What are homologous sequences what is the difference between orthologs and paralogs

Since phylogenetic inference methods produce unrooted trees, these trees will have to be rooted before reconciliation. One possibility consists in using the midpoint procedure: assuming a molecular clock, the roots correspond to the point that is at equal distance from all tree leaves.

It is however clearly established that the molecular clock assumption is often incorrect, notably in multigenic families where paralogous genes can be subject to different selective pressures. Another solution consists in defining an outgroup. For example, in a set of orthologous genes, the outgroup is constituted by the genes corresponding to the clade that diverged first in the species tree.

Thus, a tree of orthologous genes can easily be rooted, provided one has some a priori knowledge about the most basal taxa in the species tree. However, in a phylogenetic tree containing paralogous genes, defining an outgroup requires first identification of duplication nodes, and hence cannot be done independently of the tree reconciliation. As suggested by Zmasek and Eddy a parsimonious solution consists in placing the root in the gene tree so as to maximize the similarity between the gene tree and the species tree.

Thus, the procedure we use to root our gene trees consists in using the reconciliation algorithm described above, to explore all possible positions of the root in the gene tree, and retain the position that requires the minimal number of gene duplications.

In case of equality, we retain the candidate that is closest to the tree midpoint. In order to identify speciation and gene duplication events in a gene tree, it is necessary to compare gene and species trees.

As already mentioned, several algorithms dealing with that problem have been previously described Page and Charleston, ; Eulenstein et al. Thus, any error in one of the trees will result in overestimation of duplication events.

The RAP algorithm is able to cope with uncertainties, both in the gene and species trees. A node in the gene tree is considered as corresponding to a speciation event, as far as there is no strong evidence that the gene and species trees are incongruent. Moreover, RAP is able to take into account not only the topology of the trees, but also their bootstrap values or branch lengths.

Finally, RAP is also an efficient method to identify the most parsimonious root in a gene tree. Another advantage provided by our algorithm is the fact that it is rapid enough to be used for the reconciliation of very large sets of phylogenetic trees.

A problem is that RAP does not weight gene losses because no cost is associated to them. The only parameters that influence the reconciliation are the tree topologies, and the branches lengths or bootstraps.

On the other hand, as HOGENOM contains only complete genomes, losses in reconciled trees from this database could be considered as real losses, and they can be weighted. Another limitation of the tree reconciliation procedure proposed here is that it assumes that gene transmission has been entirely vertical. In animals, this assumption can be considered as correct because there are very few known cases of horizontal transfers in animals, and they are all related to transposable elements Kordis and Gubensek, In prokaryotes, however, horizontal transfers may be relatively frequent Ochman et al.

However, note that even in the absence of tree reconciliation, or if it is not trusted, it is still possible to automatically search for orthologs in these databases by using the tree pattern search facility.

In theory, taking branch lengths into account in RAP tends to underestimate the distance between genes. Consequently, in a monogenic family with some hidden paralogies, it may occur that RAP collapses some nodes and wrongly labels a duplication node as a speciation. However, a RAP parameter, the maximum length to collapse a node, can be corrected in order to take into account this underestimation of distances. Compared with a manual search, tree pattern matching presents advantages and inconveniences.

The manual expertise allows one to consider existing anomalies on phylogenetic trees and brings a better flexibility in the search. Indeed, even if the formulation is very rich, an automatic request system can never satisfy perfectly the initial objective of the biologist.

The algorithm is also sometimes dependent on reconstruction artefacts, in particular badly chosen phylogenetic roots for deep patterns. On the other hand, the tree pattern matching is a very fast operation. Searching for a pattern on an entire tree database is well compatible with an interactive application. Presently, the most frequently used approach to automatically searching for orthologous genes in different species consists in searching for sequence similarities by pairwise alignments and then selecting the best reciprocal hits: if genes X from species A and Y from species B are orthologous, then one expects that in the genome B , Y be the closest homolog of X , and reciprocally, that in the genome A , X be the closest homolog of Y.

Thus, one can automatically search for orthologs between A and B , simply by comparing all their proteins between each other e. This approach can be extended to more than two species by searching for a subset of best reciprocal hits among homologous genes, and was used for the Cluster of Orthologous Groups COGs database Tatusov et al.

An extension of this method has been developed to distinguish orthologs, in-paralogs and out-paralogs paralogs that predate the species split Remm et al.

An important limitation of these methods is that they can be used only for species for which all genes have been identified. Moreover, even when genomes have been entirely sequenced, this approach may give erroneous results because of variations in evolutionary rates within a gene family, or because some genes have been lost during evolution, or missed during the annotation process. This is more likely to happen in higher eukaryotes, where gene prediction is very difficult.

An important point that has to be highlighted is that the classification of genes into clusters of orthologs depends on the evolutionary distance between the species that are considered.

Let us consider three taxa T 1, T 2 and T 3. A set of homologous genes between T 1 and T 2 corresponds to a cluster of orthologs if and only if all these genes descend from a single gene in the last common ancestor of T 1 and T 2.

Thus, the set of clusters of orthologs between taxon T 1 and taxon T 2 corresponds to the set of genes that were present in the genome of their last common ancestor minus genes that have been lost in one lineage or the other. Hence, if the last common ancestor of T 1 and T 2 is different from the last common ancestor of T 1 and T 3, then the set of clusters of orthologs between T 1 and T 3 may differ from the set of clusters of orthologs between T 1 and T 2.

The classification proposed in the COGs database is therefore not valid for all sets of taxa. In other words, the classification of genes into clusters of orthologs should be recomputed according to the taxa that are being considered. Figure 5 illustrates an example of this problem with the phylogenetic tree of a hypothetical gene family containing sequences from human, Drosophila and chicken. In this example the X and Y genes are paralogous, and result from a duplication predating the divergence between vertebrates and insects.

Such a situation is very common in vertebrates, and might result from one or two genome duplications at the basis of this lineage. If one is interested in identifying all orthologs between mammals and birds, then one should classify Yv1 , Yv2 , Xv1 , Xv2 and Xv3 into five distinct clusters within each cluster, human and chicken genes are orthologous, but genes from different clusters are paralogous. But if one wants to identify all orthologs between Drosophila and vertebrates, then there should be only two clusters: X , Xv1 , Xv2 and Xv3 should be in one cluster since the Drosophila X gene is orthologous to Xv1 , Xv2 and Xv3 ; and Ya , Yb , Yv1 , and Yv2 should be in another cluster since the Drosophila Ya and Yb genes are orthologous to both Yv1 and Yv2.

Note that the orthology relationship is not necessarily one-to-one: because of gene duplications having occurred after the divergence of species that are being considered, one gene in a given taxon may have several orthologs in another taxon Sonnhammer and Koonin, More recently, Zmasek and Eddy , developed a more rigorous procedure that directly relies on the comparison of gene and species trees to automatically infer orthology relationships.

The approach we propose is comparable and is more general. First, our reconciliation program is applied to whole gene families databases and not only on a limited number of genes and species for which one wants specifically to identify orthologs. Second, a dedicated graphical interface has been developed in order to facilitate the composition of queries.

This is important because it makes it possible to build complex queries containing a lot of constraints on the branches and the nodes. Third, the tree pattern-matching algorithm itself is not limited to queries that allow the identification of orthologs as any kind of pattern can be entered. However, it should be mentioned that the quality of the orthology inferences depends on the reliability of the phylogenetic tree; hence the rate of false positive or false negative depends on the evolutionary distances between the species of interest.

Linked to that, gene families in which there is not enough phylogenetic information such as homeobox containing genes will give erroneous results and should be removed.

A possible improvement of RAP would be to provide an assessment of the reliability of the reconciliation. Storm and Sonnhammer proposed a procedure based on the analyses of bootstrapped trees: the results of reconciliation of each bootstrap tree are combined to give support levels of orthology inferences. Tree reconciliation between a gene tree G and a species tree S showing different topologies. The result is the reconciled tree R.

R is a variation of S , in which duplication nodes have been inserted in order to explain incongruence with G. Tree reconciliation between a gene tree G and a species tree S sharing the same topology but showing differences in branch lengths.

Topologies of G and S are identical, but the rate ratio of branch lengths is too high to consider the genes from G as orthologs.

A duplication node is created to explain this, and gives the reconciliated tree R. The two frames of the pattern editor and the tree frame of the FamFetch interface. Frame a is an interactive editor that permits one to construct any pattern, node by node and leaf by leaf. Frame b allows to choose between tools to use in the upper frame. Tools surrounded by dark grey are those that use the gene duplication predictions, and can be avoided if the user does not want to trust this information.

If the families have been selected by a tree pattern matching operation, retrieved patterns are shown with red lines on each tree in the tree frame c. In the pattern P that has been set, no Mus musculus sequences are allowed in the branch leading to Homo sapiens and no human sequences are allowed in the branch leading to the mouse.

Also, duplications are forbidden in these two branches. This is a family of interest because it contains three groups of orthologs matching the pattern, instead of a single one. Each group of orthologs is indicated by a dashed line doubling the portions of the tree corresponding to P. Note that the second group of orthologs G2 contains two human sequences while the third group G3 contains three mouse sequences. But, as these sequences are very similar, so they are considered as redundant.

An example of complex orthology relationships within a hypothetical gene family. X and Y are the duplicated copies of a gene in the common ancestor to vertebrates and insects. No duplication event occurred for X in the lineage leading to present day Drosophila species, but different duplications happened for X in vertebrates and for Y either in insects or in vertebrates. Aho, A. ACM Trans. Altschul, S. Nucleic Acids Res. Daubin, V. Genome Res. Science — Upon the completion of the chain assembly, the product is released from the solid phase to solution, deprotected, and collected.

The occurrence of side reactions sets practical limits for the length of synthetic oligonucleotides up to about nucleotide residues because the number of errors accumulates with the length of the oligonucleotide being synthesized. Products are often isolated by HPLC to obtain the desired oligonucleotides in high purity. Oligonucleotides find a variety of applications in molecular biology and medicine. They are most commonly used as antisense oligonucleotides, small interfering RNA, primers for DNA sequencing and amplification, probes for detecting complementary DNA or RNA via molecular hybridization, tools for the targeted introduction of mutations and restriction sites, and for the synthesis of artificial genes.

A further application of oligosynthesis is to make artificial genes. Artificial gene synthesis is the process of synthesizing a gene in vitro without the need for initial template DNA samples.

The main method is currently by oligonucleotide synthesis also used for other applications from digital genetic sequences and subsequent annealing of the resultant fragments. The polymerase chain reaction PCR is a biochemical technology in molecular biology used to amplify a single, or a few copies, of a piece of DNA across several orders of magnitude, generating thousands to millions of copies of a particular DNA sequence. Developed in by Kary Mullis, PCR is now a common and often indispensable technique used in medical and biological research labs for a variety of applications including the following:.

The method relies on thermal cycling, consisting of cycles of repeated heating and cooling of the reaction for DNA melting and enzymatic replication of the DNA. Primers short DNA fragments containing sequences complementary to the target region, along with a DNA polymerase after which the method is named are key components to enable selective and repeated amplification. As PCR progresses, the DNA generated is itself used as a template for replication, setting in motion a chain reaction in which the DNA template is exponentially amplified.

PCR can be extensively modified to perform a wide array of genetic manipulations. The reaction produces a limited amount of final amplified product that is governed by the available reagents in the reaction, and the feedback-inhibition of the reaction products.

A basic PCR set up requires the following components and reagents:. Typically, PCR consists of a series of repeated temperature changes, called cycles, with each cycle commonly consisting of two to three discrete temperature steps, usually three. The temperatures used, and the length of time they are applied in each cycle, depend on a variety of parameters. These include the enzyme used for DNA synthesis, the concentration of divalent ions and dNTPs in the reaction, and the melting temperature Tm of the primers.

The Steps of PCR : This illustrates a PCR reaction to demonstrate how amplification leads to the exponential growth of a short product flanked by the primers. The first cycle is complete. After elongation, the cycle goes back to step one, usually for cycles. Under optimum conditions i. Sanger sequencing is based on the incorporation and detection of labeled ddNTPs as terminal nucleotides in DNA amplification.

Sanger sequencing, also known as chain-termination sequencing, refers to a method of DNA sequencing developed by Frederick Sanger in This method is based on amplification of the DNA fragment to be sequenced by DNA polymerase and incorporation of modified nucleotides — specifically, dideoxynucleotides ddNTPs.

The ddNTPs may be radioactively or fluorescently labelled for detection in automated sequencing machines. Following rounds of template DNA extension from the bound primer, the resulting DNA fragments are heat denatured and separated by size using gel electrophoresis. This is frequently performed using a denaturing polyacrylamide-urea gel with each of the four reactions run in one of four individual lanes lanes A, T, G, C. Sanger sequencing : Different types of Sanger sequencing, all of which depend on the sequence being stopped by a terminating dideoxynucleotide black bars.

Dye-primer sequencing facilitates reading in an optical system for faster and more economical analysis and automation. The later development by Leroy Hood and coworkers of fluorescently labeled ddNTPs and primers set the stage for automated, high-throughput DNA sequencing.

Chain-termination methods have greatly simplified DNA sequencing. More recently, dye-terminator sequencing has been developed. Dye-terminator sequencing utilizes labelling of the chain terminator ddNTPs, which permits sequencing in a single reaction, rather than four reactions as in the labelled-primer method. In dye-terminator sequencing, each of the four dideoxynucleotide chain terminators is labelled with fluorescent dyes, each of which emit light at different wavelengths.

Chromatograph : This is an example of the output of a Sanger sequencing read using fluorescently labelled dye-terminators. The four DNA bases are represented by different colours which are interpreted by the software to give the DNA sequence above.

DNA sequencers carry out capillary electrophoresis for size separation, detection and recording of dye fluorescence, and data output as fluorescent peak trace chromatograms. Automation has lead to the sequencing of entire genomes.

Metagenomics is the study of metagenomes; genetic material recovered directly from environmental samples. The broad field may also be referred to as environmental genomics, ecogenomics or community genomics.

While traditional microbiology and microbial genome sequencing and genomics rely upon cultivated clonal cultures, early environmental gene sequencing cloned specific genes often the 16S rRNA gene to produce a profile of diversity in a natural sample. Such work revealed that the vast majority of microbial biodiversity had been missed by cultivation-based methods. Due to its ability to reveal the previously hidden diversity of microscopic life, metagenomics offers a powerful lens for viewing the microbial world that has the potential to revolutionize understanding of the entire living world.

Conventional sequencing begins with a culture of identical cells as a source of DNA. However, early metagenomic studies revealed that there are probably large groups of microorganisms in many environments that cannot be cultured and thus cannot be sequenced.

These early studies focused on 16S ribosomal RNA sequences which are relatively short, often conserved within a species, and generally different between species.

Many 16S rRNA sequences have been found which do not belong to any known cultured species, indicating that there are numerous non-isolated organisms.

Advances in bioinformatics, refinements of DNA amplification, and the proliferation of computational power have greatly aided the analysis of DNA sequences recovered from environmental samples, This allows the adaptation of shotgun sequencing to metagenomic samples.

The approach, used to sequence many cultured microorganisms and the human genome, randomly shears DNA, sequences many short sequences, and reconstructs them into a consensus sequence. Shotgun sequencing and screens of clone libraries reveal genes present in environmental samples.

This provides information both on which organisms are present and what metabolic processes are possible in the community. This can be helpful in understanding the ecology of a community, particularly if multiple samples are compared to each other. Shotgun metagenomics is also capable of sequencing nearly complete microbial genomes directly from the environment.

As the collection of DNA from an environment is largely uncontrolled, the most abundant organisms in an environmental sample are most highly represented in the resulting sequence data. To achieve the high coverage needed to fully resolve the genomes of under-represented community members, large samples are needed. On the other hand, the random nature of shotgun sequencing ensures that many of these organisms, which would otherwise go unnoticed using traditional culturing techniques, will be represented by at least some small sequence segments.

The first metagenomic studies conducted using high-throughput sequencing used massively parallel pyrosequencing. However, this limitation is compensated for by the much larger number of sequence reads.

Pyrosequenced metagenomes generate — megabases, while Illumina platforms generate around 20—50 gigabases. An additional advantage to short read sequencing is that this technique does not require cloning the DNA before sequencing, removing one of the main biases in environmental sampling.

As most short-read assembly software was not designed for metagenomic applications, specialized methods have been developed to utilize mate-read data in metagenomic assembly.

From these studies the microbial fauna that might reside in a sample of soil, even on the surface of a keyboard, can be more accurately and efficiently identified. In molecular biology, a reporter gene often simply reporter is a gene that researchers attach to a regulatory sequence of another gene of interest in bacteria, cell culture, animals, or plants.

Certain genes are chosen as reporters because the characteristics they confer on organisms expressing them are easily identified and measured, or because they are selectable markers. Reporter genes are often used as an indication of whether a certain gene has been taken up by or expressed in the cell or organism population. To introduce a reporter gene into an organism, scientists place the reporter gene and the gene of interest in the same DNA construct to be inserted into the cell or organism.

For bacteria or prokaryotic cells in culture, this is usually in the form of a circular DNA molecule called a plasmid. It is important to use a reporter gene that is not natively expressed in the cell or organism under study, since the expression of the reporter is being used as a marker for successful uptake of the gene of interest.

Commonly used reporter genes that induce visually identifiable characteristics usually involve fluorescent and luminescent proteins. Examples include the gene that encodes jellyfish green fluorescent protein GFP , which causes cells that express it to glow green under blue light, the enzyme luciferase, which catalyzes a reaction with luciferin to produce light, and the red fluorescent protein from the gene dsRed.

A common reporter in bacteria is the E. This enzyme causes bacteria expressing the gene to appear blue when grown on a medium that contains the substrate analog X-gal. An example of a selectable-marker which is also a reporter in bacteria is the chloramphenicol acetyltransferase CAT gene, which confers resistance to the antibiotic chloramphenicol.

Reporter genes can also be used to assay for the expression of the gene of interest, which may produce a protein that has little obvious or immediate effect on the cell culture or organism. In these cases the reporter is directly attached to the gene of interest to create a gene fusion.

The two genes are under the same promoter elements and are transcribed into a single messenger RNA molecule. Human myoglobin and dog hemoglobin, however, are homologous genes that are neither paralogous or orthologous. Since graduating with a Bachelor of Science in zoology, Elizabeth Sheldon has had the opportunity to travel between Portland, Chicago, Detroit and London, where she worked in social media marketing and wrote science-related articles.

During her time at Michigan State University, she challenged herself further in the sciences by completing concentrations in ecology, evolution and organismal biology. Five Types of Gene Splicing Mechanism. A List of Five Characteristics of Chromosomes.

What Is a Homologous Allele? What Are Deleterious Genes? How to Write a Notation of a Karyotype. Explain the Significance of Meiosis in Sexual Reproduction.