TOM – 02.03.2016

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

TOM – 17.02.2016

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

riku1

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

riku2

TOM – 03.02.2016

Group Factor Analysis

Presented by:  Suleiman Ali Khan, Post-doc, Group Aittokallio, FIMM

http://users.ics.aalto.fi/suleiman/

A key underlying problem faced by informaticians in general and computational biologists in particular is to identify the dependencies between one or more datasets. Recent advancements in machine learning have led to creation of a series of methods that could identify statistical patterns shared between multiple datasets as well as enumerating those specific to anyone. ‘Group Factor Analysis (GFA)’ is a key tool that has recently come up to address these challenges, and in the first session of TOM I will give an overview GFA and some of its practical use cases in bioinformatics and computational biology.

For more reading:

http://bioinformatics.oxfordjournals.org/content/30/17/i497.full.pdf+html

http://research.cs.aalto.fi/pml/online-papers/khan2014ecml.pdf

http://jmlr.csail.mit.edu/proceedings/papers/v22/virtanen12/virtanen12.pdf

TOM Schedule

This is a tentative schedule for spring session 2016

Date Presenter Title of presentation
03.02 Suleiman Ali Khan Group Factor Analysis
17.02 Riku Katainen Analysis and visualization tool for next generation sequencing data
02.03

Arvydas Dapkunas

Ville Rantanen

Analyzing kidney development

NiceCSV

16.03 Simon Anders DESeq
30.03 Liye He

Phylogenetic reconstruction of cancer evolution using LICHeE

12.04 Svetlana Ovchinnikova Metric learning approaches
 27.04 Mikhail Shubin Data visualisation
11.05 Christian Benner  FINEMAP: Efficient variable selection using summary data from GWAS
25.05 TBD