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Here the Human Pangenome Reference Consortium presents a first draft of the human pangenome reference. The pangenome contains 47 phased, diploid assemblies from a cohort of genetically diverse individuals1. These assemblies cover more than 99% of the expected sequence in each genome and are more than 99% accurate at the structural and base pair levels. Based on alignments of the assemblies, we generate a draft pangenome that captures known variants and haplotypes and reveals new alleles at structurally complex loci. We also add 119 million base pairs of euchromatic polymorphic sequences and 1,115 gene duplications relative to the existing reference GRCh38. Roughly 90 million of the additional base pairs are derived from structural variation. Using our draft pangenome to analyse short-read data reduced small variant discovery errors by 34% and increased the number of structural variants detected per haplotype by 104% compared with GRCh38-based workflows, which enabled the typing of the vast majority of structural variant alleles per sample.

Reconstructing ancestral gene orders is an important step towards understanding genome evolution. The Small Parsimony Problem (SPP) has been extensively studied in this regard. The problem aims at finding the gene orders at internal nodes of a given phylogenetic tree such that the overall genome rearrangement distance along the tree branches is minimized. However, this problem is intractable in most genome rearrangement models, especially when gene duplication and loss are considered. In this work, we describe an Integer Linear Program algorithm to solve the SPP for natural genomes, i.e., genomes that contain conserved, unique, and duplicated markers. The evolutionary model that we consider is the DCJ-indel model that includes the Double-Cut and Join rearrangement operation and the insertion and deletion of genome segments. We evaluate our algorithm on simulated data and show that it is able to reconstruct very efficiently and accurately ancestral gene orders in a very comprehensive evolutionary model.

Recently, we proposed an efficient ILP formulation [Rubert DP, Martinez FV, Braga MDV, Natural family-free genomic distance, Algorithms Mol Biol 16:4, 2021] for exactly computing the rearrangement distance of two genomes in a family-free setting. In such a setting, neither prior classification of genes into families, nor further restrictions on the genomes are imposed. Given two genomes, the mentioned ILP computes an optimal matching of the genes taking into account simultaneously local mutations, given by gene similarities, and large-scale genome rearrangements. Here, we explore the potential of using this ILP for inferring groups of orthologs across several species. More precisely, given a set of genomes, our method first computes all pairwise optimal gene matchings, which are then integrated into gene families in the second step. Our approach is implemented into a pipeline incorporating the pre-computation of gene similarities. It can be downloaded from gitlab.ub.uni-bielefeld.de/gi/FFGC. We obtained promising results with experiments on both simulated and real data.

The dramatic decrease in time and cost for generating genetic sequence data has opened up vast opportunities in molecular systematics, one of which is the ability to decipher the evolutionary history of strains of a species. Under this fine systematic resolution, the standard markers are too crude to provide a phylogenetic signal. Nevertheless, among prokaryotes, genome dynamics in the form of horizontal gene transfer (HGT) between organisms and gene loss seem to provide far richer information by affecting both gene order and gene content. The “synteny index” (SI) between a pair of genomes combines these latter two factors, allowing comparison of genomes with unequal gene content, together with order considerations of their common genes. Although this approach is useful for classifying close relatives, no rigorous statistical modeling for it has been suggested. Such modeling is valuable, as it allows observed measures to be transformed into estimates of time periods during evolution, yielding the “additivity” of the measure. To the best of our knowledge, there is no other additivity proof for other gene order/content measures under HGT. Here, we provide a first statistical model and analysis for the SI measure. We model the “gene neighborhood” as a “birth–death–immigration” process affected by the HGT activity over the genome, and analytically relate the HGT rate and time to the expected SI. This model is asymptotic and thus provides accurate results, assuming infinite size genomes. Therefore, we also developed a heuristic model following an “exponential decay” function, accounting for biologically realistic values, which performed well in simulations. Applying this model to 1,133 prokaryotes partitioned to 39 clusters by the rank of genus yields that the average number of genome dynamics events per gene in the phylogenetic depth of genus is around half with significant variability between genera. This result extends and confirms similar results obtained for individual genera in different manners.

The computation of genomic distances has been a very active field of computational comparative genomics over the past 25 years. Substantial results include the polynomial-time computability of the inversion distance by Hannenhalli and Pevzner in 1995 and the introduction of the double cut and join distance by Yancopoulos et al. in 2005. Both results, however, rely on the assumption that the genomes under comparison contain the same set of unique markers (syntenic genomic regions, sometimes also referred to as genes). In 2015, Shao et al. relax this condition by allowing for duplicate markers in the analysis. This generalized version of the genomic distance problem is NP-hard, and they give an integer linear programming (ILP) solution that is efficient enough to be applied to real-world datasets. A restriction of their approach is that it can be applied only to balanced genomes that have equal numbers of duplicates of any marker. Therefore, it still needs a delicate preprocessing of the input data in which excessive copies of unbalanced markers have to be removed. In this article, we present an algorithm solving the genomic distance problem for natural genomes, in which any marker may occur an arbitrary number of times. Our method is based on a new graph data structure, the multi-relational diagram, that allows an elegant extension of the ILP by Shao et al. to count runs of markers that are under- or over-represented in one genome with respect to the other and need to be inserted or deleted, respectively. With this extension, previous restrictions on the genome configurations are lifted, for the first time enabling an uncompromising rearrangement analysis. Any marker sequence can directly be used for the distance calculation. The evaluation of our approach shows that it can be used to analyze genomes with up to a few 10,000 markers, which we demonstrate on simulated and real data.

Current methods for synteny analysis provide only limited support to study large genomes at the sequence level. In this chapter, we describe a pipeline based on existing tools that, applied in a suitable fashion, enables synteny analysis of large genomic datasets. We give a hands-on description of each step of the pipeline using four avian genomes for data. We also provide integration scripts that simplify the conversion and setup of data between the different tools in the pipeline.

The comparison of genome structures across distinct species offers valuable insights into the species' phylogeny, genome organization, and gene associations. In this chapter, we review the family-free genome comparison tool FFGC which provides several methods for gene order analyses that do not require prior knowledge of evolutionary relationships between the genes across the studied genomes. Moreover, the tool features a complete workflow for genome comparison, requiring nothing but annotated genome sequences as input.

Current methods for synteny analysis provide only limited support to study large genomes at the sequence level. In this chapter, we describe a pipeline based on existing tools that, applied in a suitable fashion, enables synteny analysis of large genomic datasets. We give a hands-on description of each step of the pipeline using four avian genomes for data. We also provide integration scripts that simplify the conversion and setup of data between the different tools in the pipeline.

Yersinia pestis is the causative agent of the bubonic plague, a disease responsible for several dramatic historical pandemics. Progress in ancient DNA (aDNA) sequencing rendered possible the sequencing of whole genomes of important human pathogens, including the ancient Y. pestis strains responsible for outbreaks of the bubonic plague in London in the 14th century and in Marseille in the 18th century, among others. However, aDNA sequencing data are still characterized by short reads and non-uniform coverage, so assembling ancient pathogen genomes remains challenging and often prevents a detailed study of genome rearrangements. It has recently been shown that comparative scaffolding approaches can improve the assembly of ancient Y. pestis genomes at a chromosome level. In the present work, we address the last step of genome assembly, the gap-filling stage. We describe an optimization-based method AGapEs (ancestral gap estimation) to fill in inter-contig gaps using a combination of a template obtained from related extant genomes and aDNA reads. We show how this approach can be used to refine comparative scaffolding by selecting contig adjacencies supported by a mix of unassembled aDNA reads and comparative signal. We applied our method to two Y. pestis data sets from the London and Marseilles outbreaks, for which we obtained highly improved genome assemblies for both genomes, comprised of, respectively, five and six scaffolds with 95 % of the assemblies supported by ancient reads. We analysed the genome evolution between both ancient genomes in terms of genome rearrangements, and observed a high level of synteny conservation between these strains.

Yersinia pestis is the causative agent of the bubonic plague, a disease responsible for several dramatic historical pandemics. Progress in ancient DNA (aDNA) sequencing rendered possible the sequencing of whole genomes of important human pathogens, including the ancient Y. pestis strains responsible for outbreaks of the bubonic plague in London in the 14th century and in Marseille in the 18th century, among others. However, aDNA sequencing data are still characterized by short reads and non-uniform coverage, so assembling ancient pathogen genomes remains challenging and often prevents a detailed study of genome rearrangements. It has recently been shown that comparative scaffolding approaches can improve the assembly of ancient Y. pestis genomes at a chromosome level. In the present work, we address the last step of genome assembly, the gap-filling stage. We describe an optimization-based method AGapEs (ancestral gap estimation) to fill in inter-contig gaps using a combination of a template obtained from related extant genomes and aDNA reads. We show how this approach can be used to refine comparative scaffolding by selecting contig adjacencies supported by a mix of unassembled aDNA reads and comparative signal. We applied our method to two Y. pestis data sets from the London and Marseilles outbreaks, for which we obtained highly improved genome assemblies for both genomes, comprised of, respectively, five and six scaffolds with 95 % of the assemblies supported by ancient reads. We analysed the genome evolution between both ancient genomes in terms of genome rearrangements, and observed a high level of synteny conservation between these strains.

Yersinia pestis is the causative agent of the bubonic plague, a disease responsible for several dramatic historical pandemics. Progress in ancient DNA (aDNA) sequencing rendered possible the sequencing of whole genomes of important human pathogens, including the ancient Yersinia pestis strains responsible for outbreaks of the bubonic plague in London in the 14th century and in Marseille in the 18th century among others. However, aDNA sequencing data are still characterized by short reads and non-uniform coverage, so assembling ancient pathogen genomes remains challenging and prevents in many cases a detailed study of genome rearrangements. It has recently been shown that comparative scaffolding approaches can improve the assembly of ancient Yersinia pestis genomes at a chromosome level. In the present work, we address the last step of genome assembly, the gap-filling stage. We describe an optimization-based method AGapEs (Ancestral Gap Estimation) to fill in inter-contig gaps using a combination of a template obtained from related extant genomes and aDNA reads. We show how this approach can be used to refine comparative scaffolding by selecting contig adjacencies supported by a mix of unassembled aDNA reads and comparative signal. We apply our method to two data sets from the London and Marseilles outbreaks of the bubonic plague. We obtain highly improved genome assemblies for both the London strain and Marseille strain genomes, comprised of respectively five and six scaffolds, with 95% of the assemblies supported by ancient reads. We analyze the genome evolution between both ancient genomes in terms of genome rearrangements, and observe a high level of synteny conservation between these two strains.

Background The gene family-free framework for comparative genomics aims at providing methods for gene order analysis that do not require prior gene family assignment, but work directly on a sequence similarity graph. We study two problems related to the breakpoint median of three genomes, which asks for the construction of a fourth genome that minimizes the sum of breakpoint distances to the input genomes. Methods We present a model for constructing a median of three genomes in this family-free setting, based on maximizing an objective function that generalizes the classical breakpoint distance by integrating sequence similarity in the score of a gene adjacency. We study its computational complexity and we describe an integer linear program (ILP) for its exact solution. We further discuss a related problem called family-free adjacencies for k genomes for the special case of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$k \le 3$$\end{document}k≤3 and present an ILP for its solution. However, for this problem, the computation of exact solutions remains intractable for sufficiently large instances. We then proceed to describe a heuristic method, FFAdj-AM, which performs well in practice. Results The developed methods compute accurate positional orthologs for genomes comparable in size of bacterial genomes on simulated data and genomic data acquired from the OMA orthology database. In particular, FFAdj-AM performs equally or better when compared to the well-established gene family prediction tool MultiMSOAR. Conclusions We study the computational complexity of a new family-free model and present algorithms for its solution. With FFAdj-AM, we propose an appealing alternative to established tools for identifying higher confidence positional orthologs. Electronic supplementary material The online version of this article (doi:10.1186/s13015-017-0106-z) contains supplementary material, which is available to authorized users.

While therapeutic cell transplantations using progenitor cells are increasingly evolving towards phase I and II clinical trials and chemically defined cell culture is established, standardization in biobanking is still in the stage of infancy. In this study, the EU FP6-funded CRYSTAL (CRYo-banking of Stem cells for human Therapeutic AppLication) consortium aimed to validate novel Standard Operating Procedures (SOPs) to perform and validate xeno-free and chemically defined cryopreservation of human progenitor cells and to reduce the amount of the potentially toxic cryoprotectant additive (CPA) dimethyl sulfoxide (DMSO). To achieve this goal, three human adult progenitor and stem cell populations—umbilical cord blood (UCB)-derived erythroid cells (UCB-ECs), UCB-derived endothelial colony forming cells (UCB-ECFCs), and adipose tissue (AT)-derived mesenchymal stromal cells (AT-MSCs)—were cryopreserved in chemically defined medium supplemented with 10% or 5% DMSO. Cell recovery, cell repopulation, and functionality were evaluated postthaw in comparison to cryopreservation in standard fetal bovine serum (FBS)-containing freezing medium. Even with a reduction of the DMSO CPA to 5%, postthaw cell count and viability assays indicated no overall significant difference versus standard cryomedium. Additionally, to compare cellular morphology/membrane integrity and ice crystal formation during cryopreservation, multiphoton laser-scanning cryomicroscopy (cryo-MPLSM) and scanning electron microscopy (SEM) were used. Neither cryo-MPLSM nor SEM indicated differences in membrane integrity for the tested cell populations under various conditions. Moreover, no influence was observed on functional properties of the cells following cryopreservation in chemically defined freezing medium, except for UCB-ECs, which showed a significantly reduced differentiation capacity after cryopreservation in chemically defined medium supplemented with 5% DMSO. In summary, these results demonstrate the feasibility and robustness of standardized xeno-free cryopreservation of different human progenitor cells and encourage their use even more in the field of tissue-engineering and regenerative medicine.

International audience; In this study we demonstrate a new non-invasive imaging method for monitoring freezing processes in biological samples and to investigate life in the frozen state. It combines an laser scanning microscope with a computer-controlled cryo-stage. Near infrared femtosecond laser pulses evoke the fluorescence of endogenous fluorophores and fluorescent labels due to multiphoton absorption. The inherent optical nonlinearity of multiphoton absorption allows 3D fluorescence imaging for optical tomography of frozen biological material in situ. As an example for functional imaging we use fluorescence lifetime imaging (FLIM) to create images with chemical and physical contrast.