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This article is from the online course:. Join Now. News categories. Other top stories on FutureLearn. Category: General. We take a closer look at media literacy and what makes it so important in …. Register for free to receive relevant updates on courses and news from FutureLearn. Once you do this, your search strategies should appear in the Saved Search Strategies tab. Object: Starting with two or more sequences, compare them and find the differences.
This will search for nucleic acid sequences from humans with the word "mitochondrion" in the title. Mitochondrial DNA is often used in evolutionary comparisons because it is inherited only through the maternal lineage and changes very slowly. These are high-quality sequences that have been curated and annotated by NCBI staff. There are three Reference Sequences for the mitochondrial genome in humans: one for modern humans Homo sapiens , one for Neanderthals Homo sapiens neanderthalensis , and one for Denisovans Homo sp.
To compare sequences, check the box next to Align two or more sequences under the Query Sequence box. You should see two results, in which the query sequence modern human is compared to one of the subject sequences, Neanderthal or Denisovan. Click on the name of the first result Homo sapiens neanderthalis. You should see a base-by-base comparison of the two sequences in two lines. The top line is the query sequence modern human. In the second line, representing the subject sequence ancient human , bases where the subject sequence is identical to the query sequence are replaced by dots, and bases where the subject sequence differs from the query sequence appear in red.
Scroll down to the first coding sequence CDS. The CDS regions are displayed in four lines: the first line shows the amino acid translation for the query sequence modern human on the second line. The third line is the subject sequence ancient human , and the one below shows the amino acid translation for the subject sequence.
Finally, a little arrow, or pointer, is added to indicate which direction to follow the alignment Figure 1B. In the third stage, the algorithm starts to actually build and score the alignment in a step called fill, or induction. Figure 1B: Filling the axes of the alignment matrix. When filling the axes of the alignment matrix, start in the upper left corner and set it to 0. Next, assign a score for each letter in the row or column.
Note that there is a penalty for gaps, and that the arrow should point toward the origin of the alignment. All rights reserved. This same process continues, calculating two scores for every square in the matrix Figures 1D and 1E. At the end, there is a completely scored matrix with a series of arrows used to find the optimal alignment Figure 1F.
Figure 1D: Induction or filling in of the alignment matrix, part II. The same process is carried out for the next square in the alignment. Here, using the value in upper left brown square yields a sum of -2, using the value in the upper green square yields a sum of -3, and using the value in the left dark blue square yields a sum of Because -2 is the highest score and was initially calculated using the upper left square, -2 is recorded in the matrix along with an arrow pointing toward the brown square.
Figure Detail. The rest of the matrix is completed using the same method. The final steps of generating an alignment are called traceback , and they involve finding the optimal, highest-scoring alignment.
The traceback starts in the lower right of the matrix Figure 1F and follows the pointers to the adjacent boxes. By definition, this will be the best scoring path through the alignment Figure 1G. Although this sort of dynamic programming did a complete job of comparing every single residue of one sequence to every single residue of a second sequence and kept track of how well the sequences aligned at every step, these algorithms required a considerable amount of computer memory and processing time.
Computing speed was an especially important concern, because these exhaustive programs had to search databases that continued to grow at exponential rates. Moreover, most regions of the search space did not score very well and therefore probably could have been skipped during the calculation process. Finally, these programs required powerful computing hardware that was expensive, rare, and ultimately impractical for most scientists and labs. Researcher Stephen Altschul and colleagues wanted to bypass these challenges and develop a way for databases to be searched quickly on routinely used computers.
In order to increase the speed of alignment, the BLAST algorithm was designed to approximate the results of an alignment algorithm created by Smith and Waterman , but to do so without comparing each residue against every other residue Altschul et al. BLAST is therefore heuristic in nature, meaning it has "smart shortcuts" that allow it to run more quickly Madden, However, in this trade-off for increased speed, the accuracy of the algorithm is slightly decreased.
BLAST increases the speed of alignment by decreasing the search space or number of comparisons it makes. Specifically, instead of comparing every residue against each other, BLAST uses short "word" w segments to create alignment "seeds. Requiring only three residues to match in order to seed an alignment means that fewer sequence regions need to be compared. Larger word sizes usually mean that there are even fewer regions to evaluate e. A cutoff score S is used to select alignments over the cutoff, which means the sequences share significant homologies.
If a hit is detected, then the algorithm checks whether w is contained within a longer aligned segment pair that has a cutoff score greater than or equal to S Altschul et al. When an alignment score starts to decrease past a lower threshold score X , the alignment is terminated Figure 3C. These and many other variables can be adjusted to either increase the speed of the algorithm or emphasize its sensitivity. Altschul and colleagues tested the BLAST algorithm on a database of randomly generated sequences, and they examined the output resulting from different w and T parameters.
If T is set to be a lower threshold, then the algorithm detects more word pairs and requires a longer processing time Altschul et al. Thus, choosing the value for T was a major decision because the researchers wanted to reach a compromise between the algorithm's sensitivity and its processing time e. Next, Altschul and colleagues tested BLAST on a database of real sequences, and they found it was successful in quickly identifying alignments with high scores.
In searching the globin gene family , for example, they found that BLAST identified 88 of the 89 globin alignments that scored above Other gene families, including the immunoglobulins , protein kinases, and cytochrome c genes, were then examined to measure the number of alignments detected when using different T and S values. BLAST was also able to detect similar regions within pairs of long sequences.
These tests therefore showed that BLAST was fast, sensitive, and accurate as a tool for analyzing sequence alignments Altschul et al. One of the most notable innovations of BLAST is that the program calculates the statistical significance for each sequence alignment result.
This is known as the expect value E-value or probability value P-value , and it is calculated for each alignment. The E-value describes how many hits you can expect to see by chance when searching a database of a certain size, whereas the P-value describes the probability that the alignment you are observing is due to chance.
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