TXGP ens63 reference

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One of the most interesting questions we can ask with X. laevis genome would be how many genes it has. To construct gene models, we are mainly focusing on de novo transcriptome assembly approach with our RNA-seq data. However, de novo transcriptome assembly programs generate many 'false positive' transcripts. Also, because of allotetraploidy in X. laevis, transcriptome data may contain many transcript variants for each gene. So, to estimate the gene model from transcriptome data precisely, we would like to combine all transcripts candidates foe each gene together, and analyze them separately. Sequence-based clustering is natural way to do this, but we need to optimize parameters, such as %identity to define a cluster. To get some ideas for this, we have looked at genes and transcripts of several well-studied organisms.

Genes & Transcripts

This figure shows total number of genes and transcripts in each organisms. The number on top of green bar means total number of transcripts, and the number on top of blue bar means total number of genes (based on EnsEMBL v.63 annotation). The number on top of cyan bar means the number of genes that contain only one transcript.


Clustering of transcripts

We clustered all transcripts for each organism with usearch program with different %id cutoff. The number on top of red bar means the ratio of 'the number of clusters' to 'the number of genes'. The number on top of pink bar means the ratio of 'the number of clusters having more than one gene' to 'the number of clusters'. Although they may be very closed paralogous genes, we considered this number as 'clustering error'.

  • Ultimate goal is to match the number of genes to the number of clusters in all organisms.
  • Human and mouse have too much transcripts compared to other organisms, so we would allow 1.5x more clusters than total number of genes in these organisms.
  • Although X. tropicalis is the closest model organism, it does not have many transcripts yet. So we use D. rerio(zebrafish) to estimate 'optimal number of clusters'.
  • We would like to control 'clustering error' less than 0.10 (it may be little bit higher than conventional cutoff, i.e. 0.05. But, as mentioned earlier, it may also contain many paralogous genes, so it is unlikely that all of them are clustering errors.)

ens63.gene_vs_clusters.uc090.small.png ens63.gene_vs_clusters.uc080.small.png ens63.gene_vs_clusters.uc070.small.png ens63.gene_vs_clusters.uc060.small.png