Tree-Based Position Weight Matrix Approach to Model Transcription Factor Binding Site Profiles

Most of the position weight matrix (PWM) based bioinformatics methods developed to predict transcription factor binding sites (TFBS) assume each nucleotide in the sequence motif contributes independently to the interaction between protein and DNA sequence, usually producing high false positive predictions. The increasing availability of TF enrichment profiles from recent ChIP-Seq methodology facilitates the investigation of dependent structure and accurate prediction of TFBSs. We develop a novel Tree-based PWM (TPWM) approach to accurately model the interaction between TF and its binding site. The whole tree-structured PWM could be considered as a mixture of different conditional-PWMs. We propose a discriminative approach, called TPD (TPWM based Discriminative Approach), to construct the TPWM from the ChIP-Seq data with a pre-existing PWM. To achieve the maximum discriminative power between the positive and negative datasets, the cutoff value is determined based on the Matthew Correlation Coefficient (MCC). The resulting TPWMs are evaluated with respect to accuracy on extensive synthetic datasets. We then apply our TPWM discriminative approach on several real ChIP-Seq datasets to refine the current TFBS models stored in the TRANSFAC database. Experiments on both the simulated and real ChIP-Seq data show that the proposed method starting from existing PWM has consistently better performance than existing tools in detecting the TFBSs. The improved accuracy is the result of modelling the complete dependent structure of the motifs and better prediction of true positive rate. The findings could lead to better understanding of the mechanisms of TF-DNA interactions

Informative sequence-based models for fragment distributions in ChIP-seq, RNA-seq and ChIP-chip data

Many high throughput sequencing protocols for RNA and DNA require that the polynucleic acid is fragmented so that the identity of a limited number of nucleic acids of one or both of the ends of the fragments can be determined by sequencing. The nucleic acid sequence allows the fragment to be located within the genome, and the fragment distribution can then be used for a variety of different purposes. In the case of DNA this includes identifying the locations where specific proteins are bound to the genome. In the case of RNA this includes quantifying the expression levels of different gene variants or transcripts. If the locations of the polynucleic acid fragments are partly determined by the underlying nucleic acid sequence this could bias any results derived from the data. Unfortunately, such sequence dependencies have already been observed in the distribution of both RNA and DNA fragments. Previous analyses of such data in order to reduce the bias have examined the role of regional characteristics such as GC bias, or the bias towards a specific sequence at the start of the fragments. This thesis introduces a new method for modelling the bias which considers the degree to which the nucleotide sequence affects the likelihood of a fragment originating at that location. This shows that there is often not a single bias characteristic, but multiple, alternative sequence biases that coexist within a single dataset. This also shows that the nucleotide sequence immediately proximal to the fragment also has a significant effect on the fragment likelihood. This new approach highlights characteristics that were previously hidden and provides a more powerful basis for correcting such bias. Multiple alternative sequence biases are observed when both RNA and DNA are fragmented, but the more detailed information provided by the new technique shows in detail how the characteristics are different for RNA and DNA and indicates that very different molecular mechanisms are responsible for the biases in the two processes. This thesis also shows how removing the effect of this bias in ChIP-seq experiments can reveal more subtle features of the distribution of the fragments. This can provide information on the nature of the binding between proteins and the DNA with per-nucleotide precision, revealed through the change in likelihood of the DNA fragmenting at each position in the binding site. It is also shown how the model fitting technique developed to analyse sequence bias can also be used to obtain additional information from the results of ChIP-chip experiments. The approach is used to find the nucleotide sequence preference of DNA binding proteins, and also the cooperative effects associated with binding at multiple binding sites in close proximity

Limitations and potentials of current motif discovery algorithms

Computational methods for de novo identification of gene regulation elements, such as transcription factor binding sites, have proved to be useful for deciphering genetic regulatory networks. However, despite the availability of a large number of algorithms, their strengths and weaknesses are not sufficiently understood. Here, we designed a comprehensive set of performance measures and benchmarked five modern sequence-based motif discovery algorithms using large datasets generated from Escherichia coli RegulonDB. Factors that affect the prediction accuracy, scalability and reliability are characterized. It is revealed that the nucleotide and the binding site level accuracy are very low, while the motif level accuracy is relatively high, which indicates that the algorithms can usually capture at least one correct motif in an input sequence. To exploit diverse predictions from multiple runs of one or more algorithms, a consensus ensemble algorithm has been developed, which achieved 6–45% improvement over the base algorithms by increasing both the sensitivity and specificity. Our study illustrates limitations and potentials of existing sequence-based motif discovery algorithms. Taking advantage of the revealed potentials, several promising directions for further improvements are discussed. Since the sequence-based algorithms are the baseline of most of the modern motif discovery algorithms, this paper suggests substantial improvements would be possible for them

On the detection and refinement of transcription factor binding sites using ChIP-Seq data

Coupling chromatin immunoprecipitation (ChIP) with recently developed massively parallel sequencing technologies has enabled genome-wide detection of protein–DNA interactions with unprecedented sensitivity and specificity. This new technology, ChIP-Seq, presents opportunities for in-depth analysis of transcription regulation. In this study, we explore the value of using ChIP-Seq data to better detect and refine transcription factor binding sites (TFBS). We introduce a novel computational algorithm named Hybrid Motif Sampler (HMS), specifically designed for TFBS motif discovery in ChIP-Seq data. We propose a Bayesian model that incorporates sequencing depth information to aid motif identification. Our model also allows intra-motif dependency to describe more accurately the underlying motif pattern. Our algorithm combines stochastic sampling and deterministic ‘greedy’ search steps into a novel hybrid iterative scheme. This combination accelerates the computation process. Simulation studies demonstrate favorable performance of HMS compared to other existing methods. When applying HMS to real ChIP-Seq datasets, we find that (i) the accuracy of existing TFBS motif patterns can be significantly improved; and (ii) there is significant intra-motif dependency inside all the TFBS motifs we tested; modeling these dependencies further improves the accuracy of these TFBS motif patterns. These findings may offer new biological insights into the mechanisms of transcription factor regulation

The folded k-spectrum kernel: A machine learning approach to detecting transcription factor binding sites with gapped nucleotide dependencies

Understanding the molecular machinery involved in transcriptional regulation is central to improving our knowledge of an organism’s development, disease, and evolution. The building blocks of this complex molecular machinery are an organism’s genomic DNA sequence and transcription factor proteins. Despite the vast amount of sequence data now available for many model organisms, predicting where transcription factors bind, often referred to as ‘motif detection’ is still incredibly challenging. In this study, we develop a novel bioinformatic approach to binding site prediction. We do this by extending pre-existing SVM approaches in an unbiased way to include all possible gapped k-mers, representing different combinations of complex nucleotide dependencies within binding sites. We show the advantages of this new approach when compared to existing SVM approaches, through a rigorous set of cross-validation experiments. We also demonstrate the effectiveness of our new approach by reporting on its improved performance on a set of 127 genomic regions known to regulate gene expression along the anterio-posterior axis in early Drosophila embryos

Optimized mixed Markov models for motif identification

BACKGROUND: Identifying functional elements, such as transcriptional factor binding sites, is a fundamental step in reconstructing gene regulatory networks and remains a challenging issue, largely due to limited availability of training samples. RESULTS: We introduce a novel and flexible model, the Optimized Mixture Markov model (OMiMa), and related methods to allow adjustment of model complexity for different motifs. In comparison with other leading methods, OMiMa can incorporate more than the NNSplice's pairwise dependencies; OMiMa avoids model over-fitting better than the Permuted Variable Length Markov Model (PVLMM); and OMiMa requires smaller training samples than the Maximum Entropy Model (MEM). Testing on both simulated and actual data (regulatory cis-elements and splice sites), we found OMiMa's performance superior to the other leading methods in terms of prediction accuracy, required size of training data or computational time. Our OMiMa system, to our knowledge, is the only motif finding tool that incorporates automatic selection of the best model. OMiMa is freely available at [1]. CONCLUSION: Our optimized mixture of Markov models represents an alternative to the existing methods for modeling dependent structures within a biological motif. Our model is conceptually simple and effective, and can improve prediction accuracy and/or computational speed over other leading methods

Computational representation and discovery of transcription factor binding sites

Tesi per compendi de publicacions.The information about how, when, and where are produced the proteins has been one of the major challenge in molecular biology. The studies about the control of the gene expression are essential in order to have a better knowledge about the protein synthesis. The gene regulation is a highly controlled process that starts with the DNA transcription. This process operates at the gene level, hereditary basic units, which will be copied into primary ribonucleic acid (RNA). This first step is controlled by the binding of specific proteins, called as Transcription Factors (TF), with a sequence of the DNA (Deoxyribonucleic Acid) in the regulatory region of the gene. These DNA sequences are known as binding sites (BS). The binding sites motifs are usually very short (5 to 20 bp long) and highly degenerate. These sequences are expected to occur at random every few hundred base pairs. Besides, a TF can bind among different sites. Due to its highly variability, it is difficult to establish a consensus sequence. The study and identification binding sites is important to clarify the control of the gene expression. Due to the importance of identifying binding sites sequences, projects such as ENCODE (Encyclopedia of DNA elements), have dedicated efforts to map binding sites for large set of transcription factor to identify regulatory regions. In this thesis, we have approached the problem of the binding site detection from another angle. We have developed a set of toolkit for motif binding detection based on linear and non-linear models. First of all, we have been able to characterize binding sites using different approaches. The first one is based on the information that there is in each binding sites position. The second one is based on the covariance model of an aligned set of binding sites sequences. From these motif characterizations, we have proposed a new set of computational methods to detect binding sites. First, it was developed a new method based on parametric uncertainty measurement (Rényi entropy). This detection algorithm evaluates the variation on the total Rényi entropy of a set of sequences when a candidate sequence is assumed to be a true binding site belonging to the set. This method was found to perform especially well on transcription factors that the correlation among binding sites was null. The correlation among binding sites positions was considered through linear, Q-residuals, and non-linear models, alpha-Divergence and SIGMA. Q-residuals is a novel motif finding method which constructs a subspace based on the covariance of numerical DNA sequences. When the number of available sequences was small, The Q-residuals performance was significantly better and faster than all the others methodologies. Alpha-Divergence was based on the variation of the total parametric divergence in a set of aligned sequenced with binding evidence when a candidate sequence is added. Given an optimal q-value, the alpha-Divergence performance had a better behavior than the others methodologies in most of the studied transcription factor binding sites. And finally, a new computational tool, SIGMA, was developed as a trade-off between the good generalisation properties of pure entropy methods and the ability of position-dependency metrics to improve detection power. In approximately 70% of the cases considered, SIGMA exhibited better performance properties, at comparable levels of computational resources, than the methods which it was compared. This set of toolkits and the models for the detection of a set of transcription factor binding sites (TFBS) has been included in an R-package called MEET.La informació sobre com, quan i on es produeixen les proteïnes ha estat un dels majors reptes en la biologia molecular. Els estudis sobre el control de l'expressió gènica són essencials per conèixer millor el procés de síntesis d'una proteïna. La regulació gènica és un procés altament controlat que s'inicia amb la transcripció de l'ADN. En aquest procés, els gens, unitat bàsica d'herència, són copiats a àcid ribonucleic (RNA). El primer pas és controlat per la unió de proteïnes, anomenades factors de transcripció (TF), amb una seqüència d'ADN (àcid desoxiribonucleic) en la regió reguladora del gen. Aquestes seqüències s'anomenen punts d'unió i són específiques de cada proteïna. La unió dels factors de transcripció amb el seu corresponent punt d'unió és l'inici de la transcripció. Els punts d'unió són seqüències molt curtes (5 a 20 parells de bases de llargada) i altament degenerades. Aquestes seqüències poden succeir de forma aleatòria cada centenar de parells de bases. A més a més, un factor de transcripció pot unir-se a diferents punts. A conseqüència de l'alta variabilitat, és difícil establir una seqüència consensus. Per tant, l'estudi i la identificació del punts d'unió és important per entendre el control de l'expressió gènica. La importància d'identificar seqüències reguladores ha portat a projectes com l'ENCODE (Encyclopedia of DNA Elements) a dedicar grans esforços a mapejar les seqüències d'unió d'un gran conjunt de factors de transcripció per identificar regions reguladores. L'accés a seqüències genòmiques i els avanços en les tecnologies d'anàlisi de l'expressió gènica han permès també el desenvolupament dels mètodes computacionals per la recerca de motius. Gràcies aquests avenços, en els últims anys, un gran nombre de algorismes han sigut aplicats en la recerca de motius en organismes procariotes i eucariotes simples. Tot i la simplicitat dels organismes, l'índex de falsos positius és alt respecte als veritables positius. Per tant, per estudiar organismes més complexes és necessari mètodes amb més sensibilitat. En aquesta tesi ens hem apropat al problema de la detecció de les seqüències d'unió des de diferents angles. Concretament, hem desenvolupat un conjunt d'eines per la detecció de motius basats en models lineals i no-lineals. Les seqüències d'unió dels factors de transcripció han sigut caracteritzades mitjançant dues aproximacions. La primera està basada en la informació inherent continguda en cada posició de les seqüències d'unió. En canvi, la segona aproximació caracteritza la seqüència d'unió mitjançant un model de covariància. A partir d'ambdues caracteritzacions, hem proposat un nou conjunt de mètodes computacionals per la detecció de seqüències d'unió. Primer, es va desenvolupar un nou mètode basat en la mesura paramètrica de la incertesa (entropia de Rényi). Aquest algorisme de detecció avalua la variació total de l'entropia de Rényi d'un conjunt de seqüències d'unió quan una seqüència candidata és afegida al conjunt. Aquest mètode va obtenir un bon rendiment per aquells seqüències d'unió amb poca o nul.la correlació entre posicions. La correlació entre posicions fou considerada a través d'un model lineal, Qresiduals, i dos models no-lineals, alpha-Divergence i SIGMA. Q-residuals és una nova metodologia per la recerca de motius basada en la construcció d'un subespai a partir de la covariància de les seqüències d'ADN numèriques. Quan el nombre de seqüències disponible és petit, el rendiment de Q-residuals fou significant millor i més ràpid que en les metodologies comparades. Alpha-Divergence avalua la variació total de la divergència paramètrica en un conjunt de seqüències d'unió quan una seqüència candidata és afegida. Donat un q-valor òptim, alpha-Divergence va tenir un millor rendiment que les metodologies comparades en la majoria de seqüències d'unió dels factors de transcripció considerats. Finalment, un nou mètode computacional, SIGMA, va ser desenvolupat per tal millorar la potència de deteccióPostprint (published version

Novel computational methods for studying the role and interactions of transcription factors in gene regulation

Regulation of which genes are expressed and when enables the existence of different cell types sharing the same genetic code in their DNA. Erroneously functioning gene regulation can lead to diseases such as cancer. Gene regulatory programs can malfunction in several ways. Often if a disease is caused by a defective protein, the cause is a mutation in the gene coding for the protein rendering the protein unable to perform its functions properly. However, protein-coding genes make up only about 1.5% of the human genome, and majority of all disease-associated mutations discovered reside outside protein-coding genes. The mechanisms of action of these non-coding disease-associated mutations are far more incompletely understood. Binding of transcription factors (TFs) to DNA controls the rate of transcribing genetic information from the coding DNA sequence to RNA. Binding affinities of TFs to DNA have been extensively measured in vitro, ligands by exponential enrichment) and Protein Binding Microarrays (PBMs), and the genome-wide binding locations and patterns of TFs have been mapped in dozens of cell types. Despite this, our understanding of how TF binding to regulatory regions of the genome, promoters and enhancers, leads to gene expression is not at the level where gene expression could be reliably predicted based on DNA sequence only. In this work, we develop and apply computational tools to analyze and model the effects of TF-DNA binding. We also develop new methods for interpreting and understanding deep learning-based models trained on biological sequence data. In biological applications, the ability to understand how machine learning models make predictions is as, or even more important as raw predictive performance. This has created a demand for approaches helping researchers extract biologically meaningful information from deep learning model predictions. We develop a novel computational method for determining TF binding sites genome-wide from recently developed high-resolution ChIP-exo and ChIP-nexus experiments. We demonstrate that our method performs similarly or better than previously published methods while making less assumptions about the data. We also describe an improved algorithm for calling allele-specific TF-DNA binding. We utilize deep learning methods to learn features predicting transcriptional activity of human promoters and enhancers. The deep learning models are trained on massively parallel reporter gene assay (MPRA) data from human genomic regulatory elements, designed regulatory elements and promoters and enhancers selected from totally random pool of synthetic input DNA. This unprecedentedly large set of measurements of human gene regulatory element activities, in total more than 100 times the size of the human genome, allowed us to train models that were able to predict genomic transcription start site positions more accurately than models trained on genomic promoters, and to correctly predict effects of disease-associated promoter variants. We also found that interactions between promoters and local classical enhancers are non-specific in nature. The MPRA data integrated with extensive epigenetic measurements supports existence of three different classes of enhancers: classical enhancers, closed chromatin enhancers and chromatin-dependent enhancers. We also show that TFs can be divided into four different, non-exclusive classes based on their activities: chromatin opening, enhancing, promoting and TSS determining TFs. Interpreting the deep learning models of human gene regulatory elements required application of several existing model interpretation tools as well as developing new approaches. Here, we describe two new methods for visualizing features and interactions learned by deep learning models. Firstly, we describe an algorithm for testing if a deep learning model has learned an existing binding motif of a TF. Secondly, we visualize mutual information between pairwise k-mer distributions in sample inputs selected according to predictions by a machine learning model. This method highlights pairwise, and positional dependencies learned by a machine learning model. We demonstrate the use of this model-agnostic approach with classification and regression models trained on DNA, RNA and amino acid sequences.Monet eliöt koostuvat useista erilaisista solutyypeistä, vaikka kaikissa näiden eliöiden soluissa onkin sama DNA-koodi. Geenien ilmentymisen säätely mahdollistaa erilaiset solutyypit. Virheellisesti toimiva säätely voi johtaa sairauksiin, esimerkiksi syövän puhkeamiseen. Jos sairauden aiheuttaa viallinen proteiini, on syynä usein mutaatio tätä proteiinia koodaavassa geenissä, joka muuttaa proteiinia siten, ettei se enää pysty toimittamaan tehtäväänsä riittävän hyvin. Kuitenkin vain 1,5 % ihmisen genomista on proteiineja koodaavia geenejä. Suurin osa kaikista löydetyistä sairauksiin liitetyistä mutaatioista sijaitsee näiden ns. koodaavien alueiden ulkopuolella. Ei-koodaavien sairauksiin liitetyiden mutaatioiden vaikutusmekanismit ovat yleisesti paljon huonommin tunnettuja, kuin koodaavien alueiden mutaatioiden. Transkriptiotekijöiden sitoutuminen DNA:han säätelee transkriptiota, eli geeneissä olevan geneettisen informaation lukemista ja muuntamista RNA:ksi. Transkriptiotekijöiden sitoutumista DNA:han on mitattu kattavasti in vitro-olosuhteissa, ja monien transkriptiotekijöiden sitoutumiskohdat on mitattu genominlaajuisesti useissa eri solutyypeissä. Tästä huolimatta ymmärryksemme siitä miten transkriptioitekijöiden sitoutuminen genomin säätelyelementteihin, eli promoottoreihin ja vahvistajiin, johtaa geenien ilmentymiseen ei ole sellaisella tasolla, että voisimme luotettavasti ennustaa geenien ilmentymistä pelkästään DNA-sekvenssin perusteella. Tässä työssä kehitämme ja sovellamme laskennallisia työkaluja transkriptiotekijöiden sitoutumisesta johtuvan geenien ilmentymisen analysointiin ja mallintamiseen. Kehitämme myös uusia menetelmiä biologisella sekvenssidatalla opetettujen syväoppimismallien tulkitsemiseksi. Koneoppimismallin tekemien ennusteiden ymmärrettävyys on biologisissa sovelluksissa yleensä yhtä tärkeää, ellei jopa tärkeämpää kuin pelkkä raaka ennustetarkkuus. Tämä on synnyttänyt tarpeen uusille menetelmille, jotka auttavat tutkijoita louhimaan biologisesti merkityksellistä tietoa syväoppimismallien ennusteista. Kehitimme tässä työssä uuden laskennallisen työkalun, jolla voidaan määrittää transkriptiotekijöiden sitoutumiskohdat genominlaajuisesti käyttäen mittausdataa hiljattain kehitetyistä korkearesoluutioisista ChIP-exo ja ChIP-nexus kokeista. Näytämme, että kehittämämme menetelmä suoriutuu paremmin, tai vähintään yhtä hyvin kuin aiemmin julkaistut menetelmät tehden näitä vähemmän oletuksia signaalin muodosta. Esittelemme myös parannellun algoritmin transkriptiotekijöiden alleelispesifin sitoutumisen määrittämiseksi. Käytämme syväoppimismenetelmiä oppimaan mitkä ominaisuudet ennustavat ihmisen promoottori- ja voimistajaelementtien aktiivisuutta. Nämä syväoppimismallit on opetettu valtavien rinnakkaisten reportterigeenikokeiden datalla ihmisen genomisista säätelyelementeistä, sekä aktiivisista promoottoreista ja voimistajista, jotka ovat valikoituneet satunnaisesta joukosta synteettisiä DNA-sekvenssejä. Tämä ennennäkemättömän laaja joukko mittauksia ihmisen säätelyelementtien aktiivisuudesta - yli satakertainen määrä DNA sekvenssiä ihmisen genomiin verrattuna - mahdollisti transkription aloituskohtien sijainnin ennustamisen ihmisen genomissa tarkemmin kuin ihmisen genomilla opetetut mallit. Nämä mallit myös ennustivat oikein sairauksiin liitettyjen mutaatioiden vaikutukset ihmisen promoottoreilla. Tuloksemme näyttivät, että vuorovaikutukset ihmisen promoottorien ja klassisten paikallisten voimistajien välillä ovat epäspesifejä. MPRA-data, integroituna kattavien epigeneettisten mittausten kanssa mahdollisti voimistajaelementtien jaon kolmeen luokkaan: klassiset, suljetun kromatiinin, ja kromatiinista riippuvat voimistajat. Tutkimuksemme osoitti, että transkriptiotekijät voidaan jakaa neljään, osittain päällekkäiseen luokkaan niiden aktiivisuuksien perusteella: kromatiinia avaaviin, voimistaviin, promotoiviin ja transkription aloituskohdan määrittäviin transkriptiotekijöihin. Ihmisen genomin säätelyelementtejä kuvaavien syväoppimismallien tulkitseminen vaati sekä olemassa olevien menetelmien soveltamista, että uusien kehittämistä. Kehitimme tässä työssä kaksi uutta menetelmää syväoppimismallien oppimien muuttujien ja niiden välisten vuorovaikutusten visualisoimiseksi. Ensin esittelemme algoritmin, jonka avulla voidaan testata onko syväoppimismalli oppinut jonkin jo tunnetun transkriptiotekijän sitoutumishahmon. Toiseksi, visualisoimme positiokohtaisten k-meerijakaumien keskeisinformaatiota sekvensseissä, jotka on valittu syväoppimismallin ennusteiden perusteella. Tämä menetelmä paljastaa syväoppimismallin oppimat parivuorovaikutukset ja positiokohtaiset riippuvuudet. Näytämme, että kehittämämme menetelmä on mallin arkkitehtuurista riippumaton soveltamalla sitä sekä luokittelijoihin, että regressiomalleihin, jotka on opetettu joko DNA-, RNA-, tai aminohapposekvenssidatalla