Genome-Wide Transcript Profiling Reveals Novel Breast Cancer-Associated Intronic Sense RNAs

Non-coding RNAs (ncRNAs) play major roles in development and cancer progression. To identify novel ncRNAs that may identify key pathways in breast cancer development, we performed high-throughput transcript profiling of tumor and normal matched-pair tissue samples. Initial transcriptome profiling using high-density genome-wide tiling arrays revealed changes in over 200 novel candidate genomic regions that map to intronic regions. Sixteen genomic loci were identified that map to the long introns of five key protein-coding genes, CRIM1, EPAS1, ZEB2, RBMS1, and RFX2. Consistent with the known role of the tumor suppressor ZEB2 in the cancer-associated epithelial to mesenchymal transition (EMT), in situ hybridization reveals that the intronic regions deriving from ZEB2 as well as those from RFX2 and EPAS1 are down-regulated in cells of epithelial morphology, suggesting that these regions may be important for maintaining normal epithelial cell morphology. Paired-end deep sequencing analysis reveals a large number of distinct genomic clusters with no coding potential within the introns of these genes. These novel transcripts are only transcribed from the coding strand. A comprehensive search for breast cancer associated genes reveals enrichment for transcribed intronic regions from these loci, pointing to an underappreciated role of introns or mechanisms relating to their biology in EMT and breast cancer

Methodology for predicting semantic annotations of protein sequences by feature extraction derived of statistical contact potentials and continuous wavelet transform

En esta tesis se propone un método para la predicción de anotaciones de proteínas a partir de la estimación de características en secuencias biológicas. Dicha estimación emplea información sobre la estructura de las proteínas a partir de las estadísticas de contactos potenciales entre pares de aminoácidos. Inicialmente, una proteína es transformada a una serie numérica por medio de estos contactos potenciales. Debido a las interacciones entre aminoácidos cercanos, la transformada wavelet puede fácilmente detectar las subsecuencias pertenecientes a posiciones específicas a lo largo de la proteína. Así, todas las subsecuencias son agrupadas de acuerdo a su distribución y estos grupos son modelados empleando perfiles de Modelos Ocultos de Markov. Finalmente, los perfiles son usados como características donde proteínas de análisis son mapeadas generando así un espacio de representación que es usado para entrenar un clasificador basado en vectores de soporte. La metodología ha sido rigurosamente evaluada y comparada con tres diferentes criterios de caracterización: 1) características globales comúnmente usadas para representar proteínas, 2) características específicas como motivos y dominios, y por último 3) evaluación de el rendimiento de varios programas construidos para la predicción de anotación de proteínas. Como resultado el método propuesto ha logrado los mas altos puntajes de predicción en la mayoría de los casos de estudio. De manera que estas predicciones sugieren a nuestro método como una alternativa a los comúnmente usados algoritmos de caracterización. Por otra parte, a pesar de que el enfoque de la metodología esta diseñada para resolver problemas de clasificación, la comunidad científica puede hacer uso de ella en dos diferentes enfoques: 1) como un predictor de anotaciones en proteínas y 2) como una herramienta para encontrar motivos. Por último, el código fuente del método se encuentra para libre descarga en: http://sourceforge.net/projects/wamofi/?source=navbarAbstract : In this thesis, a method to predict semantic annotations of the proteins from its primary structure is proposed. The main contribution of this thesis lies in the implementation of a novel protein feature representation, which makes use of the pairwise statistical contact potentials describing the protein interactions and geometry at the atomic level. Initially, a protein sequence is decomposed into a numerical series by a contact potential. From the interactions between adjacent amino acids, the wavelet transform can easily detect and characterize subsequences at specific position along the protein sequence. Then, all subsequences are grouped into clusters and a Hidden Markov Model (HMM) profile is built for each one of the groups. Finally, the modeled profiles HMM are used as features in order to build a feature space with the aim to train and evaluate a support vector machine classifier. Evaluations of the proposed methodology are driven against three different views 1) known protein features 2) motif-domain based features (PFam terms) and 3) performance evaluation over several methods for protein annotation prediction. As result, The method have acquired the highest performance prediction in most of the study cases. Thus, this efficiency suggest our approach as an alternative method for the characterization of protein sequences. Although, the research in this thesis focuses on the classification problem, the scientific community can make use of the methodology in two different ways: 1) as a protein predictor and 2) as a motif finding tool. Finally, the source code of the method is free available for download at SourceForge http://sourceforge.net/projects/wamofi/?source=navbarMaestrí

DeepARG: a deep learning approach for predicting antibiotic resistance genes from metagenomic data

Abstract Background Growing concerns about increasing rates of antibiotic resistance call for expanded and comprehensive global monitoring. Advancing methods for monitoring of environmental media (e.g., wastewater, agricultural waste, food, and water) is especially needed for identifying potential resources of novel antibiotic resistance genes (ARGs), hot spots for gene exchange, and as pathways for the spread of ARGs and human exposure. Next-generation sequencing now enables direct access and profiling of the total metagenomic DNA pool, where ARGs are typically identified or predicted based on the “best hits” of sequence searches against existing databases. Unfortunately, this approach produces a high rate of false negatives. To address such limitations, we propose here a deep learning approach, taking into account a dissimilarity matrix created using all known categories of ARGs. Two deep learning models, DeepARG-SS and DeepARG-LS, were constructed for short read sequences and full gene length sequences, respectively. Results Evaluation of the deep learning models over 30 antibiotic resistance categories demonstrates that the DeepARG models can predict ARGs with both high precision (> 0.97) and recall (> 0.90). The models displayed an advantage over the typical best hit approach, yielding consistently lower false negative rates and thus higher overall recall (> 0.9). As more data become available for under-represented ARG categories, the DeepARG models’ performance can be expected to be further enhanced due to the nature of the underlying neural networks. Our newly developed ARG database, DeepARG-DB, encompasses ARGs predicted with a high degree of confidence and extensive manual inspection, greatly expanding current ARG repositories. Conclusions The deep learning models developed here offer more accurate antimicrobial resistance annotation relative to current bioinformatics practice. DeepARG does not require strict cutoffs, which enables identification of a much broader diversity of ARGs. The DeepARG models and database are available as a command line version and as a Web service at http://bench.cs.vt.edu/deeparg

MetaStorm: A Public Resource for Customizable Metagenomics Annotation.

Metagenomics is a trending research area, calling for the need to analyze large quantities of data generated from next generation DNA sequencing technologies. The need to store, retrieve, analyze, share, and visualize such data challenges current online computational systems. Interpretation and annotation of specific information is especially a challenge for metagenomic data sets derived from environmental samples, because current annotation systems only offer broad classification of microbial diversity and function. Moreover, existing resources are not configured to readily address common questions relevant to environmental systems. Here we developed a new online user-friendly metagenomic analysis server called MetaStorm (http://bench.cs.vt.edu/MetaStorm/), which facilitates customization of computational analysis for metagenomic data sets. Users can upload their own reference databases to tailor the metagenomics annotation to focus on various taxonomic and functional gene markers of interest. MetaStorm offers two major analysis pipelines: an assembly-based annotation pipeline and the standard read annotation pipeline used by existing web servers. These pipelines can be selected individually or together. Overall, MetaStorm provides enhanced interactive visualization to allow researchers to explore and manipulate taxonomy and functional annotation at various levels of resolution

Apigenin Impacts the Growth of the Gut Microbiota and Alters the Gene Expression of Enterococcus

Apigenin is a major dietary flavonoid with many bioactivities, widely distributed in plants. Apigenin reaches the colon region intact and interacts there with the human gut microbiota, however there is little research on how apigenin affects the gut bacteria. This study investigated the effect of pure apigenin on human gut bacteria, at both the single strain and community levels. The effect of apigenin on the single gut bacteria strains Bacteroides galacturonicus, Bifidobacterium catenulatum, Lactobacillus rhamnosus GG, and Enterococcus caccae, was examined by measuring their anaerobic growth profiles. The effect of apigenin on a gut microbiota community was studied by culturing a fecal inoculum under in vitro conditions simulating the human ascending colon. 16S rRNA gene sequencing and GC-MS analysis quantified changes in the community structure. Single molecule RNA sequencing was used to reveal the response of Enterococcus caccae to apigenin. Enterococcus caccae was effectively inhibited by apigenin when cultured alone, however, the genus Enterococcus was enhanced when tested in a community setting. Single molecule RNA sequencing found that Enterococcus caccae responded to apigenin by up-regulating genes involved in DNA repair, stress response, cell wall synthesis, and protein folding. Taken together, these results demonstrate that apigenin affects both the growth and gene expression of Enterococcus caccae

Additional file 4: table S4. of DeepARG: a deep learning approach for predicting antibiotic resistance genes from metagenomic data

Prediction results of the genes from MEGARes using the deepARG-LS model trained with our database. (PDF 60 kb

Pipelines.

<p>Overview of the computational pipelines implemented in the MetaStorm service for taxonomic and functional annotation.</p

Taxonomy visualization.

<p>Taxonomy levels are shown as pie charts (only Family and Genus are shown for illustration). The interactive tree allows users to follow the path of the abundant taxas and the chart displays the selected taxonomy level. The right panel shows the hits distribution to the open node in the taxonomy tree. In this example, the families under the order <i>Rhizobiales</i> are shown in the left panel.</p

Main user interface of MetaStorm.

<p><u>Create a new project</u> allows to submit a project under the user profile. <u>My Projects</u> grant access to the data management interface that includes: Upload raw files, add samples, remove samples, visualize individual samples and compare samples. <u>Customize Reference Database</u> gives access to the form for uploading a customized reference database. <u>Browse projects</u> allows to find samples by biome and/or location. <u>Comparison tool</u> allows users to compare samples from different projects. <u>Profile</u> allows users to modify their personal information and password.</p