NCI scientists have many choices available to them for bioinformatic analyses of Next Generation Sequencing (NGS) data. While some require expertise in programming, others provide a more user-friendly, point-and-click interface. These options include programs for elucidating molecular pathways influenced by changes in gene expression, building of phylogenetic trees, in silico molecular cloning, and more. CCR scientists can learn about the full range of commercial bioinformatics software available to them at the BTEP CCR Bioinformatics Resources page. This topic spotlight will focus on three popular software packages: Partek Flow, Qiagen Ingenuity Pathway Analysis (IPA), and Qlucore Omics Explorer.
Partek Flow enables scientists to build analysis workflows for data derived from sequencing modalities such as DNA, bulk RNA, single cell RNA, spatial transcriptomics, ChIP, and ATAC. Many input file formats are supported including FASTQ, BAM, and CSV (e.g., gene expression counts table), thus analysis can begin at any stage. Partek Flow has a built-in genome browser, produces publication quality visualizations (e.g., PCA, heatmap, and volcano plots), and provides insights to biological functions impacted by different conditions. The NIH High Performance Computing cluster (HPC) Biowulf hosts Partek Flow, providing investigators with abundant compute resources for analyzing big data while using a point-and-click interface. Instructions for accessing Partek Flow are available here.
Qiagen’s Ingenuity Pathway Analysis empowers researchers to discover affected pathways, networks, diseases, regulatory mechanisms, biomarkers, and drug targets using data generated from studies that examined how gene, protein, or metabolite levels change under various biological settings. Users supply a comma-separated (CSV) or tab-delimited (TXT) table formatted with columns listing identifiers, measured change, and statistical confidence of measured change for genes, proteins, or metabolites of interest. Tables containing RNA sequencing differential gene expression analysis results can be uploaded as input. Researchers can also perform keyword searches on IPA’s vast knowledgebase to learn about topics such as molecules and genes that influence diseases or biological functions and pathways influenced by various drugs or genes. In this way, the software can be used for discovery even when scientists do not have their own data.
Qlucore Omics Explorer is a visualization software for data derived from many platforms including bulk RNA-seq, single cell RNA-seq, ChIP-seq, ATAC-seq, proteomics, and metabolomics. Users start by supplying a data table containing measurements of gene or protein expression (e.g., RNA sequencing gene expression counts table), metabolite levels, or number of peaks detected per genomic region (ChIP and ATAC-seq experiments). From there, Qlucore Omics Explorer’s suite of statistical, gene set enrichment, and ontology tools generate graphical results that help researchers capture biological insights such as differentially expressed genes in an RNA sequencing experiment. Built-in machine learning algorithms allow investigators to classify samples and cells based on expression of genes, proteins, or metabolites.
Here, we highlighted three packages that cater to a variety of purposes including building comprehensive sequencing analysis workflows (Partek Flow), learning about biological pathways and networks (IPA), and sample and cell type classification using machine learning (Qlucore Omics Explorer). Table 1 emphasizes the capabilities of software mentioned above.
Table 1: Comparison of Partek Flow, Qiagen IPA, and Qlucore Omics Explorer
| Partek Flow | Qiagen IPA | Qlucore Omics Explorer | |
| Build comprehensive sequencing analysis workflow | Yes | No | No |
| Gene set/pathway analysis | Yes | Yes | Yes |
| Accepted input | FASTQ, BAM, CSV data tables and many more | CSV or tab delimited TXT data table containing gene, protein, or metabolite identifiers along with measured change between biological conditions and statistical confidence of measured change | CSV or tab delimited TXT data table containing study measurements such as RNA sequencing gene expression counts |
| Machine learning and classification | No | No | Yes |
| Produce publication quality plots | Yes | Yes | Yes |
| Runs on personal computer | No (hosted on Biowulf, the NIH High Performance Computing cluster) | Yes | Yes |
Recordings of previous trainings addressing the use of Partek Flow, Qiagen IPA, Qlucore Omics Explorer, and other licensed software are available on the BTEP Video Archive. Please see the BTEP calendar for upcoming trainings. Should you have questions regarding commercial bioinformatics software, please reach out to the BTEP team at ncibtep@nih.gov.
— Joe Wu (BTEP)