Helpful Features, Tips and Tricks
How to create analysis workflows with Blast2GO
Blast2GO provides an interface to create, edit and run workflows based on the Common Workflow Language (CWL) specification. This interface allows to describe all analysis steps using the functions and tools offered by Blast2GO and connect them to perform a complete analysis in a single run.
This video shows step-by-step how to create a workflow from scratch, define the input data, configure the parameters of each step, save and export results, generate charts and more.
Please find further information in the online user manual.
Expression Estimation at Transcript-Level
The Transcript-level Quantification feature of Blast2GO allows quantifying the gene and isoform expression of RNA-seq datasets.
This video shows step-by-step how to create a count table of aligned sequencing reads and explains in detail the different concepts of expression quantification at transcript level. The application is based on the RSEM software package, which assigns reads to the isoforms they came from modelling the uncertainty derived from multiple isoforms having overlapping sequences.
As input, sequencing reads in FASTQ format and a FASTA file containing the transcript sequences are required. The output, an un-normalized count table, can then be analysed directly within Blast2GO. Various options for differential expression analysis are available (find videos here and here).
Find more details in the online user manual.
- Li B and Dewey CN (2011). "RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome." BMC Bioinformatics, 12:323.
- Langmead B, Salzberg S (2012). "Fast gapped-read alignment with Bowtie 2." Nature Methods, 9:357-359
Expression Quantification with Blast2GO
The "Create Count Table" feature of Blast2GO allows quantifying the gene expression of RNA-seq datasets.
This video shows step-by-step how to create a count table of raw reads and explains in detail different concepts of expression quantification. The available parameters are inspired by the popular HTSeq Python Package (reference below).
As input, aligned sequencing reads in SAM/BAM format and a GTF/GFF file with coordinates of genomic features are required. The output, an un-normalized count table, can then be analysed directly within Blast2GO. Various options for differential expression analysis are available (find videos here and here).
Anders S, Theodor Pyl P, Huber W (2015). "HTSeq — A Python framework to work with high-throughput sequencing data." Bioinformatics, 31 (2), p. 166-169.
Coding-Potential Assessment Tool with Blast2GO
NCBI GenBank Submission with Blast2GO
Time course expression analysis with Blast2GO
The Time Course Expression Analysis tool allows performing a differential expression analysis of expression data arising from a time course RNA-seq experiment. This application is based on the maSigPro Bioconductor package, which implements a two-step regression strategy to detect genes with significant temporal expression changes and significant differences between experimental groups.
This video shows the analysis of count data coming from an experiment in which the expression levels of tumour and normal human cells were measured at different times.
Nueda MJ, Tarazona S and Conesa A (2014). “Next maSigPro: updating maSigPro Bioconductor package for RNA-seq time series." Bioinformatics, 30, p. 2598-2602.
Pairwise differential expression analysis with Blast2GO
Robinson MD, McCarthy DJ and Smyth GK (2010). “edgeR: a Bioconductor package for differential expression analysis of digital gene expression data.” Bioinformatics, 26, pp. -1.
How to translate longest ORFs with Blast2GO
- How to perform a Gene Set Enrichment Analysis (GSEA)
- How to use PSORTb
- Coloured Graphs: How to combine multiple functional GO profiles
- More videos about helpful features
- How to select sequences by function and create ID lists
- How to perform a Fisher's Exact Test
- How to use BioMart and GO-Slim
- How to run local Blast against a custom database
- How to use RFAM