A nullomer is an oligomer that does not occur as a subsequence in a given DNA sequence, i.e. it is an absent word of that sequence. The importance of nullomers in several applications, from drug discovery to forensic practice, is now debated in the literature. Here, we investigated the nature of nullomers, whether their absence in genomes has just a statistical explanation or it is a peculiar feature of genomic sequences. We introduced an extension of the notion of nullomer, namely high order nullomers, which are nullomers whose mutated sequences are still nullomers. We studied different aspects of them: comparison with nullomers of random sequences, CpG distribution and mean helical rise. In agreement with previous results we found that the number of nullomers in the human genome is much larger than expected by chance. Nevertheless antithetical results were found when considering a random DNA sequence preserving dinucleotide frequencies. The analysis of CpG frequencies in nullomers and high order nullomers revealed, as expected, a high CpG content but it also highlighted a strong dependence of CpG frequencies on the dinucleotide position, suggesting that nullomers have their own peculiar structure and are not simply sequences whose CpG frequency is biased. Furthermore, phylogenetic trees were built on eleven species based on both the similarities between the dinucleotide frequencies and the number of nullomers two species share, showing that nullomers are fairly conserved among close species. Finally the study of mean helical rise of nullomers sequences revealed significantly high mean rise values, reinforcing the hypothesis that those sequences have some peculiar structural features. The obtained results show that nullomers are the consequence of the peculiar structure of DNA (also including biased CpG frequency and CpGs islands), so that the hypermutability model, also taking into account CpG islands, seems to be not sufficient to explain nullomer phenomenon. Finally, high order nullomers could emphasize those features that already make simple nullomers useful in several applications.

Prediction of binding sites for transcription factors is important to understand how the latter regulate gene expression and how this regulation can be modulated for therapeutic purposes. A consistent number of references address this issue with different approaches, Machine Learning being one of the most successful. Nevertheless, we note that many such approaches fail to propose a robust and meaningful method to embed the genetic data under analysis. We try to overcome this problem by proposing a bidirectional transformer-based encoder, empowered by bidirectional long-short term memory layers and with a capsule layer responsible for the final prediction. To evaluate the efficiency of the proposed approach, we use benchmark ChIP-seq datasets of five cell lines available in the ENCODE repository (A549, GM12878, Hep-G2, H1-hESC, and Hela). The results show that the proposed method can predict TFBS within the five different cell lines very well; moreover, cross-cell predictions provide satisfactory results as well. Experiments conducted across cell lines are reinforced by the analysis of five additional lines used only to test the model trained using the others. The results confirm that prediction across cell lines remains very high, allowing an extensive cross-transcription factor analysis to be performed from which several indications of interest for molecular biology may be drawn.

Next Generation Sequencing (NGS) machines extract from a biological sample a large number of short DNA fragments (reads). These reads are then used for several applications, e.g., sequence reconstruction, DNA assembly, gene expression profiling, mutation analysis. We propose a method to evaluate the similarity between reads. This method does not rely on the alignment of the reads and it is based on the distance between the frequencies of their substrings of fixed dimensions (k-mers). We compare this alignment-free distance with the similarity measures derived from two alignment methods: Needleman-Wunsch and Blast. The comparison is based on a simple assumption: the most correct distance is obtained by knowing in advance the reference sequence. Therefore, we first align the reads on the original DNA sequence, compute the overlap between the aligned reads, and use this overlap as an ideal distance. We then verify how the alignment-free and the alignment-based distances reproduce this ideal distance. The ability of correctly reproducing the ideal distance is evaluated over samples of read pairs from Saccharomyces cerevisiae, Escherichia coli, and Homo sapiens. The comparison is based on the correctness of threshold predictors cross-validated over different samples. We exhibit experimental evidence that the proposed alignment-free distance is a potentially useful read-to-read distance measure and performs better than the more time consuming distances based on alignment. Alignment-free distances may be used effectively for reads comparison, and may provide a significant speed-up in several processes based on NGS sequencing (e.g., DNA assembly, reads classification).

In this article we present evidence for a relationship between chromosome gene loci and the topological properties of the protein-protein interaction network corresponding to the set of genes under consideration. Specifically, for each chromosome of the Saccharomyces cerevisiae genome, the distribution of the intra-chromosome inter-gene distances was analyzed and a positive correlation with the distance among the corresponding proteins of the protein-protein interaction network was found. In order to study this relationship we used concepts based on non-parametric statistics and information theory. We provide statistical evidence that if two genes are closely located, then it is likely that their protein products are closely located in the protein-protein interaction network, or in other words, that they are involved in the same biological process.

Experimental co-expression data and protein–protein interaction networks are frequently used to analyze the interactions among genes or proteins. Recent studies have investigated methods to integrate these two sources of information. We propose a new method to integrate co-expression data obtained through DNA microarray analysis (MA) and protein–protein interaction (PPI) network data, and apply it to Arabidopsis thaliana . The proposed method identifies small subsets of highly interacting proteins. Based on the analysis of the basis of co-localization and mRNA developmental expression, we show that these groups provide important biological insights; additionally, these subsets are significantly enriched with respect to KEGG Pathways and can be used to predict successfully whether proteins belong to known pathways. Thus, the method is able to provide relevant biological information and support the functional identification of complex genetic traits of economic value in plant agrigenomics research. The method has been implemented in a prototype software tool named CLAIM ( CL uster A nalysis I ntegration M ethod) and can be downloaded from http://bio.cs.put.poznan.pl/research_fields . CLAIM is based on the separate clustering of MA and PPI data; the clusters are merged in a special graph; cliques of this graph are subsets of strongly connected proteins. The proposed method was successfully compared with existing methods. CLAIM appears to be a useful semi-automated tool for protein functional analysis and warrants further evaluation in agrigenomics research.