我在中科院昆明植物开始新的研究

在经历了短暂的公司研发后, 从2018年7月起, 我又回到学术圈在中国科学院昆明植物所做PI,领导一个生物信息学研究的实验室,从事二代、三代基因组数据的从头组装、遗传变异分析和相关的比较基因组学研究。 研究方向主要侧重于应用统计/概率论理论,机器学习(统计学习)算法到最新的计算生物学问题中,特别关注的数据是植物基因组。

欢迎有兴趣的同仁加盟我们实验室。实验室现有助理研究员、博士后、研究生和客座研究生等职位开放,欢迎有计算机、数学、物理或具有生物信息应用技术背景的人应聘。

IMG_20170218_161648

 

Sclust paper published on NP

After years fighting, our Sclsut paper published on Nature Protocols finally. Enjoy!

Yupeng Cun, Tsun-Po Yang, Viktor Achter*, Ulrich Lang, Martin Peifer, Copy number analysis and inference of subclonal populations in cancer genomes using Sclust. Nature Protocols, 2018,DOI: 10.1038/nprot.2018.033


Frequent Q&A on Sclust software package uses:

a

A new fast method for copy number calling, tissue purity estimating and subclone inferring in cancer genome

Our new methods final launched on Nature Protocols, where we developed a series of methods and related C++/R combined software package,  Sclust(around 1.5Gb,大文件谨慎载). In Sclust, you can do copy number calling, cancer tissue purity estimating and clone and subclone structure inferring from normal-tumor paired whole genome/exon sequencing data.

先总结一下,我们方法的有如下点:

1. 可以准确地做copy number calling, tumor purity estimating,subclonal inferring;

2. subclonal inferring的速度超级快。4000~6000 个SNVs 的 clonal inferring 过程在个人电脑上只需3到5秒。

3. sclust 给出了每个集群的倍数树变异,目前还有少数个软件提供这个功能。

欢迎使用软件,欢迎咨询,欢迎交流。

联系邮件:yp.cun@outlook.com。 下面clonal 推断一些背景。

Continue reading

【c】Frontiers in Single Cell Genomics, Suzhou

Frontiers in Single Cell Genomics

http://www.csh-asia.org/2016meetings/cell.html

 

We are pleased to announce the Cold Spring Harbor Asia conference on Frontiers in Single Cell Genomics which will be held in Suzhou, China, located approximately 60 miles west of Shanghai. The conference will begin at 7:00pm on the evening of Monday November 7, and will conclude after lunch on November 11, 2016.

Continue reading

A brief introduction to “apply” in R

a good, practical guidline for “apply” in R.

What You're Doing Is Rather Desperate

At any R Q&A site, you’ll frequently see an exchange like this one:

Q: How can I use a loop to […insert task here…] ?
A: Don’t. Use one of the apply functions.

So, what are these wondrous apply functions and how do they work? I think the best way to figure out anything in R is to learn by experimentation, using embarrassingly trivial data and functions.

If you fire up your R console, type “??apply” and scroll down to the functions in the base package, you’ll see something like this:

Let’s examine each of those.

1. apply
Description: “Returns a vector or array or list of values obtained by applying a function to margins of an array or matrix.”

OK – we know about vectors/arrays and functions, but what are these “margins”? Simple: either the rows (1), the columns (2) or both (1:2). By “both”, we mean “apply the…

View original post 1,003 more words