Janggu makes deep learning a breeze

Date:
July 13, 2020
Source:
Max Delbru"ck Center for Molecular Medicine in the Helmholtz
Association
Summary:
Researchers have developed a new tool that makes it easier to
maximize the power of deep learning for studying genomics.



FULL STORY ========================================================================== Researchers from the MDC have developed a new tool that makes it easier to maximize the power of deep learning for studying genomics. They describe
the new approach, Janggu, in the journal Nature Communications.


========================================================================== Imagine that before you could make dinner, you first had to rebuild the kitchen, specifically designed for each recipe. You'd spend way more time
on preparation, than actually cooking. For computational biologists, it's
been a similar time-consuming process for analyzing genomics data. Before
they can even begin their analysis, they spend a lot of valuable time formatting and preparing huge data sets to feed into deep learning models.

To streamline this process, researchers from the Max Delbrueck Center
for Molecular Medicine in the Helmholtz Association (MDC) developed a
universal programming tool that converts a wide variety of genomics data
into the required format for analysis by deep learning models. "Before,
you ended up wasting a lot of time on the technical aspect, rather than focusing on the biological question you were trying to answer," says
Dr. Wolfgang Kopp, a scientist in the Bioinformatics and Omics Data
Science research group at MDC's Berlin Institute of Medical Systems
Biology (BIMSB), and first author of the paper. "With Janggu, we are
aiming to relieve some of that technical burden and make it accessible
to as many people as possible." Unique name, universal solution Janggu
is named after a traditional Korean drum shaped like an hourglass turned
on its side. The two large sections of the hourglass represent the areas
Janggu is focused: pre-processing of genomics data, results visualization
and model evaluation. The narrow connector in the middle represents a placeholder for any type of deep learning model researchers wish to use.

Deep learning models involve algorithms sorting through massive amounts
data and finding relevant features or patterns. While deep learning is a
very powerful tool, its use in genomics has been limited. Most published
models tend to only work with fixed types of data, able to answer only
one specific question. Swapping out or adding new data often requires
starting over from scratch and extensive programming efforts.



========================================================================== Janggu converts different genomics data types into a universal format
that can be plugged into any machine learning or deep learning model
that uses python, a widely-used programming language.

"What makes our approach special is that you can easily use any genomic
data set for your deep learning problem, anything goes in any format,"
Dr. Altuna Akalin, who heads the Bioinformatics and Omics Data Science
research group.

Separation is key Akalin's research group has a dual mission: developing
new machine learning tools, and using them to investigate questions
in biology and medicine. During their own research efforts, they were continually frustrated by how much time was spent formatting data. They realized part of the problem was each deep learning model included its
own data pre-processing. By separating the data extraction and formatting
from the analysis, it provides a much easier way to interchange, combine
or reuse sections of data. It's kind of like having all the kitchen
tools and ingredients at your fingertips ready to try out a new recipe.

"The difficulty was finding the right balance between flexibility
and usability," Kopp says. "If it is too flexible, people will be
drowned in different options and it will be difficult to get started."
Kopp has prepared several tutorials to help others begin using Janggu,
along with example datasets and case studies. The Nature Communications
paper demonstrates Janggu's versatility in handling very large volumes of
data, combining data streams, and answering different types of questions,
such as predicting binding sites from DNA sequences and/or chromatin accessibility, as well as for classification and regression tasks.

Endless applications While most of Janggu's benefit is on the front
end, the researchers wanted to provide a complete solution for deep
learning. Janggu also includes visualization of results after the deep
learning analysis, and evaluates what the model has learned. Notably,
the team incorporated "higher-order sequence encoding" into the package,
which allows to capture correlations between neighboring nucleotides. This helped to increase accuracy of some analyses. By making deep learning
easier and more user-friendly, Janggu helps throw open the door to
answering all kinds of biological questions.

"One of the most interesting applications is predicting the effect of
mutations on gene regulation," Akalin says. "This is exciting because
now we can start understanding individual genomes, for instance, we
can pinpoint genetic variants that cause regulatory changes, or we can interpret regulatory mutations occurring in tumors."

========================================================================== Story Source: Materials provided by Max_Delbru"ck_Center_for_Molecular_Medicine_in_the
Helmholtz_Association. Original written by Laura Petersen. Note: Content
may be edited for style and length.


========================================================================== Journal Reference:
1. Wolfgang Kopp, Remo Monti, Annalaura Tamburrini, Uwe Ohler, Altuna
Akalin. Deep learning for genomics using Janggu. Nature
Communications, 2020; 11 (1) DOI: 10.1038/s41467-020-17155-y ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2020/07/200713104357.htm

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