Identifying statistical dependence in genomic sequences via mutual information estimates

Causal discovery using compression-complexity measures

Causal inference is one of the most fundamental problems across all domains of science. We address the problem of inferring a causal direction from two observed discrete symbolic sequences X and Y. We present a framework which relies on lossless compressors for inferring context-free grammars (CFGs) from sequence pairs and quantifies the extent to which the grammar inferred from one sequence compresses the other sequence. We infer X causes Y if the grammar inferred from X better compresses Y than in the other direction. To put this notion to practice, we propose three models that use the Compression-Complexity Measures (CCMs) - Lempel-Ziv (LZ) complexity and Effort-To-Compress (ETC) to infer CFGs and discover causal directions without demanding temporal structures. We evaluate these models on synthetic and real-world benchmarks and empirically observe performances competitive with current state-of-the-art methods. Lastly, we present two unique applications of the proposed models for causal inference directly from pairs of genome sequences belonging to the SARS-CoV-2 virus. Using numerous sequences, we show that our models capture causal information exchanged between genome sequence pairs, presenting novel opportunities for addressing key issues in sequence analysis to investigate the evolution of virulence and pathogenicity in future applications.

Improved algorithm for analysis of DNA sequences using multiresolution transformation

Bioinformatics and genomic signal processing use computational techniques to solve various biological problems. They aim to study the information allied with genetic materials such as the deoxyribonucleic acid (DNA), the ribonucleic acid (RNA), and the proteins. Fast and precise identification of the protein coding regions in DNA sequence is one of the most important tasks in analysis. Existing digital signal processing (DSP) methods provide less accurate and computationally complex solution with greater background noise. Hence, improvements in accuracy, computational complexity, and reduction in background noise are essential in identification of the protein coding regions in the DNA sequences. In this paper, a new DSP based method is introduced to detect the protein coding regions in DNA sequences. Here, the DNA sequences are converted into numeric sequences using electron ion interaction potential (EIIP) representation. Then discrete wavelet transformation is taken. Absolute value of the energy is found followed by proper threshold. The test is conducted using the data bases available in the National Centre for Biotechnology Information (NCBI) site. The comparative analysis is done and it ensures the efficiency of the proposed system.

To control false positives in gene-gene interaction analysis: two novel conditional entropy-based approaches

Genome-wide analysis of gene-gene interactions has been recognized as a powerful avenue to identify the missing genetic components that can not be detected by using current single-point association analysis. Recently, several model-free methods (e.g. the commonly used information based metrics and several logistic regression-based metrics) were developed for detecting non-linear dependence between genetic loci, but they are potentially at the risk of inflated false positive error, in particular when the main effects at one or both loci are salient. In this study, we proposed two conditional entropy-based metrics to challenge this limitation. Extensive simulations demonstrated that the two proposed metrics, provided the disease is rare, could maintain consistently correct false positive rate. In the scenarios for a common disease, our proposed metrics achieved better or comparable control of false positive error, compared to four previously proposed model-free metrics. In terms of power, our methods outperformed several competing metrics in a range of common disease models. Furthermore, in real data analyses, both metrics succeeded in detecting interactions and were competitive with the originally reported results or the logistic regression approaches. In conclusion, the proposed conditional entropy-based metrics are promising as alternatives to current model-based approaches for detecting genuine epistatic effects.

Identification of specificity determining residues in peptide recognition domains using an information theoretic approach applied to large-scale binding maps

Background

Peptide Recognition Domains (PRDs) are commonly found in signaling proteins. They mediate protein-protein interactions by recognizing and binding short motifs in their ligands. Although a great deal is known about PRDs and their interactions, prediction of PRD specificities remains largely an unsolved problem.

Results

We present a novel approach to identifying these Specificity Determining Residues (SDRs). Our algorithm generalizes earlier information theoretic approaches to coevolution analysis, to become applicable to this problem. It leverages the growing wealth of binding data between PRDs and large numbers of random peptides, and searches for PRD residues that exhibit strong evolutionary covariation with some positions of the statistical profiles of bound peptides. The calculations involve only information from sequences, and thus can be applied to PRDs without crystal structures. We applied the approach to PDZ, SH3 and kinase domains, and evaluated the results using both residue proximity in co-crystal structures and verified binding specificity maps from mutagenesis studies.

Discussion

Our predictions were found to be strongly correlated with the physical proximity of residues, demonstrating the ability of our approach to detect physical interactions of the binding partners. Some high-scoring pairs were further confirmed to affect binding specificity using previous experimental results. Combining the covariation results also allowed us to predict binding profiles with higher reliability than two other methods that do not explicitly take residue covariation into account.

Conclusions

The general applicability of our approach to the three different domain families demonstrated in this paper suggests its potential in predicting binding targets and assisting the exploration of binding mechanisms.

CpG_MI: a novel approach for identifying functional CpG islands in mammalian genomes

CpG islands (CGIs) are CpG-rich regions compared to CpG-depleted bulk DNA of mammalian genomes and are generally regarded as the epigenetic regulatory regions in association with unmethylation, promoter activity and histone modifications. Accurate identification of CpG islands with epigenetic regulatory function in bulk genomes is of wide interest. Here, the common features of functional CGIs are identified using an average mutual information method to differentiate functional CGIs from the remaining CGIs. A new approach (CpG mutual information, CpG_MI) was further explored to identify functional CGIs based on the cumulative mutual information of physical distances between two neighboring CpGs. Compared to current approaches, CpG_MI achieved the highest prediction accuracy. This approach also identified new functional CGIs overlapping with gene promoter regions which were missed by other algorithms. Nearly all CGIs identified by CpG_MI overlapped with histone modification marks. CpG_MI could also be used to identify potential functional CGIs in other mammalian genomes, as the CpG dinucleotide contents and cumulative mutual information distributions are almost the same among six mammalian genomes in our analysis. It is a reliable quantitative tool for the identification of functional CGIs from bulk genomes and helps in understanding the relationships between genomic functional elements and epigenomic modifications.