NERITES
: Nitrate availability Effect over RhythmIc
diel TranscriptomEs in raphidocelis Subcapitata
Welcome to
NERITES
a web application
that allows you to easily and autonomously explore the response of
Raphidocelis subcapitata
to nitrate stress over time and
photoperiod.
Rhythmicity
Explore rhythmicity changes under stress
Gene Network
Find interesting coexpression patterns
Predictive model
Inspect our predictive model
- Explore rhythmicity . Enables the user to explore how rhythmic genes are affected by photoperiod and abiotic stress (nitrate availability) to which R. subcapitata is subjected.
- Gene Coexpression Network . If desired, the user can browse the R. subcapitata co-expression network generated from multiple conditions (nitrate availability, photoperiod and daytime) to determine potentially important connections between genes.
- Predictive model . To determine whether there is indeed a direct relationship between genes, a predictive model has been made available that shows the most notable connections between particular genes.
The freshwater Cholorophyta unicellular microalga Raphidocelis subcapitata (formerly known as Selenastrum capricornutum and Pseudokirchneriella subcapitata ) was originally harvested and isolated from the Nitelva River (Akershus, Norway) in 1959. (NORCCA 2023a). Its presence has been widely reported, all over the world. It is characterised by a crescent-shaped morphology with a dense cell wall and a single large chloroplast that occupies almost the entire interior of the cell.
This microalga began to be used as a marker of toxicity in fresh waters as it is very sensitive to many types of pollutants. However, in recent years, a potential biotechnological application of this micro-organism as a fixer of atmospheric CO2 and as a means of producing high-value compounds and biofuels has been seen.
Rhythmicity
Explore how rhythmicity changes under stress
NERITES
allows researchers to explore the expression profiles of
individual genes in
Raphidocelis subcapitata
and analyse their rhythmicity.
This data has been generated in our lab over three complete diurnal cycles under
long day (summer day, 16h light / 8h dark) and short day (winter day, 8h light / 16h dark)
conditions. In this app, users can visualize gene expression profiles of their
interest, compare their patterns under short day and long day conditions. Users can also
perform
statistical analysis
over the rhythmicity of gene expression profiles.
Follow
the next steps to perform your analysis
Photoperiod selection
Please select the desired photoperiod from the following list for performing the analysis:
Nitrate availability
Please select the desired nitrate condition from the following list for performing the analysis:
Gene of interest
Below you can write the ID associated to the gene whose rhythmicity you wish to analyze.
Nitrate availability
Please select the desired nitrate condition from the following list for performing the analysis:
Gene of interest
Below you can write the ID associated to the gene whose rhythmicity you wish to analyze.
Please be patient, building graph ...
Results
Results for the query gene are displayed below, arranged in
different tabs. The execution of each analysis is initiated from the
Start
button
within each of the tabs, with specific instructions for each analysis.
This tab shows two results. First of all you can see how your gene adapt
along the day in every condition by a graphical representation. In other instance,
you can see if your gene is rhythmic or not according to a non-parametric test (RAIN).
Finally, this tab shows several measurements (mesor, phase and amplitude)
for your gene in each condition
Gene Network
Build your own gene coexpression network
The aim of this section is to facilitate the studies over the
Raphidocelis subcapitata
transcriptome. A user can search for a set of genes of interest using the
Module Selection panel.
After selecting the module you could watch his position in the network
and explore their correlation with several fisiological effects.
Module Selection:
Results
The
Raphidocelis subcapitata
transcriptome network are
displayed below. The execution of the
Start button
enable the
visualization of the selected modules in the network. Below you can see a heatmap
with the correlations values of your selected module with some physiological factor
such as photoperiod, nitrate availability, time point and total fatty acid amount.
Correlation values range from -1 being the most negative correlation
to 1 being the most positive. In brackets we find the adjusted p-value of the correlation result.
Predictive model
Inspect your own predictive model
To determine potential relationships in fatty acid metabolism,
an
sPLS-type predictive model
has been implemented. Partial Least
Squares, or projection to latent structures (PLS), is a robust
and malleable method based on multivariate projection that can
be used to explore or explain the relationship between two
continuous data sets. Specifically,
Sparse Partial Least Squares
(sPLS)
is a modality of it that is able to perform simultaneous
selection of variables in both data sets and thus extract the
most relevant information.
Fatty Acid Selection:
Results
As a result, the weight (from 0 to 1) of the selected fatty acid in the
model and those transcription factors to which it is related are shown.
Contact and Info
Acknowledgments and other information
Authors:
Emma Serrano Pérez,
Mercedes García González and Francisco José Romero Campero.
We are strongly committed to open
access software and open science. NERITES's source code is available
at GitHub following the lateral panel link and is released under a GNU General Public License v3.0. If you
experience any problem using NERITES, please create an issue in GitHub and we will address it. For other
inquiries, send an email to eserrano3@us.es.
The architecture of this web
application was developed following the model established in our research group by PhD student Marcos Ramos González.