1,721,066 research outputs found
Iterating on multiple collections in synchrony
Get PDFModern programming languages typically provide some form of comprehension syntax which renders programs manipulating collection types more readable and understandable. However, comprehension syntax corresponds to nested loops in general. There is no simple way of using it to express efficient general synchronized iterations on multiple ordered collections, such as linear-time algorithms for low-selectivity database joins. Synchrony fold is proposed here as a novel characterization of synchronized iteration. Central to this characterization is a monotonic isBefore predicate for relating the orderings on the two collections being iterated on and an antimonotonic canSee predicate for identifying matching pairs in the two collections to synchronize and act on. A restriction is then placed on Synchrony fold, cutting its extensional expressive power to match that of comprehension syntax, giving us Synchrony generator. Synchrony generator retains sufficient intensional expressive power for expressing efficient synchronized iteration on ordered collections. In particular, it is proved to be a natural generalization of the database merge join algorithm, extending the latter to more general database joins. Finally, Synchrony iterator is derived from Synchrony generator as a novel form of iterator. While Synchrony iterator has the same extensional and intensional expressive power as Synchrony generator, the former is better dovetailed with comprehension syntax. Thereby, algorithms requiring synchronized iterations on multiple ordered collections, including those for efficient general database joins, become expressible naturally in comprehension syntax
A comparative analysis of ENCODE and Cistrome in the context of TF binding signal
Get PDFBackground: With the rise of publicly available genomic data repositories, it is now common for scientists to rely on computational models and preprocessed data, either as control or to discover new knowledge. However, different repositories adhere to the different principles and guidelines, and data processing plays a significant role in the quality of the resulting datasets. Two popular repositories for transcription factor binding sites data - ENCODE and Cistrome - process the same biological samples in alternative ways, and their results are not always consistent. Moreover, the output format of the processing (BED narrowPeak) exposes a feature, the signalValue, which is seldom used in consistency checks, but can offer valuable insight on the quality of the data. Results: We provide evidence that data points with high signalValue(s) (top 25% of values) are more likely to be consistent between ENCODE and Cistrome in human cell lines K562, GM12878, and HepG2. In addition, we show that filtering according to said high values improves the quality of predictions for a machine learning algorithm that detects transcription factor interactions based only on positional information. Finally, we provide a set of practices and guidelines, based on the signalValue feature, for scientists who wish to compare and merge narrowPeaks from ENCODE and Cistrome. Conclusions: The signalValue feature is an informative feature that can be effectively used to highlight consistent areas of overlap between different sources of TF binding sites that expose it. Its applicability extends to downstream to positional machine learning algorithms, making it a powerful tool for performance tweaking and data aggregation.Ministry of Education (MOE)National Research Foundation (NRF)Published versionThis work was supported by National Research Foundation, Singapore, under its Synthetic Biology Research and Development Programme (Award No: SBPP3); and by Ministry of Education, Singapore, Academic Research Fund Tier-1 (Award No: MOE T1 251RES1725). SC and PP are supported by the ERC AdG 693174 “Data-driven Genomic Computing (GeCo)”
The in silico prediction of promoters in bacterial genomes
No full textIn silico approaches to the identification of bacterial promoters are hampered by poor conservation of their characteristic binding sites. This suggests that the usual position weight matrix models of bacterial promoters are incomplete. A number of methods have been used to overcome this inadequacy, one of which is to incorporate structural properties of DNA. In this paper we describe an extension of the promoter description to include SIDD (stress induced duplex destabilization), DNA curvature and stacking energy. Although we report the best result to date for a realistic promoter prediction task, surprisingly, DNA structural properties did not contribute significantly to this result. We also demonstrate for the first time, that sigma-54 promoters have a stronger association with SIDD than do other promoter types
Identifying collateral and synthetic lethal vulnerabilities within the DNA-damage response.
Get PDFBackgroundA pair of genes is defined as synthetically lethal if defects on both cause the death of the cell but a defect in only one of the two is compatible with cell viability. Ideally, if A and B are two synthetic lethal genes, inhibiting B should kill cancer cells with a defect on A, and should have no effects on normal cells. Thus, synthetic lethality can be exploited for highly selective cancer therapies, which need to exploit differences between normal and cancer cells.ResultsIn this paper, we present a new method for predicting synthetic lethal (SL) gene pairs. As neighbouring genes in the genome have highly correlated profiles of copy number variations (CNAs), our method clusters proximal genes with a similar CNA profile, then predicts mutually exclusive group pairs, and finally identifies the SL gene pairs within each group pairs. For mutual-exclusion testing we use a graph-based method which takes into account the mutation frequencies of different subjects and genes. We use two different methods for selecting the pair of SL genes; the first is based on the gene essentiality measured in various conditions by means of the "Gene Activity Ranking Profile" GARP score; the second leverages the annotations of gene to biological pathways.ConclusionsThis method is unique among current SL prediction approaches, it reduces false-positive SL predictions compared to previous methods, and it allows establishing explicit collateral lethality relationship of gene pairs within mutually exclusive group pairs
NAUTICA: classifying transcription factor interactions by positional and protein-protein interaction information
Get PDF10.1186/s13062-020-00268-1Biology Direct1511
Implementing a transcription factor interaction prediction system using the genometric query language
Get PDFNovel technologies and growing interest have resulted in a large increase in the amount of data available for genomics and transcriptomics studies, both in terms of volume and contents. Biology is relying more and more on computational methods to process, investigate, and extract knowledge from this huge amount of data. In this work, we present the TICA web server (available at http://www.gmql.eu/tica/), a fast and compact tool developed to support data-driven knowledge discovery in the realm of transcription factor interaction prediction. TICA leverages both the GenoMetric Query Language, a novel query tool (based on the Apache Hadoop and Spark technologies) specialized in the integration and management of heterogeneous, large genomic datasets, and a statistical method for robust detection of co-locations across interval-based data, in order to infer physically interacting transcription factors. Notably, TICA allows investigators to upload and analyze their own ChIP-seq experiments datasets, comparing them both against ENCODE data or between themselves, achieving computation time which increases linearly with respect to dataset size and density. Using ENCODE data from three well-studied cell lines as reference, we show that TICA predictions are supported by existing biological knowledge, making the web server a reliable and efficient tool for interaction screening and data-driven hypothesis generation
TICA: Transcriptional Interaction and Coregulation Analyzer
Get PDFTranscriptional regulation is critical to cellular processes of all organisms. Regulatory mechanisms often involve more than one transcription factor (TF) from different families, binding together and attaching to the DNA as a single complex. However, only a fraction of the regulatory partners of each TF is currently known. In this paper, we present the Transcriptional Interaction and Coregulation Analyzer (TICA), a novel methodology for predicting heterotypic physical interaction of TFs. TICA employs a data-driven approach to infer interaction phenomena from chromatin immunoprecipitation and sequencing (ChIP-seq) data. Its prediction rules are based on the distribution of minimal distance couples of paired binding sites belonging to different TFs which are located closest to each other in promoter regions. Notably, TICA uses only binding site information from input ChIP-seq experiments, bypassing the need to do motif calling on sequencing data. We present our method and test it on ENCODE ChIP-seq datasets, using three cell lines as reference including HepG2, GM12878, and K562. TICA positive predictions on ENCODE ChIP-seq data are strongly enriched when compared to protein complex (CORUM) and functional interaction (BioGRID) databases. We also compare TICA against both motif/ChIP-seq based methods for physical TF–TF interaction prediction and published literature. Based on our results, TICA offers significant specificity (average 0.902) while maintaining a good recall (average 0.284) with respect to CORUM, providing a novel technique for fast analysis of regulatory effect in cell lines. Furthermore, predictions by TICA are complementary to other methods for TF–TF interaction prediction (in particular, TACO and CENTDIST). Thus, combined application of these prediction tools results in much improved sensitivity in detecting TF–TF interactions compared to TICA alone (sensitivity of 0.526 when combining TICA with TACO and 0.585 when combining with CENTDIST) with little compromise in specificity (specificity 0.760 when combining with TACO and 0.643 with CENTDIST). TICA is publicly available at http://geco.deib.polimi.it/tica/
Data-driven techniques for knowledge discovery in regulomics
Get PDFLo sviluppo di nuove tecnologie di sequenziamente ha portato ad una crescita esponenziale nel volume di dati genomici disponibili. Nonostante ciò, le tecniche di modellistica ed analisi sui dati stessi hanno solo da poco cominciato a pareggiarne lo sviluppo in termini di efficacia ed efficienza, nonostante il costante abbattimento dei costi.
Tra gli argomenti di studio della genomica vi sono i meccanismi che regolano l'espressione dei geni, cioè come le cellule selezionano ed esprimono diversi geni in risposta a situazioni differenti. Tra di questi, i fattori di trascrizione (Transcription Factors, TFs) sono proteine che si legano al DNA di organismi pro- ed eucarioti in posizioni altamente specifiche (dette Transcription Factor Binding Sites, TFBS) e modulano l'accessibilità delle aree circostanti da parte dell'RNA polimerase, responsabile della trascrizione del DNA in RNA corrispondente. Queste aree contengono solitamente sequenze che codificano per particolari geni. Per questo motivo, gli effetti di regolazione dei TF sono molto importanti per gli studi di regulomica: l'attività di singoli TF è stata studiata in diversi modi, per esempio esperimenti di laboratori ed algoritmi computazionali, ma l'interazione tra diversi TF non è stata ancora altrettanto analizzata. La regolazione congiunta dei TF è un problema più difficile dal punto di vista analitico e nuovi metodi computazionali sono necessari per affrontarlo.
Questa tesi discute un nuovo modello il cui obiettivo è la predizione e classificazione delle interazioni tra TF attraverso un modellizzazione dei dati sperimentali disponibili. L'idea fondamentale è che i TFBS e le sequenze codificanti possano essere rappresentate da un set di coordinate lineari orientate e relativo insieme di feature, e che la distanza tra i punti di legame in questo sistema di coordinate sia una feature ricca di informazioni da utilizzare per predire le interazioni TF-TF. Questo approccio si basa sulle proprietà della distribuzione delle distanze genomiche osservate tra i punti di legami più vicini tra loro dei potenziali interattori. Per raffinare ulteriormente il modello, vengono sfruttate in primo luogo le informazioni contenute nelle reti di interazioni proteiche (protein-protein interaction o PPI networks) per definire feature addizionali che siano indipendenti dalle distanze, da usare per classificare le interazioni TF-TF in co-operazioni o competizioni. L'ipotesi è quella che più interazioni due TF hanno nella rete PPI, più è probabile che i due stiano co-operando invece che competendo, e viceversa. In secondo luogo, il numero di molecole di ciascun TF che viene misurato nei punti di legami accoppiati (detto segnale di arricchimento, signal enrichment) viene usato per inferire qualore il punto di legame congiunto sia fortemente occupato o invece vi sia interferenza da parte di un competitore.
I modelli modelli risultanti sono denominati TICA, NAUTICA ed ESTETICA; i primi due dimostrano buoni livelli di perfomance sia rispetto ai database biologici di riferimento, sia rispetto alla letteratura pubblicata. Presi nell'insieme, i modelli proposti rappresentano una solida infrastruttura utile a predire e classificare i fenomeni di interazione tra TF.Novel technologies have led to exponentially increasing amounts of genomic data. However, while costs have been constantly reducing, modeling and analysis techniques have only just started to catch up in effectiveness and efficiency.
Regulomics is a sub-field of genomics which studies the mechanics of gene expression regulation, i.e. how cells select and express different genes to respond to the different situations. Among those, Transcription Factors (TFs) are proteins that attach themselves the DNA of prokaryotic and eukaryotic organisms in highly specific Transcription Factor Binding Sites (TFBS), and modulate how accessible the surrounding DNA areas is by RNA transcription machinery. Such areas usually contain coding sequences of genes. For this reason, they are of great importance in regulomics. TF activity has been studied in isolation by various means, such as wet-lab experiments and computational methods, but the interplay of several TFs has not been studied as much. TF co-regulation is significantly harder to analyse directly, requiring novel computational methods.
This thesis discusses a novel model aimed at predicting and classifying TF-TF interactions using a data-driven, model-based approach. The fundamental idea is that TFBS and coding sequences can be represented as a set of oriented, linear coordinates with features attached, and that the distance between binding sites in this coordinate system is an informative feature which can be used to predict TF-TF interactions. This approach relies on the properties of the distribution of genomic distances between matched, closest binding sites of potential interactors. To further refine this model, firstly protein-protein interaction (PPI) network data is mined to compute additional, independent features used in classification of TF-TF interactions, under the assumption that the more shared interactors two TFs have in the PPI network, the more likely it is that they are co-operating as opposed to competing for another partner; secondly, the number of detected copies of each TF at the relevant binding sites is used to infer whether the TFBS itself it highly bound or instead disrupted.
The resulting classifiers are named TICA, NAUTICA and ESTETICA; the first two show good performance with respect both to reference databases and existing literature. Taken as a whole, they represent a powerful framework for inferring and classifying TF-TF interaction phenomena.DIPARTIMENTO DI ELETTRONICA, INFORMAZIONE E BIOINGEGNERIAComputer Science and Engineering31BONARINI, ANDREAPERNICI, BARBAR
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