Arlindo L. Oliveira

The YEASTRACT database: a tool for the analysis of transcription regulatory associations in Saccharomyces cerevisiae

We present the YEAst Search for Transcriptional Regulators And Consensus Tracking (YEASTRACT; ) database, a tool for the analysis of transcription regulatory associations in Saccharomyces cerevisiae. This database is a repository of 12 346 regulatory associations between transcription factors and target genes, based on experimental evidence which was spread throughout 861 bibliographic references. It also includes 257 specific DNA-binding sites for more than a hundred characterized transcription factors. Further information about each yeast gene included in the database was obtained from Saccharomyces Genome Database (SGD), Regulatory Sequences Analysis Tools and Gene Ontology (GO) Consortium. Computational tools are also provided to facilitate the exploitation of the gathered data when solving a number of biological questions as exemplified in the Tutorial also available on the system. YEASTRACT allows the identification of documented or potential transcription regulators of a given gene and of documented or potential regulons for each transcription factor. It also renders possible the comparison between DNA motifs, such as those found to be over-represented in the promoter regions of co-regulated genes, and the transcription factor-binding sites described in the literature. The system also provides an useful mechanism for grouping a list of genes (for instance a set of genes with similar expression profiles as revealed by microarray analysis) based on their regulatory associations with known transcription factors.

The YEASTRACT database: an upgraded information system for the analysis of gene and genomic transcription regulation in Saccharomyces cerevisiae

The YEASTRACT (http://www.yeastract.com) information system is a tool for the analysis and prediction of transcription regulatory associations in Saccharomyces cerevisiae. Last updated in June 2013, this database contains over 200 000 regulatory associations between transcription factors (TFs) and target genes, including 326 DNA binding sites for 113 TFs. All regulatory associations stored in YEASTRACT were revisited and new information was added on the experimental conditions in which those associations take place and on whether the TF is acting on its target genes as activator or repressor. Based on this information, new queries were developed allowing the selection of specific environmental conditions, experimental evidence or positive/negative regulatory effect. This release further offers tools to rank the TFs controlling a gene or genome-wide response by their relative importance, based on (i) the percentage of target genes in the data set; (ii) the enrichment of the TF regulon in the data set when compared with the genome; or (iii) the score computed using the TFRank system, which selects and prioritizes the relevant TFs by walking through the yeast regulatory network. We expect that with the new data and services made available, the system will continue to be instrumental for yeast biologists and systems biology researchers.

Techniques for the creation of digital watermarks in sequential circuit designs

We present a methodology for the watermarking of synchronous sequential circuits that makes it possible to identify the authorship of designs by imposing a digital watermark on the state transition graph (STG) of the circuit. The methodology is applicable to sequential designs that are made available as firm intellectual property, the designation commonly used to characterize designs specified as structural hardware description languages or circuit netlists. The watermarking is obtained by manipulating the STG of the design in such a way as to make it exhibit a chosen property that is extremely rare in nonwatermarked circuits while, at the same time, not changing the functionality of the circuit. This manipulation is performed without ever actually computing this graph in either implicit or explicit form. Instead, the digital watermark is obtained by direct manipulation of the circuit description. We present evidence that no known algorithms for circuit manipulation can be used to efficiently remove or change the watermark and that the process is immune to a variety of other attacks. We present both theoretical and experimental results that show that the watermarking can be created and verified efficiently. We also test possible attack strategies and verify that they are inapplicable to realistic designs of medium to large complexity.

Finite state machine decomposition for low power

Clock-gating techniques have been shown to be very effective in the reduction of the switching activity in sequential logic circuits. The authors describe a new clock-gating technique based on finite state machine (FSM) decomposition. They compute two sub-FSMs that together have the same functionality as the original FSM. For all the transitions within one sub-FSM, the clock for the other sub-FSM is disabled. To minimize the average switching activity, they search for a small cluster of states with high stationary state probability and use it to create the small sub-FSM. This way one will have a small amount of logic that is active most of the time, during which is disabling a much larger circuit, the other sub-FSM. They provide a set of experimental results that show that power consumption can be substantially reduced, in some cases up to 80%.

A new algorithm for the reduction of incompletely specified finite state machines

We propose an algorithm for the problem of state reduction in incompletely specified finite state machines. This algorithm is not based on the enumeration of compatible sets, and therefore, its performance is not dependent on the number of prime compatibles. We prove that the algorithm is exact and present results that show that, in a set of hard problems, it is much more efficient than both the explicit and implicit approaches based on the enumeration of compatible sets.

Prime implicant computation using satisfiability algorithms

The computation of prime implicants has several and significant applications in different areas, including automated reasoning, non-monotonic reasoning, electronic design automation, among others. The authors describe a new model and algorithm for computing minimum-size prime implicants of propositional formulas. The proposed approach is based on creating an integer linear program (ILP) formulation for computing the minimum-size prime implicant, which simplifies existing formulations. In addition, they introduce two new algorithms for solving ILPs, both of which are built on top of an algorithm for propositional satisfiability (SAT). Given the organization of the proposed SAT algorithm, the resulting ILP procedures implement powerful search pruning techniques, including a non-chronological backtracking search strategy, clause recording procedures and identification of necessary assignments. Experimental results, obtained on several benchmark examples, indicate that the proposed model and algorithms are significantly more efficient than other existing solutions.

An Efficient Algorithm for the Identification of Structured Motifs in DNA Promoter Sequences

We propose a new algorithm for identifying cis-regulatory modules in genomic sequences. The proposed algorithm, named RISO, uses a new data structure, called box-link, to store the information about conserved regions that occur in a well-ordered and regularly spaced manner in the data set sequences. This type of conserved regions, called structured motifs, is extremely relevant in the research of gene regulatory mechanisms since it can effectively represent promoter models. The complexity analysis shows a time and space gain over the best known exact algorithms that is exponential in the spacings between binding sites. A full implementation of the algorithm was developed and made available online. Experimental results show that the algorithm is much faster than existing ones, sometimes by more than four orders of magnitude. The application of the method to biological data sets shows its ability to extract relevant consensi

Efficient Algorithms for the Inference of Minimum Size DFAs

This work describes algorithms for the inference of minimum size deterministic automata consistent with a labeled training set. The algorithms presented represent the state of the art for this problem, known to be computationally very hard.In particular, we analyze the performance of algorithms that use implicit enumeration of solutions and algorithms that perform explicit search but incorporate a set of techniques known as dependency directed backtracking to prune the search tree effectively.We present empirical results that show the comparative efficiency of the methods studied and discuss alternative approaches to this problem, evaluating their advantages and drawbacks.

A Data Mining Approach for the Detection of High-Risk Breast Cancer Groups

It is widely agreed that complex diseases are typically caused by the joint effects of multiple instead of a single genetic variation. These genetic variations may show very little effect individually but strong effect if they occur jointly, a phenomenon known as epistasis or multilocus interaction. In this work, we explore the applicability of decision trees to this problem. A case-control study was performed, composed of 164 controls and 94 cases with 32 SNPs available from the BRCA1, BRCA2 and TP53 genes. There was also information about tobacco and alcohol consumption. We used a Decision Tree to find a group with high-susceptibility of suffering from breast cancer. Our goal was to find one or more leaves with a high percentage of cases and small percentage of controls. To statistically validate the association found, permutation tests were used. We found a high-risk breast cancer group composed of 13 cases and only 1 control, with a Fisher Exact Test value of 9.7×10− 6. After running 10000 permutation tests we obtained a p-value of 0.017. These results show that it is possible to find statistically significant associations with breast cancer by deriving a decision tree and selecting the best leaf.

Implicit FSM decomposition applied to low-power design

Clock-gating techniques are very effective in the reduction of the switching activity in sequential logic circuits. In this paper, we describe a clock-gating technique based on finite-state machine (FSM) decomposition. The approach is based on the computation of two sub-FSMs that together have the same functionality as the original FSM. For all the transitions within one sub-FSM, the clock for the other sub-FSM is disabled. To minimize the average switching activity, we search for a small cluster of states with high stationary state probability and use it to create the small sub-FSM. Explicit manipulation of the state transition graph requires time and space exponential on the number of registers in the circuit, thereby restricting the applicability of explicit methods to relatively small circuits. The approach we propose is based on a method that implicitly performs the FSM decomposition. Using this technique, the FSM decomposition is performed by direct manipulation of the circuit. We provide a set of experiments that show that power consumption can be substantially reduced, in some cases by more than 70%.