Difference between revisions of "BIN-GENOME-Genome Annotation"
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Genome annotation | Genome annotation | ||
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− | + | (Genome contents; ENCODE; Genome annotation methods.) | |
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+ | <div style="padding:5px; border:1px solid #000000; background-color:#b3dbce33; font-size:85%;"> | ||
+ | <div style="font-size:118%;"> | ||
+ | <b>Abstract:</b><br /> | ||
+ | <section begin=abstract /> | ||
+ | Introduction to genome annotation: the content of genomes - what to look for; identifying genes, and keeping up-to-date on methods. | ||
+ | <section end=abstract /> | ||
+ | </div> | ||
+ | <!-- ============================ --> | ||
+ | <hr> | ||
+ | <table> | ||
+ | <tr> | ||
+ | <td style="padding:10px;"> | ||
+ | <b>Objectives:</b><br /> | ||
+ | This unit will ... | ||
+ | * ... introduce categories of genome contents, as defined eg. through the ENCODE project, and discuss annotation methods. | ||
+ | </td> | ||
+ | <td style="padding:10px;"> | ||
+ | <b>Outcomes:</b><br /> | ||
+ | After working through this unit you ... | ||
+ | * ... are familar with the contents of genomes, some methods to annotate protein genes, and sources for genomes; | ||
+ | * ... know how to get up-to-date information on genome annotation workflows. | ||
+ | </td> | ||
+ | </tr> | ||
+ | </table> | ||
+ | <!-- ============================ --> | ||
+ | <hr> | ||
+ | <b>Deliverables:</b><br /> | ||
+ | <section begin=deliverables /> | ||
+ | <li><b>Time management</b>: Before you begin, estimate how long it will take you to complete this unit. Then, record in your course journal: the number of hours you estimated, the number of hours you worked on the unit, and the amount of time that passed between start and completion of this unit.</li> | ||
+ | <li><b>Journal</b>: Document your progress in your [[FND-Journal|Course Journal]]. Some tasks may ask you to include specific items in your journal. Don't overlook these.</li> | ||
+ | <li><b>Insights</b>: If you find something particularly noteworthy about this unit, make a note in your [[ABC-Insights|'''insights!''' page]].</li> | ||
+ | <section end=deliverables /> | ||
+ | <!-- ============================ --> | ||
+ | <hr> | ||
+ | <section begin=prerequisites /> | ||
+ | <b>Prerequisites:</b><br /> | ||
+ | This unit builds on material covered in the following prerequisite units:<br /> | ||
+ | *[[BIN-Genome-Sequencing|BIN-Genome-Sequencing (Genome sequencing)]] | ||
+ | <section end=prerequisites /> | ||
+ | <!-- ============================ --> | ||
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=== Evaluation === | === Evaluation === | ||
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<b>Evaluation: NA</b><br /> | <b>Evaluation: NA</b><br /> | ||
− | :This unit is not evaluated for course marks. | + | <div style="margin-left: 2rem;">This unit is not evaluated for course marks.</div> |
+ | == Contents == | ||
− | {{ | + | {{Task|1= |
+ | *Read the introductory notes on {{ABC-PDF|BIN-Genome-Annotation|the annotation of genome sequences}}. | ||
+ | * Visit the [https://rast.nmpdr.org/ '';'' server Website] - (more publications linked from there). For many currently published genome-sequencing projects this is the standard of annotation. | ||
+ | {{#pmid:18261238}} | ||
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== Further reading, links and resources == | == Further reading, links and resources == | ||
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− | {{ | + | ;General: |
+ | {{#pmid: 31097009}} | ||
+ | {{#pmid: 32962098}} | ||
+ | {{Smallvspace}} | ||
− | + | ---- | |
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− | {{ | + | ;Encode: |
+ | <div class="reference-box">[https://www.encodeproject.org/ '''The ENCODE project''']</div> | ||
+ | {{#pmid: 29126249}} | ||
+ | {{#pmid: 21526222}} | ||
+ | {{Smallvspace}} | ||
− | + | ---- | |
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− | + | ;Annotation example papers | |
+ | {{#pmid: 28087693}}<!-- 2017 Beaver --> | ||
+ | {{#pmid: 27706134}}<!-- 2016 Korean genome with phasing --> | ||
+ | {{#pmid: 23598338}}<!-- 2013 Coelacanth --> | ||
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− | + | ---- | |
− | + | ;Specific topics | |
− | + | :;Copy Number Variation: | |
+ | :{{#pmid: 25645873}} | ||
+ | {{Smallvspace}} | ||
+ | :;miRNA: | ||
+ | :{{#pmid: 27795564}} | ||
+ | {{Smallvspace}} | ||
+ | :;Epigenomics: | ||
+ | :{{#pmid: 27867193}} | ||
− | + | == Notes == | |
+ | <references /> | ||
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<b>Version history:</b><br /> | <b>Version history:</b><br /> | ||
+ | *1.1 2020 Updates | ||
+ | *1.0 First live version | ||
*0.1 First stub | *0.1 First stub | ||
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{{CC-BY}} | {{CC-BY}} | ||
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Latest revision as of 01:28, 6 September 2021
Genome annotation
(Genome contents; ENCODE; Genome annotation methods.)
Abstract:
Introduction to genome annotation: the content of genomes - what to look for; identifying genes, and keeping up-to-date on methods.
Objectives:
|
Outcomes:
|
Deliverables:
Prerequisites:
This unit builds on material covered in the following prerequisite units:
Evaluation
Evaluation: NA
Contents
Task:
- Read the introductory notes on the annotation of genome sequences.
- Visit the ; server Website - (more publications linked from there). For many currently published genome-sequencing projects this is the standard of annotation.
Aziz et al. (2008) The RAST Server: rapid annotations using subsystems technology. BMC Genomics 9:75. (pmid: 18261238) |
[ PubMed ] [ DOI ] BACKGROUND: The number of prokaryotic genome sequences becoming available is growing steadily and is growing faster than our ability to accurately annotate them. DESCRIPTION: We describe a fully automated service for annotating bacterial and archaeal genomes. The service identifies protein-encoding, rRNA and tRNA genes, assigns functions to the genes, predicts which subsystems are represented in the genome, uses this information to reconstruct the metabolic network and makes the output easily downloadable for the user. In addition, the annotated genome can be browsed in an environment that supports comparative analysis with the annotated genomes maintained in the SEED environment. The service normally makes the annotated genome available within 12-24 hours of submission, but ultimately the quality of such a service will be judged in terms of accuracy, consistency, and completeness of the produced annotations. We summarize our attempts to address these issues and discuss plans for incrementally enhancing the service. CONCLUSION: By providing accurate, rapid annotation freely to the community we have created an important community resource. The service has now been utilized by over 120 external users annotating over 350 distinct genomes. |
Further reading, links and resources
- General
Salzberg (2019) Next-generation genome annotation: we still struggle to get it right. Genome Biol 20:92. (pmid: 31097009) |
[ PubMed ] [ DOI ] While the genome sequencing revolution has led to the sequencing and assembly of many thousands of new genomes, genome annotation still uses very nearly the same technology that we have used for the past two decades. The sheer number of genomes necessitates the use of fully automated procedures for annotation, but errors in annotation are just as prevalent as they were in the past, if not more so. How are we to solve this growing problem? |
Ejigu & Jung (2020) Review on the Computational Genome Annotation of Sequences Obtained by Next-Generation Sequencing. Biology (Basel) 9:. (pmid: 32962098) |
[ PubMed ] [ DOI ] Next-Generation Sequencing (NGS) has made it easier to obtain genome-wide sequence data and it has shifted the research focus into genome annotation. The challenging tasks involved in annotation rely on the currently available tools and techniques to decode the information contained in nucleotide sequences. This information will improve our understanding of general aspects of life and evolution and improve our ability to diagnose genetic disorders. Here, we present a summary of both structural and functional annotations, as well as the associated comparative annotation tools and pipelines. We highlight visualization tools that immensely aid the annotation process and the contributions of the scientific community to the annotation. Further, we discuss quality-control practices and the need for re-annotation, and highlight the future of annotation. |
- Encode
Davis et al. (2018) The Encyclopedia of DNA elements (ENCODE): data portal update. Nucleic Acids Res 46:D794-D801. (pmid: 29126249) |
[ PubMed ] [ DOI ] The Encyclopedia of DNA Elements (ENCODE) Data Coordinating Center has developed the ENCODE Portal database and website as the source for the data and metadata generated by the ENCODE Consortium. Two principles have motivated the design. First, experimental protocols, analytical procedures and the data themselves should be made publicly accessible through a coherent, web-based search and download interface. Second, the same interface should serve carefully curated metadata that record the provenance of the data and justify its interpretation in biological terms. Since its initial release in 2013 and in response to recommendations from consortium members and the wider community of scientists who use the Portal to access ENCODE data, the Portal has been regularly updated to better reflect these design principles. Here we report on these updates, including results from new experiments, uniformly-processed data from other projects, new visualization tools and more comprehensive metadata to describe experiments and analyses. Additionally, the Portal is now home to meta(data) from related projects including Genomics of Gene Regulation, Roadmap Epigenome Project, Model organism ENCODE (modENCODE) and modERN. The Portal now makes available over 13000 datasets and their accompanying metadata and can be accessed at: https://www.encodeproject.org/. |
ENCODE Project Consortium (2011) A user's guide to the encyclopedia of DNA elements (ENCODE). PLoS Biol 9:e1001046. (pmid: 21526222) |
[ PubMed ] [ DOI ] The mission of the Encyclopedia of DNA Elements (ENCODE) Project is to enable the scientific and medical communities to interpret the human genome sequence and apply it to understand human biology and improve health. The ENCODE Consortium is integrating multiple technologies and approaches in a collective effort to discover and define the functional elements encoded in the human genome, including genes, transcripts, and transcriptional regulatory regions, together with their attendant chromatin states and DNA methylation patterns. In the process, standards to ensure high-quality data have been implemented, and novel algorithms have been developed to facilitate analysis. Data and derived results are made available through a freely accessible database. Here we provide an overview of the project and the resources it is generating and illustrate the application of ENCODE data to interpret the human genome. |
- Annotation example papers
Lok et al. (2017) De Novo Genome and Transcriptome Assembly of the Canadian Beaver (Castor canadensis). G3 (Bethesda) 7:755-773. (pmid: 28087693) |
[ PubMed ] [ DOI ] The Canadian beaver (Castor canadensis) is the largest indigenous rodent in North America. We report a draft annotated assembly of the beaver genome, the first for a large rodent and the first mammalian genome assembled directly from uncorrected and moderate coverage (< 30 ×) long reads generated by single-molecule sequencing. The genome size is 2.7 Gb estimated by k-mer analysis. We assembled the beaver genome using the new Canu assembler optimized for noisy reads. The resulting assembly was refined using Pilon supported by short reads (80 ×) and checked for accuracy by congruency against an independent short read assembly. We scaffolded the assembly using the exon-gene models derived from 9805 full-length open reading frames (FL-ORFs) constructed from the beaver leukocyte and muscle transcriptomes. The final assembly comprised 22,515 contigs with an N50 of 278,680 bp and an N50-scaffold of 317,558 bp. Maximum contig and scaffold lengths were 3.3 and 4.2 Mb, respectively, with a combined scaffold length representing 92% of the estimated genome size. The completeness and accuracy of the scaffold assembly was demonstrated by the precise exon placement for 91.1% of the 9805 assembled FL-ORFs and 83.1% of the BUSCO (Benchmarking Universal Single-Copy Orthologs) gene set used to assess the quality of genome assemblies. Well-represented were genes involved in dentition and enamel deposition, defining characteristics of rodents with which the beaver is well-endowed. The study provides insights for genome assembly and an important genomics resource for Castoridae and rodent evolutionary biology. |
Seo et al. (2016) De novo assembly and phasing of a Korean human genome. Nature 538:243-247. (pmid: 27706134) |
[ PubMed ] [ DOI ] Advances in genome assembly and phasing provide an opportunity to investigate the diploid architecture of the human genome and reveal the full range of structural variation across population groups. Here we report the de novo assembly and haplotype phasing of the Korean individual AK1 (ref. 1) using single-molecule real-time sequencing, next-generation mapping, microfluidics-based linked reads, and bacterial artificial chromosome (BAC) sequencing approaches. Single-molecule sequencing coupled with next-generation mapping generated a highly contiguous assembly, with a contig N50 size of 17.9 Mb and a scaffold N50 size of 44.8 Mb, resolving 8 chromosomal arms into single scaffolds. The de novo assembly, along with local assemblies and spanning long reads, closes 105 and extends into 72 out of 190 euchromatic gaps in the reference genome, adding 1.03 Mb of previously intractable sequence. High concordance between the assembly and paired-end sequences from 62,758 BAC clones provides strong support for the robustness of the assembly. We identify 18,210 structural variants by direct comparison of the assembly with the human reference, identifying thousands of breakpoints that, to our knowledge, have not been reported before. Many of the insertions are reflected in the transcriptome and are shared across the Asian population. We performed haplotype phasing of the assembly with short reads, long reads and linked reads from whole-genome sequencing and with short reads from 31,719 BAC clones, thereby achieving phased blocks with an N50 size of 11.6 Mb. Haplotigs assembled from single-molecule real-time reads assigned to haplotypes on phased blocks covered 89% of genes. The haplotigs accurately characterized the hypervariable major histocompatability complex region as well as demonstrating allele configuration in clinically relevant genes such as CYP2D6. This work presents the most contiguous diploid human genome assembly so far, with extensive investigation of unreported and Asian-specific structural variants, and high-quality haplotyping of clinically relevant alleles for precision medicine. |
Amemiya et al. (2013) The African coelacanth genome provides insights into tetrapod evolution. Nature 496:311-6. (pmid: 23598338) |
[ PubMed ] [ DOI ] The discovery of a living coelacanth specimen in 1938 was remarkable, as this lineage of lobe-finned fish was thought to have become extinct 70 million years ago. The modern coelacanth looks remarkably similar to many of its ancient relatives, and its evolutionary proximity to our own fish ancestors provides a glimpse of the fish that first walked on land. Here we report the genome sequence of the African coelacanth, Latimeria chalumnae. Through a phylogenomic analysis, we conclude that the lungfish, and not the coelacanth, is the closest living relative of tetrapods. Coelacanth protein-coding genes are significantly more slowly evolving than those of tetrapods, unlike other genomic features. Analyses of changes in genes and regulatory elements during the vertebrate adaptation to land highlight genes involved in immunity, nitrogen excretion and the development of fins, tail, ear, eye, brain and olfaction. Functional assays of enhancers involved in the fin-to-limb transition and in the emergence of extra-embryonic tissues show the importance of the coelacanth genome as a blueprint for understanding tetrapod evolution. |
- Specific topics
- Copy Number Variation
Zarrei et al. (2015) A copy number variation map of the human genome. Nat Rev Genet 16:172-83. (pmid: 25645873) [ PubMed ] [ DOI ] A major contribution to the genome variability among individuals comes from deletions and duplications - collectively termed copy number variations (CNVs) - which alter the diploid status of DNA. These alterations may have no phenotypic effect, account for adaptive traits or can underlie disease. We have compiled published high-quality data on healthy individuals of various ethnicities to construct an updated CNV map of the human genome. Depending on the level of stringency of the map, we estimated that 4.8-9.5% of the genome contributes to CNV and found approximately 100 genes that can be completely deleted without producing apparent phenotypic consequences. This map will aid the interpretation of new CNV findings for both clinical and research applications.
- miRNA
Bracken et al. (2016) A network-biology perspective of microRNA function and dysfunction in cancer. Nat Rev Genet 17:719-732. (pmid: 27795564) [ PubMed ] [ DOI ] MicroRNAs (miRNAs) participate in most aspects of cellular differentiation and homeostasis, and consequently have roles in many pathologies, including cancer. These small non-coding RNAs exert their effects in the context of complex regulatory networks, often made all the more extensive by the inclusion of transcription factors as their direct targets. In recent years, the increased availability of gene expression data and the development of methodologies that profile miRNA targets en masse have fuelled our understanding of miRNA functions, and of the sources and consequences of miRNA dysregulation. Advances in experimental and computational approaches are revealing not just cancer pathways controlled by single miRNAs but also intermeshed regulatory networks controlled by multiple miRNAs, which often engage in reciprocal feedback interactions with the targets that they regulate.
- Epigenomics
Stricker et al. (2017) From profiles to function in epigenomics. Nat Rev Genet 18:51-66. (pmid: 27867193) [ PubMed ] [ DOI ] Myriads of epigenomic features have been comprehensively profiled in health and disease across cell types, tissues and individuals. Although current epigenomic approaches can infer function for chromatin marks through correlation, it remains challenging to establish which marks actually have causative roles in gene regulation and other processes. After revisiting how classical approaches have addressed this question in the past, we discuss the current state of epigenomic profiling and how functional information can be indirectly inferred. We also present new approaches that promise definitive functional answers, which are collectively referred to as 'epigenome editing'. In particular, we explore CRISPR-based technologies for single-locus and multi-locus manipulation. Finally, we discuss which level of function can be achieved with each approach and introduce emerging strategies for high-throughput progression from profiles to function.
Notes
About ...
Author:
- Boris Steipe <boris.steipe@utoronto.ca>
Created:
- 2017-08-05
Modified:
- 2020-09-25
Version:
- 1.1
Version history:
- 1.1 2020 Updates
- 1.0 First live version
- 0.1 First stub
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