Interpreting and integrating big data in the life sciences

2019 ◽  
Vol 3 (4) ◽  
pp. 335-341 ◽  
Author(s):  
Serghei Mangul
Keyword(s):  
Big Data ◽  
Life Science ◽  
Life Sciences ◽  
Reproducible Research ◽  
Computational Techniques ◽  
Omics Data ◽  
Omics Technologies ◽  
Domains Of Life ◽  
The Many ◽  
Review Current

Abstract Recent advances in omics technologies have led to the broad applicability of computational techniques across various domains of life science and medical research. These technologies provide an unprecedented opportunity to collect the omics data from hundreds of thousands of individuals and to study the gene–disease association without the aid of prior assumptions about the trait biology. Despite the many advantages of modern omics technologies, interpretations of big data produced by such technologies require advanced computational algorithms. I outline key challenges that biomedical researches are facing when interpreting and integrating big omics data. I discuss the reproducibility aspect of big data analysis in the life sciences and review current practices in reproducible research. Finally, I explain the skills that biomedical researchers need to acquire to independently analyze big omics data.

scholarly journals Interpreting and integrating big data in the life sciences

2019 ◽  
Author(s):  
Serghei Mangul
Keyword(s):  
Big Data ◽  
Life Science ◽  
Life Sciences ◽  
Reproducible Research ◽  
Computational Techniques ◽  
Omics Data ◽  
Omics Technologies ◽  
Domains Of Life ◽  
The Many ◽  
Review Current

Recent advances in omics technologies have led to the broad applicability of computational techniques across various domains of life science and medical research. These technologies provide an unprecedented opportunity to collect omics data from hundreds of thousands of individuals and to study gene-disease association without the aid of prior assumptions about the trait biology. Despite the many advantages of modern omics technologies, interpretations of big data produced by such technologies require advanced computational algorithms. Below I outline key challenges that biomedical researches are facing when interpreting and integrating big omics data. I discuss the reproducibility aspect of big data analysis in the life sciences and review current practices in reproducible research. Finally, I explain the skills which biomedical researchers need to acquire in order to independently analyze big omics data.

scholarly journals Interpreting and integrating big data in the life sciences

2019 ◽  
Author(s):  
Serghei Mangul
Keyword(s):  
Big Data ◽  
Life Science ◽  
Life Sciences ◽  
Reproducible Research ◽  
Computational Techniques ◽  
Omics Data ◽  
Omics Technologies ◽  
Domains Of Life ◽  
The Many ◽  
Review Current

Recent advances in omics technologies have led to the broad applicability of computational techniques across various domains of life science and medical research. These technologies provide an unprecedented opportunity to collect omics data from hundreds of thousands of individuals and to study gene-disease association without the aid of prior assumptions about the trait biology. Despite the many advantages of modern omics technologies, interpretations of big data produced by such technologies require advanced computational algorithms. Below I outline key challenges that biomedical researches are facing when interpreting and integrating big omics data. I discuss the reproducibility aspect of big data analysis in the life sciences and review current practices in reproducible research. Finally, I explain the skills which biomedical researchers need to acquire in order to independently analyze big omics data.

scholarly journals Interpreting and integrating big data in the life sciences

2019 ◽  
Author(s):  
Serghei Mangul
Keyword(s):  
Big Data ◽  
Life Science ◽  
Life Sciences ◽  
Reproducible Research ◽  
Computational Techniques ◽  
Omics Data ◽  
Omics Technologies ◽  
Domains Of Life ◽  
The Many ◽  
Review Current

Recent advances in omics technologies have led to the broad applicability of computational techniques across various domains of life science and medical research. These technologies provide an unprecedented opportunity to collect omics data from hundreds of thousands of individuals and to study gene-disease association without the aid of prior assumptions about the trait biology. Despite the many advantages of modern omics technologies, interpretations of big data produced by such technologies require advanced computational algorithms. Below I outline key challenges that biomedical researches are facing when interpreting and integrating big omics data. I discuss the reproducibility aspect of big data analysis in the life sciences and review current practices in reproducible research. Finally, I explain the skills which biomedical researchers need to acquire in order to independently analyze big omics data.

scholarly journals Proceedings of the “Think Tank Hackathon’’, Big Data Training School for Life Sciences Follow-up, Ljubljana 6th – 7th February 2018

2018 ◽  
Vol 24 ◽  
pp. e912
Author(s):  
Sabrina K. Schulze ◽  
Živa Ramšak ◽  
Yen Hoang ◽  
Eftim Zdravevski ◽  
Juliane Pfeil ◽  
...  
Keyword(s):  
Big Data ◽  
Life Science ◽  
Life Sciences ◽  
Science Research ◽  
Recent Experience ◽  
Think Tank ◽  
Training School ◽  
Big Data Technologies ◽  
The Cost

On 6th and 7th February 2018, a Think Tank took place in Ljubljana, Slovenia. It was a follow-up of the “Big Data Training School for Life Sciences” held in Uppsala, Sweden, in September 2017. The focus was on identifying topics of interest and optimising the programme for a forthcoming “Advanced” Big Data Training School for Life Science, that we hope is again supported by the COST Action CHARME (Harmonising standardisation strategies to increase efficiency and competitiveness of European life-science research - CA15110). The Think Tank aimed to go into details of several topics that were - to a degree - covered by the former training school. Likewise, discussions embraced the recent experience of the attendees in light of the new knowledge obtained by the first edition of the training school and how it comes from the perspective of their current and upcoming work. The 2018 training school should strive for and further facilitate optimised applications of Big Data technologies in life sciences. The attendees of this hackathon entirely organised this workshop.

scholarly journals Report on the “Big Data Training School for Life Sciences”, 18-22 September 2017, Uppsala, Sweden

2018 ◽  
Vol 23 ◽  
pp. e905
Author(s):  
Juliane Pfeil ◽  
Sabrina Kathrin Schulze ◽  
Eftim Zdravevski ◽  
Yen Hoang
Keyword(s):  
Big Data ◽  
Personal Experience ◽  
Life Science ◽  
Life Sciences ◽  
Science Research ◽  
Training School ◽  
Intensive Courses ◽  
Hands On ◽  
Diverse Backgrounds ◽  
The Cost

In September 2017 a "Big Data Training School for Life Sciences" took place in Uppsala, Sweden, jointly organised by EMBnet and the COST Action CHARME (Harmonising standardisation strategies to increase efficiency and competitiveness of European life-science research - CA15100). The week programme was divided into hands-on sessions and lectures. In both cases, insights into dealing with big amounts of data were given. This paper describes our personal experience as students’ by providing also some suggestions that we hope can help the organisers as well as other trainers to further increase the efficiency of such intensive courses for students with diverse backgrounds.

Big Data in the philippines: How do we actually use them?

2021 ◽  
pp. 1-30
Author(s):  
Lisa Grace S. Bersales ◽  
Josefina V. Almeda ◽  
Sabrina O. Romasoc ◽  
Marie Nadeen R. Martinez ◽  
Dannela Jann B. Galias
Keyword(s):  
Big Data ◽  
High Speed ◽  
The Philippines ◽  
Complex Data ◽  
Official Statistics ◽  
Group Discussions ◽  
The Public ◽  
The Government ◽  
The Many ◽  
Current Utilization

With the advancement of technology, digitalization, and the internet of things, large amounts of complex data are being produced daily. This vast quantity of various data produced at high speed is referred to as Big Data. The utilization of Big Data is being implemented with success in the private sector, yet the public sector seems to be falling behind despite the many potentials Big Data has already presented. In this regard, this paper explores ways in which the government can recognize the use of Big Data for official statistics. It begins by gathering and presenting Big Data-related initiatives and projects across the globe for various types and sources of Big Data implemented. Further, this paper discusses the opportunities, challenges, and risks associated with using Big Data, particularly in official statistics. This paper also aims to assess the current utilization of Big Data in the country through focus group discussions and key informant interviews. Based on desk review, discussions, and interviews, the paper then concludes with a proposed framework that provides ways in which Big Data may be utilized by the government to augment official statistics.

scholarly journals Captivating Perplexities of Spinareovirinae 5′ RNA Caps

Viruses ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 294
Author(s):  
Justine Kniert ◽  
Qi Feng Lin ◽  
Maya Shmulevitz
Keyword(s):  
Immune Recognition ◽  
Domains Of Life ◽  
The Many ◽  
Rna Capping ◽  
Multiple Domains ◽  
Rna Caps

RNAs with methylated cap structures are present throughout multiple domains of life. Given that cap structures play a myriad of important roles beyond translation, such as stability and immune recognition, it is not surprising that viruses have adopted RNA capping processes for their own benefit throughout co-evolution with their hosts. In fact, that RNAs are capped was first discovered in a member of the Spinareovirinae family, Cypovirus, before these findings were translated to other domains of life. This review revisits long-past knowledge and recent studies on RNA capping among members of Spinareovirinae to help elucidate the perplex processes of RNA capping and functions of RNA cap structures during Spinareovirinae infection. The review brings to light the many uncertainties that remain about the precise capping status, enzymes that facilitate specific steps of capping, and the functions of RNA caps during Spinareovirinae replication.

Closing Remarks

2019 ◽  
Vol 15 (S367) ◽  
pp. 515-517
Author(s):  
Debra Meloy Elmegreen
Keyword(s):  
Big Data ◽  
Education Research ◽  
Strategic Plan ◽  
Astronomy Education ◽  
The Public ◽  
The Many ◽  
Collaborative Efforts ◽  
Made In

AbstractThis symposium has highlighted key first steps made in addressing many goals of the IAU Strategic Plan for 2020–2030. Presentations on initiatives regarding education, with applications to development, outreach, equity, inclusion, big data, and heritage, are briefly summarized here. The many projects underway for the public, for students, for teachers, and for astronomers doing astronomy education research provide a foundation for future collaborative efforts, both regionally and globally.

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