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Supplementary Figures and legend figures Supplementary Methods

Summary of the mutational state of the expressed genes in each control SCC-PDX, and the acquired mutations in GefR and GefT SCC-PDXs, as compared to their respective control PDXs

Purpose:Recurrent and/or metastatic unresectable cutaneous squamous cell carcinomas (cSCCs) are treated with chemotherapy or radiotherapy, but have poor clinical responses. A limited response (up to 45% of cases) to EGFR-targeted therapies was observed in clinical trials with patients with advanced and metastatic cSCC. Here, we analyze the molecular traits underlying the response to EGFR inhibitors, and the mechanisms responsible for cSCC resistance to EGFR-targeted therapy.Experimental Design:We generated primary cell cultures and patient cSCC–derived xenografts (cSCC-PDXs) that recapitulate the histopathologic and molecular features of patient tumors. Response to gefitinib treatment was tested and gefitinib-resistant (GefR) cSCC-PDXs were developed. RNA sequence analysis was performed in matched untreated and GefR cSCC-PDXs to determine the mechanisms driving gefitinib resistance.Results:cSCCs conserving epithelial traits exhibited strong activation of EGFR signaling, which promoted tumor cell proliferation, in contrast to mesenchymal-like cSCCs. Gefitinib treatment strongly blocked epithelial-like cSCC-PDX growth in the absence of EGFR and RAS mutations, whereas tumors carrying the E545K PIK3CA-activating mutation were resistant to treatment. A subset of initially responding tumors acquired resistance after long-term treatment, which was induced by the bypass from EGFR to FGFR signaling to allow tumor cell proliferation and survival upon gefitinib treatment. Pharmacologic inhibition of FGFR signaling overcame resistance to EGFR inhibitor, even in PIK3CA-mutated tumors.Conclusions:EGFR-targeted therapy may be appropriate for treating many epithelial-like cSCCs without PIK3CA-activating mutations. Combined EGFR- and FGFR-targeted therapy may be used to treat cSCCs that show intrinsic or acquired resistance to EGFR inhibitors.

Scientific data analyses often combine several computational tools in automated pipelines, or workflows. Thousands of such workflows have been used in the life sciences, though their composition has remained a cumbersome manual process due to a lack of standards for annotation, assembly, and implementation. Recent technological advances have returned the long-standing vision of automated workflow composition into focus. This article summarizes a recent Lorentz Center workshop dedicated to automated composition of workflows in the life sciences. We survey previous initiatives to automate the composition process, and discuss the current state of the art and future perspectives. We start by drawing the “big picture” of the scientific workflow development life cycle, before surveying and discussing current methods, technologies and practices for semantic domain modelling, automation in workflow development, and workflow assessment. Finally, we derive a roadmap of individual and community-based actions to work toward the vision of automated workflow development in the forthcoming years. A central outcome of the workshop is a general description of the workflow life cycle in six stages: 1) scientific question or hypothesis, 2) conceptual workflow, 3) abstract workflow, 4) concrete workflow, 5) production workflow, and 6) scientific results. The transitions between stages are facilitated by diverse tools and methods, usually incorporating domain knowledge in some form. Formal semantic domain modelling is hard and often a bottleneck for the application of semantic technologies. However, life science communities have made considerable progress here in recent years and are continuously improving, renewing interest in the application of semantic technologies for workflow exploration, composition and instantiation. Combined with systematic benchmarking with reference data and large-scale deployment of production-stage workflows, such technologies enable a more systematic process of workflow development than we know today. We believe that this can lead to more robust, reusable, and sustainable workflows in the future.

Nowadays, the FAIR Principles guide data policy actions and professional practices in the public and private sectors. However, despite such global endorsements, the FAIR Principles are aspirational, remaining elusive at best, and intimidating at worst. To address the lack of practical guidance, and help with capability gaps, we developed the FAIR Cookbook, an online resource of hands-on recipes for “FAIR doers” in the Life Sciences. Launched in 2020, and created by researchers and data managers professionals in academia, pharmas and information service industries, the FAIR Cookbook covers the key steps in a FAIRification journey, the levels and indicators of FAIRness, the maturity model, the technologies, the tools and the standards available, as well as the skills required, and the challenges to achieve and improve data FAIRness. In this pre-print, we present the FAIR Cookbook, its creation and content, its value, use and adoptions, as well as the participatory process, collaborative plans for sustainability, and its adoption.The full list of FAIR Cookbook Recipes’ Authors is here.

Document detailing ELIXIR Europe's strategy for engagement with the European Open Science Cloud (EOSC)as produced by the ELIXIR-EOSC Focus Group, 2022.

The ECCB2022 Organising Committee, the ECCB2022 Programme Committee, the ECCB2022 Steering Committee, This paper was published as part of a special issue financially supported by ECCB2022.

This glossary was created by the consortium partners of the European project HealthyCloud with the aim of establishing agreed definitions of commonly used terms to harmonise the work of the project. The aim is that the glossary can also be used by other projects working in similar areas. The glossary was created through a series of glossary working group calls, during which each definition was discussed and agreed upon. This is a living document that will be updated regularly with the addition of new terms and modifications, if needed, of the terms and definitions currently there. This is the second version of the HealthyCloud glossary, in which new terms have been added based on a need perceived in the glossary working group. Some existing terms have been modified to align with the recently published Proposal for a Regulation on the European Health Data Space (EHDS). In addition, sections have been created for terms relating to specific topics (e.g., artificial intelligence, profiles as defined in HealthyCloud). &nbsp

Presented at the FAIR-IMPACT kick-off meeting. Introduces the EOSC4Cancer projects and potential synergies with FAIR-IMPACT work.

The Orthology Benchmark Service (https://orthology.benchmarkservice.org) is the gold standard for orthology inference evaluation, supported and maintained by the Quest for Orthologs consortium. It is an essential resource to compare existing and new methods of orthology inference (the bedrock for many comparative genomics and phylogenetic analysis) over a standard dataset and through common procedures. The Quest for Orthologs Consortium is dedicated to maintaining the resource up to date, through regular updates of the Reference Proteomes and increasingly accessible data through the OpenEBench platform. For this update, we have added a new benchmark based on curated orthology assertion from the Vertebrate Gene Nomenclature Committee, and provided an example meta-analysis of the public predictions present on the platform.

Next Generation Sequencing technologies significantly impact the field of Antimicrobial Resistance (AMR) detection and monitoring, with immediate uses in diagnosis and risk assessment. For this application and in general, considerable challenges remain in demonstrating sufficient trust to act upon the meaningful information produced from raw data, partly because of the reliance on bioinformatics pipelines, which can produce different results and therefore lead to different interpretations. With the constant evolution of the field, it is difficult to identify, harmonise and recommend specific methods for large-scale implementations over time. In this article, we propose to address this challenge through establishing a transparent, performance-based, evaluation approach to provide flexibility in the bioinformatics tools of choice, while demonstrating proficiency in meeting common performance standards. The approach is two-fold: first, a community-driven effort to establish and maintain “live” (dynamic) benchmarking platforms to provide relevant performance metrics, based on different use-cases, that would evolve together with the AMR field; second, agreed and defined datasets to allow the pipelines’ implementation, validation, and quality-control over time. Following previous discussions on the main challenges linked to this approach, we provide concrete recommendations and future steps, related to different aspects of the design of benchmarks, such as the selection and the characteristics of the datasets (quality, choice of pathogens and resistances, etc.), the evaluation criteria of the pipelines, and the way these resources should be deployed in the community.

COVID-19 is an infectious disease caused by the SARS-CoV-2 virus, which has spread all over the world leading to a global pandemic. The fast progression of COVID-19 has been mainly related to the high contagion rate of the virus and the worldwide mobility of humans. In the absence of pharmacological therapies, governments from different countries have introduced several non-pharmaceutical interventions to reduce human mobility and social contact. Several studies based on Anonymised Mobile Phone Data have been published analysing the relationship between human mobility and the spread of coronavirus. However, to our knowledge, none of these data-sets integrates cross-referenced geo-localised data on human mobility and COVID-19 cases into one all-inclusive open resource. Herein we present COVID-19 Flow-Maps, a cross-referenced Geographic Information System that integrates regularly updated time-series accounting for population mobility and daily reports of COVID-19 cases in Spain at different scales of time spatial resolution. This integrated and up-to-date data-set can be used to analyse the human dynamics to guide and support the design of more effective non-pharmaceutical interventions.

Background: Many types of data from genomic analyses can be represented as genomic tracks, i.e. features linked to the genomic coordinates of a reference genome. Examples of such data are epigenetic DNA methylation data, ChIP-seq peaks, germline or somatic DNA variants, as well as RNA-seq expression levels. Researchers often face difficulties in locating, accessing and combining relevant tracks from external sources, as well as locating the raw data, reducing the value of the generated information. Description of work: We propose to advance the application of FAIR data principles (Findable, Accessible, Interoperable, and Reusable) to produce searchable metadata for genomic tracks. Findability and Accessibility of metadata can then be ensured by a track search service that integrates globally identifiable metadata from various track hubs in the Track Hub Registry and other relevant repositories. Interoperability and Reusability need to be ensured by the specification and implementation of a basic set of recommendations for metadata. We have tested this concept by developing such a specification in a JSON Schema, called FAIRtracks, and have integrated it into a novel track search service, called TrackFind. We demonstrate practical usage by importing datasets through TrackFind into existing examples of relevant analytical tools for genomic tracks: EPICO and the GSuite HyperBrowser. Conclusion: We here provide a first iteration of a draft standard for genomic track metadata, as well as the accompanying software ecosystem. It can easily be adapted or extended to future needs of the research community regarding data, methods and tools, balancing the requirements of both data submitters and analytical end-users.

Web-version: https://w3id.org/ro/crate/1.1 This document specifies a method, known as RO-Crate (Research Object Crate), of aggregating and describing research data with associated metadata. RO-Crates can aggregate and describe any resource including files, URI-addressable resources, or use other addressing schemes to locate digital or physical data. RO-Crates can describe data in aggregate and at the individual resource level, with metadata to aid in discovery, re-use and long term management of data. Metadata includes the ability to describe the context of data and the entities involved in its production, use and reuse. For example: who created it, using which equipment, software and workflows, under what licenses can it be re-used, where was it collected, and/or where is it about. RO-Crate uses JSON-LD to to express this metadata using linked data, describing data resources as well as contextual entities such as people, organizations, software and equipment as a series of linked JSON-LD objects - using common published vocabularies, chiefly schema.org. The core of RO-Crate is a JSON-LD file, the RO-Crate Metadata File, named ro-crate-metadata.json. This file contains structured metadata about the dataset as a whole (the Root Data Entity) and, optionally, about some or all of its files. This provides a simple way to, for example, assert the authors (e.g. people, organizations) of the RO-Crate or one its files, or to capture more complex provenance for files, such as how they were created using software and equipment. While providing the formal specification for RO-Crate, this document also aims to be a practical guide for software authors to create tools for generating and consuming research data packages, with explanation by examples.

COVID-19 is an infectious disease caused by the SARS-CoV-2 virus, which has spread all over the world leading to a global pandemic. The fast progression of COVID-19 has been mainly related to the high contagion rate of the virus and the worldwide mobility of humans. In the absence of pharmacological therapies, governments from different countries have introduced several non-pharmaceutical interventions to reduce human mobility and social contact. Several studies based on Anonymized Mobile Phone Data have been published analysing the relationship between human mobility and the spread of coronavirus. However, to our knowledge, none of these data-sets integrates cross-referenced geo-localised data on human mobility and COVID-19 cases into one all-inclusive open resource. Herein we present COVID-19 Flow-Maps, a cross-referenced Geographic Information System that integrates regularly updated time-series accounting for population mobility and daily reports of COVID-19 cases in Spain at different scales of time spatial resolution. This integrated and up-to-date data-set can be used to analyse the human dynamics to guide and support the design of more effective non-pharmaceutical interventions., Measurement(s)COVID-19 cases • mobility • populationTechnology Type(s)Real Time PCR • mobile phoneSample Characteristic - OrganismHomo sapiens • Severe acute respiratory syndrome-related coronavirusSample Characteristic - LocationKingdom of Spain Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.15198123

PhylomeDB is a unique knowledge base providing public access to minable and browsable catalogues of pre-computed genome-wide collections of annotated sequences, alignments and phylogenies (i.e. phylomes) of homologous genes, as well as to their corresponding phylogeny-based orthology and paralogy relationships. In addition, PhylomeDB trees and alignments can be downloaded for further processing to detect and date gene duplication events, infer past events of inter-species hybridization and horizontal gene transfer, as well as to uncover footprints of selection, introgression, gene conversion, or other relevant evolutionary processes in the genes and organisms of interest. Here, we describe the latest evolution of PhylomeDB (version 5). This new version includes a newly implemented web interface and several new functionalities such as optimized searching procedures, the possibility to create user-defined phylome collections, and a fully redesigned data structure. This release also represents a significant core data expansion, with the database providing access to 534 phylomes, comprising over 8 million trees, and homology relationships for genes in over 6000 species. This makes PhylomeDB the largest and most comprehensive public repository of gene phylogenies. PhylomeDB is available at http://www.phylomedb.org. Spanish Ministry of Science and Innovation (MICINN) [PGC2018-099921-B-I00], cofounded by European Regional Development Fund (ERDF); Catalan Research Agency (AGAUR) [SGR423]; European Union's Horizon 2020 research and innovation programme [ERC-2016–724173]; Gordon and Betty Moore Foundation [GBMF9742]; Instituto de Salud Carlos III [INB Grant PT17/0009/0023 – ISCIII-SGEFI/ERDF]; U.C. was funded in part through H2020 Marie Skłodowska-Curie Actions [H2020-MSCA-IF-2017-793699]; MICINN [IJC2019- 039402-I]. Funding for open access charge: ERC.

Web-version: https://w3id.org/ro/crate/1.1 This document specifies a method, known as RO-Crate (Research Object Crate), of organizing file-based data with associated metadata, using linked data principles, in both human and machine readable formats, with the ability to include additional domain-specific metadata. The core of RO-Crate is a JSON-LD file, the RO-Crate Metadata File, named ro-crate-metadata.json. This file contains structured metadata about the dataset as a whole (the Root Data Entity) and, optionally, about some or all of its files. This provides a simple way to, for example, assert the authors (e.g. people, organizations) of the RO-Crate or one its files, or to capture more complex provenance for files, such as how they were created using software and equipment. While providing the formal specification for RO-Crate, this document also aims to be a practical guide for software authors to create tools for generating and consuming research data packages, with explanation by examples.

Copy number variations (CNVs) are major causative contributors both in the genesis of genetic diseases and human neoplasias. While "High-Throughput" sequencing technologies are increasingly becoming the primary choice for genomic screening analysis, their ability to efficiently detect CNVs is still heterogeneous and remains to be developed. The aim of this white paper is to provide a guiding framework for the future contributions of ELIXIR's recently established h uman CNV Community, with implications beyond human disease diagnostics and population genomics. This white paper is the direct result of a strategy meeting that took place in September 2018 in Hinxton (UK) and involved representatives of 11 ELIXIR Nodes. The meeting led to the definition of priority objectives and tasks, to address a wide range of CNV-related challenges ranging from detection and interpretation to sharing and training. Here, we provide suggestions on how to align these tasks within the ELIXIR Platforms strategy, and on how to frame the activities of this new ELIXIR Community in the international context. The first ELIXIR hCNV Community meeting held in Hinxton (UK) was supported by ELIXIR. The authors declare that no grants were involved in supporting this work

Web-version: https://w3id.org/ro/crate/1.0 This document specifies a method, known as RO-Crate (Research Object Crate), of organizing file-based data with associated metadata, using linked data principles, in both human and machine readable formats, with the ability to include additional domain-specific metadata. The core of RO-Crate is a JSON-LD file, the RO-Crate Metadata File, named ro-crate-metadata.jsonld. This file contains structured metadata about the dataset as a whole (the Root Data Entity) and, optionally, about some or all of its files. This provides a simple way to, for example, assert the authors (e.g. people, organizations) of the RO-Crate or one its files, or to capture more complex provenance for files, such as how they were created using software and equipment. While providing the formal specification for RO-Crate, this document also aims to be a practical guide for software authors to create tools for generating and consuming research data packages, with explanation by examples.

Human genomics is undergoing a step change from being a predominantly research-driven activity to one driven through health care as many countries in Europe now have nascent precision medicine programmes. To maximize the value of the genomic data generated, these data will need to be shared between institutions and across countries. In recognition of this challenge, 21 European countries recently signed a declaration to transnationally share data on at least 1 million human genomes by 2022. In this Roadmap, we identify the challenges of data sharing across borders and demonstrate that European research infrastructures are well-positioned to support the rapid implementation of widespread genomic data access.

Web-version: https://w3id.org/ro/crate/0.2 This document specifies a method, known as RO-Crate (Research Object Crate), of organizing file-based data with associated metadata, using linked data principles, in both human and machine readable formats, with the ability to include additional domain-specific metadata. The core of RO-Crate is a JSON-LD file, the RO-Crate Metadata File, named ro-crate-metadata.jsonld. This file contains structured metadata about the dataset as a whole (the Root Data Entity) and, optionally, about some or all of its files. This provides a simple way to, for example, assert the authors (e.g. people, organizations) of the RO-Crate or one its files, or to capture more complex provenance for files, such as how they were created using software and equipment. While providing the formal specification for RO-Crate, this document also aims to be a practical guide for software authors to create tools for generating and consuming research data packages, with explanation by examples.

Rendón-Anaya, Martha et. al., [Background] Modern civilization depends on only a few plant species for its nourishment. These crops were derived via several thousands of years of human selection that transformed wild ancestors into high-yielding domesticated descendants. Among cultivated plants, common bean (Phaseolus vulgaris L.) is the most important grain legume. Yet, our understanding of the origins and concurrent shaping of the genome of this crop plant is limited., [Results] We sequenced the genomes of 29 accessions representing 12 Phaseolus species. Single nucleotide polymorphism-based phylogenomic analyses, using both the nuclear and chloroplast genomes, allowed us to detect a speciation event, a finding further supported by metabolite profiling. In addition, we identified ~1200 protein coding genes (PCGs) and ~100 long non-coding RNAs with domestication-associated haplotypes. Finally, we describe asymmetric introgression events occurring among common bean subpopulations in Mesoamerica and across hemispheres., [Conclusions] We uncover an unpredicted speciation event in the tropical Andes that gave rise to a sibling species, formerly considered the “wild ancestor” of P. vulgaris, which diverged before the split of the Mesoamerican and Andean P. vulgaris gene pools. Further, we identify haplotypes strongly associated with genes underlying the emergence of domestication traits. Our findings also reveal the capacity of a predominantly autogamous plant to outcross and fix loci from different populations, even from distant species, which led to the acquisition by domesticated beans of adaptive traits from wild relatives. The occurrence of such adaptive introgressions should be exploited to accelerate breeding programs in the near future., This work was supported by the Ibero-American Programme for Science, Technology and Development - CYTED (PhasIbeAm project); Spanish Government - Ministry of Economy and Competitiveness (EUI2009-04052, BIO2011-26205), “Centro de Excelencia Severo Ochoa 2013-2017,” SEV-2012-0208 and Project PT13/0001/0021 (ISCIII - Subdirección General de Evaluación y Fomento de la Investigación/FEDER “Una Manera de hacer Europa”); Brazilian Government—National Council for Scientific and Technological Development - CNPq/Prosul (490725/2010-4); Ministerio de Ciencia, Tecnología e Innovación Productiva de la República Argentina; Consejo Nacional de Ciencia y Tecnología - Conacyt, Mexico (J010-214-2009, Fronteras 2015-2/814); and U.S. government: USDA National Institute of Food and Agriculture (2013-67013-21224. M. R-A. and J.M.M.-V. are indebted to Conacyt for a doctoral fellowship.

Here, we report the draft genome sequence of Solanum commersonii, which consists of ∼830 megabases with an N50 of 44,303 bp anchored to 12 chromosomes, using the potato (Solanum tuberosum) genome sequence as a reference. Compared with potato, S. commersonii shows a striking reduction in heterozygosity (1.5% versus 53 to 59%), and differences in genome sizes were mainly due to variations in intergenic sequence length. Gene annotation by ab initio prediction supported by RNA-seq data produced a catalog of 1703 predicted microRNAs, 18,882 long noncoding RNAs of which 20% are shown to target cold-responsive genes, and 39,290 protein-coding genes with a significant repertoire of nonredundant nucleotide binding site-encoding genes and 126 cold-related genes that are lacking in S. tuberosum. Phylogenetic analyses indicate that domesticated potato and S. commersonii lineages diverged ∼2.3 million years ago. Three duplication periods corresponding to genome enrichment for particular gene families related to response to salt stress, water transport, growth, and defense response were discovered. The draft genome sequence of S. commersonii substantially increases our understanding of the domesticated germplasm, facilitating translation of acquired knowledge into advances in crop stability in light of global climate and environmental changes.

The dependence of life scientists on software has steadily grown in recent years. For many tasks, researchers have to decide which of the available bioinformatics software are more suitable for their specific needs. Additionally researchers should be able to objectively select the software that provides the highest accuracy, the best efficiency and the highest level of reproducibility when integrated in their research projects.Critical benchmarking of bioinformatics methods, tools and web services is therefore an essential community service, as well as a critical component of reproducibility efforts. Unbiased and objective evaluations are challenging to set up and can only be effective when built and implemented around community driven efforts, as demonstrated by the many ongoing community challenges in bioinformatics that followed the success of CASP. Community challenges bring the combined benefits of intense collaboration, transparency and standard harmonization. Only open systems for the continuous evaluation of methods offer a perfect complement to community challenges, offering to larger communities of users that could extend far beyond the community of developers, a window to the developments status that they can use for their specific projects. We understand by continuous evaluation systems as those services which are always available and periodically update their data and/or metrics according to a predefined schedule keeping in mind that the performance has to be always seen in terms of each research domain.We argue here that technology is now mature to bring community driven benchmarking efforts to a higher level that should allow effective interoperability of benchmarks across related methods. New technological developments allow overcoming the limitations of the first experiences on online benchmarking e.g. EVA. We therefore describe OpenEBench, a novel infra-structure designed to establish a continuous automated benchmarking system for bioinformatics methods, tools and web services.OpenEBench is being developed so as to cater for the needs of the bioinformatics community, especially software developers who need an objective and quantitative way to inform their decisions as well as the larger community of end-users, in their search for unbiased and up-to-date evaluation of bioinformatics methods. As such OpenEBench should soon become a central place for bioinformatics software developers, community-driven benchmarking initiatives, researchers using bioinformatics methods, and funders interested in the result of methods evaluation.

Appropriate benchmark design is a requirement for assessing and improving bioinformatics methods and tools, and needs of best practices to pass over from raw data to valuable knowledge for decision-making. In order to build a continuous automated benchmarking infrastructure which hosts different - emerging or existing - benchmark efforts, bioinformatics tools and data-types, it is crucial to discuss storage, analysis, comparison and sharing of large heterogeneous data sets. Finally, the most appropriate format to communicate results by either exposing them to third parties resources e.g. tools registries, and/or directly via a web-portal. The increasing complexity of the constantly growing body of biological data e.g. unstructured description of resources, non-standardized input and output formats, lack of appropriate metadata and/or deprecated software source codes, represents a tremendous challenge. Thus, we must develop new technical solutions or adapt existing ones to leverage existing data and to better prepare for future challenges around the scientific, technical and functional evaluation of bioinformatics methods and tools. Deliverable 2.1. defines the data warehouse infrastructure needed to host different benchmark initiatives from a broad range of bioinformatics fields, provides a reference implementation, and oversee the overall ELIXIR Tools and Data Services Registry integration and operation. On this report we will examine the following aspects of a data warehouse: Background about the need of an automated benchmark infrastructure. An architecture overview with an emphasis in the data warehouse as central component. An explanation about the database warehouse design and implementation. A final review about future works in the context of the benchmark infrastructure.

Phaseolus vulgaris is the most important legume species for human nourishment. However, until very recently genomics resources for this plant have been scarce, which preventing fully understanding the parallel domestications occurred at two geographical regions: Mesoamerica and Andes. The first reference genome for P. vulgaris, the Andean landrace G19833, was published in 2014, followed in 2016 by the Mesoamerican reference genome, the breeding line BAT93. These resources have allowed elucidating the evolutionary trajectory of P. vulgaris as species, and of both gene pools. First, it has been possible to confirm that the common bean has not undergone a specific whole genome duplication event similarly to the one of Glycine max around ~12 million years ago. Second, there is a high degree of concordance between both gene pools in terms of gene content and evolutionary profiles. This includes also the pattern of specialization of gene expression profiles across different relative evolutionary ages. We confirmed the trend observed for the Mesoamerican genome: retained duplicated genes tend to specialize their expression profiles overtime. New analyses using available transcriptomic data gene co-expression networks for both gene pools have been generated and compared for this review in order to look for commonalities and differences. Genes associated to photosynthesis and to response to different stresses account for the largest modules of these networks, although some differences were detected which may have roles in the domestication syndrome of both gene pools. However, more sequencing data are needed to a better understanding of common bean genome function and to deepen on the domestication processes of both gene pools. It is expected that third generation sequencing technologies will play an important role in those efforts, leading to better genome assemblies and gene-sets. This will focus further efforts on improving breeding lines while keeping genetic diversity of landraces and wild accessions of P. vulgaris.

International audience; To better determine the history of modern birds, we performed a genome-scale phylogenetic analysis of 48 species representing all orders of Neoaves using phylogenomic methods created to handle genome-scale data. We recovered a highly resolved tree that confirms previously controversial sister or close relationships. We identified the first divergence in Neoaves, two groups we named Passerea and Columbea, representing independent lineages of diverse and convergently evolved land and water bird species. Among Passerea, we infer the common ancestor of core landbirds to have been an apex predator and confirm independent gains of vocal learning. Among Columbea, we identify pigeons and flamingoes as belonging to sister clades. Even with whole genomes, some of the earliest branches in Neoaves proved challenging to resolve, which was best explained by massive protein-coding sequence convergence and high levels of incomplete lineage sorting that occurred during a rapid radiation after the Cretaceous-Paleogene mass extinction event about 66 million years ago.

The Table 1 (EU declaration signatory and membership status) of the original article published online contained several errors: the IARC was incorrectly listed in the table, which only includes countries, and footnote; Croatia, Lithuania, Poland and Portugal were not listed as full EMBL members; Estonia is now listed as a Prospect Member of EMBL; Hungary, Ireland and Israel were incorrectly listed as BBMRI-ERIC members; Iceland was omitted from the table. Hyphens stand for ‘not applicable’; the table and footnote have been corrected accordingly. These errors have now been corrected online and in print.

Phylogenetic trees representing the evolutionary relationships of homologous genes are the entry point for many evolutionary analyses. For instance, the use of a phylogenetic tree can aid in the inference of orthology and paralogy relationships, and in the detection of relevant evolutionary events such as gene family expansions and contractions, horizontal gene transfer, recombination or incomplete lineage sorting. Similarly, given the plurality of evolutionary histories among genes encoded in a given genome, there is a need for the combined analysis of genome-wide collections of phylogenetic trees (phylomes). Here, we introduce a new release of PhylomeDB (http://phylomedb.org), a public repository of phylomes. Currently, PhylomeDB hosts 120 public phylomes, comprising >1.5 million maximum likelihood trees and multiple sequence alignments. In the current release, phylogenetic trees are annotated with taxonomic, protein-domain arrangement, functional and evolutionary information. PhylomeDB is also a major source for phylogeny-based predictions of orthology and paralogy, covering >10 million proteins across 1059 sequenced species. Here we describe newly implemented PhylomeDB features, and discuss a benchmark of the orthology predictions provided by the database, the impact of proteome updates and the use of the phylome approach in the analysis of newly sequenced genomes and transcriptomes. Spanish ministry of Economy and Competitiveness [BIO2012-37161]; a Grant from the Qatar National/nResearch Fund [NPRP 5-298-3-086]; a the European Research Council under the European Union’s Seventh/nFramework Programme [FP/2007-2013/ERC and ERC-/n2012-StG-310325]; Juan de La Cierva postdoctoral program (to J.H.C.) and La Caixa-CRG International Fellowship Program (to L.P.P.). Funding for open access charge: Internal budget from the CRG

Motivation: Multiple sequence alignments are generally reconstructed using a progressive approach that follows a guide-tree. During this process, gaps are introduced at a cost to maximize residue pairing, but it is unclear whether inferred gaps reflect actual past events of sequence insertions or deletions. It has been found that patterns of inferred gaps in alignments contain information towards the true phylogeny, but it is as yet unknown whether gaps are simply reflecting information that was already present in the guide-tree. Results: We here develop a framework to disentangle the phylogenetic signal carried by gaps from that which is already present in the guide-tree. Our results indicate that most gaps are incorrectly inserted in patterns that, nevertheless, follow the guide-tree. Thus, most gap patterns in current alignments are not informative per se. This affects different programs to various degrees, PRANK being the most sensitive to the guide-tree. Contact: tgabaldon@crg.es Supplementary information: Supplementary data are available at Bioinformatics online.

Achieving high accuracy in orthology inference is essential for many comparative, evolutionary and functional genomic analyses, yet the true evolutionary history of genes is generally unknown and orthologs are used for very different applications across phyla, requiring different precision–recall trade-offs. As a result, it is difficult to assess the performance of orthology inference methods. Here, we present a community effort to establish standards and an automated web-based service to facilitate orthology benchmarking. Using this service, we characterize 15 well-established inference methods and resources on a battery of 20 different benchmarks. Standardized benchmarking provides a way for users to identify the most effective methods for the problem at hand, sets a minimum requirement for new tools and resources, and guides the development of more accurate orthology inference methods., Nature Methods, 13 (5), ISSN:1548-7105, ISSN:1548-7091