Genomic benchmarks: a collection of datasets for genomic sequence classification

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14740%2F23%3A00131330" target="_blank" >RIV/00216224:14740/23:00131330 - isvavai.cz</a>

Result on the web

<a href="https://link.springer.com/article/10.1186/s12863-023-01123-8" target="_blank" >https://link.springer.com/article/10.1186/s12863-023-01123-8</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1186/s12863-023-01123-8" target="_blank" >10.1186/s12863-023-01123-8</a>

Result language

angličtina

Original language name

Genomic benchmarks: a collection of datasets for genomic sequence classification

Original language description

Background Recently, deep neural networks have been successfully applied in many biological fields. In 2020, a deep learning model AlphaFold won the protein folding competition with predicted structures within the error tolerance of experimental methods. However, this solution to the most prominent bioinformatic challenge of the past 50 years has been possible only thanks to a carefully curated benchmark of experimentally predicted protein structures. In Genomics, we have similar challenges (annotation of genomes and identification of functional elements) but currently, we lack benchmarks similar to protein folding competition. Results Here we present a collection of curated and easily accessible sequence classification datasets in the field of genomics. The proposed collection is based on a combination of novel datasets constructed from the mining of publicly available databases and existing datasets obtained from published articles. The collection currently contains nine datasets that focus on regulatory elements (promoters, enhancers, open chromatin region) from three model organisms: human, mouse, and roundworm. A simple convolution neural network is also included in a repository and can be used as a baseline model. Benchmarks and the baseline model are distributed as the Python package ‘genomic-benchmarks’, and the code is available at https://github.com/ML-Bioinfo-CEITEC/genomic_benchmarks. Conclusions Deep learning techniques revolutionized many biological fields but mainly thanks to the carefully curated benchmarks. For the field of Genomics, we propose a collection of benchmark datasets for the classification of genomic sequences with an interface for the most commonly used deep learning libraries, implementation of the simple neural network and a training framework that can be used as a starting point for future research. The main aim of this effort is to create a repository for shared datasets that will make machine learning for genomics more comparable and reproducible while reducing the overhead of researchers who want to enter the field, leading to healthy competition and new discoveries.

Czech name

—

Czech description

—

Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

10610 - Biophysics

Project

Result was created during the realization of more than one project. More information in the Projects tab.

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2023

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

BMC Genomic Data

ISSN

2730-6844

e-ISSN

2730-6844

Volume of the periodical

24

Issue of the periodical within the volume

1

Country of publishing house

GB - UNITED KINGDOM

Number of pages

9

Pages from-to

1-9

UT code for WoS article

000981254200001

EID of the result in the Scopus database

2-s2.0-85157964171