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In this chapter we will review the common steps in the analysis of whole genome singlebase-pair resolution methylation data including the pre-processing of the reads, the alignment and the read out of the methylation information of individual cytosines. We will specially focus on the possible error sources which need to be taken into account in order to generate high quality methylation maps. Several tools have been already developed to convert the sequencing data into knowledge about the methylation levels. We will review the most used tools discussing both technical aspects like user-friendliness and speed, but also biologically relevant questions as the quality control. For one of these tools, NGSmethPipe, we will give a step by step tutorial including installation and methylation profiling for different data types and species. We will conclude the chapter with a brief discussion of NGSmethDB, a database for the storage of single-base resolution methylation maps that can be used to further analyze the obtained methylation maps. ; This work was supported by the Ministry of Innovation and Science of the Spanish Government [BIO2010-20219 (M.H.), BIO2008-01353 (J.L.O.)]; 'Juan de la Cierva' grant (to M.H.) and Basque Country 'Programa de formación de investigadores' grant (to G.B.).

Next-generation sequencing (NGS) together with bisulphite conversion allows the generation of whole genome methylation maps at single-cytosine resolution. This allows studying the absence of methylation in a particular genome region over a range of tissues, the differential tissue methylation or the changes occurring along pathological conditions. However, no database exists fully addressing such requirements. We propose here NGSmethDB (http://bioinfo2.ugr.es/NGSmethDB/gbrowse/) for the storage and retrieval of methylation data derived from NGS. Two cytosine methylation contexts (CpG and CAG/CTG) are considered. Through a browser interface coupled to a MySQL backend and several data mining tools, the user can search for methylation states in a set of tissues, retrieve methylation values for a set of tissues in a given chromosomal region, or display the methylation of promoters among different tissues. NGSmethDB is currently populated with human, mouse and Arabidopsis data, but other methylomes will be incorporated through an automatic pipeline as soon as new data become available. Dump downloads for three coverage levels (1, 5 or 10 reads) are available. NGSmethDB will be useful for experimental researchers, as well as for bioinformaticians, who might use the data as input for further research. ; The Spanish Government grant (BIO2008-01353 to J.L.O.); 'Juan de la Cierva' (to M.H.); Basque Country 'Programa de formación de investigadores' grant (to G.B.). Funding for open access charge: The Spanish Government grant (BIO2008-01353 to J.L.O.).

We present a new version of miRanalyzer, a web server and stand-alone tool for the detection of known and prediction of new microRNAs in high-throughput sequencing experiments. The new version has been notably improved regarding speed, scope and available features. Alignments are now based on the ultrafast short-read aligner Bowtie (granting also colour space support, allowing mismatches and improving speed) and 31 genomes, including 6 plant genomes, can now be analysed (previous version contained only 7). Differences between plant and animal microRNAs have been taken into account for the prediction models and differential expression of both, known and predicted microRNAs, between two conditions can be calculated. Additionally, consensus sequences of predicted mature and precursor microRNAs can be obtained from multiple samples, which increases the reliability of the predicted microRNAs. Finally, a stand-alone version of the miRanalyzer that is based on a local and easily customized database is also available; this allows the user to have more control on certain parameters as well as to use specific data such as unpublished assemblies or other libraries that are not available in the web server. miRanalyzer is available at http://bioinfo2.ugr.es/miRanalyzer/miRanalyzer.php. ; The Ministry of Innovation and Science of the Spanish Government (BIO2010-20219 to M.H.); the Junta de Andalucia (P07FQM3163 to M.H.); the 'Juan de la Cierva' fellowship (to M.H.); the Department of Industry, Tourism and Trade of the Government of the Autonomous Community of the Basque Country (Etortek Research Programs 2009/20011 to A.M.A.); from the Innovation Technology Department of the Bizkaia County (to A.M.A.). Funding for open access charge: Department of Industry, Tourism and Trade of the Government of the Autonomous Community of the Basque Country (Etortek Research Programs 2009/2011 to A.M.A.).

[Background] Virtually all currently available microRNA target site prediction algorithms require the presence of a (conserved) seed match to the 5' end of the microRNA. Recently however, it has been shown that this requirement might be too stringent, leading to a substantial number of missed target sites. [Results] We developed TargetSpy, a novel computational approach for predicting target sites regardless of the presence of a seed match. It is based on machine learning and automatic feature selection using a wide spectrum of compositional, structural, and base pairing features covering current biological knowledge. Our model does not rely on evolutionary conservation, which allows the detection of species-specific interactions and makes TargetSpy suitable for analyzing unconserved genomic sequences. In order to allow for an unbiased comparison of TargetSpy to other methods, we classified all algorithms into three groups: I) no seed match requirement, II) seed match requirement, and III) conserved seed match requirement. TargetSpy predictions for classes II and III are generated by appropriate postfiltering. On a human dataset revealing fold-change in protein production for five selected microRNAs our method shows superior performance in all classes. In Drosophila melanogaster not only our class II and III predictions are on par with other algorithms, but notably the class I (no-seed) predictions are just marginally less accurate. We estimate that TargetSpy predicts between 26 and 112 functional target sites without a seed match per microRNA that are missed by all other currently available algorithms. [Conclusion] Only a few algorithms can predict target sites without demanding a seed match and TargetSpy demonstrates a substantial improvement in prediction accuracy in that class. Furthermore, when conservation and the presence of a seed match are required, the performance is comparable with state-of-the-art algorithms. TargetSpy was trained on mouse and performs well in human and drosophila, suggesting that it may be applicable to a broad range of species. Moreover, we have demonstrated that the application of machine learning techniques in combination with upcoming deep sequencing data results in a powerful microRNA target site prediction tool http://www.targetspy.org webcite. ; The work of MH was supported by the Spanish Government (Grant number: BIO2008.01353) and by the Junta de Andalucia (Grant number P07-FQM-03613).

[v2; ref status: indexed, http://f1000r.es/301] ; Whole genome methylation profiling at a single cytosine resolution is now feasible due to the advent of high-throughput sequencing techniques together with bisulfite treatment of the DNA. To obtain the methylation value of each individual cytosine, the bisulfite-treated sequence reads are first aligned to a reference genome, and then the profiling of the methylation levels is done from the alignments. A huge effort has been made to quickly and correctly align the reads and many different algorithms and programs to do this have been created. However, the second step is just as crucial and non-trivial, but much less attention has been paid to the final inference of the methylation states. Important error sources do exist, such as sequencing errors, bisulfite failure, clonal reads, and single nucleotide variants. We developed MethylExtract, a user friendly tool to: i) generate high quality, whole genome methylation maps and ii) detect sequence variation within the same sample preparation. The program is implemented into a single script and takes into account all major error sources. MethylExtract detects variation (SNVs – Single Nucleotide Variants) in a similar way to VarScan, a very sensitive method extensively used in SNV and genotype calling based on non-bisulfite-treated reads. The usefulness of MethylExtract is shown by means of extensive benchmarking based on artificial bisulfite-treated reads and a comparison to a recently published method, called Bis-SNP. MethylExtract is able to detect SNVs within High-Throughput Sequencing experiments of bisulfite treated DNA at the same time as it generates high quality methylation maps. This simultaneous detection of DNA methylation and sequence variation is crucial for many downstream analyses, for example when deciphering the impact of SNVs on differential methylation. An exclusive feature of MethylExtract, in comparison with existing software, is the possibility to assess the bisulfite failure in a statistical way. The source code, tutorial and artificial bisulfite datasets are available at http://bioinfo2.ugr.es/MethylExtract/ and http://sourceforge.net/projects/methylextract/, and also permanently accessible from 10.5281/zenodo.7144. ; This work was supported by the Spanish Government [BIO2008-01353 to JLO and BIO2010-20219 to MH], and Basque country 'AE' grant (GB).

Transcription factors (TFs), microRNAs (miRNAs), small interfering RNAs (siRNAs) and other functional non-coding small RNAs (sRNAs) are important gene regulators. Comparison of sRNA expression profiles between transgenic barley over-expressing a drought tolerant TF (TaDREB3) and non-transgenic control barley revealed many group-specific sRNAs. In addition, 42% of the shared sRNAs were differentially expressed between the two groups (|log2| >1). Furthermore, TaDREB3-derived sRNAs were only detected in transgenic barley despite the existence of homologous genes in non-transgenic barley. These results demonstrate that the TF strongly affects the expression of sRNAs and siRNAs could in turn affect the TF stability. The TF also affects size distribution and abundance of sRNAs including miRNAs. About half of the sRNAs in each group were derived from chloroplast. A sRNA derived from tRNA-His(GUG) encoded by the chloroplast genome is the most abundant sRNA, accounting for 42.2% of the total sRNAs in transgenic barley and 28.9% in non-transgenic barley. This sRNA, which targets a gene (TC245676) involved in biological processes, was only present in barley leaves but not roots. 124 and 136 miRNAs were detected in transgenic and non-transgenic barley, respectively. miR156 was the most abundant miRNA and up-regulated in transgenic barley, while miR168 was the most abundant miRNA and up-regulated in non-transgenic barley. Eight out of 20 predicted novel miRNAs were differentially expressed between the two groups. All the predicted novel miRNA targets were validated using a degradome library. Our data provide an insight into the effect of TF on the expression of sRNAs in barley. ; This work was funded by the Australian Research Council, the South Australian Government and the Grains Research and Development Corporation.

[Background] The phylogenetic distribution of large-scale genome structure (i.e. mosaic compositional patchiness) has been explored mainly by analytical ultracentrifugation of bulk DNA. However, with the availability of large, good-quality chromosome sequences, and the recently developed computational methods to directly analyze patchiness on the genome sequence, an evolutionary comparative analysis can be carried out at the sequence level. [Results] The local variations in the scaling exponent of the Detrended Fluctuation Analysis are used here to analyze large-scale genome structure and directly uncover the characteristic scales present in genome sequences. Furthermore, through shuffling experiments of selected genome regions, computationally-identified, isochore-like regions were identified as the biological source for the uncovered large-scale genome structure. The phylogenetic distribution of short- and large-scale patchiness was determined in the best-sequenced genome assemblies from eleven eukaryotic genomes: mammals (Homo sapiens, Pan troglodytes, Mus musculus, Rattus norvegicus, and Canis familiaris), birds (Gallus gallus), fishes (Danio rerio), invertebrates (Drosophila melanogaster and Caenorhabditis elegans), plants (Arabidopsis thaliana) and yeasts (Saccharomyces cerevisiae). We found large-scale patchiness of genome structure, associated with in silico determined, isochore-like regions, throughout this wide phylogenetic range. [Conclusion] Large-scale genome structure is detected by directly analyzing DNA sequences in a wide range of eukaryotic chromosome sequences, from human to yeast. In all these genomes, large-scale patchiness can be associated with the isochore-like regions, as directly detected in silico at the sequence level. ; This work was supported by the Spanish Government (BIO2005-09116-C03-01) and Plan Andaluz de Investigación (CVI-162, P06-FQM-01858, P07-FQM-03163 and TIC-640).

Isochores are long genome segments homogeneous in G+C. Here, we describe an algorithm (IsoFinder) running on the web (http://bioinfo2.ugr.es/IsoF/isofinder.html) able to predict isochores at the sequence level. We move a sliding pointer from left to right along the DNA sequence. At each position of the pointer, we compute the mean G+C values to the left and to the right of the pointer. We then determine the position of the pointer for which the difference between left and right mean values (as measured by the t-statistic) reaches its maximum. Next, we determine the statistical significance of this potential cutting point, after filtering out short-scale heterogeneities below 3 kb by applying a coarse-graining technique. Finally, the program checks whether this significance exceeds a probability threshold. If so, the sequence is cut at this point into two subsequences; otherwise, the sequence remains undivided. The procedure continues recursively for each of the two resulting subsequences created by each cut. This leads to the decomposition of a chromosome sequence into long homogeneous genome regions (LHGRs) with well-defined mean G+C contents, each significantly different from the G+C contents of the adjacent LHGRs. Most LHGRs can be identified with Bernardi's isochores, given their correlation with biological features such as gene density, SINE and LINE (short, long interspersed repetitive elements) densities, recombination rate or single nucleotide polymorphism variability. The resulting isochore maps are available at our web site (http://bioinfo2.ugr.es/isochores/), and also at the UCSC Genome Browser (http://genome.cse.ucsc.edu/). ; This work was supported by the Spanish Government (Grants Nos. BIO2002-04014-C03-01 and 02) and Plan Andaluz de Investigación (CVI-162). M.H. acknowledges a predoctoral grant from the University of Granada (Spain).

Background: Various small RNA (sRNA) sizes and varieties have been identified, but their relationship as well as relationship with their origins and allocations have not been well understood or investigated. Results: By comparing sRNAs generated from two barley cultivars, Golden Promise (GP) and Pallas, we identified that the generation of different sizes and types of sRNAs in barley was locus-, chromosome- and/or cultivar-dependent. 20-nt sRNAs mainly comprising miRNAs and chloroplast-derived sRNAs were significantly over-expressed in Pallas vs. GP on chromosomes 3H and 6H. MiRNAs-enriched 21-nt sRNAs were significantly over-expressed in Pallas vs. GP only on chromosome 4H. On chromosome 5H this size of sRNAs was significantly under-expressed in Pallas, so were 22-nt sRNAs mainly comprising miRNAs and repeat-derived sRNAs. 24-nt sRNAs mostly derived from repeats were evenly distributed in all chromosomes and expressed similarly between GP and Pallas. Unlike other sizes of sRNAs, 24-nt sRNAs were little conserved in other plant species. Abundant sRNAs were mostly generated from 3' terminal regions of chromosome 1H and 5' terminal regions of chromosome 5H. Over-expressed miRNAs in GP vs. Pallas primarily function in stress responses and iron-binding. Conclusions: Our study indicates that 23−24-nt sRNAs may be linked to repressive chromatin modifications and function in genome stability while 20−21-nt sRNAs may be important for the cultivar specificity. This study provides a novel insight into the mechanism of sRNA expression and function in barley. ; This work was funded by the Australian Research Council, the Grains Research and Development Corporation and the South Australian Government. None of these funding bodies were involved in the design of the study nor the collection, analysis, and interpretation of data and in writing the manuscript.

The updated release of 'NGSmethDB' (http://bioinfo2.ugr.es/NGSmethDB) is a repository for single-base whole-genome methylome maps for the best-assembled eukaryotic genomes. Short-read data sets from NGS bisulfite-sequencing projects of cell lines, fresh and pathological tissues are first pre-processed and aligned to the corresponding reference genome, and then the cytosine methylation levels are profiled. One major improvement is the application of a unique bioinformatics protocol to all data sets, thereby assuring the comparability of all values with each other. We implemented stringent quality controls to minimize important error sources, such as sequencing errors, bisulfite failures, clonal reads or single nucleotide variants (SNVs). This leads to reliable and high-quality methylomes, all obtained under uniform settings. Another significant improvement is the detection in parallel of SNVs, which might be crucial for many downstream analyses (e.g. SNVs and differential-methylation relationships). A next-generation methylation browser allows fast and smooth scrolling and zooming, thus speeding data download/upload, at the same time requiring fewer server resources. Several data mining tools allow the comparison/retrieval of methylation levels in different tissues or genome regions. NGSmethDB methylomes are also available as native tracks through a UCSC hub, which allows comparison with a wide range of third-party annotations, in particular phenotype or disease annotations. ; Spanish Government [BIO2008-01353 to J.L.O. and BIO2010-20219 to M.H.], and Basque country 'AE' grant (to G.B.). Funding for open access charge: Department of Genetics, University of Granada, Spain.

This research was supported by the Spanish Ministry of Science, Innovation and Universities (grants AGL2017-88702-C2-1-R and AGL2017-88702-C2-2-R). Funding was also received from the BRESOV (Breeding for resilient, efficient, and sustainable organic vegetable production) project. BRESOV was funded by the European Union's Horizon 2020 research and innovation programme under grant agreement No. 774244. We would also like to thank research facilities provided by the Campus de Excelencia Internacional Agroalimentario (CeiA3). PhD fellowships were funded by the FPU (R.F., R.Le.) Programmes of the Ministerio de Ciencia e Innovacion and the LASPAU (J.L.Q.). ; Trichomes are specialised epidermal cells developed in the aerial surface of almost every terrestrial plant. These structures form physical barriers, which combined with their capability of synthesis of complex molecules, prevent plagues from spreading and confer trichomes a key role in the defence against herbivores. In this work, the tomato gene HAIRPLUS (HAP) that controls glandular trichome density in tomato plants was characterised. HAP belongs to a group of proteins involved in histone tail modifications although some also bind methylated DNA. HAP loss of function promotes epigenomic modifications in the tomato genome reflected in numerous differentially methylated cytosines and causes transcriptomic changes in hap mutant plants. Taken together, these findings demonstrate that HAP links epigenome remodelling withmulticellular glandular trichome development and reveal that HAP is a valuable genomic tool for pest resistance in tomato breeding. ; Spanish Government AGL2017-88702-C2-1-R AGL2017-88702-C2-2-R ; European Commission 774244 ; FPU Programmes of the Ministerio de Ciencia e Innovacion ; LASPAU ; BRESOV (Breeding for resilient, efficient, and sustainable organic vegetable production) project

Hypomethylated, CpG-rich DNA segments (CpG islands, CGIs) are epigenome markers involved in key biological processes. Aberrant methylation is implicated in the appearance of several disorders as cancer, immunodeficiency, or centromere instability. Furthermore, methylation differences at promoter regions between human and chimpanzee strongly associate with genes involved in neurological/psychological disorders and cancers. Therefore, the evolutionary comparative analyses of CGIs can provide insights on the functional role of these epigenome markers in both health and disease. Given the lack of specific tools, we developed CpGislandEVO. Briefly, we first compile a database of statistically significant CGIs for the best assembled mammalian genome sequences available to date. Second, by means of a coupled browser front-end, we focus on the CGIs overlapping orthologous genes extracted from OrthoDB, thus ensuring the comparison between CGIs located on truly homologous genome segments. This allows comparing the main compositional features between homologous CGIs. Finally, to facilitate nucleotide comparisons, we lifted genome coordinates between assemblies from different species, which enables the analysis of sequence divergence by direct count of nucleotide substitutions and indels occurring between homologous CGIs. The resulting CpGislandEVO database, linking together CGIs and single-cytosine DNA methylation data from several mammalian species, is freely available at our website. ; This work was supported by the Spanish Government [BIO2008-01353 to José L. Oliver and BIO2010-20219 to Michael Hackenberg], Basque country "AE" grant (to Guillermo Barturen) and Erasmus internships (to Stefanie Geisen, Francisco Dios, and E. J. Maarten Hamberg).

Background Many k-mers (or DNA words) and genomic elements are known to be spatially clustered in the genome. Well established examples are the genes, TFBSs, CpG dinucleotides, microRNA genes and ultra-conserved non-coding regions. Currently, no algorithm exists to find these clusters in a statistically comprehensible way. The detection of clustering often relies on densities and sliding-window approaches or arbitrarily chosen distance thresholds. Results We introduce here an algorithm to detect clusters of DNA words (k-mers), or any other genomic element, based on the distance between consecutive copies and an assigned statistical significance. We implemented the method into a web server connected to a MySQL backend, which also determines the co-localization with gene annotations. We demonstrate the usefulness of this approach by detecting the clusters of CAG/CTG (cytosine contexts that can be methylated in undifferentiated cells), showing that the degree of methylation vary drastically between inside and outside of the clusters. As another example, we used WordCluster to search for statistically significant clusters of olfactory receptor (OR) genes in the human genome. Conclusions WordCluster seems to predict biological meaningful clusters of DNA words (k-mers) and genomic entities. The implementation of the method into a web server is available at http://bioinfo2.ugr.es/wordCluster/wordCluster.php webcite including additional features like the detection of co-localization with gene regions or the annotation enrichment tool for functional analysis of overlapped genes. ; The Spanish Government grants BIO2008-01353 to JLO, mobility PR2009-0285 to PC, Spanish Junta de Andalucía grants P07-FQM3163 to PC and P06-FQM1858 to PB are acknowledged. The Spanish 'Juan de la Cierva' grant to MH and Basque Country 'Programa de formación de investigadores del Departamento de Educación, Universidades e Investigación' grant to GB are also acknowledged.

[Background] Unmethylated stretches of CpG dinucleotides (CpG islands) are an outstanding property of mammal genomes. Conventionally, these regions are detected by sliding window approaches using %G + C, CpG observed/expected ratio and length thresholds as main parameters. Recently, clustering methods directly detect clusters of CpG dinucleotides as a statistical property of the genome sequence. [Results] We compare sliding-window to clustering (i.e. CpGcluster) predictions by applying new ways to detect putative functionality of CpG islands. Analyzing the co-localization with several genomic regions as a function of window size vs. statistical significance (p-value), CpGcluster shows a higher overlap with promoter regions and highly conserved elements, at the same time showing less overlap with Alu retrotransposons. The major difference in the prediction was found for short islands (CpG islets), often exclusively predicted by CpGcluster. Many of these islets seem to be functional, as they are unmethylated, highly conserved and/or located within the promoter region. Finally, we show that window-based islands can spuriously overlap several, differentially regulated promoters as well as different methylation domains, which might indicate a wrong merge of several CpG islands into a single, very long island. The shorter CpGcluster islands seem to be much more specific when concerning the overlap with alternative transcription start sites or the detection of homogenous methylation domains. [Conclusions] The main difference between sliding-window approaches and clustering methods is the length of the predicted islands. Short islands, often differentially methylated, are almost exclusively predicted by CpGcluster. This suggests that CpGcluster may be the algorithm of choice to explore the function of these short, but putatively functional CpG islands. ; We acknowledge the Spanish Government (Grant No. BIO2008-01353) and the Spanish Junta de Andalucía (Grant Nos. P06-FQM1858 and P07-FQM3163) financial support. MH acknowledges financial support from the 'Juan de la Cierva' grant from the Spanish Government. GB acknowledges financial support from the 'Programa de formación de investigadores del Departamento de Educación, Universidades e Investigación' grant from the Basque Country Government.

We would like to thank Stephanie Davis for her language editing assistance. The investigators also wish to thank the patients for kindly agreeing to donate samples to this study. We thank all the physicians who collaborated by providing clinical information. Graphical Abstract, Figs 1A, 8A and Fig. S1 were created with Biorender.com. This project has received funding from a European Union's Horizon 2020 research and innovation program under the Marie SklodowskaCurie grant agreement ELBA No 765492. ; Although many studies highlight the implication of circular RNAs (circRNAs) in carcinogenesis and tumor progression, their potential as cancer biomarkers has not yet been fully explored in the clinic due to the limitations of current quantification methods. Here, we report the use of the nCounter platform as a valid technology for the analysis of circRNA expression patterns in non-small cell lung cancer (NSCLC) specimens. Under this context, our custom-made circRNA panel was able to detect circRNA expression both in NSCLC cells and formalin-fixed paraffinembedded (FFPE) tissues. CircFUT8 was overexpressed in NSCLC, contrasting with circEPB41L2, circBNC2, and circSOX13 downregulation even at the early stages of the disease. Machine learning (ML) approaches from different paradigms allowed discrimination of NSCLC from nontumor controls (NTCs) with an 8-circRNA signature. An additional 4-circRNA signature was able to classify early-stage NSCLC samples from NTC, reaching a maximum area under the ROC curve (AUC) of 0.981. Our results not only present two circRNA signatures with diagnosis potential but also introduce nCounter processing following ML as a feasible protocol for the study and development of circRNA signatures for NSCLC. ; European Commission 765492