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RNA-seq

RNA-seq

RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases.
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...

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Updated: Jun 27, 2025

Author Spotlight: A Computational Pipeline for Analyzing Chimeric Noncoding RNA-Target RNA Interactions in High-Throughput Sequencing Data

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Published on: December 1, 2023

SUMA: a lightweight machine learning model-powered shared nearest neighbour-based clustering application interface

Hamza Umut Karakurt^1,2, Pınar Pir^1,2

¹Department of Bioengineering, Faculty of Engineering, Gebze Technical University, Kocaeli, Turkiye.

Turkish Journal of Biology = Turk Biyoloji Dergisi

|April 29, 2024

Summary

This summary is machine-generated.

SUMA is a new tool that uses a random forest model to optimize graph-based clustering for single-cell RNA sequencing (scRNA-Seq) data. It accurately predicts the optimal number of neighbors, improving cell type annotation and simplifying analysis for researchers.

Keywords:

RShiny ScRNA-Seq clustering machine learning random forest

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Author Spotlight: Deciphering the Cellular Mysteries of Intermuscular Adipose Tissue in Humans

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Published on: May 3, 2024

Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Single-cell RNA sequencing (scRNA-Seq) reveals cellular diversity but faces challenges with data sparsity, noise, and cell type identification.
Graph-based clustering is a powerful scRNA-Seq analysis method, yet its performance relies heavily on user-defined parameters like the number of neighbors.
Optimizing these parameters is crucial for accurate cell clustering and annotation in scRNA-Seq studies.

Purpose of the Study:

To develop SUMA, a lightweight tool employing a random forest model to predict optimal parameters for graph-based clustering of scRNA-Seq data.
To enhance the accuracy and ease of cell type annotation by optimizing clustering results.
To integrate SUMA into an RShiny application for user-friendly access by researchers, including non-bioinformaticians.

Main Methods:

Utilized publicly available scRNA-Seq datasets and three graph-based clustering algorithms for SUMA development.
Trained a random forest model using Scikit-learn (Python) and randomForest (R) libraries, considering a wide range of neighbor counts and variant genes.
Evaluated clustering quality using the adjusted Rand index (ARI) against true labels, splitting data into training and testing sets.

Main Results:

The developed machine learning model achieved an accuracy of 0.96 and an AUC of 0.98.
The model identified the number of cells in scRNA-Seq data as the most influential feature for determining the optimal number of neighbors.
SUMA effectively predicts optimal clustering parameters, leading to improved scRNA-Seq data analysis.

Conclusions:

SUMA provides an accurate and automated approach to optimize graph-based clustering for scRNA-Seq data.
The SUMAShiny application offers an integrated platform for clustering and visualizing scRNA-Seq data, accessible via desktop or web browser.
SUMA empowers researchers, including those without extensive bioinformatics expertise, to perform robust cell type annotation and analysis.