Liu Xin began studying classic experiments in plant research and genetics at the School of Life Sciences at Peking University in 2005. Today, he leads a team of more than 100 researchers and has contributed to major global initiatives such as the Earth BioGenome Project and the Thousand Plant Genomes Project.
Over the years, Liu Xin has grown from a young researcher into a recognised leader in genomics and has held positions including Executive Vice President of Shenzhen BGI Research.
Life Science Research Must Benefit the Public
Q: You began studying life sciences in 2005 and have worked in genomics for more than a decade. How do you reflect on the development of the field?
A: I started studying life sciences in 2005 and became involved in genome research in 2009. Looking back over the past decade, I have noticed that relatively few of my classmates continued working in the life sciences field.
This reflects, to some extent, that the development of the life sciences industry has not yet reached its full potential. The opportunities are still limited, and the industry needs further development, particularly technology-driven biotechnology industries.
Only through technological innovation can life sciences achieve the same level of integration between research and application that we have seen in fields such as computing.
Traditional life science research, especially basic research, can solve important scientific questions. However, it often remains distant from industrial applications and practical implementation. Bridging the gap between cutting-edge science and real-world applications remains one of the biggest challenges we face.
Today, emerging biotechnologies led by genome sequencing are accelerating the translation of scientific discoveries into practical applications. By analysing biological systems using large-scale genomic data and combining this with technologies such as gene editing, researchers can better understand biological processes and apply these discoveries in industry.
Much like how mobile internet technology accelerated the widespread adoption of the internet, advances in biotechnology are helping life sciences become more industrialised and accessible. We can expect significant growth in the life sciences sector in the coming years.
I feel fortunate to remain in this field and to continue working on core research. Large-scale genetic research and big data-based bioinformatics will play an important role in driving the future development of the biological industry.
The Rapid Development of Sequencing Technology Is Transforming Scientific Thinking
Q: How has the development of gene sequencing technology influenced research approaches in life sciences?
A: In recent years, the rapid development of genome sequencing technology has encouraged new research approaches across the life sciences.
Research strategies are evolving as genetic technologies advance. Increasingly, life science research is centred on analysing large-scale genomic datasets.
As sequencing technologies become more accessible and costs continue to fall, researchers across many disciplines are using genetic data as a fundamental component of their studies.
Since I first began working in genomics, one of the most striking trends has been the constant growth in data volume. This means we must develop more effective analytical tools and methods.
From this perspective, life sciences are gradually evolving into a form of big data science.
Genomics as a research method can be applied across many disciplines. Whether in neuroscience, brain science or plant biology, genomic technologies can complement traditional experimental research and provide valuable insights.
The Past and Present of Plant and Animal Genome Research
Q: How has plant and animal genome research evolved over time?
A: In the early stages of genomic research, assembling the genome of a species was itself a major scientific achievement. Many early studies focused primarily on generating genome datasets that could support future research.
However, as sequencing technologies have advanced, genome assembly alone has become less of a focal point. Today, the real value lies in using genomic data to answer fundamental questions about biology and to deepen our understanding of life.
Genomic data from plants and animals can help address two major scientific questions.
First, genome analysis can reveal the evolutionary history of species, both at the species level and at the population level.
Second, genomic data can help identify the genetic basis of species-specific traits and environmental adaptation. When combined with experimental validation, this information allows researchers to solve important biological questions.
These discoveries often lead to new industrial applications, demonstrating how genomic data can support innovation in biotechnology and agriculture.
Challenges in Animal and Plant Genome Research
Q: What are the main challenges facing genome research and its industrial applications?
A: When I first began working in this field, the scale of genomic data was relatively small. Today, advances in sequencing technology have dramatically increased both the volume of data produced and expectations around cost and efficiency.
Managing and analysing large-scale genomic datasets has therefore become one of the biggest challenges facing the industry.
Researchers must address challenges at every stage of the workflow, from sample collection and preparation to sequencing and data analysis.
For example, multi-omics studies that analyse genomes across populations or even at the single-cell level require large-scale sampling and highly precise preparation processes.
Many species found in nature have limited existing biological information, and different biological samples can vary significantly. These factors make sample collection and preparation more complex.
As genome sequencing expands, the volume of data continues to increase, which makes data analysis more challenging. Researchers must continually improve analytical methods and computational efficiency.
Another challenge arises when working with small or rare biological samples. For example, when sequencing ants or other small organisms, obtaining sufficient DNA for sequencing can be difficult.
If a sample is rare or unique, scaling up sequencing can magnify even small technical challenges.
Ultimately, cost remains one of the biggest challenges for the industry. Improving efficiency while reducing sequencing costs is essential for large-scale research.
In some applications, such as human genomics, higher sequencing costs may be acceptable. However, in fields such as crop breeding or livestock research, low-cost sequencing technologies are essential for widespread adoption.
To support broader industrial applications, sequencing technologies must continue to evolve to meet the practical requirements of real-world research and industry use cases.
