Fig 1.
Appearance and EOD waveforms of four mormyrid species used for this study.
The morphological shape of each target species is shown, while body sizes may vary. Species-specific EOD wave forms are represented in relation to a 1ms time scale.
Table 1.
Assembly statistics of the four transcriptomes.
Table 2.
List of 27 candidate genes potentially related to the EOD elongation of C. tshokwe.
Fig 2.
Proportional ratio of candidate SCO-sequences between C. tshokwe and C. compressirostris for any annotated GO-term.
The figure illustrates the proportional ratio of candidate SCO-sequences between the two Campylomormyrus species (y-axis) relative to the total candidate SCO-sequence number for any annotated GO term (x-axis). The 95% confidence interval for an equal ratio (50:50) is depicted as the gray shaded area, rendering dots (i.e., GO terms) outside of the area significant (red circles).
Table 3.
Comparison of proportional assignment of candidate SCO data among C. tshokwe and C. compressirostris for KEGG level A categories.
Fig 3.
Proportional ratio of candidate SCO-sequences among C. tshokwe and C. compressirostris with annotated KEGG pathways.
The figure represents the proportional ratio of candidate SCO-sequence counts among both species in a found KEGG level B category (y-axis), relative on the total number of candidate SCO-sequences in the respective category (x-axis). The 95% confidence interval for 50:50 ratio is depicted as gray shaded area, rendering dots (i.e., KEGG level B categories) outside of the area significant.
Fig 4.
KEGG level A category assignments among the candidate SCO sequences, compared to the entire transcriptome.
The bar chart shows the sequence percentage (y-axis) with an annotated KEGG Level A category (x-axis) in the SCO data set and entire transcriptomes of C. tshokwe (red) and C. compressirostris (blue). The error bars indicate the 95% confidence interval, taking the total absolute number of candidate SCO-sequences into account (confidence limits of proportions). Asterisks (*) depict significance at p<0.05.
Fig 5.
Overview of the workflow of the applied data-analytical approaches.
Shown are the major bioinformatical steps to create an input data set (A), steps for potential candidate gene identification (B), and the computational steps to create the candidate SCO data sets as well as their three annotation comparisons (C).