Category Archives: Computational Genomics

Inferring tumour evolution 2 – Comparison to classical phylogenetics

Scientific B-sides

Quick recap: Last time we talked about tumor evolution and I presented a toy example to introduce key concepts. I also introduced the intra-tumor phylogeny problem: Given a sample of the genomes of clones in a tumour, reconstruct its `life history’. This problem consists of two sub-problems: (1)identification of clones, and (2) inferring evolutionary relationships between clones.

This problem falls into the general area of reconstructing phylogenetic trees — so how does inferring clonal trees compare to classical phylogenetic methods?

View original post 777 more words

Inferring tumour evolution 1 – The intra-tumour phylogeny problem

Scientific B-sides

“Cancer evolves dynamically as clonal expansions supersede one another driven by shifting selective pressures, mutational processes, and disrupted cancer genes. These processes mark the genome, such that a cancer’s life history is encrypted in the somatic mutations present,”

write Nik-Zainal et al in the abstract of their 2012 Cell paper `The life history of 21 breast cancers‘. The key figure of their paper shows a phylogenetic tree of tumor development in a patient. The paper contains lots of computational work on analyzing and interpreting mutations based on deep-sequencing data, but –a big surprised but— the very last step of putting together the tree was done manually. Half the paper is describing the reasoning that Peter Campbell and his group used to condense all the evidence they had gathered from genomic data into the tree – but there is no algorithm.

View original post 951 more words

A new R package for network-based biomarker discovery released

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: 

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

NetClass can be download from souceforg ( or , CRAN ( ). For more detail of netClass, you can refer these four papers:

“Translational Bioinformatics” collection for PLOS cBio

A review collection in current approach in Translational Bioinformatics.


Image Credit: PLOS
Issue Image

‘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:

PLOS Computational Biology: published 27 Dec 2012 | info:doi/10.1371/journal.pcbi.1002796

PLOS Computational Biology: published 27 Dec 2012 | info:doi/10.1371/journal.pcbi.1002826

Chapter 2: Data-Driven View of Disease Biology

Casey S. Greene, Olga G. Troyanskaya

PLOS Computational Biology: published 27 Dec 2012 | info:doi/10.1371/journal.pcbi.1002816

PLOS Computational Biology: published 27 Dec 2012 | info:doi/10.1371/journal.pcbi.1002805

Chapter 4: Protein Interactions and Disease

Mileidy W. Gonzalez, Maricel G. Kann

PLOS Computational Biology: published 27 Dec 2012 | info:doi/10.1371/journal.pcbi.1002819

Chapter 5: Network Biology Approach to Complex Diseases

Dong-Yeon Cho, Yoo-Ah Kim, Teresa M. Przytycka

PLOS Computational Biology: published 27 Dec 2012 | info:doi/10.1371/journal.pcbi.1002820

PLOS Computational Biology: published 27 Dec 2012 | info:doi/10.1371/journal.pcbi.1002821

Chapter 7: Pharmacogenomics

Konrad J. Karczewski, Roxana Daneshjou, Russ B. Altman

PLOS Computational Biology: published 27 Dec 2012 | info:doi/10.1371/journal.pcbi.1002817

PLOS Computational Biology: published 27 Dec 2012 | info:doi/10.1371/journal.pcbi.1002858

Chapter 9: Analyses Using Disease Ontologies

Nigam H. Shah, Tyler Cole, Mark A. Musen

PLOS Computational Biology: published 27 Dec 2012 | info:doi/10.1371/journal.pcbi.1002827

Chapter 10: Mining Genome-Wide Genetic Markers

Xiang Zhang, Shunping Huang, Zhaojun Zhang, Wei Wang

PLOS Computational Biology: published 27 Dec 2012 | info:doi/10.1371/journal.pcbi.1002828

Chapter 11: Genome-Wide Association Studies

William S. Bush, Jason H. Moore

PLOS Computational Biology: published 27 Dec 2012 | info:doi/10.1371/journal.pcbi.1002822

Chapter 12: Human Microbiome Analysis

Xochitl C. Morgan, Curtis Huttenhower

PLOS Computational Biology: published 27 Dec 2012 | info:doi/10.1371/journal.pcbi.1002808

PLOS Computational Biology: published 27 Dec 2012 | info:doi/10.1371/journal.pcbi.1002823

Chapter 14: Cancer Genome Analysis

Miguel Vazquez, Victor de la Torre, Alfonso Valencia

PLOS Computational Biology: published 27 Dec 2012 | info:doi/10.1371/journal.pcbi.1002824


Use prior information to prognostic biomaker or not?

In our recent publication in BMC bioinformatics, we acompared a great deal of feature selection methods to finding prognostic biomakers in 6 breast cancer gene expresion data. No methods show significant performacne in prediction accuracy, feature selection stability and  biogical interprety, which against previeous reseach results: current network-based appraoch did not show much benift in our analysis. Meanwhile, A group from NKI also show the simliar results in PloS One. The R codes for these algorithms in our paper is availiable as request.

Prediction performance in terms of area under ROC curve (AUC)

Continue reading

Scientific B-sides

Welcome back! The last post discussed rules 1-3: the importance to do a postdoc, a concise CV and a unique research statement. Like the last post this one is inspired by a Career Development Workshop at ISMB 2012 that I contributed to (download the slides).

There is still one thing missing from a standard application pack:

4. Pretend you care! The teaching statement

Together with CV and research statement some places ask you to submit a teaching statement. So write one. But don’t be fooled, it’s pretty low on the priority list (for the hiring committee, even if maybe not for you). Academic employers want three things from you: money, papers, and … long time nothing … teaching. I’m not saying they won’t ask you to teach for many hours a week, but when it comes to you being evaluated its money and papers (in that order) which…

View original post 951 more words

Scientific B-sides

Starting your own group is one of the most important steps in your scientific career — and one of the hardest.

Being invited to a Career Development Workshop at ISMB 2012 made me write down some of the advice that I had got when I was on the jobmarket a few years ago (and even put some of it on slides).

In a diverse and interdisciplinary field like computational biology it is very quite hard to come up with general rules that fit everyone. This is why I went down the self-indulgent route and revisited the CV and research statement I had prepared 4 years ago. (You’ll find a copy in the slides.) Some things are Ok, some things I would improve now — you will see, I’ll comment on this later. Let’s start with the basics:

View original post 1,266 more words

comutational courese in Plos Computational Biology

Short introduction paper in different ares in computational biology.

Fran Lewitter, Welcome to PLoS Computational Biology “Education”

Kenzie D MacIsaac, Ernest Fraenkel, Practical Strategies for Discovering Regulatory DNA Sequence Motifs, April 2006

Duncan Brown, Kimmen Sjölander, Functional Classification Using Phylogenomic Inference,June 2006

Philip E Bourne, Johanna McEntyre, Biocurators: Contributors to the World of Science,October 2006