Team I Gene Prediction Group: Difference between revisions
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Gene prediction is the process of identifying the specific regions of genomic DNA that encode for genes. After sequencing and assembly, gene prediction is one of the first steps in understanding the genome of a species. In the past, confirming that the gene prediction is accurate demanded in vivo experimentation through gene knockout and other assays. Today, bioinformatics research has made it possible to predict the function of a gene based on its sequence alone. There are two general methods to do this: homology-based tools and ab-initio tools. | Gene prediction is the process of identifying the specific regions of genomic DNA that encode for genes. After sequencing and assembly, gene prediction is one of the first steps in understanding the genome of a species. In the past, confirming that the gene prediction is accurate demanded in vivo experimentation through gene knockout and other assays. Today, bioinformatics research has made it possible to predict the function of a gene based on its sequence alone. There are two general methods to do this: homology-based tools and ab-initio tools. | ||
There is a big difference between prokaryotic and eukaryotic gene prediction. For eukaryotes, genes may be separated by introns, which makes it challenging to find the whole genomic sequence. Promoter sequences are more complex and less well understood in eukaryotes as well. In prokaryotes, the promoter regions are well understood, which is useful when using ab initio tools, since these tools search for signs of specific signs of protein coding genes. In prokaryotic genomes there are also contiguous open reading frames (ORFs) - when paired with the high amount of stop codons in prokaryotes this can indicate, with high probability, a gene being present. | There is a big difference between prokaryotic and eukaryotic gene prediction. For eukaryotes, genes may be separated by introns, which makes it challenging to find the whole genomic sequence. Promoter sequences are more complex and less well understood in eukaryotes as well. In prokaryotes, the promoter regions are well understood, which is useful when using ab initio tools, since these tools search for signs of specific signs of protein coding genes. In prokaryotic genomes there are also contiguous open reading frames (ORFs) - when paired with the high amount of stop codons in prokaryotes this can indicate, with high probability, a gene being present. Our challenge when looking at ORFs is that every gene is a ORF, but not every ORF is a gene. | ||
===Data=== | ===Data=== |
Revision as of 08:51, 23 March 2018
Introduction
Background
Our overarching goal is to understand what causes heteroresistance in Klebsiella spp. At this step, our objective was, given assembled genomes, to predict genes for Klebsiella that could later be annotated to understand functionality.
Gene Prediction
Gene prediction is the process of identifying the specific regions of genomic DNA that encode for genes. After sequencing and assembly, gene prediction is one of the first steps in understanding the genome of a species. In the past, confirming that the gene prediction is accurate demanded in vivo experimentation through gene knockout and other assays. Today, bioinformatics research has made it possible to predict the function of a gene based on its sequence alone. There are two general methods to do this: homology-based tools and ab-initio tools.
There is a big difference between prokaryotic and eukaryotic gene prediction. For eukaryotes, genes may be separated by introns, which makes it challenging to find the whole genomic sequence. Promoter sequences are more complex and less well understood in eukaryotes as well. In prokaryotes, the promoter regions are well understood, which is useful when using ab initio tools, since these tools search for signs of specific signs of protein coding genes. In prokaryotic genomes there are also contiguous open reading frames (ORFs) - when paired with the high amount of stop codons in prokaryotes this can indicate, with high probability, a gene being present. Our challenge when looking at ORFs is that every gene is a ORF, but not every ORF is a gene.
Data
Objective
Methods
Pipeline (general workflow)
Discussion
Conclusions
References
Here is our powerpoint for background and strategy.