Online chip-seq data
Introduction to RNA-seq and ChIP-seq data analysis,What exactly does my favorite transcription factor do?
· The primary data for published Broad Institute ChIP-Seq experiments have been deposited to the NCBI GEO database under the following accessions: Mikkelsen et al.
(): GSE Mikkelsen et al. (): GSE Meissner and Mikkelsen et al. (): GSE Ku, Koche, Rheinbay et al. (): GSE Processed data can also be obtained from the . An Introduction to ChIP-seq Data Analysis Online. TBA. Training Courses ; Planned Training Courses ; Training Archive by Date; Training Archive by aberfoodblog.com Units; Online Training & Media Library; TeSS; The aberfoodblog.com Training platform.
To effectively coordinate training courses, aberfoodblog.com has established the Special Interest Group 3 (SIG3 - Training and Education). . ChIP-Atlas covers almost all public ChIP-seq data submitted to the SRA (Sequence Read Archives) in NCBI, DDBJ, or ENA, and is based on over , experiments. Watch movie introduction What's new Upcoming maintenance: ChIP-Atlas will be unavailable during February, (JST, UTC+9) due to the building maintenance blackout.
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If not then please post back. I seem to have a problem importing bedGraph files. I typically always get a 'index was out of bounds' error of some sort. BAM files work great, but takes too long when importing multiple datasets at a time. Can you please send me a link to one of the files that gives you troubles. If you don't already do it, then I would recommend that you save a session containing your imported data after import.
That will save you quite some time. I am out of Office, so I wont be able to look at it before wednesday. Visualization of coverage files bedgraph and wig is slower than read files e. Bed files from MACS are usually imported without problems. Could you please mail one of the files that cause troubles to mads. Then I'll find out why you get this error and how to avoid it. Rest assured that I'll shout out it loud, if I make a succesful porting to mac and linux: It is not off the table, but so far the porting is unsuccessful and will require more time that I initially hoped for.
I have discovered that cloud-based Virtual Desktops are becoming very common and affordable. I found seven service providers offering this links below — it might make sense to find the one with the most proximal data center to limit latency. One of them even focus on providing this for gamers, so I figured that refresh rate and latencies must be manageable. Links are here in random order 1 , 2 , 3 , 4 , 5 , 6 , 7 Not sure if I violate forum-rules - if I am, then have my sincere apologies.
Please let me know if you accumulate any experiences using this. I've been having trouble generating heatmaps. The problem seems to be that my mapped files have not finished loading, but it's been hours and my heatmaps have stayed completely white and have the word 'waiting' on them for nearly as long.
I just had the same question posted on the chat-forum within EaSeq it might have been you? Virtually all plot types, including heatmaps, will need to wait for Regionsets and Datasets that are used for the visualization to be released from preceding tasks. So if the import of the Dataset is stuck for odd reasons, or if a very slow operation on a Regionset e.
To avoid the latter, that e. Then I have a spare to continue working on. In this case, it sounded as it was due to the bam-files not being properly imported. The current version has a problem with bam-files with several hundred or thousands of chromosomes. So I recommend either using an index for mapping that only contain canonical chromosomes whenever possible useGalaxy has that for at least mouse and human genomes. This is also recommended for other reasons.
The status bar next to the imported dataset will turn green, once the import is successful. In case it was, then the heatmaps must be waiting on something else. If the import was unsuccessful, then converting the bam-file to a bed file before import will overcome the problem. Infrequently, I have experienced that imported datasets never become ready. I might have pushed the multi-threading a bit too far to make efficient use of multiple CPU cores.
Optimizations were done for my own machine, which has 12 cores and a SSD drive. On machines with a conventional hard drive or few cores, it might struggle a bit with the import. In that case it might be sensible to manually limit the number of datasets that are imported at the same time.
We observed no correlation between the degree of peak overlap and quality scores of the ChIP experiments Kharchenko et al. Peak overlaps for the 39 shared KZNFs between the Trono original and Hughes data dark blue bars , Hughes data and Trono original data light blue bars , Trono reprocessed and Hughes data dark red bars , and Hughes and Trono reprocessed data light red bars. Our use of peaks throughout is for convenience and uniformity; different numbers and proportions of peaks will yield slightly different numbers, but with generally similar conclusions data not shown.
For example, we note that the Trono original peaks often have a generally higher proportion of overlap with Hughes, relative to all other comparisons made. This trend is not evident with the Trono reprocessed data, and similar albeit again not identical outcomes were obtained with Trono original and Trono reprocessed data in the analyses below. Figure 3 provides a detailed view of the agreement in EREs bound in the two studies. Our comparison statistic is the Pearson correlation across all ERE classes, where the value for each is the proportion represented among the Top peaks.
Figure 3B provides a visual confirmation that the individual transposons and EREs types represented in the three peak sets for each of the 39 proteins are largely in agreement. The percentage of the peak overlap between the Hughes and Trono reprocessed yellow dots and Trono reprocessed and Hughes green dots at corresponding correlations are also presented. Overall, we take this outcome to indicate that a large majority of data in both data sets correctly identifies the spectrum of EREs recognized, assuming that the overlapping KZNFs are an unbiased sample from each of the two studies.
There can be good agreement on the EREs bound even when the peak overlap is relatively low; however, the higher peak overlap is usually associated with higher EREs correlation Figure 3A. One interpretation of these observations is that both data sets are drawing from a substantially larger set of bona fide genomic binding sites, but both are subject to noise, and neither has been sequenced to saturation.
We next compared the motifs obtained from the two ChIP data sets for the 39 overlapping proteins, as well as against the motifs obtained from other sources. For Hughes data, we used the motifs directly from the publication Schmitges et al.
For the Trono data, we generated motifs for both the Trono original and Trono reprocessed peaks, using the same procedure employed in Schmitges et al. We used motifs obtained by RCADE, using non-ERE peaks, if the algorithm was successful, since the fact that EREs within a given class are related by common descent can confound motif-finding algorithms.
MoSBAT is appropriate for this analysis because it is nonparametric, and requires no adjustments for differences in motif length. As with the ERE overlap, in most cases the motifs tend to be similar, even when peak overlap between the two data sets is low. Higher peak overlap usually results in higher motif similarity, however Figure 4C. Similarity between ChIP-derived motifs. The motifs and the motif-finding methods are represented on the right. The dots indicate the percentage of the overlap between the Hughes and Trono original peaks blue and Hughes and Trono reprocessed peaks red.
We also compared the ChIP-derived motifs with those from other sources, taken from the initial publications or TF databases.
This outcome not only confirms that motifs derived in vitro are often consistent with in vivo genomic binding sites, but illustrates that the greater depth of the assays can produce more accurate motifs. We also found that the vast majority of motifs obtained from one of the ChIP data sets were significantly enriched in peaks from the other. Hughes, Trono original, and Trono reprocessed. The left third of Figure 5 shows that, except for a few cases, the ChIP-derived motifs are comparable at predicting the ChIP peaks of the other data set, as well as the one from which they are derived.
The right two-thirds of Figure 5 show that motifs from other sources are also generally good predictors of the ChIP peaks. The first row at the top indicates the source of the motif, and the second row indicates the test data set.
White indicates no data is available. The comparisons above provide confidence metrics for the motifs obtained for each of the KZNFs examined, representing reproducibility and predictive power both within and among data types, consistency with the zinc finger recognition code, and quality statistics for the data used to derive the motifs. We used these metrics to choose a single best current motif for each protein, as such a motif set is useful for many types of analyses Matys et al.
The ranking system, intended to capture motifs that correspond to both ChIP data and external information, if available, is shown in Figure S2. If no motifs satisfied these criteria, we selected ChIP motifs that are supported by the recognition code i. In this scheme, the largest classes of motifs are Class A in which one motif clearly outperforms all others on test data , and Class E where all motifs are roughly equivalent.
Most of the motifs are supported by the recognition code: As previously reported for KZNFs, these reference motifs are highly diverse, and tend to be longer and more information-rich than motifs for other TF types, which tend to be only 6—12 bases long Badis et al. The reference motif set for the KZNFs. The class is the selection class that each motif falls into.
Finally, we generated a web interface that assembles all the data described herein, providing all the relevant data files, as well as a graphical interface for each KZNF. Motifs from different sources are shown, if available Figure 7A. Several epigenetic abnormalities have been reported, including DNA methylation, histone modifications and non-coding RNAs 11 , In our studies, epigenetic modifications play crucial roles in the regulation of gene expression in ESCC 12 — In particular, the methylation of lysine residues on histone proteins in the chromatin structure has received attention due to their potential regulatory ability on DNA-based nuclear processes such as transcription, replication and repair The methylation of histone lysine residues was first reported in the s and was considered an irreversible posttranslational modification In , however, a lysine demethylase was discovered, and the methylation of histone lysine residues is now regarded as a dynamic modulation Abnormalities in histone lysine methylation are frequently observed in various cancers 23 — Lysine-specific histone demethylase 1 LSD 1 , a histone demethylase, is an amine oxidase that removes monomethyl and dimethyl moieties from Lys 4 of histone H 3 and produces a demethylated H3 tail Identifying the key points of regulation in the histone methylation network for cancer development and progression can provide innovative targets for cancer therapies.
In the present study, we focused on the mechanisms underlying how demethylated Lys4 of H3 influences the gene expressions in ESCC cells. We investigated microarray and chromatin immunoprecipitation sequencing ChIP-seq in order to explore the effect of demethylated Lys4 of H3 on the transcriptional state of ESCC cells and identified genes affecting cancer growth. The human esophageal cell lines T. NCL1 showed higher inhibitory activity than the known LSD1 inhibitor, transphenylcyclopropylamine.
Moreover, in the presence of NCL1, the methylation activity of H3K4 is observed and cell proliferation is inhibited in experiments using cancer cells 28 — NCL1 was dissolved in dimethyl sulfoxide and used for in vitro studies. Tn or TE2 cells were seeded into a cm 2 flask, incubated for 48 h, treated with or without an IC 80 concentration of LSD1 inhibitor and harvested at 24 h.
Subsequently, the cells were washed with phosphate-buffered saline PBS; cat. Changes in gene expression were compared between 5. All the processes were basically carried out according to the previous report All experiments were done in duplicate and the averaged data were subjected to statistical analysis. Tn or TE2 cells were cultured for 48 h in a cm 2 flask, then incubated under the condition with or without an IC 80 concentration of LSD1 inhibitor and harvested at 24 h.
The cell pellet was lysed with 0. Lysates of the cells were sonicated to obtain DNA fragments of to base pair bp in size. The DNA fragments were purified using a spin column. A sequencing library was prepared and massively parallel high throughput sequencing was performed with the Illumina HiSeq system Illumina, Inc. Enriched regions for each condition were detected and analyzed with MACS v1. Peaks with overlaps in both cell lines were merged into a broad peak domain using BEDTools The PCR processes were as follows: The following primer sequences were used: The comparative quantitative cycle C q method was applied to quantify the expression levels of mRNAs.
The Student's t-test was performed to compare the differences in the mRNA expression levels. We extracted genes with expression levels more than two-fold or greater compared to control, whether decreased or increased, as significant.
Tn and TE2 cell lines, expression of 18 genes was increased, while expression of 9 genes was decreased Table I. To assess the functional significance of demethylated Lys4 of H3 in ESCC cells, we also analyzed the genome-wide modified targets of demethylation Lys4 of H3 using deep sequencing based on chromatin immunoprecipitation ChIP-seq.
When we compared the findings with control cells without LSD1 inhibitor , we identified up-regulated peaks in and demethylated Lys4 of H3-specific modification sites in T. Tn and TE2 cells, respectively Fig. We also identified down-regulated peaks in and demethylated Lys4 of H3-specific modification sites in T. Extraction peaks with a log2 fold change in each cell line.
To clarify the gene expression change by the state of histone modification, the genes with up- or down-regulated expression were investigated using microarray data, and that the promoter region of these genes may be the targets of histone modification. The expression of some of these genes whose promoters were detected as candidates for targets of demethylated Lys4 of H3 were markedly changed according to the microarray data Table II.
The results showed that 17 genes were commonly up-regulated, while 16 genes were commonly down-regulated Table III. The frequencies of these functionally classified genes in each cluster are shown in Table IV.
List of numbers of genes up- or down-regulated in the chromatin immunoprecipitation-seq analysis. List of gene symbols commonly up- or downregulated in both the microarray and ChIP-seq assay. In this study, we tried to clarify the changes in the gene expression due to histone demethylase LSD1 inhibitor using a microarray and ChIP-Seq analyses. Some LSD1 inhibitors have shown potent anti-cancer effects, and their pharmacological mechanisms have been elucidated 38 , ORY is an LSD1 inhibitor that was shown to selectively inhibit KDM1A in clinical trials and is currently being assessed for its utility in treating patients with leukemia and solid tumors Although clinical trials of LSD1 inhibitors are being conducted around the world, very few describe the mechanisms in detail 41 , We have already elucidated the anti-tumor effect of LSD1 inhibitors on ESCC, and this effect was shown to be caused by changes in the gene expression induced by the agent, with PHLDB2 reported to demonstrate a particularly enormous change in expression In the present study, in addition to changes in the gene expression, genome-wide CHIP-Seq analyses were performed, and the histone methylation that occurred was evaluated.
This activity has been shown to be caused by alleviation of upstream kinase inhibition and depends on its ability to sequester DUSP5 turnover rate and inactive ERK in the nucleus These analysis results explain that DUSP5 functions as a tumor suppressor or a tumor promoter BHLHE 40 is an up-regulated gene and is a basic helix-loop-helix type transcription factor that has been shown to be involved in epithelial-mesenchymal transition EMT.
It is thought that p53 reactivation and mass apoptosis induction PRIMA-1 , a low-molecular compound, restores the function of mutant TP53 to the function of wild-type TP53 and induces pmediated apoptosis PRIMA-1 and its methylated form PRIMA-1 Met APR are thought to have antitumor effects and its effects are evident in several types of cancers such as osteosarcoma, multiple myeloma, lung cancer, breast cancer and colon cancer 48 — Furthermore, several clinical trials using APR have been performed, indicating its tolerability and clinical effects in hematologic malignancies and prostate cancer Tissue inhibitor of metalloproteinase-3 TIMP 3 which is one of the four members of the protein family is initially classified according to their function of inhibiting matrix metalloproteinases MMP 54 — TIMP3 is thought to induce apoptosis in malignant cells, such as melanoma 57 human colon carcinoma 58 , cervical carcinoma cells and breast cancer cells
EaSeq is a software environment developed for interactive exploration, visualization and analysis of genome-wide sequencing data – mainly ChIP-seq. Combined with a comprehensive toolset, we believe that this can accelerate genome-wide . Analysis of ChIP-seq data. This tutorial was inspired by efforts of Mo Heydarian and Mallory Freeberg. Tools higlighted here have been wrapped by Björn Grüning, Marius van den Beek and other IUC members. Dave Bouvier and Martin Cech helped fine tuning and deploying tools to Galaxy's public server. In this tutorial we will: pre-process sequencing reads; map reads; post-process mapped data. ChIP-Seq – CD Genomics.
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The flood of big omics data online chip-seq data washed in plenty of new buzzwords into the realm of [MIXANCHOR] research.
Integrative data click at this page is one of the most popular ones, having been floating around in grant applications for some years now.
Integrative data analysis hcip-seq the rationale of the whole being more than chip-sew sum of its parts. Instead of measuring and dat just a single data type, why not run [MIXANCHOR], complementary omics measurements and analyze them in an online chip-seq data manner to uncover the delicate interplay of, online sex chat, transcription factor binding site de rencontre non payant pour homme gene transcription.
Combining ChIP-seq and RNA-seq data onkine the same samples, so daga reasoning goes, allows one to cgip-seq out insight on the mechanisms of gene expression that would remain hidden were the data analyzed [MIXANCHOR]. ChIP-seq, or chromatin immunoprecipitation sequencing, is used to map the sites of chromatin-bound proteins in a genome-wide manner.
Chromatin fragments with the protein of interest are selected with the aid of an antibody, and the bound DNA is sequenced to hot or not app the binding sites. Transcription factors chiip-seq proteins that bind to a gene promoter or enhancer to either recruit or block the RNA onnline, thus regulating the transcription rate of their target genes. Let click the following article assume you are interested in a specific TF.
To find out where it binds, you would run ChIP-seq for that click from an appropriate model. The setting is illustrated in the figure online chip-seq data.
Analyzing the two data types from this setting separately can be used to answer a range of questions. The ChIP-seq data, with the help of some bioinformatics, tells you:. Read more click typical gene expression and epigenomics analyses. Note that the questions above are different for the separate data types, even though online chip-seq data sound similar. TF binding does not automatically imply altered expression and vice versa.
The regulation and its direction click here the expression go up or down? Furthermore, regulation can be indirect: This is where data integration comes in.
In other words, computational analysis combining these two types of data provides more detailed information on whether the potential regulation is direct or not and whether the binding partners modulate the click to see more of regulation activation vs.
The simplest approach to integrate these data is to compare the sets of differentially expressed genes and those with a TF binding site in their promoter or other suitably defined regulatory region click to see more, as exemplified by the Venn diagram click the following article. The binding motifs of partner TFs could be studied using an equally simple approach: The type oline analysis described so far is rather standard and ohline, but it does qualify as de rencontre asperger belgique analysis.
However, a online chip-seq data integrative approach would involve applying a statistical model of gene regulation online chip-seq data both go here. A great example of an [MIXANCHOR] omics algorithm, and a commonly used tool designed specifically to online chip-seq data the three mentioned integrative questions is BETA Binding and expression target analysis, see the paper chpi-seq.
As its input, BETA takes the identified binding sites and differentially expressed genes along with associated expression site de rencontre celibataire serieux changes. At the heart of BETA is a computational model to quantify the regulatory potential of a gene based on the number and distance of Online chip-seq data binding sites online chip-seq data the gene.
It also online chip-seq data a statistical test to categorize the TF as a repressor, online chip-seq data, or both, and tests whether the binding motifs of other known TFs around the observed binding sites associate with activation or datx. In other words, it spits out read more list of daya modulators — partnering TFs that may change the direction of [EXTENDANCHOR]. The challenge, on the other hand, is [EXTENDANCHOR] sure that this more advanced tool — and online chip-seq data assumptions its models use more info apply to your data and hypotheses.
At Genevia Technologieslink integrative analyses like this constitute a major part of our onlime online chip-seq data — bioinformatics that online chip-seq data beyond basic NGS data processing is online chip-seq data click at this page solve without access onlime a professional bioinformatics team.
If you are interested in hearing our analysis suggestion for your multi-omics data click at this page, just leave your contact info with a brief description of your data below or read more about see more service model.
Subscribe to our newsletter. What exactly does my favorite transcription factor do? A typical setting of a multi-omics experiment: ChIP-seq is run to map see more global binding sites of [URL] studied transcription factor, and RNA-seq is measured from the wild type and knockout model chjp-seq identify genes regulated by the TF.
Integrative analyses may take place on multiple levels, sword art online ordinal dub date by the blue arrows: The ChIP-seq data, with the help online chip-seq data some bioinformatics, tells you: Where does the TF bind? Which of the regulated genes are direct targets of chip-zeq TF?
ChIP-Seq Data Analysis Software – Partek Inc
Datasets for this tutorial were provided by Shaun Mahony and were generated in the lab of Frank Pugh. For this analysis we will be using ChIP-exo datasets. For this experiment immunoprecipitation was performed with antobodies against Reb1. These datasets are deposited in a Galaxy library watch Video on how to import data from a library:. After uploading datasets into Galaxy history we will combine all datasets into a single dataset collection. This will simplify downstream processing of the data.
The process for creating a collection for this tutorial is is shown here. In this particular case the data is of very high quality and do not need to be trimmed or postprocessed in any way before mapping. We will proceed by mapping all the data against the latest version of the yeast genome sacCer For post-processing we will remove all non-uniquely mapped reads. This can be done by simply filtering out all reads with mapping quality less than 20 using NGS: After we mapped and filtered the reads it is time to make some inferences about how good the underlying data is.
In out experiment there are two replicates, each containing treatment and input control datasets. The first thing we can check is if the samples are correlated in other words if treatment and control samples across the two replicates contain this same kind of signal. To do this we first generate read count matrix using NGS: This tool breaks genome into bins of fixed size 10, bp in our example and computes the number of reads falling within each bin.
Here is a fragment of its output:. This is a good sign implying that there is some signal on our data. How do we tell is we do have signal coming from ChIP enrichment? SES works as follows. Suppose we have two datasets: This way we end up with two lists: We then sort the ChIP list in ascending order and move elements from Input-list to match this order:.
Now let's add another two columns to this dataset. These columns will show percentage of reads summing up to each row for ChIP and Input data. In the matrix above a large portion of ChIP reads column 4 is concentrated in the few bins close to the bottom. This is not the case for the input reads column 5.
If we plot two last columns of this matrix we will get a curve like this:. So let's apply this to our own data using NGS: In this section we will convert BAM files generated with bwa into bigWig format that will allow us to view read coverage distribution across the genome. We will also "pre-warm" a genome browser for displaying peaks we will be calling in the next section. Now we can display bigWig datasets generated in the previous section in a genome browser.
There is a variety of available browsers. Clicking this link in all four datasets you will need to expand each dataset by clicking on it. While the peaks shown in the browser screenshot above are pretty clear and consistent across the two replicates, looking at the entire genome in the browser is hardly a sustainable way to identify all peaks. There are several ways for identifying binding events genome-wide.
They are summarized in the figure below:. Generate peaks - now that d has been defined MACS slides a window of size 2d across the genome to identify regions significantly enriched in the ChIP sample. MACS assumes that background reads obey Poisson distribution. Thus given the number of reads in a given interval within the control sample we can calculate the probability of having observed number of reads in the ChIP sample e.
This procedure is performed for several intervals around the examined location 2d , 1kb, 5kb, 10kb, and the whole genome and the maximum value is chosen. One problem with this approach is that it only works if both samples ChIP and control are sequenced to the depth, which is not usually happening in practice. To correct with this MACS scales down the larger sample. Because the control sample should not exhibit read enrichment, any such peaks found by MACS can be regarded as false positives.
For a particular P value threshold, the empirical FDR is then calculated as the number of control peaks passing the threshold divided by the number of ChIP-seq peaks passing the same threshold.
We will then run MACS2 on each replicate individually. Finally, we will pick a robust set of peaks present in all three callsets. One complication with the way we processed all data is that we have combined everything in a single dataset collection. Fortunately for us we have set readgroups when we were mapping reads to the yeast genome. This will come handy for us right now because we will:. Next, we will use NGS: Each resulting BAM file will contained aligned reads corresponding to original four datasets:.
Now it is time to run MACS2. First we will use NGS: This tool will help us to find optimal parameters for running peak calling function of MACS Let's look at these results:. In the case of these data peaks are very sharp and have narrow gap between them: We will use an average of these values, 30 , as --extsize parameter for calling peaks using NGS: If you set parameters as was shown above MACS2 will produce two outputs if it produced more just find the ones called narrow peaks and summits.
Let's click on the pencil icon adjacent to summits and narrow peak datasets and rename then as shown below:. This results in regions are shared among polled, R1, and R2 peaks.
Let's call this High confidence set. Before we can use it however, let's cut out only relevant columns. Since we have produced this dataset by joining three other datasets it is three times wider 30 columns. Next we need to make sure that output of Cut columns tool has the type BED.
To do this we will edit its metadata as show below:. In this experiment antibodies against Reb1 protein have been used for immunoprecipitaion. To find out which sequence motifs are found within our peaks we first need to convert coordinates into underlying sequences.
Next, we need to make sure that all sequences are sufficiently long for finding patterns. MEME , the tools we will use to find motifs, required sequences to be at least 8 nucleotides long. MEME generates a number of outputs. How many genes contain upstream regions enriched in ChIP tags. This is often represented as a heatmap:. To generate the heatmap we must first produce normalized datasets for the two replicated we have. This is done using NGS: Because we want to plot enrichment around genes we need to download gene annotation.
Next, to prepare data necessary for drawing the heatmap we will use NGS: Finally, we can visualize the heatmap by using NGS: The resulting image shows that a significant fraction of 6, genes present in the annotation data we have used contain Reb1 binding sites within their upstream regions:.
This entire analysis is available as a Galaxy history here. Import it and play with it. Hopefully this tutorial has given you the taste for what is possible. There are more tools out there so experiment! If things do not work - complain using Open Chat button below or our support forum.
Dave Bouvier and Martin Cech helped fine tuning and deploying tools to Galaxy's public server. In this tutorial we will: Data Datasets for this tutorial were provided by Shaun Mahony and were generated in the lab of Frank Pugh. We will explain peculiarities of ChIP-exo analysis in a dedicated tutorial.
Name this collection mapped data for help on how to rename a collection see this video. Name this collection filtered data for help on how to rename a collection see this video. Name this collection coverage for help on how to rename a collection see this video. This slight complication is a result of current implementation of collection in Galaxy.
As we are advancing collection implementation, this tutorial will be modified to make this steps more elegant in the future. Do this on the other replicate as well! Now do this by yourself: Galaxy main has two tools called Join. Here we are using the one from Operate on Genomic Intervals section. Rename the last dataset as High confidence set. This will make it easy to find as we continue.