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TOM Spring schedule 2018

Tentative schedule for Spring session 2018


Date Presenter  Title of presentation
31.01 Jarkko Toivonen

MODER: Discovering structures of dimeric transcription factor binding sites


Ilida Sulemanyova 

A deep convolutional neural network approach for astrocyte detection


Julia Casado

CYTO – single cell analysis powered by Anduril


Rishi Das Roy

For RNA-seq: a pipeline for RNA-seq analysis


Tuomas Puoliväli

MultiPy: Multiple hypothesis testing in Python


TOM 13.12.2017

Venue: Biomedicum 1, Meeting room 8-9, P-floor

Timing: 15:30 onwards

Presenter: Chiara Facciotto, PhD student

Interactive visualizations in R using Shiny and Plotly

The first step of downstream data analysis usually involves data exploration through different types of visualizations such as barplots, boxplots, histograms, scatterplots and heatmaps. This can be a quite tedious step, often requiring to adjust parameters or to analyze different subsets of data. Moreover, browsability of the data by project collaborators can be difficult due to data sharing policies.

In this talk I will show how to use Shiny and Plotly, two R packages, to produce interactive custom-made visualizations that can be used to explore your data and that can be shared as online apps with your collaborators. Feel free to bring your computer with you if you want to try to build your own app.


TOM 15.11.2017

Venue: Biomedicum 1, Meeting room 8-9, P-floor

Timing: 15:30 onwards

Presenter: Balaguru Ravikumar, PhD student

C-SPADE: a web-tool for interactive analysis and visualization of drug screening experiments through compound-specific bioactivity dendrograms

A necessary functionality performed by every chemical biologist is to analysis the similarity or diversity of the drug molecules, prior to their screening experiments. Owing to the wide variety of methods, chemical biologist currently require the aid of a chemoinformatician to perform such analysis. Toward that end, we introduce C-SPADE, an open-source exploratory web-tool for interactive analysis and visualization of drug profiling assays (biochemical, cell-based or cell-free) using compound-centric similarity clustering. C-SPADE, in real-time, allows the users to visually map the chemical diversity of a screening panel, explore investigational compounds in terms of their similarity to the screening panel, perform polypharmacological analyses and guide drug-target interaction predictions. C-SPADE provides an intuitive representation of the chemical space by capturing and visualizing underlying patterns of compound similarities linked to their polypharmacological effects, thereby reducing the time required for manual analysis in drug development or repurposing applications. The web-tool provides a customizable visual workspace that can either be downloaded as figure or Newick tree file or shared as a hyperlink with other users.

C-SPADE is freely available at http://cspade.fimm.fi/.

In this I talk I will briefly take you through C-SPADE and its functionality, providing a demo of its usability with a case study.

For more reading:


TOM 18.10.2017

Venue: Biomedicum 1, Meeting room 8-9, P-floor

Timing: 15:30 onwards

Presenter: Andres Veidenberg, PhD student

Web-based evolutionary sequence analysis with Wasabi

Multiple sequence alignments and phylogenetic trees form the basis of comparative sequence analyses. Downstream analysis pipelines, however, can easily grow overly complex with each added tool and branching dataset. To address the issue I’m building Wasabi: a web-browser based, graphical environment for evolutionary sequence analysis. Wasabi is designed for joint visualization and analysis of trees and multiple sequence alignments, and incorporates external tools and databases to a coherent analysis platform. My talk will introduce Wasabi as an analysis tool in a public server (http://wasabiapp.org), visualization provider for web services, and interactive plugin in scientific publishing.

TOM 13.09.2017

Venue: Biomedicum 1, Meeting room 8-9, P-floor

Timing: 15:30 onwards

Presenter: Mehreen Ali, PhD student, FIMM

Missing data imputation methods in proteomic datasets

Mass spectrometry (MS)-based proteomic profiling has the potential to study comprehensive protein profiling of biological samples and thus has shifted the focus of research from qualitative to quantitative analyses. MS-based proteomics provide opportunity for more global profiling of post-translational modifications, in terms of yielding proteome-wide information about cancer cell signaling activity that is not accessible by genomics or transcriptomics alone. 

However, a substantial amount of data is missing at peptide/protein level primarily due to low-abundance peptides and/or poor ionization. Missing values in proteomics datasets limit the information extraction from proteomics datasets using statistical and machine learning methods, and thus have detrimental effect on downstream analyses.

In this talk, I will give a general overview of imputation methods adapted for proteomics datasets

TOM 16.08.2017

Venue: Biomedicum 1, Meeting room 8-9, P-floor

Timing: 15:30 onwards

Presenter: Lea Urpa, FIMM-EMBL Rotation PhD student, FIMM

FocusedMDS: Interactive, intuitive visualization of high-dimensional data

Dealing with high-dimensional data is increasingly necessary in biological sciences. When the number of features or measures in a dataset far outnumbers the individuals measured (ie gene expression data, patient registry data), discovering structures and relationships in the data can be challenging. Existing widely-used visualization methods may also give misleading representations. We present focusedMDS, an intuitive, interactive multidimensional scaling tool for high-dimensional data exploration. If time allows, we will also introduce FINEMAPviewer, an interactive visualization tool for GWAS and FINEMAP results.

TOM Fall schedule 2017

Tentative schedule for Fall session 2017

Date Presenter  Title of presentation
16.08 Lea Urpa

FocusedMDS: Interactive, intuitive visualization of high-dimensional data


Mehreen Ali 

Missing data imputation methods in proteomic datasets

Andres Veidenberg

 Web-based evolutionary sequence analysis with Wasabi

Balaguru Ravikumar

C-SPADE: a web-tool for interactive analysis and visualization of drug screening experiments through compound-specific bioactivity dendrograms


Chiara Facciotto

Interactive Visualization in R using Shiny and Plotly


ILS Mentor of the year 2016

On May 8th, the ILS Doctoral Program announced the awards for Mentor of the year 2016. Last fall, the ILS student council invited nominations from all ILS students, asking them to describe the appreciable qualities of their mentors.

A total of 8 nominations were received, highlighting the positive aspects of the mentors such as:

  • Letting the student to be independent
  • Letting them make decisions regarding their projects
  • Giving importance to brainstorming regarding the students project, especially in the beginning
  • Being excited about the students project
  • Motivating the students in the face of negative results
  • Availability
  • Having a social and friendly relationship with the student

Dr. Krister Wennerberg and Dr. David Fewer were chosen as the mentors of the year, based on the testimonials. Krister Wennerberg is a PI at the Institute for Molecular Medicine Finland (FIMM) and David Fewer works at the Division of Microbiology and Biotechnology, Department of Food and Environmental Sciences.

The awardees mentioned in their acceptance speech that they were extremely honored by receiving this award. They think that as a PI it’s also their responsibility to train the next generation of scientists, and a good way to do this is to get the students to be excited about their projects so that they are the ones who are driving it forward. And the best way to do that is to discuss about ideas and brainstorm with the students and let the student figure the way out himself/herself.

Nominations also included Eija Jokitalo (EM Unit, BI), Maarja Mikkola (Developmental Biology Program, BI), Susanna Fagerholm (Biosciences), Frederic Michon (Developmental Biology Program, BI), Andrii Domanskyi (BI), Eeva Sievi (DS Health) and Erkki Raulo (ILS) who were also acknowledged and felicitated at the event.

We congratulate both mentors and nominees and wish them to keep up their good work as role models.

ILS Student Council


Student in Spotlight – Pia Kinaret


Pia Kinaret is a PhD student at the Institute of Biotechnology, in the research group led by Dario Greco. She is studying alternative approaches for nanomaterial safety assessment, and has recently published an article on this topic. Congratulations Pia!

A suitable alternative for nanomaterial toxicity testing

Research digest:

The world is going nano! Nanoscience is a very rapidly growing field of research. Nanotechnology, nanostructure and nanoparticles are now commonplace terms and have revolutionized various aspects of our daily lives. Nanomaterials are already being used in cosmetics, cleaning products and our food, and have the potential to make supercomputers that will fit in our pockets. One very promising area is the use of nanoparticles as vehicles for drug delivery to targeted tissue sites such as tumor. But, how grave are the consequences when these particles pile up in the environment or inside us?

Technically, nanomaterials are any particles with at least one dimension less than 100 nanometers. The inherent shape and size of the nanomaterials makes them an oddball for our immune system to handle. Similar to asbestos, inhaled nanomaterials can be hazardous; cause severe asthma-type symptoms, granuloma formation, fibrosis and cardiovascular diseases. As the number of engineered nanomaterials is increasing exponentially, understanding the physiological effects of exposure to nanomaterials and developing toxicity standard for nanomaterials is of great importance. Therefore, Pia and colleagues [1] have studied an efficient and cost-effective method to assess nanomaterial toxicity.

The most common route of human exposure to nanomaterials is through respiration. State-of-the-art method for studying the airway-exposure of nanomaterials is via inhalation method, in which lab mice are exposed to aerosolized nanomaterial. However, this is cumbersome and time-consuming. Alternatively, this could be studied by oropharyngeal aspiration, in which the nanomaterial is introduced to animal airways as a liquid dispersion. Aspiration is much faster and cost-effective method compared to inhalation, however it is not yet clear whether the two methods are comparable.

Pia [1] studied the responses of lab mice exposed to carbon-based nanomaterials by inhalation and aspiration method at various doses. She found that the immune responses of the mice at low doses of aspiration were comparable to that of inhalation. Also, the responses at molecular level in terms of the gene expression changes and induced biological functions were also very similar. Pia thus concludes that aspiration is a valid alternative to the inhalation method for assessment of nanomaterial toxicity.

  1. Inhalation and Oropharyngeal Aspiration Exposure to Rod-Like Carbon Nanotubes Induce Similar Airway Inflammation and Biological Responses in Mouse Lungs. Kinaret P, Ilves M, Fortino V, Rydman E, Karisola P, Lähde A, Koivisto J, Jokiniemi J, Wolff H, Savolainen K, Greco D, Alenius H. ACS Nano. 2017 Jan 24;11(1):291-303.


TOM 10.05.2017

Venue: Biomedicum 1, Meeting room 3, P-floor

Timing: 15:30 onwards

Presenter: Abhishekh Gupta, Post-doc, Group Aittokallio, FIMM

Drug response quantification: past, present and future

Quantifying drug response is crucial for assessing vulnerabilities of cancer cells, and plays an important role in drug discovery.  In this talk, I will present state-of-the-art methods to infer drug response from high-throughput drug screening experiments. In addition, I will illustrate a novel Normalized Drug Response (NDR) metric which realistically captures full-spectrum of the drug-induced effects. The drug sensitivity scores based on this metric are able to precisely capture the drug’s biological behavior, aiding the differentiation of drugs as into distinct drug-classes, namely lethal, sub-effective, effective and growth-stimulatory.