Periodic pattern detection in sparse boolean sequences

Selection, periodicity and potential function for Highly Iterative Palindrome-1 (HIP1) in cyanobacterial genomes

Highly Iterated Palindrome 1 (HIP1, GCGATCGC) is hyper-abundant in most cyanobacterial genomes. In some cyanobacteria, average HIP1 abundance exceeds one motif per gene. Such high abundance suggests a significant role in cyanobacterial biology. However, 20 years of study have not revealed whether HIP1 has a function, much less what that function might be. We show that HIP1 is 15- to 300-fold over-represented in genomes analyzed. More importantly, HIP1 sites are conserved both within and between open reading frames, suggesting that their overabundance is maintained by selection rather than by continual replenishment by neutral processes, such as biased DNA repair. This evidence for selection suggests a functional role for HIP1. No evidence was found to support a functional role as a peptide or RNA motif or a role in the regulation of gene expression. Rather, we demonstrate that the distribution of HIP1 along cyanobacterial chromosomes is significantly periodic, with periods ranging from 10 to 90 kb, consistent in scale with periodicities reported for co-regulated, co-expressed and evolutionarily correlated genes. The periodicity we observe is also comparable in scale to chromosomal interaction domains previously described in other bacteria. In this context, our findings imply HIP1 functions associated with chromosome and nucleoid structure.

WoPPER: Web server for Position Related data analysis of gene Expression in Prokaryotes

The structural and conformational organization of chromosomes is crucial for gene expression regulation in eukaryotes and prokaryotes as well. Up to date, gene expression data generated using either microarray or RNA-sequencing are available for many bacterial genomes. However, differential gene expression is usually investigated with methods considering each gene independently, thus not taking into account the physical localization of genes along a bacterial chromosome. Here, we present WoPPER, a web tool integrating gene expression and genomic annotations to identify differentially expressed chromosomal regions in bacteria. RNA-sequencing or microarray-based gene expression data are provided as input, along with gene annotations. The user can select genomic annotations from an internal database including 2780 bacterial strains, or provide custom genomic annotations. The analysis produces as output the lists of positionally related genes showing a coordinated trend of differential expression. Graphical representations, including a circular plot of the analyzed chromosome, allow intuitive browsing of the results. The analysis procedure is based on our previously published R-package PREDA. The release of this tool is timely and relevant for the scientific community, as WoPPER will fill an existing gap in prokaryotic gene expression data analysis and visualization tools. WoPPER is open to all users and can be reached at the following URL: https://WoPPER.ba.itb.cnr.it

From multiple pathogenicity islands to a unique organized pathogenicity archipelago

Pathogenicity islands are sets of successive genes in a genome that determine the virulence of a bacterium. In a growing number of studies, bacterial virulence appears to be determined by multiple islands scattered along the genome. This is the case in a family of seven plant pathogens and a human pathogen that, under KdgR regulation, massively secrete enzymes such as pectinases that degrade plant cell wall. Here we show that their multiple pathogenicity islands form together a coherently organized, single “archipelago” at the genome scale. Furthermore, in half of the species, most genes encoding secreted pectinases are expressed from the same DNA strand (transcriptional co-orientation). This genome architecture favors DNA conformations that are conducive to genes spatial co-localization, sometimes complemented by co-orientation. As proteins tend to be synthetized close to their encoding genes in bacteria, we propose that this architecture would favor the efficient funneling of pectinases at convergent points within the cell. The underlying functional hypothesis is that this convergent funneling of the full blend of pectinases constitutes a crucial strategy for successful degradation of the plant cell wall. Altogether, our work provides a new approach to describe and predict, at the genome scale, the full virulence complement.

Analysis tools for the interplay between genome layout and regulation

BACKGROUND

Genome layout and gene regulation appear to be interdependent. Understanding this interdependence is key to exploring the dynamic nature of chromosome conformation and to engineering functional genomes. Evidence for non-random genome layout, defined as the relative positioning of either co-functional or co-regulated genes, stems from two main approaches. Firstly, the analysis of contiguous genome segments across species, has highlighted the conservation of gene arrangement (synteny) along chromosomal regions. Secondly, the study of long-range interactions along a chromosome has emphasised regularities in the positioning of microbial genes that are co-regulated, co-expressed or evolutionarily correlated. While one-dimensional pattern analysis is a mature field, it is often powerless on biological datasets which tend to be incomplete, and partly incorrect. Moreover, there is a lack of comprehensive, user-friendly tools to systematically analyse, visualise, integrate and exploit regularities along genomes.

RESULTS

Here we present the Genome REgulatory and Architecture Tools SCAN (GREAT:SCAN) software for the systematic study of the interplay between genome layout and gene expression regulation.

GREAT

SCAN is a collection of related and interconnected applications currently able to perform systematic analyses of genome regularities as well as to improve transcription factor binding sites (TFBS) and gene regulatory network predictions based on gene positional information.

CONCLUSIONS

We demonstrate the capabilities of these tools by studying on one hand the regular patterns of genome layout in the major regulons of the bacterium Escherichia coli. On the other hand, we demonstrate the capabilities to improve TFBS prediction in microbes. Finally, we highlight, by visualisation of multivariate techniques, the interplay between position and sequence information for effective transcription regulation.

GREAT: a web portal for Genome Regulatory Architecture Tools

GREAT (Genome REgulatory Architecture Tools) is a novel web portal for tools designed to generate user-friendly and biologically useful analysis of genome architecture and regulation. The online tools of GREAT are freely accessible and compatible with essentially any operating system which runs a modern browser. GREAT is based on the analysis of genome layout -defined as the respective positioning of co-functional genes- and its relation with chromosome architecture and gene expression. GREAT tools allow users to systematically detect regular patterns along co-functional genomic features in an automatic way consisting of three individual steps and respective interactive visualizations. In addition to the complete analysis of regularities, GREAT tools enable the use of periodicity and position information for improving the prediction of transcription factor binding sites using a multi-view machine learning approach. The outcome of this integrative approach features a multivariate analysis of the interplay between the location of a gene and its regulatory sequence. GREAT results are plotted in web interactive graphs and are available for download either as individual plots, self-contained interactive pages or as machine readable tables for downstream analysis. The GREAT portal can be reached at the following URL https://absynth.issb.genopole.fr/GREAT and each individual GREAT tool is available for downloading.

Global genomic arrangement of bacterial genes is closely tied with the total transcriptional efficiency

The availability of a large number of sequenced bacterial genomes allows researchers not only to derive functional and regulation information about specific organisms but also to study the fundamental properties of the organization of a genome. Here we address an important and challenging question regarding the global arrangement of operons in a bacterial genome: why operons in a bacterial genome are arranged in the way they are. We have previously studied this question and found that operons of more frequently activated pathways tend to be more clustered together in a genome. Specifically, we have developed a simple sequential distance-based pseudo energy function and found that the arrangement of operons in a bacterial genome tend to minimize the clusteredness function (C value) in comparison with artificially-generated alternatives, for a variety of bacterial genomes. Here we extend our previous work, and report a number of new observations: (a) operons of the same pathways tend to group into a few clusters rather than one; and (b) the global arrangement of these operon clusters tend to minimize a new “energy” function (C⁺ value) that reflects the efficiency of the transcriptional activation of the encoded pathways. These observations provide insights into further study of the genomic organization of genes in bacteria.

PreCisIon: PREdiction of CIS-regulatory elements improved by gene's positION

Conventional approaches to predict transcriptional regulatory interactions usually rely on the definition of a shared motif sequence on the target genes of a transcription factor (TF). These efforts have been frustrated by the limited availability and accuracy of TF binding site motifs, usually represented as position-specific scoring matrices, which may match large numbers of sites and produce an unreliable list of target genes. To improve the prediction of binding sites, we propose to additionally use the unrelated knowledge of the genome layout. Indeed, it has been shown that co-regulated genes tend to be either neighbors or periodically spaced along the whole chromosome. This study demonstrates that respective gene positioning carries significant information. This novel type of information is combined with traditional sequence information by a machine learning algorithm called PreCisIon. To optimize this combination, PreCisIon builds a strong gene target classifier by adaptively combining weak classifiers based on either local binding sequence or global gene position. This strategy generically paves the way to the optimized incorporation of any future advances in gene target prediction based on local sequence, genome layout or on novel criteria. With the current state of the art, PreCisIon consistently improves methods based on sequence information only. This is shown by implementing a cross-validation analysis of the 20 major TFs from two phylogenetically remote model organisms. For Bacillus subtilis and Escherichia coli, respectively, PreCisIon achieves on average an area under the receiver operating characteristic curve of 70 and 60%, a sensitivity of 80 and 70% and a specificity of 60 and 56%. The newly predicted gene targets are demonstrated to be functionally consistent with previously known targets, as assessed by analysis of Gene Ontology enrichment or of the relevant literature and databases.

Genomic organization of evolutionarily correlated genes in bacteria: limits and strategies

The need for efficient molecular interplay in time and space within a cell imposes strong constraints that could be partially relaxed if relative gene positions along chromosomes were appropriate. Comparative genomics studies have demonstrated the short-scale conservation of gene proximity along bacterial chromosomes. Additionally, the long-range periodic positioning of evolutionarily correlated genes within Escherichia coli has recently been highlighted. To gain further insight into these different genetic organizations, we examined the compromise between chromosomal proximity and periodicity for all available eubacterial genomes by evaluating groups of evolutionarily correlated genes from a benchmark data set. In enterobacteria, strict chromosomal proximity is found to be limited to groups under 20 genes, whereas periodicity is significant in all groups over 50. The E. coli K12 genome bears 511 periodic genes (12% of the genome), whose orthologs are found to be periodic in all eubacterial phyla. These periodic genes predominantly function in macromolecular synthesis and spatial organization of cellular components. They are enriched in essential and housekeeping genes and tend to often be constitutively expressed. On this basis, it is argued that chromosomal proximity and periodicity are ubiquitous complementary genomic strategies that favor the build-up of local concentrations of co-functional molecules. In particular, the periodic layout may facilitate chromosome folding to spatially organize the construction of major cell components. The transition at 20 genes is reminiscent of the size of the longest operons and of topological microdomains. The range for which DNA neighborhood optimizes biochemical interactions might therefore be defined by DNA topology.