Institute of Bioinformatics Münster
MetaG
2024/04/20 12:50
New Request
New Request
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Database
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Database Profile
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Filter
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Filter Profile
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Query File
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Minimum Sequence Length
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Email
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Analysis Name
Advanced Parameters
MetaG Parameters
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E-Value Cutoff
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Alignment Score Cutoff
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Confidence Cutoff
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Method for Average Confidence
LAST Alignment Parameters
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Substitution Matrix
Use Matrix or Match Score/Mismatch Cost
A C G T
A
C
G
T
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Match Score (-r)
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Mismatch Cost (-q)
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Gap Existence Cost (-a)
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Gap Extension Cost (-b)
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Insertion Existence Cost (-A)
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Insertion Extension Cost (-B)
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Score Matrix applies to Forward Strand (-S)
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Initial Matches Position (-k)
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Maximum Score Drop (-x)
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LAST-SPLIT (-m)
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.