New genomic signals underlying the emergence of human proto-genes

De novo genes are novel genes which emerge from non-coding DNA. Until now, little is known about de novo genes properties, correlated to their age and mechanisms of emergence. In this study, we investigate four properties: introns, upstream regulatory motifs, 5 prime UTRs and protein domains, in 23135 human proto-genes. We found that proto-genes contain introns, whose number and position correlates with the genomic position of proto-gene emergence. The origin of these introns is debated, as our result suggest that 41% proto-genes might have captured existing introns, as well as the fact that 13.7% of them do not splice the ORF. We show that proto-genes which emerged via overprinting tend to be more enriched in core promotor motifs, while intergenic and intronic ones are more enriched in enhancers, even if the motif TATA is most expressed upstream these genes. Intergenic and intronic 5 prime UTRs of proto-genes have a lower potential to stabilise mRNA structures than exonic proto-genes and established human genes. Finally, we confirm that proto-genes gain new putative domains with age. Overall, we find that regulatory motifs inducing transcription and translation of previously non-coding sequences may facilitate proto-gene emergence. Our paper demonstrates that introns, 5 prime UTRs, and domains have specific properties in proto-genes. We also show the importance of studying proto-genes in relation to their genomic position, as it strongly impacts these properties.

Myths and mechanisms: RecBCD and Chi hotspots as determinants of self vs. non-self

Bacteria face a challenge when DNA enters their cells by transformation, mating, or phage infection. Should they treat this DNA as an invasive foreigner and destroy it, or consider it one of their own and potentially benefit from incorporating new genes or alleles to gain useful functions? It is frequently stated that the short nucleotide sequence Chi (5' GCTGGTGG 3') recognized by RecBCD helicase-nuclease allows Escherichia coli to distinguish self (i.e., E. coli) DNA from non-self (i.e., any other) DNA and to destroy non-self DNA, and that Chi is 'overrepresented' in the E. coli genome. We show here that these dogmas are incorrect and apparently based on false assumptions. We note Chi's wide-spread occurrence and activity in distantly related species. We illustrate multiple, highly non-random features of the genomes of E. coli and coliphage P1 that account for Chi's high frequency and genomic position, leading us to propose that P1 selects for Chi's enhancement of recombination, whereas E. coli selects for the preferred codons in Chi. We discuss other, substantiated mechanisms for self vs. non-self determination involving RecBCD and for RecBCD's destruction of DNA that cannot recombine, whether foreign or domestic.

Ordinary randomness

Chapter 2 addresses how well the biological process of mutation is described by some of the ordinary meanings of “chance“ or “randomness“ in science: lack of purpose or foresight, uniformity (homogeneity), stochasticity, indeterminacy, unpredictability, spontaneity, and independence (chance). Ordinary mutations exhibit various kinds of heterogeneity (nonuniformity), e.g., by genomic position, or by cell-cycle state. The occurrence of mutations is affected by various conditions inside the cell, e.g., the spectrum of replication errors is shaped by the composition of DNA precursor pools. Many of the processes that lead to mutation are spontaneous in the sense of emerging internally, but some processes reflect external effects such as radiation or uptake of foreign DNA. Though most of the processes that lead to mutations are “macroscopic,” some processes (e.g., damage caused by radioactive decay or electromagnetic radiation) implicate quantum indeterminacy.

Detailed temporal dissection of an enhancer cluster reveals two distinct roles for individual elements

Many genes are regulated by multiple enhancers that often simultaneously activate their target gene. Yet, how individual enhancers collaborate to activate transcription is not well understood. Here, we dissect the functions and interdependencies of five enhancer elements that form a previously identified enhancer cluster and activate the Fgf5 locus during exit from naïve murine pluripotency. Four elements are located downstream of the Fgf5 gene and form a super-enhancer. Each of these elements contributes to Fgf5 induction at a distinct time point of differentiation. The fifth element is located in the first intron of the Fgf5 gene and contributes to Fgf5 expression at every time point by amplifying overall Fgf5 expression levels. This amplifier element strongly accumulates paused RNA Polymerase II but does not give rise to a mature Fgf5 mRNA. By transplanting the amplifier to a different genomic position, we demonstrate that it enriches for high levels of paused RNA Polymerase II autonomously. Based on our data, we propose a model for a mechanism by which RNA Polymerase II accumulation at a novel type of enhancer element, the amplifier, contributes to enhancer collaboration.

GenMap: ultra-fast computation of genome mappability

Motivation Computing the uniqueness of k-mers for each position of a genome while allowing for up to e mismatches is computationally challenging. However, it is crucial for many biological applications such as the design of guide RNA for CRISPR experiments. More formally, the uniqueness or (k, e)-mappability can be described for every position as the reciprocal value of how often this k-mer occurs approximately in the genome, i.e. with up to e mismatches. Results We present a fast method GenMap to compute the (k, e)-mappability. We extend the mappability algorithm, such that it can also be computed across multiple genomes where a k-mer occurrence is only counted once per genome. This allows for the computation of marker sequences or finding candidates for probe design by identifying approximate k-mers that are unique to a genome or that are present in all genomes. GenMap supports different formats such as binary output, wig and bed files as well as csv files to export the location of all approximate k-mers for each genomic position. Availability and implementation GenMap can be installed via bioconda. Binaries and C++ source code are available on https://github.com/cpockrandt/genmap.

RumimiR: a detailed microRNA database focused on ruminant species

The ever-increasing use of next-generation sequencing technologies to explore the genome has generated large quantities of data in recent years. Numerous publications have described several thousand sequences of microRNAs, all species included. A new database (RumimiR) has been created from the literature to provide a detailed description of microRNAs for three ruminant species: cattle, goats and sheep. To date, 2887, 2733 and 5095 unique microRNAs from bovine, caprine and ovine species, respectively, are included. In addition to the most recent reference genomic position and sequence of each microRNA, this database contains details about the animals, tissue origins and experimental conditions mentioned in the publications. Identity to human or mouse microRNA is also indicated. The RumimiR database allows data filtering by selecting microRNAs on the basis of defined criteria such as animal status or tissue origin. For ruminant studies, RumimiR supplements the widely used miRBase database, by using complementary criteria to allow browsing and filtering, and integrates all newly described published sequences. The principal goal of this database is to provide easy access to all the ruminant microRNAs described in the literature.

Allele frequency-free inference of close familial relationships from genotypes or low depth sequencing data

Knowledge of how individuals are related is important in many areas of research and numerous methods for inferring pairwise relatedness from genetic data have been developed. However, the majority of these methods were not developed for situations where data is limited. Specifically, most methods rely on the availability of population allele frequencies, the relative genomic position of variants, and accurate genotype data. But in studies of non-model organisms or ancient human samples, such data is not always available. Motivated by this, we present a new method for pairwise relatedness inference, which requires neither allele frequency information nor information on genomic position. Furthermore, it can be applied to both genotype data and to low-depth sequencing data where genotypes cannot be accurately called. We evaluate it using data from SNP arrays and low-depth sequencing from a range of human populations and show that it can be used to infer close familial relationships with a similar accuracy as a widely used method that relies on population allele frequencies. Additionally, we show that our method is robust to SNP ascertainment, which is important for application to a diverse range of populations and species.