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How to find Orthologous Groups with Blast2GO

Blast2GO allows assigning Cluster of Orthologous Groups (COG) to sequences via the EggNOG database. A Cluster of Orthologous Group (COG) corresponds to a group of proteins that share a high level of sequence similarity, which can be usually associated with evolutionary convergence. Due to their close relation, the orthologous group annotation can be used to infer properties that can improve the Blast2GO sequence characterization.

In this demo, we show how to find Orthologous Groups and merge COG associated GOs to existing Blast2GO annotations. We also review the results obtained and show how to generate and interpret different result summary charts. 

Prokaryotic GeneFinding with Blast2GO.

The Prokaryotic GeneFinding tool within Blast2GO, uses the Glimmer algorithm, providing an easy and fast way to locate the genes using the 'ab initio' (single run model) or 'hint based' (iterative run model) methodology on a prokaryotic query genome.

Glimmer (Gene Locator and Interpolated Markov ModelER) is a system for finding genes in microbial DNA, like bacterial, archaeal and virus genomes. It uses Interpolated Context Models (ICM) to identify the codings regions and distinguish them from noncoding DNA. 

This video shows how to use the Prokaryote GeneFinding tool in order to predict the potentially protein-coding regions of the Helicobacter Pylori genome.

Eukaryotic GeneFinding with Blast2GO

The Eukaryotic GeneFinding tool within Blast2GO allows to predicting genes in Eukaryota genomes based on the Augustus algorithm, which is one of the most accurate and widely used.

The predictions can be generated by two different methodologies: 'ab initio', which only needs the DNAseq data as input and 'hint based', which also requires a RNAseq alignment file (.bam) in order to generate hints for the prediction. This last prediction method generates a more accurate gene prediction thanks to the added gene structure information.

This video shows how to predict the genes of Sus scrofa with the genome of the wild boar ('ab initio') and by adding a bam file with some RNAseq to generate the 'hints'. Finally, we compare the run speed and the quality of the results for both methods.

How to know if all your sequences have been analyzed. 

The Analysis Progress chart is a very useful representation, which shows the step of the analysis in which the sequences in your dataset are found.

They can be in different states, each represented by a colour:

  • Total: grey
  • Without Analysis: white
  • With IPS: purple
  • With Blast Hits: orange
  • Without Blast Hits: red  

As an example of the utility of the Progress chart, the analysis progress may allow you to detect that not all your sequences have been analyzed at a specific step in the analysis due to any kind of problem. In the current entry, you will learn how to detect the problem and solve it. 


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Scientific Impact of Blast2GO

Publications, Citations and Testimonials.