Primary orthologs from local sequence context

Whole-genome sequencing and evolutionary analysis of the wild edible mushroom, Morchella eohespera

Morels (Morchella, Ascomycota) are an extremely desired group of edible mushrooms with worldwide distribution. Morchella eohespera is a typical black morel species, belonging to the Elata clade of Morchella species. The biological and genetic studies of this mushroom are rare, largely hindering the studies of molecular breeding and evolutionary aspects. In this study, we performed de novo sequencing and assembly of the M. eohespera strain m200 genome using the third-generation nanopore sequencing platform. The whole-genome size of M. eohespera was 53.81 Mb with a contig N50 of 1.93 Mb, and the GC content was 47.70%. A total of 9,189 protein-coding genes were annotated. Molecular dating showed that M. eohespera differentiated from its relative M. conica at ~19.03 Mya (million years ago) in Burdigalian. Evolutionary analysis showed that 657 gene families were contracted and 244 gene families expanded in M. eohespera versus the related morel species. The non-coding RNA prediction results showed that there were 336 tRNAs, 76 rRNAs, and 45 snRNAs in the M. eohespera genome. Interestingly, there was a high degree of repetition (20.93%) in the M. eohespera genome, and the sizes of long interspersed nuclear elements, short interspersed nuclear elements, and long terminal repeats were 0.83 Mb, 0.009 Mb, and 4.56 Mb, respectively. Additionally, selection pressure analysis identified that a total of 492 genes in the M. eohespera genome have undergone signatures of positive selection. The results of this study provide new insights into the genome evolution of M. eohespera and lay the foundation for in-depth research into the molecular biology of the genus Morchella in the future.

Colorful orthology clustering in bounded-degree similarity graphs

Clustering genes in similarity graphs is a popular approach for orthology prediction. Most algorithms group genes without considering their species, which results in clusters that contain several paralogous genes. Moreover, clustering is known to be problematic when in-paralogs arise from ancient duplications. Recently, we proposed a two-step process that avoids these problems. First, we infer clusters of only orthologs (i.e. with only genes from distinct species), and second, we infer the missing inter-cluster orthologs. In this paper, we focus on the first step, which leads to a problem we call Colorful Clustering. In general, this is as hard as classical clustering. However, in similarity graphs, the number of species is usually small, as well as the neighborhood size of genes in other species. We therefore study the problem of clustering in which the number of colors is bounded by [Formula: see text], and each gene has at most [Formula: see text] neighbors in another species. We show that the well-known cluster editing formulation remains NP-hard even when [Formula: see text] and [Formula: see text]. We then propose a fixed-parameter algorithm in [Formula: see text] to find the single best cluster in the graph. We implemented this algorithm and included it in the aforementioned two-step approach. Experiments on simulated data show that this approach performs favorably to applying only an unconstrained clustering step.

CoreCruncher: Fast and Robust Construction of Core Genomes in Large Prokaryotic Data Sets

The core genome represents the set of genes shared by all, or nearly all, strains of a given population or species of prokaryotes. Inferring the core genome is integral to many genomic analyses, however, most methods rely on the comparison of all the pairs of genomes; a step that is becoming increasingly difficult given the massive accumulation of genomic data. Here, we present CoreCruncher; a program that robustly and rapidly constructs core genomes across hundreds or thousands of genomes. CoreCruncher does not compute all pairwise genome comparisons and uses a heuristic based on the distributions of identity scores to classify sequences as orthologs or paralogs/xenologs. Although it is much faster than current methods, our results indicate that our approach is more conservative than other tools and less sensitive to the presence of paralogs and xenologs. CoreCruncher is freely available from: https://github.com/lbobay/CoreCruncher. CoreCruncher is written in Python 3.7 and can also run on Python 2.7 without modification. It requires the python library Numpy and either Usearch or Blast. Certain options require the programs muscle or mafft.

Ten Years of Collaborative Progress in the Quest for Orthologs

Accurate determination of the evolutionary relationships between genes is a foundational challenge in biology. Homology-evolutionary relatedness-is in many cases readily determined based on sequence similarity analysis. By contrast, whether or not two genes directly descended from a common ancestor by a speciation event (orthologs) or duplication event (paralogs) is more challenging, yet provides critical information on the history of a gene. Since 2009, this task has been the focus of the Quest for Orthologs (QFO) Consortium. The sixth QFO meeting took place in Okazaki, Japan in conjunction with the 67th National Institute for Basic Biology conference. Here, we report recent advances, applications, and oncoming challenges that were discussed during the conference. Steady progress has been made toward standardization and scalability of new and existing tools. A feature of the conference was the presentation of a panel of accessible tools for phylogenetic profiling and several developments to bring orthology beyond the gene unit-from domains to networks. This meeting brought into light several challenges to come: leveraging orthology computations to get the most of the incoming avalanche of genomic data, integrating orthology from domain to biological network levels, building better gene models, and adapting orthology approaches to the broad evolutionary and genomic diversity recognized in different forms of life and viruses.