MINER: exploratory analysis of gene interaction networks by machine learning from expression data

<p>Abstract</p> <p>Background</p> <p>The reconstruction of gene regulatory networks from high-throughput "omics" data has become a major goal in the modelling of living systems. Numerous approaches have been proposed, most of which attempt only "one-shot" reconstruction of the whole network with no intervention from the user, or offer only simple correlation analysis to infer gene dependencies.</p> <p>Results</p> <p>We have developed MINER (Microarray Interactive Network Exploration and Representation), an application that combines multivariate non-linear tree learning of individual gene regulatory dependencies, visualisation of these dependencies as both trees and networks, and representation of known biological relationships based on common Gene Ontology annotations. MINER allows biologists to explore the dependencies influencing the expression of individual genes in a gene expression data set in the form of decision, model or regression trees, using their domain knowledge to guide the exploration and formulate hypotheses. Multiple trees can then be summarised in the form of a gene network diagram. MINER is being adopted by several of our collaborators and has already led to the discovery of a new significant regulatory relationship with subsequent experimental validation.</p> <p>Conclusion</p> <p>Unlike most gene regulatory network inference methods, MINER allows the user to start from genes of interest and build the network gene-by-gene, incorporating domain expertise in the process. This approach has been used successfully with RNA microarray data but is applicable to other quantitative data produced by high-throughput technologies such as proteomics and "next generation" DNA sequencing.</p

Computational methods to analyze molecular determinants behind phenotypes

Phenotype is a collection of an organism's observable features that can be characterized both on individual level and on single cell level. Phenotypes are largely determined by their molecular processes which also explains their inheritance and plasticity. Some of the molecular background of phenotypes can be characterized by inherited genetic variations and alterations in gene expression. The high-throughput measurement technologies enable the measurement of molecular determinants in cells. However, measurement technologies produce remarkable large data sets and the research questions have become increasingly complex. Thus computational methods are needed to discover molecular mechanisms behind the phenotypes. In many cases, analysis of molecular determinants that contribute to the phenotype proceeds by first identifying putative candidates by using a priori information and high-throughput measurements. Then further analysis can focus on most promising molecules. In many cases, the aim is to identify relevant markers or targets from a set of candidate molecules. Often biomedical studies result in a long list of candidate genes, and to interpret these candidates, information on their context in cell functions is needed. This context information can give insight to synergistic effects of molecular machinery in cells when functions of individual molecules do not explain the observed phenotype. In addition, the context information can be used to generate candidates. One of the methods in this thesis provides a computational data integration method that provides a link in between candidate genes from molecular pathways and genetic variants. It uses publicly available biological knowledge bases to systematically create functional context of candidate genes. This approach is especially important when studying cancer, that is dependent of complex molecular signaling. Genotypes associated with inherited disease predispositions have been studied successfully in the past, however, traditional methods are not applicable in wide variety of analysis conditions. Thus, this thesis introduces a method that uses haplotype sharing to identify genetic loci inherited by multiple distantly related individuals. It is flexible and can be used in various settings, also with very limited number of samples. Increasing the number of biological replicates in gene expression analysis increases the reliability of the results. In many cases, however, the number of samples is limited. Therefore, pooling gene expression data from multiple published studies can increase the understanding of the molecular background behind cell types. This is shown in this thesis by an analysis that identifies gene expression differences in two cell types using publicly available gene expression samples from previous studies. Finally, when candidate molecules are available to characterize phenotypes, they can be compiled into biomarkers. In many cases, a combination of multiple molecules serves as a better biomarker than a single molecule. This thesis also includes a machine learning approach that is used to discover a classifier that predicts the phenotype.Fenotyyppi on joukko organismin piirteitä, jotka ovat havaittavissa joko yksilön tasolla tai yksittäisten solujen tasolla. Molekulaariset prosessit määräävät pitkälti fenotyyppien ilmentymistä, joten taustalla vaikuttavat molekulaariset prosessit myös selittävät fenotyyppien perinnöllisyyttä sekä niiden mukautumista. Fenotyyppien molekulaarista taustaa voidaan kartoittaa tunnistamalla geneettistä variaatiota sekä muutoksia geenien aktiivisuudessa. Määrääviä molekulaarisia tekijöitä voidaan havaita soluissa käyttämällä high-throughput -mittausteknologioita. Nämä mittausteknologiat tuottavat erittäin suuria data-aineistoja ja samalla tutkimuskysymykset ovat tulleet entistä monimutkaisemmiksi. Nämä seikat ovat johtaneet siihen, että laskennallisia menetelmiä tarvitaan fenotyyppien molekulaarisen mekanismien tunnistamisessa. Usein tutkimus etenee ensin tunnistamalla lupaavia kandidaatteja käyttämällä a priori tietoa sekä high-throughput -mittauksia. Jatkoanalyysit voivat keskittyä lupaavimpiin molekyyleihin. Tällöin tavoitteena saattaa olla käyttökelpoisimpien biomarkkereiden tunnistaminen tai kohdegeenien valitseminen kandidaattien joukosta. Usein biolääketieteen tutkimus tuottaa joukon kandidaattigeenejä, jolloin tulosten tulkinta vaatii tietoa kandidaattigeenien suhteesta solun muuhun molekulaariseen toimintaan. Kun tämä molekulaarinen toiminta kontekstina otetaan huomioon, on mahdollista ymmärtää geenien yhteisvaikutuksia solun toimintaan silloin kun yksittäiset geenit eivät selitä havaittua fenotyyppiä. Solun molekulaarista kontekstia voi käyttää myös kandidaattigeenien luomiseen. Yksi väitöskirjassa esitelty menetelmä tarjoaa laskennallisen menetelmän, jolla voidaan yhdistää kandidaatit tunnetuilta pathwaylta geneettisiin variantteihin. Tämä menetelmä käyttää julkisia tietokantoja, joista se systemaattisesti kerää molekulaarisen kontekstin kandidaattigeeneille. Tällainen lähestymistapa on erityisen hyödyllinen syöpätutkimuksessa, sillä syöpä on tyypillisesti riippuvainen monimutkaisista molekyylien signalointiverkoista. Perittyjen genotyyppien ja sairauksien välisiä yhteyksiä on tutkittu pitkään menestyksekkäästi, mutta perinteisesti käytetyt menetelmät soveltuvat vain tiettyihin tapauksiin. Tässä väitöskirjassa esitellään menetelmä, joka käyttää haplotyyppien jakamista tunnistaakseen genomiset alueet, jotka ovat periytyneet useille kaukaisesti sukua oleville henkilöille. Tätä menetelmää voi käyttää useissa erilaisissa tutkimuskysymyksissä, ja se tuottaa luotettavia tuloksia myös hyvin vähäisellä näytemäärällä. Geeniekspressioanalyysin tulosten luotettavuus kasvaa samalla kun biologisten kopioiden määrä aineistossa kasvaa. Huolimatta tästä, näytemäärät ovat usein rajallisia. Tämän vuoksi geeniekspressiomittausten yhdistäminen useista jo julkaistuista tutkimuksista voi lisätä ymmärrystä solutyypin määräävistä biologisista prosesseista. Tässä väitöskirjassa esitellään analyysi, jolla tunnistetaan geeniekspressioeroja käyttäen geeniekspressioainestoa, joka on yhdistetty julkaistuista tutkimuksista. Viimein, kun fenotyyppiä selittävät kandidaattimolekyylit on tunnistettu, niistä voidaan luoda biomarkkereita. Monesti useamman molekyylin mittaus on parempi biomarkkeri kuin yksikään molekyyli yksinään. Tässä väitöskirjassa esitellään myös koneoppimisanalyysi, jolla luodaan geeniekspressiomittauksista fenotyyppiä ennustava luokittelija

Computational Methods for the Analysis of Genomic Data and Biological Processes

In recent decades, new technologies have made remarkable progress in helping to understand biological systems. Rapid advances in genomic profiling techniques such as microarrays or high-performance sequencing have brought new opportunities and challenges in the fields of computational biology and bioinformatics. Such genetic sequencing techniques allow large amounts of data to be produced, whose analysis and cross-integration could provide a complete view of organisms. As a result, it is necessary to develop new techniques and algorithms that carry out an analysis of these data with reliability and efficiency. This Special Issue collected the latest advances in the field of computational methods for the analysis of gene expression data, and, in particular, the modeling of biological processes. Here we present eleven works selected to be published in this Special Issue due to their interest, quality, and originality

Machine Learning Models for Deciphering Regulatory Mechanisms and Morphological Variations in Cancer

The exponential growth of multi-omics biological datasets is resulting in an emerging paradigm shift in fundamental biological research. In recent years, imaging and transcriptomics datasets are increasingly incorporated into biological studies, pushing biology further into the domain of data-intensive-sciences. New approaches and tools from statistics, computer science, and data engineering are profoundly influencing biological research. Harnessing this ever-growing deluge of multi-omics biological data requires the development of novel and creative computational approaches. In parallel, fundamental research in data sciences and Artificial Intelligence (AI) has advanced tremendously, allowing the scientific community to generate a massive amount of knowledge from data. Advances in Deep Learning (DL), in particular, are transforming many branches of engineering, science, and technology. Several of these methodologies have already been adapted for harnessing biological datasets; however, there is still a need to further adapt and tailor these techniques to new and emerging technologies. In this dissertation, we present computational algorithms and tools that we have developed to study gene-regulation and cellular morphology in cancer. The models and platforms that we have developed are general and widely applicable to several problems relating to dysregulation of gene expression in diseases. Our pipelines and software packages are disseminated in public repositories for larger scientific community use. This dissertation is organized in three main projects. In the first project, we present Causal Inference Engine (CIE), an integrated platform for the identification and interpretation of active regulators of transcriptional response. The platform offers visualization tools and pathway enrichment analysis to map predicted regulators to Reactome pathways. We provide a parallelized R-package for fast and flexible directional enrichment analysis to run the inference on custom regulatory networks. Next, we designed and developed MODEX, a fully automated text-mining system to extract and annotate causal regulatory interaction between Transcription Factors (TFs) and genes from the biomedical literature. MODEX uses putative TF-gene interactions derived from high-throughput ChIP-Seq or other experiments and seeks to collect evidence and meta-data in the biomedical literature to validate and annotate the interactions. MODEX is a complementary platform to CIE that provides auxiliary information on CIE inferred interactions by mining the literature. In the second project, we present a Convolutional Neural Network (CNN) classifier to perform a pan-cancer analysis of tumor morphology, and predict mutations in key genes. The main challenges were to determine morphological features underlying a genetic status and assess whether these features were common in other cancer types. We trained an Inception-v3 based model to predict TP53 mutation in five cancer types with the highest rate of TP53 mutations. We also performed a cross-classification analysis to assess shared morphological features across multiple cancer types. Further, we applied a similar methodology to classify HER2 status in breast cancer and predict response to treatment in HER2 positive samples. For this study, our training slides were manually annotated by expert pathologists to highlight Regions of Interest (ROIs) associated with HER2+/- tumor microenvironment. Our results indicated that there are strong morphological features associated with each tumor type. Moreover, our predictions highly agree with manual annotations in the test set, indicating the feasibility of our approach in devising an image-based diagnostic tool for HER2 status and treatment response prediction. We have validated our model using samples from an independent cohort, which demonstrates the generalizability of our approach. Finally, in the third project, we present an approach to use spatial transcriptomics data to predict spatially-resolved active gene regulatory mechanisms in tissues. Using spatial transcriptomics, we identified tissue regions with differentially expressed genes and applied our CIE methodology to predict active TFs that can potentially regulate the marker genes in the region. This project bridged the gap between inference of active regulators using molecular data and morphological studies using images. The results demonstrate a significant local pattern in TF activity across the tissue, indicating differential spatial-regulation in tissues. The results suggest that the integrative analysis of spatial transcriptomics data with CIE can capture discriminant features and identify localized TF-target links in the tissue

Functional Analysis of Human Long Non-coding RNAs and Their Associations with Diseases

Within this study, we sought to leverage knowledge from well-characterized protein coding genes to characterize the lesser known long non-coding RNA (lncRNA) genes using computational methods to find functional annotations and disease associations. Functional genome annotation is an essential step to a systems-level view of the human genome. With this knowledge, we can gain a deeper understanding of how humans develop and function, and a better understanding of human disease. LncRNAs are transcripts greater than 200 nucleotides, which do not code for proteins. LncRNAs have been found to regulate development, tissue and cell differentiation, and organ formation. Their dysregulation has been linked to several diseases including autism spectrum disorder (ASD) and cancer. While a great deal of research has been dedicated to protein-coding genes, the relatively recently discovered lncRNA genes have yet to be characterized. LncRNA function is tied closely to when and where they are expressed. Co-expression network analysis offer a means of functional annotation of uncharacterized genes through a guilt by association approach. We have constructed two co-expression networks using known disease-associated protein-coding genes and lncRNA genes. Through clustering of the networks, gene set enrichment analysis, and centrality measures, we found enrichment for disease association and functions as well as identified high-confidence lncRNA disease gene targets. We present a novel approach to the identification of disease state associations by demonstrating genes that are associated with the same disease states share patterns that can be discerned from transcriptomes of healthy tissues. Using a machine learning algorithm, we built a model to classify ASD versus non-ASD genes using their expression profiles from healthy developing human brain tissues. Feature selection during the model-building process also identified critical temporospatial points for the determination of ASD genes. We constructed a webserver tool for the prioritization of genes for ASD association. The webserver tool has a database containing prioritization and co-expression information for nearly every gene in the human genome