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Assignment Generator :: Basic Instructions


Scientists use different methods to identify bacteria such as microscopy and biochemical tests. A different approach uses the sequence of the 16S rRNA gene. This gene is highly conserved in bacteria (and other organisms) and is strongly correlated with their evolutionary history. By sequencing many segments of the rRNA genes present in environmental DNA, we can determine what well-studied organisms are related to the microorganisms present in an environment. This methodology is widely used in scientific fields as diverse as medicine, plant pathology and geochemistry, among others.

In this exercise, the student will use two different bioinformatics methods to estimate the taxonomic placement of unknown microorganism from their rRNA gene sequences.

1. Obtain your unknown sequences

Your instructor will tell you how to obtain a file containing your rRNA gene sequences. This file is in FASTA format that can be opened with text editors such as TextEdit on Mac OS X and WordPad on PC. (If you have difficulty opening your file with the latest Mac OS 10.9, from the Finder double click on the file and choose to open it with TextEdit. If you still have trouble, change your file extension to .txt. Do not use Notepad on PC to view the file as it will show the string of text on a single line.)

The FASTA format is a very common sequence file format and should appear as follows: The first line is devoted to the name of the sequence and always begins with a right caret followed by the name and a hard line return. The sequence begins on the second line and the sequence text soft wraps (no hard line return between lines) until the second and last line return of the file signifies the end of the sequence.

>SEQUENCE_ID (hard line return)

SEQUENCE HERE…END (hard line return)

2. Classify your sequences using the SeqMatch k-nearest-neighbor classifier

The Sequence Match results show the closest matches and how they fit in the nomenclatural taxonomy.

3. Classify sequences using the RDP naïve Bayesian Classifier




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