Pan-genome

Last updated
Pangenome analysis of Streptococcus agalactiae genomes made with Anvi'o software whose development is led by A. Murat Eren. Genomes obtained from Tettelin et al. (2005). Each circle corresponds to one genome and each radius represent a gene family. At the bottom and at right are localized the core genome families. Some families in the core may have more than one homologous gene per genome. In the middle, at the left of the figure the shell genome is observed. At the top left are shown families from the dispensable genome and singletons. Example of Anvi'o software output.png
Pangenome analysis of Streptococcus agalactiae genomes made with Anvi'o software whose development is led by A. Murat Eren. Genomes obtained from Tettelin et al. (2005). Each circle corresponds to one genome and each radius represent a gene family. At the bottom and at right are localized the core genome families. Some families in the core may have more than one homologous gene per genome. In the middle, at the left of the figure the shell genome is observed. At the top left are shown families from the dispensable genome and singletons.

In the fields of molecular biology and genetics, a pan-genome (pangenome or supragenome) is the entire set of genes from all strains within a clade. More generally, it is the union of all the genomes of a clade. [2] [3] [4] [5] The pan-genome can be broken down into a "core pangenome" that contains genes present in all individuals, a "shell pangenome" that contains genes present in two or more strains, and a "cloud pangenome" that contains genes only found in a single strain. [3] [4] [6] Some authors also refer to the cloud genome as "accessory genome" containing 'dispensable' genes present in a subset of the strains and strain-specific genes. [2] [3] [4] Note that the use of the term 'dispensable' has been questioned, at least in plant genomes, as accessory genes play "an important role in genome evolution and in the complex interplay between the genome and the environment". [5] The field of study of pangenomes is called pangenomics. [2]

Contents

The genetic repertoire of a bacterial species is much larger than the gene content of an individual strain. [7] Some species have open (or extensive) pangenomes, while others have closed pangenomes. [2] For species with a closed pan-genome, very few genes are added per sequenced genome (after sequencing many strains), and the size of the full pangenome can be theoretically predicted. Species with an open pangenome have enough genes added per additional sequenced genome that predicting the size of the full pangenome is impossible. [4] Population size and niche versatility have been suggested as the most influential factors in determining pan-genome size. [2]

Pangenomes were originally constructed for species of bacteria and archaea, but more recently eukaryotic pan-genomes have been developed, particularly for plant species. Plant studies have shown that pan-genome dynamics are linked to transposable elements. [8] [9] [10] [11] The significance of the pan-genome arises in an evolutionary context, especially with relevance to metagenomics, [12] but is also used in a broader genomics context. [13] An open access book reviewing the pangenome concept and its implications, edited by Tettelin and Medini, was published in the spring of 2020. [14]

Etymology

The term 'pangenome' was defined with its current meaning by Tettelin et al. in 2005; [2] it derives 'pan' from the Greek word παν, meaning 'whole' or 'everything', while the genome is a commonly used term to describe an organism's complete genetic material. Tettelin et al. applied the term specifically to bacteria, whose pangenome "includes a core genome containing genes present in all strains and a dispensable genome composed of genes absent from one or more strains and genes that are unique to each strain." [2]

Parts of the pangenome

In the pangenome, we can identify three sets of genes: Core, Shell, and Cloud genome. The Core genome comprises the genes that are present in all genomes analyzed. To avoid dismissing families due to sequencing artifacts some authors consider the softcore (>95% occurrence). The Shell genome consists of the genes shared by the majority of genomes (10-95% occurrence). The gene families present in only one genome or <10% occurrence are described as Dispensable or Cloud genome. Parts of the pangenome.png
In the pangenome, we can identify three sets of genes: Core, Shell, and Cloud genome. The Core genome comprises the genes that are present in all genomes analyzed. To avoid dismissing families due to sequencing artifacts some authors consider the softcore (>95% occurrence). The Shell genome consists of the genes shared by the majority of genomes (10-95% occurrence). The gene families present in only one genome or <10% occurrence are described as Dispensable or Cloud genome.

Core

Is the part of the pangenome that is shared by every genome in the tested set. Some authors have divided the core pangenome in hard core, those families of homologous genes that has at least one copy of the family shared by every genome (100% of genomes) and the soft core or extended core, [15] those families distributed above a certain threshold (90%). In a study that involves the pangenomes of Bacillus cereus and Staphylococcus aureus, some of them isolated from the international space station, the thresholds used for segmenting the pangenomes were as follows: "Cloud," "Shell," and "Core" corresponding to gene families with presence in <10%, 10 to 95%, and >95% of the genomes, respectively. [16]

The core genome size and proportion to the pangenome depends on several factors, but it is especially dependent on the phylogenetic similarity of the considered genomes. For example, the core of two identical genomes would also be the complete pangenome. The core of a genus will always be smaller than the core genome of a species. Genes that belong to the core genome are often related to house keeping functions and primary metabolism of the lineage, nevertheless, the core gene can also contain some genes that differentiate the species from other species of the genus, i.e. that may be related pathogenicity to niche adaptation. [17]

Shell

Is the part of the pangenome shared by the majority of the genomes in a pangenome. [18] There is not a universally accepted threshold to define the shell genome, some authors consider a gene family as part of the shell pangenome if it shared by more than 50% of the genomes in the pangenome. [19] A family can be part of the shell by several evolutive dynamics, for example by gene loss in a lineage where it was previously part of the core genome, such is the case of enzymes in the tryptophan operon in Actinomyces , [20] or by gene gain and fixation of a gene family that was previously part of the dispensable genome such is the case of trpF gene in several Corynebacterium species. [21]

Cloud

The cloud genome consists of those gene families shared by a minimal subset of the genomes in the pangenome, [22] it includes singletons or genes present in only one of the genomes. It is also known as the peripheral genome, or accessory genome. Gene families in this category are often related to ecological adaptation.[ citation needed ]

Classification

a) Closed pangenomes are characterized by large core genomes and small accessory genomes. b) Open pangenomes tend to have small core genomes and large accessory genomes. c) The size of open pangenomes tends to increase with every added genome, meanwhile closed pangenome's size tends to be asymptotic despite adding more genomes. Due to this characteristic, complete pangenome size for closed pangenomes can be predicted. Characteristics of open and closed pangenomes.png
a) Closed pangenomes are characterized by large core genomes and small accessory genomes. b) Open pangenomes tend to have small core genomes and large accessory genomes. c) The size of open pangenomes tends to increase with every added genome, meanwhile closed pangenome's size tends to be asymptotic despite adding more genomes. Due to this characteristic, complete pangenome size for closed pangenomes can be predicted.

The pan-genome can be somewhat arbitrarily classified as open or closed based on the alpha value of the Heap law: [23] [15] 

if  then the pangenome is considered open. if  then the pangenome is considered closed.

Usually, the pangenome software can calculate the parameters of the Heap law that best describe the behavior of the data.

Open pangenome

An open pangenome occurs when in one taxonomic lineage keeps increasing the number of new gene families and this increment does not seem to be asymptotic regardless how many new genomes are added to the pangenome. Escherichia coli is an example of a species with an open pangenome. Any E. coli genome size is in the range of 4000-5000 genes and the pangenome size estimated for this species with approximately 2000 genomes is composed by 89,000 different gene families. [24] The pangenome of the domain bacteria is also considered to be open.

Closed Pangenome

A closed pangenome occurs in a lineage when only few gene families are added when new genomes are incorporated into the pangenome analysis, and the total amount of gene families in the pangenome seem to be asymptotic to one number. It is believed that parasitism and species that are specialists in some ecological niche tend to have closed pangenomes. Staphylococcus lugdunensis is an example of a commensal bacteria with closed pan-genome. [25]

History

Pangenome

The original pangenome concept was developed by Tettelin et al. [2] when they analyzed the genomes of eight isolates of Streptococcus agalactiae, where they described a core genome shared by all isolates, accounting for approximately 80% of any single genome, plus a dispensable genome consisting of partially shared and strain-specific genes. Extrapolation suggested that the gene reservoir in the S. agalactiae pan-genome is vast and that new unique genes would continue to be identified even after sequencing hundreds of genomes. [2] The pangenome comprises the entirety of the genes discovered in the sequenced genomes of a given microbial species and it can change when new genomes are sequenced and incorporated into the analysis.[ citation needed ]

The supergenome is defined as all genes accessible for a certain species, the pangenome if sequencing of all genomes of one species was available. Metapangenome is the pangenome analysis applied to metagenomic samples, where the union of genes of several species is evaluated for a given habitat. Supergenome and metapangenome.png
The supergenome is defined as all genes accessible for a certain species, the pangenome if sequencing of all genomes of one species was available. Metapangenome is the pangenome analysis applied to metagenomic samples, where the union of genes of several species is evaluated for a given habitat.

The pangenome of a genomic lineage accounts for the intra lineage gene content variability. Pangenome evolves due to: gene duplication, gene gain and loss dynamics and interaction of the genome with mobile elements that are shaped by selection and drift. [26] Some studies point that prokaryotes pangenomes are the result of adaptive, not neutral evolution that confer species the ability to migrate to new niches. [27]

Supergenome

The supergenome can be thought of as the real pangenome size if all genomes from a species were sequenced. [28] It is defined as all genes accessible for being gained by a certain species. It cannot be calculated directly but its size can be estimated by the pangenome size calculated from the available genome data. Estimating the size of the cloud genome can be troubling because of its dependence on the occurrence of rare genes and genomes. In 2011 genomic fluidity was proposed as a measure to categorize the gene-level similarity among groups of sequenced isolates. [29] In some lineages the supergenomes did appear infinite, [30] as is the case of the Bacteria domain. [31]

Metapangenome

'Metapangenome' has been defined as the outcome of the analysis of pangenomes in conjunction with the environment where the abundance and prevalence of gene clusters and genomes are recovered through shotgun metagenomes. [32] The combination of metagenomes with pangenomes, also referred to as "metapangenomics", reveals the population-level results of habitat-specific filtering of the pangenomic gene pool. [33]

Other authors consider that Metapangenomics expands the concept of pangenome by incorporating gene sequences obtained from uncultivated microorganisms by a metagenomics approach. A metapangenome comprises both sequences from metagenome-assembled genomes (MAGs) and from genomes obtained from cultivated microorganisms. [34] Metapangenomics has been applied to assess diversity of a community, microbial niche adaptation, microbial evolution, functional activities, and interaction networks of the community. [35] The Anvi'o platform developed a workflow that integrates analysis and visualization of metapangenomes by generating pangenomes and study them in conjunction with metagenomes. [32]

Examples

Prokaryote pangenome

The S. pneumoniae pan-genome. (a) Number of new genes as a function of the number of sequenced genomes. The predicted number of new genes drops sharply to zero when the number of genomes exceeds 50. (b) Number of core genes as a function of the number of sequenced genomes. The number of core genes converges to 1,647 for number of genomes n-[?]. From Donati et al. Streptococcus pneumoniae pan-genome Donati 2011.jpg
The S. pneumoniae pan-genome. (a) Number of new genes as a function of the number of sequenced genomes. The predicted number of new genes drops sharply to zero when the number of genomes exceeds 50. (b) Number of core genes as a function of the number of sequenced genomes. The number of core genes converges to 1,647 for number of genomes n→∞. From Donati et al.

In 2018, 87% of the available whole genome sequences were bacteria fueling researchers interest in calculating prokaryote pangenomes at different taxonomic levels. [22] In 2015, the pangenome of 44 strains of Streptococcus pneumoniae bacteria shows few new genes discovered with each new genome sequenced (see figure). In fact, the predicted number of new genes dropped to zero when the number of genomes exceeds 50 (note, however, that this is not a pattern found in all species). This would mean that S. pneumoniae has a 'closed pangenome'. [37] The main source of new genes in S. pneumoniae was Streptococcus mitis from which genes were transferred horizontally. The pan-genome size of S. pneumoniae increased logarithmically with the number of strains and linearly with the number of polymorphic sites of the sampled genomes, suggesting that acquired genes accumulate proportionately to the age of clones. [36] Another example of prokaryote pan-genome is Prochlorococcus , the core genome set is much smaller than the pangenome, which is used by different ecotypes of Prochlorococcus. [38] Open pan-genome has been observed in environmental isolates such as Alcaligenes sp. [39] and Serratia sp., [40] showing a sympatric lifestyle. Nevertheless, open pangenome is not exclusive to free living microorganisms, a 2015 study on Prevotella bacteria isolated from humans, compared the gene repertoires of its species derived from different body sites of human. It also reported an open pan-genome showing vast diversity of gene pool. [41]

Archaea also have some pangenome studies. Halobacteria pangenome shows the following gene families in the pangenome subsets: core (300), variable components (Softcore: 998, Cloud:36531, Shell:11784). [42]

Eukaryote pangenome

Eukaryote organisms such as fungi, animals and plants have also shown evidence of pangenomes. In four fungi species whose pangenome has been studied, between 80 and 90% of gene models were found as core genes. The remaining accessory genes were mainly involved in pathogenesis and antimicrobial resistance. [43]

In animals, the human pangenome is being studied. In 2010 a study estimated that a complete human pan-genome would contain ~19–40 Megabases of novel sequence not present in the extant reference human genome. [44] The Human Pangenome consortium has the goal to acknowledge the human genome diversity. In 2023, a draft human pangenome reference was published. [45] It is based on 47 genomes from persons of varied ethnicity. [45] Plans are underway for an improved reference capturing still more biodiversity from a still wider sample. [45]

Among plants, there are examples of pangenome studies in model species, both diploid [9] and polyploid, [10] and a growing list of crops. [46] [47] Pangenomes have shown promise as a tool in plant breeding by accounting for structural variants and SNPs in non-reference genomes, which helps to solve the problem of missing heritability that persists in genome wide association studies. [48] An emerging plant-based concept is that of pan-NLRome, which is the repertoire of nucleotide-binding leucine-rich repeat (NLR) proteins, intracellular immune receptors that recognize pathogen proteins and confer disease resistance. [49]

Virus pangenome

Virus does not necessarily have genes extensively shared by clades such as is the case of 16S in bacteria, and therefore the core genome of the full Virus Domain is empty. Nevertheless, several studies have calculated the pangenome of some viral lineages. The core genome from six species of pandoraviruses comprises 352 gene families only 4.7% of the pangenome, resulting in an open pangenome. [50]

Data structures

The number of sequenced genomes is continuously growing "simply scaling up established bioinformatics pipelines will not be sufficient for leveraging the full potential of such rich genomic data sets". [51] Pangenome graphs are emerging data structures designed to represent pangenomes and to efficiently map reads to them. They have been reviewed by Eizenga et al. [52]

Software tools

Pangenome analysis of genomes of Streptococcus agalactiae. Example of phylogenies made with BPGA software. This software allows us to generate phylogenies based on the clustering of the core genome or pangenome. Core and pan phylogenetic reconstructions are not necessarily matching. BPGA analysis of Streptococcus agalactiae.png
Pangenome analysis of genomes of Streptococcus agalactiae. Example of phylogenies made with BPGA software. This software allows us to generate phylogenies based on the clustering of the core genome or pangenome. Core and pan phylogenetic reconstructions are not necessarily matching.

As interest in pangenomes increased, there have been several software tools developed to help analyze this kind of data. To start a pangenomic analysis the first step is the homogenization of genome annotation. [23] The same software should be used to annotate all genomes used, such as GeneMark [53] or RAST. [54] In 2015, a group reviewed the different kinds of analyses and tools a researcher may have available. [55] There are seven kinds of software developed to analyze pangenomes: Those dedicated to cluster homologous genes; identify SNPs; plot pangenomic profiles; build phylogenetic relationships of orthologous genes/families of strains/isolates; function-based searching; annotation and/or curation; and visualization. [55]

The two most cited software tools for pangenomic analysis at the end of 2014 [55] were Panseq [56] and the pan-genomes analysis pipeline (PGAP). [57] Other options include BPGA – A Pan-Genome Analysis Pipeline for prokaryotic genomes, [58] GET_HOMOLOGUES, [59] Roary. [60] and PanDelos. [61] In 2015 a review focused on prokaryote pangenomes [62] and another for plant pan-genomes were published. [63] Among the first software packages designed for plant pangenomes were PanTools. [64] and GET_HOMOLOGUES-EST. [11] [59] In 2018 panX was released, an interactive web tool that allows inspection of gene families evolutionary history. [65] panX can display an alignment of genomes, a phylogenetic tree, mapping of mutations and inference about gain and loss of the family on the core-genome phylogeny. In 2019 OrthoVenn 2.0 [66] allowed comparative visualization of families of homologous genes in Venn diagrams up to 12 genomes. In 2023, BRIDGEcerealwas developed to survey and graph indel-based haplotypes from pan-genome through a gene model ID. [67]

Pangenome graph of 3 117 Acinetobacter baumannii genomes generated with PPanGGOLiN software. Edges correspond to genomic colocalization and nodes correspond to genes. The thickness of the edges is proportional to the number of genomes sharing that link. The edges between persistent (similar to core genes), shell and cloud nodes are colored in orange, green and blue, respectively. Pcbi.1007732.g002.png
Pangenome graph of 3 117 Acinetobacter baumannii genomes generated with PPanGGOLiN software. Edges correspond to genomic colocalization and nodes correspond to genes. The thickness of the edges is proportional to the number of genomes sharing that link. The edges between persistent (similar to core genes), shell and cloud nodes are colored in orange, green and blue, respectively.

In 2020 Anvi'o [1] was available as a multiomics platform that contains pangenomic and metapangenomic analyses as well as visualization workflows. In Anvi'o, genomes are displayed in concentrical circles and each radius represents a gene family, allowing for comparison of more than 100 genomes in its interactive visualization. In 2020, a computational comparison of tools for extracting gene-based pangenomic contents (such as GET_HOMOLOGUES, PanDelos, Roary, and others) has been released. [68] Tools were compared from a methodological perspective, analyzing the causes that lead a given methodology to outperform other tools. The analysis was performed by taking into account different bacterial populations, which are synthetically generated by changing evolutionary parameters. Results show a differentiation of the performance of each tool that depends on the composition of the input genomes. Again in 2020, several tools introduced a graphical representation of the pangenomes showing the contiguity of genes (PPanGGOLiN, [46] Panaroo [65] ).

Example of possible outputs of BPGA software. Pangenome analysis of genomes of Streptococcus agalactiae. At the left, the distribution of Go terms by core/dispensable/unique genome is shown. In this example, the category replication, recombination, and repair are enriched on unique gene families. On the right, a typical pan/core plot is shown, when more genomes have added the size of the core is decreasing, and on the contrary the size of the pangenome increases. Pangenome analysis with BPGA software.png
Example of possible outputs of BPGA software. Pangenome analysis of genomes of Streptococcus agalactiae . At the left, the distribution of Go terms by core/dispensable/unique genome is shown. In this example, the category replication, recombination, and repair are enriched on unique gene families. On the right, a typical pan/core plot is shown, when more genomes have added the size of the core is decreasing, and on the contrary the size of the pangenome increases.

See also

Related Research Articles

<span class="mw-page-title-main">Bioinformatics</span> Computational analysis of large, complex sets of biological data

Bioinformatics is an interdisciplinary field of science that develops methods and software tools for understanding biological data, especially when the data sets are large and complex. Bioinformatics uses biology, chemistry, physics, computer science, computer programming, information engineering, mathematics and statistics to analyze and interpret biological data. The subsequent process of analyzing and interpreting data is referred to as computational biology.

<span class="mw-page-title-main">Genomics</span> Discipline in genetics

Genomics is an interdisciplinary field of biology focusing on the structure, function, evolution, mapping, and editing of genomes. A genome is an organism's complete set of DNA, including all of its genes as well as its hierarchical, three-dimensional structural configuration. In contrast to genetics, which refers to the study of individual genes and their roles in inheritance, genomics aims at the collective characterization and quantification of all of an organism's genes, their interrelations and influence on the organism. Genes may direct the production of proteins with the assistance of enzymes and messenger molecules. In turn, proteins make up body structures such as organs and tissues as well as control chemical reactions and carry signals between cells. Genomics also involves the sequencing and analysis of genomes through uses of high throughput DNA sequencing and bioinformatics to assemble and analyze the function and structure of entire genomes. Advances in genomics have triggered a revolution in discovery-based research and systems biology to facilitate understanding of even the most complex biological systems such as the brain.

<span class="mw-page-title-main">Omics</span> Suffix in biology

The branches of science known informally as omics are various disciplines in biology whose names end in the suffix -omics, such as genomics, proteomics, metabolomics, metagenomics, phenomics and transcriptomics. Omics aims at the collective characterization and quantification of pools of biological molecules that translate into the structure, function, and dynamics of an organism or organisms.

<span class="mw-page-title-main">Comparative genomics</span>

Comparative genomics is a field of biological research in which the genomic features of different organisms are compared. The genomic features may include the DNA sequence, genes, gene order, regulatory sequences, and other genomic structural landmarks. In this branch of genomics, whole or large parts of genomes resulting from genome projects are compared to study basic biological similarities and differences as well as evolutionary relationships between organisms. The major principle of comparative genomics is that common features of two organisms will often be encoded within the DNA that is evolutionarily conserved between them. Therefore, comparative genomic approaches start with making some form of alignment of genome sequences and looking for orthologous sequences in the aligned genomes and checking to what extent those sequences are conserved. Based on these, genome and molecular evolution are inferred and this may in turn be put in the context of, for example, phenotypic evolution or population genetics.

<i>Streptomyces</i> Genus of bacteria

Streptomyces is the largest genus of Actinomycetota, and the type genus of the family Streptomycetaceae. Over 700 species of Streptomyces bacteria have been described. As with the other Actinomycetota, streptomycetes are gram-positive, and have very large genomes with high GC content. Found predominantly in soil and decaying vegetation, most streptomycetes produce spores, and are noted for their distinct "earthy" odor that results from production of a volatile metabolite, geosmin. Different strains of the same species may colonize very diverse environments.

<span class="mw-page-title-main">Metagenomics</span> Study of genes found in the environment

Metagenomics is the study of genetic material recovered directly from environmental or clinical samples by a method called sequencing. The broad field may also be referred to as environmental genomics, ecogenomics, community genomics or microbiomics.

Computational genomics refers to the use of computational and statistical analysis to decipher biology from genome sequences and related data, including both DNA and RNA sequence as well as other "post-genomic" data. These, in combination with computational and statistical approaches to understanding the function of the genes and statistical association analysis, this field is also often referred to as Computational and Statistical Genetics/genomics. As such, computational genomics may be regarded as a subset of bioinformatics and computational biology, but with a focus on using whole genomes to understand the principles of how the DNA of a species controls its biology at the molecular level and beyond. With the current abundance of massive biological datasets, computational studies have become one of the most important means to biological discovery.

<span class="mw-page-title-main">16S ribosomal RNA</span> RNA component

16S ribosomal RNA is the RNA component of the 30S subunit of a prokaryotic ribosome. It binds to the Shine-Dalgarno sequence and provides most of the SSU structure.

<span class="mw-page-title-main">MicrobesOnline</span>

MicrobesOnline is a publicly and freely accessible website that hosts multiple comparative genomic tools for comparing microbial species at the genomic, transcriptomic and functional levels. MicrobesOnline was developed by the Virtual Institute for Microbial Stress and Survival, which is based at the Lawrence Berkeley National Laboratory in Berkeley, California. The site was launched in 2005, with regular updates until 2011.

<span class="mw-page-title-main">Human Microbiome Project</span> Former research initiative

The Human Microbiome Project (HMP) was a United States National Institutes of Health (NIH) research initiative to improve understanding of the microbiota involved in human health and disease. Launched in 2007, the first phase (HMP1) focused on identifying and characterizing human microbiota. The second phase, known as the Integrative Human Microbiome Project (iHMP) launched in 2014 with the aim of generating resources to characterize the microbiome and elucidating the roles of microbes in health and disease states. The program received $170 million in funding by the NIH Common Fund from 2007 to 2016.

Pathogenomics is a field which uses high-throughput screening technology and bioinformatics to study encoded microbe resistance, as well as virulence factors (VFs), which enable a microorganism to infect a host and possibly cause disease. This includes studying genomes of pathogens which cannot be cultured outside of a host. In the past, researchers and medical professionals found it difficult to study and understand pathogenic traits of infectious organisms. With newer technology, pathogen genomes can be identified and sequenced in a much shorter time and at a lower cost, thus improving the ability to diagnose, treat, and even predict and prevent pathogenic infections and disease. It has also allowed researchers to better understand genome evolution events - gene loss, gain, duplication, rearrangement - and how those events impact pathogen resistance and ability to cause disease. This influx of information has created a need for bioinformatics tools and databases to analyze and make the vast amounts of data accessible to researchers, and it has raised ethical questions about the wisdom of reconstructing previously extinct and deadly pathogens in order to better understand virulence.

<span class="mw-page-title-main">DNA annotation</span> The process of describing the structure and function of a genome

In molecular biology and genetics, DNA annotation or genome annotation is the process of describing the structure and function of the components of a genome, by analyzing and interpreting them in order to extract their biological significance and understand the biological processes in which they participate. Among other things, it identifies the locations of genes and all the coding regions in a genome and determines what those genes do.

In metagenomics, binning is the process of grouping reads or contigs and assigning them to individual genome. Binning methods can be based on either compositional features or alignment (similarity), or both.

Mark J. Pallen is a research leader at the Quadram Institute and Professor of Microbial Genomics at the University of East Anglia. In recent years, he has been at the forefront of efforts to apply next-generation sequencing to problems in microbiology and ancient DNA research.

Metatranscriptomics is the set of techniques used to study gene expression of microbes within natural environments, i.e., the metatranscriptome.

PICRUSt is a bioinformatics software package. The name is an abbreviation for Phylogenetic Investigation of Communities by Reconstruction of Unobserved States.

Machine learning in bioinformatics is the application of machine learning algorithms to bioinformatics, including genomics, proteomics, microarrays, systems biology, evolution, and text mining.

<span class="mw-page-title-main">Genome mining</span>

Genome mining describes the exploitation of genomic information for the discovery of biosynthetic pathways of natural products and their possible interactions. It depends on computational technology and bioinformatics tools. The mining process relies on a huge amount of data accessible in genomic databases. By applying data mining algorithms, the data can be used to generate new knowledge in several areas of medicinal chemistry, such as discovering novel natural products.

<span class="mw-page-title-main">Human Pangenome Reference</span>

The Human Pangenome Reference is a collection of genomes from a diverse cohort of individuals compiled by the Human Pangenome Reference Consortium (HPRC). This first draft pangenome comprises 47 phased, diploid assemblies from a diverse cohort of individuals and was intended to capture the genetic diversity of the human population. The development of this pangenome seeks to address perceived shortcomings in the current human reference genome by offering a more comprehensive and inclusive resource for genomic research and analysis...

References

  1. 1 2 Eren AM, Kiefl E, Shaiber A, Veseli I, Miller SE, Schechter MS, et al. (January 2021). "Community-led, integrated, reproducible multi-omics with anvi'o". Nature Microbiology. 6 (1): 3–6. doi:10.1038/s41564-020-00834-3. PMC   8116326 . PMID   33349678.
  2. 1 2 3 4 5 6 7 8 9 10 11 12 Tettelin H, Masignani V, Cieslewicz MJ, Donati C, Medini D, Ward NL, Angiuoli SV, Crabtree J, Jones AL, Durkin AS, DeBoy RT (2005-09-27). "Genome analysis of multiple pathogenic isolates of Streptococcus agalactiae: Implications for the microbial "pan-genome"". Proceedings of the National Academy of Sciences. 102 (39): 13950–13955. Bibcode:2005PNAS..10213950T. doi: 10.1073/pnas.0506758102 . ISSN   0027-8424. PMC   1216834 . PMID   16172379.
  3. 1 2 3 Medini D, Donati C, Tettelin H, Masignani V, Rappuoli R (December 2005). "The microbial pan-genome". Current Opinion in Genetics & Development. 15 (6): 589–94. doi:10.1016/j.gde.2005.09.006. PMID   16185861.
  4. 1 2 3 4 Vernikos G, Medini D, Riley DR, Tettelin H (February 2015). "Ten years of pan-genome analyses". Current Opinion in Microbiology. 23: 148–54. doi:10.1016/j.mib.2014.11.016. PMID   25483351.
  5. 1 2 Marroni F, Pinosio S, Morgante M (April 2014). "Structural variation and genome complexity: is dispensable really dispensable?". Current Opinion in Plant Biology. 18: 31–36. doi:10.1016/j.pbi.2014.01.003. PMID   24548794.
  6. ↑ Wolf YI, Makarova KS, Yutin N, Koonin EV (December 2012). "Updated clusters of orthologous genes for Archaea: a complex ancestor of the Archaea and the byways of horizontal gene transfer". Biology Direct. 7: 46. doi: 10.1186/1745-6150-7-46 . PMC   3534625 . PMID   23241446.
  7. ↑ Mira A, Martín-Cuadrado AB, D'Auria G, Rodríguez-Valera F (2010). "The bacterial pan-genome:a new paradigm in microbiology". Int Microbiol. 13 (2): 45–57. doi:10.2436/20.1501.01.110. PMID   20890839.
  8. ↑ Morgante M, De Paoli E, Radovic S (April 2007). "Transposable elements and the plant pan-genomes". Current Opinion in Plant Biology. 10 (2): 149–55. doi:10.1016/j.pbi.2007.02.001. PMID   17300983.
  9. 1 2 Gordon SP, Contreras-Moreira B, Woods DP, Des Marais DL, Burgess D, Shu S, et al. (December 2017). "Extensive gene content variation in the Brachypodium distachyon pan-genome correlates with population structure". Nature Communications. 8 (1): 2184. Bibcode:2017NatCo...8.2184G. doi:10.1038/s41467-017-02292-8. PMC   5736591 . PMID   29259172.
  10. 1 2 Gordon SP, Contreras-Moreira B, Levy JH, Djamei A, Czedik-Eysenberg A, Tartaglio VS, et al. (July 2020). "Gradual polyploid genome evolution revealed by pan-genomic analysis of Brachypodium hybridum and its diploid progenitors". Nature Communications. 11 (1): 3670. Bibcode:2020NatCo..11.3670G. doi: 10.1038/s41467-020-17302-5 . PMC   7391716 . PMID   32728126.
  11. 1 2 Contreras-Moreira B, Cantalapiedra CP, García-Pereira MJ, Gordon SP, Vogel JP, Igartua E, et al. (February 2017). "Analysis of Plant Pan-Genomes and Transcriptomes with GET_HOMOLOGUES-EST, a Clustering Solution for Sequences of the Same Species". Frontiers in Plant Science. 8: 184. doi: 10.3389/fpls.2017.00184 . PMC   5306281 . PMID   28261241.
  12. ↑ Reno ML, Held NL, Fields CJ, Burke PV, Whitaker RJ (May 2009). "Biogeography of the Sulfolobus islandicus pan-genome". Proceedings of the National Academy of Sciences of the United States of America. 106 (21): 8605–10. Bibcode:2009PNAS..106.8605R. doi: 10.1073/pnas.0808945106 . PMC   2689034 . PMID   19435847.
  13. ↑ Reinhardt JA, Baltrus DA, Nishimura MT, Jeck WR, Jones CD, Dangl JL (February 2009). "De novo assembly using low-coverage short read sequence data from the rice pathogen Pseudomonas syringae pv. oryzae". Genome Research. 19 (2): 294–305. doi:10.1101/gr.083311.108. PMC   2652211 . PMID   19015323.
  14. ↑ Tettelin H, Medini D (2020). Tettelin H, Medini D (eds.). The Pangenome (PDF). doi:10.1007/978-3-030-38281-0. ISBN   978-3-030-38280-3. PMID   32633908. S2CID   217167361.
  15. 1 2 Halachev MR, Loman NJ, Pallen MJ (2011). "Calculating orthologs in bacteria and Archaea: a divide and conquer approach". PLOS ONE. 6 (12): e28388. Bibcode:2011PLoSO...628388H. doi: 10.1371/journal.pone.0028388 . PMC   3236195 . PMID   22174796.
  16. ↑ Blaustein RA, McFarland AG, Ben Maamar S, Lopez A, Castro-Wallace S, Hartmann EM (2019). "Pangenomic Approach To Understanding Microbial Adaptations within a Model Built Environment, the International Space Station, Relative to Human Hosts and Soil". mSystems. 4 (1): e00281-18. doi:10.1128/mSystems.00281-18. PMC   6325168 . PMID   30637341.
  17. ↑ Mosquera-Rendón J, Rada-Bravo AM, Cárdenas-Brito S, Corredor M, Restrepo-Pineda E, Benítez-Páez A (January 2016). "Pangenome-wide and molecular evolution analyses of the Pseudomonas aeruginosa species". BMC Genomics. 17 (45): 45. doi: 10.1186/s12864-016-2364-4 . PMC   4710005 . PMID   26754847.
  18. ↑ Snipen L, Ussery DW (January 2010). "Standard operating procedure for computing pangenome trees". Standards in Genomic Sciences. 2 (1): 135–41. doi:10.4056/sigs.38923. PMC   3035256 . PMID   21304685.
  19. ↑ Sélem-Mojica N, Aguilar C, Gutiérrez-García K, Martínez-Guerrero CE, Barona-Gómez F (December 2019). "EvoMining reveals the origin and fate of natural product biosynthetic enzymes". Microbial Genomics. 5 (12): e000260. doi:10.1099/mgen.0.000260. PMC   6939163 . PMID   30946645.
  20. ↑ Juárez-Vázquez AL, Edirisinghe JN, Verduzco-Castro EA, Michalska K, Wu C, Noda-García L, et al. (March 2017). "Evolution of substrate specificity in a retained enzyme driven by gene loss". eLife. 6 (6): e22679. doi: 10.7554/eLife.22679 . PMC   5404923 . PMID   28362260.
  21. ↑ Noda-García L, Camacho-Zarco AR, Medina-Ruíz S, Gaytán P, Carrillo-Tripp M, Fülöp V, Barona-Gómez F (September 2013). "Evolution of substrate specificity in a recipient's enzyme following horizontal gene transfer". Molecular Biology and Evolution. 30 (9): 2024–34. doi: 10.1093/molbev/mst115 . PMID   23800623.
  22. 1 2 Vernikos GS (2020). "A Review of Pangenome Tools and Recent Studies". The Pangenome. pp. 89–112. doi:10.1007/978-3-030-38281-0_4. ISBN   978-3-030-38280-3. PMID   32633917. S2CID   219011507.
  23. 1 2 Costa SS, Guimarães LC, Silva A, Soares SC, Baraúna RA (2020). "First Steps in the Analysis of Prokaryotic Pan-Genomes". Bioinformatics and Biology Insights. 14: 1177932220938064. doi:10.1177/1177932220938064. PMC   7418249 . PMID   32843837.
  24. ↑ Land M, Hauser L, Jun SR, Nookaew I, Leuze MR, Ahn TH, et al. (March 2015). "Insights from 20 years of bacterial genome sequencing". Functional & Integrative Genomics. 15 (2): 141–61. doi:10.1007/s10142-015-0433-4. PMC   4361730 . PMID   25722247.
  25. ↑ Argemi X, Matelska D, Ginalski K, Riegel P, Hansmann Y, Bloom J, et al. (August 2018). "Comparative genomic analysis of Staphylococcus lugdunensis shows a closed pan-genome and multiple barriers to horizontal gene transfer". BMC Genomics. 19 (1): 621. doi: 10.1186/s12864-018-4978-1 . PMC   6102843 . PMID   30126366.
  26. ↑ Brockhurst MA, Harrison E, Hall JP, Richards T, McNally A, MacLean C (October 2019). "The Ecology and Evolution of Pangenomes". Current Biology. 29 (20): R1094–R1103. doi: 10.1016/j.cub.2019.08.012 . PMID   31639358. S2CID   204823648.
  27. ↑ McInerney JO, McNally A, O'Connell MJ (March 2017). "Why prokaryotes have pangenomes" (PDF). Nature Microbiology. 2 (4): 17040. doi:10.1038/nmicrobiol.2017.40. PMID   28350002. S2CID   19612970.
  28. ↑ Koonin EV (June 2015). "The Turbulent Network Dynamics of Microbial Evolution and the Statistical Tree of Life". Journal of Molecular Evolution. 80 (5–6): 244–50. Bibcode:2015JMolE..80..244K. doi:10.1007/s00239-015-9679-7. PMC   4472940 . PMID   25894542.
  29. ↑ Kislyuk AO, Haegeman B, Bergman NH, Weitz JS (January 2011). "Genomic fluidity: an integrative view of gene diversity within microbial populations". BMC Genomics. 12 (12): 32. doi: 10.1186/1471-2164-12-32 . PMC   3030549 . PMID   21232151.
  30. ↑ Puigbò P, Lobkovsky AE, Kristensen DM, Wolf YI, Koonin EV (August 2014). "Genomes in turmoil: quantification of genome dynamics in prokaryote supergenomes". BMC Biology. 12 (66): 66. doi: 10.1186/s12915-014-0066-4 . PMC   4166000 . PMID   25141959.
  31. ↑ Lapierre P, Gogarten JP (March 2009). "Estimating the size of the bacterial pan-genome". Trends in Genetics. 25 (3): 107–10. doi:10.1016/j.tig.2008.12.004. PMID   19168257.
  32. 1 2 Delmont TO, Eren AM (2018). "Linking pangenomes and metagenomes: the Prochlorococcus metapangenome". PeerJ. 6: e4320. doi: 10.7717/peerj.4320 . PMC   5804319 . PMID   29423345.
  33. ↑ Utter DR, Borisy GG, Eren AM, Cavanaugh CM, Mark Welch JL (2020-12-16). "Metapangenomics of the oral microbiome provides insights into habitat adaptation and cultivar diversity". Genome Biology. 21 (1): 293. doi: 10.1186/s13059-020-02200-2 . PMC   7739467 . PMID   33323129.
  34. ↑ Ma B, France M, Ravel J (2020). "Meta-Pangenome: At the Crossroad of Pangenomics and Metagenomics". In Tettelin H, Medini D (eds.). The Pangenome: Diversity, Dynamics and Evolution of Genomes. Springer. pp. 205–218. doi:10.1007/978-3-030-38281-0_9. ISBN   978-3-030-38281-0. PMID   32633911. S2CID   219067583.
  35. ↑ Zhong C, Chen C, Wang L, Ning K (2021). "Integrating pan-genome with metagenome for microbial community profiling". Computational and Structural Biotechnology Journal. 19: 1458–1466. doi:10.1016/j.csbj.2021.02.021. PMC   8010324 . PMID   33841754.
  36. 1 2 Donati C, Hiller NL, Tettelin H, Muzzi A, Croucher NJ, Angiuoli SV, et al. (2010). "Structure and dynamics of the pan-genome of Streptococcus pneumoniae and closely related species". Genome Biology. 11 (10): R107. doi: 10.1186/gb-2010-11-10-r107 . PMC   3218663 . PMID   21034474.
  37. ↑ Rouli L, Merhej V, Fournier PE, Raoult D (September 2015). "The bacterial pangenome as a new tool for analysing pathogenic bacteria". New Microbes and New Infections. 7: 72–85. doi:10.1016/j.nmni.2015.06.005. PMC   4552756 . PMID   26442149.
  38. ↑ Kettler GC, Martiny AC, Huang K, Zucker J, Coleman ML, Rodrigue S, et al. (December 2007). "Patterns and implications of gene gain and loss in the evolution of Prochlorococcus". PLOS Genetics. 3 (12): e231. doi: 10.1371/journal.pgen.0030231 . PMC   2151091 . PMID   18159947.
  39. ↑ Basharat Z, Yasmin A, He T, Tong Y (2018). "Genome sequencing and analysis of Alcaligenes faecalis subsp. phenolicus MB207". Scientific Reports. 8 (1): 3616. Bibcode:2018NatSR...8.3616B. doi:10.1038/s41598-018-21919-4. PMC   5827749 . PMID   29483539.
  40. ↑ Basharat Z, Yasmin A (2016). "Pan-genome Analysis of the Genus Serratia". arXiv: 1610.04160 [q-bio.GN].
  41. ↑ Gupta VK, Chaudhari NM, Iskepalli S, Dutta C (March 2015). "Divergences in gene repertoire among the reference Prevotella genomes derived from distinct body sites of human". BMC Genomics. 16 (153): 153. doi: 10.1186/s12864-015-1350-6 . PMC   4359502 . PMID   25887946.
  42. ↑ Gaba S, Kumari A, Medema M, Kaushik R (December 2020). "Pan-genome analysis and ancestral state reconstruction of class halobacteria: probability of a new super-order". Scientific Reports. 10 (1): 21205. Bibcode:2020NatSR..1021205G. doi:10.1038/s41598-020-77723-6. PMC   7713125 . PMID   33273480.
  43. ↑ McCarthy CG, Fitzpatrick DA (February 2019). "Pan-genome analyses of model fungal species". Microbial Genomics. 5 (2). doi:10.1099/mgen.0.000243. PMC   6421352 . PMID   30714895.
  44. ↑ Li R, Li Y, Zheng H, Luo R, Zhu H, Li Q, Qian W, Ren Y, Tian G, Li J, Zhou G, Zhu X, Wu H, Qin J, Jin X, Li D, Cao H, Hu X, Blanche H, Cann H, Zhang X, Li S, Bolund L, Kristiansen K, Yang H, Wang J, Wang J (2010). "Building the sequence map of the human pan-genome". Nat Biotechnol. 28 (1): 57–63. doi:10.1038/nbt.1596. PMID   19997067. S2CID   205274447.
  45. 1 2 3 Liao WW, Asri M, Ebler J, Doerr D, Haukness M, Hickey G, et al. (May 2023). "A draft human pangenome reference". Nature. 617 (7960): 312–324. doi: 10.1038/s41586-023-05896-x . PMC   10172123 . PMID   37165242.
  46. 1 2 Gao L, Gonda I, Sun H, et al. (May 2019). "The tomato pan-genome uncovers new genes and a rare allele regulating fruit flavor". Nature Genetics. 51 (6): 1044–51. doi:10.1038/s41588-019-0410-2. PMID   31086351. S2CID   152283283.
  47. ↑ Jayakodi M, Padmarasu S, Haberer G, et al. (Nov 2020). "The barley pan-genome reveals the hidden legacy of mutation breeding". Nature. 588 (7837): 284–9. Bibcode:2020Natur.588..284J. doi: 10.1038/s41586-020-2947-8 . PMC   7759462 . PMID   33239781.
  48. ↑ Zhou Y, Zhang Z, Bao Z, Li H, Lyu Y, Zan Y, Wu Y, Cheng L, Fang Y, Wu K, Zhang J, Lyu H, Lin T, Gao Q, Saha S (8 July 2022). "Graph pangenome captures missing heritability and empowers tomato breeding". Nature. 606 (7914): 527–534. doi: 10.1038/s41586-022-04808-9 . hdl: 20.500.11850/553553 . ISSN   1476-4687. PMC   9200638 . PMID   35676474.
  49. ↑ Van de Weyer AL, Monteiro F, Furzer OJ, Nishimura MT, Cevik V, Witek K, Jones JD, Dangl JL, Weigel D, Bemm F (August 2019). "A Species-Wide Inventory of NLR Genes and Alleles in Arabidopsis thaliana". Cell. 178 (5): 1260–72. doi: 10.1016/j.cell.2019.07.038 . PMC   6709784 . PMID   31442410.
  50. ↑ Aherfi S, Andreani J, Baptiste E, Oumessoum A, Dornas FP, Andrade AC, et al. (2018). "A Large Open Pangenome and a Small Core Genome for Giant Pandoraviruses". Frontiers in Microbiology. 9 (9): 1486. doi: 10.3389/fmicb.2018.01486 . PMC   6048876 . PMID   30042742.
  51. ↑ The Computational Pan-Genomics Consortium (January 2018). "Computational pan-genomics: status, promises and challenges". Briefings in Bioinformatics. 19 (1): 118–135. doi:10.1093/bib/bbw089. PMC   5862344 . PMID   27769991.
  52. ↑ Eizenga JM, Novak AM, Sibbesen JA, Heumos S, Ghaffaari A, Hickey G, et al. (August 2020). "Pangenome Graphs". Annual Review of Genomics and Human Genetics. 21: 139–162. doi:10.1146/annurev-genom-120219-080406. PMC   8006571 . PMID   32453966.
  53. ↑ Besemer J, Lomsadze A, Borodovsky M (June 2001). "GeneMarkS: a self-training method for prediction of gene starts in microbial genomes. Implications for finding sequence motifs in regulatory regions". Nucleic Acids Research. 29 (12): 2607–18. doi:10.1093/nar/29.12.2607. PMC   55746 . PMID   11410670.
  54. ↑ Aziz RK, Bartels D, Best AA, DeJongh M, Disz T, Edwards RA, et al. (February 2008). "The RAST Server: rapid annotations using subsystems technology". BMC Genomics. 9 (9): 75. doi: 10.1186/1471-2164-9-75 . PMC   2265698 . PMID   18261238.
  55. 1 2 3 Xiao J, Zhang Z, Wu J, Yu J (February 2015). "A brief review of software tools for pangenomics". Genomics, Proteomics & Bioinformatics. 13 (1): 73–6. doi:10.1016/j.gpb.2015.01.007. PMC   4411478 . PMID   25721608.
  56. ↑ Laing C, Buchanan C, Taboada EN, Zhang Y, Kropinski A, Villegas A, et al. (September 2010). "Pan-genome sequence analysis using Panseq: an online tool for the rapid analysis of core and accessory genomic regions". BMC Bioinformatics. 11 (1): 461. doi: 10.1186/1471-2105-11-461 . PMC   2949892 . PMID   20843356.
  57. ↑ Zhao Y, Wu J, Yang J, Sun S, Xiao J, Yu J (February 2012). "PGAP: pan-genomes analysis pipeline". Bioinformatics. 28 (3): 416–8. doi:10.1093/bioinformatics/btr655. PMC   3268234 . PMID   22130594.
  58. ↑ Chaudhari NM, Gupta VK, Dutta C (April 2016). "BPGA- an ultra-fast pan-genome analysis pipeline". Scientific Reports. 6 (24373): 24373. Bibcode:2016NatSR...624373C. doi:10.1038/srep24373. PMC   4829868 . PMID   27071527.
  59. 1 2 Contreras-Moreira B, Vinuesa P (December 2013). "GET_HOMOLOGUES, a versatile software package for scalable and robust microbial pangenome analysis". Applied and Environmental Microbiology. 79 (24): 7696–701. Bibcode:2013ApEnM..79.7696C. doi:10.1128/AEM.02411-13. PMC   3837814 . PMID   24096415.
  60. ↑ Page AJ, Cummins CA, Hunt M, Wong VK, Reuter S, Holden MT, et al. (November 2015). "Roary: rapid large-scale prokaryote pan genome analysis". Bioinformatics. 31 (22): 3691–3. doi:10.1093/bioinformatics/btv421. PMC   4817141 . PMID   26198102.
  61. ↑ Bonnici V, Giugno R, Manca V (November 2018). "PanDelos: a dictionary-based method for pan-genome content discovery". BMC Bioinformatics. 19 (Suppl 15): 437. doi: 10.1186/s12859-018-2417-6 . PMC   6266927 . PMID   30497358.
  62. ↑ Guimarães LC, Florczak-Wyspianska J, de Jesus LB, Viana MV, Silva A, Ramos RT, et al. (August 2015). "Inside the Pan-genome - Methods and Software Overview". Current Genomics. 16 (4): 245–52. doi:10.2174/1389202916666150423002311. PMC   4765519 . PMID   27006628.
  63. ↑ Golicz AA, Batley J, Edwards D (April 2016). "Towards plant pangenomics" (PDF). Plant Biotechnology Journal. 14 (4): 1099–105. doi:10.1111/pbi.12499. PMID   26593040.
  64. ↑ Sheikhizadeh S, Schranz ME, Akdel M, de Ridder D, Smit S (September 2016). "PanTools: Representation, Storage and Exploration of Pan-Genomic Data". Bioinformatics. 32 (17): i487–i493. doi: 10.1093/bioinformatics/btw455 . PMID   27587666.
  65. 1 2 Ding W, Baumdicker F, Neher RA (January 2018). "panX: pan-genome analysis and exploration". Nucleic Acids Research. 46 (1): e5. doi:10.1093/nar/gkx977. PMC   5758898 . PMID   29077859.
  66. ↑ Xu L, Dong Z, Fang L, Luo Y, Wei Z, Guo H, et al. (July 2019). "OrthoVenn2: a web server for whole-genome comparison and annotation of orthologous clusters across multiple species". Nucleic Acids Research. 47 (W1): W52–W58. doi:10.1093/nar/gkz333. PMC   6602458 . PMID   31053848.
  67. ↑ Zhang B, Huang H, Tibbs-Cortes LE, Vanous A, Zhang Z, Sanguinet K, Garland-Campbell KA, Yu J, Li X (2023). "Streamline unsupervised machine learning to survey and graph indel-based haplotypes from pan-genomes". Molecular Plant. 16 (6): 975–978. doi: 10.1016/j.molp.2023.05.005 . PMID   37202927.
  68. ↑ Bonnici V, Maresi E, Giugno R (2020). "Challenges in gene-oriented approaches for pangenome content discovery". Briefings in Bioinformatics. 22 (3). doi:10.1093/bib/bbaa198. ISSN   1477-4054. PMID   32893299.
This page is based on this Wikipedia article
Text is available under the CC BY-SA 4.0 license; additional terms may apply.
Images, videos and audio are available under their respective licenses.