Z-flipon variants reveal the many roles of Z-DNA and Z-RNA in health and disease.

Identifying roles for Z-DNA remains challenging given their dynamic nature. Here, we perform genome-wide interrogation with the DNABERT transformer algorithm trained on experimentally identified Z-DNA forming sequences (Z-flipons). The algorithm yields large performance enhancements (F1 = 0.83) over existing approaches and implements computational mutagenesis to assess the effects of base substitution on Z-DNA formation. We show Z-flipons are enriched in promoters and telomeres, overlapping quantitative trait loci for RNA expression, RNA editing, splicing, and disease-associated variants. We cross-validate across a number of orthogonal databases and define BZ junction motifs. Surprisingly, many effects we delineate are likely mediated through Z-RNA formation. A shared Z-RNA motif is identified in SCARF2, SMAD1, and CACNA1 transcripts, whereas other motifs are present in noncoding RNAs. We provide evidence for a Z-RNA fold that promotes adaptive immunity through alternative splicing of KRAB domain zinc finger proteins. An analysis of OMIM and presumptive gnomAD loss-of-function datasets reveals an overlap of Z-flipons with disease-causing variants in 8.6% and 2.9% of Mendelian disease genes, respectively, greatly extending the range of phenotypes mapped to Z-flipons.

Unsupervised domain adaptation methods for cross-species transfer of regulatory code signals.

Due to advances in NGS technologies whole-genome maps of various functional genomic elements were generated for a dozen of species, however experiments are still expensive and are not available for many species of interest. Deep learning methods became the state-of-the-art computational methods to analyze the available data, but the focus is often only on the species studied. Here we take advantage of the progresses in Transfer Learning in the area of Unsupervised Domain Adaption (UDA) and tested nine UDA methods for prediction of regulatory code signals for genomes of other species. We tested each deep learning implementation by training the model on experimental data from one species, then refined the model using the genome sequence of the target species for which we wanted to make predictions. Among nine tested domain adaptation architectures non-adversarial methods Minimum Class Confusion (MCC) and Deep Adaptation Network (DAN) significantly outperformed others. Conditional Domain Adversarial Network (CDAN) appeared as the third best architecture. Here we provide an empirical assessment of each approach using real world data. The different approaches were tested on ChIP-seq data for transcription factor binding sites and histone marks on human and mouse genomes, but is generalizable to any cross-species transfer of interest. We tested the efficiency of each method using species where experimental data was available for both. The results allows us to assess how well each implementation will work for species for which only limited experimental data is available and will inform the design of future experiments in these understudied organisms. Overall, our results proved the validity of UDA methods for generation of missing experimental data for histone marks and transcription factor binding sites in various genomes and highlights how robust the various approaches are to data that is incomplete, noisy and susceptible to analytic bias.

DeepZ: A Deep Learning Approach for Z-DNA Prediction.

Here we describe an approach that uses deep learning neural networks such as CNN and RNN to aggregate information from DNA sequence; physical, chemical, and structural properties of nucleotides; and omics data on histone modifications, methylation, chromatin accessibility, and transcription factor binding sites and data from other available NGS experiments. We explain how with the trained model one can perform whole-genome annotation of Z-DNA regions and feature importance analysis in order to define key determinants for functional Z-DNA regions.

Conserved microRNAs and Flipons Shape Gene Expression during Development by Altering Promoter Conformations.

The classical view of gene regulation draws from prokaryotic models, where responses to environmental changes involve operons regulated by sequence-specific protein interactions with DNA, although it is now known that operons are also modulated by small RNAs. In eukaryotes, pathways based on microRNAs (miR) regulate the readout of genomic information from transcripts, while alternative nucleic acid structures encoded by flipons influence the readout of genetic programs from DNA. Here, we provide evidence that miR- and flipon-based mechanisms are deeply connected. We analyze the connection between flipon conformation and the 211 highly conserved human miR that are shared with other placental and other bilateral species. The direct interaction between conserved miR (c-miR) and flipons is supported by sequence alignments and the engagement of argonaute proteins by experimentally validated flipons as well as their enrichment in promoters of coding transcripts important in multicellular development, cell surface glycosylation and glutamatergic synapse specification with significant enrichments at false discovery rates as low as 10-116. We also identify a second subset of c-miR that targets flipons essential for retrotransposon replication, exploiting that vulnerability to limit their spread. We propose that miR can act in a combinatorial manner to regulate the readout of genetic information by specifying when and where flipons form non-B DNA (NoB) conformations, providing the interactions of the conserved hsa-miR-324-3p with RELA and the conserved hsa-miR-744 with ARHGAP5 genes as examples.

A survey of physician opinions in Russia in the field of pharmacogenetics of cardiovascular disease.

Aims: To study the readiness of Russian physicians with experience and younger physicians undergoing clinical residency and postgraduate education to apply pharmacogenetic testing in their clinical practice. Materials & methods: The sociological study involved physicians (n = 378) living in different regions of the Russian Federation, as well as residents and graduate students (n = 185) of the Russian Medical Academy of Continuing Professional Medical Education. The survey consisted of 35 questions, and 23 were created on the online platform of professional surveys, Testograf.ru. Results: Every second respondent was willing to use pharmacogenetic testing in clinical practice to predict the efficacy and safety of medications in patients with cardiovascular disease (p = 0.06). Factors impeding the clinical implementation of pharmacogenetic testing in Russia were identified: physicians' ignorance of pharmacogenetics (p = 0.015), a lack of pharmacogenetic testing in clinical guidelines and treatment standards (p = 0.175) and a lack of economic justification for using pharmacogenetic testing (p = 0.320). Conclusion: Russian physicians have a positive attitude toward pharmacogenetic testing. However, the level of test implementation remains low.

Z-RNA and the Flipside of the SARS Nsp13 Helicase: Is There a Role for Flipons in Coronavirus-Induced Pathology?

We present evidence suggesting that the severe acute respiratory syndrome (SARS) coronavirus non-structural protein 13 (Nsp13) modulates the Z-RNA dependent regulated cell death pathways . We show that Z-prone sequences [called flipons] exist in coronavirus and provide a signature (Z-sig) that enables identification of the animal viruses from which the human pathogens arose. We also identify a potential RIP Homology Interaction Motif (RHIM) in the helicase Nsp13 that resembles those present in proteins that initiate Z-RNA-dependent cell death through interactions with the Z-RNA sensor protein ZBP1. These two observations allow us to suggest a model in which Nsp13 down regulates Z-RNA activated innate immunity by two distinct mechanisms. The first involves a novel ATP-independent Z-flipon helicase (flipase) activity in Nsp13 that differs from that of canonical A-RNA helicases. This flipase prevents formation of Z-RNAs that would otherwise activate cell death pathways. The second mechanism likely inhibits the interactions between ZBP1 and the Receptor Interacting Proteins Kinases RIPK1 and RIPK3 by targeting their RHIM domains. Together the described Nsp13 RHIM and flipase activities have the potential to alter the host response to coronaviruses and impact the design of drugs targeting the Nsp13 protein. The Z-sig and RHIM domains may provide a way of identifying previously uncharacterized viruses that are potentially pathogenic for humans.

ADAR1 masks the cancer immunotherapeutic promise of ZBP1-driven necroptosis.

Only a small proportion of patients with cancer show lasting responses to immune checkpoint blockade (ICB)-based monotherapies. The RNA-editing enzyme ADAR1 is an emerging determinant of resistance to ICB therapy and prevents ICB responsiveness by repressing immunogenic double-stranded RNAs (dsRNAs), such as those arising from the dysregulated expression of endogenous retroviral elements (EREs)1-4. These dsRNAs trigger an interferon-dependent antitumour response by activating A-form dsRNA (A-RNA)-sensing proteins such as MDA-5 and PKR5. Here we show that ADAR1 also prevents the accrual of endogenous Z-form dsRNA elements (Z-RNAs), which were enriched in the 3' untranslated regions of interferon-stimulated mRNAs. Depletion or mutation of ADAR1 resulted in Z-RNA accumulation and activation of the Z-RNA sensor ZBP1, which culminated in RIPK3-mediated necroptosis. As no clinically viable ADAR1 inhibitors currently exist, we searched for a compound that can override the requirement for ADAR1 inhibition and directly activate ZBP1. We identified a small molecule, the curaxin CBL0137, which potently activates ZBP1 by triggering Z-DNA formation in cells. CBL0137 induced ZBP1-dependent necroptosis in cancer-associated fibroblasts and reversed ICB unresponsiveness in mouse models of melanoma. Collectively, these results demonstrate that ADAR1 represses endogenous Z-RNAs and identifies ZBP1-mediated necroptosis as a new determinant of tumour immunogenicity masked by ADAR1. Therapeutic activation of ZBP1-induced necroptosis provides a readily translatable avenue for rekindling the immune responsiveness of ICB-resistant human cancers.

Mono a Mano: ZBP1's Love-Hate Relationship with the Kissing Virus.

Z-DNA binding protein (ZBP1) very much represents the nuclear option. By initiating inflammatory cell death (ICD), ZBP1 activates host defenses to destroy infectious threats. ZBP1 is also able to induce noninflammatory regulated cell death via apoptosis (RCD). ZBP1 senses the presence of left-handed Z-DNA and Z-RNA (ZNA), including that formed by expression of endogenous retroelements. Viruses such as the Epstein-Barr "kissing virus" inhibit ICD, RCD and other cell death signaling pathways to produce persistent infection. EBV undergoes lytic replication in plasma cells, which maintain detectable levels of basal ZBP1 expression, leading us to suggest a new role for ZBP1 in maintaining EBV latency, one of benefit for both host and virus. We provide an overview of the pathways that are involved in establishing latent infection, including those regulated by MYC and NF-κB. We describe and provide a synthesis of the evidence supporting a role for ZNA in these pathways, highlighting the positive and negative selection of ZNA forming sequences in the EBV genome that underscores the coadaptation of host and virus. Instead of a fight to the death, a state of détente now exists where persistent infection by the virus is tolerated by the host, while disease outcomes such as death, autoimmunity and cancer are minimized. Based on these new insights, we propose actionable therapeutic approaches to unhost EBV.

Special Issue: A, B and Z: The Structure, Function and Genetics of Z-DNA and Z-RNA.

It is now difficult to believe that a biological function for the left-handed Z-DNA and Z-RNA conformations was once controversial. The papers in this Special Issue, "Z-DNA and Z-RNA: from Physical Structure to Biological Function", are based on presentations at the ABZ2021 meeting that was held virtually on 19 May 2021 and provide evidence for several biological functions of these structures. The first of its kind, this international conference gathered over 200 scientists from many disciplines to specifically address progress in research involving Z-DNA and Z-RNA. These high-energy left-handed conformers of B-DNA and A-RNA are associated with biological functions and disease outcomes, as evidenced from both mouse and human genetic studies. These alternative structures, referred to as "flipons", form under physiological conditions, regulate type I interferon responses and induce necroptosis during viral infection. They can also stimulate genetic instability, resulting in adaptive evolution and diseases such as cancer. The meeting featured cutting-edge science that was, for the most part, unpublished. We plan for the ABZ meeting to reconvene in 2022.

Randomness in Cancer Breakpoint Prediction.

Cancer genomes are susceptible to multiple rearrangements by deleting, inserting, and translocating genomic regions. Recently, the problem of finding determinants of breakpoint formations was approached with machine learning methods; however, unlike cancer point mutations, breakpoint prediction appeared to be a more difficult task, and various machine learning models did not achieve high prediction power often slightly exceeding the threshold of random guessing. This raised the question of whether the breakpoints are random noise in cancer mutagenesis or there exist determinants in structural mutagenesis. In the present study, we investigated randomness in cancer breakpoint genome distributions through the power of machine learning models to predict breakpoint hot spots. We divided all cancer types into three groups by degree of randomness in their breakpoint formation. We tested different density thresholds and explored the bias in hot spot definition. We also compared prediction of hot spots versus individual breakpoints. We found that hot spots are considerably better predicted than individual breakpoints; however, some individual breakpoints can also be predicted with a satisfactory power, and thus, it is not proper to filter them from analyses. We demonstrated that positive-unlabeled learning can provide insights into insufficiency of cancer data sets, which are not always reflected by data set sizes. Overall, the present results support the view that cancer breakpoint landscape can be represented by predictable dense breakpoint regions and scattered individual breakpoints, which are not all random noise, but some are generated by detectable mechanism.

Comprehensive analysis of cancer breakpoints reveals signatures of genetic and epigenetic contribution to cancer genome rearrangements.

Understanding mechanisms of cancer breakpoint mutagenesis is a difficult task and predictive models of cancer breakpoint formation have to this time failed to achieve even moderate predictive power. Here we take advantage of a machine learning approach that can gather important features from big data and quantify contribution of different factors. We performed comprehensive analysis of almost 630,000 cancer breakpoints and quantified the contribution of genomic and epigenomic features-non-B DNA structures, chromatin organization, transcription factor binding sites and epigenetic markers. The results showed that transcription and formation of non-B DNA structures are two major processes responsible for cancer genome fragility. Epigenetic factors, such as chromatin organization in TADs, open/closed regions, DNA methylation, histone marks are less informative but do make their contribution. As a general trend, individual features inside the groups show a relatively high contribution of G-quadruplexes and repeats and CTCF, GABPA, RXRA, SP1, MAX and NR2F2 transcription factors. Overall, the cancer breakpoint landscape can be represented by well-predicted hotspots and poorly predicted individual breakpoints scattered across genomes. We demonstrated that hotspot mutagenesis has genomic and epigenomic factors, and not all individual cancer breakpoints are just random noise but have a definite mutation signature. Besides we found a long-range action of some features on breakpoint mutagenesis. Combining omics data, cancer-specific individual feature importance and adding the distant to local features, predictive models for cancer breakpoint formation achieved 70-90% ROC AUC for different cancer types; however precision remained low at 2% and the recall did not exceed 50%. On the one hand, the power of models strongly correlates with the size of available cancer breakpoint and epigenomic data, and on the other hand finding strong determinants of cancer breakpoint formation still remains a challenge. The strength of predictive signals of each group and of each feature inside a group can be converted into cancer-specific breakpoint mutation signatures. Overall our results add to the understanding of cancer genome rearrangement processes.

Deep learning approach for predicting functional Z-DNA regions using omics data.

Computational methods to predict Z-DNA regions are in high demand to understand the functional role of Z-DNA. The previous state-of-the-art method Z-Hunt is based on statistical mechanical and energy considerations about B- to Z-DNA transition using sequence information. Z-DNA CHiP-seq experiment results showed little overlap with Z-Hunt predictions implying that sequence information only is not sufficient to explain emergence of Z-DNA at different genomic locations. Adding epigenetic and other functional genomic mark-ups to DNA sequence level can help revealing the functional Z-DNA sites. Here we take advantage of the deep learning approach that can analyze and extract information from large volumes of molecular biology data. We developed a machine learning approach DeepZ that aggregates information from genome-wide maps of epigenetic markers, transcription factor and RNA polymerase binding sites, and chromosome accessibility maps. With the developed model we not only verify the experimental Z-DNA predictions, but also generate the whole-genome annotation, introducing new possible Z-DNA regions, which have not yet been found in experiments and can be of interest to the researchers from various fields.

Interethnic differences in the prevalence of main cardiovascular pharmacogenetic biomarkers.

Background: The aim of this study was to determine the prevalence of CYP2C9, VKORC1, CYP2C19, ABCB1, CYP2D6 and SLCO1B1 genes polymorphisms among residents of the Volga region (Chuvash and Mari) and northern Caucasus (Kabardins and Ossetians). Materials & methods: The study involved 845 apparently healthy volunteers of both sexes of the four different ethnic groups living in the Russian Federation: 238 from the Chuvash ethnic group, 206 Mari, 157 Kabardins and 244 Ossetians. Results: Significant differences were identified in allele frequency of CYP2C9, VKORC1, CYP2C19, ABCB1, CYP2D6 and SLCO1B1 genes polymorphisms between the Chuvash and Kabardins, Chuvash and Ossetians, Mari and Kabardians, Mari and Ossetians.

Recognition of 3'-end L1, Alu, processed pseudogenes, and mRNA stem-loops in the human genome using sequence-based and structure-based machine-learning models.

The role of 3'-end stem-loops in retrotransposition was experimentally demonstrated for transposons of various species, where LINE-SINE retrotransposons share the same 3'-end sequences, containing a stem-loop. We have discovered that 62-68% of processed pseduogenes and mRNAs also have 3'-end stem-loops. We investigated the properties of 3'-end stem-loops of human L1s, Alus, processed pseudogenes and mRNAs that do not share the same sequences, but all have 3'-end stem-loops. We have built sequence-based and structure-based machine-learning models that are able to recognize 3'-end L1, Alu, processed pseudogene and mRNA stem-loops with high performance. The sequence-based models use only sequence information and capture compositional bias in 3'-ends. The structure-based models consider physical, chemical and geometrical properties of dinucleotides composing a stem and position-specific nucleotide content of a loop and a bulge. The most important parameters include shift, tilt, rise, and hydrophilicity. The obtained results clearly point to the existence of structural constrains for 3'-end stem-loops of L1 and Alu, which are probably important for transposition, and reveal the potential of mRNAs to be recognized by the L1 machinery. The proposed approach is applicable to a broader task of recognizing RNA (DNA) secondary structures. The constructed models are freely available at github ( https://github.com/AlexShein/transposons/ ).

Tissue-specific impact of stem-loops and quadruplexes on cancer breakpoints formation.

BACKGROUND: Chromosomal rearrangements are the typical phenomena in cancer genomes causing gene disruptions and fusions, corruption of regulatory elements, damage to chromosome integrity. Among the factors contributing to genomic instability are non-B DNA structures with stem-loops and quadruplexes being the most prevalent. We aimed at investigating the impact of specifically these two classes of non-B DNA structures on cancer breakpoint hotspots using machine learning approach. METHODS: We developed procedure for machine learning model building and evaluation as the considered data are extremely imbalanced and it was required to get a reliable estimate of the prediction power. We built logistic regression models predicting cancer breakpoint hotspots based on the densities of stem-loops and quadruplexes, jointly and separately. We also tested Random Forest models varying different resampling schemes (leave-one-out cross validation, train-test split, 3-fold cross-validation) and class balancing techniques (oversampling, stratification, synthetic minority oversampling). RESULTS: We performed analysis of 487,425 breakpoints from 2234 samples covering 10 cancer types available from the International Cancer Genome Consortium. We showed that distribution of breakpoint hotspots in different types of cancer are not correlated, confirming the heterogeneous nature of cancer. It appeared that stem-loop-based model best explains the blood, brain, liver, and prostate cancer breakpoint hotspot profiles while quadruplex-based model has higher performance for the bone, breast, ovary, pancreatic, and skin cancer. For the overall cancer profile and uterus cancer the joint model shows the highest performance. For particular datasets the constructed models reach high predictive power using just one predictor, and in the majority of the cases, the model built on both predictors does not increase the model performance. CONCLUSION: Despite the heterogeneity in breakpoint hotspots' distribution across different cancer types, our results demonstrate an association between cancer breakpoint hotspots and stem-loops and quadruplexes. Approximately for half of the cancer types stem-loops are the most influential factors while for the others these are quadruplexes. This fact reflects the differences in regulatory potential of stem-loops and quadruplexes at the tissue-specific level, which yet to be discovered at the genome-wide scale. The performed analysis demonstrates that influence of stem-loops and quadruplexes on breakpoint hotspots formation is tissue-specific.

Conserved 3' UTR stem-loop structure in L1 and Alu transposons in human genome: possible role in retrotransposition.

BACKGROUND: In the process of retrotransposition LINEs use their own machinery for copying and inserting themselves into new genomic locations, while SINEs are parasitic and require the machinery of LINEs. The exact mechanism of how a LINE-encoded reverse transcriptase (RT) recognizes its own and SINE RNA remains unclear. However it was shown for the stringent-type LINEs that recognition of a stem-loop at the 3'UTR by RT is essential for retrotransposition. For the relaxed-type LINEs it is believed that the poly-A tail is a common recognition element between LINE and SINE RNA. However polyadenylation is a property of any messenger RNA, and how the LINE RT recognizes transposon and non-transposon RNAs remains an open question. It is likely that RNA secondary structures play an important role in RNA recognition by LINE encoded proteins. RESULTS: Here we selected a set of L1 and Alu elements from the human genome and investigated their sequences for the presence of position-specific stem-loop structures. We found highly conserved stem-loop positions at the 3'UTR. Comparative structural analyses of a human L1 3'UTR stem-loop showed a similarity to 3'UTR stem-loops of the stringent-type LINEs, which were experimentally shown to be recognized by LINE RT. The consensus stem-loop structure consists of 5-7 bp loop, 8-10 bp stem with a bulge at a distance of 4-6 bp from the loop. The results show that a stem loop with a bulge exists at the 3'-end of Alu. We also found conserved stem-loop positions at 5'UTR and at the end of ORF2 and discuss their possible role. CONCLUSIONS: Here we presented an evidence for the presence of a highly conserved 3'UTR stem-loop structure in L1 and Alu retrotransposons in the human genome. Both stem-loops show structural similarity to the stem-loops of the stringent-type LINEs experimentally confirmed as essential for retrotransposition. Here we hypothesize that both L1 and Alu RNA are recognized by L1 RT via the 3'-end RNA stem-loop structure. Other conserved stem-loop positions in L1 suggest their possible functions in protein-RNA interactions but to date no experimental evidence has been reported.

Structural features of DNA that determine RNA polymerase II core promoter.

BACKGROUND: The general structure and action of all eukaryotic and archaeal RNA polymerases machinery have an astonishing similarity despite the diversity of core promoter sequences in different species. The goal of our work is to find common characteristics of DNA region that define it as a promoter for the RNA polymerase II (Pol II). RESULTS: The profiles of a large number of physical and structural characteristics, averaged over representative sets of the Pol II minimal core promoters of the evolutionary divergent species from animals, plants and unicellular fungi were analysed. In addition to the characteristics defined at the base-pair steps, we, for the first time, use profiles of the ultrasonic cleavage and DNase I cleavage indexes, informative for internal properties of each complementary strand. CONCLUSIONS: DNA of the core promoters of metazoans and Schizosaccharomyces pombe has similar structural organization. Its mechanical and 3D structural characteristics have singular properties at the positions of TATA-box. The minor groove is broadened and conformational motion is decreased in that region. Special characteristics of conformational behavior are revealed in metazoans at the region, which connects the end of TATA-box and the transcription start site (TSS). The intensities of conformational motions in the complementary strands are periodically changed in opposite phases. They are noticeable, best of all, in mammals. Such conformational features are lacking in the core promoters of S. pombe. The profiles of Saccharomyces cerevisiae core promoters significantly differ: their singular region is shifted down thus pointing to the uniqueness of their structural organization. Obtained results may be useful in genetic engineering for artificial modulation of the promoter strength.

Non-random DNA fragmentation in next-generation sequencing.

Next Generation Sequencing (NGS) technology is based on cutting DNA into small fragments, and their massive parallel sequencing. The multiple overlapping segments termed "reads" are assembled into a contiguous sequence. To reduce sequencing errors, every genome region should be sequenced several dozen times. This sequencing approach is based on the assumption that genomic DNA breaks are random and sequence-independent. However, previously we showed that for the sonicated restriction DNA fragments the rates of double-stranded breaks depend on the nucleotide sequence. In this work we analyzed genomic reads from NGS data and discovered that fragmentation methods based on the action of the hydrodynamic forces on DNA, produce similar bias. Consideration of this non-random DNA fragmentation may allow one to unravel what factors and to what extent influence the non-uniform coverage of various genomic regions.

Impact of constitutional copy number variants on biological pathway evolution.

BACKGROUND: Inherited Copy Number Variants (CNVs) can modulate the expression levels of individual genes. However, little is known about how CNVs alter biological pathways and how this varies across different populations. To trace potential evolutionary changes of well-described biological pathways, we jointly queried the genomes and the transcriptomes of a collection of individuals with Caucasian, Asian or Yoruban descent combining high-resolution array and sequencing data. RESULTS: We implemented an enrichment analysis of pathways accounting for CNVs and genes sizes and detected significant enrichment not only in signal transduction and extracellular biological processes, but also in metabolism pathways. Upon the estimation of CNV population differentiation (CNVs with different polymorphism frequencies across populations), we evaluated that 22% of the pathways contain at least one gene that is proximal to a CNV (CNV-gene pair) that shows significant population differentiation. The majority of these CNV-gene pairs belong to signal transduction pathways and 6% of the CNV-gene pairs show statistical association between the copy number states and the transcript levels. CONCLUSIONS: The analysis suggested possible examples of positive selection within individual populations including NF-kB, MAPK signaling pathways, and Alu/L1 retrotransposition factors. Altogether, our results suggest that constitutional CNVs may modulate subtle pathway changes through specific pathway enzymes, which may become fixed in some populations.

Quartet decomposition server: a platform for analyzing phylogenetic trees.

BACKGROUND: The frequent exchange of genetic material among prokaryotes means that extracting a majority or plurality phylogenetic signal from many gene families, and the identification of gene families that are in significant conflict with the plurality signal is a frequent task in comparative genomics, and especially in phylogenomic analyses. Decomposition of gene trees into embedded quartets (unrooted trees each with four taxa) is a convenient and statistically powerful technique to address this challenging problem. This approach was shown to be useful in several studies of completely sequenced microbial genomes. RESULTS: We present here a web server that takes a collection of gene phylogenies, decomposes them into quartets, generates a Quartet Spectrum, and draws a split network. Users are also provided with various data download options for further analyses. Each gene phylogeny is to be represented by an assessment of phylogenetic information content, such as sets of trees reconstructed from bootstrap replicates or sampled from a posterior distribution. The Quartet Decomposition server is accessible at http://quartets.uga.edu. CONCLUSIONS: The Quartet Decomposition server presented here provides a convenient means to perform Quartet Decomposition analyses and will empower users to find statistically supported phylogenetic conflicts.