Blast2GO Blog

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Blast2GO Supported Project:

Study of changes in the maize transcriptome in response to Maize Iranian Mosaic Virus (MIMV) infection

Researchers:

  • Mr. Abozar Ghorbani, Ph.D. candidate at Plant Virology Research Center, College of Agriculture, Shiraz University, Shiraz, Iran
  • Supervisors: Prof. Keramatollah Izadpanah, Plant Virology Research Center, College of Agriculture, Shiraz University, Shiraz, Iran and A/Prof. Ralf Dietzgen, Queensland Alliance for Agriculture and Food Innovation, the University of Queensland, Australia

Background and Project Overview:

Maize Iranian mosaic virus (MIMV, genus Nucleorhabdovirus, family Rhabdoviridae) is an economically important virus in maize in Iran. In addition to maize, it infects wheat, barley, rice and several other gramineous plant species. The virus is transmitted by the planthopper Laodelphax striatellus in a persistent-propagative manner. There is no close serological relationship between MIMV and other rhabdoviruses infecting gramineous plants such as Maize mosaic virus, Barley yellow striate mosaic virus and Cynodon chlorotic streak virus. In recent years, several differential screening techniques have been devised to identify changes in the expression of host genes in response to virus infection. Next-generation deep-sequencing techniques, such as Illumina RNA-seq, have provided new approaches to study plant transcriptomes to allow insights into plant defense responses.

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NCBI GenBank Submission with Blast2GO 

This video shows how to use the 'Create NCBI GenBank Genome Submission Files' tool which allows to generate all files (e.g. the Asn1 (.sqn) file) necessary to submit your annotated sequences to the NCBI database. It allows to combine genomic sequences and functional annotations and creates valid GenBank submission files. Additionally, this video explains how to obtain source files (.gff and .annot files), provides hints on how to prevent common validation errors and how to submit a WGS project via the NCBI website. 

 


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.  

 

References: 

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 

The Pairwise Differential Expression Analysis tool is designed to perform differential expression analysis of count data arising from an RNA-seq experiment. The application, which is based on the software package "edgeR", allows the identification of differentially expressed genes between two experimental conditions by applying quantitative statistical methods. 

This video shows the performance of a pairwise differential expression analysis in which the expression of two cell types obtained from mice at different development stages was compared. 

 

References: 

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.


 

Eukaryotic gene finding with Blast2GO

A basic evaluation of Augustus

Blast2GO allows executing eukaryotic de-novo and RNA-seq based gene finding with Augustus. In this way, it is possible to discover novel, putative coding genes and their genomic positions for yet uncharacterized genome. Based on the Augustus algorithm an 'ab-initio' (DNA sequences only), as well as RNA-seq guided (BAM files) gene predictions, are supported. As shown below, the latter increases the prediction accuracy significantly.

In this evaluation, we will guide you through a typical gene finding process while comparing the different results obtained using the 'ab-initio' and the RNA-seq supported approach. The performance (time) is also compared with the standalone Augustus version.

 

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Time Course Expression Analysis with Blast2GO

A simple use-case comparing Blast2GO with R chunks

The Blast2GO feature “Time Course Expression Analysis” is designed to perform time-course expression analysis of count data arising from RNA-seq technology. Based on the software package 'maSigPro', which belongs to the Bioconductor project, this tool allows the detection of genomic features with significant temporal expression changes and significant differences between experimental groups by applying a two steps regression strategy. This use case shows the basic analysis workflow, comparing the results obtained with R Bioconductor and Blast2GO. 

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Pairwise Differential Expression Analysis with Blast2GO

A simple use-case comparing Blast2GO with R chunks

The Blast2GO feature “Pairwise Differential Expression Analysis” is designed to perform differential expression analysis of count data arising from RNA-seq technology. This tool allows the identification of differentially expressed genes considering two different conditions based on the software package ‘edgeR’, which belongs to the Bioconductor project. This use case shows the basic analysis workflow, comparing the results obtained with R Bioconductor and Blast2GO.

Blast2GO and R Logo

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How to translate longest ORFs with Blast2GO

The Blast2GO "Translate Longest ORF" tool searches for the longest Open Reading Frame (ORF) in nucleotide sequences and translates them into their protein sequences. You may choose one or multiple of the six possible DNA frames or select the reading frame based on the frame of the best blastx hit. In this video, we will explain how to translate a set of Salmonella enterica genes and the available parameters (e.g how to select the reading frames, the genetic codes that can be used for translation, how to allow open-ended translations, etc). We also show the "Batch Rename" functions to undo sequence name changes.


Blast2GO - Major Release Version 4

(Release Date: 13/09/2016)

We are very happy to announce the release of Blast2GO 4. This new version contains many new bioinformatic features like Differential Expression Analysis, Gene Finding or Gene Set Enrichment Analysis.

 

Blast2GO will update automatically if activated. Otherwise please download the latest version online from here.

Feedback, questions, as well as feature requests, are most welcome. Please write us to: This email address is being protected from spambots. You need JavaScript enabled to view it.

 

New Blast2GO Version 3.3

Highlight Features of Version 4:

  • Differential Expression Analysis for Pairwise and Time Course
  • Gene Finding for Eukaryotes and Prokaryotes
  • Gene Set Enrichment Analysis (GSEA)
  • ID and Sequences Conversion via Blast/Blat Top-Hit Sequences
  • App Manager with new Blast2GO “Featured” Apps
  • Create NCBI Submission Files Feature as “Featured” App
  • Ortholog Group Finder (COG/EggNOG) via App Manager
  • And many more ...

Functional Analysis of Pancreatic Cancer Expression Profiles

This use case shows how to perform a functional analysis of pancreatic cancer expression data with Blast2GO. The performed steps are explained more in detail with short video tutorial linked to each section.

Malignant (PDAC), benign (CP) and normal tissue (NP) gene lists are compared against each other. The human genome functional annotation data is retrieved via BioMart and used as the reference. The data set is analysed using two different enrichment-analysis strategies, a Fisher's Exact Test and GSEA and results are visualized in various different ways within Blast2GO. 

Analysis Workflow

  1. Extract and Review data with Blast2GO.
    • Load the complete human genome GO annotation by BioMart.
    • Make several Project Statistics to check coverage of annotated sequences. 
    • Reduce the functional information with GO-Slim.
  2. Import differential expressed gene list.
    • Import two datasets from Pancreatic expression Database: Pancreatic Ductal Adenocarcinoma (PDAC) vs Normal Pancreas (NP) and Chronic Pancreatitis (CP) vs Normal Pancreas (NP).
  3. Enrichment analysis:
    1. Fisher's Exact Test
    2. Gene Set Enrichment Analysis
  4. Methods to visualize functional profiles.
    • Word Cloud
    • Coloured Graph
  5. Conclusions
    Workflow21

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