The 22,000-year-old cave painting of an Atlantic salmon (Salmo salar) near the Vézère River in France is a reminder of our fascination with, and dependence on, Atlantic salmon throughout human history. Atlantic salmon belongs to the salmonid lineage which comprises 11 genera, with at least 70 species that exhibit a wide range of ecological adaptations and use a variety of marine and freshwater life history strategies 1 . Salmonids hold important positions as socially iconic species and economic resources within aquaculture, wild fisheries and recreational sport fisheries. Moreover, they serve as key indicator species of the health of North Atlantic and Pacific coastal and river ecosystems.All teleosts share at least three rounds of whole-genome duplication (WGD), 1R and 2R before the divergence of lamprey from the jawed vertebrates 2 , and a third teleost-specific WGD (Ts3R) at the base of the teleosts ~320 million years ago (Mya) [3][4][5] . Very little is known about the mechanisms of genomic and chromosomal reorganization after WGD in vertebrates because the 1R, 2R and Ts3R occurred so long ago that few clear signatures of post-WGD reorganization events remain. In contrast, a fourth WGD (the Ss4R salmonid-specific autotetraploidization event) occurred in the common ancestor of salmonids ~80 Mya after their divergence from Esociformes ~125 Mya 6-8 (Fig. 1), and the continued presence of multivalent pairing at meiosis and evidence of tetrasomic inheritance in salmonid species suggests that diploidy is not yet fully re-established 6,9,10 . Salmonids thus appear to provide an unprecedented opportunity for studying vertebrate genome evolution after an autotetraploid WGD 11,12 over a time period that is long enough to reveal long-term evolutionary patterns, but short enough to give a high-resolution picture of the process. In addition, they provide an excellent setting for contextualizing genome evolution with a dramatic post-WGD species radiation and intricate adaptations to a whole range of life history regimes.Here we present a high-quality reference genome assembly of the Atlantic salmon, and use it to describe major patterns characterizing the post-Ss4R salmonid genome evolution over the past 80 million years (Myr). Our results challenge the recent claim that rediploidization in salmonids has been a gradual process unlinked to significant genome rearrangements 13 . They also challenge current views about the relative importance of sub-and neofunctionalization in vertebrate genomes (reviewed in ref. 14), and the importance of dosage balance as a gene duplicate retention mechanism 15 . Genome characterizationThe Atlantic salmon reference genome assembly (GenBank: GCA_000233375.4) adds up to 2.97 gigabases (Gb) with aThe whole-genome duplication 80 million years ago of the common ancestor of salmonids (salmonid-specific fourth vertebrate whole-genome duplication, Ss4R) provides unique opportunities to learn about the evolutionary fate of a duplicated vertebrate genome in 70 extant lineages. Here we present a high...

COnstraint-Based Reconstruction and Analysis (COBRA) provides a molecular mechanistic framework for integrative analysis of experimental data and quantitative prediction of physicochemically and biochemically feasible phenotypic states. The COBRA Toolbox is a comprehensive software suite of interoperable COBRA methods. It has found widespread applications in biology, biomedicine, and biotechnology because its functions can be flexibly combined to implement tailored COBRA protocols for any biochemical network. Version 3.0 includes new methods for quality controlled reconstruction, modelling, topological analysis, strain and experimental design, network visualisation as well as network integration of chemoinformatic, metabolomic, transcriptomic, proteomic, and thermochemical data. New multi-lingual code integration also enables an expansion in COBRA application scope via high-precision, high-performance, and nonlinear numerical optimisation solvers for multi-scale, multi-cellular and reaction kinetic modelling, respectively. This protocol can be adapted for the generation and analysis of a constraint-based model in a wide variety of molecular systems biology scenarios. This protocol is an update to the COBRA Toolbox 1.0 and 2.0. The COBRA Toolbox 3.0 provides an unparalleled depth of constraint-based reconstruction and analysis methods. ]); 61 | The MUST sets are the sets of reactions that must increase or decrease their flux in order to achieve the desired phenotype in the mutant strain. As shown in Figure 6, the first order MUST sets are MustU and MustL while second order MUST sets are denoted as MustUU, MustLL, and MustUL. After parameters and constraints are defined, the functions findMustL and findMustU are run to determine the mustU and mustL sets, respectively. Define an ID of the run with:Each time the MUST sets are determined, folders are generated to read inputs and store outputs, i.e., reports. These folders are located in the directory defined by the uniquely defined runID.62 | In order to find the first order MUST sets, constraints should be defined: >> constrOpt = struct('rxnList', {{'EX_gluc', 'R75', 'EX_suc'}}, 'values', [-100; 0; 155.5]); 63 | The first order MUST set MustL is determined by running: >> [mustLSet, pos_mustL] = findMustL(model, minFluxesW, maxFluxesW, ... 'constrOpt', constrOpt, 'runID', runID);If runID is set to 'TestoptForceL', a folder TestoptForceL is created, in which two additional folders InputsMustL and OutputsMustL are created. The InputsMustL folder contains all the inputs required to run the function findMustL, while the OutputsMustL folder contains the mustL set found and a report that summarises all the inputs and outputs. In order to maintain a chronological order of computational experiments, the report is timestamped.64 | Display the reactions that belong to the mustL set using: >> disp(mustLSet) 65 | The first order MUST set MustU is determined by running: >> [mustUSet, pos_mustU] = findMustU(model, minFluxesW, maxFluxesW, ... 'constrOpt', constrOpt, 'runID', runID);...

The BioMart Community Portal (www.biomart.org) is a community-driven effort to provide a unified interface to biomedical databases that are distributed worldwide. The portal provides access to numerous database projects supported by 30 scientific organizations. It includes over 800 different biological datasets spanning genomics, proteomics, model organisms, cancer data, ontology information and more. All resources available through the portal are independently administered and funded by their host organizations. The BioMart data federation technology provides a unified interface to all the available data. The latest version of the portal comes with many new databases that have been created by our ever-growing community. It also comes with better support and extensibility for data analysis and visualization tools. A new addition to our toolbox, the enrichment analysis tool is now accessible through graphical and web service interface. The BioMart community portal averages over one million requests per day. Building on this level of service and the wealth of information that has become available, the BioMart Community Portal has introduced a new, more scalable and cheaper alternative to the large data stores maintained by specialized organizations.

The genome of potato, a major global food crop, was recently sequenced. The work presented here details the integration of the potato reference genome (DM) with a new sequence-tagged site marker−based linkage map and other physical and genetic maps of potato and the closely related species tomato. Primary anchoring of the DM genome assembly was accomplished by the use of a diploid segregating population, which was genotyped with several types of molecular genetic markers to construct a new ~936 cM linkage map comprising 2469 marker loci. In silico anchoring approaches used genetic and physical maps from the diploid potato genotype RH89-039-16 (RH) and tomato. This combined approach has allowed 951 superscaffolds to be ordered into pseudomolecules corresponding to the 12 potato chromosomes. These pseudomolecules represent 674 Mb (~93%) of the 723 Mb genome assembly and 37,482 (~96%) of the 39,031 predicted genes. The superscaffold order and orientation within the pseudomolecules are closely collinear with independently constructed high density linkage maps. Comparisons between marker distribution and physical location reveal regions of greater and lesser recombination, as well as regions exhibiting significant segregation distortion. The work presented here has led to a greatly improved ordering of the potato reference genome superscaffolds into chromosomal “pseudomolecules”.

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