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RNA Assembly Optimization #

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3회 업데이트 됨.

  • 최초 작성자
  • 최근 업데이트

Structured data


Introduction: #

Next-generation sequencing has made it possible to perform differential Gene expression studies in non-model organisms. For these studies, reference genome were prepared from De novo assembly using the RNA-seq data. However, Transcriptome assembly produces a multitude of contigs with differnt isoform which can also be created by sequence artifacts and assembler artifacts. Those can create a false prediction at the down stream analysis. so to remove those assembled contigs must be clustered into unitigs prior to differential gene expression detection. Based on these objective the Corset, a method were developed, which can cluster contigs based on the reference mapped read count from multiple samples.


Clustering: #

Corset is the best methods to cluster the assembled contigs from mapped reads, when compared to CAP3 and CD-HIT-EST

Commands : #

Step 1: #

Map the reads to assembled transcriptome

Step 2: #

/data/Bioinformatics/Tools/corset-1.03-linux64/corset test.bam,sample1.bam,sample2.bam,

Step 3: #

/data/Bioinformatics/Tools/corset-1.03-linux64/corset_fasta_ID_changer clusters.txt ../All.fasta > Corset.fasta

source code #

Reference #

  1. Corset: enabling differential gene expression analysis for de novo assembled transcriptomes, Genome Biology

Suggested Pages #