This work was supported by NIH grants including DP1HD087990 from NICHD, R01HG008135 from NHGRI. We thank members of the Zhong lab for valuable feedback.
Author Contributions: 
X.C. and A.T.Z. updated GeNemo by coding new interface and features; A.T.Z. produced the in-house sample video; A.T.Z., X.C and S.Z. wrote the paper.

NOTE: The protocol can be paused anywhere.
1. Basic Setup
<ol>
	<li>Obtain a BED, Peaks format, or BigWig7 file containing the data to be input into Genome. The file should have extension name &#34;bed&#34;, &#34;broadpeaks&#34;&#34;narrowpeaks&#34;, or &#34;bigWig&#34; respectively. 
	&#8203;NOTE: Zipped versions of these type of files will also work.</li>
	<li>Use an internet browser to go to genemo.org. Any operating system capable of running most common internet browsers should be able to use GeNemo.
	<ol>
		<li>Choose which species to search against using the dropdown menu. Currently available species include human and mouse.</li>
		<li>Upload user file using a url or a direct upload. BigWig files only work with the url upload method. BED and Peaks format files work with both methods (wiggle files cannot be uploaded as the main data as of now).</li>
	</ol>
	</li>
</ol>
2. Optional Setup
<ol>
	<li>Provide an email address in the corresponding box in order to receive the search results by email when the search is done. 
	&#8203;NOTE: When searching a large part of the genome and/or against a large number of tracks (see below), it is recommended that the user provides his/her email, since the search may take a long time. For example, a 100 megabase search takes around 15 s. A link to the search results will be sent to the email address provided when the search is completed. The link will expire in 7 days after the completion of a search.</li>
	<li>Provide a bigwig file or the wiggle display file may be from a url. This display file will not affect the results; it will only be shown alongside the results.</li>
	<li>Specify a search range (including the chromosome and base pair positions) in the corresponding box.
	<ol>
		<li>List the chromosome, start base pair, and end base pair.</li>
		<li>Use &#39;chrN&#39; for the chromosome format, where &#39;N&#39; is the chromosome number/letter (1, 2, &#8230; X, or Y). For the base pairs, just type in the numbers.</li>
		<li>Include spaces between all three entries, or include a colon (:) between chromosome number and the first base pair, and/or a hyphen between the two base pairs. For example: chr1:1000000-2000000, chr1 1000000 2000000, chr1 1000000-2000000, chr1:1000000 2000000. 
		NOTE: Steps 2.1 - 2.3 are optional.</li>
	</ol>
	</li>
</ol>
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/56136/56136fig1.jpg" /> 
Figure 1: GeNemo&#39;s front page with the necessary areas filled out.&#160;A user needs to input the species, search file and search range, and select tracks he/she wishes to search against. Email address and display file are optional.&#160;<a href="//cloudfront-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/56136/56136fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
3. Data Selection
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/56136/56136fig2.jpg" /> 
Figure 2: Track selection window.&#160;This is brought up by clicking the &#34;DATA SELECTION&#34; button on the front page. Here, users select tracks to search the input file against. Some of the tracks are already selected by default.&#160;<a href="//cloudfront-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/56136/56136fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<ol>
	<li>After clicking the data selection button, choose which types of tracks to search against (i.e., to add to the query). The track collection includes many different datasets from labs around the world.
	<ol>
		<li>As the list of tracks is quite long, users may want to use the filter button (on the top) to facilitate track selections. Tracks may be filtered by Experiment, Tissue, Cell Line and/or Lab.</li>
		<li>There are five buttons on the bottom to help execute track selection: Select All, Select None, Add, Filter, Exclude.</li>
		<li>Select All&#34; and &#34;Select None&#34; are self-explanatory.</li>
		<li>The &#34;Add&#34; button adds currently selected tracks to the query. It serves as the logic gate &#34;OR&#34;. Note that selecting the filter(s) above (e.g., certain Experiments, Tissues, Cell Lines or Labs) does not automatically add corresponding tracks to the search query. Users must first select tracks (e.g., brain, liver under tissue), and then click the &#34;Add&#34; button to add them to the query. When selecting tracks, note that only the filters specified in the opened tab in the filter window will be applied to the search query. Selections on other tabs will be saved in the filter window, but not applied to the search query.</li>
		<li>The &#34;Filter&#34; button retains only the types of tracks currently selected in the filter window in the query and removes all other types of tracks. It serves as the logic gate &#34;AND&#34;. Essentially, &#34;Filter&#34; allows the selection of the interaction between two categories of tracks (e.g., certain tissues with certain Labs). Note that &#34;Filter&#34; does not add the selected types of tracks to the query if they are not already in the query.</li>
		<li>The &#34;Exclude&#34; button removes all types of tracks that are currently selected in the filter window from the query. It serves as the logic gate &#34;NOT&#34;, in opposition to the &#34;Filter&#34; function. Again, &#34;Exclude&#34; does not add any tracks currently not selected in the filter window to the query.</li>
	</ol>
	</li>
</ol>
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/56136/56136fig3.jpg" /> 
Figure 3: Filter window. This is brought up by clicking the &#34;FILTER&#34; button on the Track selection window. Here, users can select many tracks at the same time, with relative ease.&#160;<a href="//cloudfront-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/56136/56136fig3large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/56136/56136fig4.jpg" /> 
Figure 4: How to use the filter function.&#160;<a href="//cloudfront-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/56136/56136fig4large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<ol start="2">
	<li>After adding the desired tracks to the query, click the &#34;Update&#34; button on the bottom right. This is needed in order to accommodate two ways to select data: selecting individual data tracks or filtering/excluding. The &#34;Reset View&#34; button resets the query to the default tracks related to gene expression regulation in human/mouse embryonic stem cells. 
	NOTE: Selecting tracks to be searched against via &#34;Data selection&#34; is optional but recommended because the default search tracks are most likely not suited to the user&#39;s needs.</li>
</ol>
4. Search and Results
<ol>
	<li>Click the &#34;Search&#34; button after data selection. The search may take some time.</li>
	<li>Once the search is completed, users will see various boxes in the Results page. Each box represents a section of the genome where a user&#39;s data file has a closely matched pattern with one or more of the tracks the user has queried.
	<ol>
		<li>If there are no boxes visible, try searching more types of tracks or making the search range bigger with the same input file. An easy way to do this without redoing everything is clicking the &#34;&#9776;&#34; button next to the logo. This will open up a sidebar that allows the user to modify the search.</li>
		<li>The results may be exported as a BED file by clicking on the &#34;DOWNLOAD BED FILE&#34; button on the bottom of the Results page.</li>
	</ol>
	</li>
	<li>Click the Visualize button on the top right of each box to visualize the results.
	<ol>
		<li>In the Visualization panel on the right, multiple things are displayed including the data, which incorporates the user input file, the display file if one was inputted, matching tracks, and some default tracks. From the results, the user may compare known ENCODE datasets against the provided dataset for further investigation. The user may also refer to UCSC genes to see the context of the query results. If tracks from multiple cell lines/tissues are selected, the user may use such results to gain insights about the tissue specificity of the similarities between the given dataset and ENCODE datasets.</li>
		<li>On the Results page, user may drag on any tracks to move upstream or downstream of the genome; when the mouse cursor is on the coordinates, the user may use the mouse wheel and/or zoom in and out.</li>
	</ol>
	</li>
</ol>
<img alt="Figure 5" class="xfigimg" src="/files/ftp_upload/56136/56136fig5.jpg" /> 
Figure 5: Results page. This particular search returned 363 matching regions. Displaying the first matching region can be done by clicking the &#34;SHOW&#34; button on the bottom left of each resulting region box. On the left part of the display window it can be seen that the two data files (input and selected track) are similar in signal strength pattern.&#160;<a href="//cloudfront-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/56136/56136fig5large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>

<ol>
	<li>The ENCODE Project Consortium. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=An+integrated+encyclopedia+of+DNA+elements+in+the+human+genome.">An integrated encyclopedia of DNA elements in the human genome.</a> Nature. 489, 57-74 (2012).</li><li>Barski, A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=High-Resolution+Profiling+of+Histone+Methylations+in+the+Human+Genome.">High-Resolution Profiling of Histone Methylations in the Human Genome.</a> Cell. 129 (4), 823-837 (2007).</li><li>Meaney, M. J., Ferguson-Smith, A. C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Epigenetic+regulation+of+the+neural+transcriptome:+the+meaning+of+the+marks.">Epigenetic regulation of the neural transcriptome: the meaning of the marks.</a> Nature Neuroscience. 13, 1313-1318 (2010).</li><li>Roh, T. -. Y., Cuddapah, S., Cui, K., Zhao, K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+genomic+landscape+of+histone+modifications+in+human+T+cells.">The genomic landscape of histone modifications in human T cells.</a> PNAS. 103 (43), 15782-15787 (2006).</li><li>Zhang, Y., Cao, X., Zhong, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=GeNemo:+a+search+engine+for+web-based+functional+genomic+data.">GeNemo: a search engine for web-based functional genomic data.</a> Nucleic Acids Res. 44, W122-W127 (2016).</li><li>Fujita, P. A., Rhead, B., Zweig, A. S., Hinrichs, A. S., Karolchik, D., Cline, M. S., Goldman, M., Barber, G. P., Clawson, H., Coelho, A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+UCSC+Genome+Browser+database:+update+2011.">The UCSC Genome Browser database: update 2011.</a> Nucleic Acids Res. 39, 876-882 (2011).</li><li>Neph, S., Vierstra, J., Stergachis, A. B., Reynolds, A. P., Haugen, E., Vernot, B., Thurman, R. E., John, S., Sandstrom, R., Johnson, A. K., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=An+expansive+human+regulatory+lexicon+encoded+in+transcription+factor+footprints.">An expansive human regulatory lexicon encoded in transcription factor footprints.</a> Nature. 489, 83-90 (2012).</li><li>Sarda, S., Hannenhalli, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Next-generation+sequencing+and+epigenomics+research:+a+hammer+in+search+of+nails.">Next-generation sequencing and epigenomics research: a hammer in search of nails.</a> Genomics Inform. 12 (1), 2-11 (2014).</li><li>Storre, J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Silencing+of+the+Meiotic+Genes+SMC1&#946;+and+STAG3+in+Somatic+Cells+by+E2F6.">Silencing of the Meiotic Genes SMC1&#946; and STAG3 in Somatic Cells by E2F6.</a> J Biol Chem. 280, 41380-41386 (2005).</li><li>Liu, B., Shats, I., Angus, S. P., Gatza, M. L., Nevins, J. R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Interaction+of+E2F7+Transcription+Factor+with+E2F1+and+C-terminal-binding+Protein+(CtBP)+Provides+a+Mechanism+for+E2F7-dependent+Transcription+Repression.">Interaction of E2F7 Transcription Factor with E2F1 and C-terminal-binding Protein (CtBP) Provides a Mechanism for E2F7-dependent Transcription Repression.</a> J Biol Chem. 288, 24581-24589 (2013).</li></ol>

The authors have no competing financial interests to disclose.

A thorough understanding of the epigenome is required to achieve the full potential of human genome sequencing in providing new biological insights8. Currently there are only ways to search online epigenomic datasets by their data description and title (i.e., metadata)1. This severely limits the types of search one can do with epigenomic data. Pattern-based search tools for epigenomic data are essential for exploring the relationship between different epigenomic marks, which may lead to new biological insights. GeNemo, which searches by the content of the data and not metadata, is the first service of its kind to compare patterns in epigenomic data from published depositories such as the ENCODE database with a user-generated or downloaded dataset5. This marks the beginning of the availability of an epigenomic search tool that is widely accessible to researchers around the world just as text-based sequence search tool became widely available in the 1990s. Currently, there are no alternatives for pattern-based online search tools for epigenomic data other than GeNemo.
One potential example of using GeNemo is to search the co-appearing histone modifications and other epigenetic marks with the transcriptional factor E2F6 in human embryonic stem cells (an example E2F6 binding signal file is available at ENCODE data portal or at https://sysbio.ucsd.edu/public/xcao3/ENCODESample/ENCFF001UBC.bed). By using this file as query to search against all ENCODE datasets in H1-hESC, GeNemo will show that E2F6 binding signal is heavily enriched with H3K4me1, H3K4me2, H3K4me3, and H3K27me3, which agrees with existing research showing that E2F6 regulates some genes via methylation of H3K279. On the other hand, there appears to be colocalization of E2F6 and CtBP2 binding sites, which is known to interact with a factor in the same family, E2F710. These results for the entire genome against a large number of epigenetic marks, transcriptional factor binding signals, and other signals included in ENCODE can be fairly easily obtained with GeNemo, which can provide all potential targets for further analysis.
Since the first publication5 of GeNemo as a web-based epigenomic data search tool, the Results section of GeNemo has been updated to have a matching appearance with GeNemo&#39;s front page. The old Results section closely mirrored the UCSC genome browser results section, and was largely dependent on the remote UCSC server for display. With the new interface, GeNemo is more user-friendly and no longer dependent on the UCSC genome server (even though data are still fetched remotely). This makes GeNemo more robust and less susceptible to problems due to code changes at the UCSC server. Furthermore, the new, faster polymer interface of GeNemo gives the user more tools to visualize and analyze patterns in the data.
Critical steps include providing the appropriate input file and selecting data tracks to search against. Users are strongly encouraged to experiment with various track selection functions to become familiar with the selection process and how different commands can be combined to achieve the intended outcome. In particular, note that the &#34;Add&#34; function is required to add desired tracks selected to the query, while &#34;Filter&#34; or &#34;Exclude&#34; can be used as logic gate commands &#34;AND&#34; and &#34;OR&#34;, respectively. The &#34;Update&#34; function is required to affect all the selections before implementing the search. When no results are returned, a user may check the input data file, search more tracks or increase the search range. Whenever there is an error, there will be a window popping up defining what exactly the error is. There are some ambiguous errors, though. For example, when the window says that &#39;no file was uploaded,&#39; either no file was uploaded, or the uploaded file was not of an acceptable format and, consequently, the program was not able to read it correctly. Acceptable file formats for file upload include BED and Peaks format file for both upload methods, and bigWig for online link upload only. The zipped versions of these file formats are also acceptable.
Current limitations of this approach include the yet-to-be-optimized algorithms and functions employed in GeNemo. GeNemo cannot yet provide any guidance on the interpretation of any datasets returned. This task is up to the users, which requires significant knowledge and expertise in the biology of the genome and epigenome. In addition, another current limitation is that users cannot change the sensitivity and noise level of the searches. We expect to continue to improve and expand GeNemo on its pattern searching capabilities and dataset collection in future.

Recent technological advances have allowed for a rapid expansion of epigenomic or functional genomic data depositories, which have outpaced the development of relevant analytic tools to extract biological insights. One important way to analyze epigenomic data is to search user-generated data against data depositories and especially those from the Encyclopedia of DNA Elements (ENCODE)1 projects for matching patterns that could lead to new knowledge. For instance, identifying similarities in the patterns of two different epigenomic marks at defined loci across the genome may indicate coordinated action by different molecular players on chromatin conformation and transcriptional regulation2,3,4.
Conventional text-based search engines are ineffective in this regard because, unlike DNA sequence, epigenomic data predominantly exist in the format of intensities or functional genomic regions. GeNemo, standing for Gene Nemo (as in Finding Nemo), was developed to address this unmet need using pattern-based searches5. Its algorithm utilizes a Markov Chain Monte Carlo maximization process5. Users take their own data or a dataset downloaded from depositories and search an array of online epigenomic data to identify similarities in patterns.
The current version of GeNemo has an updated display, interfaces more robustly with the University of California, Santa Cruz (UCSC) genome browser6, and is less susceptible to issues caused by changes in the latter. In particular, while GeNemo&#39;s Results page used to be based on the UCSC genome browser interface, the current version of GeNemo supports its own Results page and consequently is no longer adversely affected by structural changes to the UCSC genome browser. GeNemo can use any genomic signal, including protein-binding, histone modification, chromatin accessibility, topological domains, and so on, as a query to find colocalized/similar segments among known data sets from large consortia. Therefore, it is an important tool to study the relationship between different epigenomic data of interest and known data generated in large scale genomic projects.

<table><tbody><tr><td>GENEMO</td><td>https://www.genemo.org</td><td></td><td>Comparative Epigenome Browser</td></tr></tbody></table>

pattern-based search of epigenomic data using genemo

Compared with the robust text-based search tools for genomic or RNA sequencing data, current methodologies for pattern-based searches of epigenomic and other functional genomic data are very limited. GeNemo is the first online search tool that accomplishes this goal. Users input their functional genomic data in the Browser Extensible Data (BED), Peaks, and bigWig formats, and may search for data in any of the three formats. Users may specify which types of datasets to search against, choosing from a variety of online datasets, with the Encyclopedia of DNA Elements (ENCODE) representing different epigenomic marks, transcriptional factor binding sites, and chromatin hypersensitivities or accessibilities in specific cell types, and developmental stages or species (mouse or human). GeNemo returns a list of genomic regions with matching patterns to the input data, which may be viewed in the browser as well as downloaded in the BED file format. The upgraded GeNemo has improved graphical display, has more robust interface, and is no longer prone to errors due to changes in the University of California, Santa Cruz (UCSC) genome browser. Troubleshooting steps for common problems are discussed. As the amount of functional genomic data is expanding exponentially, there is a critical need to develop and refine new bioinformatic tools such as GeNemo for data analyses and interpretation.

Shown here in Figure 5 is a simulated search. The human species was selected, and the corresponding sample file was used as the input data file. In addition, the default tracks, as seen in Figure 3, were selected. There were a total of 363 matching regions, and the first region is shown in the display page. It can be seen that the intensity pattern from base 17036000 to 17038000 on chromosome 1 for the input file and one of the selected tracks is very similar.

Watch this Scientific Journal Video about Pattern-based Search of Epigenomic Data Using GeNemo at JoVE.com

Pattern-based Search of Epigenomic Data Using GeNemo

Unlike DNA sequence data, epigenomic data are not readily subjected to text-based searches. Presented here are the procedures to use an upgraded version of GeNemo, a web-based bioinformatics tool, to conduct pattern-based searches for similarities in epigenomic data comparing available online databases including Encyclopedia of DNA Elements with user&#39;s data.

Compared with the robust text-based search tools for genomic or RNA sequencing data, current methodologies for pattern-based searches of epigenomic and ...

pattern-based-search-of-epigenomic-data-using-genemo

Universidad San Sebastian

Research

JoVE Journal

Bioengineering

5.0K Views.  University of California San Diego. Unlike DNA sequence data, epigenomic data are not readily subjected to text-based searches. Presented here are the procedures to use an upgraded version of GeNemo, a web-based bioinformatics tool, to conduct pattern-based searches for similarities in epigenomic data comparing available online databases including Encyclopedia of DNA Elements with user's data.

Video: Pattern-based Search of Epigenomic Data Using GeNemo

Methyl-binding DNA capture Sequencing for Patient Tissues

Methylation is one of the essential epigenetic modifications to the DNA, which is responsible for the precise regulation of genes required for stable development and differentiation of different tissue types. Dysregulation of this process is often the hallmark of various diseases like cancer. Here, we outline one of the recent sequencing techniques, Methyl-Binding DNA Capture sequencing (MBDCap-seq), used to quantify methylation in various normal and disease tissues for large patient cohorts. We describe a detailed protocol of this affinity enrichment approach along with a bioinformatics pipeline to achieve optimal quantification. This technique has been used to sequence hundreds of patients across various cancer types as a part of the 1,000 methylome project (Cancer Methylome System).

Here we present a protocol to investigate genome wide DNA methylation in large scale clinical patient screening studies using the Methyl-Binding DNA Capture sequencing (MBDCap-seq or MBD-seq) technology and the subsequent bioinformatics analysis pipeline.

Methylation is one of the essential epigenetic modifications to the DNA, which is responsible for the precise regulation of genes required for stable ...

Genetics

An Integrated Platform for Genome-wide Mapping of Chromatin States Using High-throughput ChIP-sequencing in Tumor Tissues

Histone modifications constitute a major component of the epigenome and play important regulatory roles in determining the transcriptional status of associated loci. In addition, the presence of specific modifications has been used to determine the position and identity non-coding functional elements such as enhancers. In recent years, chromatin immunoprecipitation followed by next generation sequencing (ChIP-seq) has become a powerful tool in determining the genome-wide profiles of individual histone modifications. However, it has become increasingly clear that the combinatorial patterns of chromatin modifications, referred to as Chromatin States, determine the identity and nature of the associated genomic locus. Therefore, workflows consisting of robust high-throughput (HT) methodologies for profiling a number of histone modification marks, as well as computational analyses pipelines capable of handling myriads of ChIP-Seq profiling datasets, are needed for comprehensive determination of epigenomic states in large number of samples. The HT-ChIP-Seq workflow presented here consists of two modules: 1) an experimental protocol for profiling several histone modifications from small amounts of tumor samples and cell lines in a 96-well format; and 2) a computational data analysis pipeline that combines existing tools to compute both individual mark occupancy and combinatorial chromatin state patterns. Together, these two modules facilitate easy processing of hundreds of ChIP-Seq samples in a fast and efficient manner. The workflow presented here is used to derive chromatin state patterns from 6 histone mark profiles in melanoma tumors and cell lines. Overall, we present a comprehensive ChIP-seq workflow that can be applied to dozens of human tumor samples and cancer cell lines to determine epigenomic aberrations in various malignancies.

Here, we describe an optimized high-throughput ChIP-sequencing protocol and computational analyses pipeline for the determination of genome-wide chromatin state patterns from frozen tumor tissues and cell lines.

Histone modifications constitute a major component of the epigenome and play important regulatory roles in determining the transcriptional status of ...

Cancer Research

A Fast and Quantitative Method for Post-translational Modification and Variant Enabled Mapping of Peptides to Genomes

Cross-talk between genes, transcripts, and proteins is the key to cellular responses; hence, analysis of molecular levels as distinct entities is slowly being extended to integrative studies to enhance the understanding of molecular dynamics within cells. Current tools for the visualization and integration of proteomics with other omics datasets are inadequate for large-scale studies. Furthermore, they only capture basic sequence identify, discarding post-translational modifications and quantitation. To address these issues, we developed PoGo to map peptides with associated post-translational modifications and quantification to reference genome annotation. In addition, the tool was developed to enable the mapping of peptides identified from customized sequence databases incorporating single amino acid variants. While PoGo is a command line tool, the graphical interface PoGoGUI enables non-bioinformatics researchers to easily map peptides to 25 species supported by Ensembl genome annotation. The generated output borrows file formats from the genomics field and, therefore, visualization is supported in most genome browsers. For large-scale studies, PoGo is supported by TrackHubGenerator to create web-accessible repositories of data mapped to genomes that also enable an easy sharing of proteogenomics data. With little effort, this tool can map millions of peptides to reference genomes within only a few minutes, outperforming other available sequence-identity based tools. This protocol demonstrates the best approaches for proteogenomics mapping through PoGo with publicly available datasets of quantitative and phosphoproteomics, as well as large-scale studies.

Here we present the proteogenomic tool PoGo and protocols for fast, quantitative, post-translational modification and variant enabled mapping of peptides identified through mass spectrometry onto reference genomes. This tool is of use to integrate and visualize proteogenomic and personal proteomic studies interfacing with orthogonal genomics data.

Cross-talk between genes, transcripts, and proteins is the key to cellular responses; hence, analysis of molecular levels as distinct entities is slowly ...

Optimized Analysis of DNA Methylation and Gene Expression from Small, Anatomically-defined Areas of the Brain

Exposure to diet, drugs and early life adversity during sensitive windows of life 1,2 can lead to lasting changes in gene expression that contribute to the display of physiological and behavioural phenotypes. Such environmental programming is likely to increase the susceptibility to metabolic, cardiovascular and mental diseases 3,4.
DNA methylation and histone modifications are considered key processes in the mediation of the gene-environment dialogue and appear also to underlay environmental programming 5. In mammals, DNA methylation typically comprises the covalent addition of a methyl group at the 5-position of cytosine within the context of CpG dinucleotides.
CpG methylation occurs in a highly tissue- and cell-specific manner making it a challenge to study discrete, small regions of the brain where cellular heterogeneity is high and tissue quantity limited. Moreover, because gene expression and methylation are closely linked events, increased value can be gained by comparing both parameters in the same sample.
Here, a step-by-step protocol (Figure 1) for the investigation of epigenetic programming in the brain is presented using the 'maternal separation' paradigm of early life adversity for illustrative purposes. The protocol describes the preparation of micropunches from differentially-aged mouse brains from which DNA and RNA can be simultaneously isolated, thus allowing DNA methylation and gene expression analyses in the same sample.

A streamlined workflow to study DNA methylation and gene expression changes upon early-life stress is shown. Starting from maternal separation of newborn mice and isolation of discrete brain tissues, we represent a protocol to simultaneously isolate DNA and RNA from brain tissue punches for subsequent bisulfite sequencing and RT-PCR analysis.

Exposure to diet, drugs and early life adversity during sensitive windows of life 1,2 can lead to lasting changes in gene expression that contribute to ...

Neuroscience

Genome-Wide Analysis of DNA Methylation in Gastrointestinal Cancer

DNA methylation is an important epigenetic change that is biologically meaningful and a frequent focus of cancer research. Genome-wide DNA methylation is a useful measure to provide an accurate analysis of the methylation status of gastrointestinal (GI) malignancies. Given the multiple potential translational uses of DNA methylation analysis, practicing clinicians and others new to DNA methylation studies need to be able to understand step by step how these genome-wide analyses are performed. The goal of this protocol is to provide a detailed description of how this method is used for the biomarker identification in GI malignancies. Importantly, we describe three critical steps that are needed to obtain accurate results during genome-wide analysis. Clearly and concisely written, these three methods are often lacking and not noticeable to those new to epigenetic studies. We used 48 samples of a GI malignancy (gastric cancer) to highlight practically how genome-wide DNA methylation analysis can be performed for GI malignancies.

Herein, we describe a procedure for genome-wide analysis of DNA methylation in gastrointestinal cancers. The procedure is of relevance to studies that investigate relationships between methylation patterns of genes and factors contributing to carcinogenesis in gastrointestinal cancers.

DNA methylation is an important epigenetic change that is biologically meaningful and a frequent focus of cancer research. Genome-wide DNA methylation is ...

Sample Preparation to Bioinformatics Analysis of DNA Methylation: Association Strategy for Obesity and Related Trait Studies

Obesity is directly connected to lifestyle and has been associated with DNA methylation changes that may cause alterations in the adipogenesis and lipid storage processes contributing to the development of the disease. We demonstrate a complete protocol from selection to epigenetic data analysis of patients with and without obesity. All steps from the protocol were tested and validated in a pilot study. 32 women participated in the study, in which 15 individuals were classified with obesity according to Body Mass Index (BMI) (45.1 &#177; 5.4 kg/m2); and 17 individuals were classified without obesity according to BMI (22.6 &#177; 1.8 kg/m2). In the group with obesity, 564 CpG sites related to fat mass were identified by linear regression analysis. The CpG sites were in the promoter regions. The differential analysis found 470 CpGs hypomethylated and 94 hypermethylated sites in individuals with obesity. The most hypomethylated enriched pathwayswere in the RUNX, WNT signaling, and response to hypoxia. The hypermethylated pathways were related to insulin secretion, glucagon signaling, and Ca2+. We conclude that the protocol effectively identified DNA methylation patterns and trait-related DNA methylation. These patterns could be associated with altered gene expression, affecting adipogenesis and lipid storage. Our results confirmed that an obesogenic lifestyle could promote epigenetic changes in human DNA.

The present study describes the workflow to manage DNA methylation data obtained by microarray technologies. The protocol demonstrates steps from sample preparation to data analysis. All procedures are described in detail, and the video shows the significant steps.

Obesity is directly connected to lifestyle and has been associated with DNA methylation changes that may cause alterations in the adipogenesis and lipid ...

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Routine and systematic use of bacterial whole-genome sequencing (WGS) is enhancing the accuracy and resolution of epidemiological investigations carried out by Public Health laboratories and regulatory agencies. Large volumes of publicly available WGS data can be used to study pathogenic populations at a large scale. Recently, a freely available computational platform called ProkEvo was published to enable reproducible, automated, and scalable hierarchical-based population genomic analyses using bacterial WGS data. This implementation of ProkEvo demonstrated the importance of combining standard genotypic mapping of populations with mining of accessory genomic content for ecological inference. In particular, the work highlighted here used ProkEvo-derived outputs for population-scaled hierarchical analyses using the R programming language. The main objective was to provide a practical guide for microbiologists, ecologists, and epidemiologists by showing how to: i) use a phylogeny-guided mapping of hierarchical genotypes; ii) assess frequency distributions of genotypes as a proxy for ecological fitness; iii) determine kinship relationships and genetic diversity using specific genotypic classifications; and iv) map lineage differentiating accessory loci. To enhance reproducibility and portability, R markdown files were used to demonstrate the entire analytical approach. The example dataset contained genomic data from 2,365 isolates of the zoonotic foodborne pathogen Salmonella Newport. Phylogeny-anchored mapping of hierarchical genotypes (Serovar -&#62; BAPS1 -&#62; ST -&#62; cgMLST) revealed the population genetic structure, highlighting sequence types (STs) as the keystone differentiating genotype. Across the three most dominant lineages, ST5 and ST118 shared a common ancestor more recently than with the highly clonal ST45 phylotype. ST-based differences were further highlighted by the distribution of accessory antimicrobial resistance (AMR) loci. Lastly, a phylogeny-anchored visualization was used to combine hierarchical genotypes and AMR content to reveal the kinship structure and lineage-specific genomic signatures. Combined, this analytical approach provides some guidelines for conducting heuristic bacterial population genomic analyses using pan-genomic information.

This analytical computational platform provides practical guidance for microbiologists, ecologists, and epidemiologists interested in bacterial population genomics. Specifically, the work presented here demonstrated how to perform: i) phylogeny-guided mapping of hierarchical genotypes; ii) frequency-based analysis of genotypes; iii) kinship and clonality analyses; iv) identification of lineage differentiating accessory loci.

Routine and systematic use of bacterial whole-genome sequencing (WGS) is enhancing the accuracy and resolution of epidemiological investigations carried ...

In Vitro Selection of Engineered Transcriptional Repressors for Targeted Epigenetic Silencing

Gene inactivation is instrumental to study gene function and represents a promising strategy for the treatment of a broad range of diseases. Among traditional technologies, RNA interference suffers from partial target abrogation and the requirement for life-long treatments. In contrast, artificial nucleases can impose stable gene inactivation through induction of a DNA double strand break (DSB), but recent studies are questioning the safety of this approach. Targeted epigenetic editing via engineered transcriptional repressors (ETRs) may represent a solution, as a single administration of specific ETR combinations can lead to durable silencing without inducing DNA breaks.
ETRs are proteins containing a programmable DNA-binding domain (DBD) and effectors from naturally occurring transcriptional repressors. Specifically, a combination of three ETRs equipped with the KRAB domain of human ZNF10, the catalytic domain of human DNMT3A and human DNMT3L, was shown to induce heritable repressive epigenetic states on the ETR-target gene. The hit-and-run nature of this platform, the lack of impact on the DNA sequence of the target, and the possibility to revert to the repressive state by DNA demethylation on demand, make epigenetic silencing a game-changing tool. A critical step is the identification of the proper ETRs&#39; position on the target gene to maximize on-target and minimize off-target silencing. Performing this step in the final ex vivo or in vivo preclinical setting can be cumbersome.
Taking the CRISPR/catalytically dead Cas9 system as a paradigmatic DBD for ETRs, this paper describes a protocol consisting of the in vitro screen of guide RNAs (gRNAs) coupled to the triple-ETR combination for efficient on-target silencing, followed by evaluation of the genome-wide specificity profile of top hits. This allows for reduction of the initial repertoire of candidate gRNAs to a short list of promising ones, whose complexity is suitable for their final&#160;evaluation in the therapeutically relevant setting of interest.

In Vitro Selection of Engineered Transcriptional Repressors for Targeted Epigenetic Silencing

Here, we present a protocol for the in vitro selection of engineered transcriptional repressors (ETRs) with high, long-term, stable, on-target silencing efficiency and low genome-wide, off-target activity. This workflow allows for reducing an initial, complex repertoire of candidate ETRs to a short list, suitable for further evaluation in therapeutically relevant settings.

Gene inactivation is instrumental to study gene function and represents a promising strategy for the treatment of a broad range of diseases. Among ...

Enhanced Reduced Representation Bisulfite Sequencing for Assessment of DNA Methylation at Base Pair Resolution

DNA methylation pattern mapping is heavily studied in normal and diseased tissues. A variety of methods have been established to interrogate the cytosine methylation patterns in cells. Reduced representation of whole genome bisulfite sequencing was developed to detect quantitative base pair resolution cytosine methylation patterns at GC-rich genomic loci. This is accomplished by combining the use of a restriction enzyme followed by bisulfite conversion. Enhanced Reduced Representation Bisulfite Sequencing (ERRBS) increases the biologically relevant genomic loci covered and has been used to profile cytosine methylation in DNA from human, mouse and other organisms. ERRBS initiates with restriction enzyme digestion of DNA to generate low molecular weight fragments for use in library preparation. These fragments are subjected to standard library construction for next generation sequencing. Bisulfite conversion of unmethylated cytosines prior to the final amplification step allows for quantitative base resolution of cytosine methylation levels in covered genomic loci. The protocol can be completed within four days. Despite low complexity in the first three bases sequenced, ERRBS libraries yield high quality data when using a designated sequencing control lane. Mapping and bioinformatics analysis is then performed and yields data that can be easily integrated with a variety of genome-wide platforms. ERRBS can utilize small input material quantities making it feasible to process human clinical samples and applicable in a range of research applications. The video produced demonstrates critical steps of the ERRBS protocol.

Enhanced Reduced Representation Bisulfite Sequencing is a method for the preparation of sequencing libraries for DNA methylation analysis based on restriction enzyme digestion combined with cytosine bisulfite conversion. This protocol requires 50 ng of starting material and yields base pair resolution data at GC-rich genomic regions.

DNA methylation pattern mapping is heavily studied in normal and diseased tissues. A variety of methods have been established to interrogate the cytosine ...

Biology

Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay (EMSA) and DNA-affinity Precipitation Assay (DAPA)

Population and family-based genetic studies typically result in the identification of genetic variants that are statistically associated with a clinical disease or phenotype. For many diseases and traits, most variants are non-coding, and are thus likely to act by impacting subtle, comparatively hard to predict mechanisms controlling gene expression. Here, we describe a general strategic approach to prioritize non-coding variants, and screen them for their function. This approach involves computational prioritization using functional genomic databases followed by experimental analysis of differential binding of transcription factors (TFs) to risk and non-risk alleles. For both electrophoretic mobility shift assay (EMSA) and DNA affinity precipitation assay (DAPA) analysis of genetic variants, a synthetic DNA oligonucleotide (oligo) is used to identify factors in the nuclear lysate of disease or phenotype-relevant cells. For EMSA, the oligonucleotides with or without bound nuclear factors (often TFs) are analyzed by non-denaturing electrophoresis on a tris-borate-EDTA (TBE) polyacrylamide gel. For DAPA, the oligonucleotides are bound to a magnetic column and the nuclear factors that specifically bind the DNA sequence are eluted and analyzed through mass spectrometry or with a reducing sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE) followed by Western blot analysis. This general approach can be widely used to study the function of non-coding genetic variants associated with any disease, trait, or phenotype.

Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay EMSA and DNA-affinity Precipitation Assay DAPA

We present a strategic plan and protocol for identifying non-coding genetic variants affecting transcription factor (TF) DNA binding. A detailed experimental protocol is provided for electrophoretic mobility shift assay (EMSA) and DNA affinity precipitation assay (DAPA) analysis of genotype-dependent TF DNA binding.

Population and family-based genetic studies typically result in the identification of genetic variants that are statistically associated with a clinical ...

Pattern-based Search of Epigenomic Data Using GeNemo

Summary

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Chapters in this video

Methyl-binding DNA capture Sequencing for Patient Tissues

An Integrated Platform for Genome-wide Mapping of Chromatin States Using High-throughput ChIP-sequencing in Tumor Tissues

A Fast and Quantitative Method for Post-translational Modification and Variant Enabled Mapping of Peptides to Genomes

Optimized Analysis of DNA Methylation and Gene Expression from Small, Anatomically-defined Areas of the Brain

Genome-Wide Analysis of DNA Methylation in Gastrointestinal Cancer

Sample Preparation to Bioinformatics Analysis of DNA Methylation: Association Strategy for Obesity and Related Trait Studies

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

In Vitro Selection of Engineered Transcriptional Repressors for Targeted Epigenetic Silencing

Enhanced Reduced Representation Bisulfite Sequencing for Assessment of DNA Methylation at Base Pair Resolution

Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay (EMSA) and DNA-affinity Precipitation Assay (DAPA)