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Evolutionary Relationships through Genome Comparisons

Evolutionary Relationships through Genome Comparisons

Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...

Genome Annotation and Assembly

Genome Annotation and Assembly

The genome refers to all of the genetic material in an organism. It can range from a few million base pairs in microbial cells to several billion base pairs in many eukaryotic organisms. Genome assembly refers to the process of taking the DNA sequencing data and putting it all back together in a correct order to create a close representation of the original genome. This is followed by the identification of functional elements on the newly assembled genome, a process called genome annotation.

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: Jul 7, 2025

Ultra-long Read Sequencing for Whole Genomic DNA Analysis

Ultra-long Read Sequencing for Whole Genomic DNA Analysis

Published on: March 15, 2019

Benchmarking long-read genome sequence alignment tools for human genomics applications.

Jonathan LoTempio^1,2, Emmanuele Delot^3,4, Eric Vilain^1,2

¹Institute for Clinical and Translational Science, University of California, Irvine, CA, United States of America.

|December 22, 2023

Summary

This summary is machine-generated.

Benchmarking long-read sequencing alignment tools for human genomics reveals no single tool is sufficient. A combined approach using multiple alignment tools, such as Minimap2 and Winnowmap2, is recommended for comprehensive genomic variant detection.

Keywords:

Benchmark Genome alignment Genomic medicine Long-read sequence Short-read sequence

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Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Long-read genome sequencing (LRS) offers utility in various fields, but its application in reference-guided human genomics for genomic medicine remains less understood.
Evaluating the suitability of LRS for human genomics is crucial for advancing genomic medicine.

Purpose of the Study:

To benchmark platform-agnostic alignment tools for their suitability in reference-guided human genomics using LRS data.
To assess the performance of different alignment tools in producing accurate human genome representations.

Main Methods:

Utilized publicly available LRS datasets from NA12878 (Oxford Nanopore) and NA24385 (Pacific Biosciences).
Employed and benchmarked state-of-the-art sequence alignment tools: GraphMap2, LRA, Minimap2, NGMLR, and Winnowmap2.
Evaluated tools based on computational efficiency, resource requirements, and ability to align reads and detect structural variants.

Main Results:

Minimap2 and Winnowmap2 were computationally efficient; NGMLR was resource-intensive but consistent; LRA was fast but platform-specific (PacBio).
Significant disagreement among tools regarding unaligned reads impacted genome coverage and breakpoint detection.
No single tool independently identified all large structural variants (1,001-100,000 bp) in the tested samples.

Conclusions:

A combined approach leveraging multiple LRS alignment tools is necessary for comprehensive human genomics.
Utilizing alignments from at least three tools, prioritizing lightweight options like Minimap2 and Winnowmap2, is recommended for a complete genomic variability picture.
NGMLR and LRA can serve as valuable third tools depending on specific research questions, data availability, and time constraints, while Graphmap2 is not ideal for whole human genome exploration with LRS data.