Position Weight Matrix or Acyclic Probabilistic Finite Automaton: Which model to use? A decision rule inferred for the prediction of transcription factor binding sites.

Prediction of transcription factor binding sites (TFBS) is an example of application of Bioinformatics where DNA molecules are represented as sequences of A, C, G and T symbols. The most used model in this problem is Position Weight Matrix (PWM). Notwithstanding the advantage of being simple, PWMs cannot capture dependency between nucleotide positions, which may affect prediction performance. Acyclic Probabilistic Finite Automata (APFA) is an alternative model able to accommodate position dependencies. However, APFA is a more complex model, which means more parameters have to be learned. In this paper, we propose an innovative method to identify when position dependencies influence preference for PWMs or APFAs. This implied using position dependency features extracted from 1106 sets of TFBS to infer a decision tree able to predict which is the best model - PWM or APFA - for a given set of TFBSs. According to our results, as few as three pinpointed features are able to choose the best model, providing a balance of performance (average precision) and model simplicity.

Global repression by tailless during segmentation.

The orphan nuclear receptor Tailless (Tll) exhibits conserved roles in brain formation and maintenance that are shared, for example, with vertebrate orthologous forms (Tlx). However, the early expression of tll in two gap domains in the segmentation cascade of Drosophila is unusual even for most other insects. Here we investigate tll regulation on pair-rule stripes. With ectopic misexpression of tll we detected unexpected repression of almost all pair-rule stripes of hairy (h), even-skipped (eve), runt (run), and fushi-tarazu (ftz). Examining Tll embryonic ChIP-chip data with regions mapped as Cis-Regulatory Modules (CRMs) of pair-rule stripes we verified Tll interactions to these regions. With the ChIP-chip data we also verified Tll interactions to the CRMs of gap domains and in the misexpression assay, Tll-mediated repression on Kruppel (Kr), kni (kni) and giant (gt) according to their differential sensitivity to Tll. These results with gap genes confirmed previous data from the literature and argue against indirect repression roles of Tll in the striped pattern. Moreover, the prediction of Tll binding sites in the CRMs of eve stripes and the mathematical modeling of their removal using an experimentally validated theoretical framework shows effects on eve stripes compatible with the absence of a repressor binding to the CRMs. In addition, modeling increased tll levels in the embryo results in the differential repression of eve stripes, agreeing well with the results of the misexpression assay. In genetic assays we investigated eve 5, that is strongly repressed by the ectopic domain and representative of more central stripes not previously implied to be under direct regulation of tll. While this stripe is little affected in tll-, its posterior border is expanded in gt- but detected with even greater expansion in gt-;tll-. We end up by discussing tll with key roles in combinatorial repression mechanisms to contain the expression of medial patterns of the segmentation cascade in the extremities of the embryo.

Computer-aided autism diagnosis using visual attention models and eye-tracking: replication and improvement proposal.

BACKGROUND: Autism Spectrum Disorder (ASD) diagnosis can be aided by approaches based on eye-tracking signals. Recently, the feasibility of building Visual Attention Models (VAMs) from features extracted from visual stimuli and their use for classifying cases and controls has been demonstrated using Neural Networks and Support Vector Machines. The present work has three aims: 1) to evaluate whether the trained classifier from the previous study was generalist enough to classify new samples with a new stimulus; 2) to replicate the previously approach to train a new classifier with a new dataset; 3) to evaluate the performance of classifiers obtained by a new classification algorithm (Random Forest) using the previous and the current datasets. METHODS: The previously approach was replicated with a new stimulus and new sample, 44 from the Typical Development group and 33 from the ASD group. After the replication, Random Forest classifier was tested to substitute Neural Networks algorithm. RESULTS: The test with the trained classifier reached an AUC of 0.56, suggesting that the trained classifier requires retraining of the VAMs when changing the stimulus. The replication results reached an AUC of 0.71, indicating the potential of generalization of the approach for aiding ASD diagnosis, as long as the stimulus is similar to the originally proposed. The results achieved with Random Forest were superior to those achieved with the original approach, with an average AUC of 0.95 for the previous dataset and 0.74 for the new dataset. CONCLUSION: In summary, the results of the replication experiment were satisfactory, which suggests the robustness of the approach and the VAM-based approaches feasibility to aid in ASD diagnosis. The proposed method change improved the classification performance. Some limitations are discussed and additional studies are encouraged to test other conditions and scenarios.

Computer aided diagnosis of neurodevelopmental disorders and genetic syndromes based on facial images - A systematic literature review.

Neurodevelopment disorders can result in facial dysmorphisms. Therefore, the analysis of facial images using image processing and machine learning techniques can help construct systems for diagnosing genetic syndromes and neurodevelopmental disorders. The systems offer faster and cost-effective alternatives for genotyping tests, particularly when dealing with large-scale applications. However, there are still challenges to overcome to ensure the accuracy and reliability of computer-aided diagnosis systems. This article presents a systematic review of such initiatives, including 55 articles. The main aspects used to develop these diagnostic systems were discussed, namely datasets - availability, type of image, size, ethnicities and syndromes - types of facial features, techniques used for normalization, dimensionality reduction and classification, deep learning, as well as a discussion related to the main gaps, challenges and opportunities.

A Stochastic Grammar Approach to Mass Classification in Mammograms.

Breast cancer is responsible for approximately 15% of all cancer-related deaths among women worldwide, and early and accurate diagnosis increases the chances of survival. Over the last decades, several machine learning approaches have been used to improve the diagnosis of this disease, but most of them require a large set of samples for training. Syntactic approaches were barely used in this context, although it can present good results even if the training set has few samples. This article presents a syntactic approach to classify masses as benign or malignant. There were used features extracted from a polygonal representation of masses combined with a stochastic grammar approach to discriminate the masses found in mammograms. The results were compared with other machine learning techniques, and the grammar-based classifiers showed superior performance in the classification task. The best accuracies achieved were from 96% to 100%, indicating that grammatical approaches are robust and able to discriminate the masses even when trained with small samples of images. Syntactic approaches could be more frequently employed in the classification of masses, since they can learn the pattern of benign and malignant masses from a small sample of images achieving similar results when compared to the state of art.

MicroRNA target prediction tools for animals: Where we are at and where we are going to - A systematic review.

MicroRNAs (miRNAs) are non-coding RNAs containing 19-26 nucleotides, and they directly regulate the translation of mRNAs by binding to them. MiRNAs participate in various physiological processes and are associated with the development of diseases, such as cancer. Therefore, understanding miRNAs regulation on targets is crucial for understanding the mechanisms of diseases and for obtaining a more suitable treatment. In animals, the base complementarity between miRNAs and the mRNA is imperfect, hindering the prediction of these targets. Thus, over the past 15 years, several computational tools have emerged for the prediction of miRNA targets in animals, generally with a focus on human expression data. Taking into account the wide range of prediction tools, a systematic review is presented here to analyze and classify these methods and features to enable the most appropriate choice according to the needs of each researcher. In this study, only articles whose methods met the inclusion and exclusion criteria established in the protocol were considered. The search was performed in November 2020, in two search engines PubMed and VHL Regional Portal. Among the initial 5315 journals found in the two searches, 78 articles were accepted, comprising 49 different tools analyzed and grouped by features and method similarities. As we limited our criteria to animals, all tools found in our search were suitable for human studies. The results demonstrated the evolution of prediction tools, including the most used features, such as alignment and thermodynamics, the methods used, as well as performance issues. It is possible to conclude that the currently available miRNA target prediction tools and methods can be aggregated with new features or other methods to improve accuracy.

An additive repression mechanism sets the anterior limits of anterior pair-rule stripes 1.

Segments are repeated anatomical units forming the body of insects. In Drosophila, the specification of the body takes place during the blastoderm through the segmentation cascade. Pair-rule genes such as hairy (h), even-skipped (eve), runt (run), and fushi-tarazu (ftz) are of the intermediate level of the cascade and each pair-rule gene is expressed in seven transversal stripes along the antero-posterior axis of the embryo. Stripes are formed by independent cis-regulatory modules (CRMs) under the regulation of transcription factors of maternal source and of gap proteins of the first level of the cascade. The initial blastoderm of Drosophila is a syncytium and it also coincides with the mid-blastula transition when thousands of zygotic genes are transcribed and their products are able to diffuse in the cytoplasm. Thus, we anticipated a complex regulation of the CRMs of the pair-rule stripes. The CRMs of h 1, eve 1, run 1, ftz 1 are able to be activated by bicoid (bcd) throughout the anterior blastoderm and several lines of evidence indicate that they are repressed by the anterior gap genes slp1 (sloppy-paired 1), tll (tailless) and hkb (huckebein). The modest activity of these repressors led to the premise of a combinatorial mechanism regulating the expression of the CRMs of h 1, eve 1, run 1, ftz 1 in more anterior regions of the embryo. We tested this possibility by progressively removing the repression activities of slp1, tll and hkb. In doing so, we were able to expose a mechanism of additive repression limiting the anterior borders of stripes 1. Stripes 1 respond depending on their distance from the anterior end and repressors operating at different levels.

Computer-aided autism diagnosis based on visual attention models using eye tracking.

An advantage of using eye tracking for diagnosis is that it is non-invasive and can be performed in individuals with different functional levels and ages. Computer/aided diagnosis using eye tracking data is commonly based on eye fixation points in some regions of interest (ROI) in an image. However, besides the need for every ROI demarcation in each image or video frame used in the experiment, the diversity of visual features contained in each ROI may compromise the characterization of visual attention in each group (case or control) and consequent diagnosis accuracy. Although some approaches use eye tracking signals for aiding diagnosis, it is still a challenge to identify frames of interest when videos are used as stimuli and to select relevant characteristics extracted from the videos. This is mainly observed in applications for autism spectrum disorder (ASD) diagnosis. To address these issues, the present paper proposes: (1) a computational method, integrating concepts of Visual Attention Model, Image Processing and Artificial Intelligence techniques for learning a model for each group (case and control) using eye tracking data, and (2) a supervised classifier that, using the learned models, performs the diagnosis. Although this approach is not disorder-specific, it was tested in the context of ASD diagnosis, obtaining an average of precision, recall and specificity of 90%, 69% and 93%, respectively.

Visual Search Efficiency in Mild Cognitive Impairment and Alzheimer's Disease: An Eye Movement Study.

BACKGROUND: Visual search abilities are essential to everyday life activities and are known to be affected in Alzheimer's disease (AD). However, little is known about visual search efficiency in mild cognitive impairment (MCI), a transitive state between normal aging and dementia. Eye movement studies and machine learning methods have been recently used to detect oculomotor impairments in individuals with dementia. OBJECTIVE: The aim of the present study is to investigate the association between eye movement metrics and visual search impairment in MCI and AD. METHODS: 127 participants were tested: 43 healthy controls, 51 with MCI, and 33 with AD. They completed an eyetracking visual search task where they had to find a previously seen target stimulus among distractors. RESULTS: Both patient groups made more fixations on the screen when searching for a target, with longer duration than controls. MCI and AD fixated the distractors more often and for a longer period of time than the target. Healthy controls were quicker and made less fixations when scanning the stimuli for the first time. Machine-learning methods were able to distinguish between controls and AD subjects and to identify MCI subjects with a similar oculomotor profile to AD with a good accuracy. CONCLUSION: Results showed that eye movement metrics are useful for identifying visual search impairments in MCI and AD, with possible implications in the early identification of individuals with high-risk of developing AD.

Down syndrome detection based on facial features using a geometric descriptor.

Down syndrome is one of the most common genetic disorders caused by chromosome abnormalities in humans. Among other physical characteristics, certain facial features are typically associated in people with Down syndrome. We investigate the problem of Down syndrome detection from a collection of face images. As the main contribution, a compact geometric descriptor is used to extract facial features from the images. Experiments are conducted on an available dataset to demonstrate the performance of the proposed methodology.

MicroRNAs discriminate familial from sporadic non-BRCA1/2 breast carcinoma arising in patients ≤35 years.

The influence of genetic factors may contribute to the poor prognosis of breast cancer (BC) at a very young age. However BRCA1/2 mutations could not explain the majority of cases arising in these patients. MicroRNAs (miRs) have been implicated in biological processes associated with BC. Therefore, we investigated differences in miRs expression between tumors from young patients (≤35 years) with sporadic or familial history and non-carriers of BRCA1/2 mutations. Thirty-six young Brazilian patients were divided into 2 groups: sporadic (NF-BC) or familial breast cancer (F-BC). Most of the samples were classified as luminal A and B and the frequency of subtypes did not differ between familial or sporadic cases. Using real time qPCR and discriminant function analysis, we identified 9 miRs whose expression levels rather than miR identity can discriminate between both patient groups. Candidate predicted targets were determined by combining results from miRWalk algorithms with mRNA expression profiles (n = 91 differently expressed genes). MiR/mRNA integrated analysis identified 91 candidate genes showing positive or negative correlation to at least 1 of the 9 miRs. Co-expression analysis of these genes with 9 miRs indicated that 49 differentially co-expressed miR-gene interactions changes in F-BC tumors as compared to those of NF-BC tumors. Out of 49, 17 (34.6%) of predicted miR-gene interactions showed an inverse correlation suggesting that miRs act as post-transcriptional regulators, whereas 14 (28.6%) miR-gene pairs tended to be co-expressed in the same direction indicating that the effects exerted by these miRs pointed to a complex level of target regulation. The remaining 18 pairs were not predicted by our criteria suggesting involvement of other regulators. MiR-mRNA co-expression analysis allowed us to identify changes in the miR-mRNA regulation that were able to distinguish tumors from familial and sporadic young BC patients non-carriers of BRCA mutations.

Huckebein is part of a combinatorial repression code in the anterior blastoderm.

The hierarchy of the segmentation cascade responsible for establishing the Drosophila body plan is composed by gap, pair-rule and segment polarity genes. However, no pair-rule stripes are formed in the anterior regions of the embryo. This lack of stripe formation, as well as other evidence from the literature that is further investigated here, led us to the hypothesis that anterior gap genes might be involved in a combinatorial mechanism responsible for repressing the cis-regulatory modules (CRMs) of hairy (h), even-skipped (eve), runt (run), and fushi-tarazu (ftz) anterior-most stripes. In this study, we investigated huckebein (hkb), which has a gap expression domain at the anterior tip of the embryo. Using genetic methods we were able to detect deviations from the wild-type patterns of the anterior-most pair-rule stripes in different genetic backgrounds, which were consistent with Hkb-mediated repression. Moreover, we developed an image processing tool that, for the most part, confirmed our assumptions. Using an hkb misexpression system, we further detected specific repression on anterior stripes. Furthermore, bioinformatics analysis predicted an increased significance of binding site clusters in the CRMs of h 1, eve 1, run 1 and ftz 1when Hkb was incorporated in the analysis, indicating that Hkb plays a direct role in these CRMs. We further discuss that Hkb and Slp1, which is the other previously identified common repressor of anterior stripes, might participate in a combinatorial repression mechanism controlling stripe CRMs in the anterior parts of the embryo and define the borders of these anterior stripes.

Investigating giant (Gt) repression in the formation of partially overlapping pair-rule stripes.

Drosophila pair-rule genes are expressed in striped patterns with a precise order of overlap between stripes of different genes. We investigated the role of Giant (Gt) in the regulation of even-skipped, hairy, runt, and fushi tarazu stripes formed in the vicinity of Gt expression domains. In gt null embryos, specific stripes of eve, h, run, and ftz are disrupted. With an ectopic expression system, we verified that stripes affected in the mutant are also repressed. Simultaneously hybridizing gt misxpressing embryos with two pair-rule gene probes, we were able to distinguish differences in the repression of pairs of stripes that overlap extensively. Together, our results showed Gt repression roles in the regulation of two groups of partially overlapping stripes and that Gt morphogen activity is part of the mechanism responsible for the differential positioning of these stripes borders. We discuss the possibility that other factors regulate Gt stripe targets as well.

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. This is the scoring technique used by sequence analysis tools such as HMMER, SAM and INFERNAL. The most prevalent null models are position-independent residue distributions that include: the uniform distribution, genomic distribution, family-specific distribution and the target sequence distribution. This paper presents a study to evaluate the impact of the choice of a null model in the final result of classifications. In particular, we are interested in minimizing the number of false predictions in a classification. This is a crucial issue to reduce costs of biological validation. RESULTS: For all the tests, the target null model presented the lowest number of false positives, when using random sequences as a test. The study was performed in DNA sequences using GC content as the measure of content bias, but the results should be valid also for protein sequences. To broaden the application of the results, the study was performed using randomly generated sequences. Previous studies were performed on aminoacid sequences, using only one probabilistic model (HMM) and on a specific benchmark, and lack more general conclusions about the performance of null models. Finally, a benchmark test with P. falciparum confirmed these results. CONCLUSIONS: Of the evaluated models the best suited for classification are the uniform model and the target model. However, the use of the uniform model presents a GC bias that can cause more false positives for candidate sequences with extreme compositional bias, a characteristic not described in previous studies. In these cases the target model is more dependable for biological validation due to its higher specificity.

Sequence and structural analysis of the 5' noncoding region of hepatitis C virus in patients with chronic infection.

Hepatitis C virus (HCV), exhibits considerable genetic diversity, but presents a relatively well conserved 5' noncoding region (5' NCR) among all genotypes. In this study, the structural features and translational efficiency of the HCV 5' NCR sequences were analyzed using the programs RNAfold, RNAshapes and RNApdist and with a bicistronic dual luciferase expression system, respectively. RNA structure prediction software indicated that base substitutions will alter potentially the 5' NCR structure. The heterogeneous sequence observed on 5' NCR led to important changes in their translation efficiency in different cell culture lines. Interactions of the viral RNA with cellular transacting factors may vary according to the cell type and viral genome polymorphisms that may result in the translational efficiency observed.

Analysis of single-nucleotide polymorphisms in the crt-o and mdr1 genes of Plasmodium vivax among chloroquine-resistant isolates from the Brazilian Amazon region.

Plasmodium vivax parasites with chloroquine resistance (CQR) are already circulating in the Brazilian Amazon. Complete single-nucleotide polymorphism (SNP) analyses of coding and noncoding sequences of the pvmdr1 and pvcrt-o genes revealed no associations with CQR, even if some mutations had not been randomly selected. In addition, striking differences in the topologies and numbers of SNPs in these transporter genes between P. vivax and P. falciparum reinforce the idea that mechanisms other than mutations may explain this virulent phenotype in P. vivax.

No-match ORESTES explored as tumor markers.

Sequencing technologies and new bioinformatics tools have led to the complete sequencing of various genomes. However, information regarding the human transcriptome and its annotation is yet to be completed. The Human Cancer Genome Project, using ORESTES (open reading frame EST sequences) methodology, contributed to this objective by generating data from about 1.2 million expressed sequence tags. Approximately 30% of these sequences did not align to ESTs in the public databases and were considered no-match ORESTES. On the basis that a set of these ESTs could represent new transcripts, we constructed a cDNA microarray. This platform was used to hybridize against 12 different normal or tumor tissues. We identified 3421 transcribed regions not associated with annotated transcripts, representing 83.3% of the platform. The total number of differentially expressed sequences was 1007. Also, 28% of analyzed sequences could represent noncoding RNAs. Our data reinforces the knowledge of the human genome being pervasively transcribed, and point out molecular marker candidates for different cancers. To reinforce our data, we confirmed, by real-time PCR, the differential expression of three out of eight potentially tumor markers in prostate tissues. Lists of 1007 differentially expressed sequences, and the 291 potentially noncoding tumor markers were provided.

Searching for molecular markers in head and neck squamous cell carcinomas (HNSCC) by statistical and bioinformatic analysis of larynx-derived SAGE libraries.

BACKGROUND: Head and neck squamous cell carcinoma (HNSCC) is one of the most common malignancies in humans. The average 5-year survival rate is one of the lowest among aggressive cancers, showing no significant improvement in recent years. When detected early, HNSCC has a good prognosis, but most patients present metastatic disease at the time of diagnosis, which significantly reduces survival rate. Despite extensive research, no molecular markers are currently available for diagnostic or prognostic purposes. METHODS: Aiming to identify differentially-expressed genes involved in laryngeal squamous cell carcinoma (LSCC) development and progression, we generated individual Serial Analysis of Gene Expression (SAGE) libraries from a metastatic and non-metastatic larynx carcinoma, as well as from a normal larynx mucosa sample. Approximately 54,000 unique tags were sequenced in three libraries. RESULTS: Statistical data analysis identified a subset of 1,216 differentially expressed tags between tumor and normal libraries, and 894 differentially expressed tags between metastatic and non-metastatic carcinomas. Three genes displaying differential regulation, one down-regulated (KRT31) and two up-regulated (BST2, MFAP2), as well as one with a non-significant differential expression pattern (GNA15) in our SAGE data were selected for real-time polymerase chain reaction (PCR) in a set of HNSCC samples. Consistent with our statistical analysis, quantitative PCR confirmed the upregulation of BST2 and MFAP2 and the downregulation of KRT31 when samples of HNSCC were compared to tumor-free surgical margins. As expected, GNA15 presented a non-significant differential expression pattern when tumor samples were compared to normal tissues. CONCLUSION: To the best of our knowledge, this is the first study reporting SAGE data in head and neck squamous cell tumors. Statistical analysis was effective in identifying differentially expressed genes reportedly involved in cancer development. The differential expression of a subset of genes was confirmed in additional larynx carcinoma samples and in carcinomas from a distinct head and neck subsite. This result suggests the existence of potential common biomarkers for prognosis and targeted-therapy development in this heterogeneous type of tumor.

TNF-alpha polymorphisms are associated with obsessive-compulsive disorder.

INTRODUCTION: Several lines of evidence support an immunologic involvement in obsessive-compulsive disorder (OCD): the increased prevalence of OCD in patients with rheumatic fever (RF), and the aggregation of obsessive-compulsive spectrum disorders among relatives of RF probands. Tumor necrosis factor alpha is a proinflammatory cytokine involved in RF and other autoimmune diseases. Polymorphisms in the promoter region of the TNFA gene have been associated with RF. Given the association between OCD and RF, the goal of the present study was to investigate a possible association between polymorphisms within the promoter region of TNFA and OCD. MATERIALS AND METHODS: Two polymorphisms were investigated: -308 G/A and -238 G/A. The allelic and genotypic frequencies of these polymorphisms were examined in 111 patients who fulfilled DSM-IV criteria for OCD and compared with the frequencies in 250 controls. RESULTS: Significant associations were observed between both polymorphisms and OCD. For -238 G/A, an association between the A allele and OCD was observed (chi(2)=12.05, p=0.0005). A significant association was also observed between the A allele of the -308 G/A polymorphism and OCD (chi(2)=7.09, p=0.007). Finally, a haplotype consisting of genotypes of these two markers was also examined. Significant association was observed for the A-A haplotype (p=0.0099 after correcting for multiple testing). DISCUSSION: There is association between the -308 G/A and -238 G/A TNFA polymorphisms and OCD in our Brazilian sample. However, these results need to be replicated in larger samples collected from different populations.

Computational methods in noncoding RNA research.

Non protein-coding RNAs (ncRNAs) are a research hotspot in bioinformatics. Recent discoveries have revealed new ncRNA families performing a variety of roles, from gene expression regulation to catalytic activities. It is also believed that other families are still to be unveiled. Computational methods developed for protein coding genes often fail when searching for ncRNAs. Noncoding RNAs functionality is often heavily dependent on their secondary structure, which makes gene discovery very different from protein coding RNA genes. This motivated the development of specific methods for ncRNA research. This article reviews the main approaches used to identify ncRNAs and predict secondary structure.