João M. O. S. Rodrigues

On finding minimal absent words

BackgroundThe problem of finding the shortest absent words in DNA data has been recently addressed, and algorithms for its solution have been described. It has been noted that longer absent words might also be of interest, but the existing algorithms only provide generic absent words by trivially extending the shortest ones.ResultsWe show how absent words relate to the repetitions and structure of the data, and define a new and larger class of absent words, called minimal absent words, that still captures the essential properties of the shortest absent words introduced in recent works. The words of this new class are minimal in the sense that if their leftmost or rightmost character is removed, then the resulting word is no longer an absent word. We describe an algorithm for generating minimal absent words that, in practice, runs in approximately linear time. An implementation of this algorithm is publicly available at ftp://www.ieeta.pt/~ap/maws.ConclusionBecause the set of minimal absent words that we propose is much larger than the set of the shortest absent words, it is potentially more useful for applications that require a richer variety of absent words. Nevertheless, the number of minimal absent words is still manageable since it grows at most linearly with the string size, unlike generic absent words that grow exponentially. Both the algorithm and the concepts upon which it depends shed additional light on the structure of absent words and complement the existing studies on the topic.

Methodologies to assess muscle co-contraction during gait in people with neurological impairment – A systematic literature review

PURPOSE To review the methodologies used to assess muscle co-contraction (MCo) with surface electromyography (sEMG) during gait in people with neurological impairment. METHODS The Scopus (1995-2013), Web of Science (1970-2013), PubMed (1948-2013) and B-on (1999-2013) databases were searched. Articles were included when sEMG was used to assess MCo during gait in people with impairment due to central nervous system disorders (CNS). RESULTS Nineteen articles met the inclusion criteria and most studied people with cerebral palsy and stroke. No consensus was identified for gait assessment protocols (surfaces, speed, distance), sEMG acquisition (electrodes position), analysis of sEMG data (filters, normalisation techniques) and quantification of MCo (agonist-antagonist linear envelopes overlapping or agonist-antagonist overlapping periods of muscles activity, onset delimited). CONCLUSION Given the wide range of methodologies employed, it is not possible to recommend the most appropriate for assessing MCo. Researchers should adopt recognized standards in future work. This is needed before consensus about the role that MCo plays in gait impairment in neurological diseases and its potential as a target for gait rehabilitation can be determined.

Minimal Absent Words in Prokaryotic and Eukaryotic Genomes

Minimal absent words have been computed in genomes of organisms from all domains of life. Here, we explore different sets of minimal absent words in the genomes of 22 organisms (one archaeota, thirteen bacteria and eight eukaryotes). We investigate if the mutational biases that may explain the deficit of the shortest absent words in vertebrates are also pervasive in other absent words, namely in minimal absent words, as well as to other organisms. We find that the compositional biases observed for the shortest absent words in vertebrates are not uniform throughout different sets of minimal absent words. We further investigate the hypothesis of the inheritance of minimal absent words through common ancestry from the similarity in dinucleotide relative abundances of different sets of minimal absent words, and find that this inheritance may be exclusive to vertebrates.

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.

Sensor and information fusion applied to a robotic soccer team

This paper is focused on the sensor and information fusion techniques used by a robotic soccer team. Due to the fact that the sensor information is affected by noise, and taking into account the multi-agent environment, these techniques can significantly improve the accuracy of the robot world model. One of the most important elements of the world model is the robot self-localisation. Here, the team localisation algorithm is presented focusing on the integration of visual and compass information. To improve the ball position and velocity reliability, two different techniques have been developed. A study of the visual sensor noise is presented and, according to this analysis, the resulting noise variation depending on the distance is used to define a Kalman filter for ball position. Moreover, linear regression is used for velocity estimation purposes, both for the ball and the robot. This implementation of linear regression has an adaptive buffer size so that, on hard deviations from the path (detected using the Kalman filter), the regression converges more quickly. A team cooperation method based on sharing of the ball position is presented. Besides the ball, obstacle detection and identification is also an important challenge for cooperation purposes. Detecting the obstacles is ceasing to be enough and identifying which obstacles are team mates and opponents is becoming a need. An approach for this identification is presented, considering the visual information, the known characteristics of the team robots and shared localisation among team members. The same idea of distance dependent noise, studied before, is used to improve this identification. Some of the described work, already implemented before RoboCup2008, improved the team performance, allowing it to achieve the 1st place in the Portuguese robotics open Robotica2008 and in the RoboCup2008 world championship.

XS: a FASTQ read simulator

BackgroundThe emerging next-generation sequencing (NGS) is bringing, besides the natural huge amounts of data, an avalanche of new specialized tools (for analysis, compression, alignment, among others) and large public and private network infrastructures. Therefore, a direct necessity of specific simulation tools for testing and benchmarking is rising, such as a flexible and portable FASTQ read simulator, without the need of a reference sequence, yet correctly prepared for producing approximately the same characteristics as real data.FindingsWe present XS, a skilled FASTQ read simulation tool, flexible, portable (does not need a reference sequence) and tunable in terms of sequence complexity. It has several running modes, depending on the time and memory available, and is aimed at testing computing infrastructures, namely cloud computing of large-scale projects, and testing FASTQ compression algorithms. Moreover, XS offers the possibility of simulating the three main FASTQ components individually (headers, DNA sequences and quality-scores).ConclusionsXS provides an efficient and convenient method for fast simulation of FASTQ files, such as those from Ion Torrent (currently uncovered by other simulators), Roche-454, Illumina and ABI-SOLiD sequencing machines. This tool is publicly available at http://bioinformatics.ua.pt/software/xs/.

Improving the Traffic Prediction Capability of Neural Networks Using Sliding Window and Multi-task Learning Mechanisms

Due to the diversity of network services and the unpredictability of their behaviors, there is an increasing need for tools that can aid in the global management of IP networks. Being able to predict network data can be very useful to anticipate network upgrading decisions or changes on the network functional operation. This paper proposes a practical approach, based on neural networks, that is able to predict network traffic in a specific network link. In order to improve the prediction capabilities of the different neural network models, sliding window and multi-task learning mechanisms are introduced and tested. By applying this prediction framework to different network links, it will be possible to predict the evolution of the global network traffic and use this information for network security, management and planning purposes. The results obtained by applying the proposed model to realistic network scenarios show that this concept can achieve excellent performance in the prediction of the network traffic on the selected links. The prediction is accurate even when there are significant changes in the number of users and their respective profiles. Moreover, the proposed prediction approach is generic and can be used to predict different network data with a very satisfactory accuracy, even with simple and small NN models.

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.