Thank you all for reading my blog entries. My blog has now more than 1,000 visitors with more than 2,000 views. Hopefully will bring more content to my entries and eventually be a useful resource for all of us!
Happy to share that we have just published a review paper on “Animal Coronavirus Diseases: Parallels with COVID-19 in Humans.” This review is written with Chao Nan Lin, whom I have worked very closely with since I was a PhD student. It is hence a great privilege to be able to collaborate with him on his topic of expertise: Animal coronaviruses. This manuscript describes the similarities and differences between animal and human coronaviruses with regards to genome organization and recombination, immunopathogenesis, transmission, viral shedding, diagnosis, treatment, and prevention. We hope that the synthesised knowledge can help us better in managing coronavirus epidemics and design of interventions that can reduce the spread of these viruses.
It’s good to pace yourself, and take breaks when necessary. As the saying goes: Slow and steady wins the race. This week, I had the privilege to have a short break from work to have a lunch with my lab mates.
The best part: I solved a few python codes (that I had trouble previously) right after the lunch. Grateful for all the support from my lab mates!
Had the chance to complete my training for fundamentals of python, NUMPY and PANDAs, all of which are essential for data science research. The knowledge is particularly relevant for analysing dataframes, and provide the essential building blocks to execute more sophisticated commands found in other online resources. As the saying goes, learning never stops, and I certainly hope that you are having this positive attitude wherever you go!
Happy to announce that my blog has reached 1,000 viewers. I would like to take this opportunity to thank everyone for reading my entries and hope that you have enjoyed the content thus far!
Happy to have completed the course on data visualisation fundamentals by Dataquest. Key packages required are:
import numpy as np import pandas as pd import matplotlib.pyplot as plt import seaborn as sns
numpy and pandas packages are important for storing and manipulating datasets, as well as for performing calculations for correlation. matplotlib.pyplot package is useful for plotting scatterplots, histograms and cumulative frequency distributions. Finally seaborn package is can plot multi-dimensional data involving multiple variables. The nitty gritty details of how to change to settings of graphs, such as adding graph titles, changing x or y-axis ticks, rotation of labelling, sizes of symbols and colours are also covered in this course. Satisfied with the learning experience, which is definitely much more important than the certificate itself. To all others trying to learn coding like me, Gambatte!
Learning a programming language can be challenging, especially without guidance or mentorship. Recently, due to my interest in creating JupyterLab, I decided to leverage on online platforms to learn more about Python. Within a short period of time, I was able to narrow down to either Dataquest or DataCamp. Eventually, I chose Dataquest because:
- Dataquest allows selection of a career track that allows me to prioritise learning the essentials first.
- Have more open-ended questions allow better retaining of information and knowledge.
- Provides realistic datasets (especially dataframes) so that you can directly apply some of these codes to real-life scenarios.
- Contents given are engaging and interactive. You can also choose to skip sections that may not be of interest to your applications.
In summary, I have been learning from Dataquest for almost a week and was so satisfied with the course design that I bought the premium membership after a day of testing out. You may also consider using the referral address: app.dataquest.io/referral-signup/gcrv9rw5/ to get $15 discount for the premium membership. This COVID-19 pandemic is probably the best chance to learn a new skill at your convenience and bioinformatics could be something of interest to you!
My lab, from the Emerging Infectious Diseases (EID) Programme, Singapore, is seeking to employ one postdoctoral researcher . This position presents an exciting opportunity to be a global leader in the application of systems biology and omics technologies to guide development of vaccines and therapeutics against emerging infectious diseases. The applicant will be working with a team of scientists and clinicians to use data-science to streamline clinical trials. The job scopes will include, but not limited to, curating existing omics datasets from our clinical trials and other published clinical trials, creating a resource database documenting biological data and designing algorithms/jupyter notebooks for query optimisation. The position comes with the potential for further career development, as well as engaging in translational research.
Applicants must have a PhD in computer sciences, bioinformatics, biological sciences or in any other related disciplines. Each applicant should a strong background in using statistical computer languages, especially R, Python and SQL to enable them to build database systems of high availability and quality. Knowledge on query optimization, analysis and troubleshooting interactions is also highly preferred. With the support from a network of scientist and clinician collaborators, applicants should be highly motivated researchers who wish to make an impact in research.
The post will be subjected to the Duke-NUS Medical School terms and conditions of service, and the salaries will be based on applicants’ experience and track records. To apply, please visit the following address:
Welcome to my blog! I am Kuan Rong, currently a principal research scientist in Duke-NUS medical school. My scientific career begun in 2008 , interrogating how antibodies can influence outcome of dengue virus infection. In my PhD with Prof. Ooi Eng Eong, we identified the molecular mechanisms involved in dengue virus neutralisation and enhancement (Chan et al., PNAS, 2011; Chan et al., PNAS, 2014). After graduation, I had the unique opportunity to work with Prof. Ooi Eng Eong and Dr. Jenny Low to investigate if antibodies can impact the outcome of the live-attenuated Yellow Fever virus infection. Since then, I became very interested in data science. By integrating various omics platforms (e.g. genomics, proteomics and metabolomics), we were able to understand how human variations and host responses affect immunogenicity and adverse events to the Yellow Fever vaccine (Chan et al., Nature Microbiology, 2016; Chan et al., Nature Medicine, 2019).
My motivation for setting up this blog is to share my knowledge on big data analysis. With the advent of high-throughput genomics, proteomics and metabolomics platforms, these tools are increasingly used to provide insights of biological processes involved in virus infection and disease manifestation, which can help guide the development of therapeutics against viral infections. However, to a virologist, extracting biologically meaningful data from omics data can be challenging, as many of us do not have formal training in bioinformatics. Fortunately, many experts in the field have developed tools that are free and easy to use. I hope that the information provided in this website will be useful for individuals with a passion for infectious diseases and systems biology.