8/1/21: New grant for the lab

In collaboration with two MSU faculty Jiliang Tang and Yuying Xie, Melissa and Shin-Han got an NSF Plant Genome Research Program grant titled Connecting sequences to functions within and between species through computational modeling and experimental studies. To have more complete knowledge of how plants work, we will connect DNA sequences with traits they control using an Artificial Intelligence-based approach, machine learning where computers are used to uncover hidden patterns from a wide range of biological data. In addition, we will apply transfer learning to translate knowledge from one plant species to another so we can later transfer what we know about model plants to other species. The outcome of the project will be computer programs that can predict the connections between DNA sequence and traits and transfer information across species. Using these programs, scientists can better understand how plants work and this knowledge can ultimately be used to create more productive and resilient plants.

7/29/21: Siobhan’s study on modeling plant genetic redundancy is published

Genetic redundancy refers to a situation where an individual with a loss-of-function mutation in one gene (single mutant) does not show an apparent phenotype until one or more paralogs are also knocked out (double/higher-order mutant). Siobhan led a study aiming at understanding the factors contributing to genetic redundancy with machine learning approaches. In addition to Siobhan, Peipei, Serena, Beth, Fanrui, and Melissa have contributed significantly to the study. This work is now published in Molecular Biology & Evolution.

7/20/21: An essay on overcoming challenges to enhancing experimental biology with computational modeling is published

Computational modeling approaches are underutilized in plant biology. Lead by Renee Dale from Danforth Plant Science Center, Shinhan and seven other colleagues published an opinion piece in Frontiers in Plant Science identifying challenges one may face in adopting modeling approach.

7/5/21: The Lab RETURNS!

For the past school year 2020-2021 the lab has been working remotely. Today was the first time all members were able to experience the lab environment together in over a year! This was Kenia and Ally’s first oppertunity to settle into the lab space due to their first year being entirely remote.

6/2/21: Kenia is awarded NRT-IMPACTS and PBHS fellowships

Congratulations to Kenia for receiving an NRT-IMPACTS fellowship, a training program for doctoral students to apply computational approaches to address challenges in plant biology. Kenia also received the PBHS fellowship, which is an NIH funded T32 training program for predoctoral students to pursue research related to plant biotechnology and chemical engineering.

6/1/21: Peipei’s study on metabolic pathway membership prediction is published

Genes in the same metabolic pathway are assumed to be co-expressed. This study, led by Peipei, aiming to use multiple strategies (including unsupervised and supervised machine learning) to evaluated the utility of gene expression data in the prediction of plant metabolic pathway membership. In addition to Peipei, Bethany, Sahra, Melissa, and Cornelius have contributed significantly to the study. This work is now published in New Phytologist.

5/11/21: Serena presents an introduction to machine learning at the GLBRC Annual Science Meeting

Machine learning (ML) is an important tool for making and interpreting predictions in biology. Serena gave a talk introducing the basic concepts of ML and how it can be used to answer biological questions at GLBRC’s Annual Science Meeting. A recording of the presentation can be found here

5/10/21: Serena becomes a PhD candidate

Serena gave a one-hour seminar on her proposed thesis research as part of the second half of her comprehensive exam. Her talk was about automated information extraction and knowledge graphs, and how we can apply them to generate novel hypotheses in the plant sciences. Afterwards she met with her committee for the proposal defense, and passed!

4/17/21: Why are there no symptoms when a plant is infected with a fungal pathogen?

The broad host range of Fusarium virguliforme represents a unique comparative system to identify and define differentially induced responses between an asymptomatic monocot host, maize (Zea mays), and a symptomatic eudicot host, soybean (Glycine max). Led by Amy Baetsen-Young from Brad Day’s lab, Shinhan collaborated on a project examining how asymptomatic and symptomatic hosts respond to F. virguliforme transcriptionally. This work is now published in Plant Cell

2/2/21: Peipei’s study on genome misassembly is published

Tandemly duplicated genomic regions can be misassembled together due to high sequence similarity in short read-based plant assembly, resulting higher read coverages than other regions. Peipei, Fanrui, and Bethany used comparative genomics and machine learning approaches to investigate the factors contributing to the uneven distribution of read depth and the potential misassembly. This work is now published in BMC Genomics.

12/18/20: Lab member Abigail Seeger completed her undergraduate career

Abigail graduated from Lyman Briggs College with degrees in Statistics and Plant Biology in a virtual commencement ceremony. She plans to continue working with the lab in the spring ‘21.

12/7/20: Welcome the newest member of our lab - Kenia Segura Abá

Kenia is a first year graduate student in the Genetics and Genomic Science Program. She rotated over the summer and has made the decision to join our lab today! She graduated from University of Texas-Austin with families in the San Antonio area. Welcome to our lab Kenia!

12/3/20: Serena’s paper is published with gazillion authors

The paper that Serena worked on during the IMPACTS course Foundations in Computational Plant Science, Composite modeling of leaf shape along shoots discriminates Vitis species better than individual leaves, has been published! A collaborative effort amont more than 30 authors, the paper is in the December issue of the journal Applications in Plant Sciences

9/21/20: Serena gave lesson in IMPACTS class

Today during the Foundations of Computational Plant Sciences course, Serena and the team of IMPACTS trainees from the Spring 2020 Forum in Computational Plant Sciences presented their lesson plan to students. This lesson plan will soon be submitted for publication in the journal CourseSource.

9/1/20: Collaborative work on maize ANT1 regulatory network is published!

Arabidopsis AINTEGUMENTA (ANT), an AP2 transcription factor, is known to control plant growth and floral organogenesis. This study is led by Dr. Wen-Hsiung Li from Academia Sinica, Taiwan, which reveals biological roles of ANT1 in several developmental processes beyond its known roles in plant growth and floral organogenesis. It is published in the Sep. 1st issue of Proc. Natl. Acad. Sci., USA.

9/1/20: Christina’s story on predictions of plant stress response through multi-omics data integration is published!

Plants respond to their environment by dynamically modulating gene expression. Our findings demonstrate how in silico approaches can improve our understanding of the complex codes regulating response to combined stress and help us identify prime targets for future characterization. It is published in Nuc. Acid. Res. Genomics and Bioinformatics.

7/30/20: Beth’s story on using Arabidopsis annotation information to improve predictions of tomato metabolic genes is published!

Plant specialized metabolites mediate interactions between plants and the environment and have significant agronomical/pharmaceutical value. This study demonstrates that specialized and general metabolic genes can be better predicted by leveraging cross-species information. Additionally, our findings provide an example for transfer learning in genomics where knowledge can be transferred from an information-rich species to an information-poor one. The findings is now available form In Silico Plant.

7/2/20: Collaborative work on the evolution of a gene cluster in Solanaceae is published!

The hairs on the surface of some species in the nightshade family (Solanaceae) produce acylsugars which deter herbivores and pests from damaging the plants. A tomato gene cluster involved in medium chain acylsugar accumulation was described, and the evolution of this gene cluster was reconstructed. This study is led by Dr. Pengxiang Fan from Prof. Robert L Last’s lab, and Peipei has contributed significantly to the evolutionary history reconstruction. It is published in Elife.

7/1/20: Ally’s offically first day!

Ally has known she was joing the lab and has been occationally joing the lab for lunch meetings up to this point. Now it is the first day as an offical graduate student here at MSU for Ally. Welcoming her to the lab with virtual open arms!

6/25/20: Serena won!!

Serena is the winner of the SciComm Blog Contest! Here is her winning post KNOWLEDGE GRAPHS: A New Way To Reason. Congrats Serena!

6/21/20: Virtual lunch in its 3rd month… Now with Fanrui!!

Fanrui, our deep learning expert, Peipei’s beloved husband, and a great cook, is finally back after getting stuck in China for >6 months due to the COVID19, repeated flight canceldation, and long stay in the secondary screen through the immigration. Welcome back!

5/13/20: Virtual lab lunch at its second month

In our lab lunch today, Ronan is biting her mom’s face!

5/11/20: Christina’s review on interpretable machine learning is published

In collaboration with Jiliang Tang from Comp. Sci. & Engr, MSU, Christina’s review on Opening the Black Box: Interpretable Machine Learning for Geneticists is now published in Trends in Genetics. This work highlights current progress in making machine learning model interpretable and explanable using applications in genetics as examples. And her graphics was picked as the cover as shown on the right!

4/8/20: Virtual lab lunch for almost a month

With the social distancing in place, we continue to meet over Zoom. It seems that folks are doing ok!

3/31/20: Collaborative work with Dr. Yan Bao is published

Dr. Bao’s work on COST1 regulates autophagy to control plant drought tolerance is published where Peipei has contributed significantly using comparative genomics approaches.

3/19/20: Siobhan defended today

Siobahn defended her thesis titled Modeling and prediction of genetic redundancy in Arabidopsis thaliana and Saccharomyces cerevisiae today virtually via Zoom with 89 people in the audience! Both us showed up in the lab (while keeping >6 ft distance) to get the virtual defense setup. Originally due to move to England next week, Siobhan will stay in East Lansing to finish her work. Good job Siobhan! The picture on the right shows Siobhan right after the defense.

3/13/20: Our first virtual lab lunch

With all of us working remotely, we decide to host a 15-min lab lunch to check in with everyone. The photo on the right is all of us experimenting with the background feature in Zoom - the two “ghosts” are Siobhan and Serena!

3/11/20: Social distancing starts

Michigan State has indicated all classes will be online only today. Given the nature of most of our work is computational and considering the risks of COVID-19, from today on all lab personnel will be working remotely, except for those who need to keep our plant alive.

3/5/20: Welcome to the lab Ally

Ally has decided to join the Plant Biology Program and, instead of doing any rotation, is coming to our lab! She is currently finishing up her Bachelor’s degree in Manchester University, a liberal art institution. She is awarded an MSU College of Natural Science Fellowship as well as an Early-Start Fellowship to begin her doctoral training on July 1st! Welcome to our lab Ally!

2/21/20: Welcome to the lab Serena

Serena is a new graduate student in the, hold your breath, Plant Biology Program, the Computational Math., Sci., Math Program, AND the Moleculr Plant Science Program. She is a MSU Plant Science Fellow and an NSF Fellow of the IMPACTS training program. She graduated from the Cornell University in 2019 and has been rotating in two other labs. Here is the funny Slack exchange when I found out she is joining us!

1/1/20: Christina’s study on using genomic and transcriptomic information to predict trait is published

Can transcriptome data be used to predict traits? In the study titled Transcriptome-Based Prediction of Complex Traits in Maize, Christina, in collaboration with Jeremy, a rotating student at the time, and Bob VanBuren tested this and found out that not only we can predict traits with transcriptomes, but they may tell us more than genomic data can. The finding is featured in MSU Today!

10/25/19: Beth defended her PhD

Beth defended her PhD thesis today titled Specilized metabolism and stress response: studies in predicting gene function and regulation. The seminar was held in Plant Biology Lab 247 and was attended by both her colleagues, friends, and families. Beth is moving to University of Wisconsin-Madison to take a postdoctoral position applying molecular evolutionary and computaional approaches to plant biochemistry. We wish her all the best! The picture on the right shows Beth right before her defense.

10/20/19: Ronan is here!

Siobhan gave birth today to her baby boy Ronan! Congrats Siobhan and Geoff!

9/27/19: Christina defeneded her PhD

Christina today defended her PhD thesis titled Interpretable machine laerning in plant genomes: studies in the complex relationship between genotype and phenotype. The seminar was held in Plant Biology Lab 247 and was full! Christina’s parents also attended her seminar. In the talk, Christina laid out the overarching Christina is now moving to Australia working in algorithm development in single cell genomics studies. All the best Christina! The picture on the right showing Chrisitna leaving the lab for good on 10/3/2020…

9/24/19: Sahra and Christina’s work on the regulatory mechanisms of cell-type stress response is published

Our story titled Cis-regulatory code for predicting plant cell-type transcriptional response to high salinity is now published in Plant Physiology with Sahra and Christina as joint first authors. Using the root cell-type transcriptome data, Sahra and Christina identified cis-regulatory sequences likely specify cell-type response to high salinity stress. More importantly, machine learning models were built to ask how well the identified regulatory sequences can predict cell-type response. The findings not only advance our understanding of the regulatory mechanisms of the plant spatial transcriptional response, but also suggest broad applicability of the approach to any species, particularly those with little or no trans regulatory data.

9/18/19: Christina’s work on benchmarking genomic prediction algorithms is published

Chrisitina’s paper on Benchmarking Parametric and Machine Learning Models for Genomic Prediction of Complex Traits is published today in G3. This is a collaborative between our colleague Gustavo de los Compos, Chrisitna’s internship mentors Andrew McCarren and Mark Roantree, and Emily Bolger a highly productive summer research student. Using data of 18 traits across six plant species with different marker densities and training population sizes, Christina spearheaded a comparison of the performance of six linear and six non-linear algorithms. The findings highlight that simpler, linear algorithm can beat out more sophiscated machine learning approaches, and the importance of algorithm selection for the prediction of trait values.

8/20/19: John’s work on poaceae intergenic transcription is published

John’s paper on Evolutionary characteristics of intergenic transcribed regions indicate rare novel genes and widespread noisy transcription in the Poaceae is published in Scientific Report. Christina as well as Rosalie Sowers, a highly talented undergrad from Penn State also contribute to this work. Extensive transcriptional activity occurring in intergenic regions of genomes has raised the question whether intergenic transcription represents the activity of novel genes or noisy expression. To assess this, John et al. evaluated cross-species and post-duplication sequence and expression conservation of intergenic transcribed regions. This study provides a framework to identify novel genes using comparative transcriptomic data to improve genome annotation that is fundamental for connecting genotype to phenotype in crop and model systems.

8/19/19: Our newest addition to the lab, Thilanka

Thilanka is a new graduate student in the Plant Biology Graduate Program. He graduated from the University of Peradeniya in Sri Lanka in 2016 and worked as a teaching assistant in the past two years in Peradeniya. He is energetic, communicative, and excited about learning new topics. He is also easy to get alone with, and has a black belt in Taekwondo!! Welcome to the lab!

7/24/19: Siobhan got the Dissertation Completion Fellowship

Siobhan has been selected to receive a $7,500 College of Natural Science Dissertation Completion Fellowship during fall semester 2019. Congrats Siobhan!

7/13/19: Nick’s work on regulatory assymmetry of plant duplicate genes is published

Nick’s paper on Expression and regulatory asymmetry of retained Arabidopsis thaliana transcription factor genes derived from whole genome duplication is published in BMC Evoltionary Biology. Christina and Eamon Winship, an undergraduate reseracher at the time also contributed. Transcription factors (TFs) play a key role in gene regulation and tend to be retained after duplication. Nick’s work provide answers about about how TF duplicates have diverged in their expression and regulation that contribute to a better understanding of the elevated retention rate among TFs.

2/19/19: Our collaborative work on maize time-series transcriptome is published

Led by Dr. Wen-Hsiung Li’s group in Taiwan, Shinhan has contributed to the study: Comparative transcriptomics method to infer gene coexpression networks and its applications to maize and rice leaf transcriptomes published in PNAS. This study involves a comprehensive time series data and a novel approach to build time-resolved co-expression network to hypothesize regulatory relationships between genes.

Beth’s work on Robust predictions of specialized metabolism genes through machine learning is published in PNAS with Peipei, Johnny, Melissa, and multiple colleagues in Rob Last’s group as co-authors. This work features a machine learning approach to predict general and specialized metabolic gene that are notoriously hard to distinquish. The work is featured in MSU Today and in NSF’s Awesome Discoveries You Probably Didn’t Hear About This Week.

Lots happened before this point.. But we transition to Github after early 2019, so…