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How to export Gene Finder GFF output with GO terms?

After running Gene Finding within Blast2GO I ended up with a GFF containing the genome coordinates and also a Blast2GO project.
With the project, I can run the full functional annotation steps Blast, InterProScan, Mapping and Annotation.
Now it would be nice if I could export a GFF file retrieved from Gene Finding containing the GOs from the annotated project.
This is possible with Blast2GO PRO.

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How to perform a Functional Enrichment Analysis

The main steps of the bioinformatic process to perform a de novo transcriptomic analysis have been described in previous blogs. A common RNA-seq analysis pipeline includes the quality control and preprocessing of raw RNA-seq reads, the structural and functional characterization of the transcriptome, the expression quantification and the differential expression analysis. Although RNA-seq analysis has become a routine procedure in biological research, extracting biological insight from such information is a major challenge.

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Expression quantification and differential expression analysis

One of the most common applications of RNA-seq is to estimate gene and transcript expression. It starts with the alignment or mapping of reads and there are two possible alternatives: mapping to the genome when a reference sequence is available or mapping to the transcriptome (e.g. de novo assembled transcriptome). Reads may map uniquely or could be multi-mapped reads, while multi-mapped reads arise more often when the reference is the transcriptome because one read could map equally well to all gene isoforms in the transcriptome that share the exon.

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Learn about de-novo transcriptome assembly with Blast2GO

High-throughput sequencing of RNA has revolutionized the study of species for which a reference genome is not available or incomplete by enabling the large-scale analysis of their transcriptomes. While analyses of model organisms generally rely on a reference genome, studies of non-model organisms usually lack this advantage. In the absence of an appropriate reference genome, de novo transcriptome assembly is performed.

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How to launch an Enrichment Analysis from pairwise differential expression results in Blast2GO?

It is possible to directly run Functional Enrichment Analysis either GSEA or Fisher Exact Test from a pairwise differential expression analysis executed in Blast2GO.
The list of interesting genes will be generated automatically by Blast2GO and there is no need to create it.

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How to create a gene list within Blast2GO to run the functional enrichment analysis?

Blast2GO offers the option to create either single or ranked ID lists depending on the algorithm that will be used for the functional enrichment analysis, the Fisher Exact Test or GSEA.

On one hand, the Fisher Exact Test expects a single column with the sequence identifiers, which have to match the ones with the Blast2GO annotation project.

On the other hand, the GSEA needs a ranked list, where the first column is the sequence identifier and the second one could be e.g the logFC, P-Value of a differential expression project.

 

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Basic Transcriptome Characterization with Blast2GO

Analysis workflow

  • Objective: To describe the process of a transcriptome characterization using Blast2GO.
  • Input data: A mouse RNA-seq dataset composed of 140803 contigs.
  • Pipeline:
    1. Blastx and InterproScan were performed with the complete dataset to identify proteins. Sequences with no Blastx hits nor IPS results were selected and further analyzed.
    2. RFAM and Local Blast (against an EST mouse db) were performed with the sequences with no Blastx nor IPS result in order to find structural and non-coding RNAs and ESTs, respectively.
    3. "Retrieve Blast Top-Hit" was used to replace the query sequences by the best EST hit sequences obtained in the local Blast result. Then, Blastx was performed using EST hits as queries in order to find new protein matches.
    4. In the results, we show how many of the contigs were characterized as proteins or structural RNAs in each of the above-mentioned steps.

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Figure 1. Transcriptome characterization workflow.

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How to use "Retrieve Blast Top-hit" to improve functional annotations with Blast2GO

RNA-seq data is sometimes difficult to match with proteins, due to the short length of the reads. When this is the case, it might be useful to try to find EST hits, which can then be used to find new protein matches. In this demo, we will show how to retrieve top EST hits and the different options that this tool offers. 

In this video, we use an RNA-seq dataset in which many sequences did not find a Blast result. To further characterize sequences without result, we first show how to select and extract sequences to a new project. Then, we perform a local Blast against an EST database and finally we explain in detail how to retrieve the top EST hits. Using the top EST hits as queries, we perform a Blastx search in order to find new protein matches and improve functional characterization.  

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How to change sequence name coming from Prokaryotic GeneFinding?

When running GeneFinding the sequences receive a name with the predicted genes.

The first part of the sequence identifier comes from the genome reference sequence name (de-novo assembly) and then a _orfx is appended, where x is a number.

Sometimes this name is not useful to proceed with downstream analysis or compare results from other experiments.

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Generate a Venn Diagram in Blast2GO

When running a pairwise differential expression analysis, I was interested in rapidly visualise the genes that overlap between KOvsCtrl, TretvsCtrl and KOvsTret comparisons.
The Venn Diagram tool offered by Blast2GO allows selecting multiple ID lists or ID value lists in text or B2G format to draw the intersection of the elements of the lists.
These ID lists can easily be generated within Blast2GO. It is possible to do all in one place, without the need to go back and forth between Blast2GO and a text editor.
Propotional Venn Daigram

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