RNA-SEQ ANALYSIS

Which RNA-seq analysis methods are used to identify differentially expressed genes?

BioConductor’s DESEQ2 and EdgeR statistical packages can be used to identify differentially expressed genes. Start by using the SeqMan NGen wizard to specify a Bioconductor statistical package as the normalization method, then perform the assembly. Open the finished assembly in ArrayStar to perform downstream analysis.

How do I visualize mRNA isoforms?

If you used SeqMan NGen to align PacBio Iso-Seq data, you can view the results in GenVision Pro, which also provides a specialized data track to aid in transcript discovery. To visualize mRNA isoforms for other data types, use GenVision Pro’s Sashimi plot data track or view the isoforms in text format using ArrayStar’s Isoform table.

Can I align RNA-Seq sequence data to mRNA sequences?

Yes, RNA-seq assemblies can be run with either mRNA reference sequences or whole genome reference sequences.

What types of read technology are supported for an RNA-Seq alignment?

Lasergene Genomics supports Illumina, 454, Ion Torrent, Sanger, PacBio, and ONT read technologies.

Where do I get the reference sequence for my RNA-Seq alignment?

If your organism of interest is a commonly-studied species, you can choose to download a DNASTAR genome template package or import a reference sequence from NCBI from within the SeqMan NGen wizard while setting up your RNA-Seq project.

If I have multiple samples, can I run them as separate RNA-Seq alignments?

Yes. To run multiple samples as separate RNA-Seq alignments, choose Multi Sample as the experiment type in the Input Sequence screen of the SeqMan NGen wizard. You will be prompted to group and name each experiment, which you can do automatically or manually. Each data set will be run against the reference sequence independently and an .assembly package will be created for each sample.

What types of post-alignment RNA-seq analysis options are available?

Both GenVision Pro and ArrayStar support the analysis of gene expression, isoforms, variants, annotations and ontology. You can compare gene expression values between experiments using scatter plots, heat maps, and line graphs (ArrayStar) or whole-genome views with data analysis tracks and customizable tables (GenVision Pro). Advanced filtering options help you home in on genes, isoforms and variants of interest based on signal, annotation values or many other options.

If the criteria for DESEQ2 or EdgeR analysis have been met, you can also specify that gene set enrichment analysis (GSEA) results be included with the assembly output. GSEA focuses on groups of genes that share some commonality, such as regulation, chromosomal location, or biological function. GSEA is available for human, mouse, C. elegans, and yeast data sets and lets you see whether a predefined set of genes shows statistically significant differences between two biological states (e.g., phenotypes). View the output in a specialized bubble plot that lists genes from the chosen analysis set that are also differentially expressed, and in clustering plots showing up-regulated and down-regulated gene sets.

Can Lasergene Genomics analyze differential gene expression values for miRNA data as well as RNA-Seq data?

Yes. When setting up your miRNA project in SeqMan NGen, select RNA-seq/Transcriptomics>miRNA as your workflow. Then proceed through the wizard following the same steps you would for an RNA-Seq project: input your reference sequence(s) and reads, name and group your replicates, and define a control.