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In silico tools to boost shrimp farming

In silico tools to boost shrimp farming


In the past few months, we have been working a lot with the immunological responses of shrimps, and that is why we always have at hand the review of Fajardo & colleagues (2021)

We like it so much that we’d like to share some aspects of the review on our blog today, for customers who haven’t taken full advantage of their trials and challenges, or for omics enthusiasts like us  (the passionomics ☺).

Thanks to the progress made in the field of transcriptomic analysis through massive mRNA sequencing technology, currently exists a significant amount of data about the crustacean immune system.

The review showed shrimps’ genes and metabolic pathways associated with the immune system through the development of a meta-analysis. The researchers analyzed 1,195 transcriptomic data sets from 14 species from the Penaeus genus from the Sequence Read Archive (SRA) database of the National Center for Biotechnology Information (NCBI). 

The literature about these data sets explored how the shrimp’s immune response changes owing to challenges with pathogens, abiotic stressors (ammonia, temperature, salinity, oxygen deprivation, and pH), dietary factors, and beneficial microbes (pre-and probiotics).

Certainly, the in silico information from sequences’ database can be converted into commercial applications to boost shrimp farming, develop more sustainable feeds, and select more disease-resistant strains… But such a vast amount of information needs to be analyzed using multidisciplinary approaches and reliable data analysis tools.

This is particularly important in the so-called training immunology, a concept that has been studied in P. vannamei, P. monodon, P. japonicus, and Procambarus clarkia and that considers the effects of ‘vaccines’ on the memory of the innate immune system and in nutrigenomics, a new discipline that studies metabolic pathways to get better combinations of nutrients and optimize feeds.

Furthermore, NGS technology, which generated massive amounts of sequencing data, requires more computing capacity and more efficient software to produce accurate results, or the incorporation of algorithms based on artificial intelligence (AI) and machine learning, which can aid in the use of such large data sets by allowing the understanding of immune responses, for example.

At the same time, cloud-based services can also be useful since they can incorporate AI-based approaches to provide vast graphical processing units to support AI applications. This, in conjunction with next-generation AI pipelines, is introducing new algorithms to obtain predictive models, allowing researchers to design better tests and obtain better data to improve future iterations.

Finally, the review highlights the importance of unifying shrimp databases to establish ‘multi-omic’ pipelines to be used by industry and academia to develop tools that can be used to improve shrimp farming. 

Every day researchers advance more and more with complete genome assemblies, enabling the industry to have a better comprehension of genes’ function, but, can anyone manage all of this information alone?


Do your department need help? 

If you are interested in exploring training immunology, nutrigenomics or have a try with artificial intelligence and machine learning,

Contact us!



Fajardo C, Martinez-Rodriguez G, Costas B, et al. Shrimp immune response: A transcriptomic perspective. Rev Aquac. 2022.

Roy S, Bossier P, Norouzitallab P, Vanrompay D. Trained immunity and perspectives for shrimp aquaculture. Rev Aquac. 2019;12(4):2351-2370. 

Santos CA, Blanck DV, de Freitas PD. RNA-seq as a powerful tool for penaeid shrimp genetic progress. Front Genet. 2014;5:298.


DataOmics on Aquafeed!

DataOmics on Aquafeed


We are very excited to share in our blog some results from our collaboration with Adisseo. A study about the health-promoting properties of Sanacore® GM in farmed seabream has been included in the latest Aquafeed Vol 14 Issue 4 2022.

Feed manufacturers and fish farmers are aware of the importance of health-promoting additives to prevent diseases in aquaculture and maintain successful production. But, are the mechanisms that are responsible for the diminution of the infection known to them?

Genomics, metabolomics, and proteomics, ‘Omics’ as we love to call them at DataOmics, can facilitate the characterization and quantification of millions of biological molecules. The genes’ expression and their interaction with them and with other molecules can define the immunocompetence and healthy status of a fish. 


Similarly, the response to diseases, stress and even nutritional challenges can be explored in-depth through the detection and quantification of proteins, including the characterization of their metabolic pathways and interaction patterns. 


Understanding such complex relationships can help identify what happens in the fish under specific conditions. This knowledge can be used to develop more suitable health-promoting products to improve fish health status and productive performance. 


At DataOmics, we performed shotgun proteomics for Adisseo, to better understand the health-promoting properties of Sanacore® GM in gilthead seabream and recollect accurate information about the physiological status of three key tissues of fish health and performance: the anterior intestine, liver, and head kidney.


The proteomics analysis carried out by DataOmics provided a snapshot into the mechanisms of action of Sanacore® GM in gilthead seabream under healthy conditions. We shed light on how Sanacore® GM could improve fish’s ability to ward off illnesses through the modulation of almost 1.500 proteins across the head-kidney, intestine, and liver.


We observed modifications in the level of proteins that participate in important immune functions that are relevant to interconnected mechanisms within innate and acquire immunity. By the shotgun proteomics performed at DataOmics, Adisseo demonstrated that Sanacore® GM seabream supplementation stimulates proteins linked with integrity and immune response in the intestine and impacted proteins linked with innate and acquired immunity in the liver offering an optimal degree of protection to the fish.


Furthermore, we observed the highest number of altered proteins abundances in the head-kidney in fish-fed Sanacore® GM evidencing the regulation of innate immunity mechanisms such as the destruction of damaged or infected cells via apoptosis and the inhibition of pro-inflammatory cytokines. By the shotgun proteomics performed at DataOmics, Adisseo also demonstrated that Sanacore® GM could enable the elimination of injured cells being a useful resource to restore immunocompetence and support fish in dealing with production conditions. 


You find these results stimulating! Don’t you? 


And what do you think about the potential of ‘Omics? Can they be useful in your research?

If you are interested in exploring all the pluses that genomics, metabolomics, and proteomics can bring to your research do not waste time and contact us!



Proteomics to gain insight into the mechanism of action of a health-promoting additive. Rayner Gonzalez-Prendes, Carla Dos Anjos de Souza, DataOmics, Waldo G. Nuñez-Ortín, Adisseo.


Gut microbiota single cell

Gut microbiota single cell identification: increasing accuracy and resolution


Among the topics that are the subject of our Monitoring and Intelligence System’s activities, there is one that we think could be particularly interesting for our clients.

Have you ever heard anything about microbiota? If you are interested in animal nutrition, it is very likely that the answer is yes!

We like to think of microbiota as a community of many individual bacteria that are from many different species and that live symbiotically.

Considering that these commensal bacterial communities are strictly associated with the physiology of the host, it is expected that such community can change or even affect the health of the host.

Therefore, to understand these interactions, it is important to know what kind of bacteria (taxa) are in the microbiota and how many cells of each kind are in these bacterial communities.

Although measuring the microbiota composition at the single-cell level is difficult, good news comes from Japan where a new method called Barcoding Bacteria for Identification and Quantification (BarBIQ) has been developed. A key difference between BarBIQ and usual methods is that BarBIQ quantifies the number of cells for each OTU, while conventional methods measure the number of amplified sources of 16S rRNA gene molecules (i.e: 16S rRNA gene abundance from cells, genome copy number and extracellular DNA), which may affect the quantification of the intestinal microbiota.

Feature image: Pathompong Thongsan, licensed under iStock.

According to the results of the study recently published in Nature, BarBIQ approach allows the classification of single bacterial cells into taxa and quantifies how many cells there are in each taxon. Since BarBIQ can define microbiota at the single-cell level, it is expected that it can provide a deep and more accurate taxonomic classification and functional inferences of microbial ecosystems.

In the original work, Jin et al., (2022) demonstrate the usefulness of BarBIQ to robustly quantify cultured bacterial strains from the human gut in a model community. Furthermore, by applying this method to murine cecal microbiota, they were able to get a highly reproducible cell abundance quantification, showing also that BarBIQ allowed to discover that dietary vitamin A impacts the composition of microbiota in two different locations of the murine cecum.

This condition, that other methods were not able to identify, shows that BarBIQ can be able not only to visualize the global microbiota and individual bacterial members but also allows an effective understanding of gut microbiota functions.

Since BarBIQ enables microbiota qualitative and quantitative characterization at the single-cells level, we can say that the method is meant to be a keystone for microbiota studies.

Therefore, it could be very useful to develop new ingredients, additives, or nutraceutical products such probiotics or prebiotics.


Our client knows that we can help them to identify, characterize and quantify microbial populations and strains present in their products.

And you? Are you aware that DATAOMICS can assist you in the development of new microbiota-based products?

If you are curious to explore this field…

Contact us!

Reference of the open access paper:

Jin, J., Yamamoto, R., Takeuchi, T. et al. High-throughput identification and quantification of single bacterial cells in the microbiota. Nat Commun 13, 863 (2022).


Real-age predicted from transcriptome

Real-age predicted from transcriptome information: A new computational strategy that makes feel us inspired!


Among this week’s readings, a prestigious study has caught our attention. Once again, the importance of a strong computational strategy for data analysis in genomics, transcriptomics, proteomics, and metabolomics has been highlighted.

It seems that gene expression data analysis takes a huge step forward. In fact, extraordinary news comes from France, where researchers at Claude Bernard Lyon University have invented a method for studying the transcriptome more efficiently.

The new method is the solution to a very common problem in the analysis of gene expression, the existence of uncontrolled and unknown sources of variance that can mask or confound the effects of variables of interest.

The new computational strategy, called RAPToR and recently presented in the journal Nature Methods by Bulteau and Francesconi, has been successfully tested on models’ gene expression profiles and it works on whole organisms, dissected tissues, and single-cell samples.

The great innovation of RAPToR (real-age prediction from transcriptome staging on reference) consists of being able to study a transcriptome variant exhaustively, excluding factors related to the stage of development of the organism which could confuse or even mask the result.

RAPToR can determine the physiological age of a sample based on the analysis of its transcriptome and eliminate this factor to study the variable of interest properly and without disturbance. It will be especially useful in large-scale single-organism profiling because it eliminates the need for staging before profiling.

Furthermore, even though RAPToR has been programmed to be used for models (especially for fast life cycle species), such as fruit mosquitoes, zebrafish, and mice, the method can work with other species.

In fact, RAPToR performs well for non-model organisms. In close species, when data are available as a reference and even in distant species for stages with conserved developmental dynamics.


The results of this rigorous study are both fascinating and inspiring to us …

And you? What do you think about this amazing innovation?


DATAOMICS customers know that gene expression analysis is becoming increasingly affordable and are turning to molecular biology techniques for in-depth studies of their products.

At DATAOMICS we know that the barrier lies in transforming the enormous amount of data that the techniques provide into helpful knowledge for decision-making in companies. 

Therefore, our clients turn to us, because they know that we can integrate all types of data to identify unique patterns and offer a holistic interpretation.

AI methods such as Machine Learning in DATAOMICS make predictive studies, very useful in the identification of biomarkers.

If you are interested in genomics, transcriptomics, proteomics, metagenomics, and metabolomics, DATAOMICS can guide you to maximise your experimental designs.


We will help you define the appropriate sampling, techniques and transform the results into useful information for decision-making in your organisation.


Contact us!