In microbiome research (including metagenomics and 16S rRNA sequencing), the terms absolute abundance and relative abundance are frequently encountered. However, what exactly do these terms mean, and why is it important to differentiate between them?

What is Relative Abundance?

Relative abundance refers to the proportion of a specific microorganism within the entire microbial community. In other words, it does not provide the actual number of microorganisms but rather indicates the proportion of that microorganism relative to the total microbial count. The sum of relative abundances typically equals 100% (or 1).

Example:

Assume that in a sample, a total of 300,000 bacteria are detected, with 100,000 being species A and 200,000 being species B. The relative abundance can be calculated as follows:

• Relative abundance of species A = 100,000 / 300,000 = 33.33%
• Relative abundance of species B = 200,000 / 300,000 = 66.67%

Relative abundance is relatively straightforward to calculate, and since it is normalized to the total microbial count, it is unaffected by the total sample size. High-throughput sequencing techniques, such as 16S rRNA sequencing, are commonly used to obtain relative abundance data.

What is Absolute Abundance?

Absolute abundance refers to the actual number of a specific microorganism present in a sample. It is typically quantified as the "number of microbial cells per gram/milliliter of sample." This measure directly informs us about the actual quantity of microorganisms in the sample.

Example:

In a water sample, suppose 100,000 bacteria of species A and 200,000 bacteria of species B are detected. The absolute abundance of species A is 100,000 cells, and for species B, it is 200,000 cells.

Absolute abundance data is usually obtained through quantitative techniques such as quantitative PCR (qPCR), which require additional experimental steps and precise measurement tools.

Key Differences Between Absolute and Relative Abundance

The main differences between absolute and relative abundance are as follows:

• Absolute abundance provides the actual count of microorganisms, which reflects the true number of microbes in the sample.
• Relative abundance describes the proportional relationship between different microorganisms within a sample, allowing for comparison of their relative distributions.

However, a limitation of relative abundance is that it may not accurately reflect the true changes in a microorganism's abundance when the total sample size varies. For instance, if the numbers of both species A and species B decrease proportionally, the relative abundance might remain unchanged, even though the actual number of these microorganisms has decreased. In contrast, absolute abundance would reveal the actual decrease in microbial numbers.

Figure 1. The distinction between absolute abundances and relative abundances (Huang Lin et al., 2020)

When to Use Absolute Abundance and When to Use Relative Abundance?

Absolute abundance: If the goal is to determine the actual number of microorganisms (such as in disease monitoring or precise quantification of microbial load), absolute abundance is more reliable.

Relative abundance: If the focus is on understanding the community structure and comparing the proportions of different microorganisms within a sample (such as in ecological studies of microbial populations), relative abundance is often preferred. This approach highlights the proportional relationships among microbes within the community.

By understanding these two different approaches, researchers can select the appropriate method for their specific study objectives, ensuring that the data obtained provides meaningful and accurate insights into the microbiome.

Absolute and Relative Abundance in 16S rRNA Sequencing

16S rRNA sequencing is a widely employed technique for analyzing microbial community structure. It works by amplifying the 16S rRNA gene of bacteria and archaea, which helps identify the types of microorganisms present in a sample.

In 16S sequencing, relative abundance is commonly used. This is because the sequencing results typically provide the sequence reads for each bacterial taxon (e.g., operational taxonomic units (OTUs) or amplicon sequence variants (ASVs)), rather than the actual quantity of organisms. Variations in sequencing depth and efficiency can influence the total number of sequences across different samples, prompting the conversion of sequence counts into relative abundance for comparison between samples.

Absolute Abundance in 16S Sequencing

To determine absolute abundance in 16S rRNA sequencing, additional methods, such as qPCR or flow cytometry, are required to quantify the total microbial load in the sample. The absolute abundance of each microbial species can then be calculated by multiplying the relative abundance by the total microbial quantity.

Example:

If qPCR reveals that the total bacterial count in a sample is 1 million, and the relative abundance of species A is 20%, the absolute abundance of species A would be:

Figure 2. The formula for calculating absolute abundance

Summary

In 16S sequencing, relative abundance is the primary method of analysis because it eliminates the variability caused by differences in sequencing depth. If absolute abundance is required, quantitative techniques must be incorporated to supplement the data.

Absolute and Relative Abundance in Metagenomic Sequencing

Metagenomic sequencing involves directly sequencing the genomes of all microorganisms present in a sample, providing a more comprehensive analysis. This method allows for the detection of bacteria, fungi, viruses, and other microbial genetic information. Metagenomic sequencing offers higher resolution and enables direct insights into microbial functional characteristics.

Similar to 16S rRNA sequencing, metagenomic sequencing typically utilizes relative abundance for data analysis. This is due to the fact that the total number of sequence reads in metagenomic sequencing can be influenced by sequencing depth, leading to significant variation in the total read count between samples. Therefore, the use of relative abundance ensures comparability across samples.

Absolute Abundance in Metagenomics

To determine the absolute abundance of each microbial species in metagenomic sequencing, total microbial abundance data for the sample is required. As with 16S rRNA sequencing, methods such as qPCR or other quantitative techniques can be employed to estimate total microbial load. The absolute abundance for each microorganism can then be calculated by multiplying the relative abundance by the estimated total abundance.

Metagenomics vs. 16S rRNA Sequencing: Abundance Metrics

16S rRNA Sequencing: This method is more suitable for quickly assessing the composition and changes in microbial communities, especially when budget constraints exist. The calculation of relative abundance is straightforward; however, the resolution is limited due to the sequencing of only a specific fragment of the 16S gene, making it challenging to obtain accurate absolute counts.

Metagenomic Sequencing: This approach captures a broader range of microbial taxa, including bacteria, viruses, fungi, and other organisms, while also providing more in-depth analysis of microbial functional traits. Although metagenomic sequencing is more costly, it offers richer information, including gene functions and ecological roles. Like 16S rRNA sequencing, metagenomic sequencing primarily relies on relative abundance analysis unless supplemented with quantitative techniques to derive absolute abundance.

Introduction of Evolutionary Analysis

Evolutionary analysis can reveal the genetic sequence relationship of classification in the evolution process from the perspective of molecular evolution. In the process of biological information analysis, phylogenetic trees are often used to present the analysis results. The phylogenetic tree can reveal the biological evolution process and mechanism of related species or genes. The evolutionary tree is mainly constructed based on the gene sequence to compare and analyze the changes and differences of genes in different environments, specific species or functions at the level of molecular evolution.

Learn other: 16s rrna sequencing

Evolutionary Analysis Method

Building a phylogenetic tree generally includes the following steps: multiple sequence alignment, choose a method to construct a phylogenetic tree, build evolutionary tree, evaluate evolutionary tree, and lastly beautify the evolutionary tree.

Fig 1. Phylogenetic tree construction pipeline

The first step of phylogenetic tree construction is to perform multiple sequence alignment. Commonly used software includes MEGA, cluster X, Muscle, phylip, etc.

Methods of phylogenetic tree construction generally include distance-based and character-based methods.

Bootstrap is usually used for evolutionary tree evaluation and testing.

Commonly used evolutionary tree beautification software includes AI, PS, ggtree, GraPhlAn, treeview, Figtree, and online website ITOL.

Fig 2. Phylogenetic tree of 199 B. rapa and 119 B. oleracea accessions. The tree was constructed using 6,707 SNP loci selected from gene pairs that were syntenic in the B. rapa and B. oleracea genomes. (Cheng F, et al. 2016)

Application Field

Human, animal and plant evolution analysis.

The evolutionary relationship of a gene or gene family in different species.

During genetic evolution research, the biological evolution process of species or genes can often be displayed intuitively by constructing evolutionary trees. Using various biological information analysis software and tools, a phylogenetic tree can be easily constructed. However, constructing a phylogenetic tree accurately and intuitively requires certain biological information analysis skills. CD Genomics' professional biological information analysis team, with excellent analysis and mapping skills, can provide phylogenetic tree construction services at any time. If you have any questions, please feel free to contact us.

Reference

1. Cheng F, et al. Subgenome parallel selection is associated with morphotype diversification and convergent crop domestication in Brassica rapa and Brassica oleracea. Nat Genet. 2016;48(10):1218-1224. doi:10.1038/ng.3634

Population genetics and precision health research rely on large genomic datasets. Long-read sequencing from Pacific Biosciences and Oxford Nanopore Technologies (ONT) has achieved a level of accuracy and throughput that allows for the progression from single genomes and small populations of individuals to the detection of variation in large-scale populations. Population-scale genomic studies are important, including reflecting the genetic diversity of target populations, detecting challenging genomic regions, serving as a resource for population genetics, translational research, and drug discovery, etc.

Overview of population-scale studies using long-read sequencing. (De Coster et al., 2021)

Overview

Sequencing the deoxyribonucleic acid (DNA) or messenger ribonucleic acid (mRNA) of different individuals in single or multispecies populations (known as population-scale sequencing) is fundamentally aimed at revealing allelic variation in macroscopic population profiles. This approach provides a critical scaffold for addressing multifaceted queries spanning the research fields of evolutionary biology, agronomic biotechnology, and translational medicine. Historical precedents of population-centric genomic studies, especially genome-wide association studies (GWAS), have always faced challenges in capturing the full range of genetic determinants of human phenotypic expression and pathological manifestations. This gap in understanding can largely be attributed to the intricate network of structural variation (SV). These SVs include inversions, deletions, and other complex chromosomal rearrangements that often remain elusive in the face of traditional sequencing methods.

High-throughput short-read sequencing platforms are characterized by read lengths that fluctuate between 25 base pairs (bp) and an upper limit of 400 bp. Their abilities are often hampered when they are tasked with deciphering variations hidden within the "dark matter" regions of the genome. Furthermore, they do not perform well in accurately resolving broad or complex variants. These obstacles not only compromise the integrity of genetic inferences derived from ancestry cohort datasets, but ultimately lead to a weakened, if not fragmented, understanding of the intricate interplay between genetic markers and disease etiology.

Emerging on this horizon is the promising field of long-read sequencing. This format enables the interrogation of genomic fragments spanning considerable contiguous lengths. The resulting capability is a holistic characterization of SVs across the human genomic landscape, setting the stage for an era dominated by population-scale long-read sequencing. By leveraging this cutting-edge technique, researchers are poised to unearth previously mysterious SVs with important links to phenotypic expression in humans, crops, fruit flies, and even birds such as songbirds. This paradigm shift is not just a technological advance, but marks a transformative leap in metagenomic research, heralding unprecedented insights and breakthroughs.

Project Strategies for Population-scale Sequencing

At the start of a population-scale sequencing project, there are multiple strategies with specific budget requirements to consider, as shown below. These strategies allow for different sizes and budgets, which can have an impact on the level of resolution at which genetic variants are detected.

Full Coverage Approach

This strategy is designed to sequence every sample from a population with moderate to high coverage, allowing for the highest level of resolution. The main criterion for determining the coverage required for each sample is whether it is assembled from scratch (requiring >40-fold coverage) or a reference-based comparison method (requiring >12-fold coverage42 ). The advantages of this strategy are its comprehensiveness, simplicity of study design, and relatively simple computational workflow. In addition, the samples are similarly covered and therefore equally well-studied, and rare variations in each sample can be easily detected.

Mixed Coverage Approach

In a "mixed-coverage" approach, a subset of samples representing subgroups (e.g., ethnicities or subgroups) of a cohort is sequenced at high coverage, and the remaining samples are sequenced at low coverage. Although this approach is generally less expensive than the full coverage approach, it still achieves higher overall detection sensitivity and is therefore particularly suitable for studies with a large number of individuals or a limited budget. However, some analytical challenges remain, especially in achieving high accuracy of genotypes across multiple samples or in distinguishing somatic versus heterozygous germline variants, which is further complicated by regions exhibiting recurrent mutations. In addition, this hybrid coverage approach will certainly bias against common alleles, as many rare alleles may be missed, especially when a locus is heterozygous and alternative alleles are therefore sparsely covered.

Hybrid Sequencing Methods

This approach involves sequencing only a small number of samples (e.g., 10-20% of all samples) with long reads, sequencing the remaining samples with short reads, and genotyping the variants found in the long reads. Once a subset of samples has been sequenced using the long read technique to produce a set of identified SVs, they can be genotyped for their breakpoint coordinates in the short read long sequence dataset. In this way, robust allele frequencies for the identified variants can be obtained. This strategy has been applied to diversity panels of human SVs to discover new expression quantitative trait loci (eQTL) and evolutionarily adapted traits.

Overview of long-read population study design. (De Coster et al., 2021)

The Importance of Long-Read Sequencing Technology in Population-Scale Study

One of the inherent challenges of population genetics is the accurate phasing of haplotypes-determining specific combinations of alleles located on a single chromosome. Long-read sequencing provides an effective solution by capturing longer DNA fragments, which can directly determine haplotype structure without relying on computational prediction or family-based studies. This capability is transformative for population-scale studies, where understanding the distribution and combination of specific allele sets can help decipher population history, migration patterns, and shared inheritance patterns.

Structural variants, such as deletions, duplications, and inversions, can have profound effects on gene function and expression. Capturing these variants at high resolution is critical when studying large populations. Long-read sequencing can identify structural variants that may be overlooked or inaccurately represented by short-read methods.

Population-scale Sequencing Downstream Analysis Methods

The choice of analytical tools is critical for downstream analysis at the population scale. Prior to downstream analysis, quality control must be performed on experimental factors that directly affect the performance of assembly, SV detection, and read-sequencing phases. There are several strategies for population-scale downstream analysis:

Read Alignment-based Analysis

Comparison-based methods are often the preferred approach for population-scale studies because they facilitate the comparison of all samples to a common coordinate system (i.e., the reference genome). In addition, these methods are usually less computationally demanding and require much less coverage than compilation-based methods. Comparison-based methods rely on matching sequencing reads to a reference genome, the overall correctness of which will affect the analysis of the read data.

Software for analyzing long-read sequence data, such as NGMLR and LAST methods, speeds up the matching process and improves the accuracy of long-read matching. In addition, a variety of tools for detecting genetic variation can eliminate the need for high sequencing coverage by enabling SV calling and genotyping at lower coverage.

Population-scale De Novo Assemblies

Traditional reference genomes, often based on short-read sequencing, can be fragmented and may miss key sequences. Such omissions may lead to significant differences, including false-positive or false-negative variant identifications. Therefore, there is an urgent need to construct and compare scratch assemblies.

The increased availability and affordability of long-read sequencing data have led to an explosion of faster and more accurate genome assembly tools. De novo assembly-based methods are often more sensitive and better suited to reconstructing highly diverse regions of the genome than comparison-based methods. The increasing yield of long-read sequencing technologies will allow sufficient coverage of each sample to be sequenced for high-quality de novo assembly.

Graph Genome Methods

Both read matching and de novo assembly methods can have systematic problems with complex structural variants, missing insertion sequences in the reference genome, repetitive variants, and highly polymorphic loci. A major benefit of graph genomes is the use of short reads for genotyping SVs. In addition, with this graph-based approach, for population studies, the often discussed dichotomy of using an existing reference genome for alignment or constructing a new reference genome by assembling it from scratch can be avoided since downstream of this step all sequences have to be aligned with the backbone of the individual (reference) assembly or pan-genome map for identification of variants, annotation, and statistical evaluation.

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circular rna sequencing

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