103:). The logic is simple: a base call with a high probability of being correct should never be aligned with another high quality but different base. However, Phrap does not rule out such alignments entirely, and the cross_match alignment gap and alignment penalties used while looking for local alignments are not always optimal for typical sequencing errors and a search for overlapping (contiguous) sequences. (Affine gaps are helpful for homology searches but not usually for sequencing error alignment). Phrap attempts to classify chimeras, vector sequences and low quality end regions all in a single alignment and will sometimes make mistakes. Furthermore, Phrap has more than one round of assembly building internally and later rounds are less stringent - Greedy algorithm.
131:: to determine the correct consensus sequence at all positions where the assembled sequences had discrepant bases. This approach had been suggested by Bonfield and Staden in 1995, and was implemented and further optimized in Phrap. Basically, at any consensus position with discrepant bases, Phrap examines the quality scores of the aligned sequences to find the highest quality sequence. In the process, Phrap takes confirmation of local sequence by other reads into account, after considering direction and sequencing chemistry.
99:. Phrap uses quality scores to tell if any observed differences in repeated regions are likely to be due to random ambiguities in the sequencing process, or more likely to be due to the sequences being from different copies of the Alu repeat. Typically, Phrap had no problems differentiating between the different Alu copies in a cosmid, and to correctly assemble the cosmids (or, later,
154:. Phred and Phrap, and similar programs who picked up on the ideas pioneered by these two programs, enabled the assembly of large parts of the human genome (and many other genomes) at an accuracy that was substantially higher (less than 1 error in 10,000 bases) than the typical accuracy of carefully hand-edited sequences that had been submitted to the
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regions that are not covered by high quality sequence (which will also have low quality), and (b) to quickly calculate a reasonably accurate estimate of the error rate of the consensus sequence. This information can then be used to direct finishing efforts, for example re-sequencing of problem regions.
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are logarithmically linked to error probabilities. This means that the quality scores of confirming reads can simply be added, as long as the error distributions are sufficiently independent. To satisfy this independence criterion, reads must typically be in different direction, since peak patterns
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If a consensus base is covered by both high-quality sequence and (discrepant) low-quality sequence, Phrap's selection of the higher quality sequence will in most cases be correct. Phrap then assigns the confirmed base quality to the consensus sequence base. This makes it easy to (a) find consensus
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by Phrap that contributed to the program's success was the determination of consensus sequences using sequence qualities. In effect, Phrap automated a step that was a major bottleneck in the early phases of the
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Phrap was written as a command line program for easy integration into automated data workflows in genome sequencing centers. For users who want to use Phrap from a graphical interface, the commercial programs
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Bonfield JK, Staden R (1995): The application of numerical estimates of base calling accuracy to DNA sequencing projects. Nucleic Acids Res. 1995 Apr 25;23(8):1406-10.
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Krawetz SA (1989): Sequence errors described in GenBank: a means to determine the accuracy of DNA sequence interpretation. Nucleic Acids Res. 1989 May 25;17(10):3951-7
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118:Quality based consensus sequences
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223:DNA Baser Command Line Tool
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65:CodonCode Aligner
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