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Next-generation Sequencing

Next-generation Sequencing

The first human genome sequencing project cost $2.7 billion and was declared complete in 2003, after 15 years of international cooperation and collaboration between several research teams and funding agencies. Today, with the advent of next-generation sequencing technologies, the cost and time of sequencing a human genome have dropped over 100 fold.
Next-Generation Sequencing Methods
Although all next-generation methods use different technologies, they all share a set of standard features....

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Updated: Mar 28, 2026

Next Generation Sequencing for the Detection of Actionable Mutations in Solid and Liquid Tumors

Next Generation Sequencing for the Detection of Actionable Mutations in Solid and Liquid Tumors

Published on: September 20, 2016

Next-Generation Pathology.

Peter D Caie¹, David J Harrison²

¹Quantitative and systems pathology, University of St Andrews, North Haugh, Fife, St Andrews, KY16 9TF, UK.

Methods in Molecular Biology (Clifton, N.J.)

|December 18, 2015

Summary

This summary is machine-generated.

Pathology is becoming a big data science, integrating multi-omics data and spatial information. Systems pathology uses mathematical modeling to understand complex diseases and personalize patient care.

Keywords:

Cancer pathology Histopathology Image analysis Integrative pathology Multi-omics Predictive models Spatial heterogeneity Systems pathology

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Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

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Published on: July 11, 2025

Area of Science:

Computational pathology
Systems biology
Bioinformatics

Background:

Pathology is transitioning from empirical methods to a big data discipline.
Tissue samples yield complex, multi-omics data, but heterogeneity and artifacts pose challenges.
Reductionist approaches may limit biomarker discovery and clinical trial success.

Purpose of the Study:

To highlight the necessity of integrating multi-omics data with spatial heterogeneity information.
To propose systems pathology as a method for analyzing complex biological data.
To emphasize the potential of next-generation pathology in personalized medicine.

Main Methods:

Mathematical modeling and systems pathology approaches.
Integration of standardized multi-omics big data.
Retention of spatial heterogeneity information from tissue samples.

Main Results:

Systems pathology can distill significant information from large, multi-parametric datasets.
Mathematical modeling enables prediction of disease progression and therapy response.
This approach addresses the complexity and heterogeneity inherent in tissue samples.

Conclusions:

Integrating multi-omics data and spatial information is crucial for understanding complex diseases.
Systems pathology offers a powerful framework for next-generation pathology.
This paradigm shift will personalize patient care through improved prognostic and predictive capabilities.