Biology subjects

Imada, E. L.

Publications and source records attributed to Imada, E. L..

7 recordsLinked to original sources

bioRxiv2022

Unleashing alternative polyadenylation analyses with REPAC

Alternative polyadenylation (APA) is an important post-transcriptional mechanism that has major implications in biological processes and diseases. Although specialized sequencing methods for polyadenylation exist, their presence in public repositories is extremely limited when compared to traditional RNA-sequencing. To overcome this, we developed REPAC, a framework for the analysis of APA from RNA-sequencing data. REPAC implements a new method for detection of APA and is designed to take advantage of recount3 which enables a streamlined way to analyze over 750,000 publicly available samples. Using REPAC, we investigated the landscape of APA caused by activation of B cells. Our analysis revealed that during this process, hundreds of genes are regulated by APA, most notably genes involved in the secretion pathway which is central for the transition to antibody-secreting B-cells. Moreover, we also showed that many genes associated with interferon response are also shortened, suggesting that APA might also play a significant role in the immune response. We also show that REPAC is faster than alternative methods by at least 7-fold and that it scales well to analysis involving hundreds of samples. Overall, the REPAC method offers an accurate, easy, and convenient solution for the exploration of APA across many phenotypes.

bioinformatics↗

bioRxiv2021

CALANGO: an annotation-based, phylogeny-aware comparative genomics framework for exploring and interpreting complex genotypes and phenotypes

The increasing availability of genomic, annotation, evolutionary and phenotypic data for species contrasts with the lack of studies that adequately integrate these heterogeneous data sources to produce biologically meaningful knowledge. Here, we present CALANGO, a phylogeny-aware comparative genomics tool that uncovers functional molecular convergences and homologous regions associated with quantitative genotypes and phenotypes across species, enabling the fast discovery of novel statistically sound, biologically relevant phenotype-genotype associations. We demonstrate the usefulness of CALANGO in two case studies. The first one unveils potential causal links between prophage density and the pathogenicity phenotype in Escherichia coli, and confidently demonstrates how CALANGO supports the investigation of basic causal relationships by enabling a level of counterfactual investigation of observed associations in the data. As a second case study, we used our tool to search for homologous regions associated with a complex phenotypic trait in a major group of eukaryotes: the evolution of maximum height in angiosperms. We confidently identify a previously unknown association between maximum plant height and the expansion of the self-incompatibility system, a molecular mechanism that prevents inbreeding and increases genetic diversity. Taller species also have lower rates of molecular evolution due to their longer generation times, a critical concern for their long-term viability. The new mechanism we report could counterbalance this fact, and have far-reaching consequences for fields as diverse as conservation biology and agriculture. CALANGO is provided as a fully operational R package that can be freely installed from CRAN.

bioinformatics↗

bioRxiv2021

Biological Constraints Can Improve Prediction inPrecision Oncology

Many gene signatures have been developed by applying machine learning (ML) on omics profiles, however, their clinical utility is often hindered by limited interpretability and unstable performance in different datasets. Here, we show the importance of embedding prior biological knowledge in the decision rules yielded by ML approaches to build robust classifiers. We tested this by applying different ML algorithms on gene expression data to predict three difficult cancer phenotypes: bladder cancer progression to muscle invasive disease; response to neoadjuvant chemotherapy in triple-negative breast cancer, and prostate cancer metastatic progression. We developed two sets of classifiers: mechanistic, by restricting the training process to features capturing a specific biological mechanism; and agnostic, in which the training didnt use any a priori biological information. Mechanistic models had a similar or better performance to their agnostic counterparts in the testing data, with enhanced stability, robustness, and interpretability. Our findings support the use of biological constraints to develop robust and interpretable gene signatures with high translational potential. MotivationOmics-based gene signatures often suffer from overfitting and reduced performance when tested on independent data. This usually results from the discrepancy between the high number of features compared to the much smaller number of samples used in the training process, which results in the machine learning algorithm perfectly fitting the training data with a subsequent deterioration in performance in independent cohorts. We introduce a mechanistic framework to mitigate overfitting and improve interpretability by constraining the training process to simple rank-based decision rules recapitulating relevant, cancer-related, biological mechanisms. Our approach aims at reducing the number of training variables to a pre-defined set of biologically important features in the form of gene pairs. The classification mechanism depends entirely on the relative ordering of these pairs, making it robust to data preprocessing techniques, improving the overall interpretability of the resulting models with significant translational implications. Most importantly, these pairs are configured in such a way that the decision rules resulting from the genes relative order embed and recapitulate specific biological mechanism, inherently enhancing the classifiers interpretability.

bioinformatics↗

bioRxiv2021

recount3: summaries and queries for large-scale RNA-seq expression and splicing

We present recount3, a resource consisting of over 750,000 publicly available human and mouse RNA sequencing (RNA-seq) samples uniformly processed by our new Monorail analysis pipeline. To facilitate access to the data, we provide the recount3 and snapcount R/Bioconductor packages as well as complementary web resources. Using these tools, data can be downloaded as study-level summaries or queried for specific exon-exon junctions, genes, samples, or other features. Monorail can be used to process local and/or private data, allowing results to be directly compared to any study in recount3. Taken together, our tools help biologists maximize the utility of publicly available RNA-seq data, especially to improve their understanding of newly collected data. recount3 is available from http://rna.recount.bio.

genomics↗

bioRxiv2020

Transcriptional landscape of PTEN loss in primary prostate cancer

PTEN is the most frequently lost tumor suppressor in primary prostate cancer (PCa) and its loss is associated with aggressive disease. However, the transcriptional changes associated with PTEN loss in PCa have not been described in detail. Here, we applied a meta-analysis approach, leveraging two large PCa cohorts with experimentally validated PTEN and ERG status, to derive a transcriptomic signature of PTEN loss, while also accounting for potential confounders due to ERG rearrangements. Strikingly, the signature indicates a strong activation of both innate and adaptive immune systems upon PTEN loss, as well as an expected activation of cell-cycle genes. Moreover, we made use of our recently developed FC-R2 expression atlas to expand this signature to include many non-coding RNAs recently annotated by the FANTOM consortium. With this resource, we analyzed the TCGA-PRAD cohort, creating a comprehensive transcriptomic landscape of PTEN loss in PCa that comprises both the coding and an extensive non-coding counterpart.

cancer biology↗

bioRxiv2019

Functional Annotation of Human Long Non-Coding RNAs via Molecular Phenotyping

Long non-coding RNAs (lncRNAs) constitute the majority of transcripts in the mammalian genomes and yet, their functions remain largely unknown. We systematically knockdown 285 lncRNAs expression in human dermal fibroblasts and quantified cellular growth, morphological changes, and transcriptomic responses using Capped Analysis of Gene Expression (CAGE). Antisense oligonucleotides targeting the same lncRNA exhibited global concordance, and the molecular phenotype, measured by CAGE, recapitulated the observed cellular phenotypes while providing additional insights on the affected genes and pathways. Here, we disseminate the largest to-date lncRNA knockdown dataset with molecular phenotyping (over 1,000 CAGE deep-sequencing libraries) for further exploration and highlight functional roles for ZNF213-AS1 and lnc-KHDC3L-2.

genomics↗

bioRxiv2019

Recounting the FANTOM Cage Associated Transcriptome

Long non-coding RNAs (lncRNAs) have emerged as key coordinators of biological and cellular processes. Characterizing lncRNA expression across cells and tissues is key to understanding their role in determining phenotypes including human diseases. We present here FC-R2, a comprehensive expression atlas across a broadly-defined human transcriptome, inclusive of over 109,000 coding and non-coding genes, as described in the FANTOM CAGE-Associated Transcriptome (FANTOM-CAT) study. This atlas greatly extends the gene annotation used in the original recount2 resource. We demonstrate the utility of the FC-R2 atlas by reproducing key findings from published large studies and by generating new results across normal and diseased human samples. In particular, we (a) identify tissue specific transcription profiles for distinct classes of coding and non-coding genes, (b) perform differential expression analyses across thirteen cancer types, providing new insights linking promoter and enhancer lncRNAs expression to tumor pathogenesis, and (c) confirm the prognostic value of several enhancers in cancer. Comprised of over 70,000 samples, the FC-R2 atlas will empower other researchers to investigate functions and biological roles of both known coding genes and novel lncRNAs. Most importantly, access to the FC-R2 atlas is available from https://jhubiostatistics.shinyapps.io/recount/, the recount Bioconductor package, and http://marchionnilab.org/fcr2.html.

genomics↗