DeepReGraph co-clusters temporal gene expression and cis-regulatory elements through heterogeneous graph representation learning

This work presents DeepReGraph, a novel method for co-clustering genes and cis-regulatory elements (CREs) into candidate regulatory networks. Gene expression data, as well as data from three CRE activity markers from a publicly available dataset of mouse fetal heart tissue, were used for DeepReGraph concept proofing. In this study we used open chromatin accessibility from ATAC-seq experiments, as well as H3K27ac and H3K27me3 histone marks as CREs activity markers. However, this method can be executed with other sets of markers. We modelled all data sources as a heterogeneous graph and adapted a state-of-the-art representation learning algorithm to produce a low-dimensional and easy-to-cluster embedding of genes and CREs. Deep graph auto-encoders and an adaptive-sparsity generative model are the algorithmic core of DeepReGraph. The main contribution of our work is the design of proper combination rules for the heterogeneous gene expression and CRE activity data and the computational encoding of well-known gene expression regulatory mechanisms into a suitable objective function for graph embedding. We showed that the co-clusters of genes and CREs in the final embedding shed light on developmental regulatory mechanisms in mouse fetal-heart tissue. Such clustering could not be achieved by using only gene expression data. Function enrichment analysis proves that the genes in the co-clusters are involved in distinct biological processes. The enriched transcription factor binding sites in CREs prioritize the candidate transcript factors which drive the temporal changes in gene expression. Consequently, we conclude that DeepReGraph could foster hypothesis-driven tissue development research from high-throughput expression and epigenomic data. Full source code and data are available on the DeepReGraph GitHub project

Micro momentos pedagógicos: ¿Cómo potenciar el aprendizaje colaborativo en programas de posgrado en línea?

El concepto de los micro momentos es quizás uno de los más revolucionarios en el tema de la psicología del comportamiento y está en el centro de la más reciente revolución del Marketing. En el contexto educativo un micro momento pedagógico se define como el conjunto de instrucciones que acompañadas de la información correcta inducen una reflexión profunda en el estudiante. El presente proyecto describe una estrategia innovadora para generar competencias de aprendizaje colaborativo (una debilidad actual con un impacto negativo en el éxito académico), en estudiantes de programas de posgrado en línea mediante la manipulación de micro momentos pedagógicos. Se diseñó una intervención educativa que consistió en la estructuración de sesiones síncronas de programas de posgrado en línea en cuatro micro momentos pedagógicos principales (activación, desarrollo, reflexión y conclusión). En la experiencia participaron 16 docentes y un total de 291 maestrantes: 154 en el grupo control (aprendizaje tradicional en línea) y 137 en el grupo experimental (aprendizaje en línea mediante la pedagogía de los micro momentos).  Al cabo de ocho semanas de trabajo se comprobó que los maestrantes del grupo experimental mostraron un nivel de desarrollo de competencias para el aprendizaje colaborativo significativamente mayor que aquellos en el grupo control. Dada la muestra utilizada consideramos que nuestra innovación es reproducible y que aportará al desarrollo de competencias para el aprendizaje en línea en programas de posgrado con un concomitante aumento en la tasa de éxito académico terminal de dichos programas

Instant messaging to humanize online learning: lessons from the use of WhatsApp in a higher education context

La inclusión de herramientas tecnológicas es frecuente y un referente del sistema de estudios de educación superior en línea, aunque no garantiza el éxito estudiantil, son importantes la interacción y acompañamiento docente, la motivación y autonomía del estudiante. La finalidad del estudio es determinar factores de éxito académico e impacto del uso de mensajería instantánea (WhatsApp) como herramienta de apoyo académico para humanizar la experiencia de aprendizaje de estudiantes de educación superior, estudios en línea. Se utilizó el enfoque metodológico de orden cuantitativo. Los principales resultados dan cuenta que, el aprendizaje autónomo, la estructura del curso y la flexibilidad de la tutoría, son factores del éxito académico de mayor influencia. La interacción lograda (profesor estudiante) a través del WhatsApp, influye en la motivación, además, se evidenció mayor actividad en la utilización de contenidos auto dirigidos y actividades colaborativas que en conjunto, estimulan la humanización de la educación en línea.The inclusion of technological tools is frequently seen as a landmark of online study programs. However, the use of technological advancements per se does not guarantee students’ success. Interaction among peers and with the teacher are important success factors often linked to students’ motivation. Skills for autonomous learning are also crucial in the context of online learning. The purpose of this study was to highlight factors of academic success and to investigate the impact of instant messaging tools like WhatsApp, to humanize the learning experience of higher education students enrolled in online programs. Our results show that autonomous learning, the structure of the course, and the flexibility of mentoring sessions are key academic success factors. The amount of interaction student-student and student-teacher interaction favored by the use of WhatsApp influenced to a large degree students’ motivation. In addition, students showed a greater consumption of self-directed content and larger participation in collaborative activities. Altogether these factors contributed to the humanization of online education

A network-based approach to identify substrate classes of bacterial glycosyltransferases

Background: Bacterial interactions with the environment-and/or host largely depend on the bacterial glycome. The specificities of a bacterial glycome are largely determined by glycosyltransferases (GTs), the enzymes involved in transferring sugar moieties from an activated donor to a specific substrate. Of these GTs their coding regions, but mainly also their substrate specificity are still largely unannotated as most sequence-based annotation flows suffer from the lack of characterized sequence motifs that can aid in the prediction of the substrate specificity. Results: In this work, we developed an analysis flow that uses sequence-based strategies to predict novel GTs, but also exploits a network-based approach to infer the putative substrate classes of these predicted GTs. Our analysis flow was benchmarked with the well-documented GT-repertoire of Campylobacter jejuni NCTC 11168 and applied to the probiotic model Lactobacillus rhamnosus GG to expand our insights in the glycosylation potential of this bacterium. In L. rhamnosus GG we could predict 48 GTs of which eight were not previously reported. For at least 20 of these GTs a substrate relation was inferred. Conclusions: We confirmed through experimental validation our prediction of WelI acting upstream of WelE in the biosynthesis of exopolysaccharides. We further hypothesize to have identified in L. rhamnosus GG the yet undiscovered genes involved in the biosynthesis of glucose-rich glycans and novel GTs involved in the glycosylation of proteins. Interestingly, we also predict GTs with well-known functions in peptidoglycan synthesis to also play a role in protein glycosylation

Phylogenomics and Systematics of Overlooked Mesoamerican and South American Polyploid Broad-Leaved Festuca Grasses Differentiate F. sects. Glabricarpae and Ruprechtia and F. subgen. Asperifolia, Erosiflorae, Mallopetalon and Coironhuecu (subgen. nov.)

Allopolyploidy is considered a driver of diversity in subtribe Loliinae. We investigate the evolution and systematics of the poorly studied Mesoamerican and South American polyploid broad-leaved Festuca L. species of uncertain origin and unclear taxonomy. A taxonomic study of seven diagnostic morphological traits was conducted on a representation of 22 species. Phylogenomic analyses were performed on a representation of these supraspecific taxa and all other Loliinae lineages using separate data from the entire plastome, nuclear rDNA 45S and 5S genes, and repetitive DNA elements. F. subgen. Mallopetalon falls within the fine-leaved (FL) Loliinae clade, whereas the remaining taxa are nested within the broad-leaved (BL) Loliinae clade forming two separate Mexico–Central–South American (MCSAI, MCSAII) lineages. MCSAI includes representatives of F. sect. Glabricarpae and F. subgen. Asperifolia plus F. superba, and MCSAII of F. subgen. Erosiflorae and F. sect. Ruprechtia plus F. argentina. MCSAII likely had a BL Leucopoa paternal ancestor, MCSAI and MCSAII a BL Meso-South American maternal ancestor, and Mallopetalon FL, American I–II ancestors. Plastome vs. nuclear topological discordances corroborated the hybrid allopolyploid origins of these taxa, some of which probably originated from Northern Hemisphere ancestors. The observed data indicate rapid reticulate radiations in the Central–South American subcontinent. Our systematic study supports the reclassification of some studied taxa in different supraspecific Festuca ranks

Identificación de secuencias que se expresan diferencialmente en la interacción no compatible Musa acuminata - Mycosphaerella fijiensis Morelet

Black leaf streak disease caused by Mycosphaerella fijiensis Morelet is considered the most destructive and costly foliar disease of bananas and plantain. An important step for the elucidation of molecular mechanisms of the disease resistance is to know about genes involved in plant defense response to pathogens. Identification of differentially expressed sequences in resistant genotype ‘Calcutta 4’ (Musa acuminata, AA) at an early stage of infection with M. fijiensis (6 to 12 days post inoculation) from a suppressed subtractive library (SSH) was carried out. A number of 63 ESTs, which include 42 singletons and 21 contigs were obtained by assembling 97 sequences with CAP3 algorithm. Identification of sequences according to their homology with sequences stated at protein non-redundant database GenBank, allowed to gather them in six functional categories: protein destiny (1.6%), oxidative stress (4.8%), metabolism (6.3%), energy production (6.3%), unknown function (38.1%) and without homology (42.8%). Results obtained will contribute to a better understanding of pathosystem, which allow the design of new strategies related to genetic improvement of banana.Keywords: Black leaf streak disease, expressed sequence tags, functional gene classification, Musa spp.,subtractive libraryLa Sigatoka negra se considera la enfermedad foliar más destructiva y costosa a nivel mundial que afecta la producción de bananos y plátanos. El conocimiento acerca de los genes involucrados en la respuesta de defensa en la planta ante el ataque por patógenos, constituye un paso importante para la elucidación de los mecanismos moleculares de resistencia a enfermedades. En este estudio a partir de una biblioteca sustractiva de ácido desoxirribonucleico complementaria (ADNc) realizada en el genotipo resistente ‘Calcutta 4’ (Musa acuminata, AA) en un estadio temprano de la infección con M. fijiensis (6 a 12 días posteriores a la inoculación), se realizó la identificación de las secuencias expresadas diferencialmente. Como resultado del ensamblaje con la herramienta bioinformática CAP3, de 97 secuencias de la biblioteca sustractiva fueron obtenidas 63 secuencias blanco expresadas, que incluían 42 secuencias aisladas y 21 ensamblajes. La identificación de las mismas según su homología con secuencias anotadas en la base de datos para proteínas no redundante (GenBank), permitió agruparlas en: destino de proteínas (1.6%), estrés oxidativo (4.8%), metabolismo (6.3%), producción de energía (6.3%), función desconocida (38.1%) y sin homología (42.8%). Los resultados obtenidos contribuirán a un mejor entendimiento del patosistema, lo cual permitirá el diseño de nuevas estrategias relacionadas con el mejoramiento genético en banano.Palabras clave: biblioteca sustractiva, clasificación funcional de genes, Musa spp., secuencias blanco expresadas, Sigatoka negr

An Alignment-Free Approach for Eukaryotic ITS2 Annotation and Phylogenetic Inference

The ITS2 gene class shows a high sequence divergence among its members that have complicated its annotation and its use for reconstructing phylogenies at a higher taxonomical level (beyond species and genus). Several alignment strategies have been implemented to improve the ITS2 annotation quality and its use for phylogenetic inferences. Although, alignment based methods have been exploited to the top of its complexity to tackle both issues, no alignment-free approaches have been able to successfully address both topics. By contrast, the use of simple alignment-free classifiers, like the topological indices (TIs) containing information about the sequence and structure of ITS2, may reveal to be a useful approach for the gene prediction and for assessing the phylogenetic relationships of the ITS2 class in eukaryotes. Thus, we used the TI2BioP (Topological Indices to BioPolymers) methodology [1], [2], freely available at http://ti2biop.sourceforge.net/ to calculate two different TIs. One class was derived from the ITS2 artificial 2D structures generated from DNA strings and the other from the secondary structure inferred from RNA folding algorithms. Two alignment-free models based on Artificial Neural Networks were developed for the ITS2 class prediction using the two classes of TIs referred above. Both models showed similar performances on the training and the test sets reaching values above 95% in the overall classification. Due to the importance of the ITS2 region for fungi identification, a novel ITS2 genomic sequence was isolated from Petrakia sp. This sequence and the test set were used to comparatively evaluate the conventional classification models based on multiple sequence alignments like Hidden Markov based approaches, revealing the success of our models to identify novel ITS2 members. The isolated sequence was assessed using traditional and alignment-free based techniques applied to phylogenetic inference to complement the taxonomy of the Petrakia sp. fungal isolate