How Is Sequence Data Utilized?

Sequence data is a type of data that consists of a series of elements, such as numbers, letters, words, symbols, or events, that are ordered in a meaningful way. It can represent widely varying types of information, from text to speech, music, DNA, and actions. Sequence data is widely used in many fields and applications, such as data mining, natural language processing, bioinformatics, and machine learning. This article focuses on the uses and benefits of sequence data in the field of bioinformatics along with the challenges scientists face in analyzing the data and how the SEQUIN app addresses those challenges.

Sequence Data & Bioinformatics

Bioinformatics is the field of science that applies computational methods to biological data, such as DNA, RNA, or proteins. Sequence data is a major form of biological data, as DNA, RNA, and proteins are all sequences of nucleotides or amino acids. It’s used to answer key questions in biology and medicine, such as how sequence variation and cellular levels of RNA and proteins influence physiology and disease. These fundamental questions are addressed through bioinformatics tasks, such as sequence alignment, sequence search, sequence annotation, sequence prediction, and sequence quantification.

Sequence Alignment

Sequence alignment is the process of arranging two or more sequences to identify regions of similarity or difference. It can be used to measure the evolutionary distance, functional similarity, or structural similarity among sequences. It’s also a key step toward sequencing an individual’s entire genome and quantifying cellular levels of RNA and proteins, as raw sequence data typically comes in the form of fragments which much be mapped to a reference genome, transcriptome, or proteome.

Sequence Search

Biological function is dictated not just from the literal sequence of DNA, RNA, and proteins, but also from patterns within them. For instance, sequence patterns determine where proteins and small molecules bind to DNA and RNA and where proteins interact with each other. Sequence search attempts to find these patterns, including motifs, domains, and signatures, which improves our understanding of biological function and plays an important role in therapeutics and personalized medicine. 

Sequence Annotation

Sequence annotation adds information and metadata to sequences, including names, descriptions, functions, and locations along a genome. This enriches the understanding and interpretation of sequences and provides useful and accessible information or resources. For instance, sequence annotation can be used to label genes, exons, introns, and promoters in a genome and provide their names, functions, and interactions, which is especially important for downstream analysis.

Sequence Prediction

Sequence prediction is the process of filling in missing pieces and inferring information about a sequence, such as its structure, function, or evolution. This can be used to complete or improve the knowledge and analysis of sequences and provide novel and valuable insights or hypotheses. For example, sequence prediction can be used to predict the secondary or tertiary structure of a protein, the function or activity of a gene, or the evolutionary origin or fate of a sequence.

Sequence Quantification

Sequence quantification attempts to determine the levels of sequences present in a biological sample, such as cells and tissues. It relies on upstream bioinformatics tasks, including alignment and annotation, for determining expression levels of specific genes and proteins, and is a critical step toward analysis and interpretation of sequence data.

Challenges for Scientists

Bulk and single-cell RNA sequencing are among the most commonly utilized technologies for examining gene expression patterns, both at the population level and the single-cell level. The sheer size of datasets produced by these analyses poses computational challenges in data interpretation, often requiring proficiency in bioinformatic methods for effective data visualization. The constant evolution of sequencing techniques and statistical methods adds an extra element of complexity, often creating a bottleneck for scientists who are eager to delve into RNA-seq datasets but lack extensive coding knowledge to tackle a new software tool or programming language. 

SEQUIN: Empowering Scientists by Democratizing Data Analysis

In response to these challenges, Rancho BioSciences collaborated with the National Center for Advancing Translational Sciences (NCATS), specifically the Stem Cell Translation Laboratory (SCTL), to develop SEQUIN, a free web-based R/Shiny app designed to empower scientists without bioinformatics expertise. SEQUIN allows users to effortlessly load, analyze, and visualize bulk and single-cell RNA-seq datasets, facilitating rapid data exploration and interpretation.

SEQUIN is designed to serve as a comprehensive tool for the swift, interactive, and user-friendly analysis of RNA sequencing data for single cells, model organisms, and tissues. The integrated functionalities of the app facilitate seamless processes such as data loading, visualization, dimensionality reduction, quality control, differential expression analysis, and gene set enrichment. A key feature of the app enables users to create tables and figures that are ready for publication.

As a free resource that’s available to the public, SEQUIN empowers scientists employing interdisciplinary approaches to directly explore and present transcriptome data by leveraging state-of-the-art statistical methods. Consequently, SEQUIN plays a role in democratizing and enhancing the efficiency of probing biological inquiries using next-generation sequencing data at the single-cell resolution level.

Rancho BioSciences boasts extensive expertise in delivering services related to RNA-seq data, encompassing transcriptomics analysis, scRNA-seq analysis, clustering, and differential gene expression (DEG) analysis. As part of our innovative Single Cell Data Science Consortium, we’ve established a Four-Tier Data Model tailored for RNA-seq data. Our team has successfully integrated hundreds of datasets, constituting millions of samples. Additionally, Rancho BioSciences has developed atlases organized by therapeutic area and has supported customers with large-scale dataset ingestion workflows. Furthermore, we offer the flexibility to install SEQUIN behind your firewall, allowing for local deployment to meet your specific requirements.

If you’re looking for a reliable and experienced partner to help you with your data science projects, look no further than Rancho BioSciences. We’re a global leader in bioinformatics services, data curation, analysis, and visualization for life sciences and healthcare. Our team of experts can handle any type of data, from genomics to clinical trials, and deliver high-quality results in a timely and cost-effective manner. Whether you need to clean, annotate, integrate, visualize, or interpret your data, Rancho BioSciences can provide you with customized solutions that meet your specific needs and goals. Contact us today to learn how we can help you with your data science challenges.