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ELIXIR (ELIXIR Europe 2019a) 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, each known as “platform”. The ELIXIR Tools Platform works to improve the discovery, quality and sustainability of software resources. The Software Development Best Practices task of the Tools Platform aims to raise the quality and sustainability of research software by producing, adopting, and promoting information standards and best practices relevant to the software development life cycle. We have published four (4OSS) simple recommendations to encourage best practices in research software (Jiménez et al. 2017) and the Top 10 metrics for recommended life science software practices (Artaza et al. 2016). The 4OSS simple recommendations are as follows: Develop a publicly accessible open source code from day one. Make software easy to discover by providing software metadata via a popular community registry. Adopt a license and comply with the licenses of third-party dependencies. Have clear and transparent contribution, governance and communication processes. Develop a publicly accessible open source code from day one. Make software easy to discover by providing software metadata via a popular community registry. Adopt a license and comply with the licenses of third-party dependencies. Have clear and transparent contribution, governance and communication processes. In order to encourage researchers and developers to adopt the 4OSS recommendations and build FAIR (Findable, Accessible, Interoperable and Reusable) software, the best practices group, in partnership with the ELIXIR Training platform, The Carpentries (Carpentries 2019, ELIXIR Europe 2019b), and other communities, are creating a collection of training materials (Kuzak et al. 2019). The next step is to adopt, promote, and recognise these information standards and best practices. The group will address this by (i) developing comprehensive guidelines for software curation, (ii) through training researchers and developers towards the adoption of software best practices and (iii) improvement of the usability of Tools Platform products. Additionally, a direct outcome of this task will be a software management plan template, connected to a concise description of the guidelines for open research software; and production of a white paper for the software development management plan for ELIXIR, which can be consequently used to produce training materials. We will work with the newly formed ReSA (Research Software Alliance) to facilitate the adoption of this plan for the broader community.

The most commonly employed mammalian model organism is the laboratory mouse. A wide variety of genetically diverse inbred mouse strains, representing distinct physiological states, disease susceptibilities, and biological mechanisms have been developed over the last century. We report full length draft de novo genome assemblies for 16 of the most widely used inbred strains and reveal for the first time extensive strain-specific haplotype variation. We identify and characterise 2,567 regions on the current Genome Reference Consortium mouse reference genome exhibiting the greatest sequence diversity between strains. These regions are enriched for genes involved in 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. Several immune related loci, some in previously identified QTLs for disease response have novel haplotypes not present in the reference that may explain the phenotype. We used these genomes to improve the mouse reference genome resulting in the completion of 10 new gene structures, and 62 new coding loci were added to the reference genome annotation. Notably this high quality collection of genomes revealed a previously unannotated gene (Efcab3-like) encoding 5,874 amino acids, one of the largest known in the rodent lineage. Interestingly, Efcab3-like−/− mice exhibit severe size anomalies in four regions of the brain suggesting a mechanism of Efcab3-like regulating brain development.

The transcriptome is the readout of the genome. Identifying common features in it across distant species can reveal fundamental principles. To this end, the ENCODE and modENCODE consortia have generated large amounts of matched RNA-sequencing data for human, worm and fly. Uniform processing and comprehensive annotation of these data allow comparison across metazoan phyla, extending beyond earlier within-phylum transcriptome comparisons and revealing ancient, conserved features1, 2, 3, 4, 5, 6. Specifically, we discover co-expression modules shared across animals, many of which are enriched in developmental genes. Moreover, we use expression patterns to align the stages in worm and fly development and find a novel pairing between worm embryo and fly pupae, in addition to the embryo-to-embryo and larvae-to-larvae pairings. Furthermore, we find that the extent of non-canonical, non-coding transcription is similar in each organism, per base pair. Finally, we find in all three organisms that the gene-expression levels, both coding and non-coding, can be quantitatively predicted from chromatin features at the promoter using a ‘universal model’ based on a single set of organism-independent parameters. In particular, this work was funded by a contract from the National Human Genome Research Institute modENCODE Project, contract U01 HG004271 and U54 HG006944, to S.E.C. (principal investigator) and P.C., T.R.G., R.A.H. and B.R.G. (co-principal investigators) with additional support from R01 GM076655 (S.E.C.) both under Department of Energy contract no. DE-AC02-05CH11231, and U54 HG007005 to B.R.G. J.B.B.’s work was supported by NHGRI K99 HG006698 and DOE DE-AC02-05CH11231. Work in P.J.B.’s group was supported by the modENCODE DAC sub award 5710003102, 1U01HG007031-01 and the ENCODE DAC 5U01HG004695-04. Work in M.B.G.’s group was supported by NIH grants HG007000 and HG007355. Work in Bloomington was supported in part by the Indiana METACyt Initiative of Indiana University, funded by an award from the Lilly Endowment, Inc. Work in E.C.L.’s group was supported by U01-HG004261 and RC2-HG005639. P.J.P. acknowledges support from the National Institutes of Health (grant no. U01HG004258). We thank the HAVANA team for providing annotation of the human reference genome, whose work is supported by National Institutes of Health (grant no. 5U54HG004555), the Wellcome Trust (grant no. WT098051). R.G. acknowledges support from the Spanish Ministry of Education (grant BIO2011-26205). We also acknowledge use of the Yale University Biomedical High Performance Computing Center. R.W.'s lab was supported by grant no. U01 HG 004263.

Sequencing the coding regions, the exome, of the human genome is one of the major current strategies to identify low frequency and rare variants associated with human disease traits. So far, the most widely used commercial exome capture reagents have mainly targeted the consensus coding sequence (CCDS) database. We report the design of an extended set of targets for capturing the complete human exome, based on annotation from the GENCODE consortium. The extended set covers an additional 5594 genes and 10.3 Mb compared with the current CCDS-based sets. The additional regions include potential disease genes previously inaccessible to exome resequencing studies, such as 43 genes linked to ion channel activity and 70 genes linked to protein kinase activity. In total, the new GENCODE exome set developed here covers 47.9 Mb and performed well in sequence capture experiments. In the sample set used in this study, we identified over 5000 SNP variants more in the GENCODE exome target (24%) than in the CCDS-based exome sequencing.

Non-obese diabetic (NOD) mice spontaneously develop type 1 diabetes (T1D) due to the progressive loss of insulin-secreting β-cells by an autoimmune driven process. NOD mice represent a valuable tool for studying the genetics of T1D and for evaluating therapeutic interventions. Here we describe the development and characterization by end-sequencing of bacterial artificial chromosome (BAC) libraries derived from NOD/MrkTac (DIL NOD) and NOD/ShiLtJ (CHORI-29), two commonly used NOD substrains. The DIL NOD library is composed of 196,032 BACs and the CHORI-29 library is composed of 110,976 BACs. The average depth of genome coverage of the DIL NOD library, estimated from mapping the BAC end-sequences to the reference mouse genome sequence, was 7.1-fold across the autosomes and 6.6-fold across the X chromosome. Clones from this library have an average insert size of 150 kb and map to over 95.6% of the reference mouse genome assembly (NCBIm37), covering 98.8% of Ensembl mouse genes. By the same metric, the CHORI-29 library has an average depth over the autosomes of 5.0-fold and 2.8-fold coverage of the X chromosome, the reduced X chromosome coverage being due to the use of a male donor for this library. Clones from this library have an average insert size of 205 kb and map to 93.9% of the reference mouse genome assembly, covering 95.7% of Ensembl genes. We have identified and validated 191,841 single nucleotide polymorphisms (SNPs) for DIL NOD and 114,380 SNPs for CHORI-29. In total we generated 229,736,133 bp of sequence for the DIL NOD and 121,963,211 bp for the CHORI-29. These BAC libraries represent a powerful resource for functional studies, such as gene targeting in NOD embryonic stem (ES) cell lines, and for sequencing and mapping experiments.

We describe the generation and analysis of an integrated sequence map of a 2.4-Mb region of pig chromosome 7, comprising the classical class I region, the extended and classical class II regions, and the class III region of the major histocompatibility complex (MHC), also known as swine leukocyte antigen (SLA) complex. We have identified and manually annotated 151 loci, of which 121 are known genes (predicted to be functional), 18 are pseudogenes, 8 are novel CDS loci, 3 are novel transcripts, and 1 is a putative gene. Nearly all of these loci have homologues in other mammalian genomes but orthologues could be identified with confidence for only 123 genes. The 28 genes (including all the SLA class I genes) for which unambiguous orthology to genes within the human reference MHC could not be established are of particular interest with respect to porcine-specific MHC function and evolution. We have compared the porcine MHC to other mammalian MHC regions and identified the differences between them. In comparison to the human MHC, the main differences include the absence of HLA-A and other class I-like loci, the absence of HLA-DP-like loci, and the separation of the extended and classical class II regions from the rest of the MHC by insertion of the centromere. We show that the centromere insertion has occurred within a cluster of BTNL genes located at the boundary of the class II and III regions, which might have resulted in the loss of an orthologue to human C6orf10 from this region.

We have generated an improved assembly and gene annotation of the pig X chromosome, and a first draft assembly of the pig Y chromosome, by sequencing BAC and fosmid clones, and incorporating information from optical mapping and fibre-FISH. The X chromosome carries 1,014 annotated genes, 689 of which are protein-coding. Gene order closely matches that found in Primates (including humans) and Carnivores (including cats and dogs), which is inferred to be ancestral. Nevertheless, several protein-coding genes present on the human X chromosome were absent from the pig (e.g. the cancer/testis antigen family) or inactive (e.g. AWAT1), and 38 pig-specific X-chromosomal genes were annotated, 22 of which were olfactory receptors. The pig Y chromosome assembly focussed on two clusters of male-specific low-copy number genes, separated by an ampliconic region including the HSFY gene family, which together make up most of the short arm. Both clusters contain palindromes with high sequence identity, presumably maintained by gene conversion. The long arm of the chromosome is almost entirely repetitive, containing previously characterised sequences. Many of the ancestral X-related genes previously reported in at least one mammalian Y chromosome are represented either as active genes or partial sequences. This sequencing project has allowed us to identify genes - both single copy and amplified - on the pig Y, to compare the pig X and Y chromosomes for homologous sequences, and thereby to reveal mechanisms underlying pig X and Y chromosome evolution.

There is increasing evidence for epistatic interactions between gene products (e.g. KIR) encoded within the Leukocyte Receptor Complex (LRC) with those (e.g. HLA) of the Major Histocompatibility Complex (MHC), resulting in susceptibility to disease. Identification of such associations at the DNA level requires comprehensive knowledge of the genetic variation and haplotype structure of the underlying loci. The LRC haplotype project aims to provide this knowledge by sequencing common LRC haplotypes.

International audience; During the last decade there has been a great increase in the number of noncoding RNA genes identified, including new classes such as microRNAs and piRNAs. There is also a large growth in the amount of experimental characterization of these RNA components. Despite this growth in information, it is still difficult for researchers to access RNA data, because key data resources for noncoding RNAs have not yet been created. The most pressing omission is the lack of a comprehensive RNA sequence database, much like UniProt, which provides a comprehensive set of protein knowledge. In this article we propose the creation of a new open public resource that we term RNAcentral, which will contain a comprehensive collection of RNA sequences and fill an important gap in the provision of biomedical databases. We envision RNA researchers from all over the world joining a federated RNAcentral network, contributing specialized knowledge and databases. RNAcentral would centralize key data that are currently held across a variety of databases, allowing researchers instant access to a single, unified resource. This resource would facilitate the next generation of RNA research and help drive further discoveries, including those that improve food production and human and animal health. We encourage additional RNA database resources and research groups to join this effort. We aim to obtain international network funding to further this endeavor.

The Human and Vertebrate Analysis and Annotation (HAVANA) group at the Wellcome Trust Sanger Institute produced the manually annotated geneset for the Encyclopedia of DNA Elements (ENCODE) pilot project and, as part of the Gencode subgroup, are reprising this role in the scale up to cover the whole human genome. Our manual annotation is checked computationally and validated experimentally. Loci and transcripts predicted to be absent from the initial annotation are identified by comparison with a number of state-of-the-art algorithms for identifying exons, splice sites, transcripts and pseudogenes. Where novel features are confirmed the annotation is updated. Annotated coding transcripts are analysed to assess their coding potential by investigating patterns of conservation within the coding sequence (CDS) and comparing predicted secondary structures of annotated CDSs to similar proteins with solved structures. Annotated coding transcripts are also checked against the current set of human Consensus CDSs (CCDS) to check agreement with other participating centres (EBI, NCBI, & UCSC).An initial round of annotation and analysis of chromosomes 21 and 22 has shown that while HAVANA annotation is both comprehensive and robust, it has benefitted from computational review. 13 novel non-coding loci, 27 novel splice variants and 6 extensions to existing variants were identified, many of which were found using supporting EST/mRNA sequences that were not present at the time of initial annotation. Fewer than 10 annotated CDSs required reclassification, no CCDS sequences required updating and 26 novel pseudogene were added. The annotation of human chromosome 2 is complete and we are currently annotating chromosomes 3 and 7. Data from all members of Gencode is distributed via DAS and is now visible in our Zmap annotation interface, allowing assessment of computational predictions contemporaneous with first-pass gene annotation.

The zebrafish genome, which consists of 25 linkage groups and is ~1.4Gb in size, is being sequenced, finished and analysed in its entirety at the Wellcome Trust Sanger Institute. The manual annotation is provided by the Human and Vertebrate Analysis and Annotation (HAVANA) group and is released at regular intervals onto the Vertebrate Genome Annotation (Vega) database ("http://vega.sanger.ac.uk":http://vega.sanger.ac.uk) and may be viewed as a DAS source in Ensembl ("http://www.ensembl.org/Danio_rerio":http://www.ensembl.org/Danio_rerio). Our annotation is compiled in close collaboration with the Zebrafish Information Network (ZFIN) ("http://zfin.org/":http://zfin.org/), which has enabled us to provide an accurate, dynamic and distinct resource for the zebrafish community as a whole.Annotation is based on the reference genome sequence, which is derived from a minimal tile path assembly composed of clones that have been mapped, sequenced and meticulously finished to a sequence accuracy of over 99.9% per 100Kb. We expect to have 90% of the zebrafish genome to a finished standard by the end of 2009. Our approach to annotation uses two strategies. Firstly, the generation and annotation of gene lists comprising of cDNA (8995 in total) found in ZFIN that maps to our current reference assembly. And, secondly, by using clone by clone annotation, where we have annotated over 3200 genes, 1100 transcripts and 130 pseudogenes across 11 linkage groups and 3530 clones. As well as our on-going genome annotation we also welcome external annotation requests for specific genes and regions, which already include the annotation of 93 genes associated with human obesity and the scheduled annotation of the Major Histocompatability Complex, which will utilise reference sequence taken from libraries of a double haploid fish and complement our previous work on the human and mouse MHC already published. External requests and any feedback, questions or requests can be sent to zfish-help [at] sanger.ac.uk.

This article is part of the supplement: Beyond the Genome: The true gene count, human evolution and disease genomics, Boston, MA, USA. 11-13 October 2010., The GENCODE consortium is a sub group of the ENCODE consortium. Its aim is to provide complete annotation of genes in the human genome including protein-coding loci, non-coding loci and pseudogenes, based on experimental evidence. The final aim is for the HAVANA team to manually annotate the complete genome. This is a time-consuming process which will be completed over the course of the ENCODE project. Currently, to provide a set of annotation covering the complete genome, rather than just the regions that have been manually annotated, a merge of manual annotation from HAVANA with automatic annotation from the Ensembl automatically annotated gene set is created. This process also adds unique full-length CDS predictions from the Ensembl protein coding set into manually annotated genes, to provide the most complete up to date annotation of the genome possible. Also included in the set are short and long ncRNA genes predicted by the Ensembl prediction pipelines and a consensus set of pseudogene predictions agreed between Havana, Yale and UCSC. The CCDS set is also fully represented within the GENCODE set. The GENCODE set is the default annotation available in Ensembl and is also available in the UCSC genome browser. All the annotation is tagged as to whether it is produced by manual annotation alone, automatic annotation alone, or by both approaches. We are currently working to provide confidence levels for annotation, based on depth and type of evidence supporting it.