Venue: Biomedicum 1, Meeting room 1 

Timing: 15:30 – 17:00

Speaker: Christian Benner, PhD Student

FINEMAP: Ultrafast high-resolution fine-mapping using summary data from genome-wide association studies

Genome-Wide Association Studies (GWAS) have identified thousands of loci associated with complex diseases. A next crucial step is fine-mapping: identifying causal variants that point to molecular mechanisms behind the associations and, eventually, suggest therapeutic targets. Recently, fine-mapping methods have been extended to use only GWAS summary data together with pairwise correlations of the variants. Common to these approaches is that they rely on computationally expensive exhaustive search restricting their use to a few hundred variants. We introduce a software package FINEMAP that replaces the exhaustive search by an ultrafast stochastic search and thereby allows fine-mapping analyses to scale up to whole chromosomes.

We show that FINEMAP (1) opens up completely new opportunities by, e.g., exploring 15q21/LIPC locus for high-density-lipoprotein (HDL) with 20,000 variants in less than 90 seconds while exhaustive search would require more than 9,000 years, (2) provides similar accuracy to exhaustive search when the latter can be completed, (3) achieves even higher accuracy when the latter must be restricted due to computational reasons, and (4) identifies more plausible variant combinations than conditional analysis. At 15q21/LIPC locus with at least a 3-SNP association pattern with HDL, a missense variant and a promoter polymorphism are likely to be causal whereas the lead variant in single-SNP testing has less evidence than a regulatory variant correlated with it.

We believe that FINEMAP’s approach of jointly modeling the whole locus together with its unprecedented computational efficiency will help reveal valuable knowledge that could otherwise remain hidden due to limitations of existing fine-mapping methods.    

http://bioinformatics.oxfordjournals.org/content/early/2016/01/14/bioinformatics.btw018.abstract

Venue: Biomedicum 1, Meeting room 3, P floor 

Timing: 15:30 – 17:00

Speaker: Mikhail Shubin, PhD Student

Data Visualization

Everybody loves visualization! It is used to search the data for new discoveries and to communicate these discoveries to fellow scientists.

But are we doing it right? Could we do it better? Is it possible we were lost in a hype?

I will try to give a brief overview of scientific visualization from the point of graphic design. I will use keywords like “information/noise ratio”, “human visual cognition”, “fashion”, “colour blindness” and “everybody is doing it wrong”. But I would prefer having a discussion instead of a lecture; I will bring a number of examples which (I hope) we can talk about together.

About me: My name is Mikhail, I have a masters degree in CS and bioinformatics. Now I’m spending my last months as a PhD student in Statistics. I have a blog about scientific visualization https://ctg2pi.wordpress.com.

Venue: Biomedicum 1, 5th Floor, Meeting room 7

Timing: 15:30 – 17:00

Speaker: Svetlana Ovchinnikova, PhD Student, Group Anders, FIMM

Metric Learning Algorithms

Many popular machine learning approaches strongly rely on distance/similarity between samples. Yet a pre-defined distance metric is not always relevant for the considered property and an appropriate transformation of the feature space can increase the effectiveness of a machine learning algorithm.

In my presentation, I will give an overview of existing approaches to metric learning and talk about mathematical background behind these algorithms. I will also demonstrate two Matlab packages: LMNN (Large Margin Nearest Neighbours) and MLKR (Metric Learning for Kernel Regression), which can be used for classification and regression problems respectively.

Weinberger, K. Q., Blitzer, J., & Saul, L. K. (2005). Distance metric learning for large margin nearest neighbor classification. In Advances in neural information processing systems (pp. 1473-1480). – http://machinelearning.wustl.edu/mlpapers/paper_files/NIPS2005_265.pdf

Weinberger, K. Q., & Tesauro, G. (2007). Metric learning for kernel regression. In International Conference on Artificial Intelligence and Statistics (pp. 612-619). – http://machinelearning.wustl.edu/mlpapers/paper_files/AISTATS07_WeinbergerT.pdf

Kireeva, N. V., Ovchinnikova, S. I., Kuznetsov, S. L., Kazennov, A. M., & Tsivadze, A. Y. (2014). Impact of distance-based metric learning on classification and visualization model performance and structure–activity landscapes. Journal of computer-aided molecular design, 28(2), 61-73. – http://link.springer.com/article/10.1007/s10822-014-9719-1

Venue: Biomedicum 1, Meeting room B236a

Timing: 15:30 – 17:00

Speaker: Liye He, PhD Student, Group Aittokallio, FIMM

LICHeE (Lineage Inference for Cancer Heterogeneity and Evolution) is a novel computational model to reconstruct the tumour phylogenies and tumour sub-clone decomposition by utilising the presence patterns of somatic single nucleotide variants (SSNVs) across multiple samples and their variant allele frequencies (VAFs).

The link for the paper:  http://www.ncbi.nlm.nih.gov/pubmed/25944252

Venue: Biomedicum 1, Seminar room 1-2

Timing: 16:00 – 17:30

Speaker: Simon Anders, FIMM-EMBL Group Leader

(i) DESeq is a Bioconductor package for analysis of RNA-Seq data that we developed and which became one of the most widely used tools for this purpose. The history of this project offers some useful lessons on how to design, author, publish, disseminate and advertise bioinformatics software with impact, i.e, such that it finds wide use by the research community.

(ii) Recently, I directed the focus of my research on interactive visualization tools for high-dimensional data. Our platform of choice is D3, a data visualization library written in Javascript. I will give a short overview of its library and show how we used it.

Venue: Biomedicum 1, Meeting room B236a, 2nd floor

Arvydas Dapkunas

Hello Folks! 

The coming Wednesday I will present you a show case of image analysis toolkit ANIMA running on Anduril.

ANIMA is an image analysis workflow environment on top of Anduril an open source component-based workflow framework for scientific data analysis (developed in S.Hautaniemi lab). The Anima was used in visualizing and quantifying properties of 3D cell culture of kidney stem cells. In the presentation I’ll introduce key principles and questions of kidney development. Then I’ll pop the hood of ANIMA with the examples of how scripted image analysis answers question of the project.

WARNING: Lots of images, so you may want to bring popcorn!

Ville Rantanen

NiceCSV is an interactive tabular data browser, as well as a text based tabular data formatter. The browser is capable of giving a quick overview of the data using a simple keyboard interface. The user may view basic statistics, sort by column, format the values, change alignment, or search with a keyword.

NiceCSV works in unison with other Unix tools, such as grep, cut and sed, reusing their capabilities for tabular data processing. 

NiceCSV is freely downloadable and the source code is available at https://bitbucket.org/MoonQ/ncsv

It has no dependencies other than the standard Python libraries. The program is fully portable to all operating systems that run Python. (Cygwin required for Windows for the interactive browser)

PGP http://goo.gl/Iayn8j 

TOM

Analysis and visualization software for massive next-generation sequencing data sets

Presented by: Riku Katainen, PhD student, Aaltonen lab, Genome-Scale Biology Program

http://research.med.helsinki.fi/gsb/aaltonen/

Due to the massive availability of next- and third generation sequencing (NGS) data, the processing and analysis now constitute a serious challenge for research in life sciences. I will introduce a highly efficient and user friendly analysis and visualization software tool (BasePlayer), which is designed for huge NGS data sets. BasePlayer offers comparative variant analyses not only in genes but also in intergenic regions by integrating regulatory annotation, transcription factor (TF) binding sites and motifs.

I am going to show basic functions of the program in real time with an actual cancer data. BasePlayer is still under development and will be published soon, so I’ll be happy to hear your suggestions and comments.

Screenshot of somatic mutations in APC gene and annotation in Base Player

Screenshot of split screen view at TTC28 LINE1-Element in Base Player