Using Machine Learning Pipelines in Bioinformatics

Bioinformatics is a particularly interesting field when it comes to applying data science – it involves a lot of data processing and analysis within the chemical, genomics, pharma, and medicine fields. Due to the complex nature of that data and the large amounts of it that continuously needs to be worked with, choosing a data processing stack that is both productive and cost-effective can be a challenge.

As the amount of data available to bioinformatics applications expands in detail (increasing your datum size) and scale (increasing your overall processing and storage needs) grows substantially, the ability to effectively ingest, transform and use that information for data science pipelines becomes increasingly complex.

Fortunately, data processing tools are being built to catch up to the needs of bioinformatics teams: these tools can now automate data ingestion and processing, manage version control of datasets and process only the diff when large-scale datasets are updated, so you can extrapolate useful information that drives your business practices forward without the added cost of re-processing large sets of data.

In this article, we’ll dive a bit deeper into all the key information you need to know about bioinformatics pipelines. We’ll be answering four questions, including:

  • What is a pipeline in bioinformatics?
  • How do you build a bioinformatics pipeline for complex data processing?
  • Why do bioinformatics teams need automated data lineage?
  • Why should you use datum-focused sequence analysis tools for bioinformatics analysis?

What is a Pipeline in Bioinformatics?

Like any machine learning pipeline, bioinformatics pipelines are composed of countless software algorithms that process raw sequencing data and turn it into a list of annotated sequence variants. One key difference between business analytics and bioinformatics is the size of a datum: life sciences sample data files can be much larger than a row of financial data, and this leaves bioinformatics teams with a unique set of needs.

Once the hurdles of datum size and data access are overcome, however, the needs for a data pipeline start to look more familiar to those in other industries: the need for data lineage and version control, concerns about what coding languages will be accepted by your pipeline software, and how data scientists will be able to use and re-purpose pipelines in the future.

Bioinformatics requires transformations that combine structured and unstructured data like images or voice data with clinical chemical, genomic, and other similar data. Each pipeline plays a different role in ensuring you get accurate information that drives better clinical outcomes.

With other pipelining tools, this can be a challenge because most are set up to only process structured data. A data pipelining tool like Pachyderm provides more flexibility for all the different needs presented by bioinformatics teams by being code- and file-agnostic. Pachyderm’s pipelines readily integrate with any files you need to process, including data warehouses, and unstructured data object storage.

Because Pachyderm pipelines are container-based, scientists and researchers can use any coding language they’re familiar with to perform data transformation and processing needed for a given pipeline.

Check out how this works in a real scenario.

Why Should You Use Datum-Focused Bioinformatics Sequence Analysis Tools?

Most approaches to data pipeline management are model-centric, focusing on the processing and output of data. For bioinformatics, however, the complications often come from the data, not the model.

When it comes to processing data in any industry, using a datum-centric approach is a must. This is especially important for companies within the bioinformatics field because the databases will be made up of very large files. With a model-driven pipeline tool, your data will be processed and re-processed over and over again, which will inevitably waste your time, make your team less productive, and make your business less profitable overall.

With a data-focused machine learning pipeline, on the other hand, your teams will be able to quickly filter and manage datasets without having to duplicate them. And that means you will more easily have the necessary information at your fingertips that you need to make unbiased conclusions about different outcomes. With Pachyderm, your pipeline can recognize new data and only process what has changed, saving you time, resources, and money; this same process allows Pachyderm to parallelize your data processing across all available computing resources.

Why Do Bioinformatics Teams Need Automated Data Lineage?

One of the most important parts of any machine learning project is reproducibility. Like any scientific discipline, a core concern for bioinformatics is data versioning and lineage, which basically builds a history of data changes, so you can track its life cycle over time.

By using bioinformatics sequence analysis tools within automated machine learning pipelines, you can automatically track data versions used in any previous versions of your model, so you can automate an ever-growing chain of data, pipelines, and algorithms with confidence in your data having a complete and accurate history.

In addition to providing confidence in your results, reproducibility also means auditability for potentially sensitive datasets. With immutable data lineage, organizations stay compliant with requirements set in place by the Health Insurance Portability and Accountability Act (HIPAA) and General Data Protection Regulation (GDPR).

Since you’ll be able to trace the origins of all information, you can be sure you’re not sharing confidential information and breaking privacy laws when starting new machine learning experiments. Plus, understanding the full story of your data ensures accurate outcomes and gives you a full breakdown of any and all data changes you make.

Connect with Pachyderm to Learn More About Complex Data Processing Today

When it comes to bioinformatics data science, your data needs to have a strong and scalable foundation in order to work in real-world situations within the genomics, chemical, and medical industries as well as natural language processing bioinformatics applications.

And the bottom line is that using machine learning helps automate complex processes and improve datasets that are used for all types of important projects on a day-to-day basis.

Ready to learn more about improving practical applications of ML pipelines and data versioning with bioinformatics sequence analysis tools? Check out our eBook, What Does Data-Centric AI Mean for On-the-Ground MLOps? and get in touch with our team to request a demo today.