Commit e7028196 authored by Martin Larralde's avatar Martin Larralde
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Update Figure 1 to make node boundaries more obvious

parent dde3ed0c
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......@@ -87,13 +87,12 @@ nucleotide hexamers inside the gene sequence.
First, the sequence is analysed to find start and stop in the 6 reading frames (1). Then
dynamic programming nodes are created for each codon, each storing the strand and the
type (green for start, red for stop) of the codon they were obtained from. They are then
scored on biological criteria, such as the presence of a Shine-Dalgarno sequence (2).
Putative genes are identified between all start and stop codons in a given
window (a gene cannot span between any pair of nodes; some invalid connections are
shown with red dashed lines). Then putative genes are then scored using a dynamic
programming approach (3). Once all connections have been scored, the dynamic programming
matrix is traversed to find the highest scoring path, giving the final predictions (4).
\label{fig:method}](figure1.svg){width=100%}
scored on biological criteria (2). Putative genes are identified between all start and
stop codons in a given window (a gene cannot span between any pair of nodes;
some invalid connections are shown with dashed lines as examples). Then putative
genes are scored using a dynamic programming approach (3). Once all connections
have been processed, the dynamic programming matrix is traversed to find the highest scoring
path, giving the final predictions (4). \label{fig:method}](figure1.svg){width=100%}
Pyrodigal adapts the first two steps so that the dynamic programming nodes
can be extracted directly from a Python string containing the sequence data,
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