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

Posted on 24/02/2018 by Y. Cun

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，目前只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。下面是clonal 推断一些背景。

继续阅读 →

A new R package for network-based biomarker discovery released

Posted on 24/05/2013 by Y. Cun

A new R package, netClass, has been release. netClass integrate network information, such as protein-protein interaction network or KEGG, to mRNA classification, but also incorporate miRNA to mRNA with mi-mRNA interaction network for biomarker discovery. This methods we called stSVM and already published in PloS ONE (Cun et al 2013). Apart from stSVM, we also implement the flowing methods in netClass:

AEP (average gene expression of pathway), Guo et al., BMC Bioinformatics 2005, 6:58.
PAC (pathway activitive classification), Lee E, et al., PLoS Comput Biol 4(11): e1000217.
hubc (Hub nodes classification), Taylor et al.(2009) Nat. Biotech.: doi: 10.1038/nbt.152
frSVM (filter via top ranked genes), Cun et al. arXiv:1212.3214 ; Winter etal., PLoS Comput Biol 8(5): e1002511.
stSVM (network smoothed t-statistic) , Cun et al., PloS One,.

NetClass can be download from souceforg ( http://sourceforge.net/projects/netclassr/) or , CRAN (http://cran.r-project.org/web/packages/netClass/ ). For more detail of netClass, you can refer these four papers:

Yupeng Cun, Holger Fröhlich (2014) netClass: An R-package for network based, integrative biomarker signature discovery. Bioinformatics; doi: 10.1093/bioinformatics/btu025
Yupeng Cun, Holger Fröhlich (2013) Network and Data Integration for Biomarker Signature Discovery via Network Smoothed T-Statistics, PLoS ONE 8(9): e73074. doi:10.1371/journal.pone.0073074.
Yupeng Cun, Holger Fröhlich (2012) Integrating Prior Knowledge Into Prognostic Biomarker Discovery Based on Network Structure. arXiv:1212.3214 ( Link for R codes and Supplemental meterials)
Yupeng Cun, Holger Fröhlich (2012) Prognostic Gene Signatures for Patient Stratification in Breast Cancer – Accuracy, Stability and Interpretability of Gene Selection Approaches Using Prior Knowledge on Protein-Protein Interactions. BMC Bioinformatics, 13:69

“Translational Bioinformatics” collection for PLOS cBio

Posted on 31/12/2012 by Y. Cun

A review collection in current approach in Translational Bioinformatics.

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COVER

Image Credit: PLOS

‘Translational Bioinformatics’ is a collection of PLOS Computational Biology Education articles which reads as a “book” to be used as a reference or tutorial for a graduate level introductory course on the science of translational bioinformatics.

Translational bioinformatics is an emerging field that addresses the current challenges of integrating increasingly voluminous amounts of molecular and clinical data. Its aim is to provide a better understanding of the molecular basis of disease, which in turn will inform clinical practice and ultimately improve human health.

The concept of a translational bioinformatics introductory book was originally conceived in 2009 by Jake Chen and Maricel Kann. Each chapter was crafted by leading experts who provide a solid introduction to the topics covered, complete with training exercises and answers. The rapid evolution of this field is expected to lead to updates and new chapters that will be incorporated into this collection.

Collection editors: Maricel Kann, Guest Editor, and Fran Lewitter, PLOS Computational Biology Education Editor.

Download the full Translational Bioinformatics collection here: PDF

Collection URL: www.ploscollections.org/translationalbioinformatics

计算基因组学研究组

Category Archives: Medicine Genomics

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

A new R package for network-based biomarker discovery released

“Translational Bioinformatics” collection for PLOS cBio

Introduction to Translational Bioinformatics Collection

Chapter 1: Biomedical Knowledge Integration

Chapter 2: Data-Driven View of Disease Biology

Chapter 3: Small Molecules and Disease

Chapter 4: Protein Interactions and Disease

Chapter 5: Network Biology Approach to Complex Diseases

Chapter 6: Structural Variation and Medical Genomics

Chapter 7: Pharmacogenomics

Chapter 8: Biological Knowledge Assembly and Interpretation

Chapter 9: Analyses Using Disease Ontologies

Chapter 10: Mining Genome-Wide Genetic Markers

Chapter 11: Genome-Wide Association Studies

Chapter 12: Human Microbiome Analysis

Chapter 13: Mining Electronic Health Records in the Genomics Era

Chapter 14: Cancer Genome Analysis