Sequence analysis of origins of replication in the Saccharomyces cerevisiae genomes

Identification of 1600 replication origins in S. cerevisiae

There are approximately 500 known origins of replication in the yeast genome, and the process by which DNA replication initiates at these locations is well understood. In particular, these sites are made competent to initiate replication by loading of the Mcm replicative helicase prior to the start of S phase; thus, 'a site that binds Mcm in G1' might be considered to provide an operational definition of a replication origin. By fusing a subunit of Mcm to micrococcal nuclease, we previously showed that known origins are typically bound by a single Mcm double hexamer, loaded adjacent to the ARS consensus sequence (ACS). Here, we extend this analysis from known origins to the entire genome, identifying candidate Mcm binding sites whose signal intensity varies over at least three orders of magnitude. Published data quantifying single-stranded DNA (ssDNA) during S phase revealed replication initiation among the most abundant 1600 of these sites, with replication activity decreasing with Mcm abundance and disappearing at the limit of detection of ssDNA. Three other hallmarks of replication origins were apparent among the most abundant 5500 sites. Specifically, these sites: (1) appeared in intergenic nucleosome-free regions flanked on one or both sides by well-positioned nucleosomes; (2) were flanked by ACSs; and (3) exhibited a pattern of GC skew characteristic of replication initiation. We conclude that, if sites at which Mcm double hexamers are loaded can function as replication origins, then DNA replication origins are at least threefold more abundant than previously assumed, and we suggest that replication may occasionally initiate in essentially every intergenic region. These results shed light on recent reports that as many as 15% of replication events initiate outside of known origins, and this broader distribution of replication origins suggest that S phase in yeast may be less distinct from that in humans than widely assumed.

Identification of 1600 replication origins in S. cerevisiae

There are approximately 500 known origins of replication in the yeast genome, and the process by which DNA replication initiates at these locations is well understood. In particular, these sites are made competent to initiate replication by loading of the Mcm replicative helicase prior to the start of S phase; thus, "a site to which MCM is bound in G1" might be considered to provide an operational definition of a replication origin. By fusing a subunit of Mcm to micrococcal nuclease, a technique referred to as "Chromatin Endogenous Cleavage", we previously showed that known origins are typically bound by a single Mcm double hexamer, loaded adjacent to the ARS consensus sequence (ACS). Here we extend this analysis from known origins to the entire genome, identifying candidate Mcm binding sites whose signal intensity varies over at least 3 orders of magnitude. Published data quantifying the production of ssDNA during S phase showed clear evidence of replication initiation among the most abundant 1600 of these sites, with replication activity decreasing in concert with Mcm abundance and disappearing at the limit of detection of ssDNA. Three other hallmarks of replication origins were apparent among the most abundant 5,500 sites. Specifically, these sites (1) appeared in intergenic nucleosome-free regions that were flanked on one or both sides by well-positioned nucleosomes; (2) were flanked by ACSs; and (3) exhibited a pattern of GC skew characteristic of replication initiation. Furthermore, the high resolution of this technique allowed us to demonstrate a strong bias for detecting Mcm double-hexamers downstream rather than upstream of the ACS, which is consistent with the directionality of Mcm loading by Orc that has been observed in vitro. We conclude that, if sites at which Mcm double-hexamers are loaded can function as replication origins, then DNA replication origins are at least 3-fold more abundant than previously assumed, and we suggest that replication may occasionally initiate in essentially every intergenic region. These results shed light on recent reports that as many as 15% of replication events initiate outside of known origins, and this broader distribution of replication origins suggest that S phase in yeast may be less distinct from that in humans than is widely assumed.

Identity preserving multi-pose facial expression recognition using fine tuned VGG on the latent space vector of generative adversarial network

Facial expression is the crucial component for human beings to express their mental state and it has become one of the prominent areas of research in computer vision. However, the task becomes challenging when the given facial image is non-frontal. The influence of poses on facial images is alleviated using an encoder of a generative adversarial network capable of learning pose invariant representations. State-of-art results for image generation are achieved using styleGAN architecture. An efficient model is proposed to embed the given image into the latent vector space of styleGAN. The encoder extracts high-level features of the facial image and encodes them into the latent space. Rigorous analysis of semantics hidden in the latent space of styleGAN is performed. Based on the analysis, the facial image is synthesized, and facial expressions are recognized using an expression recognition neural network. The original image is recovered from the features encoded in the latent space. Semantic editing operations like face rotation, style transfer, face aging, image morphing and expression transfer can be performed on the image obtained from the image generated using the features encoded latent space of styleGAN. L2 feature-wise loss is applied to warrant the quality of the rebuilt image. The facial image is then fed into the attribute classifier to extract high-level features, and the features are concatenated to perform facial expression classification. Evaluations are performed on the generated results to demonstrate that state-of-art results are achieved using the proposed method.

Evidence of gene nucleotide composition favoring replication and growth in a fastidious plant pathogen

Nucleotide composition (GC content) varies across bacteria species, genome regions, and specific genes. In Xylella fastidiosa, a vector-borne fastidious plant pathogen infecting multiple crops, GC content ranges between ∼51-52%; however, these values were gathered using limited genomic data. We evaluated GC content variations across X. fastidiosa subspecies fastidiosa (N = 194), subsp. pauca (N = 107), and subsp. multiplex (N = 39). Genomes were classified based on plant host and geographic origin; individual genes within each genome were classified based on gene function, strand, length, ortholog group, Core vs. Accessory, and Recombinant vs. Non-recombinant. GC content was calculated for each gene within each evaluated genome. The effects of genome and gene level variables were evaluated with a mixed effect ANOVA, and the marginal-GC content was calculated for each gene. Also, the correlation between gene-specific GC content vs. natural selection (dN/dS) and recombination/mutation (r/m) was estimated. Our analyses show that intra-genomic changes in nucleotide composition in X. fastidiosa are small and influenced by multiple variables. Higher AT-richness is observed in genes involved in replication and translation, and genes in the leading strand. In addition, we observed a negative correlation between high-AT and dN/dS in subsp. pauca. The relationship between recombination and GC content varied between core and accessory genes. We hypothesize that distinct evolutionary forces and energetic constraints both drive and limit these small variations in nucleotide composition.

Delving into Eukaryotic Origins of Replication Using DNA Structural Features

DNA replication in eukaryotes is an intricate process, which is precisely synchronized by a set of regulatory proteins, and the replication fork emanates from discrete sites on chromatin called origins of replication (Oris). These spots are considered as the gateway to chromosomal replication and are stereotyped by sequence motifs. The cognate sequences are noticeable in a small group of entire origin regions or totally absent across different metazoans. Alternatively, the use of DNA secondary structural features can provide additional information compared to the primary sequence. In this article, we report the trends in DNA sequence-based structural properties of origin sequences in nine eukaryotic systems representing different families of life. Biologically relevant DNA secondary structural properties, namely, stability, propeller twist, flexibility, and minor groove shape were studied in the sequences flanking replication start sites. Results indicate that Oris in yeasts show lower stability, more rigidity, and narrow minor groove preferences compared to genomic sequences surrounding them. Yeast Oris also show preference for A-tracts and the promoter element TATA box in the vicinity of replication start sites. On the contrary, Drosophila melanogaster, humans, and Arabidopsis thaliana do not have such features in their Oris, and instead, they show high preponderance of G-rich sequence motifs such as putative G-quadruplexes or i-motifs and CpG islands. Our extensive study applies the DNA structural feature computation to delve into origins of replication across organisms ranging from yeasts to mammals and including a plant. Insights from this study would be significant in understanding origin architecture and help in designing new algorithms for predicting DNA trans-acting factor recognition events.

A computational platform to identify origins of replication sites in eukaryotes

The locations of the initiation of genomic DNA replication are defined as origins of replication sites (ORIs), which regulate the onset of DNA replication and play significant roles in the DNA replication process. The study of ORIs is essential for understanding the cell-division cycle and gene expression regulation. Accurate identification of ORIs will provide important clues for DNA replication research and drug development by developing computational methods. In this paper, the first integrated predictor named iORI-Euk was built to identify ORIs in multiple eukaryotes and multiple cell types. In the predictor, seven eukaryotic (Homo sapiens, Mus musculus, Drosophila melanogaster, Arabidopsis thaliana, Pichia pastoris, Schizosaccharomyces pombe and Kluyveromyces lactis) ORI data was collected from public database to construct benchmark datasets. Subsequently, three feature extraction strategies which are k-mer, binary encoding and combination of k-mer and binary were used to formulate DNA sequence samples. We also compared the different classification algorithms' performance. As a result, the best results were obtained by using support vector machine in 5-fold cross-validation test and independent dataset test. Based on the optimal model, an online web server called iORI-Euk (http://lin-group.cn/server/iORI-Euk/) was established for the novel ORI identification.

Using extreme gradient boosting to identify origin of replication in Saccharomyces cerevisiae via hybrid features

DNA replication is a fundamental task that plays a crucial role in the propagation of all living things on earth. Hence, the accurate identification of its origin could be the key to giving an insightful understanding of the regulatory mechanism of gene expression. Indeed, with the robust development of computational techniques and the abundant biological sequencing data, it has become possible for scientists to identify the origin of replication accurately and promptly. This growing concern has drawn a lot of attention among experts in this field. However, to gain better outcomes, more work is required. Therefore, this study is designed to explore the combination of state-of-the-art features and extreme gradient boosting learning system in classifying DNA sequences. Our hybrid approach is able to identify the origin of DNA replication with achieved sensitivity of 85.19%, specificity of 93.83%, accuracy of 89.51%, and MCC of 0.7931. Evidence is presented to show that our proposed method is superior to the state-of-the-art methods on the same benchmark dataset. Moreover, the research results represent a further step towards developing the prediction models for DNA replication in particular and DNA sequences in general.

iRO-PsekGCC: Identify DNA Replication Origins Based on Pseudo k-Tuple GC Composition

Identification of replication origins is playing a key role in understanding the mechanism of DNA replication. This task is of great significance in DNA sequence analysis. Because of its importance, some computational approaches have been introduced. Among these predictors, the iRO-3wPseKNC predictor is the first discriminative method that is able to correctly identify the entire replication origins. For further improving its predictive performance, we proposed the Pseudo k-tuple GC Composition (PsekGCC) approach to capture the "GC asymmetry bias" of yeast species by considering both the GC skew and the sequence order effects of k-tuple GC Composition (k-GCC) in this study. Based on PseKGCC, we proposed a new predictor called iRO-PsekGCC to identify the DNA replication origins. Rigorous jackknife test on two yeast species benchmark datasets (Saccharomyces cerevisiae, Pichia pastoris) indicated that iRO-PsekGCC outperformed iRO-3wPseKNC. It can be anticipated that iRO-PsekGCC will be a useful tool for DNA replication origin identification. Availability and implementation: The web-server for the iRO-PsekGCC predictor was established, and it can be accessed at http://bliulab.net/iRO-PsekGCC/.

Sir2 suppresses transcription-mediated displacement of Mcm2-7 replicative helicases at the ribosomal DNA repeats

Repetitive DNA sequences within eukaryotic heterochromatin are poorly transcribed and replicate late in S-phase. In Saccharomyces cerevisiae, the histone deacetylase Sir2 is required for both transcriptional silencing and late replication at the repetitive ribosomal DNA arrays (rDNA). Despite the widespread association between transcription and replication timing, it remains unclear how transcription might impinge on replication, or vice versa. Here we show that, when silencing of an RNA polymerase II (RNA Pol II)-transcribed non-coding RNA at the rDNA is disrupted by SIR2 deletion, RNA polymerase pushes and thereby relocalizes replicative Mcm2-7 helicases away from their loading sites to an adjacent region with low nucleosome occupancy, and this relocalization is associated with increased rDNA origin efficiency. Our results suggest a model in which two of the major defining features of heterochromatin, transcriptional silencing and late replication, are mechanistically linked through suppression of polymerase-mediated displacement of replication initiation complexes.

Eukaryotic genomes typically contain large regions of repetitive DNA, referred to as heterochromatin, that are both transcriptionally silent and late replicating. We provide a possible explanation for the association between transcriptional silencing and late replication. Budding yeast contains a histone deacetylase called SIR2 that was originally identified as a transcriptional repressor, but was later also found to ensure late replication of repetitive ribosomal DNA (rDNA) sequences. We show that the transcription that occurs in the absence of SIR2 directly displaces the helicase required for replication initiation at the rDNA. This work represents an important advance in understanding the interplay between transcription and replication at repetitive sequences by directly linking transcription with replication machinery in heterochromatin.

Recent Advances on the Machine Learning Methods in Identifying DNA Replication Origins in Eukaryotic Genomics

The initiate site of DNA replication is called origins of replication (ORI) which is regulated by a set of regulatory proteins and plays important roles in the basic biochemical process during cell growth and division in all living organisms. Therefore, the study of ORIs is essential for understanding the cell-division cycle and gene expression regulation so that scholars can develop a new strategy against genetic diseases by using the knowledge of DNA replication. Thus, the accurate identification of ORIs will provide key clues for DNA replication research and clinical medicine. Although, the conventional experiments could provide accurate results, they are time-consuming and cost ineffective. On the contrary, bioinformatics-based methods can overcome these shortcomings. Especially, with the emergence of DNA sequences in the post-genomic era, it is highly expected to develop high throughput tools to identify ORIs based on sequence information. In this review, we will summarize the current progress in computational prediction of eukaryotic ORIs including the collection of benchmark dataset, the application of machine learning-based techniques, the results obtained by these methods, and the construction of web servers. Finally, we gave the future perspectives on ORIs prediction. The review provided readers with a whole background of ORIs prediction based on machine learning methods, which will be helpful for researchers to study DNA replication in-depth and drug therapy of genetic defect.

Euclidean Distance Analysis Enables Nucleotide Skew Analysis in Viral Genomes

Nucleotide skew analysis is a versatile method to study the nucleotide composition of RNA/DNA molecules, in particular to reveal characteristic sequence signatures. For instance, skew analysis of the nucleotide bias of several viral RNA genomes indicated that it is enriched in the unpaired, single-stranded genome regions, thus creating an even more striking virus-specific signature. The comparison of skew graphs for many virus isolates or families is difficult, time-consuming, and nonquantitative. Here, we present a procedure for a more simple identification of similarities and dissimilarities between nucleotide skew data of coronavirus, flavivirus, picornavirus, and HIV-1 RNA genomes. Window and step sizes were normalized to correct for differences in length of the viral genome. Cumulative skew data are converted into pairwise Euclidean distance matrices, which can be presented as neighbor-joining trees. We present skew value trees for the four virus families and show that closely related viruses are placed in small clusters. Importantly, the skew value trees are similar to the trees constructed by a “classical” model of evolutionary nucleotide substitution. Thus, we conclude that the simple calculation of Euclidean distances between nucleotide skew data allows an easy and quantitative comparison of characteristic sequence signatures of virus genomes. These results indicate that the Euclidean distance analysis of nucleotide skew data forms a nice addition to the virology toolbox.

iRSpot-Pse6NC: Identifying recombination spots in Saccharomyces cerevisiae by incorporating hexamer composition into general PseKNC

Meiotic recombination caused by meiotic double-strand DNA breaks. In some regions the frequency of DNA recombination is relatively higher, while in other regions the frequency is lower: the former is usually called "recombination hotspot", while the latter the "recombination coldspot". Information of the hot and cold spots may provide important clues for understanding the mechanism of genome revolution. Therefore, it is important to accurately predict these spots. In this study, we rebuilt the benchmark dataset by unifying its samples with a same length (131 bp). Based on such a foundation and using SVM (Support Vector Machine) classifier, a new predictor called "iRSpot-Pse6NC" was developed by incorporating the key hexamer features into the general PseKNC (Pseudo K-tuple Nucleotide Composition) via the binomial distribution approach. It has been observed via rigorous cross-validations that the proposed predictor is superior to its counterparts in overall accuracy, stability, sensitivity and specificity. For the convenience of most experimental scientists, the web-server for iRSpot-Pse6NC has been established at http://lin-group.cn/server/iRSpot-Pse6NC, by which users can easily obtain their desired result without the need to go through the detailed mathematical equations involved.

Context based computational analysis and characterization of ARS consensus sequences (ACS) of Saccharomyces cerevisiae genome

Genome-wide experimental studies in Saccharomyces cerevisiae reveal that autonomous replicating sequence (ARS) requires an essential consensus sequence (ACS) for replication activity. Computational studies identified thousands of ACS like patterns in the genome. However, only a few hundreds of these sites act as replicating sites and the rest are considered as dormant or evolving sites. In a bid to understand the sequence makeup of replication sites, a content and context-based analysis was performed on a set of replicating ACS sequences that binds to origin-recognition complex (ORC) denoted as ORC-ACS and non-replicating ACS sequences (nrACS), that are not bound by ORC. In this study, DNA properties such as base composition, correlation, sequence dependent thermodynamic and DNA structural profiles, and their positions have been considered for characterizing ORC-ACS and nrACS. Analysis reveals that ORC-ACS depict marked differences in nucleotide composition and context features in its vicinity compared to nrACS. Interestingly, an A-rich motif was also discovered in ORC-ACS sequences within its nucleosome-free region. Profound changes in the conformational features, such as DNA helical twist, inclination angle and stacking energy between ORC-ACS and nrACS were observed. Distribution of ACS motifs in the non-coding segments points to the locations of ORC-ACS which are found far away from the adjacent gene start position compared to nrACS thereby enabling an accessible environment for ORC-proteins. Our attempt is novel in considering the contextual view of ACS and its flanking region along with nucleosome positioning in the S. cerevisiae genome and may be useful for any computational prediction scheme.

A deformation energy-based model for predicting nucleosome dyads and occupancy

Nucleosome plays an essential role in various cellular processes, such as DNA replication, recombination, and transcription. Hence, it is important to decode the mechanism of nucleosome positioning and identify nucleosome positions in the genome. In this paper, we present a model for predicting nucleosome positioning based on DNA deformation, in which both bending and shearing of the nucleosomal DNA are considered. The model successfully predicted the dyad positions of nucleosomes assembled in vitro and the in vitro map of nucleosomes in Saccharomyces cerevisiae. Applying the model to Caenorhabditis elegans and Drosophila melanogaster, we achieved satisfactory results. Our data also show that shearing energy of nucleosomal DNA outperforms bending energy in nucleosome occupancy prediction and the ability to predict nucleosome dyad positions is attributed to bending energy that is associated with rotational positioning of nucleosomes.

Survey of Programs Used to Detect Alternative Splicing Isoforms from Deep Sequencing Data In Silico

Next-generation sequencing techniques have been rapidly emerging. However, the massive sequencing reads hide a great deal of unknown important information. Advances have enabled researchers to discover alternative splicing (AS) sites and isoforms using computational approaches instead of molecular experiments. Given the importance of AS for gene expression and protein diversity in eukaryotes, detecting alternative splicing and isoforms represents a hot topic in systems biology and epigenetics research. The computational methods applied to AS prediction have improved since the emergence of next-generation sequencing. In this study, we introduce state-of-the-art research on AS and then compare the research methods and software tools available for AS based on next-generation sequencing reads. Finally, we discuss the prospects of computational methods related to AS.

Recent advances in the genome-wide study of DNA replication origins in yeast

DNA replication, one of the central events in the cell cycle, is the basis of biological inheritance. In order to be duplicated, a DNA double helix must be opened at defined sites, which are called DNA replication origins (ORIs). Unlike in bacteria, where replication initiates from a single replication origin, multiple origins are utilized in the eukaryotic genomes. Among them, the ORIs in budding yeast Saccharomyces cerevisiae and the fission yeast Schizosaccharomyces pombe have been best characterized. In recent years, advances in DNA microarray and next-generation sequencing technologies have increased the number of yeast species involved in ORIs research dramatically. The ORIs in some non-conventional yeast species such as Kluyveromyces lactis and Pichia pastoris have also been genome-widely identified. Relevant databases of replication origins in yeast were constructed, then the comparative genomic analysis can be carried out. Here, we review several experimental approaches that have been used to map replication origins in yeast and some of the available web resources related to yeast ORIs. We also discuss the sequence characteristics and chromosome structures of ORIs in the four yeast species, which can be utilized to improve yeast replication origins prediction.