diff --git a/contact.html b/contact.html index d2ca2ea..64d3717 100644 --- a/contact.html +++ b/contact.html @@ -3,26 +3,38 @@ - - - - - - - Contact Us - - - - - - - - - - - - - + + + + + + + + + + + + + + + + @@ -31,53 +43,51 @@ - - - - - -
- - -
-
-

Contact us - CSBIO -

-
-
- - - -
- -
- - -
- -
-

CSBIO Lab

-

- Molecular and Cellular Biology Building
Minneapolis, MN 55455
-

-

- P: (612)-625-8089

-

- E: name@example.umn.edu -

-
-
- + + + + + +
+ + +
+
+

Contact Us + CSBIO +

+
+
+ + + +
+ +
+ + +
+ +
+

CSBIO Lab

+

+ Molecular and Cellular Biology Building
Minneapolis, MN 55455
+

+

+ P: (612)-625-8089

+

+ E: name@example.umn.edu +

+
+
+ - - - @@ -87,4 +97,4 @@

CSBIO Lab

- + \ No newline at end of file diff --git a/css/styles.css b/css/styles.css index 77124e6..ad6fad9 100644 --- a/css/styles.css +++ b/css/styles.css @@ -110,7 +110,7 @@ body { .banner-img { height: 40vh; min-height: 300px; - background-image: url("../images/GeneticNetwork.png"); + background-image: url("../images/cellmap.png"); background-size: cover; background-position: center; background-repeat: no-repeat; diff --git a/index.html b/index.html index 9cc7a75..65d2f18 100644 --- a/index.html +++ b/index.html @@ -58,28 +58,27 @@

Recent News

-
New paper
+
Updated website
-

Check out our new paper on between-pathway genetic interactions

- Read here +

Our lab website has a shiny new look!

+ See here
-
Lab members graduate
+
New paper
-

Congratulations to Dr. Nelson, who recently graduated from the BICB program

- Learn more +

Check out our new paper on between-pathway genetic interactions

+ Read here
-
New student in the lab
+
Lab members graduate
-

Kevin Lin has joined the lab as a MD/PhD student in the Medical School and the BICB - program

+

Congratulations to Dr. Nelson, who recently graduated from the BICB program

Learn more
@@ -114,15 +113,12 @@

About Us

- +
- - - \ No newline at end of file diff --git a/positions.html b/positions.html index 511fd25..02f795b 100644 --- a/positions.html +++ b/positions.html @@ -3,101 +3,103 @@ - - - - - - - Positions - - - - - - - - - - - - - + + + + + + + Positions + + + + + + + + + + + + + + - - - - - -
- - -
-
-

Current openings - -

-
- - - -
-
-

- Graduate students -

-

- We are looking for graduate students interested in computational biology. No formal biology background is necessary, but you must be interested in learning. -

-
-
-

- Post-Doctoral Scientist -

-

- Join our team to map the yeast genetic interaction network! A computational biology post-doc position is available in the Computational Biology and Functional Genomics group in the Computer Science and Engineering Department at the University of Minnesota. -

-

- The successful candidate will have the opportunity to work on an exciting project mapping the global yeast genetic interaction network in collaboration with experimental labs. Our focus will be developing algorithms for inferring biological networks by integrating these large-scale genetic interaction data with other genome-scale data. -

-

- The ideal candidate would have a computational background with a Ph.D. in Computer Science, Statistics, Physics, Math or Molecular Biology. Substantial experience programming in R, Matlab, Perl, Java or C++ is required. A basic understanding of biology is preferred, but no formal background is necessary as long as he/she has a genuine interest and aptitude for learning about functional genomics. This project is highly collaborative, and thus, strong verbal and written communication skills are essential. Previous experience in data mining or machine learning is a plus. -

-

- Interested applicants should send a CV with the names of at least three references to Chad Myers (cmyers at cs.umn.edu). Please indicate when you are able to start (as soon as possible is preferred). -

- -
- - -
- - -
-
-
-

Copyright © CSBIOLAB

-

Website made by Jean-Michel Michno "Meesh" (mich0391@umn.edu)

-
-
-
+ + + + + +
+ +
+
+

Current Openings

+
+
+ + + +
+
+

+ Graduate students +

+

+ We are looking for graduate students interested in computational biology. No formal biology background is + necessary, but you must be interested in learning. +

+
+
+

+ Post-Doctoral Scientist +

+

+ Join our team to map the yeast genetic interaction network! A computational biology post-doc position is + available in the Computational Biology and Functional Genomics group in the Computer Science and Engineering + Department at the University of Minnesota. +

+

+ The successful candidate will have the opportunity to work on an exciting project mapping the global yeast + genetic interaction network in collaboration with experimental labs. Our focus will be developing algorithms + for inferring biological networks by integrating these large-scale genetic interaction data with other + genome-scale data. +

+

+ The ideal candidate would have a computational background with a Ph.D. in Computer Science, Statistics, + Physics, Math or Molecular Biology. Substantial experience programming in R, Matlab, Perl, Java or C++ is + required. A basic understanding of biology is preferred, but no formal background is necessary as long as + he/she has a genuine interest and aptitude for learning about functional genomics. This project is highly + collaborative, and thus, strong verbal and written communication skills are essential. Previous experience in + data mining or machine learning is a plus. +

+

+ Interested applicants should send a CV with the names of at least three references to Chad Myers (cmyers at + cs.umn.edu). Please indicate when you are able to start (as soon as possible is preferred). +

+ +
- - - + + - - + +
- + \ No newline at end of file diff --git a/publication.html b/publication.html index 0f1b668..c491d9b 100644 --- a/publication.html +++ b/publication.html @@ -82,6 +82,77 @@

CSBIO Research

Type + + + + Fang G, Wang W, Paunic V, Heydari H, Costanzo M, Lie X, Liu X, VanderSluis B, + Oately B, Steinbach M, Van Ness B, Schadt EE, Pankratz D, Boone C, Kumar V^, + Myers CL^ +
+ Nature Comm + + 9/2019 + Article + + + + Zhou F, Li SC, Zhu Y, Guo W, Shao L, Nelson J, Simpkins SW, Yang D, Liu Q, + Yashiroda Y, Xu J, Fan Y, Yue J, Yoshida M, Xia T, Myers CL^, Boone C^, + Wang M^ +
+ Acta Pharmacol Sin + + 5/2019 + Article + + + + Simpkins SW, Deshpande R, Nelson J, Li SC, Piotrowski JS, Ward HN, Yashiroda Y, + Osada H, Yoshida M, Boone C, Myers CL +
+ Nature Protoc + + 2/2019 + Article + + + + Schaefer RJ, Michno J, Jeffers J, Hoekenga O, Dilkes B, Baxter I, Myers CL +
+ + Plant Cell + + 12/2018 + Article + + + + Author/Title +
+ * denotes equal author contribution +
+ ^ denotes equal corresponding author contribution + + Journal + + Date + + Type + + diff --git a/research.html b/research.html index 52126fd..685eddb 100644 --- a/research.html +++ b/research.html @@ -155,13 +155,15 @@

Genetic interactions in complex human diseasess

Co-expression networks in plants

-

Our lab aims to understand the biological context of quantitative traits in plant species to better aide in - identifying high-value genes important to plant breeding. Using technologies such as RNA-Seq and microarrays, - it is possible to measure quantitative levels of expression throughout the genome across multiple different - conditions, timepoints, tissues or samples. We build co-expression networks to infer a gene’s function by - “guilt by association”, leveraging the principle that genes that are highly associated with each other are - more likely to share a common function. Co-expression networks are built with the purpose of discovering and - characterizing highly associated groups of genes or modules to better understand various phenotypic functions. +

In collaboration with the Katagiri lab here at UMN, we are developing novel computational approaches to + interpret dynamic transcriptome data in Arabidopsis generated during the plant’s immune response. Although + clustering methods can help group genes based on the profile similarity, they fail to provide a mechanistic + understanding of the gene clusters thus undermine the information within time-series data. In our research, we + use multi-compartment models to generate dynamic profiles with fast and slow response patterns. Based on how + well the time-series transcript data of a gene are fit, we can assign the gene to the compartment with the + optimal response pattern. We find gene clusters enriched for transcription factor motifs that regulate early + and late immune response. Our approach provides a mechanistic way to classify immune response genes and allows + us to better interpret Arabidopsis transcriptome dynamics.