The term “bioinformatics” has gained popularity over the past decade. Bioinformatics is a word used to describe the type of computational analysis carried out on datasets from biological science studies.
Bioinformatics is an important step in a genomic experiment. However, the significance of this step is often lost due to the broad context in which the term is applied. Bioinformatics is not simply data analysis but is the computational strategy used to organize data and provide statistical value to a dataset by utilizing a myriad of methodologies. This type of analysis is essential when dealing with very large data files, as is common with genomic, high-throughput, studies.
One way to think about bioinformatics and the significance of the role it plays in your genomic study is to subtract it from the equation. If you remove the bioinformatics step, what are you left with? Without bioinformatics, all that you have is genomic data without a story (but the story is the best part).
Another factor that plays into the underemphasized importance of bioinformatics is the fact that it comes last in the experimental workflow. The thought process to the last step is sometimes easy to postpone, especially when you’re busy thinking about the experimental assay. But how do you plan a genomic study without knowing how to do the analysis? Hint: You can’t – You need a plan!
Misinterpretation of the data can be detrimental to a study, especially after going through the pains (and costs) of carrying out a flawless experimental design and assay; but it does happen. Next-generation sequencing (NGS) experiments are a perfect example of how the experimental design (sample number, sequencing depth) is directly tied to the analysis protocol. In NGS experiments, finding a true result is difficult due to the amount of background noise. Distinguishing a significant signal from a false positive involves the use of specific programs and algorithms that depend on optimal sample number and sequencing depth.
A comparative study by Allali et. al. (2017) highlights the potential complexities that underlie NGS studies and the need for careful consideration of the experimental question when considering both sequencing and bioinformatic protocol. In the study, researchers aimed to analyze how differences in 16S sequencing protocol (platform and bioinformatics pipelines) may lead to differences in results. Sequencing was carried out using 3 different platforms and 7 different bioinformatics pipelines to identify the bacterial species composition of gut microbiome samples from chicken. While the overall biological conclusions regarding sample differentiation were similar, researchers found variability in the phylogenetic diversity identified depending on the bioinformatic strategy employed.
There have been many advances in the methods used when analyzing large genomic datasets. However, the “gold standards” that exist for microarray studies, still do not exist for NGS studies. For this reason, we recommend careful consideration of bioinformatics approaches and in-depth discussion with professionals in the field (i.e. scientists, data scientists, bioinformaticians, statisticians, etc.).
NGS Bioinformatics – How we can help
Depending on the size of the study and project objectives, the analysis portion of an NGS study can be lengthy and costly. Turnaround time and cost are two limiting factors when considering most NGS projects.
This is where we come in – At PhalanxBio we work to provide bioinformatic results that are in line with your study objectives. Our goal is to provide you with the story that underlies your genomic data files in the most efficient and cost-effective manner possible. As part of the project workflow, we aim to engage with our clients during the early phases of the experiment in order to map out a plan-of-action.
We work with experts that have streamlined bioinformatic pipelines such as RNA-seq gene expression analysis and ChIP-seq peak analysis. The enhanced workflow allows us to provide rapid results at very affordable prices. In addition to RNA-seq and ChIP-seq, we also offer a variety of other bioinformatics analysis options including variant analysis for data from whole exome sequencing and whole genome sequencing.
Bioinformatics packages include:
- Project consultation
- Data filtering and quality control
- Data spreadsheets and tables
- Graphical interpretation of the results
- Data storage
- Access to interactive analysis results*
- Comprehensive results report
Contact us for more information and find out about the interactive analysis tools available for your project.
*Interactive analysis results available for RNA-seq and ChIP-seq data
For additional information, consider reading:
Transcriptome Sequencing – Applications and Preparation