These first-hand experiences spurred a more widespread appreciation of statistical methods for phylogenetic analysis, which was clearly reflected in the fast-growing citations of the NJ method over 16, citations to date and software packages, such as MEGA 1, providing access to statistical methods in phylogenetics Fig. Such GUIs enhanced the user experience by providing more screen space for displaying larger amounts of data and results, allowed for a more sophisticated and intuitive display of information, and enabled more intuitive and efficient data manipulations.
MEGA 2, released in , vastly improved the capabilities of MEGA 1 by facilitating analyses of larger datasets, enabling analyses of grouped sequences, specification of multiple domains and genes, and expansion of the repertoire of statistical methods for molecular evolutionary studies Fig.
It was made available over the Internet www. Over time, the use of the Windows version has been replacing the DOS version. In , the release of MEGA 3 addressed the longstanding need for making the sequence data retrieval and alignment less frustrating and less error-prone [ 12 ].
Now researchers could edit DNA sequence data from auto-sequencers, retrieve data from web-databases, and perform automatic and manual sequence alignments in MEGA.
This integrated sequence data acquisition and evolutionary analysis tool was downloaded by 50, unique e-mail addresses from — The next release in MEGA 4 offered facilities to generate detailed captions for many different types of analyses and results discussed in detail later , a maximum composite likelihood method for evolutionary distance estimation, multi-threading and multi-user support, and Linux support via the Wine application compatibility layer [ 13 ]. It has already seen over 15, downloads in the first six months of its release.
This growing impact of MEGA testifies to the widespread utility of statistical methods in deciphering evolutionary patterns and inferring the phylogenetic relationships of DNA and protein sequences in these times of momentous sequence data abundance.
Indeed, a select group of phylogenetic analysis software tools has experienced extensive growth over the last few years see Fig. In the following, we describe our biologist-centric philosophy behind the user-interface design, priorities for programming new methods and tools, and future plan of the MEGA project.
Here we focus on conceptual aspects though some technical details are discussed. The availability of expansive screen display area in modern computing environments entices developers into presenting access to all of the software's functionality to the user up front in the form of complex hierarchical menu systems.
This often leads to an over-populated interface and, consequently, steep learning curve for new users. In MEGA, we circumvented this pitfall by programming the user-interface to render itself dynamically: it only displays buttons and menu options to the user that are context appropriate for the currently active data set and analysis conditions.
Users specify models of sequence evolution and the data subset to employ only when needed by the program for calculations. Many new users have shared that they are able to learn MEGA functionality without much assistance, which we attribute in part to this context-dependent interface model.
The imprint of context-dependence principle is seen throughout MEGA. For example, the distribution of the computational functionalities and display properties into input data explorers and output result explorers is also a product of the context-dependence design imperative, as it enables the user to conduct simple downstream analyses easily using the results presented. All versions of MEGA contain visual modules for browsing, editing, and computing basic statistical quantities for the input data.
For example, the user can also calculate base frequencies and relative synonymous codon usage for all positions across all selected sequences or for only positions they highlight Fig.
These basic statistical quantities are necessary to assess the DNA and protein sequence variability, location of positions that harbor evolutionary change, and inequality of the usage of 4 nucleotides, 20 amino acid residues, and 64 codons. MEGA 4. Therefore, MEGA separates the building of the primary data subsets from the evolutionary analysis of data. In addition, users are offered many context-dependent data subset options, including the selection of codon positions to include, automatic translation of codons, and the handling of sites containing alignment gaps by removing them for sequence pairs pairwise-deletion option or completely complete-deletion option across all sequences.
MEGA contains many visual explorers for results produced, which offer built-in capabilities to carry out additional calculations and prepare results for publication.
The Tree Explorer is a prime example. It contains facilities for showing multiple representations of the tree, building consensus trees, compressing sub-trees to focus on higher-level relationships among sequences, printing trees as Windows metafiles and TIFF files, and exporting them in the Newick-compatible format for use in other evolutionary analysis programs. The Tree Explorer also estimates ancestral states of nucleotide or amino acids on each node in the tree using the maximum parsimony method.
In MEGA 3, capabilities for associating and displaying images for individual taxa and clusters were included to make it easier to produce an informative phylogenetic representation fit for publication.
The Distance Matrix Explorer, first made available in MEGA 2, is another visual result explorer that displays pairwise distances along with the estimates of standard errors, with flexibility to rearrange species sequences or taxa by dragging-and-dropping. It has the built-in capacity to calculate overall, within, and between group averages when the user has arranged taxa into groups.
In MEGA 4. This frequently happens when applying phylogenetic analyses to datasets containing a large number of distantly related DNA and protein sequences. Researchers need to quickly find and eliminate some of these offending sequences to be able to build phylogenetic trees.
Therefore, our focus is on enhancing the capability of result explorers to aid researchers in analysis of large datasets. Most bench scientists use a web browser to obtain gene sequences from databanks in a complex process that results in the mundane and frustrating task of cutting and pasting sequences from the web-browsers, or saving them to files before processing them for sequence alignment. In developing MEGA 3, we emulated and enhanced the researchers' data assembly and alignment workflow under our assist-rather-than- reinvent design principle.
We began by integrating a fully-functional web-browsing facility that had the ability to download sequence data directly into MEGA with a single click, which replaced a time consuming, multi-step, and error-prone manual process with a simple and intuitive procedure see [ 12 ] for a description. Because experimental biologists frequently work with trace data, we also included facilities for viewing and editing the trace files electropherogram produced by the automated DNA sequencers.
MEGA 3 also featured a full function Alignment Explorer containing an extensive graphical user interface for handling and aligning sequences datasets gathered in MEGA through the web-browsing facility and by importing data from FASTA and trace files [ 12 ]. It included a native implementation of selected CLUSTALW source code [ 15 ] for automated sequence alignment with facilities for the manual refinement of the alignments.
Because biologists often need to align subsets of sequences and positions in a larger alignment, we programmed Alignment Editor to align any user-selected rectangular region in all, or a subset of sequences, and insert it in the context of the larger alignment. For the protein-coding regions, users can click to translate the selected sequences or selected rectangular subset into protein sequences, align the translated protein sequences using CLUSTALW, and switch their context back to DNA sequences—all with a few mouse clicks.
However, users must select which data subset to use e. In MEGA 2, these options were available for selection in a series of tabs in a Dialog box, with selections in only one tab displayed at any time. Soon after its release, we realized that many users did not change the default options while analyzing data. Default options are not suitable in all circumstances. So, we revamped the main Analysis Preferences dialog box in MEGA 3 in order to make the user more aware of all the choices available at the same time.
This development removed the burden on users of flipping through tabs to examine all options, enhancing the users' knowledge regarding the underlying assumptions and data-handling options chosen in each analysis. Caption Expert generates detailed descriptions for every result produced by MEGA, which informs the user of all the options selected and includes specific citations for any method, algorithm, and software employed in the given analysis.
Furthermore, it gives the number of sequences used, the number of aligned positions included, and the units of the resulting statistical quantities. All captions are context-dependent; that is, they change with the type of result displayed and the way the user is viewing the results.
For example, if a user is viewing a phylogeny without branch lengths cladograms , then Caption Expert writes about the branching pattern rather than the number of substitutions or the units in which the branch lengths are expressed. Therefore, we follow the What-you-see-is-what-you-get principle in generating descriptions. All descriptions appear in natural language text, which is aimed at promoting a better understanding of the underlying assumptions and finer details of the results presented.
Such written descriptions of methods and results are useful for archival purposes, and they should aid students and researchers in preparing tables and figures for presentations. For example, researchers often require multiple days to complete aligning sequences, frequently needing to add sequences to an alignment as new data as they become available.
By saving the current Alignment Explorer session to a file, the users can return to a precise, visual snapshot of any previous alignment, along with any specified display or alignment parameters. This will assist researchers in assembling larger datasets incrementally and will not require saving of the data in flat text files that lose visual information associated with the sequence alignment and the phylogeny display.
In the future, we plan to add a facility to save the input data session in order to obviate the need to save intermediate data subsets selection of taxa, groups, and genes and other settings to text files; text files are cumbersome for very large and complex datasets.
We are currently planning to make a number of fundamental enhancements prompted by the growing needs of contemporary researchers to analyze large datasets, to use MEGA on multiple platforms, and to utilize additional statistical and computational methods.
In the following, we discuss a few of these aspects briefly. Historically, MEGA has included likelihood methods for estimating evolutionary distances between sequence pairs as well as distance-based and Maximum Parsimony methods for inferring phylogenetic trees. Many novel methodological and algorithmic developments, combined with a manifold increase in the computing power on average desktops, have made it possible to carry out computationally-intensive statistical methods of phylogenetic analysis based on the Maximum Likelihood ML principle [ 3 , 5 , 6 ].
Starting with MEGA 5, we will gradually add facilities for selecting the best-fit model of DNA and protein substitution, estimating the extent of rate variation among sites, testing molecular clocks among species and paralogous genes, reconstructing nucleotides and amino acids in the ancestral sequences, and inferring phylogenetic trees. These additions will provide an integrated solution for analysis of molecular sequences using a variety of statistical methods. An increasingly greater number of biologists are now interested in automating their analyses in MEGA.
This automation need has arisen from the profound success of sequencing efforts, which have fundamentally altered the nature and scope of investigations by evolutionary and molecular biologists. Biologists are routinely analyzing large sequence datasets, which, until recently, used to be the exclusive domain of bioinformatics investigators.
However, a majority of researchers using MEGA do not wish to trade their GUI environments for the cumbersome command line interfaces and learn unintuitive commands [ 10 ]. While the use of scripting in large-scale analysis provides a general solution to many bioinformatics problems, we find that researchers commonly turn to scripting to repeat the same analysis for different genes and genomic regions.
Therefore, we plan to add a point-and-click iteration system that will enable scientists to launch the same analysis for distinct and overlapping data subsets, including different genes, codon positions, sliding windows, and groups of sequences, without the use of scripting languages or the need to learn command. Using this system, researchers will be able to build gene-by-gene phylogenies and their consensus , estimate average sequence divergence for individual genes in multigene datasets, conduct gene-by-gene tests of selection, and estimate substitution parameters e.
The porting of MEGA source code, especially the graphical user-interface, to multiple platforms has not been feasible; we estimate that this will require multiple years of development and debugging. However, the recent rise of the web browser as the centerpiece of the desktop as well as the cultural shift among computer users in regards to web-based application utilization, offers a unique opportunity to develop cross-platform solutions. The user interface experience offered by Web 2.
The general availability of advanced, open-source JavaScript frameworks would allow us to implement many dynamic elements i. MEGA for the WebTop will also use a web service model so the user can experience a perpetual upgrade cycle wherein improvements and bug fixes can be implemented and delivered whenever they are available. However, if the second amino acid is lysine, which is also frequently the case, methionine is not removed at least in the sample proteins that have been studied thus far.
These proteins therefore begin with methionine followed by lysine Flinta et al. Table 1 shows the N-terminal sequences of proteins in prokaryotes and eukaryotes, based on a sample of prokaryotic and eukaryotic proteins Flinta et al. In the table, M represents methionine, A represents alanine, K represents lysine, S represents serine, and T represents threonine.
Once the initiation complex is formed on the mRNA, the large ribosomal subunit binds to this complex, which causes the release of IFs initiation factors. The large subunit of the ribosome has three sites at which tRNA molecules can bind. The A amino acid site is the location at which the aminoacyl-tRNA anticodon base pairs up with the mRNA codon, ensuring that correct amino acid is added to the growing polypeptide chain.
The P polypeptide site is the location at which the amino acid is transferred from its tRNA to the growing polypeptide chain. Finally, the E exit site is the location at which the "empty" tRNA sits before being released back into the cytoplasm to bind another amino acid and repeat the process.
The ribosome is thus ready to bind the second aminoacyl-tRNA at the A site, which will be joined to the initiator methionine by the first peptide bond Figure 5. Figure 5: The large ribosomal subunit binds to the small ribosomal subunit to complete the initiation complex. The initiator tRNA molecule, carrying the methionine amino acid that will serve as the first amino acid of the polypeptide chain, is bound to the P site on the ribosome.
The A site is aligned with the next codon, which will be bound by the anticodon of the next incoming tRNA. Next, peptide bonds between the now-adjacent first and second amino acids are formed through a peptidyl transferase activity. For many years, it was thought that an enzyme catalyzed this step, but recent evidence indicates that the transferase activity is a catalytic function of rRNA Pierce, After the peptide bond is formed, the ribosome shifts, or translocates, again, thus causing the tRNA to occupy the E site.
The tRNA is then released to the cytoplasm to pick up another amino acid. In addition, the A site is now empty and ready to receive the tRNA for the next codon.
This process is repeated until all the codons in the mRNA have been read by tRNA molecules, and the amino acids attached to the tRNAs have been linked together in the growing polypeptide chain in the appropriate order.
At this point, translation must be terminated, and the nascent protein must be released from the mRNA and ribosome. No tRNAs recognize these codons. Thus, in the place of these tRNAs, one of several proteins, called release factors, binds and facilitates release of the mRNA from the ribosome and subsequent dissociation of the ribosome. The translation process is very similar in prokaryotes and eukaryotes.
Although different elongation, initiation, and termination factors are used, the genetic code is generally identical. As previously noted, in bacteria, transcription and translation take place simultaneously, and mRNAs are relatively short-lived.
In eukaryotes, however, mRNAs have highly variable half-lives, are subject to modifications, and must exit the nucleus to be translated; these multiple steps offer additional opportunities to regulate levels of protein production, and thereby fine-tune gene expression.
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Copy Number Variation. Copy Number Variation and Genetic Disease. Copy Number Variation and Human Disease. Tandem Repeats and Morphological Variation. Chemical Structure of RNA. Eukaryotic Genome Complexity. RNA Functions. Citation: Clancy, S.
Nature Education 1 1 How does the cell convert DNA into working proteins? The process of translation can be seen as the decoding of instructions for making proteins, involving mRNA in transcription as well as tRNA. Aa Aa Aa. Figure Detail. Where Translation Occurs. Figure 3: A DNA transcription unit. A DNA transcription unit is composed, from its 3' to 5' end, of an RNA-coding region pink rectangle flanked by a promoter region green rectangle and a terminator region black rectangle.
Genetics: A Conceptual Approach , 2nd ed. All rights reserved. The Elongation Phase. Figure 6. Termination of Translation. Comparing Eukaryotic and Prokaryotic Translation. References and Recommended Reading Chapeville, F. European Journal of Biochemistry , — Grunberger, D. Nucleic Acids Research 15 , — Pierce, B. Article History Close. Share Cancel.
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