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The large diversity of experimental methods in proteomics as well as their increasing usage across biological and clinical research has led to the development of hundreds if not thousands of software tools to aid in the analysis and interpretation of the resulting data. Detailed information about these tools needs to be collected, categorized, and validated to guarantee their optimal utilization. A tools registry like bio.tools enables users and developers to identify new tools with more powerful algorithms or to find tools with similar functions for comparison. Here we present the content of the registry, which now comprises more than 1000 proteomics tool entries. Furthermore, we discuss future applications and engagement with other community efforts resulting in a high impact on the bioinformatics landscape.

Data Management Plans are now considered a key element of Open Science. They describe the data management life cycle for the data to be collected, processed and/or generated within the lifetime of a particular project or activity. A Software Manag ement Plan (SMP) plays the same role but for software. Beyond its management perspective, the main advantage of an SMP is that it both provides clear context to the software that is being developed and raises awareness. Although there are a few SMPs already available, most of them require significant technical knowledge to be effectively used. ELIXIR has developed a low-barrier SMP, specifically tailored for life science researchers, aligned to the FAIR Research Software principles. Starting from the Four Recommendations for Open Source Software, the ELIXIR SMP was iteratively refined by surveying the practices of the community and incorporating the received feedback. Currently available as a survey, future plans of the ELIXIR SMP include a human- and machine-readable version, that can be automatically queried and connected to relevant tools and metrics within the ELIXIR Tools ecosystem and beyond.

A new inter-governmental research infrastructure, ELIXIR, aims to unify bioinformatics resources and life science data across Europe, thereby facilitating their mining and (re-)use.© 2021 European Molecular Biology Laboratory. Published under the terms of the CC BY 4.0 license.

The software has become essential for research. To improve the Findability, Accessibility, Interoperability, and Reuse of research software, it is desirable to develop and apply a set of FAIR Guiding Principles for software. Application of the FAIR principles to software will continue to advance the aims of the open science movement. The FAIR 4 Research Software Working Group (FAIR4RS WG) aims to enable coordination of existing community-led discussions on how to define and apply FAIR principles to research software, and achieve adoption of these principles. The FAIR4RS WG is jointly convened as an RDA Working Group, FORCE11 Working Group, and Research Software Alliance (ReSA) Taskforce, in recognition of the importance of this work. Since July 2020, the group has been analysing existing work in this area and has started drafting community-agreed-upon FAIR principles for research software. This workshop will provide the following opportunities: Build skills in the development of principles and standards by contributing to the drafting of a set of FAIR principles relevant to research software. Understand the fundamental properties and uses of research software. Build new collaborations and engage with other RSEs and community members. Identify pathways to create (or synthesise existing work on) other elements of the application of FAIR to research software for future collaborations, such as the development of FAIR software indicators, maturity models, curriculums and competence centres. Consider how RSEs could play a critical role in advancing the FAIR for research software agenda, as early adopters, champions, case study contributors, and in ways that support regional/disciplinary RSE priorities. The workshop will encourage collaboration around the drafting of FAIR for research software principles and will include progress updates on the four FAIR4RS subgroups

Pseudogenes are ideal markers of genome remodelling. In turn, the mouse is an ideal platform for studying them, particularly with the recent availability of strain-sequencing and transcriptional data. Here, combining both manual curation and automatic pipelines, we present a genome-wide annotation of the pseudogenes in the mouse reference genome and 18 inbred mouse strains (available via the mouse.pseudogene.org resource). We also annotate 165 unitary pseudogenes in mouse, and 303, in human. The overall pseudogene repertoire in mouse is similar to that in human in terms of size, biotype distribution, and family composition (e.g. with GAPDH and ribosomal proteins being the largest families). Notable differences arise in the pseudogene age distribution, with multiple retro-transpositional bursts in mouse evolutionary history and only one in human. Furthermore, in each strain about a fifth of all pseudogenes are unique, reflecting strain-specific evolution. Finally, we find that ~15% of the mouse pseudogenes are transcribed, and that highly transcribed parent genes tend to give rise to many processed pseudogenes., Pseudogenes are key markers of genome remodelling processes. Here the authors present genome-wide annotation of the pseudogenes in the mouse reference genome and 18 inbred mouse strains, update human pseudogene annotations, and characterise the transcription and evolution of mouse pseudogenes.

Background Olfactory receptor (OR) genes are the largest multi-gene family in the mammalian genome, with 874 in human and 1483 loci in mouse (including pseudogenes). The expansion of the OR gene repertoire has occurred through numerous duplication events followed by diversification, resulting in a large number of highly similar paralogous genes. These characteristics have made the annotation of the complete OR gene repertoire a complex task. Most OR genes have been predicted in silico and are typically annotated as intronless coding sequences. Results Here we have developed an expert curation pipeline to analyse and annotate every OR gene in the human and mouse reference genomes. By combining evidence from structural features, evolutionary conservation and experimental data, we have unified the annotation of these gene families, and have systematically determined the protein-coding potential of each locus. We have defined the non-coding regions of many OR genes, enabling us to generate full-length transcript models. We found that 13 human and 41 mouse OR loci have coding sequences that are split across two exons. These split OR genes are conserved across mammals, and are expressed at the same level as protein-coding OR genes with an intronless coding region. Our findings challenge the long-standing and widespread notion that the coding region of a vertebrate OR gene is contained within a single exon. Conclusions This work provides the most comprehensive curation effort of the human and mouse OR gene repertoires to date. The complete annotation has been integrated into the GENCODE reference gene set, for immediate availability to the research community.

Accurate annotations of genes and their transcripts is a foundation of genomics, but no annotation technique presently combines throughput and accuracy. As a result, reference gene collections remain incomplete: many gene models are fragmentary, while thousands more remain uncatalogued–particularly for long noncoding RNAs (lncRNAs). To accelerate lncRNA annotation, the GENCODE consortium has developed RNA Capture Long Seq (CLS), combining targeted RNA capture with third-generation long-read sequencing. We present an experimental re-annotation of the GENCODE intergenic lncRNA population in matched human and mouse tissues, resulting in novel transcript models for 3574 / 561 gene loci, respectively. CLS approximately doubles the annotated complexity of targeted loci, outperforming existing short-read techniques. Full-length transcript models produced by CLS enable us to definitively characterize the genomic features of lncRNAs, including promoter- and gene-structure, and protein-coding potential. Thus CLS removes a longstanding bottleneck of transcriptome annotation, generating manual-quality full-length transcript models at high-throughput scales.Abbreviationsbpbase pairFLfull lengthntnucleotideROIread of insert, i.e. PacBio readSJsplice junctionSMRTsingle-molecule real-timeTMtranscript model

ELIXIR [1] is an intergovernmental organization that brings together life science resources across Europe. These resources include databases, software tools, training materials, cloud storage, and supercomputers. One of the goals of ELIXIR is to coordinate these resources so that they form a single infrastructure. This infrastructure makes it easier for scientists to find and share data, exchange expertise, and agree on best practices. ELIXIR's activities are divided into the following five areas Data, Tools, Interoperability, Compute and Training known as “platforms”. The ELIXIR Tools Platform works to improve the discovery, quality and sustainability of software resources. Software Best Practices task of the Tools Platform aims to raise the quality and sustainability of research software by producing, adopting, promoting and measuring information standards and best practices applied to the software development life cycle. We have published four (4OSS) simple recommendations to encourage best practices in research software [2] and the Top 10 metrics for life science software good practices [3]. The next step is to adopt, promote, and recognise these information standards and best practices, by developing comprehensive guidelines for software curation, and through workshops for training researchers and developers towards the adoption of software best practices.

The early infantile epileptic encephalopathies (EIEE) are a group of rare, severe neurodevelopmental disorders, where even the most thorough sequencing studies leave 60-65% of patients without a molecular diagnosis. Here, we explore the incompleteness of transcript models used for exome and genome analysis as one potential explanation for lack of current diagnoses. Therefore, we have updated the GENCODE gene annotation for 191 epilepsy-associated genes, using human brain-derived transcriptomic libraries and other data to build 3,550 novel putative transcript models. The extended transcriptional footprint of these genes allowed for 294 intronic or intergenic variants, found in human mutation databases, to be reclassified as exonic, while a further 70 intronic variants were reclassified as splice-site proximal. Using SCN1A as a case study due to its close phenotype/genotype correlation with Dravet syndrome, we screened 122 people with Dravet syndrome, or a similar phenotype, with a panel of novel exon sequences representing eight established genes and identified two de novo SCN1A variants that now, through improved gene annotation can be ascribed to residing among novel exons. These two (from 122 screened patients, 1.6%) new molecular diagnoses carry significant clinical implications. Furthermore, we identified a previously-classified SCN1A intronic Dravet-associated variant that now lies within a deeply conserved novel exon. Our findings illustrate the potential gains of thorough gene annotation in improving diagnostic yields for genetic disorders. We would expect to find new molecular diagnoses in our 191 genes that were originally suspected by clinicians for patients, with a negative diagnosis.

The developmental and epileptic encephalopathies (DEE) are a group of rare, severe neurodevelopmental disorders, where even the most thorough sequencing studies leave 60–65% of patients without a molecular diagnosis. Here, we explore the incompleteness of transcript models used for exome and genome analysis as one potential explanation for a lack of current diagnoses. Therefore, we have updated the GENCODE gene annotation for 191 epilepsy-associated genes, using human brain-derived transcriptomic libraries and other data to build 3,550 putative transcript models. Our annotations increase the transcriptional ‘footprint’ of these genes by over 674 kb. Using SCN1A as a case study, due to its close phenotype/genotype correlation with Dravet syndrome, we screened 122 people with Dravet syndrome or a similar phenotype with a panel of exon sequences representing eight established genes and identified two de novo SCN1A variants that now - through improved gene annotation - are ascribed to residing among our exons. These two (from 122 screened people, 1.6%) molecular diagnoses carry significant clinical implications. Furthermore, we identified a previously classified SCN1A intronic Dravet syndrome-associated variant that now lies within a deeply conserved exon. Our findings illustrate the potential gains of thorough gene annotation in improving diagnostic yields for genetic disorders.

A genome sequence is worthless if it cannot be deciphered; therefore, efforts to describe - or 'annotate' - genes began as soon as DNA sequences became available. Whereas early work focused on individual protein-coding genes, the modern genomic ocean is a complex maelstrom of alternative splicing, non-coding transcription and pseudogenes. Scientists - from clinicians to evolutionary biologists - need to navigate these waters, and this has led to the design of high-throughput, computationally driven annotation projects. The catalogues that are being produced are key resources for genome exploration, especially as they become integrated with expression, epigenomic and variation data sets. Their creation, however, remains challenging.

Pseudogenes are ideal markers of genome remodeling. In turn, the mouse is an ideal platform for studying them, particularly with the availability of developmental transcriptional data and the sequencing of 18 strains. Here, we present a comprehensive genome-wide annotation of the pseudogenes in the mouse reference genome and associated strains. We compiled this by combining manual curation of over 10,000 pseudogenes with results from automatic annotation pipelines. Also, by comparing the human and mouse, we annotated 165 unitary pseudogenes in mouse, and 303 unitaries in human. We make all our annotation available throughmouse.pseudogene.org. The overall mouse pseudogene repertoire (in the reference and strains) is similar to human in terms of overall size, biotype distribution (~80% processed/~20% duplicated) and top family composition (with many GAPDH and ribosomal pseudogenes). However, notable differences arise in the pseudogene age distribution, with multiple retro-transpositional bursts in mouse evolutionary history and only one in human. Furthermore, in each strain about a fifth of the pseudogenes are unique, reflecting strain-specific functions and evolution. Additionally, we find that ~15% of the pseudogenes are transcribed, a fraction similar to that for human, and that pseudogene transcription exhibits greater tissue and strain specificity compared to protein-coding genes. Finally, we show that highly transcribed parent genes tend to give rise to processed pseudogenes.

We report full-length draft de novo genome assemblies for 16 widely used inbred mouse strains and find extensive strain-specific haplotype variation. We identify and characterize 2,567 regions on the current mouse reference genome exhibiting the greatest sequence diversity. These regions are enriched for genes involved in pathogen defence and immunity and exhibit enrichment of transposable elements and signatures of recent retrotransposition events. Combinations of alleles and genes unique to an individual strain are commonly observed at these loci, reflecting distinct strain phenotypes. We used these genomes to improve the mouse reference genome, resulting in the completion of 10 new gene structures. Also, 62 new coding loci were added to the reference genome annotation. These genomes identified a large, previously unannotated, gene (Efcab3-like) encoding 5,874 amino acids. Mutant Efcab3-like mice display anomalies in multiple brain regions, suggesting a possible role for this gene in the regulation of brain development. Medical Research Council and the Wellcome Trust

Annotation on the reference genome of the C57BL6/J mouse has been an ongoing project ever since the draft genome was first published. Initially, the principle focus was on the identification of all protein-coding genes, although today the importance of describing long non-coding RNAs, small RNAs, and pseudogenes is recognized. Here, we describe the progress of the GENCODE mouse annotation project, which combines manual annotation from the HAVANA group with Ensembl computational annotation, alongside experimental and in silico validation pipelines from other members of the consortium. We discuss the more recent incorporation of next-generation sequencing datasets into this workflow, including the usage of mass-spectrometry data to potentially identify novel protein-coding genes. Finally, we will outline how the C57BL6/J genebuild can be used to gain insights into the variant sites that distinguish different mouse strains and species. Electronic supplementary material The online version of this article (doi:10.1007/s00335-015-9583-x) contains supplementary material, which is available to authorized users.

The Human Genome Project and advances in DNA sequencing technologies have revolutionized the identification of genetic disorders through the use of clinical exome sequencing. However, in a considerable number of patients, the genetic basis remains unclear. As clinicians begin to consider whole-genome sequencing, an understanding of the processes and tools involved and the factors to consider in the annotation of the structure and function of genomic elements that might influence variant identification is crucial. Here, we discuss and illustrate the strengths and weaknesses of approaches for the annotation and classification of important elements of protein-coding genes, other genomic elements such as pseudogenes and the non-coding genome, comparative-genomic approaches for inferring gene function, and new technologies for aiding genome annotation, as a practical guide for clinicians when considering pathogenic sequence variation. Complete and accurate annotation of structure and function of genome features has the potential to reduce both false-negative (from missing annotation) and false-positive (from incorrect annotation) errors in causal variant identification in exome and genome sequences. Re-analysis of unsolved cases will be necessary as newer technology improves genome annotation, potentially improving the rate of diagnosis.

The field of non-coding RNA biology has been hampered by the lack of availability of a comprehensive, up-to-date collection of accessioned RNA sequences. Here we present the first release of RNAcentral, a database that collates and integrates information from an international consortium of established RNA sequence databases. The initial release contains over 8.1 million sequences, including representatives of all major functional classes. A web portal (http://rnacentral.org) provides free access to data, search functionality, cross-references, source code and an integrated genome browser for selected species.

Ensembl (http://www.ensembl.org) is a genomic interpretation system providing the most up-to-date annotations, querying tools and access methods for chordates and key model organisms. This year we released updated annotation (gene models, comparative genomics, regulatory regions and variation) on the new human assembly, GRCh38, although we continue to support researchers using the GRCh37.p13 assembly through a dedicated site (http://grch37.ensembl.org). Our Regulatory Build has been revamped to identify regulatory regions of interest and to efficiently highlight their activity across disparate epigenetic data sets. A number of new interfaces allow users to perform large-scale comparisons of their data against our annotations. The REST server (http://rest.ensembl.org), which allows programs written in any language to query our databases, has moved to a full service alongside our upgraded website tools. Our online Variant Effect Predictor tool has been updated to process more variants and calculate summary statistics. Lastly, the WiggleTools package enables users to summarize large collections of data sets and view them as single tracks in Ensembl. The Ensembl code base itself is more accessible: it is now hosted on our GitHub organization page (https://github.com/Ensembl) under an Apache 2.0 open source license.

Determining the full complement of protein-coding genes is a key goal of genome annotation. The most powerful approach for confirming protein-coding potential is the detection of cellular protein expression through peptide massspectrometry(MS) experiments. Here, we mapped peptides detected in seven large-scale proteomics studies to almost 60\% of the protein-coding genes in the GENCODE annotation of the human genome. We found a strong relationship between detection in proteomics experiments and both gene family age and cross-species conservation. Most of the genes for which we detected peptides were highly conserved. We found peptides for >96\% of genes that evolved before bilateria. At the opposite end of the scale, we identified almost no peptides for genes that have appeared since primates, for genes that did not have any protein-like features or for genes with poor cross-species conservation. These results motivated us to describe a set of 2001 potential non-coding genes based on features such as weak conservation, a lack of protein features, or ambiguous annotations from major databases, all of which correlated with low peptide detection across the seven experiments. We identified peptides for just 3\% of these genes. We show that many of these genes behave more like non-coding genes than protein-coding genes and suggest that most are unlikely to code for proteins under normal circumstances. We believe that their inclusion in the human protein-coding gene catalogue should be revised as part of the ongoing human genome annotation effort. This work was supported by the National Institutes of Health (NIH, grant number U41 HG007234) and by the Spanish Ministry of Science and Innovation (grant numbers BIO2007-666855, RD07-0067-0014, COMBIOMED). J.M.R. is supported by the Spanish National Institute of Bioinformatics (www.inab.org), a platform of the `Instituto de Salud Carlos III'. Funding to pay the Open Access publication charges for this article was provided by the National Institutes of Health (NIH, grant number U41 HG007234). Sí

Ensembl (http://www.ensembl.org) creates tools and data resources to facilitate genomic analysis in chordate species with an emphasis on human, major vertebrate model organisms and farm animals. Over the past year we have increased the number of species that we support to 77 and expanded our genome browser with a new scrollable overview and improved variation and phenotype views. We also report updates to our core datasets and improvements to our gene homology relationships from the addition of new species. Our REST service has been extended with additional support for comparative genomics and ontology information. Finally, we provide updated information about our methods for data access and resources for user training.

Complete annotation of the human genome is indispensable for medical research. The GENCODE consortium strives to provide this, augmenting computational and experimental evidence with manual annotation. The rapidly developing field of proteogenomics provides evidence for the translation of genes into proteins and can be used to discover and refine gene models. However, for both the proteomics and annotation groups, there is a lack of guidelines for integrating this data. Here we report a stringent workflow for the interpretation of proteogenomic data that could be used by the annotation community to interpret novel proteogenomic evidence. Based on reprocessing of three large-scale publicly available human data sets, we show that a conservative approach, using stringent filtering is required to generate valid identifications. Evidence has been found supporting 16 novel protein-coding genes being added to GENCODE. Despite this many peptide identifications in pseudogenes cannot be annotated due to the absence of orthogonal supporting evidence., Identifying and annotating functional elements in the human genome remains a challenging but important task. Here the authors propose a priority annotation score to rank identifications and suggest how proteogenomics evidence can be interpreted and what additional information substantiates protein-coding potential for annotation.

Long non-coding RNAs (lncRNAs) constitute a large, yet mostly uncharacterized fraction of the mammalian transcriptome. Such characterization requires a comprehensive, high-quality annotation of their gene structure and boundaries, which is currently lacking. Here we describe RACE-Seq, an experimental workflow designed to address this based on RACE (rapid amplification of cDNA ends) and long-read RNA sequencing. We apply RACE-Seq to 398 human lncRNA genes in seven tissues, leading to the discovery of 2,556 on-target, novel transcripts. About 60% of the targeted loci are extended in either 5′ or 3′, often reaching genomic hallmarks of gene boundaries. Analysis of the novel transcripts suggests that lncRNAs are as long, have as many exons and undergo as much alternative splicing as protein-coding genes, contrary to current assumptions. Overall, we show that RACE-Seq is an effective tool to annotate an organism's deep transcriptome, and compares favourably to other targeted sequencing techniques., Long non-coding RNAs are increasingly recognised to be important factors in regulating cellular processes and comprise a large faction of the transcriptome, however most are uncharacterised. Here the authors present RACE-Seq, a tool to improve and extend the annotation of low-expression transcripts.

Advances in high-throughput mass spectrometry are making proteomics an increasingly important tool in genome annotation projects. Peptides detected in mass spectrometry experiments can be used to validate gene models and verify the translation of putative coding sequences (CDSs). Here, we have identified peptides that cover 35% of the genes annotated by the GENCODE consortium for the human genome as part of a comprehensive analysis of experimental spectra from two large publicly available mass spectrometry databases. We detected the translation to protein of “novel” and “putative” protein-coding transcripts as well as transcripts annotated as pseudogenes and nonsense-mediated decay targets. We provide a detailed overview of the population of alternatively spliced protein isoforms that are detectable by peptide identification methods. We found that 150 genes expressed multiple alternative protein isoforms. This constitutes the largest set of reliably confirmed alternatively spliced proteins yet discovered. Three groups of genes were highly overrepresented. We detected alternative isoforms for 10 of the 25 possible heterogeneous nuclear ribonucleoproteins, proteins with a key role in the splicing process. Alternative isoforms generated from interchangeable homologous exons and from short indels were also significantly enriched, both in human experiments and in parallel analyses of mouse and Drosophila proteomics experiments. Our results show that a surprisingly high proportion (almost 25%) of the detected alternative isoforms are only subtly different from their constitutive counterparts. Many of the alternative splicing events that give rise to these alternative isoforms are conserved in mouse. It was striking that very few of these conserved splicing events broke Pfam functional domains or would damage globular protein structures. This evidence of a strong bias toward subtle differences in CDS and likely conserved cellular function and structure is remarkable and strongly suggests that the translation of alternative transcripts may be subject to selective constraints.

Defective Gene Detective Identifying genes that give rise to diseases is one of the major goals of sequencing human genomes. However, putative loss-of-function genes, which are often some of the first identified targets of genome and exome sequencing, have often turned out to be sequencing errors rather than true genetic variants. In order to identify the true scope of loss-of-function genes within the human genome, MacArthur et al. (p. 823 ; see the Perspective by Quintana-Murci ) extensively validated the genomes from the 1000 Genomes Project, as well as an additional European individual, and found that the average person has about 100 true loss-of-function alleles of which approximately 20 have two copies within an individual. Because many known disease-causing genes were identified in “normal” individuals, the process of clinical sequencing needs to reassess how to identify likely causative alleles.

For 10,000 years pigs and humans have shared a close and complex relationship. From domestication to modern breeding practices, humans have shaped the genomes of domestic pigs. Here we present the assembly and analysis of the genome sequence of a female domestic Duroc pig (Sus scrofa) and a comparison with the genomes of wild and domestic pigs from Europe and Asia. Wild pigs emerged in South East Asia and subsequently spread across Eurasia. Our results reveal a deep phylogenetic split between European and Asian wild boars ∼1 million years ago, and a selective sweep analysis indicates selection on genes involved in RNA processing and regulation. Genes associated with immune response and olfaction exhibit fast evolution. Pigs have the largest repertoire of functional olfactory receptor genes, reflecting the importance of smell in this scavenging animal. The pig genome sequence provides an important resource for further improvements of this important livestock species, and our identification of many putative disease-causing variants extends the potential of the pig as a biomedical model., The research leading to these results has received funding from the European Research Council under the European Community’s Seventh Framework Programme (FP7/2007-2013)/ERC grant agreement no. 249894 (SelSweep)

Alternative splicing (AS) has the potential to greatly expand the functional repertoire of mammalian transcriptomes. However, few variant transcripts have been characterized functionally, making it difficult to assess the contribution of AS to the generation of phenotypic complexity and to study the evolution of splicing patterns. We have compared the AS of 309 protein-coding genes in the human ENCODE pilot regions against their mouse orthologs in unprecedented detail, utilizing traditional transcriptomic and RNAseq data. The conservation status of every transcript has been investigated, and each functionally categorized as coding (separated into coding sequence [CDS] or nonsense-mediated decay [NMD] linked) or noncoding. In total, 36.7% of human and 19.3% of mouse coding transcripts are species specific, and we observe a 3.6 times excess of human NMD transcripts compared with mouse; in contrast to previous studies, the majority of species-specific AS is unlinked to transposable elements. We observe one conserved CDS variant and one conserved NMD variant per 2.3 and 11.4 genes, respectively. Subsequently, we identify and characterize equivalent AS patterns for 22.9% of these CDS or NMD-linked events in nonmammalian vertebrate genomes, and our data indicate that functional NMD-linked AS is more widespread and ancient than previously thought. Furthermore, although we observe an association between conserved AS and elevated sequence conservation, as previously reported, we emphasize that 30% of conserved AS exons display sequence conservation below the average score for constitutive exons. In conclusion, we demonstrate the value of detailed comparative annotation in generating a comprehensive set of AS transcripts, increasing our understanding of AS evolution in vertebrates. Our data supports a model whereby the acquisition of functional AS has occurred throughout vertebrate evolution and is considered alongside amino acid change as a key mechanism in gene evolution.

Recent advances in proteomic mass spectrometry (MS) offer the chance to marry high-throughput peptide sequencing to transcript models, allowing the validation, refinement, and identification of new protein-coding loci. We present a novel pipeline that integrates highly sensitive and statistically robust peptide spectrum matching with genome-wide protein-coding predictions to perform large-scale gene validation and discovery in the mouse genome for the first time. In searching an excess of 10 million spectra, we have been able to validate 32%, 17%, and 7% of all protein-coding genes, exons, and splice boundaries, respectively. Moreover, we present strong evidence for the identification of multiple alternatively spliced translations from 53 genes and have uncovered 10 entirely novel protein-coding genes, which are not covered in any mouse annotation data sources. One such novel protein-coding gene is a fusion protein that spans the Ins2 and Igf2 loci to produce a transcript encoding the insulin II and the insulin-like growth factor 2–derived peptides. We also report nine processed pseudogenes that have unique peptide hits, demonstrating, for the first time, that they are not just transcribed but are translated and are therefore resurrected into new coding loci. This work not only highlights an important utility for MS data in genome annotation but also provides unique insights into the gene structure and propagation in the mouse genome. All these data have been subsequently used to improve the publicly available mouse annotation available in both the Vega and Ensembl genome browsers (http://vega.sanger.ac.uk).

The first wave of personal genomes documents how no single individual genome contains the full complement of functional genes. Here, we describe the extent of variation in gene and pseudogene numbers between individuals arising from inactivation events such as premature termination or aberrant splicing due to single-nucleotide polymorphisms. This highlights the inadequacy of the current reference sequence and gene set. We present a proposal to define a reference gene set that will remain stable as more individuals are sequenced. In particular, we recommend that the ancestral allele be used to define the reference sequence from which a core human reference gene annotation set can be derived. In addition, we call for the development of an expanded gene set to include human-specific genes that have arisen recently and are absent from the ancestral set.

The Vertebrate Genome Annotation (Vega) database (http://vega.sanger.ac.uk) has been designed to be a community resource for browsing manual annotation of finished sequences from a variety of vertebrate genomes. Its core database is based on an Ensembl-style schema, extended to incorporate curation-specific metadata. In collaboration with the genome sequencing centres, Vega attempts to present consistent high-quality annotation of the published human chromosome sequences. In addition, it is also possible to view various finished regions from other vertebrates, including mouse and zebrafish. Vega displays only manually annotated gene structures built using transcriptional evidence, which can be examined in the browser. Attempts have been made to standardize the annotation procedure across each vertebrate genome, which should aid comparative analysis of orthologues across the different finished regions.

The Ensembl project (http://www.ensembl.org) is a system for genome annotation, analysis, storage and dissemination designed to facilitate the access of genomic annotation from chordates and key model organisms. It provides access to data from 87 species across our main and early access Pre! websites. This year we introduced three newly annotated species and released numerous updates across our supported species with a concentration on data for the latest genome assemblies of human, mouse, zebrafish and rat. We also provided two data updates for the previous human assembly, GRCh37, through a dedicated website (http://grch37.ensembl.org). Our tools, in particular the VEP, have been improved significantly through integration of additional third party data. REST is now capable of larger-scale analysis and our regulatory data BioMart can deliver faster results. The website is now capable of displaying long-range interactions such as those found in cis-regulated datasets. Finally we have launched a website optimized for mobile devices providing views of genes, variants and phenotypes. Our data is made available without restriction and all code is available from our GitHub organization site (http://github.com/Ensembl) under an Apache 2.0 license.

A report on the Wellcome Trust retreat on devising a consensus framework for the validation of novel human protein coding loci, held in Hinxton, U.K., May 11-13, 2015.

Background: A vast amount of DNA variation is being identified by increasingly large-scale exome and genome sequencing projects. To be useful, variants require accurate functional annotation and a wide range of tools are available to this end. McCarthy et al recently demonstrated the large differences in prediction of loss-of-function (LoF) variation when RefSeq and Ensembl transcripts are used for annotation, highlighting the importance of the reference transcripts on which variant functional annotation is based. Results: We describe a detailed analysis of the similarities and differences between the gene and transcript annotation in the GENCODE and RefSeq genesets. We demonstrate that the GENCODE Comprehensive set is richer in alternative splicing, novel CDSs, novel exons and has higher genomic coverage than RefSeq, while the GENCODE Basic set is very similar to RefSeq. Using RNAseq data we show that exons and introns unique to one geneset are expressed at a similar level to those common to both. We present evidence that the differences in gene annotation lead to large differences in variant annotation where GENCODE and RefSeq are used as reference transcripts, although this is predominantly confined to non-coding transcripts and UTR sequence, with at most ~30% of LoF variants annotated discordantly. We also describe an investigation of dominant transcript expression, showing that it both supports the utility of the GENCODE Basic set in providing a smaller set of more highly expressed transcripts and provides a useful, biologically-relevant filter for further reducing the complexity of the transcriptome. Conclusions: The reference transcripts selected for variant functional annotation do have a large effect on the outcome. The GENCODE Comprehensive transcripts contain more exons, have greater genomic coverage and capture many more variants than RefSeq in both genome and exome datasets, while the GENCODE Basic set shows a higher degree of concordance with RefSeq and has fewer unique features. We propose that the GENCODE Comprehensive set has great utility for the discovery of new variants with functional potential, while the GENCODE Basic set is more suitable for applications demanding less complex interpretation of functional variants.

Homeobox genes are a group of genes coding for transcription factors with a DNA-binding helix-turn-helix structure called a homeodomain and which play a crucial role in pattern formation during embryogenesis. Many homeobox genes are located in clusters and some of these, most notably the HOX genes, are known to have antisense or opposite strand long non-coding RNA (lncRNA) genes that play a regulatory role. Because automated annotation of both gene clusters and non-coding genes is fraught with difficulty (over-prediction, under-prediction, inaccurate transcript structures), we set out to manually annotate all homeobox genes in the mouse and human genomes. This includes all supported splice variants, pseudogenes and both antisense and flanking lncRNAs. One of the areas where manual annotation has a significant advantage is the annotation of duplicated gene clusters. After comprehensive annotation of all homeobox genes and their antisense genes in human and in mouse, we found some discrepancies with the current gene set in RefSeq regarding exact gene structures and coding versus pseudogene locus biotype. We also identified previously un-annotated pseudogenes in the DUX, Rhox and Obox gene clusters, which helped us re-evaluate and update the gene nomenclature in these regions. We found that human homeobox genes are enriched in antisense lncRNA loci, some of which are known to play a role in gene or gene cluster regulation, compared to their mouse orthologues. Of the annotated set of 241 human protein-coding homeobox genes, 98 have an antisense locus (41%) while of the 277 orthologous mouse genes, only 62 protein coding gene have an antisense locus (22%), based on publicly available transcriptional evidence.

Pseudogenes are degraded fossil copies of genes. Here, we report a comparison of pseudogenes spanning three phyla, leveraging the completed annotations of the human, worm, and fly genomes, which we make available as an online resource. We find that pseudogenes are lineage specific, much more so than protein-coding genes, reflecting the different remodeling processes marking each organism’s genome evolution. The majority of human pseudogenes are processed, resulting from a retrotranspositional burst at the dawn of the primate lineage. This burst can be seen in the largely uniform distribution of pseudogenes across the genome, their preservation in areas with low recombination rates, and their preponderance in highly expressed gene families. In contrast, worm and fly pseudogenes tell a story of numerous duplication events. In worm, these duplications have been preserved through selective sweeps, so we see a large number of pseudogenes associated with highly duplicated families such as chemoreceptors. However, in fly, the large effective population size and high deletion rate resulted in a depletion of the pseudogene complement. Despite large variations between these species, we also find notable similarities. Overall, we identify a broad spectrum of biochemical activity for pseudogenes, with the majority in each organism exhibiting varying degrees of partial activity. In particular, we identify a consistent amount of transcription (∼15%) across all species, suggesting a uniform degradation process. Also, we see a uniform decay of pseudogene promoter activity relative to their coding counterparts and identify a number of pseudogenes with conserved upstream sequences and activity, hinting at potential regulatory roles.

Historically pseudogenes were believed to represent nonfunctional genomic fossils; however, there is emerging evidence that many of them could be biologically active. This possibility has ignited interest in pseudogene loci and made the need for their high-quality annotation more pressing as an accurate knowledge of all pseudogenes in the human reference genome sequence facilitates confident functional analysis. GENCODE have undertaken the first genome-wide pseudogene assignment for protein-coding genes combining both large-scale manual annotation and computational pseudogene prediction pipelines. Multiple computational predictions provide an unbiased set of hints for manual annotators to investigate, both during first-pass annotation and as part of QC to identify any potential missing pseudogene loci. Where a pseudogene is identified, the extent of its homology to the parent locus is fully investigated by a manual annotator; a pseudogene model is built and assigned to one of eight pseudogene biotypes depending on the mechanism of creation and on the presence of locus-specific transcriptional or proteomic data. The high-quality, information-rich set of pseudogenes created has been integrated with ENCODE functional genomics data, specifically expression level, transcription factor and RNA polymerase II binding, and chromatin marks. In this way we have been able to identify some pseudogenes that possess conventional characteristics of functionality as well as others with interesting patterns of partial activity, which might suggest that putatively inactive loci could be gaining a novel function, for example as long noncoding RNAs. The activity data associated with every pseudogene is stored in the psiDR resource.

Determining the full complement of protein-coding genes is a key goal of genome annotation. The most powerful approach for confirming protein coding potential is the detection of cellular protein expression through peptide mass spectrometry experiments. Here we map the peptides detected in 7 large-scale proteomics studies to almost 60% of the protein coding genes in the GENCODE annotation the human genome. We find that conservation across vertebrate species and the age of the gene family are key indicators of whether a peptide will be detected in proteomics experiments. We find peptides for most highly conserved genes and for practically all genes that evolved before bilateria. At the same time there is almost no evidence of protein expression for genes that have appeared since primates, or for genes that do not have any protein-like features or cross-species conservation. We identify 19 non-protein-like features such as weak conservation, no protein features or ambiguous annotations in major databases that are indicators of low peptide detection rates. We use these features to describe a set of 2,001 genes that are potentially non-coding, and show that many of these genes behave more like non-coding genes than protein-coding genes. We detect peptides for just 3% of these genes. We suggest that many of these 2,001 genes do not code for proteins under normal circumstances and that they should not be included in the human protein coding gene catalogue. These potential non-coding genes will be revised as part of the ongoing human genome annotation effort.

To access publisher's full text version of this article, please click on the hyperlink in Additional Links field or click on the hyperlink at the top of the page marked Files. This article is open access. The genetic contribution to the variation in human lifespan is ∼ 25%. Despite the large number of identified disease-susceptibility loci, it is not known which loci influence population mortality. We performed a genome-wide association meta-analysis of 7729 long-lived individuals of European descent (≥ 85 years) and 16 121 younger controls (