Identifying orthologs with OMA: A primer.

The Orthologous Matrix (OMA) is a method and database that allows users to identify orthologs among many genomes. OMA provides three different types of orthologs: pairwise orthologs, OMA Groups and Hierarchical Orthologous Groups (HOGs). This Primer is organized in two parts. In the first part, we provide all the necessary background information to understand the concepts of orthology, how we infer them and the different subtypes of orthology in OMA, as well as what types of analyses they should be used for. In the second part, we describe protocols for using the OMA browser to find a specific gene and its various types of orthologs. By the end of the Primer, readers should be able to (i) understand homology and the different types of orthologs reported in OMA, (ii) understand the best type of orthologs to use for a particular analysis; (iii) find particular genes of interest in the OMA browser; and (iv) identify orthologs for a given gene. The data can be freely accessed from the OMA browser at https://omabrowser.org

Identifying orthologs with OMA: A primer [version 1; peer review: 2 approved]

The Orthologous Matrix (OMA) is a method and database that allows users to identify orthologs among many genomes. OMA provides three different types of orthologs: pairwise orthologs, OMA Groups and Hierarchical Orthologous Groups (HOGs). This Primer is organized in two parts. In the first part, we provide all the necessary background information to understand the concepts of orthology, how we infer them and the different subtypes of orthology in OMA, as well as what types of analyses they should be used for. In the second part, we describe protocols for using the OMA browser to find a specific gene and its various types of orthologs. By the end of the Primer, readers should be able to (i) understand homology and the different types of orthologs reported in OMA, (ii) understand the best type of orthologs to use for a particular analysis; (iii) find particular genes of interest in the OMA browser; and (iv) identify orthologs for a given gene.  The data can be freely accessed from the OMA browser at https://omabrowser.org

A hands-on introduction to querying evolutionary relationships across multiple data sources using SPARQL [version 1; peer review: 1 approved, 2 approved with reservations]

The increasing use of Semantic Web technologies in the life sciences, in particular the use of the Resource Description Framework (RDF) and the RDF query language SPARQL, opens the path for novel integrative analyses, combining information from multiple sources. However, analyzing evolutionary data in RDF is not trivial, due to the steep learning curve required to understand both the data models adopted by different RDF data sources, as well as the SPARQL query language. In this article, we provide a hands-on introduction to querying evolutionary data across multiple sources that publish orthology information in RDF, namely: The Orthologous MAtrix (OMA), the European Bioinformatics Institute (EBI) RDF platform, the Database of Orthologous Groups (OrthoDB) and the Microbial Genome Database (MBGD). We present four protocols in increasing order of complexity. In these protocols, we demonstrate through SPARQL queries how to retrieve pairwise orthologs, homologous groups, and hierarchical orthologous groups. Finally, we show how orthology information in different sources can be compared, through the use of federated SPARQL queries

The Use of a Stringent Selection System Allows the Identification of DNA Elements that Augment Gene Expression

The use of high stringency selection systems often results in the induction of very few recombinant mammalian cell lines, which limits the ability to isolate a cell line with favorable characteristics. The employment of for instance STAR elements in DNA constructs elevates the induced number of colonies and also the protein expression levels in these colonies. Here, we describe a method to systematically identify genomic DNA elements that are able to induce many stably transfected mammalian cell lines. We isolated genomic DNA fragments upstream from the human Rb1 and p73 gene loci and cloned them around an expression cassette that contains a very stringent selection marker. Due to the stringency of the selection marker, hardly any colony survives without flanking DNA elements. We tested fourteen ~3500 bp DNA stretches from the Rb1 and p73 loci. Only two ~3500 bp long DNA fragments, called Rb1E and Rb1F, induced many colonies in the context of the stringent selection system and these colonies displayed high protein expression levels. Functional analysis showed that the Rb1 DNA fragments contained no enhancer, promoter, or STAR activity. Our data show the potential of a methodology to identify novel gene expression augmenting DNA elements in an unbiased manner

Meiotic Recombination Hotspots of Fission Yeast Are Directed to Loci that Express Non-Coding RNA

Polyadenylated, mRNA-like transcripts with no coding potential are abundant in eukaryotes, but the functions of these long non-coding RNAs (ncRNAs) are enigmatic. In meiosis, Rec12 (Spo11) catalyzes the formation of dsDNA breaks (DSBs) that initiate homologous recombination. Most meiotic recombination is positioned at hotspots, but knowledge of the mechanisms is nebulous. In the fission yeast genome DSBs are located within 194 prominent peaks separated on average by 65-kbp intervals of DNA that are largely free of DSBs.). Furthermore, we tested and rejected the hypothesis that the ncRNA loci and DSB peaks localize preferentially, but independently, to a third entity on the chromosomes.Meiotic DSB hotspots are directed to loci that express polyadenylated ncRNAs. This reveals an unexpected, possibly unitary mechanism for what directs meiotic recombination to hotspots. It also reveals a likely biological function for enigmatic ncRNAs. We propose specific mechanisms by which ncRNA molecules, or some aspect of RNA metabolism associated with ncRNA loci, help to position recombination protein complexes at DSB hotspots within chromosomes

Use of the chicken lysozyme 5' matrix attachment region to generate high producer CHO cell lines.

Scaffold or matrix attachment region (S/MAR) genetic elements have previously been proposed to insulate transgenes from repressive effects linked to their site of integration within the host cell genome. We have evaluated their use in various stable transfection settings to increase the production of recombinant proteins such as monoclonal antibodies from Chinese hamster ovary (CHO) cell lines. Using the green fluorescent protein coding sequence, we show that S/MAR elements mediate a dual effect on the population of transfected cells. First, S/MAR elements almost fully abolish the occurrence of cell clones that express little transgene that may result from transgene integration in an unfavorable chromosomal environment. Second, they increase the overall expression of the transgene over the whole range of expression levels, allowing the detection of cells with significantly higher levels of transgene expression. An optimal setting was identified as the addition of a S/MAR element both in cis (on the transgene expression vector) and in trans (co-transfected on a separate plasmid). When used to express immunoglobulins, the S/MAR element enabled cell clones with high and stable levels of expression to be isolated following the analysis of a few cell lines generated without transgene amplification procedures