cirCodAn: A GHMM-based tool for accurate prediction of coding regions in circRNA.

Studies focusing on characterizing circRNAs with the potential to translate into peptides are quickly advancing. It is helping to elucidate the roles played by circRNAs in several biological processes, especially in the emergence and development of diseases. While various tools are accessible for predicting coding regions within linear sequences, none have demonstrated accurate open reading frame detection in circular sequences, such as circRNAs.

CodAn: predictive models for precise identification of coding regions in eukaryotic transcripts.

Motivation: Characterization of the coding sequences (CDSs) is an essential step in transcriptome annotation. Incorrect identification of CDSs can lead to the prediction of non-existent proteins that can eventually compromise knowledge if databases are populated with similar incorrect predictions made in different genomes. Also, the correct identification of CDSs is important for the characterization of the untranslated regions (UTRs), which are known to be important regulators of the mRNA translation process.

Sequence motif finder using memetic algorithm.

Background: De novo prediction of Transcription Factor Binding Sites (TFBS) using computational methods is a difficult task and it is an important problem in Bioinformatics. The correct recognition of TFBS plays an important role in understanding the mechanisms of gene regulation and helps to develop new drugs.

Results: We here present Memetic Framework for Motif Discovery (MFMD), an algorithm that uses semi-greedy constructive heuristics as a local optimizer.

DNA methylation landscape of hepatoblastomas reveals arrest at early stages of liver differentiation and cancer-related alterations.

Hepatoblastomas are uncommon embryonal liver tumors accounting for approximately 80% of childhood hepatic cancer. We hypothesized that epigenetic changes, including DNA methylation, could be relevant to hepatoblastoma onset. The methylomes of eight matched hepatoblastomas and non-tumoral liver tissues were characterized, and data were validated in an independent group (11 hepatoblastomas).

DNA Methylation Levels of Melanoma Risk Genes Are Associated with Clinical Characteristics of Melanoma Patients.

In melanoma development, oncogenic process is mediated by genetic and epigenetic mutations, and few studies have so far explored the role of DNA methylation either as predisposition factor or biomarker. We tested patient samples for germline CDKN2A methylation status and found no evidence of inactivation by promoter hypermethylation. We have also investigated the association of clinical characteristics of samples with the DNA methylation pattern of twelve genes relevant for melanomagenesis.

LINE-1 hypermethylation in peripheral blood of cutaneous melanoma patients is associated with metastasis.

Aberrant DNA methylation pattern is a well-known epigenetic marker of cancer cells. Recently, aberrant methylation was also reported in the peripheral blood of cancer patients and it could potentially serve as a biomarker for cancer risk. We investigated the methylation pattern of LINE-1 and other repetitive DNA elements in peripheral blood of cutaneous melanoma patients in order to search for an association with clinical characteristics.

ToPS: a framework to manipulate probabilistic models of sequence data.

Discrete Markovian models can be used to characterize patterns in sequences of values and have many applications in biological sequence analysis, including gene prediction, CpG island detection, alignment, and protein profiling. We present ToPS, a computational framework that can be used to implement different applications in bioinformatics analysis by combining eight kinds of models: (i) independent and identically distributed process; (ii) variable-length Markov chain; (iii) inhomogeneous Markov chain; (iv) hidden Markov model; (v) profile hidden Markov model; (vi) pair hidden Markov model; (vii) generalized hidden Markov model; and (viii) similarity based sequence weighting. The framework includes functionality for training, simulation and decoding of the models.

Decreasing the number of false positives in sequence classification.

Background: A large number of probabilistic models used in sequence analysis assign non-zero probability values to most input sequences. To decide when a given probability is sufficient the most common way is bayesian binary classification, where the probability of the model characterizing the sequence family of interest is compared to that of an alternative probability model. We can use as alternative model a null model.

EGene: a configurable pipeline generation system for automated sequence analysis.

Unlabelled: EGene is a generic, flexible and modular pipeline generation system that makes pipeline construction a modular job. EGene allows for third-party programs to be used and integrated according to the needs of distinct projects and without any previous programming or formal language experience being required. EGene comes with CoEd, a visual tool to facilitate pipeline construction and documentation.

Characterization of SCAR markers of Eimeria spp. of domestic fowl and construction of a public relational database (The Eimeria SCARdb).

This study reports the development and characterization of 151 sequence characterized amplified region (SCAR) markers for the seven Eimeria species that infect the domestic fowl. From this set, 84 markers are species-specific and 67 present partial specificity. The complete nucleotide sequence was derived for all markers, revealing the presence of micro- and minisatellite repetitive units in 22 SCARs, with up to five distinct repeat units being observed per marker.