Margherita Francescatto

Brain-specific noncoding RNAs are likely to originate in repeats and may play a role in up-regulating genes in cis

The mouse and human brain express a large number of noncoding RNAs (ncRNAs). Some of these are known to participate in neural progenitor cell fate determination, cell differentiation, neuronal and synaptic plasticity and transposable elements derived ncRNAs contribute to somatic variation. Dysregulation of specific long ncRNAs (lncRNAs) has been shown in neuro-developmental and neuro-degenerative diseases thus highlighting the importance of lncRNAs in brain function. Even though it is known that lncRNAs are expressed in cells at low levels in a tissue-specific manner, bioinformatics analyses of brain-specific ncRNAs has not been performed. We analyzed previously published custom microarray ncRNA expression data generated from twelve human tissues to identify tissue-specific ncRNAs. We find that among the 12 tissues studied, brain has the largest number of ncRNAs. Our analyses show that genes in the vicinity of brain-specific ncRNAs are significantly up regulated in the brain. Investigations of repeat representation show that brain-specific ncRNAs are significantly more likely to originate in repeat regions especially DNA/TcMar-Tigger compared with non-tissue-specific ncRNAs. We find SINE/Alus depleted from brain-specific dataset when compared with non-tissue-specific ncRNAs. Our data provide a bioinformatics comparison between brain-specific and non tissue-specific ncRNAs. This article is part of a Directed Issue entitled: The Non-coding RNA Revolution.

C9orf72 is differentially expressed in the central nervous system and myeloid cells and consistently reduced in C9orf72, MAPT and GRN mutation carriers

A non-coding hexanucleotide repeat expansion (HRE) in C9orf72 is a common cause of amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD) acting through a loss of function mechanism due to haploinsufficiency of C9orf72 or a gain of function mediated by aggregates of bidirectionally transcribed HRE-RNAs translated into di-peptide repeat (DPR) proteins. To fully understand regulation of C9orf72 expression we surveyed the C9orf72 locus using Cap Analysis of Gene Expression sequence data (CAGEseq). We observed C9orf72 was generally lowly expressed with the exception of a subset of myeloid cells, particularly CD14+ monocytes that showed up to seven fold higher expression as compared to central nervous system (CNS) and other tissues. The expression profile at the C9orf72 locus showed a complex architecture with differential expression of the transcription start sites (TSSs) for the annotated C9orf72 transcripts between myeloid and CNS tissues suggesting cell and/or tissue specific functions. We further detected novel TSSs in both the sense and antisense strand at the C9orf72 locus and confirmed their existence in brain tissues and CD14+ monocytes. Interestingly, our experiments showed a consistent decrease of C9orf72 coding transcripts not only in brain tissue and monocytes from C9orf72-HRE patients, but also in brains from MAPT and GRN mutation carriers together with an increase in antisense transcripts suggesting these could play a role in regulation of C9orf72. We found that the non-HRE related expression changes cannot be explained by promoter methylation but by the presence of the C9orf72-HRE risk haplotype and unknown functional interactions between C9orf72, MAPT and GRN.

Comprehensive promoter level expression quantitative trait loci analysis of the human frontal lobe

BackgroundExpression quantitative trait loci (eQTL) analysis is a powerful method to detect correlations between gene expression and genomic variants and is widely used to interpret the biological mechanism underlying identified genome wide association studies (GWAS) risk loci. Numerous eQTL studies have been performed on different cell types and tissues of which the majority has been based on microarray technology.MethodsWe present here an eQTL analysis based on cap analysis gene expression sequencing (CAGEseq) data created from human postmortem frontal lobe tissue combined with genotypes obtained through genotyping arrays, exome sequencing, and CAGEseq. Using CAGEseq as an expression profiling technique combined with these different genotyping techniques allows measurement of the molecular effect of variants on individual transcription start sites and increases the resolution of eQTL analysis by also including the non-annotated parts of the genome.ResultsWe identified 2410 eQTLs and show that non-coding transcripts are more likely to contain an eQTL than coding transcripts, in particular antisense transcripts. We provide evidence for how previously identified GWAS loci for schizophrenia (NRGN), Parkinson’s disease, and Alzheimer’s disease (PARK16 and MAPT loci) could increase the risk for disease at a molecular level. Furthermore, we demonstrate that CAGEseq improves eQTL analysis because variants obtained from CAGEseq are highly enriched for having a functional effect and thus are an efficient method towards the identification of causal variants.ConclusionOur data contain both coding and non-coding transcripts and has the added value that we have identified eQTLs for variants directly adjacent to TSS. Future eQTL studies would benefit from combining CAGEseq with RNA sequencing for a more complete interpretation of the transcriptome and increased understanding of eQTL signals.

Highlights from the Third European International Society for Computational Biology (ISCB) Student Council Symposium 2014

In this meeting report, we give an overview of the talks, presentations and posters presented at the third European Symposium of the International Society for Computational Biology (ISCB) Student Council. The event was organized as a satellite meeting of the 13th European Conference for Computational Biology (ECCB) and took place in Strasbourg, France on September 6th, 2014.

The ISCB Student Council Internship Program: Expanding computational biology capacity worldwide

Education and training are two essential ingredients for a successful career. On one hand, universities provide students a curriculum for specializing in one’s field of study, and on the other, internships complement coursework and provide invaluable training experience for a fruitful career. Consequently, undergraduates and graduates are encouraged to undertake an internship during the course of their degree. The opportunity to explore one’s research interests in the early stages of their education is important for students because it improves their skill set and gives their career a boost. In the long term, this helps to close the gap between skills and employability among students across the globe and balance the research capacity in the field of computational biology. However, training opportunities are often scarce for computational biology students, particularly for those who reside in less-privileged regions. Aimed at helping students develop research and academic skills in computational biology and alleviating the divide across countries, the Student Council of the International Society for Computational Biology introduced its Internship Program in 2009. The Internship Program is committed to providing access to computational biology training, especially for students from developing regions, and improving competencies in the field. Here, we present how the Internship Program works and the impact of the internship opportunities so far, along with the challenges associated with this program.

Multi-omics integration for neuroblastoma clinical endpoint prediction

BackgroundHigh-throughput methodologies such as microarrays and next-generation sequencing are routinely used in cancer research, generating complex data at different omics layers. The effective integration of omics data could provide a broader insight into the mechanisms of cancer biology, helping researchers and clinicians to develop personalized therapies.ResultsIn the context of CAMDA 2017 Neuroblastoma Data Integration challenge, we explore the use of Integrative Network Fusion (INF), a bioinformatics framework combining a similarity network fusion with machine learning for the integration of multiple omics data. We apply the INF framework for the prediction of neuroblastoma patient outcome, integrating RNA-Seq, microarray and array comparative genomic hybridization data. We additionally explore the use of autoencoders as a method to integrate microarray expression and copy number data.ConclusionsThe INF method is effective for the integration of multiple data sources providing compact feature signatures for patient classification with performances comparable to other methods. Latent space representation of the integrated data provided by the autoencoder approach gives promising results, both by improving classification on survival endpoints and by providing means to discover two groups of patients characterized by distinct overall survival (OS) curves.ReviewersThis article was reviewed by Djork-Arné Clevert and Tieliu Shi.

Evaluating reproducibility of AI algorithms in digital pathology with DAPPER

Artificial Intelligence is exponentially increasing its impact on healthcare. As deep learning is mastering computer vision tasks, its application to digital pathology is natural, with the promise of aiding in routine reporting and standardizing results across trials. Deep learning features inferred from digital pathology scans can improve validity and robustness of current clinico-pathological features, up to identifying novel histological patterns, e.g. from tumor infiltrating lymphocytes. In this study, we examine the issue of evaluating accuracy of predictive models from deep learning features in digital pathology, as an hallmark of reproducibility. We introduce the DAPPER framework for validation based on a rigorous Data Analysis Plan derived from the FDA’s MAQC project, designed to analyse causes of variability in predictive biomarkers. We apply the framework on models that identify tissue of origin on 787 Whole Slide Images from the Genotype-Tissue Expression (GTEx) project. We test 3 different deep learning architectures (VGG, ResNet, Inception) as feature extractors and three classifiers (a fully connected multilayer, Support Vector Machine and Random Forests) and work with 4 datasets (5, 10, 20 or 30 classes), for a total 53000 tiles at 512 × 512 resolution. We analyze accuracy and feature stability of the machine learning classifiers, also demonstrating the need for random features and random labels diagnostic tests to identify selection bias and risks for reproducibility. Further, we use the deep features from the VGG model from GTEx on the KIMIA24 dataset for identification of slide of origin (24 classes) to train a classifier on 1060 annotated tiles and validated on 265 unseen ones. The DAPPER software, including its deep learning backbone pipeline and the HINT (Histological Imaging - Newsy Tiles) benchmark dataset derived from GTEx, is released as a basis for standardization and validation initiatives in AI for Digital Pathology. Author summary In this study, we examine the issue of evaluating accuracy of predictive models from deep learning features in digital pathology, as an hallmark of reproducibility. It is indeed a top priority that reproducibility-by-design gets adopted as standard practice in building and validating AI methods in the healthcare domain. Here we introduce DAPPER, a first framework to evaluate deep features and classifiers in digital pathology, based on a rigorous data analysis plan originally developed in the FDA’s MAQC initiative for predictive biomarkers from massive omics data. We apply DAPPER on models trained to identify tissue of origin from the HINT benchmark dataset of 53000 tiles from 787 Whole Slide Images in the Genotype-Tissue Expression (GTEx) project. We analyze accuracy and feature stability of different deep learning architectures (VGG, ResNet and Inception) as feature extractors and classifiers (a fully connected multilayer, SVMs and Random Forests) on up to 20 classes. Further, we use the deep features from the VGG model (trained on HINT) on the 1300 annotated tiles of the KIMIA24 dataset for identification of slide of origin (24 classes). The DAPPER software is available together with the HINT benchmark dataset.

Highlights from the 11th ISCB Student Council Symposium 2015: Dublin, Ireland. 10 July 2015

Table of contentsA1 Highlights from the eleventh ISCB Student Council Symposium 2015Katie Wilkins, Mehedi Hassan, Margherita Francescatto, Jakob Jespersen, R. Gonzalo Parra, Bart Cuypers, Dan DeBlasio, Alexander Junge, Anupama Jigisha, Farzana RahmanO1 Prioritizing a drug’s targets using both gene expression and structural similarityGriet Laenen, Sander Willems, Lieven Thorrez, Yves MoreauO2 Organism specific protein-RNA recognition: A computational analysis of protein-RNA complex structures from different organismsNagarajan Raju, Sonia Pankaj Chothani, C. Ramakrishnan, Masakazu Sekijima; M. Michael GromihaO3 Detection of Heterogeneity in Single Particle Tracking TrajectoriesPaddy J Slator, Nigel J BurroughsO4 3D-NOME: 3D NucleOme Multiscale Engine for data-driven modeling of three-dimensional genome architecturePrzemysław Szałaj, Zhonghui Tang, Paul Michalski, Oskar Luo, Xingwang Li, Yijun Ruan, Dariusz PlewczynskiO5 A novel feature selection method to extract multiple adjacent solutions for viral genomic sequences classificationGiulia Fiscon, Emanuel Weitschek, Massimo Ciccozzi, Paola Bertolazzi, Giovanni FeliciO6 A Systems Biology Compendium for Leishmania donovaniBart Cuypers, Pieter Meysman, Manu Vanaerschot, Maya Berg, Hideo Imamura, Jean-Claude Dujardin, Kris LaukensO7 Unravelling signal coordination from large scale phosphorylation kinetic dataWesta Domanova, James R. Krycer, Rima Chaudhuri, Pengyi Yang, Fatemeh Vafaee, Daniel J. Fazakerley, Sean J. Humphrey, David E. James, Zdenka Kuncic

Highlights from the 11th ISCB Student Council Symposium 2015

Table of contentsA1 Highlights from the eleventh ISCB Student Council Symposium 2015Katie Wilkins, Mehedi Hassan, Margherita Francescatto, Jakob Jespersen, R. Gonzalo Parra, Bart Cuypers, Dan DeBlasio, Alexander Junge, Anupama Jigisha, Farzana RahmanO1 Prioritizing a drug’s targets using both gene expression and structural similarityGriet Laenen, Sander Willems, Lieven Thorrez, Yves MoreauO2 Organism specific protein-RNA recognition: A computational analysis of protein-RNA complex structures from different organismsNagarajan Raju, Sonia Pankaj Chothani, C. Ramakrishnan, Masakazu Sekijima; M. Michael GromihaO3 Detection of Heterogeneity in Single Particle Tracking TrajectoriesPaddy J Slator, Nigel J BurroughsO4 3D-NOME: 3D NucleOme Multiscale Engine for data-driven modeling of three-dimensional genome architecturePrzemysław Szałaj, Zhonghui Tang, Paul Michalski, Oskar Luo, Xingwang Li, Yijun Ruan, Dariusz PlewczynskiO5 A novel feature selection method to extract multiple adjacent solutions for viral genomic sequences classificationGiulia Fiscon, Emanuel Weitschek, Massimo Ciccozzi, Paola Bertolazzi, Giovanni FeliciO6 A Systems Biology Compendium for Leishmania donovaniBart Cuypers, Pieter Meysman, Manu Vanaerschot, Maya Berg, Hideo Imamura, Jean-Claude Dujardin, Kris LaukensO7 Unravelling signal coordination from large scale phosphorylation kinetic dataWesta Domanova, James R. Krycer, Rima Chaudhuri, Pengyi Yang, Fatemeh Vafaee, Daniel J. Fazakerley, Sean J. Humphrey, David E. James, Zdenka Kuncic