A method for identification of the methylation level of CpG islands from NGS data

In the course of sample preparation for Next Generation Sequencing (NGS), DNA is fragmented by various methods. Fragmentation shows a persistent bias with regard to the cleavage rates of various dinucleotides. With the exception of CpG dinucleotides the previously described biases were consistent with results of the DNA cleavage in solution. Here we computed cleavage rates of all dinucleotides including the methylated CpG and unmethylated CpG dinucleotides using data of the Whole Genome Sequencing datasets of the 1000 Genomes project. We found that the cleavage rate of CpG is significantly higher for the methylated CpG dinucleotides. Using this information, we developed a classifier for distinguishing cancer and healthy tissues based on their CpG islands statuses of the fragmentation. A simple Support Vector Machine classifier based on this algorithm shows an accuracy of 84%. The proposed method allows the detection of epigenetic markers purely based on mechanochemical DNA fragmentation, which can be detected by a simple analysis of the NGS sequencing data

NGSmethDB 2017: enhanced methylomes and differential methylation.

The 2017 update of NGSmethDB stores whole genome methylomes generated from short-read data sets obtained by bisulfite sequencing (WGBS) technology. To generate high-quality methylomes, stringent quality controls were integrated with third-part software, adding also a two-step mapping process to exploit the advantages of the new genome assembly models. The samples were all profiled under constant parameter settings, thus enabling comparative downstream analyses. Besides a significant increase in the number of samples, NGSmethDB now includes two additional data-types, which are a valuable resource for the discovery of methylation epigenetic biomarkers: (i) differentially methylated single-cytosines; and (ii) methylation segments (i.e. genome regions of homogeneous methylation). The NGSmethDB back-end is now based on MongoDB, a NoSQL hierarchical database using JSON-formatted documents and dynamic schemas, thus accelerating sample comparative analyses. Besides conventional database dumps, track hubs were implemented, which improved database access, visualization in genome browsers and comparative analyses to third-part annotations. In addition, the database can be also accessed through a RESTful API. Lastly, a Python client and a multiplatform virtual machine allow for program-driven access from user desktop. This way, private methylation data can be compared to NGSmethDB without the need to upload them to public servers. Database website: http://bioinfo2.ugr.es/NGSmethDB

Metilación diferencial en el genoma humano y su asociación con la transcripción

In this Doctoral Thesis the reliability in detecting the methylation levels of individual cytosines from WGBS reads has been significantly improved, taking into account all the sources of error known today. This has allowed to test the hypothesis which argues that the sign of the association between methylation and transcription depends on the genomic context in which methylation occurs and the type of transcription factors that are involved. In the light of the obtained results, it has not been possible to refute this hypothesis. An unexpected finding was that the positive association between methylation and transcription appears to be more frequent than it had been previously described, being even more frequent than the negative association. In relation to this, in transcription factors binding sites with greater affinity for methylated sites, green CpG-TLs are over-represented, but red CpG-TLs are underrepresented. These positive associations may be due to a hitherto unknown transcription regulation mechanism, but there are also likely to be cases where hydroxymethylation is positively associated with transcription, as the WGBS method is unable to discriminate between methylation and hydroxymethylation. In further studies, OxBS-seq or TAB-seq methods should be used in order to clarify the true nature of green CpG-TLs.En esta Tesis Doctoral se ha mejorado notablemente la fiabilidad en la detección de los niveles de metilación de las citosinas individuales a partir de lecturas de WGBS, tomando en cuenta todas fuentes de error conocidas en la actualidad. Esto ha permitido poner a prueba la hipótesis de que el signo de la asociación entre la metilación y la transcripción depende del contexto genómico en que se produce la metilación y del tipo de factores de transcripción implicados. A la vista de los resultados obtenidos, no ha sido posible refutar esta hipótesis. Un hallazgo inesperado fue que la asociación positiva entre la metilación y la transcripción parece ser más frecuente de lo que previamente se había descrito, llegando incluso a ser más frecuente que la asociación negativa. En relación a esto, los sitios de unión a factores de transcripción con mayor afinidad por sitios metilados son ricos en CpGTLs verdes pero pobres en CpG-TLs rojos. Estas asociaciones positivas podrían deberse a un mecanismo de regulación de la transcripción hasta ahora desconocido, pero también es probable que en realidad se trate de casos en los que la hidroximetilación se asocia positivamente con la transcripción, ya que el método WGBS es incapaz de discriminar entre metilación e hidroximetilación. En futuros estudios, se deberían utilizar los métodos OxBS-seq o TAB-seq para tratar de esclarecer la verdadera naturaleza de los CpG-TLs verdes.Tesis Univ. Granada.Financiado por la Junta de Andalucía, a través del Programa de Ayudas a Grupos de Investigación (Grupo PAI BIO-162) y de dos proyectos del Ministerio de Economía, Industria y Competitividad, a través del Programa Estatal de Investigación, Desarrollo e Innovación Orientada a los Retos de la Sociedad (AGL2013-49090-C2-2-R y AGL2017-88702-C2-2-R)