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Molecular Evolutionary Genetics Analysis

Molecular Evolutionary Genetics Analysis is a biology topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand Molecular Evolutionary Genetics Analysis rather than just read about it. In short: Molecular Evolutionary Genetics Analysis (MEGA) is computer software for conducting statistical analysis of molecular evolution and for constructing phylogenetic trees. It includes many sophisticated methods and tools for phylogenomics and phylomedicine.

Molecular Evolutionary Genetics Analysis — main illustration
Molecular Evolutionary Genetics Analysis — illustration

Key takeaways

  • Molecular Evolutionary Genetics Analysis belongs to biology; place it in that map before memorising details.
  • Learn the definition first, then one example that makes the definition concrete.
  • Connect Molecular Evolutionary Genetics Analysis to a quantity you can measure, compute or draw — that is where exam questions come from.
  • Reproduce the core statement of Molecular Evolutionary Genetics Analysis from memory before moving on to harder problems.

Reference excerpt

Molecular Evolutionary Genetics Analysis (MEGA) is computer software for conducting statistical analysis of molecular evolution and for constructing phylogenetic trees. It includes many sophisticated methods and tools for phylogenomics and phylomedicine. It is licensed as proprietary freeware. The project for developing this software was initiated by the leadership of Masatoshi Nei in his laboratory at the Pennsylvania State University in collaboration with his graduate student Sudhir Kumar and postdoctoral fellow Koichiro Tamura. Nei wrote a monograph (pp. 130) outlining the scope of the software and presenting new statistical methods that were included in MEGA. The entire set of computer programs was written by Kumar and Tamura. The personal computers then lacked the ability to send the monograph and software electronically, so they were delivered by postal mail. From the start, MEGA was intended to be easy to use and include solid statistical methods only. MEGA version 2 (MEGA2), which was coauthored by an additional investigator Ingrid Jakobson, was released in 2001. All the computer programs and the readme files of this version could be sent electronically due to advances in computer technology. Around this time, the leadership of the MEGA project was taken over by Kumar (now at Temple University) and Tamura (now at Tokyo Metropolitan University). The monograph Molecular Evolutionary Genetics Analysis was often used as a textbook for new ways to study molecular evolution. MEGA has been updated and expanded several times and currently all these versions are available from the MEGA website. The latest release, MEGA7, has been optimized for use on 64-bit computing systems. MEGA is in two version. A graphical user interface is available as a native Microsoft Windows program. A command line version, MEGA-Computing Core (MEGA-CC), is available for native cross-platform operation. The method is widely used and cited. With millions of downloads across the releases, MEGA is cited in more than 85,000 papers. The 5th version has been cited over 25,000 times in 4 years.

Features

Sequence alignment construction Alignment Editor ― Within MEGA, the Alignment Editor is a tool that may be used for editing and building multiple sequence alignments. The Alignment Editor in MEGA includes an integrated tool for both ClustalW and MUSCLE programs. All actions take place in the Analysis Explorer, which can be found in the main menu of MEGA. When a new alignment is being created, the user is presented with three options: create a new alignment, open a saved alignment session, or retrieve sequences from a file (importing sequences from NCBI). Once an option is selected, the user can choose either ClustalW or MUSCLE from the Alignment tab located at the top of the page. Parameters for the selected alignment program can then be specified and a progress bar will appear while the tool is being computer. Aligned sequences will replace unaligned ones in the main section of the Alignment Editor. To perform further analysis in MEGA, it is advisable to save the alignment session in either MEGA or FASTA format. Trace Data File Viewer/Editor ― The Trace Data File Viewer/Editor has many functionalities in the following three menus. All the commands are used to help specialize searches and alignments in MEGA.

Data Menu consists of Open File in New Window, Open File, Save File, Print, Add to Alignment Explore, Export FASTA File, and Exit. Edit Menu consists of Undo, Copy, Mask Upstream, Mask Downstream, and Reverse Complement. The difference between Mask Upstream and Mask Downstream is upstream is used to mask/unmask a region to the left of a cursor while downstream does the opposite. Reverse Complement will be used in situations where the complements may need to be reversed in a sequence. Search Menu consists of Find, Find Next, Find Previous, Next N, Find in File, and DO BLAST Search. The Find, Find Next, and Find Previous are used to find occurrences in certain sections of a query sequence. Next N is a command the will be able to go to the next indeterminate (N) nucleotide. Find in a File allows a user to search another file for selected sequences. Do BLAST Search command will perform a BLAST search in a separate web browser. The user may be able to either select certain date to BLAST or all sequences in the session will be used. Integrated web browser, sequence fetching ― MEGA comes with a built-in web browser that allows users to access GenBank sequence data from the NCBI website. The integrated web browser can be accessed when creating a new alignment in the Alignment Editor. To successfully use sequences from NCBI, it is advised to change the searches to FASTA format and use the “Add to Alignment” button. Once completed, all the sequences will be imported into the MEGA application. Multiple sequence alignment

Data handling One of the challenges associated with evolutionary genetic analysis is the presence of ambiguous states such as R, Y, and T. These states often arise from sequence errors or incomplete datasets. However, MEGA offers several resources to handle ambiguous states, including the deletion of sites that have an ambiguity score higher than the Site Coverage Cutoff parameter. MEGA's extended format allows users to save all data attributes, such as sequence length, nucleotide positions, gaps, and ambiguous states. Additionally, MEGA supports data import from other formats, such as Clustal, which ensures a seamless transition between popular file types. After importing a dataset, MEGA provides multiple different data viewer options. For example, users can view statistical attributes and select subsets in the Sequence Data Explorer or use the Distance Data Explorer to inspect pairwise distance data. Another feature of MEGA is the visual specification of domain groups. This allows users to group sequences by a specific characteristic and view subsequent phylogenetic trees.

Genetic code table MEGA offers support for modifying the genetic code used for translating DNA sequences. By default, MEGA has 23 built-in genetic code variations including the standard code, vertebrate mitochondrial code, Drosophila mitochondrial code, and yeast mitochondrial code. Users may add, remove, or edit any genetic code table.

In addition, MEGA can also compute the degeneracy of each codon position in a genetic code table as well as the number of synonymous sites and non-synonymous sites using the Nei-Gojobori method.

… excerpt ends here. Continue reading the full article.

Illustrations

Molecular Evolutionary Genetics Analysis: A phylogenetic tree in MEGA using a Neighbor Joining Method and Bootstrapping. The data sequences being used is from the study Initial diversification of living amphibians predated the breakup of Pangaea. MEGA has created captions using the Real-Time Caption Editor to be able to analyze the properties of the results of the phylogenetic tree. This allows a user to be able to follow and interpret final results.[14]
A phylogenetic tree in MEGA using a Neighbor Joining Method and Bootstrapping. The data sequences being used is from the study Initial diversification of living amphibians predated the breakup of Pangaea. MEGA has created captions using the Real-Time Caption Editor to be able to analyze the properties of the results of the phylogenetic tree. This allows a user to be able to follow and interpret final results.[14]
Molecular Evolutionary Genetics Analysis: The original sequence in MEGA, composed of 12 nucleotides.
The original sequence in MEGA, composed of 12 nucleotides.
Molecular Evolutionary Genetics Analysis: The reverse complement of the original sequence, produced by MEGA.
The reverse complement of the original sequence, produced by MEGA.
Molecular Evolutionary Genetics Analysis: A traditional phylogenetic tree generated using a maximum likelihood algorithm
A traditional phylogenetic tree generated using a maximum likelihood algorithm
Molecular Evolutionary Genetics Analysis: A circular phylogenetic tree generated using a maximum likelihood algorithm
A circular phylogenetic tree generated using a maximum likelihood algorithm

Worked examples

Example 1 — a first encounter with Molecular Evolutionary Genetics Analysis

Start with the simplest possible case. Write down what Molecular Evolutionary Genetics Analysis claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In biology, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to Molecular Evolutionary Genetics Analysis before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about Molecular Evolutionary Genetics Analysis ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of Molecular Evolutionary Genetics Analysis

In research
Molecular Evolutionary Genetics Analysis appears in biology research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry
Engineering practice reuses Molecular Evolutionary Genetics Analysis in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom
Molecular Evolutionary Genetics Analysis is common in secondary-school and first-year university syllabi. It links to neighbouring topics Data and information visualization software, Phylogenetics software, so understanding it makes those chapters shorter.
In everyday life
Look for Molecular Evolutionary Genetics Analysis outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.

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How to study Molecular Evolutionary Genetics Analysis in 20 minutes

  1. Read the reference excerpt below once, without taking notes.
  2. Close the page and write down what Molecular Evolutionary Genetics Analysis means in your own words.
  3. Compare your version with the excerpt and mark what you missed.
  4. Work through the three examples above with pen and paper.
  5. Explain Molecular Evolutionary Genetics Analysis out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Molecular Evolutionary Genetics Analysis in simple terms?

Molecular Evolutionary Genetics Analysis (MEGA) is computer software for conducting statistical analysis of molecular evolution and for constructing phylogenetic trees. It includes many sophisticated methods and tools for phylogenomics and phylomedicine.

Why does Molecular Evolutionary Genetics Analysis matter?

Because it connects several biology ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study Molecular Evolutionary Genetics Analysis?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on Molecular Evolutionary Genetics Analysis.

Tags

  • Data and information visualization software
  • Phylogenetics software

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