What Do Zebrafish Have To Do With Bioinformatics?

What is bioinformatics?

Before reading the bioinformatics glossary it is important to know exactly what bioinformatics is. Bioinformatics uses computational methods in order to analyse biological datasets. It sits in the centre of Biology, Computer Science, and also Statistics, allowing you to understand changes in your data through a biologically-relevant lens.  

Bioinformatics is particularly used in areas such as oncology and rare disease research. Research such as large-scale clinical studies or mining of public data are well within the scope of bioinformatics analysis.  

So, where do Zebrafish come in?

Zebrafish play a particularly important role in biological research, you can find out why in our A-Z of Bioinformatics glossary below.

Glossary: A-Z of Bioinformatics

A – Analysis

Bioinformatics is analysis of biological data. It helps to make sense of large or complex datasets, and find answers in your results.  

B – Biological focus

Without a biological focus, bioinformatics would just be analysis of data. However, with a biological focus, we can give more information for your research question. It is not only that gene A has increased in expression between timepoints 1 and 2, but also what relevance that has for your research. 

C – CRISPR

CRISPR is one of the best-known methods for genome editing and allows researchers to make changes to DNA sequences as well as modify gene function and expression. CRISPR editing can be applied to a wide variety of organisms.

D – DNA

As the building blocks of life, DNA underpins a great deal of biological research. In fact, changes in DNA affect all omics analyses, from the genomic to the metabolomic.

E – Epigenetics

Epigenetics encompasses changes in gene expression that do not arise from modifications in the genetic sequence. DNA methylation and histone modifications are two areas that can be analysed to study epigenetic changes.

Read our recent whitepaper on epigenetics here.

F – Functional enrichment analysis

Functional enrichment analysis, also known as gene set enrichment analysis, identifies those genes and proteins that are over-expressed in a list of genes or proteins. This can help pinpoint biological pathways that are enriched, which can often be associated with particular disease phenotypes.

G – GWAS

Genome-wide association studies identify associations between genetic variants and traits such as disease phenotypes. GWAS studies often include large numbers of people and look for genetic markers that could be used in order to predict disease.

H – Haplotype analysis

Haplotypes are the set of variations in DNA that tend to be inherited together. This can include allelic combinations or sets of SNPs (single nucleotide polymorphisms). Haplotype can be carried out regardless of the data generation platform used.

I – Independent verification

By using an external bioinformatics company, you can get independent verification for your results. External bioinformaticians can either conduct the analysis for you, or give you third-party confirmation of the findings that you completed in-house.

J – Justification

Bioinformatics analysis often gives justification for your research. Whether it is the justification for stop/go decisions (depending on the outcome of your study) or for repositioning a drug (and giving reasons why your drug may or may not be suitable for a separate therapeutic indication).

K – Kick-off meetings

At Fios, kick-off meetings happen both internally and externally when a project is signed. This ensures that all relevant information for the project is passed between our Business Development and Operations teams. External meetings include our clients, generally for more bespoke projects to ensure that the analysis undertaken fully answers their research question.

L – Landscaping data

Data landscaping is growing in popularity. By finding what is already out there, the costs for start-ups or companies trying to reposition their research are lowered. It can also help in the early stages of decision making, when trying to decide if preclinical or indeed clinical research, should be pursued.

M – Microbiome

An up-and-coming research area, the microbiome is now much more widely studied. Bioinformatics has played a key part in understanding more about the microbiome’s role in many diseases.

Find our recent whitepaper on the microbiome here.

N – Next-Generation Sequencing

Next-generation sequencing has allowed for huge leaps forward in genomic research. NGS methods can now sequence entire human genomes within hours, rather than days or months. This has allowed for research on a larger and faster scale, for a whole host of research areas. Discover the power of next-generation sequencing analysis.

Meet Dr Paul McAdam, Head of Next Generation Sequencing at Fios.

O – Oncology

Oncology is a key area that bioinformatics can help with. From preclinical phases such as trying to reposition a drug for a new therapeutic indication, through to phased trials to ensure efficacy and endpoint analysis.

At Fios, over 35% of projects each year are in some area of oncology research.

P – Proteomics

Proteomics studies the proteins that are encoded for in a cell, tissue or organism depending on the area of research. As diseases progress, proteomics can also help build understanding of how changes in proteins occur. We can use proteomics to identify relevant biomarkers for your research or identify proteins and sequences.

Q – Quality control

‘Rubbish in, rubbish out.’ At Fios, quality control is the first step we take in any project. From ensuring that the data is in the correct format for analysis, to checking the quality of the data to highlight any of poor quality. Depending on the project and type of data, we evaluate data for reads duplication rate, raw sequencing depth and alignment quality.

Read more about the lifecycle of a project at Fios..

R – Respiratory diseases

Respiratory diseases are another area where bioinformatics can play a key role. From lung cancer to COPD, bioinformatics can analyse the large datasets produced from studies to help with target identification, patient stratification or even diagnostic testing.

S – Single-cell analysis

Getting down to the level of a single cell allows for mechanisms to be seen that would be lost when studying a large population of cells. Cell to cell variation in gene expression levels or expressed proteins could be key for many unanswered biological questions.

Read our recent whitepaper on single-cell analysis here.

T – Transcriptomics

Transcriptomics looks at all RNA transcripts in an organism. This allows researchers to see which genes are active or inactive in a particular cell or tissue. Transcriptomic data can be generated through high-throughput screening or microarray methods.

U – Understanding

At Fios, we want to help you understand your data. If we handed back static gene lists with no extra information or analysis, that would obviously not be helpful. We help you to dive deeper into your results and truly understand what your data holds.

As part of this, at the end of a project we hold technical calls to walk through your final report with an analyst, so you fully understand the results and conclusions made.

V – Vaccines

The need to develop new therapeutics to defend against disease is omnipresent. Consequently, vaccine research is constantly evolving as genes of interest are identified. With increased usage of public data mining, larger ‘omics datasets could also be used to find potential vaccine candidates.

W – Why?

The question that underpins so much biological research. Bioinformatics helps you to answer it by revealing more about your datasets. You can find more information on mechanisms of action, and also pinpoint the ‘why not’ when a potential drug may not meet your endpoints.

X – Xenografts

Xenografts, or patient derived xenografts (PDX), are models of cancer where cells or tissue from a patient’s tumour is implanted into a mouse. This gives a ‘natural’ environment for cancer growth and allows for monitoring and treatment evaluation, the results of which can then be fed back to the patient.

Y – Yield

Yield is the amount of sequencing data that is produced from a particular sample. Some analysis workflows need high yield for quality control purposes in order to ensure there is no contamination.

Z – Zebrafish

Zebrafish are a common model for research. They share 70% of the same genes with humans, and 84% of human genes that have known associations with diseases have a counterpart in zebrafish. They also share many of the same structures that humans have, such as two eyes, mouth, brain, spinal cord and kidneys.

Zebrafish bring our bioinformatics glossary to an end.

We hope you found this bioinformatics glossary educational. If you have a bioinformatics question or would like to discuss what type of bioinformatic analyses would be suitable for your next project then please contact us.