ISMB/ECCB 2017 conference – highlights

I would like to comment on two great lectures held on ISBM/ECCB 2017 conference.

Overall, the conference was great, however, there was so many tracks, it was impossible to be on every interesting talks as they often overlapped. I am aware that topic covered is really broad and even with tracks being hold simultaneously, it was pretty long conference but for me it was a bit worrying that I have to choose between many interesting talks.

What caught my eye was still increasing interest in Machine Learning use and single cell sequencing. Attending this conference one can assume that these were ‘hot topics’ of bioinformatics for 2017.

I would like to highlight two talks that were not really about bioinformatics, rather on social bioinformatic-somehow-related problems.

(1)Open Humans: Opening human health data – Talk by Madeleine Ball

Really inspiring talk about open data movement, what are pros, cons and ambivalent features of gathering open data (understood mainly but not only as genome data). Open humans project, which Madeleine Ball is co-founder and advocate,  is a platform to share your data with selected scientific projects (you decide on every step whether you want to share or not). It may help to push research to faster and more reliable answers. However, shared data is sensitive as may  serve to identify a person so we need to remember about the right not to share. World is never black and white.

(2) Bioinformatics: A Servant or the Queen of Molecular Biology? – Talk by Pavel Pevzner

This talk was really about education and what are the next steps for MOOCs (Massive Open Online Courses) and not bioinformatics itself. The discussion afterwards was also interesting as showed different point of views what people believe is the best way to share knowledge and learn yourself. Is it really needed to have academic lectures? Is it necessary to have personal contact with your professor and your colleagues? Or you can do similarly well (or better) with online peer-to-peer help? The discussion showed that the optimum solution can be actually pretty personal (can we do something about it? This is really important question to pedagogues and educators.). I think it was amazing ending for this conference as we cannot do science without knowledge sharing and teaching. We cannot be unavailable for those who want to learn and work in our field. We have to show why this is so amazing and worth every effort to push science even a little bit further.

I was a student of Pavel Pevzner during online courses on Coursera and the form of knowledge sharing they developed is really amazing and I think acceptable to learn bioinformatics for both programmers as well as biologists (but what can I know).

Both lectures should be soon available  at conference’s website and I really recommend to listen it yourself.

Why use Jupyter Notebook for data analyses?

I think that everyone interested in data science and data analysis somewhere, somehow during their education or internet searches comes across Jupyter Notebook.  Jupyter notebook is an aplication that enables you to create (and share) document that contains code (in various programming languages), explanaitions (text) and visualizations. Jupyter Notebook is super useful when you want to show your workflow and prepare how-to for future analyses for yourself or your team.

I use Jupyter Notebook with Python3 but you can use it with various programming languages if you prefer to. Python has very broad offer of libraries for statistical analysis, data visualizations and machine learning.

With Jupyter Notebook you can show every step of data transformation showing, e.g. pandas’ DataFrames in really nice shape:

Screen Shot 2017-07-01 at 07.53.29

Moreover you can include plots with the code you used to create it so you can easily reproduce it for other data:

Screen Shot 2017-07-01 at 07.51.01

Just to mention, super useful thing in Jupyter Notebook is:

%matplotlib inline

that makes your plots appear as you execute a cell without calling

plt.show()

I hope you see what are indisputable perks of using Jupyter Notebook, I encourage you strongly to try it out.

If you’re into Jupyter Notebook, this year there is a conference in August in New York, called Jupytercon (https://conferences.oreilly.com/jupyter/jup-ny).

There are a lot of interesting projects around Jupyter Notebook, like JupyterHub (https://jupyterhub.readthedocs.io/en/latest/) that allows Jupyter server to be used by multiple users or nteract (https://nteract.io/) that transforms Jupyter Notebooks to desktop application so it’s even easier to use.

Subjective guide to upcoming conferences/workshops in 2017

This is a very subjective guide to upcoming conferences in 2017 (in area of Data Science, Bioinformatics, Programming in general). Order is quite random.

  • BOSC 2017

Prague, Czech Republic

July 22 – 23, 2017

https://www.open-bio.org/wiki/BOSC

 

  • EMBL Summer School in Bioinformatics 2017

Heidelberg, Germany

June 26 – 30 June, 2017

http://www.ebi.ac.uk/training/events/2017/summer-school-bioinformatics-0

 

  • EuroPython 2017

Rimini, Italy

July 9 – 16, 2017

https://ep2017.europython.eu/en/

 

  • AMC/BCB – WABI

Boston, US

August 20 – 23, 2017

http://acm-bcb.org/2017/index.php

http://www.acm-bcb.org/WABI/2017/

 

  • RECOMB 2017

Hong Kong

May 3 – 7, 2017

http://cb.csail.mit.edu/cb/recomb2017/

 

Enjoy :)!