoint-to-gene mapping is implemented. Finally, dedicated cohort-based statistics is incorporated with correction for covariates that influence the probability to be a breakpoint gene. In addition, multiple testing correction is integrated to reveal recurrent breakpoint events. This easy-to-use algorithm, ‘GeneBreak’, is implemented in R (www.cran.r-project.org) and is available from Bioconductor (www.bioconductor.org/packages/release/bioc/html/GeneBreak.html).

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43

Li, Xiangyang, Fang Chen, and Yunpeng Chen. "Gcluster: a simple-to-use tool for visualizing and comparing genome contexts for numerous genomes." Bioinformatics 36, no. 12 (March 28, 2020): 3871–73. http://dx.doi.org/10.1093/bioinformatics/btaa212.

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Abstract Motivation Comparing the organization of gene, gene clusters and their flanking genomic contexts is of critical importance to the determination of gene function and evolutionary basis of microbial traits. Currently, user-friendly and flexible tools enabling to visualize and compare genomic contexts for numerous genomes are still missing. Results We here present Gcluster, a stand-alone Perl tool that allows researchers to customize and create high-quality linear maps of the genomic region around the genes of interest across large numbers of completed and draft genomes. Importantly, Gcluster integrates homologous gene analysis, in the form of a built-in orthoMCL, and mapping genomes onto a given phylogeny to provide superior comparison of gene contexts. Availability and implementation Gcluster is written in Perl and released under GPLv3. The source code is freely available at https://github.com/Xiangyang1984/Gcluster and http://www.microbialgenomic.com/Gcluster_tool.html. Gcluster can also be installed through conda: ‘conda install -c bioconda gcluster’. Supplementary information Supplementary data are available at Bioinformatics online.

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44

Nik-Zainal, S. "Abstract MS1-2: Genomics of DNA repair defects in breast cancer." Cancer Research 82, no. 4_Supplement (February 15, 2022): MS1–2—MS1–2. http://dx.doi.org/10.1158/1538-7445.sabcs21-ms1-2.

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Abstract While driver mutations in cancer genomes were the focus of cancer research for a long time, passenger mutational signatures - the imprints of DNA damage and DNA repair processes that have been operative during tumorigenesis - are also biologically informative. In this lecture, I provide an update of what has been uncovered in breast cancers in relation to genomic imprints of DNA repair defects and showcase how we have developed computational applications that we hope to translate toward clinical utility. Citation Format: S Nik-Zainal. Genomics of DNA repair defects in breast cancer [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr MS1-2.

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Raza, Shahid, and Hira Mubeen. "Computational Analysis of Genomic Regions of Human Insulin Receptor Gene." Journal of Advances in Biology & Biotechnology 8, no. 2 (January 10, 2016): 1–7. http://dx.doi.org/10.9734/jabb/2016/26715.

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46

Suhai, Sándor. "Computational Methods in Cancer Research The Hierarchy of Genomic Information." Interdisciplinary Science Reviews 14, no. 3 (September 1, 1989): 225–32. http://dx.doi.org/10.1179/030801889789797989.

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47

Lehrbach, N. J., and E. A. Miska. "Functional genomic, computational and proteomic analysis of C. elegans microRNAs." Briefings in Functional Genomics and Proteomics 7, no. 3 (March 9, 2008): 228–35. http://dx.doi.org/10.1093/bfgp/eln024.

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48

Carbone, Alessandra. "Computational Prediction of Genomic Functional Cores Specific to Different Microbes." Journal of Molecular Evolution 63, no. 6 (November 10, 2006): 733–46. http://dx.doi.org/10.1007/s00239-005-0250-9.

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49

Tadei, Roberto, and Nicola Bellomo. "Special issue on modeling and computational methods in genomic sciences." Computers & Mathematics with Applications 55, no. 5 (March 2008): 863–66. http://dx.doi.org/10.1016/j.camwa.2006.12.087.

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50

Kravchenko-Balasha, Nataly, Simcha Simon, R. D. Levine, F. Remacle, and Iaakov Exman. "Computational Surprisal Analysis Speeds-Up Genomic Characterization of Cancer Processes." PLoS ONE 9, no. 11 (November 18, 2014): e108549. http://dx.doi.org/10.1371/journal.pone.0108549.

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