Exploring DNA Classification with Next-Generation Sequencing (NGS) and Machine Learning

Author(s): Souradip Pal

Originally published on Towards AI.

Unlocking insights into DNA sequences using machine learning and bioinformatics techniques.

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DNA is often described as the blueprint of life, encoding the genetic instructions for every living organism. But reading and interpreting these instructions requires advanced tools. In recent years, Next-Generation Sequencing (NGS) has revolutionized the field by making it possible to decode DNA sequences at an unprecedented scale.

In this guide, we’ll dive into a fascinating bioinformatics project. We’ll use NGS data to classify DNA sequences and identify promoter regions — key areas in DNA that initiate gene transcription. Using machine learning, we’ll transform these sequences into a format suitable for algorithms and compare their performance.

Ready to explore the world of DNA classification?

Before we can dive into machine learning, we need data. Here’s how we’ll load a dataset of DNA sequences for our analysis.

# Import dataset from UCI repositoryurl = "https://archive.ics.uci.edu/ml/machine-learning-databases/molecular-biology/promoter-gene-sequences/promoters.data"names = ["Class", "id", "Sequence"]data = pd.read_csv(url, names=names)data.head()

This dataset consists of DNA sequences labeled with “+” or “-” to indicate promoter or non-promoter regions. Our job is to train a classifier to predict these labels based on the sequence data.

Our DNA sequences are stored as strings, but machine learning models require numerical input. Let’s convert each nucleotide (A, C, G,… Read the full blog for free on Medium.

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Published via Towards AI