In
computational biology, gene prediction or gene finding refers to the process of identifying the regions of genomic DNA that encode
genes. This includes protein-coding
gene
In biology, the word gene (from , ; "... Wilhelm Johannsen coined the word gene to describe the Mendelian units of heredity..." meaning ''generation'' or ''birth'' or ''gender'') can have several different meanings. The Mendelian gene is a b ...
s as well as
RNA genes, but may also include prediction of other functional elements such as
regulatory regions. Gene finding is one of the first and most important steps in understanding the genome of a species once it has been
sequenced.
In its earliest days, "gene finding" was based on painstaking experimentation on living cells and organisms. Statistical analysis of the rates of
homologous recombination
Homologous recombination is a type of genetic recombination in which genetic information is exchanged between two similar or identical molecules of double-stranded or single-stranded nucleic acids (usually DNA as in cellular organisms but may be ...
of several different genes could determine their order on a certain
chromosome
A chromosome is a long DNA molecule with part or all of the genetic material of an organism. In most chromosomes the very long thin DNA fibers are coated with packaging proteins; in eukaryotic cells the most important of these proteins ar ...
, and information from many such experiments could be combined to create a
genetic map specifying the rough location of known genes relative to each other. Today, with comprehensive genome sequence and powerful computational resources at the disposal of the research community, gene finding has been redefined as a largely computational problem.
Determining that a sequence is functional should be distinguished from determining
the function of the gene or its product. Predicting the function of a gene and confirming that the gene prediction is accurate still demands ''
in vivo'' experimentation
through
gene knockout and other assays, although frontiers of
bioinformatics
Bioinformatics () is an interdisciplinary field that develops methods and software tools for understanding biological data, in particular when the data sets are large and complex. As an interdisciplinary field of science, bioinformatics combin ...
research are making it increasingly possible to predict the function of a gene based on its sequence alone.
Gene prediction is one of the key steps in
genome annotation, following
sequence assembly, the filtering of non-coding regions and repeat masking.
Gene prediction is closely related to the so-called 'target search problem' investigating how
DNA-binding proteins (
transcription factors) locate specific
binding sites within the
genome
In the fields of molecular biology and genetics, a genome is all the genetic information of an organism. It consists of nucleotide sequences of DNA (or RNA in RNA viruses). The nuclear genome includes protein-coding genes and non-coding ...
.
Many aspects of structural gene prediction are based on current understanding of underlying
biochemical processes in the
cell such as gene
transcription,
translation
Translation is the communication of the Meaning (linguistic), meaning of a #Source and target languages, source-language text by means of an Dynamic and formal equivalence, equivalent #Source and target languages, target-language text. The ...
,
protein–protein interactions and
regulation processes, which are subject of active research in the various
omics fields such as
transcriptomics,
proteomics,
metabolomics, and more generally
structural
A structure is an arrangement and organization of interrelated elements in a material object or system, or the object or system so organized. Material structures include man-made objects such as buildings and machines and natural objects such as ...
and
functional genomics.
Empirical methods
In empirical (similarity, homology or evidence-based) gene finding systems, the target genome is searched for sequences that are similar to extrinsic evidence in the form of the known
expressed sequence tags,
messenger RNA (mRNA),
protein
Proteins are large biomolecules and macromolecules that comprise one or more long chains of amino acid residues. Proteins perform a vast array of functions within organisms, including catalysing metabolic reactions, DNA replication, respon ...
products, and homologous or orthologous sequences. Given an mRNA sequence, it is trivial to derive a unique genomic DNA sequence from which it had to have been
transcribed. Given a protein sequence, a family of possible coding DNA sequences can be derived by reverse translation of the
genetic code
The genetic code is the set of rules used by living cells to translate information encoded within genetic material ( DNA or RNA sequences of nucleotide triplets, or codons) into proteins. Translation is accomplished by the ribosome, which links ...
. Once candidate DNA sequences have been determined, it is a relatively straightforward algorithmic problem to efficiently search a target genome for matches, complete or partial, and exact or inexact. Given a sequence, local alignment algorithms such as
BLAST,
FASTA and
Smith-Waterman look for regions of similarity between the target sequence and possible candidate matches. Matches can be complete or partial, and exact or inexact. The success of this approach is limited by the contents and accuracy of the sequence database.
A high degree of similarity to a known messenger RNA or protein product is strong evidence that a region of a target genome is a protein-coding gene. However, to apply this approach systemically requires extensive sequencing of mRNA and protein products. Not only is this expensive, but in complex organisms, only a subset of all genes in the organism's genome are expressed at any given time, meaning that extrinsic evidence for many genes is not readily accessible in any single cell culture. Thus, to collect extrinsic evidence for most or all of the genes in a complex organism requires the study of many hundreds or thousands of
cell types, which presents further difficulties. For example, some human genes may be expressed only during development as an embryo or fetus, which might be difficult to study for ethical reasons.
Despite these difficulties, extensive transcript and protein sequence databases have been generated for human as well as other important model organisms in biology, such as mice and yeast. For example, the
RefSeq database contains transcript and protein sequence from many different species, and the
Ensembl
Ensembl genome database project is a scientific project at the European Bioinformatics Institute, which provides a centralized resource for geneticists, molecular biologists and other researchers studying the genomes of our own species and other ...
system comprehensively maps this evidence to human and several other genomes. It is, however, likely that these databases are both incomplete and contain small but significant amounts of erroneous data.
New high-throughput
transcriptome sequencing technologies such as
RNA-Seq and
ChIP-sequencing open opportunities for incorporating additional extrinsic evidence into gene prediction and validation, and allow structurally rich and more accurate alternative to previous methods of measuring
gene expression
Gene expression is the process by which information from a gene is used in the synthesis of a functional gene product that enables it to produce end products, protein or non-coding RNA, and ultimately affect a phenotype, as the final effect. ...
such as
expressed sequence tag or
DNA microarray
A DNA microarray (also commonly known as DNA chip or biochip) is a collection of microscopic DNA spots attached to a solid surface. Scientists use DNA microarrays to measure the expression levels of large numbers of genes simultaneously or to ...
.
Major challenges involved in gene prediction involve dealing with sequencing errors in raw DNA data, dependence on the quality of the
sequence assembly, handling short reads,
frameshift mutations,
overlapping genes and incomplete genes.
In prokaryotes it's essential to consider
horizontal gene transfer
Horizontal gene transfer (HGT) or lateral gene transfer (LGT) is the movement of genetic material between unicellular and/or multicellular organisms other than by the ("vertical") transmission of DNA from parent to offspring ( reproduction). ...
when searching for gene
sequence homology. An additional important factor underused in current gene detection tools is existence of gene clusters —
operons (which are functioning units of
DNA containing a cluster of
gene
In biology, the word gene (from , ; "... Wilhelm Johannsen coined the word gene to describe the Mendelian units of heredity..." meaning ''generation'' or ''birth'' or ''gender'') can have several different meanings. The Mendelian gene is a b ...
s under the control of a single
promoter) in both prokaryotes and eukaryotes. Most popular gene detectors treat each gene in isolation, independent of others, which is not biologically accurate.
''Ab initio'' methods
Ab Initio gene prediction is an intrinsic method based on gene content and signal detection. Because of the inherent expense and difficulty in obtaining extrinsic evidence for many genes, it is also necessary to resort to ''
ab initio'' gene finding, in which the
genomic DNA sequence alone is systematically searched for certain tell-tale signs of protein-coding genes. These signs can be broadly categorized as either ''signals'', specific sequences that indicate the presence of a gene nearby, or ''content'', statistical properties of the protein-coding sequence itself. ''Ab initio'' gene finding might be more accurately characterized as gene ''prediction'', since extrinsic evidence is generally required to conclusively establish that a putative gene is functional.
In the genomes of
prokaryotes, genes have specific and relatively well-understood
promoter sequences (signals), such as the
Pribnow box and
transcription factor
In molecular biology, a transcription factor (TF) (or sequence-specific DNA-binding factor) is a protein that controls the rate of transcription of genetic information from DNA to messenger RNA, by binding to a specific DNA sequence. The fu ...
binding site
In biochemistry and molecular biology, a binding site is a region on a macromolecule such as a protein that binds to another molecule with specificity. The binding partner of the macromolecule is often referred to as a ligand. Ligands may inclu ...
s, which are easy to systematically identify. Also, the sequence coding for a protein occurs as one contiguous
open reading frame (ORF), which is typically many hundred or thousands of
base pairs long. The statistics of
stop codon
In molecular biology (specifically protein biosynthesis), a stop codon (or termination codon) is a codon ( nucleotide triplet within messenger RNA) that signals the termination of the translation process of the current protein. Most codons in ...
s are such that even finding an open reading frame of this length is a fairly informative sign. (Since 3 of the 64 possible codons in the genetic code are stop codons, one would expect a stop codon approximately every 20–25 codons, or 60–75 base pairs, in a
random sequence.) Furthermore, protein-coding DNA has certain
periodicities and other statistical properties that are easy to detect in a sequence of this length. These characteristics make prokaryotic gene finding relatively straightforward, and well-designed systems are able to achieve high levels of accuracy.
''Ab initio'' gene finding in
eukaryotes
Eukaryotes () are organisms whose cells have a nucleus. All animals, plants, fungi, and many unicellular organisms, are Eukaryotes. They belong to the group of organisms Eukaryota or Eukarya, which is one of the three domains of life. Bact ...
, especially complex organisms like humans, is considerably more challenging for several reasons. First, the promoter and other regulatory signals in these genomes are more complex and less well-understood than in prokaryotes, making them more difficult to reliably recognize. Two classic examples of signals identified by eukaryotic gene finders are
CpG islands and binding sites for a
poly(A) tail.
Second,
splicing mechanisms employed by eukaryotic cells mean that a particular protein-coding sequence in the genome is divided into several parts (
exons
An exon is any part of a gene that will form a part of the final mature RNA produced by that gene after introns have been removed by RNA splicing. The term ''exon'' refers to both the DNA sequence within a gene and to the corresponding sequenc ...
), separated by non-coding sequences (
introns). (Splice sites are themselves another signal that eukaryotic gene finders are often designed to identify.) A typical protein-coding gene in humans might be divided into a dozen exons, each less than two hundred base pairs in length, and some as short as twenty to thirty. It is therefore much more difficult to detect periodicities and other known content properties of protein-coding DNA in eukaryotes.
Advanced gene finders for both prokaryotic and eukaryotic genomes typically use complex
probabilistic models, such as
hidden Markov models (HMMs) to combine information from a variety of different signal and content measurements. The
GLIMMER
In bioinformatics, GLIMMER (Gene Locator and Interpolated Markov ModelER) is used to find genes in prokaryotic DNA. "It is effective at finding genes in bacteria, archea, viruses, typically finding 98-99% of all relatively long protein coding ge ...
system is a widely used and highly accurate gene finder for prokaryotes.
GeneMark is another popular approach. Eukaryotic ''ab initio'' gene finders, by comparison, have achieved only limited success; notable examples are the
GENSCAN and
geneid programs. The SNAP gene finder is HMM-based like Genscan, and attempts to be more adaptable to different organisms, addressing problems related to using a gene finder on a genome sequence that it was not trained against. A few recent approaches like mSplicer, CONTRAST, or
mGene also use
machine learning
Machine learning (ML) is a field of inquiry devoted to understanding and building methods that 'learn', that is, methods that leverage data to improve performance on some set of tasks. It is seen as a part of artificial intelligence.
Machine ...
techniques like
support vector machines for successful gene prediction. They build a
discriminative model Discriminative models, also referred to as conditional models, are a class of logistical models used for classification or regression. They distinguish decision boundaries through observed data, such as pass/fail, win/lose, alive/dead or healthy/sic ...
using
hidden Markov support vector machine
Hidden or The Hidden may refer to:
Film and television Film
* ''The Hidden'' (film), a 1987 American science fiction/horror film
* ''Hidden'' (2005 film) or ''Caché'', a French thriller film
* ''Hidden'' (2009 film), a Norwegian horror film
...
s or
conditional random fields to learn an accurate gene prediction scoring function.
''Ab Initio'' methods have been benchmarked, with some approaching 100% sensitivity,
however as the sensitivity increases, accuracy suffers as a result of increased
false positives.
Other signals
Among the derived signals used for prediction are statistics resulting from the sub-sequence statistics like
k-mer statistics,
Isochore (genetics) or
Compositional domain GC composition/uniformity/entropy, sequence and frame length, Intron/Exon/Donor/Acceptor/Promoter and
Ribosomal binding site vocabulary,
Fractal dimension,
Fourier transform
A Fourier transform (FT) is a mathematical transform that decomposes functions into frequency components, which are represented by the output of the transform as a function of frequency. Most commonly functions of time or space are transformed, ...
of a pseudo-number-coded DNA,
Z-curve
The Z curve (or Z-curve) method is a bioinformatics algorithm for genome analysis. The Z-curve is a three-dimensional curve that constitutes a unique representation of a DNA sequence, i.e., for the Z-curve and the given DNA sequence each can be ...
parameters and certain run features.
It has been suggested that signals other than those directly detectable in sequences may improve gene prediction. For example, the role of
secondary structure
Protein secondary structure is the three dimensional form of ''local segments'' of proteins. The two most common secondary structural elements are alpha helices and beta sheets, though beta turns and omega loops occur as well. Secondary struct ...
in the identification of regulatory motifs has been reported.
In addition, it has been suggested that RNA secondary structure prediction helps splice site prediction.
Neural networks
Artificial neural networks are computational models that excel at
machine learning
Machine learning (ML) is a field of inquiry devoted to understanding and building methods that 'learn', that is, methods that leverage data to improve performance on some set of tasks. It is seen as a part of artificial intelligence.
Machine ...
and
pattern recognition. Neural networks must be
trained with example data before being able to generalise for experimental data, and tested against benchmark data. Neural networks are able to come up with approximate solutions to problems that are hard to solve algorithmically, provided there is sufficient training data. When applied to gene prediction, neural networks can be used alongside other ''ab initio'' methods to predict or identify biological features such as splice sites.
One approach
involves using a sliding window, which traverses the sequence data in an overlapping manner. The output at each position is a score based on whether the network thinks the window contains a donor splice site or an acceptor splice site. Larger windows offer more accuracy but also require more computational power. A neural network is an example of a signal sensor as its goal is to identify a functional site in the genome.
Combined approaches
Programs such as Maker combine extrinsic and ''ab initio'' approaches by mapping protein and
EST data to the genome to validate ''ab initio'' predictions. Augustus, which may be used as part of the Maker pipeline, can also incorporate hints in the form of EST alignments or protein profiles to increase the accuracy of the gene prediction.
Comparative genomics approaches
As the entire genomes of many different species are sequenced, a promising direction in current research on gene finding is a
comparative genomics approach.
This is based on the principle that the forces of
natural selection
Natural selection is the differential survival and reproduction of individuals due to differences in phenotype. It is a key mechanism of evolution, the change in the heritable traits characteristic of a population over generations. Cha ...
cause genes and other functional elements to undergo mutation at a slower rate than the rest of the genome, since mutations in functional elements are more likely to negatively impact the organism than mutations elsewhere. Genes can thus be detected by comparing the genomes of related species to detect this evolutionary pressure for conservation. This approach was first applied to the mouse and human genomes, using programs such as SLAM, SGP and TWINSCAN/N-SCAN and CONTRAST.
Multiple informants
TWINSCAN examined only human-mouse synteny to look for orthologous genes. Programs such as N-SCAN and CONTRAST allowed the incorporation of alignments from multiple organisms, or in the case of N-SCAN, a single alternate organism from the target. The use of multiple informants can lead to significant improvements in accuracy.
CONTRAST is composed of two elements. The first is a smaller classifier, identifying donor splice sites and acceptor splice sites as well as start and stop codons. The second element involves constructing a full model using machine learning. Breaking the problem into two means that smaller targeted data sets can be used to train the classifiers,
and that classifier can operate independently and be trained with smaller windows. The full model can use the independent classifier, and not have to waste computational time or model complexity re-classifying intron-exon boundaries. The paper in which CONTRAST is introduced proposes that their method (and those of TWINSCAN, etc.) be classified as ''de novo'' gene assembly, using alternate genomes, and identifying it as distinct from ''ab initio'', which uses a target 'informant' genomes.
Comparative gene finding can also be used to project high quality annotations from one genome to another. Notable examples include Projector, GeneWise, GeneMapper and GeMoMa. Such techniques now play a central role in the annotation of all genomes.
Pseudogene prediction
Pseudogenes are close relatives of genes, sharing very high sequence homology, but being unable to code for the same
protein
Proteins are large biomolecules and macromolecules that comprise one or more long chains of amino acid residues. Proteins perform a vast array of functions within organisms, including catalysing metabolic reactions, DNA replication, respon ...
product. Whilst once relegated as byproducts of
gene sequencing Gene Sequencing may refer to:
* DNA sequencing
* or a comprehensive variant of it: Whole genome sequencing
Whole genome sequencing (WGS), also known as full genome sequencing, complete genome sequencing, or entire genome sequencing, is the pro ...
, increasingly, as regulatory roles are being uncovered, they are becoming predictive targets in their own right.
Pseudogene prediction utilises existing sequence similarity and ab initio methods, whilst adding additional filtering and methods of identifying pseudogene characteristics.
Sequence similarity methods can be customised for pseudogene prediction using additional filtering to find candidate pseudogenes. This could use disablement detection, which looks for nonsense or frameshift mutations that would truncate or collapse an otherwise functional coding sequence.
Additionally, translating DNA into proteins sequences can be more effective than just straight DNA homology.
Content sensors can be filtered according to the differences in statistical properties between pseudogenes and genes, such as a reduced count of CpG islands in pseudogenes, or the differences in G-C content between pseudogenes and their neighbours. Signal sensors also can be honed to pseudogenes, looking for the absence of introns or polyadenine tails.
Metagenomic gene prediction
Metagenomics is the study of genetic material recovered from the environment, resulting in sequence information from a pool of organisms. Predicting genes is useful for
comparative metagenomics.
Metagenomics tools also fall into the basic categories of using either sequence similarity approaches (MEGAN4) and ab initio techniques (GLIMMER-MG).
Glimmer-MG
is an extension to
GLIMMER
In bioinformatics, GLIMMER (Gene Locator and Interpolated Markov ModelER) is used to find genes in prokaryotic DNA. "It is effective at finding genes in bacteria, archea, viruses, typically finding 98-99% of all relatively long protein coding ge ...
that relies mostly on an ab initio approach for gene finding and by using training sets from related organisms. The prediction strategy is augmented by classification and clustering gene data sets prior to applying ab initio gene prediction methods. The data is clustered by species. This classification method leverages techniques from metagenomic phylogenetic classification. An example of software for this purpose is, Phymm, which uses interpolated markov models—and PhymmBL, which integrates BLAST into the classification routines.
MEGAN4
uses a sequence similarity approach, using local alignment against databases of known sequences, but also attempts to classify using additional information on functional roles, biological pathways and enzymes. As in single organism gene prediction, sequence similarity approaches are limited by the size of the database.
FragGeneScan and MetaGeneAnnotator are popular gene prediction programs based on
Hidden Markov model. These predictors account for sequencing errors, partial genes and work for short reads.
Another fast and accurate tool for gene prediction in metagenomes is MetaGeneMark.
This tool is used by the DOE Joint Genome Institute to annotate IMG/M, the largest metagenome collection to date.
See also
*
List of gene prediction software
This is a list of software tools and web portals used for gene prediction.
See also
* Gene prediction
* List of RNA structure prediction software
* Comparison of software for molecular mechanics modeling
References
{{Reflist, 2
Predicti ...
*
Phylogenetic footprinting
*
Protein function prediction
Protein function prediction methods are techniques that bioinformatics researchers use to assign biological or biochemical roles to proteins. These proteins are usually ones that are poorly studied or predicted based on genomic sequence data. Thes ...
*
Protein structure prediction
*
Protein–protein interaction prediction
*
Pseudogene (database)
Pseudogene is a database of pseudogenes annotations compiled from various sources.
See also
* Gene prediction
* Glossary of genetics
A glossary (from grc, γλῶσσα, ''glossa''; language, speech, wording) also known as a vocabulary or cl ...
*
Sequence mining
*
Sequence similarity (homology)
References
External links
AugustusFGENESHGeMoMa- Homology-based gene prediction based on amino acid and intron position conservation as well as RNA-Seq data
geneidSGP2GlimmerGlimmerHMM
GenomeThreaderGeneMarkGismomGeneStarORF— A multi-platform and web tool for predicting ORFs and obtaining reverse complement sequence
- A portable and easily configurable genome annotation pipeline
{{DEFAULTSORT:Gene Prediction
Bioinformatics
Mathematical and theoretical biology
Markov models