The video was produced as part of InformAnimation project, a Summer School organized within the Erasmus Intensive Programme, by the Alghero School of Architecture of the University of Sassari in collaboration with a consortium of European academic partners. The goal of InformAnimation is to explore and teach the use of animation to communicate complex information.

I was offered to propose a brief on one of our projects as a challenge for the students to create an animation out of it. So we quickly decided that it would be nice to have a video explaining IntOGen project. I prepared a series of materials and information for the students and I had a couple of skype conversations with them. They quickly grasped the idea and came up with this nice video.

I want to thank and congratulate the organizers and the students of InformAnimation for this nice work.

The analysis has been done using Anduril, a workflow framework developed at Hautaniemi lab. The overall goal in the analyses is to identify common genomic regions or transcripts that have survival effect in individual cancers. To this end we have identified in all these four cancers differentially expressed genes, genomic regions with copy number aberrations or differential methylation, single nucleotide polymorphisms (SNPs), genes and genomic regions with significant survival association with Kaplan-Meier method, and genes that have simultaneous copy number alteration and significant expression changes.

Since we created IntOGen we had the motivation to let the user do simple analyses over the data and browse their results next to our datasets. For this reason we are developing Onexus, analysis management system that integrates the IntOGen browser with Anduril. This allows to create websites where the final user can define new analysis, run them on the fly and browse the results next to precalculated datasets. Onexus is under development, we are planning a first public release by the first 2012 quarter, if you want to learn more about it visit the Onexus web site.

Today Kristian Ovaska, from Hautaniemi lab, is presenting this work at the TCGA symposium.

This time, we shall have three full days to cover the multiple features of IntOGen and Gitools. We will start with background lectures on integrative onco- and other genomics as well as on the statistical meaning of each of the analysis. Then we will cover, as a first in our course, some very practical aspects which shall encourage participants to use the tools on their own data, such as data formats and how to prepare data sets for the analyses in Gitools. There will be enough time to work on a number of hands-on exercises for analyses such as functional enrichment, combination of experiments, prioritization of disease-related genes, correlation, overlapping and clustering of experiments. For data from next generation sequencing, we will present Condel, a tool that classifies the point mutations you might find in your sequences. Visit the course page to see the detailed agenda and to register.

We are always curious to find out who are the people attending our courses, so we will ask participants to present themselves and their work and to let us know which is their favourite analysis during the course, and we are more than happy to receive follow-up questions by email weeks and weeks after the course from all over the world.

The DATABASE Oxford Journal has published a new volume in September dedicated to Biomart related databases and tools.

We have had a Biomart portal for IntOGen data since 2010 and we were invited to write a manuscript describing it. We immediately thought it could be a good opportunity to describe in more detail the information included in IntOGen and specially how to query its results using Biomart. So, Gunes and me joined efforts to prepare the manuscript and tune the IntOGen Biomart portal with the new IntOGen release. Now we are happy to announce that the manuscript is published and freely available at:

http://database.oxfordjournals.org/content/2011/bar039.full

It has been a good chance to complement the information previously published in the correspondence letter at Nature Methods in 2010. In this manuscript we explain in more detail how data is organized and analysed in IntOGen, which results are obtained and how can be queried and downloaded from the Biomart portal.

We wish you find it interesting

We have worked hard during the last months to release the new version of IntOGen, version 03, which we have published today. We are very proud of this new release, as it includes some major improvements. Although the look and functionalities of the web service does not change, the data content and, therefore, the results have changed substantially. The changes that have been made can be summarized as follows:

The Pipeline: Christian has re-written the pipeline from scratch. The following are some of the new features of the current version:

• The way studies are organized have changed (see below) and the pipeline is able to select and process them automatically based on their annotations.

• Christian has developed a system to manage workflows called Wok. It is written in python and it is quite flexible, allowing fast development and supporting many different execution platforms. The state of execution can be tracked through a web interface and also the logs generated by the jobs. As a result the IntOGen pipeline is much easier to run and validate now.

• In the new pipeline we have automated some processes that were performed manually before, such as the preprocessing of expression and Progenetix CNA data, the creation of the different databases involved in IntOGen and the generation of reports for quality control of the results.

Oncogenomic Data: We changed the oncogenomic data substantially. Alba, Michi, Christian and myself have work hard in organizing the data and annotating it thoroughly. Overall, the new data is of higher quality and better annotated. Here are the changes:

• We created an ontology to annotate new data entities, i.e. sample, assays.

• All the oncogenomic data was re-annotated using the ontology.

• Our main source of CNA data is Progenetix. The current version of Progenetix excludes poor resolution data and contains only CGH and aCGH data. In IntOGen v03, we have decided to adopt a similar strategy, and thus the number of CNA studies has been reduced, as poor resolution ones have been excluded (See Table 1).

• We added 50 new transcriptomic studies while we excluded some studies of breast cancer since we detected assays common to multiple studies. Repetitive data biases the results. Some studies were excluded due to a lack of minimum clinical information (See Table 1).

• In the case of some studies that existed in the previous version, some assays have been omitted due to lack of proper annotation.
• We have updated COSMIC data with the help of Xavi. IntOGen v03 now includes data from COSMIC v53.

• IntOGen v03 uses the version 60 of Ensembl. This affects the mapping of the probes on a platform to Ensembl genes, which results in changes in the results.

As a result of all these changes in data content, some results in IntOGen v03 are different than those in v02. We recommend all IntOGen users to move to v03 of the data as it is of higher quality.

Table 1. Comparison of the data content of version 2 and 3 of IntOGen.

I also would like to remind you that we recently included ICGC somatic mutations in IntOGen. Look at this post for further information about browsing ICGC data in IntOGen.

For that we have prepared an automatic pipeline to import somatic variants from ICGC projects directly from the ICGC Data Coordination Center. We use the genomic coordinates of the variants and their alternative alleles to assess their likely functional impact on genes. We also determine the recurrence of each variant within each project, for each tumor type (topography) and across all ICGC projects.

Finally, we have prepared a modified version of IntOGen browser to effectively navigate ICGC data, which provides an alternative way to navigate and visualize ICGC results. Browsing ICGC data in IntOGen provides some distinctive features such as:

• Functional assessment of mutations
• Browsing ICGC data together with IntOGen data
• Visualizing ICGC mutations in a Genome Browser view

Related to the functional assessment of mutations, among other things, we compute the condel score of all non-synonymous variants detected by ICGC projects.

For more information about Condel look at this post and/or the Condel web page.

The IntOGen browser for ICGC data is still in a beta stage, as there are somethings that we plan to improve, but we are delighted to share it with you at this stage and we are open to suggestions by users.

To start navigating ICGC data in IntOGen go directly to http://icgc.intogen.org or click the ICGC link from the menu at IntOGen web page

We hope you like it.

Attendees will learn how to handle and analyze genomic data to answer specific questions using tools developed in the Biomedical Genomics lab: IntOGen, Gitools and Condel. We will deal with questions such as:

• Which mutations are likely to affect the function of the protein and which are probably neutral?
• Which of the genes affected by those mutations are already known to be involved in cancer or other diseases?
• Which pathways or biological processes are affected by the transcriptomic alterations detected in my experiment?

The course will be held in the UPF (Universitat Pompeu Fabra) facilities in the Life and Health Sciences building, next to the PRBB in Barcelona, Spain. For more information on how to register please visit its webpage: Course on Bioinformatics for Integrative Genomics.

We encourage you to register asap if you are considering to come, since the number of places is limited. Please feel free to spread the word amongst your colleagues!

So then… what do you do when you have a list of GO terms? Already if it is only 100 GO terms, it is quite hard to get an idea which are the affected compartments. To understand better you have to identify the more general terms that are affected. Here I explain quickly how I solved this problem and share it with you.

• Ingredient 1: With the help of the BioStar community I found an easy way to query for the descendants for a certain GO term. So I can find out the hierarchical relationship between my GO terms.
• Ingredient 2: I needed to create a hierarchical tree for my GO Terms. For this tree representation I adapted the idea Node class and tree function from this blog entry.

So I mixed the two ingredients in a python script which re-creates the ontology hierarchy for my subset of GO Terms and prints it. This way I can see in what trees your terms collapse in and have a structured overview.

Of course this is more helpful if your list is relatively small. Also after I removed all the part-terms (the ones that follow the pattern “Some-compartment part”), the resulting tree structure is less repetitive.

The output of the python script looks like this (this is an excerpt):

| integral to membrane
    | integral to plasma membrane
        | integrin complex
        | voltage-gated potassium channel complex
| cell projection
    | ruffle
    | microvillus
    | neuron projection
        | axon
        | dendrite
            | dendritic spine
        | growth cone
| membrane fraction
    | synaptosome
    | vesicular fraction
        | microsome
| proteasome complex
    | proteasome core complex
| cell surface
    | external side of plasma membrane
| chromosome
    | condensed chromosome
        | condensed chromosome, centromeric region
            | condensed chromosome kinetochore

And in the end: Here is the python script if you are interested:

# settings
###########################################################3
filename = "intogen-combinations.go-cc.significant.unique.nopart.tsv"
go_term_col = 2     # the column with the go term names
header_length = 1   # nb of rows header is occupying in the file

# database access
###########################################################3
import MySQLdb
db = MySQLdb.connect(host="mysql.ebi.ac.uk",
                     user="go_select",
                     db="go_latest",
                     passwd="amigo",
                     port=4085)
cur = db.cursor()

# Create class and functions needed for tree represantation
###########################################################3

# constants
ROOT_NODE = "the_root_node"
ORPHAN_NODE = "an_orphan_node"

class Node:
    def __init__(self, n, s):
        self.id = n
        self.title = s
        self.children = []

def add_node(tree, nodeId, title, parentId=ROOT_NODE):
   newNode = False
   if not nodeId in treeMap:
       newNode = True
       tree[nodeId] = Node(nodeId, title)
   else:
       tree[nodeId].id = nodeId
       tree[nodeId].title = title
       if tree[nodeId] in tree[ROOT_NODE].children:
           tree[ROOT_NODE].children.remove(tree[nodeId])

   if not parentId in treeMap and parentId != ROOT_NODE:
       tree[parentId] = Node(parentId, parentId)
       tree[ROOT_NODE].children.append(treeMap[parentId])

   if parentId != ROOT_NODE or newNode:
       tree[parentId].children.append(treeMap[nodeId])

def print_map(node, lvl=0):
    for n in sorted(node.children, cmp=lambda x,y: cmp(x.title, y.title)):
        print '\t' * lvl + str("|") + " " + n.title
        if len(n.children) > 0:
            print_map(n, lvl+1)

treeMap = {}
Root = Node(ROOT_NODE, ROOT_NODE)
treeMap[Root.id] = Root

# read the file with your go term names
###########################################################3
go_terms = []
f = open(filename, "r")

for line in f:
    cols = line.rstrip().split("\t")
    go_terms.append(cols[go_term_col-1])
for i in xrange(0,header_length):
    del(go_terms[0])

# query descendants and reconstruct tree
###########################################################3
placeholder = '%s'
placeholders = ', '.join(placeholder for unused in go_terms)
for t in go_terms:
    query = "SELECT DISTINCT descendant.acc, descendant.name \
    FROM \
     term \
     INNER JOIN graph_path ON (term.id=graph_path.term1_id) \
     INNER JOIN term AS descendant ON (descendant.id=graph_path.term2_id) \
    WHERE term.name='%s' \
     AND distance < 2 AND distance > 0 \
     AND descendant.name IN (%s);" % (t,placeholders)
    cur.execute(query,go_terms)
    output = cur.fetchall()
    if len(output) == 0:
        add_node(treeMap,t,t)
        #if t == "mitochondrial membrane": print t,"added","solo"
    for o in output:
        add_node(treeMap, o[1],o[1],t)
        #if o[1] == "mitochondrial membrane": print o[1],"added","aschildof",t

cur.close()
db.close()

print_map(Root)

For this analysis I used the same dataset that was introduced in the previous post, namely a microarray dataset profiling 156 lung tumors and adjacent normal lung tissue samples by Hou et al. 2010. However, in this case I preprocessed the it to have a matrix of log2 ratios between tumor and normal samples.

As my objective was to identify genes that are significantly up-regulated in this experiment, I used Oncodrive. This is a simple statistical method that, given a matrix of alterations for genes and samples, assesses for each gene if it is altered in more samples than expected by chance. This is the method that we use in IntOGen to identify significantly altered genes. The result is a p-value per gene that indicates if the gene is significantly altered (up-regulated in this case).

I applied this method to the matrix of log2 ratios of Hou et al. 2010, considering up-regulation any fold change higher than 1.297. This is the optimal cutoff for this dataset obtained as explained in the supplementary material of IntOGen paper. As a results I obtained a Gitools matrix with a single column with a p-value per gene. I found, for example, that CSK2 and SOX4 genes are signifincantly up-regulated, and I know that those two genes are also up-regulated in other lung cancer experiments in IntOGen.

You can try to reproduce this same analysis following this step by step tutorial. Enjoy it!

In a next post I plan to explain how to compare if the genes identified as significantly up-regulated in this dataset are also up-regulated in other experiments from IntOGen.

IntOGen search for CDKN2A gene sorted by genes more significantly upregulated

2. Are you interested in a particular type of cancer? Would you like to know which genes are the more significantly altered in samples of that tumour type? Just search for that tumour type in IntOGen and browse the results. Follow this tutorial for details on how to do this.

IntOGen search for Lung cancer sorted by genes with higher number of mutations in this tumour type

3. Would you like to know which biological processes or pathways are altered in that tumour type of your interest? You can also answer this question by browsing biological modules in IntOGen. You will find how to do that in the same tutorial as the previous question.

IntOGen search of pathways affected in colon cancer sorted by those in which genes tend to be upregulated

IntOGen is a resource that integrates multidimensional OncoGenomics Data for the identification of genes and groups of genes (biological modules) involved in cancer development. The great advantage of IntOGen is that it includes a large number of oncogenomic experiments (more than 800) studying diverse types of cancer alterations (mutations, copy number alterations and expression) in a common framework.

To find details on how data is collected and analysed in IntOGen read the paper in Nature Methods or check the frequently asked questions.