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Recent progress in mass spectrometry (MS) lipidomics has led to a rapid proliferation of studies across biology and biomedicine and facilitating important discoveries. These generate extremely large raw datasets requiring sophisticated tailored solutions to support automated data processing. To address this, numerous software tools have been developed, tailored for specific tasks in the data analysis pipeline. However, for researchers, deciding which approach best suits their application relies on ad hoc testing, which is inefficient and time-consuming. Here, we first review the data processing pipeline, summarizing the number and scope of available tools. Next, to support researchers, LIPID MAPS provides an interactive online portal listing open-access tools with a graphical user interface. This guides users towards appropriate solutions within major areas in data processing including (1) lipid-oriented databases, (2) MS data repositories, (3) analysis of targeted lipidomics datasets, (4) lipid identification, and (5) quantification from untargeted lipidomics datasets, (6) statistical analysis and visualization, and (7) data integration solutions. Detailed descriptions of functions and requirements are provided to guide customized data analysis workflows.

Ensembl (https://www.ensembl.org) is unique in its flexible infrastructure for access to genomic data and annotation. It has been designed to efficiently deliver annotation at scale for all eukaryotic life, and it also provides deep comprehensive annotation for key species. Genomes representing a greater diversity of species are increasingly being sequenced. In response, we have focussed our recent efforts on expediting the annotation of new assemblies. Here, we report the release of the greatest annual number of newly annotated genomes in the history of Ensembl via our dedicated Ensembl Rapid Release platform (http://rapid.ensembl.org). We have also developed a new method to generate comparative analyses at scale for these assemblies and, for the first time, we have annotated non-vertebrate eukaryotes. Meanwhile, we continually improve, extend and update the annotation for our high-value reference vertebrate genomes and report the details here. We have a range of specific software tools for specific tasks, such as the Ensembl Variant Effect Predictor (VEP) and the newly developed interface for the Variant Recoder. All Ensembl data, software and tools are freely available for download and are accessible programmatically.

Ensembl (https://www.ensembl.org) has produced high-quality genomic resources for vertebrates and model organisms for more than twenty years. During that time, our resources, services and tools have continually evolved in line with both the publicly available genome data and the downstream research and applications that utilise the Ensembl platform. In recent years we have witnessed a dramatic shift in the genomic landscape. There has been a large increase in the number of high-quality reference genomes through global biodiversity initiatives. In parallel, there have been major advances towards pangenome representations of higher species, where many alternative genome assemblies representing different breeds, cultivars, strains and haplotypes are now available. In order to support these efforts and accelerate downstream research, it is our goal at Ensembl to create high-quality annotations, tools and services for species across the tree of life. Here, we report our resources for popular reference genomes, the dramatic growth of our annotations (including haplotypes from the first human pangenome graphs), updates to the Ensembl Variant Effect Predictor (VEP), interactive protein structure predictions from AlphaFold DB, and the beta release of our new website.

The Ensembl project (https://www.ensembl.org) annotates genomes and disseminates genomic data for vertebrate species. We create detailed and comprehensive annotation of gene structures, regulatory elements and variants, and enable comparative genomics by inferring the evolutionary history of genes and genomes. Our integrated genomic data are made available in a variety of ways, including genome browsers, search interfaces, specialist tools such as the Ensembl Variant Effect Predictor, download files and programmatic interfaces. Here, we present recent Ensembl developments including two new website portals. Ensembl Rapid Release (http://rapid.ensembl.org) is designed to provide core tools and services for genomes as soon as possible and has been deployed to support large biodiversity sequencing projects. Our SARS-CoV-2 genome browser (https://covid-19.ensembl.org) integrates our own annotation with publicly available genomic data from numerous sources to facilitate the use of genomics in the international scientific response to the COVID-19 pandemic. We also report on other updates to our annotation resources, tools and services. All Ensembl data and software are freely available without restriction.

The lipid envelope of SARS-CoV2 is an essential component of the virus, however its molecular composition is unknown. Addressing this knowledge gap could support the design of anti-viral agents, and further understanding of viral interaction with extracellular host proteins, infectivity, pathogenicity, and innate immune system clearance. Lipidomics analysis of SARS-CoV2 particles generated from Vero or A549 cells revealed that the virus envelope comprised mainly of phospholipids (PL), primarily phosphatidylcholine (PC), phosphatidylethanolamine (PE) and phosphatidylinositol (PI), with very little cholesterol, sphingolipids or other lipids, indicating significant differences from host membranes. Unlike healthy cellular membranes, procoagulant aminoPL (aPL), specifically PE and phosphatidylserine (PS), were present on the external side at levels far exceeding those seen on activated platelets. As a result, purified virions directly promoted coagulation. To investigate whether these differences enabled the viral envelope to be selectively targeted at relevant sites in vivo, we tested whether non-toxic oral rinses containing lipid disrupting chemicals could reduce viral infectivity. Products containing PL-disrupting surfactant solutions (cetylpyridinium chloride (CPC) or ethyl lauroyl arginate) met EN14476 virucidal standards in vitro, however products containing essential oils, PVP-I, or Chlorhexidine did not, nor did rinses containing components that altered the critical micelle concentration of CPC. This result was recapitulated in vivo, where a 30-second oral rinse with CPC-mouthwash eliminated live virus in the oral cavity of COVID19 patients for at least 1hr, while PVP-Iodine and saline mouthwashes were ineffective. Thus, the SARS-CoV2 lipid envelope is distinct from the host plasma membrane which may enable design of selective anti-viral approaches, it exposes PE and PS which may influence thrombosis, pathogenicity, and inflammation, and can be selectively targeted in vivo by specific oral rinses.

Ensembl (https://www.ensembl.org) has produced high-quality genomic resources for vertebrates and model organisms for more than twenty years. During that time, our resources, services and tools have continually evolved in line with both the publicly available genome data and the downstream research and applications that utilise the Ensembl platform. In recent years we have witnessed a dramatic shift in the genomic landscape. There has been a large increase in the number of high-quality reference genomes through global biodiversity initiatives. In parallel, there have been major advances towards pangenome representations of higher species, where many alternative genome assemblies representing different breeds, cultivars, strains and haplotypes are now available. In order to support these efforts and accelerate downstream research, it is our goal at Ensembl to create high-quality annotations, tools and services for species across the tree of life. Here, we report our resources for popular reference genomes, the dramatic growth of our annotations (including haplotypes from the first human pangenome graphs), updates to the Ensembl Variant Effect Predictor (VEP), interactive protein structure predictions from AlphaFold DB, and the beta release of our new website.

Ensembl (https://www.ensembl.org) is unique in its flexible infrastructure for access to genomic data and annotation. It has been designed to efficiently deliver annotation at scale for all eukaryotic life, and it also provides deep comprehensive annotation for key species. Genomes representing a greater diversity of species are increasingly being sequenced. In response, we have focussed our recent efforts on expediting the annotation of new assemblies. Here, we report the release of the greatest annual number of newly annotated genomes in the history of Ensembl via our dedicated Ensembl Rapid Release platform (http://rapid.ensembl.org). We have also developed a new method to generate comparative analyses at scale for these assemblies and, for the first time, we have annotated non-vertebrate eukaryotes. Meanwhile, we continually improve, extend and update the annotation for our high-value reference vertebrate genomes and report the details here. We have a range of specific software tools for specific tasks, such as the Ensembl Variant Effect Predictor (VEP) and the newly developed interface for the Variant Recoder. All Ensembl data, software and tools are freely available for download and are accessible programmatically.

Ensembl Genomes (http://www.ensemblgenomes.org) is an integrating resource for genome-scale data from non-vertebrate species, complementing the resources for vertebrate genomics developed in the context of the Ensembl project (http://www.ensembl.org). Together, the two resources provide a consistent set of interfaces to genomic data across the tree of life, including reference genome sequence, gene models, transcriptional data, genetic variation and comparative analysis. Data may be accessed via our website, online tools platform and programmatic interfaces, with updates made four times per year (in synchrony with Ensembl). Here, we provide an overview of Ensembl Genomes, with a focus on recent developments. These include the continued growth, more robust and reproducible sets of orthologues and paralogues, and enriched views of gene expression and gene function in plants. Finally, we report on our continued deeper integration with the Ensembl project, which forms a key part of our future strategy for dealing with the increasing quantity of available genome-scale data across the tree of life.

The Ensembl project (https://www.ensembl.org) annotates genomes and disseminates genomic data for vertebrate species. We create detailed and comprehensive annotation of gene structures, regulatory elements and variants, and enable comparative genomics by inferring the evolutionary history of genes and genomes. Our integrated genomic data are made available in a variety of ways, including genome browsers, search interfaces, specialist tools such as the Ensembl Variant Effect Predictor, download files and programmatic interfaces. Here, we present recent Ensembl developments including two new website portals. Ensembl Rapid Release (http://rapid.ensembl.org) is designed to provide core tools and services for genomes as soon as possible and has been deployed to support large biodiversity sequencing projects. Our SARS-CoV-2 genome browser (https://covid-19.ensembl.org) integrates our own annotation with publicly available genomic data from numerous sources to facilitate the use of genomics in the international scientific response to the COVID-19 pandemic. We also report on other updates to our annotation resources, tools and services. All Ensembl data and software are freely available without restriction.

We present LipidFinder 2.0, incorporating four new modules that apply artefact filters, remove lipid and contaminant stacks, in-source fragments and salt clusters, and a new isotope deletion method which is significantly more sensitive than available open-access alternatives. We also incorporate a novel false discovery rate (FDR) method, utilizing a target-decoy strategy, which allows users to assess data quality. A renewed lipid profiling method is introduced which searches three different databases from LIPID MAPS and returns bulk lipid structures only, and a lipid category scatter plot with color blind friendly pallet. An API interface with XCMS Online is made available on LipidFinder’s online version. We show using real data that LipidFinder 2.0 provides a significant improvement over non-lipid metabolite filtering and lipid profiling, compared to available tools.AvailabilityLipidFinder 2.0 is freely available at https://github.com/ODonnell-Lipidomics/LipidFinder and http://lipidmaps.org/resources/tools/lipidfinder.Contactlipidfinder@cardiff.ac.ukSupplementary informationSupplementary data are available at Bioinformatics online.

Supplemental Digital Content is available in the text., Background: Common chromosome 9p21 single nucleotide polymorphisms (SNPs) increase coronary heart disease risk, independent of traditional lipid risk factors. However, lipids comprise large numbers of structurally related molecules not measured in traditional risk measurements, and many have inflammatory bioactivities. Here, we applied lipidomic and genomic approaches to 3 model systems to characterize lipid metabolic changes in common Chr9p21 SNPs, which confer ≈30% elevated coronary heart disease risk associated with altered expression of ANRIL, a long ncRNA. Methods: Untargeted and targeted lipidomics was applied to plasma from NPHSII (Northwick Park Heart Study II) homozygotes for AA or GG in rs10757274, followed by correlation and network analysis. To identify candidate genes, transcriptomic data from shRNA downregulation of ANRIL in HEK-293 cells was mined. Transcriptional data from vascular smooth muscle cells differentiated from induced pluripotent stem cells of individuals with/without Chr9p21 risk, nonrisk alleles, and corresponding knockout isogenic lines were next examined. Last, an in-silico analysis of miRNAs was conducted to identify how ANRIL might control lysoPL (lysophosphospholipid)/lysoPA (lysophosphatidic acid) genes. Results: Elevated risk GG correlated with reduced lysoPLs, lysoPA, and ATX (autotaxin). Five other risk SNPs did not show this phenotype. LysoPL-lysoPA interconversion was uncoupled from ATX in GG plasma, suggesting metabolic dysregulation. Significantly altered expression of several lysoPL/lysoPA metabolizing enzymes was found in HEK cells lacking ANRIL. In the vascular smooth muscle cells data set, the presence of risk alleles associated with altered expression of several lysoPL/lysoPA enzymes. Deletion of the risk locus reversed the expression of several lysoPL/lysoPA genes to nonrisk haplotype levels. Genes that were altered across both cell data sets were DGKA, MBOAT2, PLPP1, and LPL. The in-silico analysis identified 4 ANRIL-regulated miRNAs that control lysoPL genes as miR-186-3p, miR-34a-3p, miR-122-5p, and miR-34a-5p. Conclusions: A Chr9p21 risk SNP associates with complex alterations in immune-bioactive phospholipids and their metabolism. Lipid metabolites and genomic pathways associated with coronary heart disease pathogenesis in Chr9p21 and ANRIL-associated disease are demonstrated.

Contribution: Participation in the conception the work and performed data analysis; International audience; The life cycle of spirochetes of the genus Borrelia includes complex networks of vertebrates and ticks. The tripartite association of Borrelia-vertebrate-tick has proved ecologically successful for these bacteria, which have become some of the most prominent tick-borne pathogens in the northern hemisphere. To keep evolutionary pace with its double-host life history, Borrelia must adapt to the evolutionary pressures exerted by both sets of hosts. In this review, we attempt to reconcile functional, phylogenetic, and ecological perspectives to propose a coherent scenario of Borrelia evolution. Available empirical information supports that the association of Borrelia with ticks is very old. The major split between the tick families Argasidae-Ixodidae (dated some 230-290 Mya) resulted in most relapsing fever (Rf) species being restricted to Argasidae and few associated with Ixodidae. A further key event produced the diversification of the Lyme borreliosis (Lb) species: the radiation of ticks of the genus Ixodes from the primitive stock of Ixodidae (around 217 Mya). The ecological interactions of Borrelia demonstrate that Argasidae-transmitted Rf species remain restricted to small niches of one tick species and few vertebrates. The evolutionary pressures on this group are consequently low, and speciation processes seem to be driven by geographical isolation. In contrast to Rf, Lb species circulate in nested networks of dozens of tick species and hundreds of vertebrate species. This greater variety confers a remarkably variable pool of evolutionary pressures, resulting in large speciation of the Lb group, where different species adapt to circulate through different groups of vertebrates. Available data, based on ospA and multilocus sequence typing (including eight concatenated in-house genes) phylogenetic trees, suggest that ticks could constitute a secondary bottleneck that contributes to Lb specialization. Both sets of adaptive pressures contribute to the resilience of highly adaptable meta-populations of bacteria.

Background: Molecular evolution studies involve many different hard computational problems solved, in most cases, with heuristic algorithms that provide a nearly optimal solution. Hence, diverse software tools exist for the different stages involved in a molecular evolution workflow. Results: We present MEvoLib, the first molecular evolution library for Python, providing a framework to work with different tools and methods involved in the common tasks of molecular evolution workflows. In contrast with already existing bioinformatics libraries, MEvoLib is focused on the stages involved in molecular evolution studies, enclosing the set of tools with a common purpose in a single high-level interface with fast access to their frequent parameterizations. The gene clustering from partial or complete sequences has been improved with a new method that integrates accessible external information (e.g. GenBank''s features data). Moreover, MEvoLib adjusts the fetching process from NCBI databases to optimize the download bandwidth usage. In addition, it has been implemented using parallelization techniques to cope with even large-case scenarios. Conclusions: MEvoLib is the first library for Python designed to facilitate molecular evolution researches both for expert and novel users. Its unique interface for each common task comprises several tools with their most used parameterizations. It has also included a method to take advantage of biological knowledge to improve the gene partition of sequence datasets. Additionally, its implementation incorporates parallelization techniques to enhance computational costs when handling very large input datasets.

The wide availability of sequenced biological data has challenged the conventional methods and tools used in molecular biology to compute the conservation index. As the size of input datasets increases, the time-cost of current conservation methods is becoming unafTordable. We propose a new software tool that combines several estimation methods applied to the conservation computation process with parallelization and divide-and-conquer techniques substantially improving its performance without affecting its accuracy. We have also made an in-depth analysis of the impact of different methods and parameter selection on the alignment process applied to input datasets prior to their conservation analysis. We have used sets of mitochondrial DNA sequences with different levels of heterogeneity and length, to provide a full case study. Both the software tool and the input datasets used in this research are freely available at http://www.zaramit.orglconservation_index

Sequencing errors can be difficult to detect due to the high rate of production of new data, which makes manual curation unfeasible. To address these shortcomings we have developed a phylogenetic inspired algorithm to assess the quality of new sequences given a related phylogeny. Its performance and efficiency have been evaluated with human mitochondrial DNA data.

Background Several methods have been developed to predict the pathogenicity of missense mutations but none has been specifically designed for classification of variants in mtDNA-encoded polypeptides. Moreover, there is not available curated dataset of neutral and damaging mtDNA missense variants to test the accuracy of predictors. Because mtDNA sequencing of patients suffering mitochondrial diseases is revealing many missense mutations, it is needed to prioritize candidate substitutions for further confirmation. Predictors can be useful as screening tools but their performance must be improved. Results We have developed a SVM classifier (Mitoclass.1) specific for mtDNA missense variants. Training and validation of the model was executed with 2,835 mtDNA damaging and neutral amino acid substitutions, previously curated by a set of rigorous pathogenicity criteria with high specificity. Each instance is described by a set of three attributes based on evolutionary conservation in Eukaryota of wildtype and mutant amino acids as well as coevolution and a novel evolutionary analysis of specific substitutions belonging to the same domain of mitochondrial polypeptides. Our classifier has performed better than other web-available tested predictors. We checked performance of three broadly used predictors with the total mutations of our curated dataset. PolyPhen-2 showed the best results for a screening proposal with a good sensitivity. Nevertheless, the number of false positive predictions was too high. Our method has an improved sensitivity and better specificity in relation to PolyPhen-2. We also publish predictions for the complete set of 24,201 possible missense variants in the 13 human mtDNA-encoded polypeptides. Conclusions Mitoclass.1 allows a better selection of candidate damaging missense variants from mtDNA. A careful search of discriminatory attributes and a training step based on a curated dataset of amino acid substitutions belonging exclusively to human mtDNA genes allows an improved performance. Mitoclass.1 accuracy could be improved in the future when more mtDNA missense substitutions will be available for updating the attributes and retraining the model. Electronic supplementary material The online version of this article (doi:10.1186/s12859-017-1562-7) contains supplementary material, which is available to authorized users.

Since the creation of the bioinformatics field, and even more since the creation of the so called nextgeneration sequencing, the relevance of computer methods and technologies has significantly increased. We here present several contributions we have published as solution for different problems involved in the study of the evolution process.

La filogenetica es la ciencia que estudia las relaciones entre organismos basandose en su proximidad evolutiva. La forma mas visual y conveniente de representar estas relaciones es a traves de los arboles filogeneticos. El analisis filogenetico es un proceso formado por distintas etapas cuya finalidad es poder reconstruir dichos arboles. Estas etapas pueden incluir: estudio de modelos evolutivos (modelos matematicos que intentan explicar de la forma mas fiel posible la evolucion real de los organismos), analisis estadistico, alineamiento de secuencias,... Actualmente el coste computacional limita de forma practica tanto la realizacion de filogenias extensivas (tratando miles y decenas de miles de secuencias) como la aplicacion de modelos evolutivos mas generales, interesantes y explicativos que el modelo uniforme (usado en tamanos de problema reducidos). Por otro lado, los procesos de secuenciacion de cadenas biologicas no estan exentos de errores, los cuales pueden aparecer en cualquier lugar de la secuencia. Hasta la fecha, todo proceso de verificacion requiere la actuacion manual de un experto en el campo, lo que es un proceso muy costoso. El trabajo aborda tres aspectos especificos: i) el desarrollo e implementacion de un sistema de inferencia filogenetica que concentra varios metodos sobre analisis de secuencias y estudio de filogenias que no se habian unido hasta el momento; ii) el desarrollo e implementacion de una aplicacion para la deteccion automatica de errores en las cadenas obtenidas en los procesos de secuenciacion; iii) el estudio teorico de nuevos algoritmos para la caracterizacion de problemas entre aquellos que se consideran como no resolubles en la actualidad.