Genome annotation pdf

Using the contains information for the alternative transcripts. After clicking one of the QTLs additional information for that transcript, such as exon the user would see that there are small overlapping QTLs coordinates.

The last section of each gene page is the record for that particular locus. Finally, each gene page Chromosome GBrowse. Once in GBrowse the user can provides a link to the wiki page for that gene. The view different tracks to identify genome features that wiki allows researchers to take an active part in underlie this QTL. After zooming in, the user would contributing knowledge to the database.

Users must many spliced EST alignments and several gene models register to use the wiki before they can add comments within the locus, providing a starting point for further about a gene, even if they have already registered for research.

Searching for information on Gene Pages. The Bovine OGSv2 contains 23 gene models. The gene pages genes, the curator updates the site with news and are a new Ruby on Rails application developed in our literature.

Page creation is handled started. This of HTML tags in the page body. Contributors have access to create and edit the GO ID in the gene search box to retrieve a smaller, content, while administrators also have additional privil- and likely more relevant, set of 13 genes.

If the OGSv2 eges, such as to modify the sites appearance. The inclu- gene symbol is already known, entering it into the search sion of different standard modules allows for the box will take the searcher straight to the relevant gene different features available at BGD, like a custom page. D Nucleic Acids Research, , Vol. Genome Res. The Bovine Genome Database project is ongoing. We will 8. Genome Biol. We will also annotate and create a genome 9. This Marygold,S.

We will incorporate Ontology annotations. Nucleic Acids Res. Finally, we plan to create tools AgriLife; start-up funds from Georgetown University. Bioinformatics, Chapter 4, Unit 4 3. None declared. Bioinformatics, 21, — BMC Detection of quantitative trait loci affecting milk Bioinformatics, 6, Dairy Sci. BMC Bioinformatics, 7, BMC Genomics, 10, BMC Genomics, 11, Characterization of lymphocyte subpopulations and major RNA-Seq or when a new assembly is released.

Special attention is given to tracking of models and genes from one release of the annotation to the next. Previous and current models annotated at overlapping genomic locations are identified and the locus type and GeneID of the previous models are taken into consideration when assigning GeneIDs to the new models. If the assembly was updated between the two rounds of annotation, the assemblies are aligned to each other and the alignments used to match previous and current models in mapped regions.

The quality of the annotation is assessed prior to publishing, based on the intrinsic characteristics of the annotated models and on the expectations for the species. Indicators of a low quality annotation may disqualify a genome from being included in RefSeq. These indicators are: high count of coding genes that lack near-full coverage by alignments of experimental evidence, high count of partial coding genes lacking a start or stop codon, or internal exons , high count of low-quality genes with suspected frameshifts or premature stop codons, low BUSCO completeness score see below , and, for vertebrates, low count of genes with orthologs to a reference species.

BUSCO run in "protein" mode provides an estimate of the completeness of the gene set. National Center for Biotechnology Information , U. Important features of the pipeline include: flexibility and speed higher weight given to curated evidence than non-curated evidence utilization of RNA-Seq for gene prediction production of models that compensate for assembly issues tracking of gene loci from one annotation to the next ability to co-annotate multiple assemblies for the same organism The products of an annotation run chromosome, scaffolds and model transcripts and proteins are labeled with an Annotation Release number.

Process The figure below provides an overview of the annotation process. Transcript alignments The set of transcripts selected for alignment to the genome varies by species, and may include transcripts from other organisms.

However, additional steps are performed to address the short length, redundancy and abundance of the reads: only single representatives of identical sequences retrieved from SRA are aligned alignments with the same splice structure and the same or similar start and end points are collapsed into a single representative alignment alignments representing very rare introns likely to be background noise are filtered out At each step, information is recorded about the samples and number of reads represented by each read and alignment, so the level of support can be used to filter alignments and evaluate gene predictions.

Protein alignments The set of proteins selected for alignment to the genome varies by species, and may include proteins from other organisms. Choosing the best models for a gene The final set of annotated features comprises, in order of preference, pre-existing RefSeq sequences and a subset of well-supported Gnomon -predicted models.

Models based on known and curated RefSeq RefSeq transcripts are given precedence over overlapping Gnomon models with the same splice pattern. Integrating RefSeq and Gnomon annotations As a result of the model selection process, a gene may be represented by multiple splice variants, with some of them known RefSeq and others model RefSeq originating from Gnomon predictions.

Protein naming and determination of locus type Genes represented by known or curated RefSeq sequences inherit the Gene symbol, name and locus type e. Gnomon models that appear to be single-exon retrocopies of protein-coding genes may also be annotated as pseudogenes When multiple assemblies are annotated , a partial or imperfect model may be called coding because a complete model exists at the corresponding locus on one of the other annotated assemblies Assignment of GeneIDs Genes in the final set of models are assigned GeneIDs in NCBI's Gene database.

All alternative splice forms of a gene get the same GeneID. As much as possible, GeneIDs are carried forward from one annotation run to the next, using the mapping of the new assembly to the previous one if the assembly was updated. Gene features mapped to equivalent locations of co-annotated assemblies are assigned the same GeneIDs.

Starting with software version 8. Annotation of transcription start sites TSS Starting with software release 9. Special considerations Annotation of multiple assemblies When multiple assemblies of good quality are available for a given organism, annotation of all is done in coordination. Assembly-assembly alignment results are used to rank the transcript and the curated genomic alignments: for a given query sequence, alignments to corresponding regions of two assemblies receive the same rank.

Corresponding loci of multiple assemblies are assigned the same GeneID and locus type. Re-annotation Organisms are periodically re-annotated when new evidence is available e. One could be tempted to extend the small network of domains shown in Figure 5. It appears, however, that such an extension would have been ill-advised.

Domain fusions often are found only within a specialized, narrow group of orthologous protein domains, and translating their functional interaction into a general prediction for the respective domains is likely to be grossly misleading.

CBS, PAS, GAF domains , combine with a variety of other domains that otherwise have nothing in common and therefore significantly increase the number of false-positives among the Rosetta Stone predictions.

Manual detection of such cases is relatively straightforward, but automation of this process may be complicated. As already mentioned in Chapter 2 , comparisons of complete bacterial genomes have revealed the lack of large-scale conservation of the gene order even between relatively close species, such as E.

Although these pairs of genomes have numerous similar strings of adjacent genes most of them predicted operons , comparisons of more distantly related bacterial and archaeal genomes have shown that, at large phylogenetic distances, even most of the operons are extensively rearranged [ , ].

The few operons that are conserved across distantly related genomes typically encode physically interacting proteins, such as ribosomal proteins or subunits of the H-ATPase and ABC-type transporter complexes [ , , , ]. It should be noted that only a relatively small number of operons have been identified experimentally, primarily in well-characterized bacteria, such as E. However, analysis of gene strings that are conserved in bacterial and archaeal genome strongly suggested that the great majority of them do form operons [ ].

This conclusion was based on the following principal arguments: i as shown by Monte Carlo simulations, the likelihood that identical strings of more than two genes are found by chance in more than two genomes is extremely low; ii most of those conserved strings that include characterized genes either are known operons or include functionally linked genes and can be predicted to form operons; iii typical conserved gene strings include 2 to 4 genes, which is the characteristic size of operons; iv conserved gene strings that include genes from adjacent, independent operons are extremely rare; v nearly all conserved gene strings consist of genes that are transcribed in the same direction [ ].

As a result, one can usually assume that conserved gene strings are co-regulated, i. These observations indicate that conserved gene strings are under stabilizing selection that prevents their disruption. For functionally related genes e. We believe that the selfish operon hypothesis seems to put the cart ahead of the horse: operons certainly do spread via HGT, but their transfer leads to fixation more often than transfer of individual genes because of the selective advantage conferred to the recipient by the acquired operon.

In contrast, for functionally unrelated genes, there would be no selection towards coexpression. Therefore, an observation of similar operons found in phylogenetically distant species can be considered an indication of a potential functional relationship between the corresponding genes, even if these genes are scattered in other genomes.

Because of the simplicity and elegance of this approach to functional analysis of complete genomes, there are several web sites that offer slightly different approaches to delineation of the conserved gene strings. This tool identifies conserved gene strings by searching for pairs of homologous proteins that are encoded by genes located no more than bp apart on the same DNA strand in each of the analyzed genomes.

Each of these pairs is then assigned a score based on the evolutionary distance between the respective species on the rRNA-based phylogenetic tree.

It is expected that chance occurrence of pairs of homologous genes in distantly related species is less likely than in closely related ones, so such pairs are more likely to be functionally relevant. Homologous genes are defined as bidirectional best hits in all-against-all BLAST comparisons, which is similar to the method used in constructing the COG database [ ].

Because the number of potential gene linkages grows exponentially with the number of the analyzed genomes [ ], the sensitivity of methods based on the detection of conserved gene strings can be significantly improved by taking into consideration even unfinished genome sequences. This approach was used in the successful reconstruction of several known metabolic pathways and led to the correct prediction of candidate genes for some previously uncharacterized metabolic enzymes [ 82 , , ].

Unfortunately, while this book was in preparation, the ERGO database has been closed for the public, while WIT was still missing some of the useful functionality. We will therefore illustrate the use of the method by exploiting a somewhat similar tool in the COG database.

Genes whose products belong to the same COG are identically colored. This provides for easy identification of sets of COGs that tend to be clustered in genomes. Of course, this tool only works for the genes whose products belong to COGs, so the relationships between genes that are found in only two complete genomes and hence do not belong to any COG would be missed. For a practical example of the use of this method, let us consider the search for the archaeal shikimate kinase, the enzyme that is not homologous to the bacterial shikimate kinase AroK and hence was not found by traditional sequence similarity searches [ ].

Reconstruction of the aromatic amino acids biosynthesis pathway in archaea showed that genomes of A. Two of these missing enzymes catalyze first and second reactions of the pathway, indicating that aromatic acids biosynthesis in most archaea uses different precursors than in bacteria, whereas the third reaction, phosphorylation of shikimate, was attributed to a non-orthologous kinase, encoded only in archaea [ ].

Daugherty and coworkers made a list of the genes involved in aromatic amino acid biosynthesis in archaea and looked for potential neighbors of the aroE gene whose product, shikimate dehydrogenase, catalyzes the reaction immediately preceding the phosphorylation of shikimate Figure 7.

This was also the case in A. Genes encoding PAB orthologs COG were also found in other archaeal genomes, but not in any of the bacterial genomes that contain the typical aroK gene Figure 5. Given this connection, Daugherty et al. Each line corresponds to an individual genome: aful, Archaeoglobus fulgidus ; hbsp, Halobacterium sp. Orthologs are identified as bidirectional best hits using Smith-Waterman comparisons.

The sequence entered in FASTA format is compared against the database of all proteins encoded in complete genomes so that the user could choose one of the best hits for further examination.

The default option in STRING further reduces the number of analyzed genomes by eliminating closely related ones this option can be switched off by the user.

If this does not happen even after five consequent search cycles, the program would just tabulate how many times was each particular gene found in the output.

Combined with impressive graphics, this approach makes STRING a fast and convenient tool to search for consistent gene associations in complete genomes.

In addition, SNAP does not require the related genes to form conserved gene strings, they only need to be in the vicinity of each other. SNAPper looks for the homologs of the given protein, than takes neighbors of the corresponding genes, looks for their homologs, and so on [ ].

The program then builds a similarity-neighborhood graph SN-graph , which consists of the chains of orthologous genes in different genomes and adjacent genes in the same genome. The hits that form a closed SN-graph, i. The advanced version of SNAPper offers the choice of several parameters, which allow fine-tuning the performance of the tool depending on the particular query protein.

It allows one to find all genes in any two selected complete genomes whose products are sufficiently similar to each other and are separated by no more than five genes. The user can specify the desired degree of similarity between the proteins in terms of the minimal pairwise BLAST score or maximal Evalue , the minimal length of the alignment, and the type of BLAST hits bidirectional or unidirectional hits, or just any hits with the specified BLAST score.

The user can also specify maximum allowable distances between the genes in either organism, limiting it to any number of genes from zero to five. This option allows one to retrieve much more distant gene pairs than those detected by the ERGO tool. The downside of this richness is that unless one uses fairly strict criteria for protein similarity and the intergenic distances, he or she will end up with dozens or even hundreds of reported gene pairs, few of which would have predictive power.

Nonetheless, a sensible use of this tool can bring some very interesting results [ ]. To evaluate the power of gene order-based methods for making functional predictions, we have isolated those cases where a substantial functional prediction did not appear possible without explicit use of gene adjacency information [ ].

Given that, as noted above, homology-based approaches already allow functional predictions for a majority of the genes in each sequenced prokaryotic genome, this places gene-string analysis in the position of an important accessory methodology in the hierarchy of genome annotation approaches. Other genome context-based methods may also be useful but are clearly less powerful. This is, of course, a pessimistic assessment because more subtle changes in prediction for gene already annotated by homology-based methods were not taken into account.

These limitations notwithstanding, some of the predictions made on the basis of gene order conservation combined with homology information seem to be exceptionally important.

This finding was made by examination of archaeal genome alignments, which led to the detection of a large superoperon, which, in its complete form, consists of 15 genes. This full complement of co-localized genes, however, is present in only one species, M. Remarkably, the predicted exosomal superoperon also includes genes for proteasome subunits. According to the logic outlined above, this points to a hitherto unknown functional and possibly even physical association between the proteasome and the exosome, the machines for controlled degradation of RNA and proteins, respectively.

Gene order-based functional prediction seems to be impossible for eukaryotes because of the apparent lack of clustering of functionally linked genes. However, several operons that have been identified in C. Besides, the above prediction of proteasome-exosome association might potentially extend to eukaryotes, offering yet another example of the use of prokaryotic genome comparisons for understanding the eukaryotic cell.

Given the fluidity of gene order in prokaryotes, detection of subtle conservation patterns requires fairly sophisticated computational procedures that search for gene neighborhoods , sets of genes that tend to cluster together in multiple genomes, but do not necessarily show extensive conservation of exact gene order [ , , , , ].

One of the interesting findings that have been made possible through these approaches is the prediction of a new DNA repair system in archaeal and bacterial hyperthemophiles [ ]. As shown in Figure 5. However, the overall conservation of the neighborhood is obvious once the analysis is completed and the results are summarized as in Figure 5. In an already familiar theme, prediction of this repair system involved a combination of genomic neighborhood detection with fairly complicated protein sequence analysis and structure prediction.

Finally, this is where we encounter, once again, COG, the protein family already discussed in 4. When we first analyzed those proteins, we were inclined to predict that they were novel enzymes, perhaps with a hydrolytic activity. Context analysis allows us to make a much more specific prediction: these proteins mostly likely are nucleases involved in DNA repair. Predicted DNA repair system in hyperthermophiles.

The pink boxes show optimal growth temperatures for each of the analyzed species A. The genes are not drawn to scale; arrows more In this chapter, we discussed both traditional methods for genome annotation based on homology detection and newer approaches united under the umbrella of genome context analysis. We noted that, although functions can be predicted, at some level of precision, for a substantial majority of genes in each sequenced prokaryotic genome, current annotations are replete with inaccuracies, inconsistencies and incompleteness.

This should not be construed as any kind of implicit criticism of those researchers who are involved in genome annotation: the task is objectively hard and is getting progressively more difficult with the growth of databases and accumulation of inconsistencies. Fortunately, we believe that the remedy is already at hand see 3. Specialized databases, designed as genome annotation tools, seem to be capable of dramatically improving the situation, if not solving the annotation problem completely.

Prototypes of such databases already exist and function and their extensive growth in the near future seems assured. The context-based methods of genome annotation are quite new: the development of these approaches started only after multiple genome sequences became available. These approaches have a lot of appeal because they are, indeed, true genomic methods based on the notion that the genome and, especially, many compared genomes is much more than the sum of its parts.

The results produced by these methods are often very intuitive and even visually appealing as in gene string analysis. Objectively, however, these methods yield considerably less information on gene function than homology-based methods, at least for the foreseeable future. Nevertheless, different genome context approaches substantially complement each other and homology-based methods. In fact, homology-based and context-based methods often produce different and complementary types of functional predictions.

The former tend to predict biochemical functions activities , whereas the latter result in biological predictions, such as involvement of a gene in a particular cellular process e. DNA repair in the example above , even if the exact activity cannot be predicted. We would like to end this chapter on an upbeat note by stating, in large part on the basis of personal experience, that genome annotation is not a routine, mundane activity as it might seem to an outside observer.

On the contrary, this is exciting research, somewhat akin to detective work, which has the potential of teasing out deep mysteries of life from genome sequences. Turn recording back on. National Center for Biotechnology Information , U. Boston: Kluwer Academic ; Search term. Chapter 5 Genome Annotation and Analysis. Methods, Approaches and Results in Genome Annotation 5. Genome annotation: data flow and performance What is genome annotation?

Automation of genome annotation Terry Gaasterland and Christoph Sensen once estimated that annotating genomic sequence by hand would require as much as one year per person per one megabase [ ]. SEALS In addition to completely automated systems, some tools that greatly facilitate and accelerate manual genome annotation are worth a mention. Accuracy of genome annotation, sources of errors, and some thoughts on possible improvements Benchmarking the accuracy of genome annotation is extremely hard.

A case study on genome annotation: the crenarchaeon Aeropyrum pernix Aeropyrum pernix was the first representative of the Crenarchaeota one of the two major branches of archaea; see Chapter 6 and the first aerobic archaeon whose genome has been sequenced [ ]. Genome Context Analysis and Functional Prediction All the preceding discussion in this chapter centered on prediction of the functions of proteins encoded in sequenced genomes by extrapolating from the functions of their experimentally characterized homologs.

Phyletic patterns profiles Genes coding for proteins that function in the same cellular system or pathway tend to have similar phyletic patterns [ , ].

Gene clusters and genomic neighborhoods As already mentioned in Chapter 2 , comparisons of complete bacterial genomes have revealed the lack of large-scale conservation of the gene order even between relatively close species, such as E.

Genome context tools in genome annotation To evaluate the power of gene order-based methods for making functional predictions, we have isolated those cases where a substantial functional prediction did not appear possible without explicit use of gene adjacency information [ ]. Conclusions and Outlook In this chapter, we discussed both traditional methods for genome annotation based on homology detection and newer approaches united under the umbrella of genome context analysis.

Further Reading 1. Brenner S. Errors in genome annotation. Trends in Genetics. Who's your neighbor? New computational approaches for functional genomics. Nature Biotechnology. Huynen MA, Snel B.