Most of the human genome consists of DNA that does not code for proteins.
Annotating functional regions in the non-coding genome involves two complementary analysis techniques: comparative analysis, which involves examining DNA sequences, and functional analysis, which involves examining the output of functional genomics experiments.
With the exponential increase in DNA sequence data, it is now possible to compare sequences within a single human haplotype, between cell types in a single person, across the human population and between species. Integrating the analysis across all these scales is useful.
There are two main methods of sequence comparison: scanning for regions of high sequence similarity above some operational threshold, and building statistical models of sequence families. Model-based sequence analysis can incorporate more biological knowledge than sequence similarity scans and provide more refined results.
The output of most high-throughput functional genomics experiments can be treated as a continuous signal mapped onto the genome and analysed with a standardized signal processing approach.
Signal processing involves smoothing the raw signal, then thresholding and segmenting the signal into discrete annotated blocks.
Integration of multiple types of signals generates a progression of more and more complex annotations; these smaller annotations are clustered into groups and then into functional networks that begin to represent the state of biological knowledge about the genome.
A chronic problem with annotation based on functional genomics data is the lack of sufficient validation by more low-throughput methods.
Techniques such as paired-end sequencing and chromosome conformation capture (and its descendants) enable annotation of connectivity between elements and necessitate a move beyond the one-dimensional signal approach to annotation.
Most of the human genome consists of non-protein-coding DNA. Recently, progress has been made in annotating these non-coding regions through the interpretation of functional genomics experiments and comparative sequence analysis. One can conceptualize functional genomics analysis as involving a sequence of steps: turning the output of an experiment into a 'signal' at each base pair of the genome; smoothing this signal and segmenting it into small blocks of initial annotation; and then clustering these small blocks into larger derived annotations and networks. Finally, one can relate functional genomics annotations to conserved units and measures of conservation derived from comparative sequence analysis.
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The authors thank members of the Gerstein laboratory for helpful discussions and careful reading of the manuscript. We acknowledge support from the US NIH and from the Albert L. Williams Professorship funds.
The authors declare no competing financial interests.
- Targeted exome sequencing
A technique that involves filtering genomic DNA by capturing regions of interest (often protein-coding exons) on a microarray, then sequencing the captured DNA using next-generation techniques.
- Structural variants
Chromosomal rearrangements (deletions, duplications, novel sequence insertions or inversions) that are inherited and polymorphic across the human population. Structural variants are by definition longer than SNPs and can be hundreds of thousands of base pairs long.
- Copy-number variants
Structural variants that arise from deletion or duplication and thus lead to a change in copy number of the underlying region of the genome.
- Segmental duplication
The operational definition of a segmental duplication rests on finding two regions in the same genome ranging in length from a thousand to several million nucleotides with at least 90% sequence identity. Segmental duplications are inherited but not necessarily polymorphic across the human population.
Copies of protein-coding genes with mutations that disrupt their coding sequence and demolish their original protein-coding function.
- Syntenic blocks
Segments that align between genome sequences from two species and that are believed to define an orthologous relationship.
- DNA-based transposons
Transposable DNA elements that rely on a transposase enzyme to excise themselves from one region of the genome and insert themselves into a different region, without increasing in copy number.
- RNA-based retrotransposons
Transposable elements generated when reverse transcriptase enzymes copy RNA elements into DNA and insert the DNA copies back into the genome.
- Duplicated pseudogenes
Pseudogenes that result from whole-genome or segmental duplications, in which one copy maintains its ancestral function and the other copy degrades into a pseudogene.
- Processed pseudogenes
Pseudogenes that arise when the mRNA of a parent gene is retrotranscribed back into DNA and inserted into the genome.
- Unitary pseudogenes
A rare class of pseudogene in which a single-copy parent gene becomes non-functional.
- Chromatin immunoprecipitation
(ChIP.) A technique for identifying potential regulatory sequences that are bound by the protein of interest. Soluble DNA–chromatin extracts (complexes of DNA and protein) are isolated by using antibodies that recognize specific DNA-binding proteins. In ChIP–chip, the ChIP step is followed by microarray analysis, whereas in ChIP–seq, it is followed by sequencing.
- Tiling arrays
A class of microarray in which probes of a specific length and spacing provide uniform coverage of an entire genome or portion of a genome to a desired resolution.
- RNA sequencing
The use of high-throughput sequencing of RNA that has been reverse-transcribed into DNA to characterize the set of RNA transcripts produced by a cell.
The process of filtering noise from a signal by removing fine-scale variation.
The process of discretizing a continuous signal by choosing a signal value above which the signal is considered 'on' or 'active' and below which the signal is considered 'off' or 'inactive'.
The result of thresholding in signal processing — that is, segments are those regions defined as 'on' or 'active' after discretization of the signal.
Highly compact and therefore inactive regions of the genome. Largely composed of repetitive DNA, heterochromatin forms dark bands after Giemsa staining.
The lightly staining regions of the genome that are generally decondensed during interphase and contain transcriptionally active regions.
A low-copy vector for the construction of stable genomic libraries that uses the Escherichia coli F-factor origin of replication. Each fosmid clone can store ∼40 kb of library DNA. Cloned sequences are more stable in fosmids than in high-copy vectors.
A measure of the proportion of true negatives correctly identified as such (for example, the percentage of healthy people who are identified as not having a disease).
- Regulatory forests
Regions of the genome that are enriched with binding sites for regulatory factors, such as transcription factors.
- Principal components analysis
A statistical method used to simplify data sets by transforming a series of correlated variables into a smaller number of uncorrelated factors.
- Non-allelic homologous recombination
Recombination between segmental duplications that leads to local duplication, deletion or inversion of genome sequence.
- Ultraconserved elements
Operationally defined as non-coding elements that are hundreds of base pairs long and 100% identical across human, mouse and rat genomes.
A measure of the proportion of true positives that are correctly identified as such (for example, the percentage of sick people who are identified as having a disease).
- Paired-end sequencing
Determination of the sequence at both ends of a fragment of DNA of known size.
- Chromosome conformation capture
A technique used to study the long-distance interactions between genomic regions, which in turn can be used to study the three-dimensional architecture of chromosomes within a cell nucleus.
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Alexander, R., Fang, G., Rozowsky, J. et al. Annotating non-coding regions of the genome. Nat Rev Genet 11, 559–571 (2010). https://doi.org/10.1038/nrg2814
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