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This is part four of our Q&A article series with several leading sequencing analysis providers. We’re interviewing these experts to learn how they handle different aspects of the analysis process.
During this segment of our series, we ask the experts about the intricacies of data visualization.
In the first installment, we learned about quality control, the second installment covered assemblies and alignments, and the third installment focused on transcript analysis.
What types of visualization tools are available and how do you decide which is best for your data?
QIAGEN Digital Insights has a number of visualization and interpretation tools available. The best known of these is QIAGEN Ingenuity Pathway Analysis (IPA) which is a platform that lets you discover and interpret the biology in your differential expression data. You can also leverage the IPA knowledge base without data to explore disease networks, pathways and more.
One powerful feature of IPA is called Analysis Match. This gives you the ability to automatically compare and visualize your data against a library of over 135,000 curated datasets to find other datasets that have similar or divergent patterns of expression and underlying biological effects.
Then you can go further with the IPA Land Explorer feature, which empowers you to see how your genes of interest are expressed across diseases, tissues, treatments, etc., from a library of over 650,000 biological samples across about 2000 diseases. You can see if certain mutations affect expression and generate survival curves as you go. It completes the picture for your visualization and interpretation.
The Illumina product development team chooses the best visualizations available for each application, leveraging open-source and proprietary tools. For example, we’ve used the open-source Pangolin to develop lineage visualizations within our COVIDSeq application. We have developed proprietary visualization libraries, such as our sequencing QC output in our DRAGEN All Caller application. Within Illumina Connected Analytics, we also provide tools for customers to develop their own visualizations within Jupytr Notebooks and R-Shiny apps.
Visualization is arguably Geneious Prime’s greatest strength, offering a variety of dynamic visualizations that are readily available at every step of a workflow. We make it easy to find the best visualization for any given task with smart defaults and an intuitive interface for switching between the available options.
The sequence view is the key here, providing a highly configurable and interactive experience for viewing and editing most data types in one place. This includes raw reads and chromatograms, alignments and assemblies, whole chromosomes and proteins as well as circular molecules like vectors and bacterial genomes. Of particular note, Geneious Prime correctly displays and generates live summary statistics for a circular assembly or alignment where reads may map over the origin.
Amongst the many other options we provide, tabular viewing of annotated data is another key visualization feature of Geneious Prime. It provides rapid sorting and filtering of large data sets to identify items of interest and export the results into CSV for specialized analysis using downstream software.
Our mission at MGI is to minimize the customer’s manual work and maximize data quality and security by providing visualization tools which cover the whole NGS workflow- the ZTRON series of products. Using the ZTRON series of products, we can manage biological samples, use them for laboratory management, as well as manage data generated by sequencing machines, analyze them, and generate relevant reports.
For each of the tools, we have dedicated a lot of efforts and resources to their development, testing and launch. These tools all have very unique designs and comprehensive functions, so it is difficult for us to pick up a so-called “best”. If I were doing bioinformatics, I would find PaaZ most useful as it allows checking and safety deletion. Perfectly integrated into the whole automated system, it supports customized pipelines at its user interface and therefore requires the least amount of bioinformatics capabilities.
The analysis system provides a visual interface for the scheduling of biometric analyses, such as:
a) Visual view of analysis progress, status, and logs;
b) Automatic management of genetic data, backup, and customization of governance rules;
c) Customization of bioinformatic analysis pipeline through “drag and pull” at a visual interface. According to the quality control standards provided by the user, the quality control status of each sample can be set and displayed.In the QC process, statistical tables and pictures can be used to visually present data performance. During sample analysis, users can rely on tools like samtools1 to view the detailed status of the comparison and visualize results such as variant calling using IGV2 view.
References:
1. Li H, Handsaker B, Wysoker A, et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics. 2009;25(16):2078-2079. doi:10.1093/bioinformatics/btp352.
2. Thorvaldsdóttir H, Robinson JT, Mesirov JP. Integrative Genomics Viewer (IGV): high-performance genomics data visualization and exploration. Brief Bioinform. 2013;14(2):178-192. doi:10.1093/bib/bbs017.
There are many ways to visualize genomic data, with an even longer list of available tools. One of the most common ways to visualize genomic data is with a genome browser such as IGV or UCSC’s browser which allow you to look directly at your data in a given region of the genome. While this is essential for “getting a feel for your data,” or maybe doing a quick QC or sanity-check, it’s inefficient for performing genome-wide analyses. Another great way to visualize genomic data is by using deeptools to produce heatmaps and profile plots which give you a birds-eye view of your entire dataset. More quantitative visualizations such as violin plots, histograms, and bar plots are critical for understanding and asking questions of your data.
In the single-cell space, the Seurat toolkit has essentially become the industry standard for data analysis and visualization, so incorporating it into the Basepair platform along with a suite of other genomics tools was a no-brainer. Altogether, the best way to visualize genomic data (whether it’s RNA-seq, ChIP-seq, ATAC-seq, scRNA-seq, WGS/WES, etc.) is by using a combination of tools that work together to summarize your biological signal while offering the flexibility to quickly explore the data at different resolutions.
Check out the fifth and sixth (final) installment of our Q&A series.