Blast2GO Blog


Quality Check and preprocessing: how to perform them with Blast2GO

Functional genomics attempts to describe functions of genes and proteins by making use of data derived from genomic and transcriptomic experiments. Some bioinformatics tools, like Blast2GO, offers biologists to get from raw NGS data through all steps up to the generation of biological insights for a species without any previous genome resources.


The bioinformatic process to perform a de novo transcriptomic analysis can be divided into 4 phases:


  1. Quality check and pre-processing raw sequence data
  2. De novo transcript reconstruction and functional characterization
  3. Expression quantification and differential expression analysis
  4. Functional Enrichment Analysis


In this article we will analyze the first point and we will explain how to perform a quality check and pre-processing raw sequence data.


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Blast2GO - Release of Version 5.2

(Release Date: 02/08/2018)

What's new in Blast2GO 5.2?

We are happy to announce that Blast2GO 5.2 has now been released. This update makes our bioinformatics tool for straightforward functional genomics even better.
Read below to learn which features and improvements are part of this release.

Main features:

  • Venn Diagrams that are fully customizable and allows multiple list comparisons
  • FastQ Pre-Processing (Adapters, Quality, Length) with multiple trimming options.


new version 5


  • Workflows allow regular files as input and output
  • Context Menu for GFF and VCF tables
  • GSEA with "No Filter" option
  • Table context menu: create a category chart using two columns
  • File Manager context menu: merge two statistics objects
  • Improved Enzyme Statistics Wizard
  • "Export Annotations" includes enzymes with 'One annotation per row'
  • Minor fixes and improvements


How to update:

If you are already working with Blast2GO 5 you don’t have to do anything to update to the latest version. Blast2GO will update automatically. Otherwise, you can download the latest version online from

If you are still working with version 4, we highly recommend upgrading to version 5. You can download the latest version from here. 


Feedback, questions and feature requests are most welcome. As always, please write us: This email address is being protected from spambots. You need JavaScript enabled to view it.


De novo genome assembly and annotation of sheath rot fungus Sarocladium oryzae with Blast2GO

This project reproduces a study carried out by Hittalmani S. et al. in 2016 about the S. oryzae fungus which causes sheath rot of rice. We used Blast2GO for the main analysis steps like the structural and functional annotation and analysis.
(Original research paper:


1.  Introduction 

Sheath rot disease caused by S. oryzae is an emerging threat to rice cultivation. This fungus invades rice through stomata and wounds caused by insects. Upon infection, the fungus engenders necrotic lesions at the mesophyll and vascular tissues, which impede the adequate translocation of nutrients from the foliage to the panicle. This leads to the production of partially filled chaffy grains, affecting the production yield by up to 85%. Nonetheless, despite the threat this fungus entails, little is known about its life cycle and infection biology.

Thereby, as an attempt to attain information about putative genes involved in pathogenicity, Hittalmani S. et al. carried out a de novo genome assembly and annotation of S. oryzae. The goal was to create genomic resources, which could be translated into designing better disease management strategies and widen the understanding of rice-Sarocladium pathosystem.

Sarocladium oryzae

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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 worklfows 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 to quantify 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


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 to quantify 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.

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