Frequency-based haplotype reconstruction from deep sequencing data of bacterial populations

Clonal populations accumulate mutations over time, resulting in different haplotypes. Deep sequencing of such a population in principle provides information to reconstruct these haplotypes and the frequency at which the haplotypes occur. However, this reconstruction is technically not trivial, especially not in clonal systems with a relatively low mutation frequency. The low number of segregating sites in those systems adds ambiguity to the haplotype phasing and thus obviates the reconstruction of genome-wide haplotypes based on sequence overlap information.

Therefore, we present EVORhA, a haplotype reconstruction method that complements phasing information in the non-empty read overlap with the frequency estimations of inferred local haplotypes. As was shown with simulated data, as soon as read lengths and/or mutation rates become restrictive for state-of-the-art methods, the use of this additional frequency information allows EVORhA to still reliably reconstruct genome-wide haplotypes. On real data, we show the applicability of the method in reconstructing the population composition of evolved bacterial populations and in decomposing mixed bacterial infections from clinical samples.

SomInaClust: detection of cancer genes based on somatic mutation patterns of inactivation and clustering

Background

With the advances in high throughput technologies, increasing amounts of cancer somatic mutation data are being generated and made available. Only a small number of (driver) mutations occur in driver genes and are responsible for carcinogenesis, while the majority of (passenger) mutations do not influence tumour biology. In this study, SomInaClust is introduced, a method that accurately identifies driver genes based on their mutation pattern across tumour samples and then classifies them into oncogenes or tumour suppressor genes respectively.

Results

SomInaClust starts from the observation that oncogenes mainly contain mutations that, due to positive selection, cluster at similar positions in a gene across patient samples, whereas tumour suppressor genes contain a high number of protein-truncating mutations throughout the entire gene length. The method was shown to prioritize driver genes in 9 different solid cancers. Furthermore it was found to be complementary to existing similar-purpose methods with the additional advantages that it has a higher sensitivity, also for rare mutations (occurring in less than 1% of all samples), and it accurately classifies candidate driver genes in putative oncogenes and tumour suppressor genes. Pathway enrichment analysis showed that the identified genes belong to known cancer signalling pathways, and that the distinction between oncogenes and tumour suppressor genes is biologically relevant.

Conclusions

SomInaClust was shown to detect candidate driver genes based on somatic mutation patterns of inactivation and clustering and to distinguish oncogenes from tumour suppressor genes. The method could be used for the identification of new cancer genes or to filter mutation data for further data-integration purposes.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-015-0555-7) contains supplementary material, which is available to authorized users.

PheNetic: network-based interpretation of molecular profiling data

Molecular profiling experiments have become standard in current wet-lab practices. Classically, enrichment analysis has been used to identify biological functions related to these experimental results. Combining molecular profiling results with the wealth of currently available interactomics data, however, offers the opportunity to identify the molecular mechanism behind an observed molecular phenotype. In this paper, we therefore introduce ‘PheNetic’, a user-friendly web server for inferring a sub-network based on probabilistic logical querying. PheNetic extracts from an interactome, the sub-network that best explains genes prioritized through a molecular profiling experiment. Depending on its run mode, PheNetic searches either for a regulatory mechanism that gave explains to the observed molecular phenotype or for the pathways (in)activated in the molecular phenotype. The web server provides access to a large number of interactomes, making sub-network inference readily applicable to a wide variety of organisms. The inferred sub-networks can be interactively visualized in the browser. PheNetic's method and use are illustrated using an example analysis of differential expression results of ampicillin treated Escherichia coli cells. The PheNetic web service is available at http://bioinformatics.intec.ugent.be/phenetic/.

Vitamin D3 Induces Tolerance in Human Dendritic Cells by Activation of Intracellular Metabolic Pathways

Metabolic switches in various immune cell subsets enforce phenotype and function. In the present study, we demonstrate that the active form of vitamin D, 1,25-dihydroxyvitamin D₃ (1,25(OH)₂D₃), induces human monocyte-derived tolerogenic dendritic cells (DC) by metabolic reprogramming. Microarray analysis demonstrated that 1,25(OH)₂D₃ upregulated several genes directly related to glucose metabolism, tricarboxylic acid cycle (TCA), and oxidative phosphorylation (OXPHOS). Although OXPHOS was promoted by 1,25(OH)₂D₃, hypoxia did not change the tolerogenic function of 1,25(OH)₂D₃-treated DCs. Instead, glucose availability and glycolysis, controlled by the PI3K/Akt/mTOR pathway, dictate the induction and maintenance of the 1,25(OH)₂D₃-conditioned tolerogenic DC phenotype and function. This metabolic reprogramming is unique for 1,25(OH)₂D₃, because the tolerogenic DC phenotype induced by other immune modulators did not depend on similar metabolic changes. We put forward that these metabolic insights in tolerogenic DC biology can be used to advance DC-based immunotherapies, influencing DC longevity and their resistance to environmental metabolic stress.

ASP-G: an ASP-based method for finding attractors in genetic regulatory networks

MOTIVATION

Boolean network models are suitable to simulate GRNs in the absence of detailed kinetic information. However, reducing the biological reality implies making assumptions on how genes interact (interaction rules) and how their state is updated during the simulation (update scheme). The exact choice of the assumptions largely determines the outcome of the simulations. In most cases, however, the biologically correct assumptions are unknown. An ideal simulation thus implies testing different rules and schemes to determine those that best capture an observed biological phenomenon. This is not trivial because most current methods to simulate Boolean network models of GRNs and to compute their attractors impose specific assumptions that cannot be easily altered, as they are built into the system.

RESULTS

To allow for a more flexible simulation framework, we developed ASP-G. We show the correctness of ASP-G in simulating Boolean network models and obtaining attractors under different assumptions by successfully recapitulating the detection of attractors of previously published studies. We also provide an example of how performing simulation of network models under different settings help determine the assumptions under which a certain conclusion holds. The main added value of ASP-G is in its modularity and declarativity, making it more flexible and less error-prone than traditional approaches. The declarative nature of ASP-G comes at the expense of being slower than the more dedicated systems but still achieves a good efficiency with respect to computational time.

AVAILABILITY AND IMPLEMENTATION

The source code of ASP-G is available at http://bioinformatics.intec.ugent.be/kmarchal/Supplementary_Information_Musthofa_2014/asp-g.zip.

Photoreceptor-mediated bending towards UV-B in Arabidopsis

Plants reorient their growth towards light to optimize photosynthetic light capture--a process known as phototropism. Phototropins are the photoreceptors essential for phototropic growth towards blue and ultraviolet-A (UV-A) light. Here we detail a phototropic response towards UV-B in etiolated Arabidopsis seedlings. We report that early differential growth is mediated by phototropins but clear phototropic bending to UV-B is maintained in phot1 phot2 double mutants. We further show that this phototropin-independent phototropic response to UV-B requires the UV-B photoreceptor UVR8. Broad UV-B-mediated repression of auxin-responsive genes suggests that UVR8 regulates directional bending by affecting auxin signaling. Kinetic analysis shows that UVR8-dependent directional bending occurs later than the phototropin response. We conclude that plants may use the full short-wavelength spectrum of sunlight to efficiently reorient photosynthetic tissue with incoming light.

A network-based approach to identify substrate classes of bacterial glycosyltransferases

Background

Bacterial interactions with the environment- and/or host largely depend on the bacterial glycome. The specificities of a bacterial glycome are largely determined by glycosyltransferases (GTs), the enzymes involved in transferring sugar moieties from an activated donor to a specific substrate. Of these GTs their coding regions, but mainly also their substrate specificity are still largely unannotated as most sequence-based annotation flows suffer from the lack of characterized sequence motifs that can aid in the prediction of the substrate specificity.

Results

In this work, we developed an analysis flow that uses sequence-based strategies to predict novel GTs, but also exploits a network-based approach to infer the putative substrate classes of these predicted GTs. Our analysis flow was benchmarked with the well-documented GT-repertoire of Campylobacter jejuni NCTC 11168 and applied to the probiotic model Lactobacillus rhamnosus GG to expand our insights in the glycosylation potential of this bacterium. In L. rhamnosus GG we could predict 48 GTs of which eight were not previously reported. For at least 20 of these GTs a substrate relation was inferred.

Conclusions

We confirmed through experimental validation our prediction of WelI acting upstream of WelE in the biosynthesis of exopolysaccharides. We further hypothesize to have identified in L. rhamnosus GG the yet undiscovered genes involved in the biosynthesis of glucose-rich glycans and novel GTs involved in the glycosylation of proteins. Interestingly, we also predict GTs with well-known functions in peptidoglycan synthesis to also play a role in protein glycosylation.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-349) contains supplementary material, which is available to authorized users.

A putative de-N-acetylase of the PIG-L superfamily affects fluoroquinolone tolerance in Pseudomonas aeruginosa

A major cause of treatment failure of infections caused by Pseudomonas aeruginosa is the presence of antibiotic-insensitive persister cells. The mechanism of persister formation in P. aeruginosa is largely unknown, and so far, only few genetic determinants have been linked to P. aeruginosa persistence. Based on a previous high-throughput screening, we here present dnpA (de-N-acetylase involved in persistence; gene locus PA14_66140/PA5002) as a new gene involved in noninherited fluoroquinolone tolerance in P. aeruginosa. Fluoroquinolone tolerance of a dnpA mutant is strongly reduced both in planktonic culture and in a biofilm model, whereas overexpression of dnpA in the wild-type strain increases the persister fraction. In addition, the susceptibility of the dnpA mutant to different classes of antibiotics is not affected. dnpA is part of the conserved LPS core oligosaccharide biosynthesis gene cluster. Based on primary sequence analysis, we predict that DnpA is a de-N-acetylase, acting on an unidentified substrate. Site-directed mutagenesis suggests that this enzymatic activity is essential for DnpA-mediated persistence. A transcriptome analysis indicates that DnpA primarily affects the expression of genes involved in surface-associated processes. We discuss the implications of these findings for future antipersister therapies targeted at chronic P. aeruginosa infections.

Improved linkage analysis of Quantitative Trait Loci using bulk segregants unveils a novel determinant of high ethanol tolerance in yeast

Background

Bulk segregant analysis (BSA) coupled to high throughput sequencing is a powerful method to map genomic regions related with phenotypes of interest. It relies on crossing two parents, one inferior and one superior for a trait of interest. Segregants displaying the trait of the superior parent are pooled, the DNA extracted and sequenced. Genomic regions linked to the trait of interest are identified by searching the pool for overrepresented alleles that normally originate from the superior parent. BSA data analysis is non-trivial due to sequencing, alignment and screening errors.

Results

To increase the power of the BSA technology and obtain a better distinction between spuriously and truly linked regions, we developed EXPLoRA (EXtraction of over-rePresented aLleles in BSA), an algorithm for BSA data analysis that explicitly models the dependency between neighboring marker sites by exploiting the properties of linkage disequilibrium through a Hidden Markov Model (HMM).

Reanalyzing a BSA dataset for high ethanol tolerance in yeast allowed reliably identifying QTLs linked to this phenotype that could not be identified with statistical significance in the original study. Experimental validation of one of the least pronounced linked regions, by identifying its causative gene VPS70, confirmed the potential of our method.

Conclusions

EXPLoRA has a performance at least as good as the state-of-the-art and it is robust even at low signal to noise ratio’s i.e. when the true linkage signal is diluted by sampling, screening errors or when few segregants are available.

Improving the adaptability of simulated evolutionary swarm robots in dynamically changing environments

One of the important challenges in the field of evolutionary robotics is the development of systems that can adapt to a changing environment. However, the ability to adapt to unknown and fluctuating environments is not straightforward. Here, we explore the adaptive potential of simulated swarm robots that contain a genomic encoding of a bio-inspired gene regulatory network (GRN). An artificial genome is combined with a flexible agent-based system, representing the activated part of the regulatory network that transduces environmental cues into phenotypic behaviour. Using an artificial life simulation framework that mimics a dynamically changing environment, we show that separating the static from the conditionally active part of the network contributes to a better adaptive behaviour. Furthermore, in contrast with most hitherto developed ANN-based systems that need to re-optimize their complete controller network from scratch each time they are subjected to novel conditions, our system uses its genome to store GRNs whose performance was optimized under a particular environmental condition for a sufficiently long time. When subjected to a new environment, the previous condition-specific GRN might become inactivated, but remains present. This ability to store 'good behaviour' and to disconnect it from the novel rewiring that is essential under a new condition allows faster re-adaptation if any of the previously observed environmental conditions is reencountered. As we show here, applying these evolutionary-based principles leads to accelerated and improved adaptive evolution in a non-stable environment.

Phenotypical and molecular responses of Arabidopsis thaliana roots as a result of inoculation with the auxin-producing bacterium Azospirillum brasilense

The auxin-producing bacterium Azospirillum brasilense Sp245 can promote the growth of several plant species. The model plant Arabidopsis thaliana was chosen as host plant to gain an insight into the molecular mechanisms that govern this interaction. The determination of differential gene expression in Arabidopsis roots after inoculation with either A. brasilense wild-type or an auxin biosynthesis mutant was achieved by microarray analysis. Arabidopsis thaliana inoculation with A. brasilense wild-type increases the number of lateral roots and root hairs, and elevates the internal auxin concentration in the plant. The A. thaliana root transcriptome undergoes extensive changes on A. brasilense inoculation, and the effects are more pronounced at later time points. The wild-type bacterial strain induces changes in hormone- and defense-related genes, as well as in plant cell wall-related genes. The A. brasilense mutant, however, does not elicit these transcriptional changes to the same extent. There are qualitative and quantitative differences between A. thaliana responses to the wild-type A. brasilense strain and the auxin biosynthesis mutant strain, based on both phenotypic and transcriptomic data. This illustrates the major role played by auxin in the Azospirillum-Arabidopsis interaction, and possibly also in other bacterium-plant interactions.

Inferring the relation between transcriptional and posttranscriptional regulation from expression compendia

Background

Publicly available expression compendia that measure both mRNAs and sRNAs provide a promising resource to simultaneously infer the transcriptional and the posttranscriptional network. To maximally exploit the information contained in such compendia, we propose an analysis flow that combines publicly available expression compendia and sequence-based predictions to infer novel sRNA-target interactions and to reconstruct the relation between the sRNA and the transcriptional network.

Results

We relied on module inference to construct modules of coexpressed genes (sRNAs). TFs and sRNAs were assigned to these modules using the state-of-the-art inference techniques LeMoNe and Context Likelihood of Relatedness (CLR). Combining these expressions with sequence-based sRNA-target interactions allowed us to predict 30 novel sRNA-target interactions comprising 14 sRNAs. Our results highlight the role of the posttranscriptional network in finetuning the transcriptional regulation, e.g. by intra-operonic regulation.

Conclusion

In this work we show how strategies that combine expression information with sequence-based predictions can help unveiling the intricate interaction between the transcriptional and the posttranscriptional network in prokaryotic model systems.

MAGIC: access portal to a cross-platform gene expression compendium for maize

To facilitate the exploration of publicly available Zea mays expression data, we constructed a maize expression compendium, making use of an integration methodology and a consistent probe to gene mapping based on the 5b.60 sequence release of Z. mays. The compendium is made available through a web portal MAGIC that hosts a variety of analysis tools to easily browse and analyze the data. Our compendium is different from previous initiatives in combining expression values across different experiments by providing a consistent gene annotation across different platforms.

MLVA as a tool for public health surveillance of human Salmonella Typhimurium: prospective study in Belgium and evaluation of MLVA loci stability

Surveillance of Salmonella enterica subsp. enterica serovar Typhimurium (S. Typhimurium) is generally considered to benefit from molecular techniques like multiple-locus variable-number of tandem repeats analysis (MLVA), which allow earlier detection and confinement of outbreaks. Here, a surveillance study, including phage typing, antimicrobial susceptibility testing and the in Europe most commonly used 5-loci MLVA on 1,420 S. Typhimurium isolates collected between 2010 and 2012 in Belgium, was used to evaluate the added value of MLVA for public health surveillance. Phage types DT193, DT195, DT120, DT104, DT12 and U302 dominate the Belgian S. Typhimurium population. A combined resistance to ampicillin, streptomycin, sulphonamides and tetracycline (ASSuT) with or without additional resistances was observed for 42.5% of the isolates. 414 different MLVA profiles were detected, of which 14 frequent profiles included 44.4% of the S. Typhimurium population. During a serial passage experiment on selected isolates to investigate the in vitro stability of the 5 MLVA loci, variations over time were observed for loci STTR6, STTR10, STTR5 and STTR9. This study demonstrates that MLVA improves public health surveillance of S. Typhimurium. However, the 5-loci MLVA should be complemented with other subtyping methods for investigation of possible outbreaks with frequent MLVA profiles. Also, variability in these MLVA loci should be taken into account when investigating extended outbreaks and studying dynamics over longer periods.

COLOMBOS v2.0: an ever expanding collection of bacterial expression compendia

The COLOMBOS database (http://www.colombos.net) features comprehensive organism-specific cross-platform gene expression compendia of several bacterial model organisms and is supported by a fully interactive web portal and an extensive web API. COLOMBOS was originally published in PLoS One, and COLOMBOS v2.0 includes both an update of the expression data, by expanding the previously available compendia and by adding compendia for several new species, and an update of the surrounding functionality, with improved search and visualization options and novel tools for programmatic access to the database. The scope of the database has also been extended to incorporate RNA-seq data in our compendia by a dedicated analysis pipeline. We demonstrate the validity and robustness of this approach by comparing the same RNA samples measured in parallel using both microarrays and RNA-seq. As far as we know, COLOMBOS currently hosts the largest homogenized gene expression compendia available for seven bacterial model organisms.

PheNetic: network-based interpretation of unstructured gene lists in E. coli

At the present time, omics experiments are commonly used in wet lab practice to identify leads involved in interesting phenotypes. These omics experiments often result in unstructured gene lists, the interpretation of which in terms of pathways or the mode of action is challenging. To aid in the interpretation of such gene lists, we developed PheNetic, a decision theoretic method that exploits publicly available information, captured in a comprehensive interaction network to obtain a mechanistic view of the listed genes. PheNetic selects from an interaction network the sub-networks highlighted by these gene lists. We applied PheNetic to an Escherichia coli interaction network to reanalyse a previously published KO compendium, assessing gene expression of 27 E. coli knock-out mutants under mild acidic conditions. Being able to unveil previously described mechanisms involved in acid resistance demonstrated both the performance of our method and the added value of our integrated E. coli network. PheNetic is available at .

EPSILON: an eQTL prioritization framework using similarity measures derived from local networks

MOTIVATION

When genomic data are associated with gene expression data, the resulting expression quantitative trait loci (eQTL) will likely span multiple genes. eQTL prioritization techniques can be used to select the most likely causal gene affecting the expression of a target gene from a list of candidates. As an input, these techniques use physical interaction networks that often contain highly connected genes and unreliable or irrelevant interactions that can interfere with the prioritization process. We present EPSILON, an extendable framework for eQTL prioritization, which mitigates the effect of highly connected genes and unreliable interactions by constructing a local network before a network-based similarity measure is applied to select the true causal gene.

RESULTS

We tested the new method on three eQTL datasets derived from yeast data using three different association techniques. A physical interaction network was constructed, and each eQTL in each dataset was prioritized using the EPSILON approach: first, a local network was constructed using a k-trials shortest path algorithm, followed by the calculation of a network-based similarity measure. Three similarity measures were evaluated: random walks, the Laplacian Exponential Diffusion kernel and the Regularized Commute-Time kernel. The aim was to predict knockout interactions from a yeast knockout compendium. EPSILON outperformed two reference prioritization methods, random assignment and shortest path prioritization. Next, we found that using a local network significantly increased prioritization performance in terms of predicted knockout pairs when compared with using exactly the same network similarity measures on the global network, with an average increase in prioritization performance of 8 percentage points (P < 10(-5)).

AVAILABILITY

The physical interaction network and the source code (Matlab/C++) of our implementation can be downloaded from http://bioinformatics.intec.ugent.be/epsilon.

CONTACT

lieven.verbeke@intec.ugent.be, kamar@psb.ugent.be, jan.fostier@intec.ugent.be

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Identifying differentially expressed genes in the absence of replication

In microarray data analysis, the comparison of gene expression levels in different conditions and selection of biologically relevant genes are essential tasks. In this study, we propose a novel statistical procedure based on standardised conditional residuals from a linear mixed-effects model which allows comparison of conditions, even if only one replicate per experimental condition is available. We illustrate this method by using three publicly available datasets. We show that this method can be extended to handle more complex designs. Finally, simulations show that the tests developed have good statistical power to detect true differences among conditions at the gene level.

Association of the vascular endothelial growth factor gene polymorphisms (-460C/T, +405G/C and +936T/C) with endometriosis: a meta-analysis

Published data on the association between the vascular endothelial growth factor (VEGF) gene -460C/T (rs833061), +405G/C (rs2010963), +936T/C (rs3025039) polymorphisms and endometriosis risk are inconclusive. Eleven eligible case-control studies including 2690 cases and 2803 controls were included in this meta-analysis through searching the databases of PubMed and CBMdisc (up to August 1, 2011). In the overall analysis, no significant association between the -460C/T and +405G/C polymorphisms and risk of endometriosis was observed. However, significant associations were observed between endometriosis risk and VEGF+936T polymorphism with summarized odds ratio of 1.19 (95%CI, 1.02-1.37), 1.18 (95%CI, 1.03-1.37), 1.15 (95%CI, 1.01-1.30) for CT versus CC genotype, dominant mode (CT/TT vs. CC) and allele comparison (T vs. C), respectively. Furthermore, stratified analysis showed that significantly strong association between +936T/C polymorphism and endometriosis was present only in stage III-IV (OR = 1.32 for dominant mode; OR = 1.30 for T vs. C), but not in stage I-II. However, no significantly increased risk of endometriosis was found in any of the genetic models in Asians or in Caucasians. This meta-analysis supports that VEGF+936T/C polymorphism is capable of causing endometriosis susceptibility.

Expression divergence between Escherichia coli and Salmonella enterica serovar Typhimurium reflects their lifestyles

Escherichia coli K12 is a commensal bacteria and one of the best-studied model organisms. Salmonella enterica serovar Typhimurium, on the other hand, is a facultative intracellular pathogen. These two prokaryotic species can be considered related phylogenetically, and they share a large amount of their genetic material, which is commonly termed the "core genome." Despite their shared core genome, both species display very different lifestyles, and it is unclear to what extent the core genome, apart from the species-specific genes, plays a role in this lifestyle divergence. In this study, we focus on the differences in expression domains for the orthologous genes in E. coli and S. Typhimurium. The iterative comparison of coexpression methodology was used on large expression compendia of both species to uncover the conservation and divergence of gene expression. We found that gene expression conservation occurs mostly independently from amino acid similarity. According to our estimates, at least more than one quarter of the orthologous genes has a different expression domain in E. coli than in S. Typhimurium. Genes involved with key cellular processes are most likely to have conserved their expression domains, whereas genes showing diverged expression are associated with metabolic processes that, although present in both species, are regulated differently. The expression domains of the shared "core" genome of E. coli and S. Typhimurium, consisting of highly conserved orthologs, have been tuned to help accommodate the differences in lifestyle and the pathogenic potential of Salmonella.

Identification of a complex genetic network underlying Saccharomyces cerevisiae colony morphology

When grown on solid substrates, different microorganisms often form colonies with very specific morphologies. Whereas the pioneers of microbiology often used colony morphology to discriminate between species and strains, the phenomenon has not received much attention recently. In this study, we use a genome-wide assay in the model yeast Saccharomyces cerevisiae to identify all genes that affect colony morphology. We show that several major signalling cascades, including the MAPK, TORC, SNF1 and RIM101 pathways play a role, indicating that morphological changes are a reaction to changing environments. Other genes that affect colony morphology are involved in protein sorting and epigenetic regulation. Interestingly, the screen reveals only few genes that are likely to play a direct role in establishing colony morphology, with one notable example being FLO11, a gene encoding a cell-surface adhesin that has already been implicated in colony morphology, biofilm formation, and invasive and pseudohyphal growth. Using a series of modified promoters for fine-tuning FLO11 expression, we confirm the central role of Flo11 and show that differences in FLO11 expression result in distinct colony morphologies. Together, our results provide a first comprehensive look at the complex genetic network that underlies the diversity in the morphologies of yeast colonies.

Nucleosomes affect local transformation efficiency

Genetic transformation is a natural process during which foreign DNA enters a cell and integrates into the genome. Apart from its relevance for horizontal gene transfer in nature, transformation is also the cornerstone of today's recombinant gene technology. Despite its importance, relatively little is known about the factors that determine transformation efficiency. We hypothesize that differences in DNA accessibility associated with nucleosome positioning may affect local transformation efficiency. We investigated the landscape of transformation efficiency at various positions in the Saccharomyces cerevisiae genome and correlated these measurements with nucleosome positioning. We find that transformation efficiency shows a highly significant inverse correlation with relative nucleosome density. This correlation was lost when the nucleosome pattern, but not the underlying sequence was changed. Together, our results demonstrate a novel role for nucleosomes and also allow researchers to predict transformation efficiency of a target region and select spots in the genome that are likely to yield higher transformation efficiency.