This is a very short post to share the poster that I am presenting this week at the Biology of Genomes meeting in CSHL.
Alba is a PhD!
Last Monday, April 29th, Alba defended her PhD thesis at the PRBB. It was the highest point of a story that started some five years ago, when she joined our group to do her Master’s thesis. From there, she moved on to do her PhD, tackling a very ambitious project trying to unravel the possible roles of the Polycomb complex in tumorigenesis. The title of her thesis, “Integrative study of the regulatory and epigenomic programs involved in cancer development” is only the tip of the iceberg of what her PhD project has covered. Moving on from her Biology degree, she has taken up all the bioinformatics and computational skills required for the endeavor. Although she always worked intensively, the last six months, after she returned from a four-mont stay in Chicago, at Elizaveta Benevolentskaya‘s group, were particularly intense. During this time she finished writing one paper that she’d been working on in Chicago, and wrote three others, one of which has been already submitted for publication.
For Alba, it’s been four years of struggle and learning, but for the group it’s been four very enriching years. Not only has she been an accomplished PhD student (and now graduate), but also an amazing colleague and friend to all of us. A new stage of her professional life starts now, and we wish her the best in this new journey.
Congratulations, Alba!
SVGMap 1.5.0 released
After some kind feedback from the Bergman Lab, we have released a new version of SVGmap, 1.5.0. In the Bergman Lab they are using SVGmap for displaying tissue-specific data of gene expression and insertion preferention of transposable elements in the drosophila and have written up a useful guide about how to install and use SVGmap. As you also can read in the same blog post, they have implemented a ‘stripped-down R implementation of the SVGmap approach, called FlyFig’.
A capture of the SVGmap server interface: Cell compartments enriched for downregulation in kidney (data from IntOGen)
New OncodriveFM release 0.3.2
Some time ago Abel wrote about how to identify cancer drivers from tumor somatic mutations, and presented OncodriveFM. Nuria also posted a nice poster explaining it together with TransFIC.
Initially, OncodriveFM was written by Abel as a Perl script and distributed through our web. Later on I had to implement the analysis workflows of IntOGen SM, which required to use it intensively. However,
we realized that the code of OncodriveFM could be significantly improved in terms of performance, as there is a part of the analysis that may take quite a lot of time depending on the input data. This is why I decided to implement it again, starting from a prototype written by Abel, using Python. Read the rest of this entry »
Oncogenomics workshop: my slides on the analysis of Somatic Mutations in IntOGen
Last week I attended the Oncogenomics Workshop in Hinxton. This was a really interesting Workshop organized by the Industry program of the EBI. It addressed the issues of which data are becoming available and how to access them, how to best analyze the oncogenomics data and interpret them, and which are the challenges ahead in terms of translating these data and knowledge into therapeutic opportunities. I was invited to present IntOGen and I thought it would be of interest to some followers of our blog if I shared the slides of the talk.
New IntOGen Somatic Mutations Analysis version available
We are proud to announce the brand new version of the IntOGen Somatic Mutations Analysis (IntOGen SM) pipeline. We call it version 2.0.0 as it has been completely rewritten from scratch with a strong focus on quality, efficiency and scalability.
The IntOGen SM pipeline addresses the challenge of identifying which somatic mutations are important for the development of tumors. The input for the analysis is a list of somatic mutations detected in a cohort of tumors. Read the rest of this entry »
Gitools in GenomeSpace and a new user mail list
As some already may know, Gitools has been integrated with GenomeSpace. Together with the GenomeSpace team we have set up a tutorial that demonstrates the power behind the GenomeSpace approach. The tutorial may be found here and helps the user to extract data from ArrayExpress, normalize it on GenePattern and then load and browse it in Gitools. All these steps are being done without the need to manually download any tool nor data set. You can sign up for GenomeSpace and try it right away.
Oncodrive-CIS: a method to reveal likely driver genes based on the impact of their copy number changes on expression.
One of the current challenges of oncogenomics is to elucidate which of the aberrations observed in the cancer genome is involved in the disease and which of them are just bystanders occurring stochastically due to the cell genomic instability, i.e. to distinguish driver events from passengers. This is indeed demanding when analyzing copy number alterations (CNAs), in which very large regions of the DNA (and thus a large number of genes) may be affected. The current approach for dealing with this issue is to assess the recurrence of the alteration across multiple samples, since those alterations occurring more than expected by chance should point out significant events in terms of the disease. However, this exhibits some drawbacks, as the underestimation of low-recurrent drivers, the difficulty of assessing the background model and, finally, that it does not take into account how the alteration may impact the normal behavior of the gene.
How to visualize multidimensional cancer genomics data?
A challenge to all cancer genomic studies is to visually explore the generated complex data in a meaningful way to extract relevant knowledge. We have written a review on that topic which has been published last week in Genome Medicine.
Gitools 1.8 for improved data managament and transfer
We have started to distribute Gitools in the version 1.8.x. This latest version include important new features in terms of communication of Gitools with other tools (i.e. IGV, GenomeSpace, GSEA, excel). Now the user will get more flexibility for loading data in Gitools. These are the most important improvements in that aspect:
- Send matrices from IGV (Integrative Genomics Viewer) directly to Gitools
- Load matrices from GenomeSpace
- .GCT file support for matrix files (format used by GSEA).
- Import matrices from Excel data sheets
Go download latest version