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Impacts of variation in the human genome on gene regulation.

Journal of molecular biology | 2013

Recent advances in fast and inexpensive DNA sequencing have enabled the extensive study of genomic and transcriptomic variation in humans. Human genomic variation is composed of sequence and structural changes including single-nucleotide and multinucleotide variants, short insertions or deletions (indels), larger copy number variants, and similarly sized copy neutral inversions and translocations. It is now well established that any two genomes differ extensively and that structural changes constitute a prominent source of this variation. There have also been major technological advances in RNA sequencing to globally quantify and describe diversity in transcripts. Large consortia such as the 1000 Genomes Project and the ENCODE (ENCyclopedia Of DNA Elements) Project are producing increasingly comprehensive maps outlining the regions of the human genome containing variants and functional elements, respectively. Integration of genetic variation data and extensive annotation of functional genomic elements, along with the ability to measure global transcription, allow the impacts of genetic variants on gene expression to be resolved. There are several well-established models by which genetic variants affect gene regulation depending on the type, nature, and position of the variant with respect to the affected genes. These effects can be manifested in two ways: changes to transcript sequences and isoforms by coding variants, and changes to transcript abundance by dosage or regulatory variants. Here, we review the current state of how genetic variations impact gene regulation locally and globally in the human genome.

Pubmed ID: 23871684 RIS Download

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Cufflinks (tool)

RRID:SCR_014597

Software tool for transcriptome assembly and differential expression analysis for RNA-Seq. Includes script called cuffmerge that can be used to merge together several Cufflinks assemblies. It also handles running Cuffcompare as well as automatically filtering a number of transfrags that are likely to be artifacts. If the researcher has a reference GTF file, the researcher can provide it to the script to more effectively merge novel isoforms and maximize overall assembly quality.

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