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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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previous diagram

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: 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. RFAM and Local Blast (against an EST mouse db) were performed with the sequences with

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Retrieve Top Blast Hit

How to change sequence names 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. Is there any way

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Generate Venn Diagram

A Venn Diagram is a graphical representation of sets or groups of elements or IDs in form of circles where common elements/IDs of the sets are represented by the areas of overlap among the circles. In bioinformatics this is a popular graphical representation to compare subsets, for example when running a pairwise differential expression analysis it might be interesting to

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