Computational Approaches Leveraging Integrated Connections of Multi-Omic Data toward Clinical Applications

2021 ◽  
Author(s):  
Cansu H Demirel ◽  
Kaan M Arici ◽  
Nurcan Tuncbag
Keyword(s):  
High Throughput ◽  
Biological Systems ◽  
Clinical Applications ◽  
Data Type ◽  
Omics Data ◽  
Computational Approaches ◽  
Omic Data

In line with the advances in high-throughput technologies, multiple omic datasets have accumulated to study biological systems and diseases coherently. No single omics data type is capable of fully representing...

Studying how genetic variants affect mechanism in biological systems

2018 ◽  
Vol 62 (4) ◽  
pp. 575-582
Author(s):  
Francesco Raimondi ◽  
Robert B. Russell
Keyword(s):  
Genetic Variation ◽  
Molecular Mechanism ◽  
Genetic Variants ◽  
Biological Function ◽  
Biological Systems ◽  
Protein Structures ◽  
Clinical Applications ◽  
Biological Pathways ◽  
The Relationship

Genetic variants are currently a major component of system-wide investigations into biological function or disease. Approaches to select variants (often out of thousands of candidates) that are responsible for a particular phenomenon have many clinical applications and can help illuminate differences between individuals. Selecting meaningful variants is greatly aided by integration with information about molecular mechanism, whether known from protein structures or interactions or biological pathways. In this review we discuss the nature of genetic variants, and recent studies highlighting what is currently known about the relationship between genetic variation, biomolecular function, and disease.

scholarly journals A Review of Recent Advancement in Integrating Omics Data with Literature Mining towards Biomedical Discoveries

2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
Kalpana Raja ◽  
Matthew Patrick ◽  
Yilin Gao ◽  
Desmond Madu ◽  
Yuyang Yang ◽  
...  
Keyword(s):  
Text Mining ◽  
High Throughput ◽  
Literature Mining ◽  
Biological Knowledge ◽  
Omics Data ◽  
Huge Number ◽  
Automated Generation ◽  
The Past ◽  
Independent Information ◽  
Recent Advancement

In the past decade, the volume of “omics” data generated by the different high-throughput technologies has expanded exponentially. The managing, storing, and analyzing of this big data have been a great challenge for the researchers, especially when moving towards the goal of generating testable data-driven hypotheses, which has been the promise of the high-throughput experimental techniques. Different bioinformatics approaches have been developed to streamline the downstream analyzes by providing independent information to interpret and provide biological inference. Text mining (also known as literature mining) is one of the commonly used approaches for automated generation of biological knowledge from the huge number of published articles. In this review paper, we discuss the recent advancement in approaches that integrate results from omics data and information generated from text mining approaches to uncover novel biomedical information.

scholarly journals Therapeutic Drugs Targeting 2019-nCoV Main Protease by High-Throughput Screening

2020 ◽  
Author(s):  
Yan Li ◽  
Jinyong Zhang ◽  
Ning Wang ◽  
Haibo Li ◽  
Yun Shi ◽  
...  
Keyword(s):  
High Throughput ◽  
High Throughput Screening ◽  
Binding Capacity ◽  
Effective Means ◽  
Clinical Applications ◽  
Therapeutic Drugs ◽  
Main Protease ◽  
Novel Coronavirus ◽  
Clinical Drug ◽  
Specific Inhibitors

Abstract2019 Novel Coronavirus (2019-nCoV) is a virus identified as the cause of the outbreak of pneumonia first detected in Wuhan, China. Investigations on the transmissibility, severity, and other features associated with this virus are ongoing. Currently, there is no vaccine or therapeutic antibody to prevent the infection, and more time is required to develop an effective immune strategy against the pathogen. In contrast, specific inhibitors targeting the key protease involved in replication and proliferation of the virus are the most effective means to alleviate the epidemic. The main protease of SARS-CoV is essential for the life cycle of the virus, which showed 96.1% of similarity with the main proteaseof 2019-nCoV, is considered to be an attractive target for drug development. In this study, we have identified 4 small molecular drugs with high binding capacity with SARS-CoV main protease by high-throughput screening based on the 8,000 clinical drug libraries, all these drugs have been widely used in clinical applications with guaranteed safety, which may serve as promising candidates to treat the infection of 2019-nCoV.

scholarly journals SpiderSeqR: an R package for crawling the web of high-throughput multi-omic data repositories for data-sets and annotation

2020 ◽  
Author(s):  
Anna M. Sozanska ◽  
Charles Fletcher ◽  
Dóra Bihary ◽  
Shamith A. Samarajiwa
Keyword(s):  
High Throughput ◽  
R Package ◽  
Data Reuse ◽  
Massively Parallel ◽  
Data Sets ◽  
Similar Data ◽  
Data Generation ◽  
Data Repositories ◽  
Public Data ◽  
Omic Data

AbstractMore than three decades ago, the microarray revolution brought about high-throughput data generation capability to biology and medicine. Subsequently, the emergence of massively parallel sequencing technologies led to many big-data initiatives such as the human genome project and the encyclopedia of DNA elements (ENCODE) project. These, in combination with cheaper, faster massively parallel DNA sequencing capabilities, have democratised multi-omic (genomic, transcriptomic, translatomic and epigenomic) data generation leading to a data deluge in bio-medicine. While some of these data-sets are trapped in inaccessible silos, the vast majority of these data-sets are stored in public data resources and controlled access data repositories, enabling their wider use (or misuse). Currently, most peer reviewed publications require the deposition of the data-set associated with a study under consideration in one of these public data repositories. However, clunky and difficult to use interfaces, subpar or incomplete annotation prevent discovering, searching and filtering of these multi-omic data and hinder their re-purposing in other use cases. In addition, the proliferation of multitude of different data repositories, with partially redundant storage of similar data are yet another obstacle to their continued usefulness. Similarly, interfaces where annotation is spread across multiple web pages, use of accession identifiers with ambiguous and multiple interpretations and lack of good curation make these data-sets difficult to use. We have produced SpiderSeqR, an R package, whose main features include the integration between NCBI GEO and SRA databases, enabling an integrated unified search of SRA and GEO data-sets and associated annotations, conversion between database accessions, as well as convenient filtering of results and saving past queries for future use. All of the above features aim to promote data reuse to facilitate making new discoveries and maximising the potential of existing data-sets.Availabilityhttps://github.com/ss-lab-cancerunit/SpiderSeqR

scholarly journals Systems Biology and Multi-Omics Integration: Viewpoints from the Metabolomics Research Community

Metabolites ◽  
2019 ◽  
Vol 9 (4) ◽  
pp. 76 ◽  
Author(s):  
Farhana R. Pinu ◽  
David J. Beale ◽  
Amy M. Paten ◽  
Konstantinos Kouremenos ◽  
Sanjay Swarup ◽  
...  
Keyword(s):  
New Zealand ◽  
Systems Biology ◽  
Life Science ◽  
Biological Systems ◽  
Research Community ◽  
Data Sets ◽  
Omics Data ◽  
Omics Integration ◽  
Traditional Approaches ◽  
Multiple Interactions

The use of multiple omics techniques (i.e., genomics, transcriptomics, proteomics, and metabolomics) is becoming increasingly popular in all facets of life science. Omics techniques provide a more holistic molecular perspective of studied biological systems compared to traditional approaches. However, due to their inherent data differences, integrating multiple omics platforms remains an ongoing challenge for many researchers. As metabolites represent the downstream products of multiple interactions between genes, transcripts, and proteins, metabolomics, the tools and approaches routinely used in this field could assist with the integration of these complex multi-omics data sets. The question is, how? Here we provide some answers (in terms of methods, software tools and databases) along with a variety of recommendations and a list of continuing challenges as identified during a peer session on multi-omics integration that was held at the recent ‘Australian and New Zealand Metabolomics Conference’ (ANZMET 2018) in Auckland, New Zealand (Sept. 2018). We envisage that this document will serve as a guide to metabolomics researchers and other members of the community wishing to perform multi-omics studies. We also believe that these ideas may allow the full promise of integrated multi-omics research and, ultimately, of systems biology to be realized.

scholarly journals Toward a Better Understanding of Species Interactions through Network Biology

mSystems ◽  
2019 ◽  
Vol 4 (3) ◽  
Author(s):  
Ryan S. McClure
Keyword(s):  
Complex Systems ◽  
Species Interactions ◽  
Omics Data ◽  
New Methods ◽  
Strong Position ◽  
Multiple Species ◽  
Network Approaches ◽  
Natural Settings ◽  
Microbial Systems ◽  
Omic Data

ABSTRACT Within the last decade, there has been an explosion of multi-omics data generated for several microbial systems. At the same time, new methods of analysis have emerged that are based on inferring networks that link features both within and between species based on correlation in abundance. These developments prompt two important questions. What can be done with network approaches to better understand microbial species interactions? What challenges remain in applying network approaches to query the more complex systems of natural settings? Here, I briefly describe what has been done and what questions still need to be answered. Over the next 5 to 10 years, we will be in a strong position to infer networks that contain multiple kinds of omic data and describe systems with multiple species. These applications will open the door for a better understanding and use of microbiomes across a variety of fields.

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