Vera Afreixo

Comparative context analysis of codon pairs on an ORFeome scale

Codon context is an important feature of gene primary structure that modulates mRNA decoding accuracy. We have developed an analytical software package and a graphical interface for comparative codon context analysis of all the open reading frames in a genome (the ORFeome). Using the complete ORFeome sequences of Saccharomyces cerevisiae, Schizosaccharomyces pombe, Candida albicans and Escherichia coli, we show that this methodology permits large-scale codon context comparisons and provides new insight on the rules that govern the evolution of codon-pair context.

Fourier analysis of symbolic data: A brief review

We overview and discuss several methods for the Fourier analysis of symbolic data, such as DNA sequences, emphasizing their mutual connections. We consider the indicator sequence approach, the vector and the symbolic autocorrelation methods, and methods such as the spectral envelope, that for each frequency optimize the symbolic-no-numeric mapping to emphasize any periodic data features. We discuss the equivalence or connections between these methods. We show that it is possible to define the autocorrelation function of symbolic data, assuming only that we can compare any two symbols and decide if they are equal or distinct. The autocorrelation is a numeric sequence, and its Fourier transform can also be obtained by summing the squares of the Fourier transform of indicator sequences (zero/one sequences indicating the position of the symbols). Another interpretation of the spectrum is given, borrowing from the spectral envelope concept: among all symbolic-to-numeric mappings there is one that maximizes the spectral energy at each frequency, and leads to the spectrum.

Genome analysis with inter-nucleotide distances

Motivation: DNA sequences can be represented by sequences of four symbols, but it is often useful to convert the symbols into real or complex numbers for further analysis. Several mapping schemes have been used in the past, but they seem unrelated to any intrinsic characteristic of DNA. The objective of this work was to find a mapping scheme directly related to DNA characteristics and that would be useful in discriminating between different species. Mathematical models to explore DNA correlation structures may contribute to a better knowledge of the DNA and to find a concise DNA description. Results: We developed a methodology to process DNA sequences based on inter-nucleotide distances. Our main contribution is a method to obtain genomic signatures for complete genomes, based on the inter-nucleotide distances, that are able to discriminate between different species. Using these signatures and hierarchical clustering, it is possible to build phylogenetic trees. Phylogenetic trees lead to genome differentiation and allow the inference of phylogenetic relations. The phylogenetic trees generated in this work display related species close to each other, suggesting that the inter-nucleotide distances are able to capture essential information about the genomes. To create the genomic signature, we construct a vector which describes the inter-nucleotide distance distribution of a complete genome and compare it with the reference distance distribution, which is the distribution of a sequence where the nucleotides are placed randomly and independently. It is the residual or relative error between the data and the reference distribution that is used to compare the DNA sequences of different organisms. Contact: vera@ua.pt

A Three-State Model for DNA Protein-Coding Regions

It is known that the protein-coding regions of DNA are usually characterized by a three-base periodicity. In this paper, we exploit this property, studying a DNA model based on three deterministic states, where each state implements a finite-context model. The experimental results obtained confirm the appropriateness of the proposed approach, showing compression gains in relation to the single finite-context model counterpart. Additionally, and potentially more interesting than the compression gain on its own, is the observation that the entropy associated to each of the three base positions of a codon differs and that this variation is not the same among the organisms analyzed

The Use of Procalcitonin (PCT) for Diagnosis of Sepsis in Burn Patients: A Meta-Analysis.

The continuous development of resuscitation techniques and intensive care reduced the mortality rate induced by the initial shock in burn patients and, currently, infections (especially sepsis) are the main causes of mortality of these patients. The misuse of antimicrobial agents is strongly related to antimicrobial and adverse patient outcomes, development of microbial resistance and increased healthcare-related costs. To overcome these risks, antimicrobial stewardship is mandatory and biomarkers are useful to avoid unnecessary medical prescription, to monitor antimicrobial therapy and to support the decision of its stop. Among a large array of laboratory tests, procalcitonin (PCT) emerged as the leading biomarker to accurately and time-effectively indicate the presence of systemic infection. In the presence of systemic infection, PCT blood levels undergo a sudden and dramatic increase, following the course of the infection, and quickly subside after the control of the septic process. This work is a meta-analysis on PCT performance as a biomarker for sepsis. This meta–analysis showed that overall pooled area under the curve (AUC) is 0.83 (95% CI = 0.76 to 0.90); the estimated cut-off is 1.47 ng/mL. The overall sepsis effect in PCT levels is significant and strong (Cohens d is 2.1 and 95% CI = 1.1 to 3.2). This meta–analysis showed PCT may be considered as a biomarker with a strong diagnostic ability to discriminate between the septic from the non-septic burn patients. Thus, this work encourages the determination of PCT levels in clinical practice for the management of these patients, in order to timely identify the susceptibility to sepsis and to initiate the antimicrobial therapy, improving the patients’ outcomes.

Analysis of single-strand exceptional word symmetry in the human genome: new measures

Some previous studies suggest the extension of Chargaffs second rule (the phenomenon of symmetry in a single DNA strand) to long DNA words. However, in random sequences generated under an independent symbol model where complementary nucleotides have equal occurrence probabilities, we expect the phenomenon of symmetry to hold for any word length. In this work, we develop new statistical methods to measure the exceptional symmetry. Exceptional symmetry is a refinement of Chargaffs second parity rule that highlights the words whose frequency of occurrence is similar to that of its reversed complement but dissimilar to the frequencies of occurrence of other words which contain the same number of nucleotides A or T. We analyze words of lengths up to 12 in the complete human genome and in each chromosome separately. We assess exceptional symmetry globally, by word group, and by word. We conclude that the global symmetry present in the human genome is clearly exceptional and significant. The chromosomes present distinct exceptional symmetry profiles. There are several exceptional word groups and exceptional words with a strong exceptional symmetry.

The breakdown of the word symmetry in the human genome.

Previous studies have suggested that Chargaffs second rule may hold for relatively long words (above 10nucleotides), but this has not been conclusively shown. In particular, the following questions remain open: Is the phenomenon of symmetry statistically significant? If so, what is the word length above which significance is lost? Can deviations in symmetry due to the finite size of the data be identified? This work addresses these questions by studying word symmetries in the human genome, chromosomes and transcriptome. To rule out finite-length effects, the results are compared with those obtained from random control sequences built to satisfy Chargaffs second parity rule. We use several techniques to evaluate the phenomenon of symmetry, including Pearsons correlation coefficient, total variational distance, a novel word symmetry distance, as well as traditional and equivalence statistical tests. We conclude that word symmetries are statistical significant in the human genome for word lengths up to 6nucleotides. For longer words, we present evidence that the phenomenon may not be as prevalent as previously thought.

Methicillin-resistant Staphylococcus aureus carrying the new mecC gene--a meta-analysis.

In 2011, a new mecA gene homolog, named mecC gene, was found in isolates from both humans and animals. The discovery of methicillin-resistant Staphylococcus aureus (MRSA) carrying the mecC gene has caused speculations about the origin, epidemiology, and impact of these isolates. The objective of this work is to perform a meta-analysis on the prevalence of mecC MRSA, based on previously published results. Meta-analysis showed that the overall pooled prevalence is 0.009% (95% confidence interval=0.05-0.013) and that there was evidence of heterogeneity (P<0.01, I(2)=97%). In conclusion, the very low reported prevalence provides an important baseline to monitor the epidemiology of this emerging form of MRSA.

A Genomic Distance for Assembly Comparison Based on Compressed Maximal Exact Matches

Genome assemblies are typically compared with respect to their contiguity, coverage, and accuracy. We propose a genome-wide, alignment-free genomic distance based on compressed maximal exact matches and suggest adding it to the benchmark of commonly used assembly quality metrics. Maximal exact matches are perfect repeats, without gaps or misspellings, which cannot be further extended to either their left- or right-end side without loss of similarity. The genomic distance here proposed is based on the normalized compression distance, an information-theoretic measure of the relative compressibility of two sequences estimated using multiple finite-context models. This measure exposes similarities between the sequences, as well as, the nesting structure underlying the assembly of larger maximal exact matches from smaller ones. We use four human genome assemblies for illustration and discuss the impact of genome sequencing and assembly in the final content of maximal exact matches and the genomic distance here proposed.

Inter-dinucleotide distances in the human genome: an analysis of the whole-genome and protein-coding distributions.

We study the inter-dinucleotide distance distributions in the human genome, both in the whole-genome and protein-coding regions. The inter-dinucleotide distance is defined as the distance to the next occurrence of the same dinucleotide. We consider the 16 sequences of inter-dinucleotide distances and two reading frames. Our results show a period-3 oscillation in the protein-coding inter-dinucleotide distance distributions that is absent from the whole-genome distributions. We also compare the distance distribution of each dinucleotide to a reference distribution, that of a random sequence generated with the same dinucleotide abundances, revealing the CG dinucleotide as the one with the highest cumulative relative error for the first 60 distances. Moreover, the distance distribution of each dinucleotide is compared to the distance distribution of all other dinucleotides using the Kullback-Leibler divergence. We find that the distance distribution of a dinucleotide and that of its reversed complement are very similar, hence, the divergence between them is very small. This is an interesting finding that may give evidence of a stronger parity rule than Chargaffs second parity rule.