DNA physical properties outperform sequence compositional information in classifying nucleosome-enriched and -depleted regions

Competitive Chemical Reaction Kinetic Model of Nucleosome Assembly Using the Histone Variant H2A.Z and H2A In Vitro

Nucleosomes not only serve as the basic building blocks for eukaryotic chromatin but also regulate many biological processes, such as DNA replication, repair, and recombination. To modulate gene expression in vivo, the histone variant H2A.Z can be dynamically incorporated into the nucleosome. However, the assembly dynamics of H2A.Z-containing nucleosomes remain elusive. Here, we demonstrate that our previous chemical kinetic model for nucleosome assembly can be extended to H2A.Z-containing nucleosome assembly processes. The efficiency of H2A.Z-containing nucleosome assembly, like that of canonical nucleosome assembly, was also positively correlated with the total histone octamer concentration, reaction rate constant, and reaction time. We expanded the kinetic model to represent the competitive dynamics of H2A and H2A.Z in nucleosome assembly, thus providing a novel method through which to assess the competitive ability of histones to assemble nucleosomes. Based on this model, we confirmed that histone H2A has a higher competitive ability to assemble nucleosomes in vitro than histone H2A.Z. Our competitive kinetic model and experimental results also confirmed that in vitro H2A.Z-containing nucleosome assembly is governed by chemical kinetic principles.

Moderation of Structural DNA Properties by Coupled Dinucleotide Contents in Eukaryotes

Dinucleotides are known as determinants for various structural and physiochemical properties of DNA and for binding affinities of proteins to DNA. These properties (e.g., stiffness) and bound proteins (e.g., transcription factors) are known to influence important biological functions, such as transcription regulation and 3D chromatin organization. Accordingly, the question arises of how the considerable variations in dinucleotide contents of eukaryotic chromosomes could still provide consistent DNA properties resulting in similar functions and 3D conformations. In this work, we investigate the hypothesis that coupled dinucleotide contents influence DNA properties in opposite directions to moderate each other's influences. Analyzing all 2478 chromosomes of 155 eukaryotic species, considering bias from coding sequences and enhancers, we found sets of correlated and anti-correlated dinucleotide contents. Using computational models, we estimated changes of DNA properties resulting from this coupling. We found that especially pure A/T dinucleotides (AA, TT, AT, TA), known to influence histone positioning and AC/GT contents, are relevant moderators and that, e.g., the Roll property, which is known to influence histone affinity of DNA, is preferably moderated. We conclude that dinucleotide contents might indirectly influence transcription and chromatin 3D conformation, via regulation of histone occupancy and/or other mechanisms.

Nucleosome Assembly and Disassembly in vitro Are Governed by Chemical Kinetic Principles

As the elementary unit of eukaryotic chromatin, nucleosomes in vivo are highly dynamic in many biological processes, such as DNA replication, repair, recombination, or transcription, to allow the necessary factors to gain access to their substrate. The dynamic mechanism of nucleosome assembly and disassembly has not been well described thus far. We proposed a chemical kinetic model of nucleosome assembly and disassembly in vitro. In the model, the efficiency of nucleosome assembly was positively correlated with the total concentration of histone octamer, reaction rate constant and reaction time. All the corollaries of the model were well verified for the Widom 601 sequence and the six artificially synthesized DNA sequences, named CS1–CS6, by using the salt dialysis method in vitro. The reaction rate constant in the model may be used as a new parameter to evaluate the nucleosome reconstitution ability with DNAs. Nucleosome disassembly experiments for the Widom 601 sequence detected by Förster resonance energy transfer (FRET) and fluorescence thermal shift (FTS) assays demonstrated that nucleosome disassembly is the inverse process of assembly and can be described as three distinct stages: opening phase of the (H2A–H2B) dimer/(H3–H4)₂ tetramer interface, release phase of the H2A–H2B dimers from (H3–H4)₂ tetramer/DNA and removal phase of the (H3–H4)₂ tetramer from DNA. Our kinetic model of nucleosome assembly and disassembly allows to confirm that nucleosome assembly and disassembly in vitro are governed by chemical kinetic principles.

Recent development of bioinformatics tools for microRNA target prediction

MicroRNAs (miRNAs) are central players that regulate the post-transcriptional processes of gene expression. Binding of miRNAs to target mRNAs can repress their translation by inducing the degradation or by inhibiting the translation of the target mRNAs. High-throughput experimental approaches for miRNA target identification are costly and time-consuming, depending on various factors. It is vitally important to develop the bioinformatics methods for accurately predicting miRNA targets. With the increase of RNA sequences in the post-genomic era, bioinformatics methods are being developed for miRNA studies specially for miRNA target prediction. This review summarizes the current development of state-of-the-art bioinformatics tools for miRNA target prediction, points out the progress and limitations of the available miRNA databases, and their working principles. Finally, we discuss the caveat and perspectives of the next-generation algorithms for the prediction of miRNA targets.

Epigenetic Marks and Variation of Sequence-Based Information Along Genomic Regions Are Predictive of Recombination Hot/Cold Spots in Saccharomyces cerevisiae

Characterization and identification of recombination hotspots provide important insights into the mechanism of recombination and genome evolution. In contrast with existing sequence-based models for predicting recombination hotspots which were defined in a ORF-based manner, here, we first defined recombination hot/cold spots based on public high-resolution Spo11-oligo-seq data, then characterized them in terms of DNA sequence and epigenetic marks, and finally presented classifiers to identify hotspots. We found that, in addition to some previously discovered DNA-based features like GC-skew, recombination hotspots in yeast can also be characterized by some remarkable features associated with DNA physical properties and shape. More importantly, by using DNA-based features and several epigenetic marks, we built several classifiers to discriminate hotspots from coldspots, and found that SVM classifier performs the best with an accuracy of ∼92%, which is also the highest among the models in comparison. Feature importance analysis combined with prediction results show that epigenetic marks and variation of sequence-based features along the hotspots contribute dominantly to hotspot identification. By using incremental feature selection method, an optimal feature subset that consists of much less features was obtained without sacrificing prediction accuracy.

A deformation energy model reveals sequence-dependent property of nucleosome positioning

We present a deformation energy model for predicting nucleosome positioning, in which a position-dependent structural parameter set derived from crystal structures of nucleosomes was used to calculate the DNA deformation energy. The model is successful in predicting nucleosome occupancy genome-wide in budding yeast, nucleosome free energy, and rotational positioning of nucleosomes. Our model also indicates that the genomic regions underlying the MNase-sensitive nucleosomes in budding yeast have high deformation energy and, consequently, low nucleosome-forming ability, while the MNase-sensitive non-histone particles are characterized by much lower DNA deformation energy and high nucleosome preference. In addition, we also revealed that remodelers, SNF2 and RSC8, are likely to act in chromatin remodeling by binding to broad nucleosome-depleted regions that are intrinsically favorable for nucleosome positioning. Our data support the important role of position-dependent physical properties of DNA in nucleosome positioning.

Deform-nu: A DNA Deformation Energy-Based Predictor for Nucleosome Positioning

The structure and function of chromatin can be regulated through positioning patterns of nucleosomes. DNA-based processes are regulated via nucleosomes. Therefore, it is significant to determine nucleosome positions in DNA-based processes. A deformation energy model was proposed to predict nucleosome positions in our previous study. A free web server based on the model (http://lin-group.cn/server/deform-nu/) was firstly established to estimate the occupancy and rotational positioning of nucleosomes in the study. Then, the performance of the model was verified by several examples. The results indicated that nucleosome positioning relied on the physical properties of DNA, such as deformation energy.

Prediction of Sphingosine protein-coding regions with a self adaptive spectral rotation method

Identifying protein coding regions in DNA sequences by computational methods is an active research topic. Welan gum produced by Sphingomonas sp. WG has great application potential in oil recovery and concrete construction industry. Predicting the coding regions in the Sphingomonas sp. WG genome and addressing the mechanism underlying the explanation for the synthesis of Welan gum metabolism is an important issue at present. In this study, we apply a self adaptive spectral rotation (SASR, for short) method, which is based on the investigation of the Triplet Periodicity property, to predict the coding regions of the whole-genome data of Sphingomonas sp. WG without any previous training process, and 1115 suspected gene fragments are obtained. Suspected gene fragments are subjected to a similarity search against the non-redundant protein sequences (nr) database of NCBI with blastx, and 762 suspected gene fragments have been labeled as genes in the nr database.

Bastion3: a two-layer ensemble predictor of type III secreted effectors

MOTIVATION

Type III secreted effectors (T3SEs) can be injected into host cell cytoplasm via type III secretion systems (T3SSs) to modulate interactions between Gram-negative bacterial pathogens and their hosts. Due to their relevance in pathogen-host interactions, significant computational efforts have been put toward identification of T3SEs and these in turn have stimulated new T3SE discoveries. However, as T3SEs with new characteristics are discovered, these existing computational tools reveal important limitations: (i) most of the trained machine learning models are based on the N-terminus (or incorporating also the C-terminus) instead of the proteins' complete sequences, and (ii) the underlying models (trained with classic algorithms) employed only few features, most of which were extracted based on sequence-information alone. To achieve better T3SE prediction, we must identify more powerful, informative features and investigate how to effectively integrate these into a comprehensive model.

RESULTS

In this work, we present Bastion3, a two-layer ensemble predictor developed to accurately identify type III secreted effectors from protein sequence data. In contrast with existing methods that employ single models with few features, Bastion3 explores a wide range of features, from various types, trains single models based on these features and finally integrates these models through ensemble learning. We trained the models using a new gradient boosting machine, LightGBM and further boosted the models' performances through a novel genetic algorithm (GA) based two-step parameter optimization strategy. Our benchmark test demonstrates that Bastion3 achieves a much better performance compared to commonly used methods, with an ACC value of 0.959, F-value of 0.958, MCC value of 0.917 and AUC value of 0.956, which comprehensively outperformed all other toolkits by more than 5.6% in ACC value, 5.7% in F-value, 12.4% in MCC value and 5.8% in AUC value. Based on our proposed two-layer ensemble model, we further developed a user-friendly online toolkit, maximizing convenience for experimental scientists toward T3SE prediction. With its design to ease future discoveries of novel T3SEs and improved performance, Bastion3 is poised to become a widely used, state-of-the-art toolkit for T3SE prediction.

AVAILABILITY AND IMPLEMENTATION

http://bastion3.erc.monash.edu/.

CONTACT

selkrig@embl.de or wyztli@163.com or or trevor.lithgow@monash.edu.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.