RNA sequencing and swarm intelligence-enhanced classification algorithm development for blood-based disease diagnostics using spliced blood platelet RNA

Blood-based diagnostics tests, using individual or panels of biomarkers, may revolutionize disease diagnostics and enable minimally invasive therapy monitoring. However, selection of the most relevant biomarkers from liquid biosources remains an immense challenge. We recently presented the thromboSe...

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Bibliographic Details
Published in:Nature protocols Vol. 14; no. 4; pp. 1206 - 1234
Main Authors: Best, Myron G, In 't Veld, Sjors G J G, Sol, Nik, Wurdinger, Thomas
Format: Journal Article
Language:English
Published: England Nature Publishing Group 01.04.2019
Subjects:
Algorithms
Automation
Bioinformatics
Biomarkers
Biomarkers - blood
Blood
Blood platelets
Blood Platelets - chemistry
Blood Platelets - metabolism
Cancer
Classification
Computational Biology - methods
Computer applications
Diagnostic systems
DNA, Complementary - analysis
DNA, Complementary - genetics
Gene sequencing
High-Throughput Nucleotide Sequencing - methods
Humans
Intelligence
Learning algorithms
Machine learning
Molecular biology
mRNA
Next-generation sequencing
Platelets
Ribonucleic acid
RNA
RNA Splicing
RNA, Messenger - analysis
RNA, Messenger - genetics
Sequence Analysis, RNA - methods
Sequence Analysis, RNA - statistics & numerical data
Support Vector Machine - statistics & numerical data
Support vector machines
Swarm intelligence
ISSN:1754-2189, 1750-2799, 1750-2799
Online Access:Get full text
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Summary:Blood-based diagnostics tests, using individual or panels of biomarkers, may revolutionize disease diagnostics and enable minimally invasive therapy monitoring. However, selection of the most relevant biomarkers from liquid biosources remains an immense challenge. We recently presented the thromboSeq pipeline, which enables RNA sequencing and cancer classification via self-learning and swarm intelligence-enhanced bioinformatics algorithms using blood platelet RNA. Here, we provide the wet-lab protocol for the generation of platelet RNA-sequencing libraries and the dry-lab protocol for the development of swarm intelligence-enhanced machine-learning-based classification algorithms. The wet-lab protocol includes platelet RNA isolation, mRNA amplification, and preparation for next-generation sequencing. The dry-lab protocol describes the automated FASTQ file pre-processing to quantified gene counts, quality controls, data normalization and correction, and swarm intelligence-enhanced support vector machine (SVM) algorithm development. This protocol enables platelet RNA profiling from 500 pg of platelet RNA and allows automated and optimized biomarker panel selection. The wet-lab protocol can be performed in 5 d before sequencing, and the algorithm development can be completed in 2 d, depending on computational resources. The protocol requires basic molecular biology skills and a basic understanding of Linux and R. In all, with this protocol, we aim to enable the scientific community to test platelet RNA for diagnostic algorithm development.
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ISSN:1754-2189
1750-2799
1750-2799
DOI:10.1038/s41596-019-0139-5