Blast2GO Blog


Reanalyzing the transcriptomic response of A. galli to an anthelmintic drug with Blast2GO

In this analysis, we will reproduce the study that was carried out by Michaela M. Martis et al. in 2017 (doi: with Blast2GO. 


Ascaridia galli is an intestinal parasite which infects a wide range of domestic birds. It is especially important in European farms, where it parasites laying hens and cause some economic problems. The only available treatments are the Benzimidazole derivatives, such as Flubendazole (FLBZ), a group of anthelmintic drugs. However, their overuse could be problematic in the future, as A. galli could develop resistance mechanisms.

The objectives of this transcriptomics study were:

  • To develop a reference transcriptome of A. galli.
  • To understand the response in gene expression before and after the exposure to FLBZ through an RNA-Seq Differential Expression Analysis.
  • To investigate b-tubulin transcripts expression and phylogeny, since several studies have reported the relation between b-tubulin and Benzimidazoles resistance.


A. galli


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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

Is Repeat-Masking necessary in Blast2GO?

Does RNA-seq-based Eukaryotic GeneFinding of Blast2GO require repeat-masking the whole genome shotgun (WGS) sequence? A case study in jute (Corchorus olitorius L., Malvaceae s. l.)

Debabrata Sarkar1Carlos Menor2 and Nagendra Kumar Singh3

1Biotechnology Unit, Division of Crop Improvement, ICAR-Central Research Institute for Jute and Allied Fibres (CRIJAF), Nilganj, Barrackpore, Kolkata 700120, West Bengal, India. E-mail: This email address is being protected from spambots. You need JavaScript enabled to view it. . ORCID iD: 0000-0003-3943-9646
2Blast2GO Team, BioBam Bioinformatics S.L., Avenida Peris y Valero 87-19 46006 Valencia, Spain. E-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.
3Rice Genome Lab, ICAR-National Research Centre on Plant Biotechnology (NRCPB), Pusa Campus, New Delhi 110012, India. E-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.


Prior to gene prediction in eukaryotes, it is important to mask repetitive sequences including low-complexity regions and transposable elements (Yandell and Ence 2012; Ekblom and Wolf 2014). In the present study, we investigated the effectiveness of RNA-seq-based [WebAUGUSTUS (Hoff and Stanke 2013)] gene prediction using the Eukaryotic GeneFinding (EGF) module of the software Blast2GO (Conesa et al. 2005) with or without masking the whole genome shotgun (WGS) sequence of Corchorus olitorius L. (Sarkar et al. 2017). Since genome masking is not a prerequisite for EGF (in Blast2GO), our overreaching objective was to assess whether repeat-masking would improve the precision of protein-coding gene prediction and annotations using evidence from RNA-seq alignments as implemented in Blast2GO.

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Blast2GO Supported Project:

Evolutionary analysis of three Pneumocystis genomes


  • Prof. Luis Delaye, CINVESTAV Irapuato, México
  • Prof. Enrique Calderon, Instituto de Biomedicina de Sevilla, España
  • Prof. Andrés Moya, I2SysBio, Universidad de Valencia, España


Cyst from Pneumocystis showing eight endospores. Giemsa stain. 

Background and Project Overview:

Fungi from the genus Pneumocystis parasites lungs of mammals. These fungi show extensive stenoxenism, meaning that each Pneumocystis specie parasites a single mammal species. In humans, Pneumocystis causes pneumonia in immunocompromised hosts. Pneumocystis genomes are endowed with several copies of a gene coding for a Major Surface Glycoprotein (MSG). Pneumocystis uses MSG proteins for host attachment and immune system evasion.

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Blast2GO Supported Project:

Study of gene expression patterns associated with reproductive plasticity in the peacock blenny Salaria pavo


  • MSc Sara D. Cardoso, Ph.D. candidate at Gulbenkian Institute for Science (IGC), Oeiras, Portugal
  • Supervisors: Prof. Rui F. Oliveira, Gulbenkian Institute for Science (IGC), Oeiras, Portugal and Prof. Adelino V. M. Canário, CCMAR - Centre of Marine Sciences, University of Algarve, Faro, Portugal

Salaria pavo nest-holder male inside its nest being courted by a female.

Background and Project Overview:

The peacock blenny Salaria pavo (family Blenniidae) is a small intertidal fish usually found in rocky shores of the Mediterranean Sea and adjacent Atlantic areas. Populations of the peacock blenny under divergent ecological conditions (i.e. nest site availability) exhibit profound differences in their mating system, involving sex role reversal in courtship behaviour and the expression of sequential alternative reproductive tactics (ARTs).

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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

A basic evaluation of the Coding-Potential Assessment Tool in Blast2GO

RNA-seq technologies detect coding as well as multiple forms of noncoding RNA. RNA-seq can accurately measure gene and transcript abundance as well as identify known and novel features of a transcriptome. While the coding transcripts will lead to effector proteins, the non-coding transcripts are usually involved in the gene expression regulation and in the transcription and translation machinery.

In this evaluation, we will predict the coding potential for the transcripts of an RNA-seq experiment showing the different options of this tool which is based on the 'Coding-Potential Assessment Tool 1 ' and an overview of the results.

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Blast2GO - Release of Version 5

(Release Date: 13/01/2018)

Just in time for the PAG Plant & Animal Genome Conference, we launched Blast2GO 5! This major release brings many new features. Most importantly, Blast2GO is now connected to the powerful BioBam Cloud, an AWS  architecture which gives a big boost to many analysis steps. Details below. 

To upgrade from version 4 to 5 you have to download the latest version here. Minor updates will be automatic, as usual.

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 version 5

Highlight Features of Version 5:

  • InterProScan in the Cloud (CloudIPS)
  • Workflows (Create, View, Run)
  • De-Novo Transcript Level Quantification (Fasta + FastQ)
  • Blast Result Alignment Viewer
  • High-Performance GFF File Viewer
  • PDF Reports for "RNA-Seq" and "Gene Finding" features
  • New Gene Ontology ID-Mapping Strategy (faster, more complete, "gi" independent)
  • New design for many result pages
  • "Find Similar Sequences" performance improvements
  • Faster Species Taxonomy Queries
  • Preferences: Define Memory and Temporary Files Settings
  • Preferences: Choose Gene Ontology Version: latest or month
  • Improved "Cloud Usage" Monitor

Coding-Potential Assessment Tool with Blast2GO

This video shows how to use the 'Coding-Potential Assessment Tool' which allows distinguishing the coding transcripts from the non-coding transcripts. This can be achieved using prebuilt models or building a species-specific model from the NCBI database. The results of the coding potential can be cross-checked with the Blast results and may allow discovering some novel mRNA.

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