Analyze your reads based on this database.
Analysis parameters.
Remove reads matching this database before the taxon calling.
Filter parameters.
Your sequencing reads. Must be one fasta/fastq file or one archive.
Reads shorter than this value will not be analyzed. No value or zero to deactivate this filter.
[Integer]
Receive a mail, when the analysis is finished.
Provide a name for the analysis to help you identify it later on.
Enter a substitution matrix. Cannot be used with "Match Score" and "Mismatch Cost".
The button "init" initiliazes the matrix with 0 values. This means, it will be discarded.
Score for a nucleotide match between database entry and query read.
[Integer]
Penalty for a nucleotide mismatch between database entry and query read.
[Integer]
Penalty for introducing a gap into the query read. [Integer]
Penalty for extending a sequence of multiple gaps in the query read.
First gap in gap sequence gets penalty -a; further gaps get -b.
[Integer]
Penalty for introducing a nucleotide into the query read. [Integer]
Penalty for extending a sequence of multiple insertions in the
query read. First insertion in the sequence gets penalty -A;
further insertions get -B. [Integer]
Look for initial matches starting at every k-th position in each query. [Integer]
Maximum score drop for gapped alignments. [Integer]
How can the matrix be applied to the forward and reverse strand of the query?
0: Use the matrix as-is. 1: Reverse the matrix for the reverse strand.
When you used LAST-TRAIN, double check with its parameter -S. [INTEGER]
Filter ambigous alignments with an error probability > m.
Between 0.0 and 1.0. 1.0 is most relaxed. [Float]
Alignments with a e-value <= e^cutoff are chosen for taxonomy
calling. E.g: -3 to consider only alignments with e-value <= e^-3.
[Integer]
From alignments chosen by -e for each query, choose only the top -ac %
(decimal notion) as ranked by the alignment score. [Float: 0.0 to 1.0]
Confidence for the taxonomy calling. Lower values allow for more resolution at
lower ranks in ambigous samples. [Float: 0.0 to 1.0]
Method to calculate average confidence for each taxon in your sample. Note: The geometric
mean and the harmonic mean return 0, if any of the single confidences for a taxon is 0.