Research area

The Nakato lab's research area is computational genomics. Using various NGS assays and informatics methods, we aim to elucidate the unknown mechanisms of the genome. A big feature is that our lab has aspects of both "informatics", which involves developing accurate and efficient methods for complex problems, and "life sciences", which involves uncovering new knowledge that nobody knows yet through various experiments.

For example, suppose you are interested in estimating gene co-expression networks from single-cell data. Questions such as "Is there a way to estimate the network more accurately?" and "Can we infer causal relationships that are not included in the co-expression network?" fall under the category of informatics (bioinformatics). On the other hand, questions such as "What functions or properties of the sample does the estimated network reflect?" and "Can we discover new marker genes for cell differentiation by estimating and comparing networks from stem cell and non-stem cell data?" belong to the life sciences (computational biology). In the Nakato lab, research in both areas is possible.

The current research themes

We are interested in the relationship between epigenomic state (ChIP-seq), 3D genome structure (Hi-C, Micro-C) and gene expression (RNA-seq) and how they change during cell differentiation and diseases. We also focus on cell trajectory analysis and gene network analysis using single-cell transcriptome data (scRNA-seq). More detailed research and previous results can be found on the Research page.

Examples of topics include the following. There are variations, including those that are more theoretical and those that require less programming. There are many other topics, so if you have an idea you want to try, feel free to suggest it.

• Identification of novel functional regulatory regions using large-scale epigenomic data:The International Human Epigenome Consortium (IHEC), in which we participate, has established an epigenomic database consisting of over 1,000 cell types. We use this resource to infer important functional regions of the genome.
• Large-scale NGS analysis for enhancer regions: Enhancers that regulate gene activityhave a variety of attractive features. They are highly cell-type specific. Multiple enhancers cooperatively regulate a single gene. There are special regulatory regions called "super enhancers". We implement large-scale analysis to clarify the functions of enhancers and their relationship to diseases.
• Relationship between 3D genome and gene expression, and functional analysis of cohesin: The 3D genome structure is important for epigenome and transcriptome, in which the cohesin complex plays an crutial role. The functions of cohesin are diverse, and many details remain unresolved. Through knockdown experiments of cohesin and analysis of cohesin disease patient cells, we will elucidate the importance of the 3D genome and the regulatory mechanism by cohesin.
• Gene co-expression network and cell trajectory analysis using single cell data: Single cell data can be used in a variety of ways. We are particularly interested in gene network analysis and cell differentiation trajectory, and are developing novel techniques and acquiring knowledge.

Ideal candidate profile

◆ Interest in both life science and theory
Since the questions in our research are closely related to the life sciences, it is important to have an interest in the life sciences themselves. On the other hand, when reading methodological papers, we also go into mathematical formulas and theoretical content, so it is important that even non-informatics people are willing to learn theory and take on challenges.

◆ People who are willing to learn on their own
Students are not required to understand all of the knowledge when they first join the lab. People with no programming experience or knowledge of genomics can join the lab. Everything can be learned after enrollment.
However, simply enrolling doesn't automatically impart knowledge. You must study and acquire the necessary knowledge on your own. If you do not have the knowledge that others have already acquired, you will have to work twice as hard to keep up with them. For those who say "I still want to give it my all!", we promise to provide the utmost support.
The attitude of proactively acquiring the necessary knowledge on your own is essential at the Nakato lab. If you don't ask questions, no one will teach you, so such a passive attitude of waiting for someone to teach you may not work well.

◆ Interest in various fields
Nakato Lab's research is interdisciplinary, spanning several fields. Thus, students will have many opportunities to be exposed to new techniques and knowledge that they have not yet learned. A desire to learn what you don't know and an attitude of listening with interest to topics outside your field are important. Students who think "This is an unfamiliar field, so I don't know all the details, but it's interesting!" are desirable.

◆ Proactive communicators
The research topics of the lab members are diverse, and everyone leverages their expertise in the research. You will learn a lot from others, and you will teach other members in your area of expertise. Nakato also learns a lot from the members. Therefore, it is important that you can enjoy interacting with the people around you. If you do not want to interact in any way, you can still do your research alone, but you may get less out of the lab.

Resources at Nakato Lab

◆ Abundant computing resources
　◇ The Nakato lab has shared computing servers (many-core, GPU, and storage servers). Lab members can use all of them. Supercomputers are not usually used, but you can.
　◇ Various tools for different NGS analyses are installed on the servers. In addition to data from the publicly available NGS databases, we have a lot of valuable data available for our research, including data generated through collaborations or unpublished data.

◆ A wide range of research topics related to NGS analysis
　◇ Almost all NGS assays are the subject of research in the Nakato lab, and we have members and collaborators who are strong in their respective fields. Their support makes it easy to incorporate multiple NGS assays into your research.

◆ A strong network of researchers
　◇ Our institute has many excellent wet labs and daily interlaboratory interactions, such as internal student presentations. There are also joint lab seminars with other labs.
　◇ Nakato Lab also has many exchanges and joint research projects with other research institutions through research-funded projects. We can provide opportunities to make friends and expand exchanges with experts in the fields of life science, medics, informatics, physics, mathematics, etc. If you have ambitions to go abroad, we can give you the opportunity to work on topics related to international collaborative projects.

◆ Supports through the research budget
　◇ All lab members will be provided with a laptop PC and a portable hard drive (and a personal workstation as needed).
　◇ All academic conferences can be attended at the lab's expense (travel to a distant location requires a presentation). Tokyo has a great advantage in that many conferences are held in the neighborhood, so you can easily attend many conferences.
　◇ Paid tools such as ChatGPT, MS Office, Adobe, etc. are also provided.
　◇ Ph.D. students are paid RA salary.

Required skills

◆ Linux (including Mac terminal or WSL), programming skills (Python)
　◇ We welcome students from non-computer fields, but in any case, you will be using these techniques once you join our lab; if you have no experience at all, you will have a hard time learning everything from scratch, so we strongly recommend that you learn some of these techniques on your own beforehand. It is also necessary to see if you can enjoy "life in front of a PC all day" before joining the lab.
　◇ Since we are choosing Python as our first language, Python is the preferred first language to learn, but R is also fine.
　◇ It would be even better if you can use VSCode, Git, Jupyter Notebook, Rstudio, Docker, etc., but this is not a problem even after you enroll.

◆ Experience with NGS analysis
　◇ All members of the Nakato lab will work with NGS analysis. We recommend that you try some NGS analysis beforehand.
　◇ The main NGS assays used in the Nakato lab are ChIP-seq, RNA-seq, Hi-C, and scRNA-seq. Of these, RNA-seq and scRNA-seq would be easy to try first.
　◇ Reference books include Bioinformatics Data Skills for an introduction. Ming Tang's introduction tweet is also useful.
　◇ It is also good to use the web browser-based analysis tools such as Galaxy and iDEP, which are easy to use without installing any tools on your PC.

◆ Experience with exploratory Data Analysis
　◇ The Nakato lab uses exploratory data analysis (EDA). This approach attempts to extract meaningful information from data by identifying features contained in the data with little or no prior knowledge of the data. Since there are several known pitfalls in EDA, learning about them in advance will help you proceed more smoothly with your research.
　◇ Reference books include Statistics Done Wrong as an introduction. Comparing Python Clustering Algorithms is also informative content.

Research in the Nakato Lab (for undergraduates)

◆ The first step: Let's ask questions
Research starts with asking questions. These questions essentially represent your ideas and constitute the core of your research (the experimental results are not the most important aspect). The first step in research is to learn "how to make good questions" through literature reviews and discussions with members.
Deciding the question you want to address is also important for understanding your own academic interests. For this, the literature review is essential. The excitement of discovering a new topic - "I didn't know there was such a world!" - is one of the most exciting parts of research.

◆ The second step: Let's review the literature
Once you have decided on a research topic based on your questions, the next step is to review the existing literature. Since computational genomics is a highly competitive field, you will find many related papers regardless of the topic you choose. It is essential for beginners to read a lot of literature. Keep reading. As you read through the papers, you may find that some problems in your questions have already been solved, and you may develop new related questions. Through such reviews, you will find that your questions become more detailed and refined as you "re-ask" your questions.

◆ The third step: Let's experiment and re-ask the question
Next, you will design and conduct experiments to address your questions. You may not know how to do an experiment or what dataset to use. Ask the faculty or seniors first. Any member will be happy to teach you, because everyone has gone through the same struggles. The experimental results may sometimes be fantastic, but most experiments will fail. However, please remember that we can learn a lot from failure. There are many things that you couldn't understand just by thinking, but they become clear when you actually experiment and observe the results. Asking yourself "Why didn't it work?" and "How should I experiment next?" is the third step in research. Research often spends more time thinking than actually working. Train yourself to think for longer periods of time.

◆ The final step: Write a manuscript and give a presentation
Finally, you will summarize your research in a manuscript and give an oral presentation. You will learn how to clearly summarize the research questions, methods, results, and discussion. Such technical writing and presentation skills are essential ones you can learn in the lab. Practice presenting in lab seminars. Since lab members have different research fields, your presentation needs to be interesting to people with different backgrounds. Such skills will be a powerful weapon for you even after graduation.

Q&A

◆ When are the seminars held? Is the language of presentation English?
We have a lab seminar every Monday from 10:30 to 12:30 for research progress and journal club. English is recommended, but Japanese is also used. Before each seminar, we have a "How was your weekend?" time to share weekend stories. It's a nice time to practice English and learn what other members usually do.
In addition to the seminar, we have team meetings (about once a month) for the 3D genome and single-cell teams, where we discuss methods and new ideas in more detail.

◆ Are there core hours?
The lab seminar is the only core time in our lab. As long as you can lead an independent research life, there is no problem. We have an environment that allows us to connect to the server from home, and we encourage remote work in times of typhoons or heavy snowfall, for example. Nakato sometimes works from home or leaves early because he's raising three kids. ;) However, most of us actually come to the lab every day because of the comfortable research environment in the lab. The general announcement is managed via Slack.

◆ Any lab events?
We have welcome and farewell parties for newcomers and graduates. We also have a lab trip once a year (we recently went to Mt. Takao for our first lab trip: photo). See the Gallery page for more photos.

◆ Can I present in academic conferences?
We provide opportunities for you to present your research at a conference (oral or poster) at least once a year. The main domestic conferences we participate in are the annual meetings of the Molecular Biology Society of Japan (MBSJ), the Japanese Society for Bioinformatics (JSBi), and the Japanese Society for Epigenetics, as well as the International Genome Conference. Students briefly report on their participation in a lab seminar after attending a conference.

◆ Is there financial support?
We can help you apply for JSPS and other scholarships, including assistance in deciding on a research topic and writing an application form. In addition, Foundation Scholarships, and DSTEP are offered by the Department of Frontier Medical Informatics and Life Sciences. Eligibility depends on student status.

◆ What is the career path after graduation?
We will respect and support the student's wishes, whether in academia or business. The skills how to conduct research, analyze data, discuss with others, and give presentations that you learn in our lab will be a powerful weapon no matter what kind of job you find.

Entrance examination

Admission to the Nakato lab is available from the two departments below.

◆ The Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences: The exam consists of an English score sheet (TOEIC, TOEFLiBT, etc.), a written exam, and an oral exam. The application period is mid-June and the examination date is early August. See the official website for the accurate information.

◆ The Medical Science Graduate Program: The medical program is a 6-year program, including the first and second semesters of the doctoral program. Admissions guidance is usually held in May. If you are interested in applying, please contact us.

Interns

We can welcome interns for a limited period of time (unpaid, from two weeks to several months, mainly in summer) to conduct research in the Nakato lab. Interns are assigned an individual topic, one or more journal clubs, and a presentation of their research progress at the end of the program. You will be listed as an author if the research result is published. The program is open to undergraduate and master's students interested in the Nakato lab, as well as Ph.D. students who wish to study computational techniques intensively for a limited period. If interested, contact Ryuichiro Nakato.

Postdoc positions

Postdoc applicants are expected to have a strong background in data analysis/machine learning/statistics AND/OR substantial experience generating and/or analyzing large NGS datasets. If interested, please contact Ryuichiro Nakato with your CV (including contact information for at least one person who can write a letter of recommendation for you), references, and a short research statement describing your background and research interests in the Nakato lab. The expected research topics are listed below:

• An integrated analysis method for multiple NGS assays (multi-omics data). In particular, analysis methods for integrating data consisting of multiple cell types.
• Data imputation using deep learning to remove technical noise and generate virtual data for epigenomic and 3D conformation data.
• Mathematical modeling of time-series ChIP-seq and Hi-C data for RNA polymerase II elongation.