What is bioinformatics?

Bioinformatics is a new, computationally-oriented Life Science domain. Its primary goal is to make sense of the information stored within living organisms. Bioinformatics relies on and combines concepts and approaches from biology, computer science, and data analysis. Bioinformaticians evaluate and define their success primarily in terms of the new insights they produce about biological processes through digitally parsing genomic information.

Bioinformatics is a data science that investigates how information is stored within and processed by living organisms.

How has bioinformatics changed?

In its early days––perhaps until the beginning of the 2000s––bioinformatics was synonymous with sequence analysis. Scientists typically obtained just a few DNA sequences, then analyzed them for various properties. Today, sequence analysis is still central to the work of bioinformaticians, but it has also grown well beyond it.

In the mid-2000s, the so-called next-generation, high-throughput sequencing instruments (such as the Illumina HiSeq) made it possible to measure the full genomic content of a cell in a single experimental run. With that, the quantity of data shot up immensely as scientists were able to capture a snapshot of everything that is DNA-related.

These new technologies have transformed bioinformatics into an entirely new field of data science that builds on the "classical bioinformatics" to process, investigate, and summarize massive data sets of extraordinary complexity.

What subfields of bioinformatics exist?

DNA sequencing was initially valued for revealing the DNA content of a cell. It may come as a surprise to many, however, that the greatest promise for the future of bioinformatics might lie in other applications. In general, most bioinformatics problems fall under one of four categories:

  1. Assembly: establishing the nucleotide composition of genomes
  2. Resequencing: identifying mutations and variations in genomes
  3. Classification: determining the species composition of a population of organisms
  4. Quantification: using DNA sequencing to measure the functional characteristics of a cell

The Human Genome Project fell squarely in the assembly category. Since its completion, scientists have assembled the genomes of thousands of others species. The genomes of many millions of species, however, remain completely unknown.

Studies that attempt to identify changes relative to known genomes fall into the resequencing field of study. DNA mutations and variants may cause phenotypic changes like emerging diseases, changing fitness, different survival rates, etc. For example, there are several ongoing efforts to compile all variants present in the human genome––these efforts would fall into the resequencing category. Thanks to the work of bioinformaticians, massive computing efforts are underway to produce clinically valuable information from the knowledge gained through resequencing.

Living micro-organisms surround us, and we coexist with them in complex collectives that can only survive by maintaining interdependent harmony. Classifying these mostly-unknown species of micro-organisms by their genetic material is a fast-growing subfield of bioinformatics.

Finally, and perhaps most unexpectedly, bioinformatics methods can help us better understand biological processes, like gene expressions, through quantification. In these protocols, the sequencing procedures are used to determine the relative abundances of various DNA fragments that were made to correlate with other biological processes

Over the decades biologists have become experts at manipulating DNA and are now able to co-opt the many naturally-occurring molecular processes to copy, translate, and reproduce DNA molecules and connect these actions to biological processes. Sequencing has opened a new window into this world, new methods and sequence manipulations are being continuously discovered. The various methods are typically named as ???-Seq for example RNA-Seq, Chip-Seq, RAD-Seq to reflect on what phenomena is being captured/connected to sequencing. For example, RNA-Seq reveals the messenger RNA by turning it into DNA. Sequencing this construct allows for simultaneously measuring the expression levels of all genes of a cell.

Is there a list of functional assays used in bioinformatics?

In the Life Sciences, an assay is an investigative procedure used to assess or measure the presence, amount, or function of some target (like a DNA fragment). Dr. Lior Pachter, professor of Mathematics at Caltech, maintains a list of "functional genomics" assay technologies on the page called Star-Seq.

All of these techniques fall into the quantification category. Each assay uses DNA sequencing to quantify another measure, and many are examples of connecting DNA abundances to various biological processes.

Notably, the list now contains nearly 100 technologies. Many people, us included, believe that these applications of sequencing are of greater importance and impact than identifying the base composition of genomes.

Below are some examples of the assay technologies on Dr. Pachter's list:


But what is bioinformatics, really?

So now that you know what bioinformatics is all about, you're probably wondering what it's like to practice it day-in-day-out as a bioinformatician. The truth is, it's not easy. Just take a look at this "Biostar Quote of the Day" from Brent Pedersen in Very Bad Things:

I've been doing bioinformatics for about 10 years now. I used to joke with a friend of mine that most of our work was converting between file formats. We don't joke about that anymore.

Jokes aside, modern bioinformatics relies heavily on file and data processing. The data sets are large and contain complex interconnected information. A bioinformatician's job is to simplify massive datasets and search them for the information that is relevant for the given study. Essentially, bioinformatics is the art of finding the needle in the haystack.

Is creativity required?

Bioinformatics requires a dynamic, creative approach. Protocols should be viewed as guidelines, not as rules that guarantee success. Following protocols by the letter is usually quite counterproductive. At best, doing so leads to sub-optimal outcomes; at worst, it can produce misinformation that spells the end of a research project.

Living organisms operate in immense complexity. Bioinformaticians need to recognize this complexity, respond dynamically to variations, and understand when methods and protocols are not suited to a data set. The myriad complexities and challenges of venturing at the frontiers of scientific knowledge always require creativity, sensitivity, and imagination. Bioinformatics is no exception.

Unfortunately, the misconception that bioinformatics is a procedural skill that anyone can quickly add to their toolkit rather than a scientific domain in its own right can lead some people to underestimate the value of a bioinformatician's individual contributions to the success of a project.

As observed in Core services: Reward bioinformaticians, Nature, (2015),

Biological data will continue to pile up unless those who analyze it are recognized as creative collaborators in need of career paths.

Bioinformatics requires multiple skill sets, extensive practice, and familiarity with multiple analytical frameworks. Proper training, a solid foundation and an in-depth understanding of concepts are required of anyone who wishes to develop the particular creativity needed to succeed in this field.

This need for creativity and the necessity for a bioinformatician to think "outside the box" is what this Handbook aims to teach. We don't just want to list instructions: "do this, do that". We want to help you establish that robust and reliable foundation that will allow you to be creative when (not if) that time comes.

What are common characteristics of bioinformatics projects?

Most bioinformatics projects start out with a "standardized" plan, like the ones you'll find in this Handbook. But these plans are never set in stone. Depending on the types and features of observations and results of analyses, additional tasks will inevitably deviate from the original plan to account for variances observed in the data. Frequently, the studies need substantive customization.

As the authors of Core services: Reward bioinformaticians, Nature, (2015) have observed of their own projects,

No project was identical, and we were surprised at how common one-off requests were. There were a few routine procedures that many people wanted, such as finding genes expressed in a disease. But 79% of techniques applied to fewer than 20% of the projects. In other words, most researchers came to the bioinformatics core seeking customized analysis, not a standardized package.

In summary, this question is difficult to answer because there isn't a "typical" bioinformatics project. It is quite common for projects to deviate from the standardized workflow.

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