TOM – 14.12.2016

Venue: Biomedicum 1, Seminar room 1-2

Timing: 15:30 onwards

Presenter: Boris Vassilev, Ikonen Lab, Department of Anatomy, Biomedicum Helsinki

Document, automate, and share your data analysis with Lir

Lir is a software tool for documenting, automating, and sharing computational work. We have demonstrated that Lir can improve the reproducibility of a complex data analysis. With Lir, it takes less effort to document the analysis, while the generation of all results is automated. Importantly, Lir supports the use of multiple programming languages and approaches in the same data analysis. This allows the user to employ whatever tool is best suited for each step of the analysis

See more details from the publication:

Student in Spotlight – Petra Tauscher

For the month of November 2016


Petra Tauscher is a PhD student at the Institute of Biotechnology, Developmental Biology Program with Group Leader Dr. Osamu Shimmi. She is studying the post-transcriptional regulation of TGF-beta type signaling in Drosophila development, and has recently published an article on this topic. Congratulations Petra!

Developmental patterning and intrinsic properties of BMP ligands

Research Digest:

A fascinating area of research in biology is to understand how animals transform from a single-cell zygote to a fully-grown adult with sophisticated organ systems. Previous studies have shown that the genetic mechanisms of a huge variety of animal species during development is conserved, i.e. a similar group of genes carry out a conserved set of molecular functions. How is it possible that animals look so different, despite using the same mechanisms?

Because the developmental processes are conserved, it allows researchers to conduct studies in model organisms. For instance, invertebrates like fruit flies, as well as vertebrates use the same set of molecules for dorsal/ventral patterning (DV) during embryogenesis which ultimately determines the back/belly in adult form. The Bone Morphogenetic Protein (BMP) signaling pathway is one such conserved signaling that plays a crucial role during this process. It is also important for the posterior cross-vein (PCV) formation during pupal stage, which develops into the wings in flies. So how does the BMP signaling pathway result in DV patterning in one, and PCV in another context of development?

Previous studies suggest that changes in gene regulation affect BMP signaling in a spatiotemporal manner. Petra’s experiments [1] add a new aspect to this idea. She compared the protein sequences of BMP-type ligands from various species and found that the BMP-type ligands contain a highly conserved N-glycosylation motif. However, Screw, a BMP-type ligand in fly, carries an additional unique N-glycosylation motif that is not present in other BMP-type ligands. While Screw is critical and exclusively found in the context of dorsal/ventral patterning, Glass bottom boat, a paralog of Screw plays a role during PCV formation in fly. Petra et al. found that Screw, if expressed artificially in the wing can replace the function of Glass Bottom Boat during PCV formation, but vice-versa is not true. Furthermore, BMP-type ligands lacking N-glycosylation motifs were more efficient in the context of PCV formation, whereas lack of N-glycosylation in the Screw ligand led to a reduced viability. This suggests that N-glycosylations provide an advantage for DV patterning, i.e. during early stages of embryogenesis but are detrimental during PCV formation. Hence, N-glycosylation motifs affect BMP function in a context dependent manner.

Petra thereby concludes that apart from the spatial and temporal changes in gene expression, post-translational modifications may be one way how evolutionarily conserved molecules and signaling pathways adapt to different developmental processes.

  1. Tauscher PM, Gui J, Shimmi O. Adaptive protein divergence of BMP ligands takes place under developmental and evolutionary constraints. Development. 2016 Oct 15;143(20):3742-3750.


TOM – 16.11.2016

Venue: Biomedicum 1, Seminar room 1-2

Timing: 15:30 onwards

Presenter: Teemu Kivioja, Taipale Lab, Genome-Scale Biology Program

Utilizing integer linear programming in experiment design and analysis

Many tasks in bioinformatics involve choosing the best combination
from a vast number of different alternatives, for example the optimal
set of molecular probes from a large number of candidate probes. Many
such discrete optimization problems can be conveniently expressed in
Integer Linear Programming (ILP) or more generally Mixed Integer
Linear Programming (MILP) framework. The improvement of general ILP
solvers together with the increase in computing power has made it
possible to optimally solve many problem instances of small to medium
sizes without developing a task-specific algorithm or resorting to
heuristics. I will introduce how one can use ILP to solve experiment
design and analysis problems through simple examples taken from my own
research including molecular barcode design, CRISPR/Cas9 guide RNA
selection, and selecting a representative set of transcription factor
binding motifs.

Battle de suprême: CRISPR vs. RNAi based screening

Written by: An Uncharacterized ORF


(Commentators speech log)

Dear XXs and XYs, Welcome to the LoF Screening Championship hosted by the World Federation for Functional Genomics (WFFG). We’re having a gloriously sunny day and a more perfect stage couldn’t be asked for an ultimate entertaining extravaganza. LoF, sounds eerily like ‘laugh’ so you may think that this is a competition for the king of comedy of sorts, the one who can leave you Laughing-on-Floor, but sadly it stands for Loss-of-Function.

Loss-of-function screens are concerned with understanding the function of genes by quintessentially removing them from a cell and observing the consequences. For instance, it has been applied in identifying genes that are essential for survival of cancer cells, with the intention to pick out diagnostic markers and targets for drugs, giving them the ability to kill cancer cells. Similar to other functional genomic tools like transcriptome and proteome profiling, LoF screens have also been adapted to high-throughput settings. By nullifying the function of many genes in one go, these techniques can produce a copious amount of data leaving the analyst melancholically overwhelmed.

Today on stage, we have two contestants both of whom have been adapted for high-throughput LoF screens. While one works by knocking down the mRNAs to suppress gene expression, the other works by knocking the gene out. On this sombre thought of gene annihilation, let’s watch this crown battle for the favourite toolkit of natural philosophers, aka scientists.

But first, here’s a quick look at their bios.



Popularly known as RNAi, Ribonucleic Acid interference is the veteran here. Make no mistake, despite the nominal reference to a recreational drug, RNAi’s character remains unblemished by any doping scandals, and most likely is a scornful quip on the scandalous.

Brought out from obscurity by Andrew Fire and Craig Mello in 1998, RNAi generated a huge fire, ahem, fan following and swiftly became the poster child of the Silencer movement that rose to its prominence post-millennium. The arrival of RNAi to the scene provided immense psychological boost to her fans, who could then use it to write their own stories of explorations on suppressing their favourite genes. No wonder RNAi became a heartthrob; for it empowered the people for targeted gene suppression and gave rise to a democratic wave of explorations.

RNAi’s prime strengths are its dicing and slicing abilities that it derives from the adventuring Argonautes. RNAi’s two avatars, siRNA and shRNA, have been successfully deployed by her fans for quite some time. However, as they grew familiar with the technique, RNAi has fallen prey to the old maxim, “familiarity breeds contempt”. Sufficient time has already been devoted to expose RNAi’s shortcomings, inefficient knockdown and off-target effects, that has MELLO-wed the FIRE in RNAi.



on the other side is a new kid on the block. Clustered Regularly Interspaced Short Palindromic Repeats (catches breath) under the nom de guerre CRISPR has ascended to stellar heights within a very short term. Legend has it that, when CRISPR was young and still hadn’t realised his full potential, he was found protecting bacteria in yogurt from viruses. It was Emanuelle Charpentier, Jennifer Doudna and Feng Zhang who trained CRISPR and helped unleash his capabilities.

CRISPR’s strengths come from its Cas9 arm with which it can break DNA at any address directed by the Guide RNA. CRISPR has become a sensational star and has found a cult fan following in the Genome Editors. Ever since CRISPR came into the scene, the Genome Editing community has been very gung-ho about it, almost to the level that their infectious enthusiasm has raised eyebrows of the Human Germline Ethical Brigade.

Like RNAi, CRISPR has also been morphed to perform high-throughput LoF screens. The question remains, who is better at it? Let’s hope we will get the answer today.


… Continued …

Originally published at: FIMMSights Blog!

TOM – 19.10.2016

Venue: Biomedicum 1, Seminar room 1-2

Timing: 15:30 onwards

Presenter: Gopal Peddinti, Senior Researcher, FIMM

Genome scale metabolic modeling of cancer cells

Genome based research in cancer has identified a plethora of mutational events involved in the initiation and progression of cancers. Surprisingly, the huge diversity of genomic alterations appear to converge in altering the tumor metabolism. Therefore, “starving the tumor cells of their energy supplies” (i.e. targeting tumor metabolism) appears to be a universal bullet to treat cancers. To explore the therapeutic potential of metabolism, genome scale metabolic models (GSMM) emerged as powerful in silico tools in cancer systems biology research for predicting biomarkers, therapeutic targets, and treatment side effects.

GSMMs are stoichiometric models providing a comprehensive view of cell metabolism. Reconstruction and simulation of GSMMs is achieved by constraint based modeling framework. COnstraints Based Reconstruction and Analysis (COBRA) is a comprehensive toolbox used widely in metabolic modeling. I will briefly review the GSMM approach, some of its successful applications, and show the COBRA matlab toolbox (

Dance your PhD – Maarja Laos

“Creativity is a great motivator because it makes people interested in what they are doing. Creativity gives hope that there can be a worthwhile idea. Creativity gives the possibility of some sort of achievement to everyone. Creativity makes life more fun and more interesting.”   –   Edward de Bono


Here’s a submission from Maarja Laos, an ILS PhD Student and Student Council member, for the Dance your PhD contest organised by the Science magazine. No talk, only Dance! Kudos to Maarja for being able to put all the hard work to get it done and inspiring us fellow students to look at our projects from a creative perspective and add fun in our journey as scientists. Enjoy!



The cochlea of the inner ear functions to enable us to hear sounds. It contains hair cells that via the hair bundles on their apical surface detect sound waves and transmit them to the brain to generate hearing.

Human hair cells are very sensitive and die easily due to loud noise and other insults (e.g. some antibiotics, chemotherapy drugs). Unfortunately, following injury the surviving hair cells in mammals, including in humans, are unable to divide leading to a gradual deterioration of hearing.

Scientists are trying to restore human hearing by designing strategies to replace lost hair cells either by forcing the surviving hair cells to divide, by repairing the damaged essential parts of the hair cell, such as the hair bundle or by making the surrounding supporting cells to convert into hair cells. The cochlear auditory sensory epithelium containing the hair cells and supporting cells can be grown in culture on a filter membrane. The filter membrane is placed on top of a metal mesh so that the cells are located at the interface of the culturing media and air, allowing both nutrients from the culturing media and oxygen from the air to reach them.

Scientists face many difficulties trying to make these regenerative strategies to work. When mammalian hair cells are forced to divide, the cell division often fails, resulting in death of a cell or daughter hair cells with abnormal number of chromosomes. The strategies aiming to repair the damaged hair bundle of a hair cell are not able to fully restore the hair bundle, resulting in hair cells that are unable to properly detect sound. The strategies that convert supporting cells into hair cells usually produce hair cells that are not fully functional and retain characteristics of supporting cells. In my PhD project I have tried to understand the reasons why these regenerative strategies are not successful.

TOM – 21.09.2016

Venue: Biomedicum 1, Meeting room 10

Timing: 15:30 onwards

Presenter: Tuomo Hartonen, PhD student

Transcription factor binding site discovery from ChIP-exo and ChIP-nexus data

Novel chromatin immunoprecipitation (ChIP) experiments ChIP-nexus and ChIP-exo allow studying transcription factor binding with unprecedented accuracy. True transcription factor binding locations are separated from noise by peak calling softwares.

Most peak calling softwares search binding events by creating a model of “true” peaks from the sites with highest enrichment in the ChIP experiments and then accepting only the peaks resembling this model. It is however known that most transcription factors bind cooperatively with other factors, form dimers or interact with other proteins. Moreover, the currently most used approach to predict transcription factor binding sites with simple Position Weight Matrix (PWM) models dos not explain all binding that is observed in vivo. These different types of binding create different ChIP-nexus/exo fingerprints. Fitting the peaks to just one model may lead to missing important binding events.

PeakXus is a peak caller specifically designed to leverage the increased resolution of ChIP-nexus/exo experiments. PeakXus is developed with the aim of making as few assumptions of the data as possible to allow novel discoveries. PeakXus supports use of Unique Molecular Identifiers (UMI) to remove PCR-duplicates that can create artefacts closely resembling true ChIP-nexus/exo binding events. We show that PeakXus consistently finds more peaks overlapping with transcription factor specific PWM-hits than published methods.

I will try to give a clear introduction to the topic so that also those who are not so familiar with the ChIP experiments are able to follow. I will compare the ChIP-exo/nexus protocols to ChIP-seq in order to highlight applications where the novel experiments are more suitable (for example allele specific binding analysis). I will also briefly explain the kind of output the algorithm provides.

PeakXus is published at:

PeakXus code at GitHub:

Other useful references for those who are interested in reading more:

He et al. (2015). ChIP-Nexus enables improved detection of in vivo transcription factor binding footprints. Nature biotechnology.

Rhee & Pugh (2011). Comprehensive genome-wide protein-DNA interactions detected at single-nucleotide resolution. Cell.

Kivioja  et al. (2012). Counting absolute numbers of molecules using unique molecular identifiers. Nature methods.

TOM – Course description


Usually starts at 15.30
Biomedicum 1

First things first, this meetings’ setting is a very casual one. We want to create a society for exchange of knowledge and ideas of people doing/interested in Bioinformatics/Applied Statistics. The idea is to bring them together and we hope that this will be beneficial for all of us.

Tool Of the Month (TOM) is a platform to discuss the various tools and methods that we use in our research. We can discuss various bioinformatics algorithms, softwares, packages in R/Python/Perl/any-of-your-favourite-language. And if you want to talk about your research that’s even better!

Each time we meet, we will have one or two talks about a method/tool/idea in a format the that can be freely decided by the presenter. And if you want to continue the exciting conversation with the eclectic crowd, feel free to take it to nearby restaurants/pubs. What’s more, you can receive credits, by only presenting once and actively participating to TOM meetings!


Students can obtain credits from presenting and/or attending TOM-meetings as follows:
a) attending 6 meetings & filling the feedback form =1 cr
b) doing a) and giving a presentation = 2 cr

Attendances do not all have to be from the same year
Credits can be registered after each spring term


TOM mailing list
If you would like to receive notifications of the upcoming meetings, you can join the TOM mailing list following the instructions shown here:

Name of the mailing list is ils-tom

TOM Fall Schedule

Tentative schedule for Fall session 2016

Date Presenter       Title of presentation
31.08 Abhishekh Gupta SGNS2: a tool for systems modeling
21.09 Tuomo Hartonen Transcription factor binding site discovery from ChIP-exo/Nexus data
19.10 Gopal Peddinti COBRA: Constraint Based Modeling for Genome scale metabolic networks
16.11 Teemu Kivioja Integer Linear Programming
14.12 Boris Vassiliev

Document, automate and share your data analysis with Lir


TOM – 31.08.2016

Venue: Biomedicum 1, Seminar room 1-2

Timing: 16:00 onwards

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

SGNS2: a tool for systems modeling

To move beyond the trajectory of traditional scientific efforts focused on studying individual genes or single mutation and its relationship with a phenotype, we need to channel efforts to integrate information from various technologies (omics datasets) to enable a holistic view of biological systems (my current research focus is on Cancer systems). Therefore, what I will be presenting to you can be categorized into the area of Cancer Systems Biology.


Interactions in biological systems differ from physical and chemical systems, as biological processes are mostly stochastic in nature and involve a low copy numbers of interacting species (for ex: mRNA, proteins, substrate). Transcriptional regulation of gene expression is a dynamic process, and inherently noisy due to the fluctuations in number of biomolecules involved in such processes. This dynamic behavior of transcriptomic landscape can have a significant effect on the overall cellular response to a given stimulus, for instance, how a cancer cell responds to a drug. For such systems, a stochastic formulation is usually preferred.

I will be presenting how the tool SGNS2 can be used in systems biology project. SGNS2 is an open-source simulator of chemical reaction systems according to the Stochastic Simulation Algorithm (SSA). SGNS2 is based on an enhanced Next Reaction Method, one of the efficient sampling procedures of the SSA. The simulator also uses the concepts of dynamic compartments. The simulator is optimized for simulation of models with large number interacting species.

Lloyd-Price J, Gupta A, Ribeiro AS. SGNS2: a compartmentalized stochastic
chemical kinetics simulator for dynamic cell populations. Bioinformatics. 2012
Nov 15;28(22):3004-5.