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Multilocus Sequence Typing MLST Analysis Tool

The Multilocus Sequence Typing Analysis Tool of OmicsBox (MLST) is a nucleotide sequence-based approach of characterizing isolates of bacterial species using the sequences of internal fragments of seven housekeeping genes. This video shows step-by-step how to analyze bacterial sequences with the MLST App in OmicsBox. As input, users can provide either DNA-Seq reads in FASTQ or contigs/scaffolds in FASTA format.

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Expression quantification and differential expression analysis

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

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de-novo transcriptome overview

Learn about de-novo transcriptome assembly

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

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Blast2GO poster exhibited at ECCB 2018

BioBam Bioinformatics attended the 17th European Conference on Computational Biology (ECCB) in Athens, Greece. It is the main computational biology event in Europe and congregates scientists working in a variety of disciplines, including bioinformatics, computational biology, biotechnology, medicine, and systems biology. We presented all the new features developed, during the last month, in Blast2GO:

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quality check

NGS Preprocessing and Quality Control: How-To with OmicsBox

Functional genomics attempts to describe the functions of genes and proteins by making use of data derived from genomic and transcriptomic experiments. Some bioinformatics tools, like OmicsBox, 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

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Sarocladium oryzae

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: https://doi.org/10.1186/s12864-016-2599-0) 1.  Introduction Sheath rot disease caused by S. oryzae is an emerging threat to rice

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How to create analysis workflows

OmicsBox/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

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Expression Estimation at Transcript-Level

The Transcript-level Quantification feature of OmicsBox/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

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Expression Quantification at Gene-Level

The “Create Count Table” feature of OmicsBox/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).

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