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Lipid transcriptome profiling of indigenous Dunaliella sp. isolate

BioBam Bioinformatics S.L. Supported Project. Researchers: Nahid Hosseinzadeh Gharajeh Mohammad Amin Hejazi Mostafa Valizadeh Ebrahim Dorani Background and Project Overview: Dunaliella is a unicellular, halotolerant, biflagellate microalga which is already exploited as a platform in food, feed, aquaculture, biomedical, pharmaceutical, nutraceutical and cosmeceutical industries, specifically due to its high production of β-carotene and fine chemicals. There is a wide range

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Interview with Mariana Monteiro

Interview with Mariana Monteiro, a Bioinformatician, Sales and Customer Manager at BioBam. What I value the most about BioBam is that it is a very human company very close to its employees.

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BioBam at PAG January 2019

We attended PAG XXVII, the largest Ag-Genomics Meeting in the world. https://www.intlpag.org/30/ We launched OmicsBox Beta and gave away a whole bunch of free subscriptions. Here are a few pics of the happy winners! Hopefully, they took advantage. The PAG 2019 Team with our new OmcisBox Booth :-). See you next year!

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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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