Name: informatic analysis of sequence data from batch yeast 2-hybrid screens
Uploaded: 2018-06-28T14:45:00
Description: Watch this Scientific Journal Video about Informatic Analysis of Sequence Data from Batch Yeast 2-Hybrid Screens at JoVE.com

This work was supported by National Institutes of Health: NIH R21 EB021870-01A1 and by NSF Research Project Grant: 1517110.

1. Mapping Fastq Files
NOTE: DEEPN software as well as many bioinformatics programs use DNA sequence data wherein each sequence read has been mapped for its position in reference DNA. A variety of mapping programs can be used for this including the MAPster interface here that uses the HISTAT2 program to produce .sam files used in subsequent steps.
<ol>
	<li>Map the sequence data to the correct version of the genome. For Y2H libraries of mouse origin, use the UCSC mm10 genome; for those using...

<ol>
	<li>Fields, S., Song, O. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+novel+genetic+system+to+detect+protein-protein+interactions.">A novel genetic system to detect protein-protein interactions.</a> Nature. 340 (6230), 245-246 (1989).</li><li>Rajagopala, S. V. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Mapping+the+Protein-Protein+Interactome+Networks+Using+Yeast+Two-Hybrid+Screens.">Mapping the Protein-Protein Interactome Networks Using Yeast Two-Hybrid Screens.</a> Advances in Experimental Medicine and Biology. 883, 187-214 (2015).</li><li>Weimann, M., 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=A+Y2H-seq+approach+defines+the+human+protein+methyltransferase+interactome.">A Y2H-seq approach defines the human protein methyltransferase interactome.</a> Nature Methods. 10 (4), 339-342 (2013).</li><li>Yachie, N., 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=Pooled-matrix+protein+interaction+screens+using+Barcode+Fusion+Genetics.">Pooled-matrix protein interaction screens using Barcode Fusion Genetics.</a> Molecular Systems Biology. 12 (4), 863 (2016).</li><li>Trigg, S. 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=CrY2H-seq:+a+massively+multiplexed+assay+for+deep-coverage+interactome+mapping.">CrY2H-seq: a massively multiplexed assay for deep-coverage interactome mapping.</a> Nature Methods. , (2017).</li><li>Pashkova, N., 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=DEEPN+as+an+Approach+for+Batch+Processing+of+Yeast+2-Hybrid+Interactions.">DEEPN as an Approach for Batch Processing of Yeast 2-Hybrid Interactions.</a> Cell Reports. 17 (1), 303-315 (2016).</li><li>Kim, D., Langmead, B., Salzberg, S. L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=HISAT:+a+fast+spliced+aligner+with+low+memory+requirements.">HISAT: a fast spliced aligner with low memory requirements.</a> Nature Methods. 12 (4), 357-360 (2015).</li><li>Reinert, K., Langmead, B., Weese, D., Evers, D. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Alignment+of+Next-Generation+Sequencing+Reads.">Alignment of Next-Generation Sequencing Reads.</a> Annual Review of Genomics and Human Genetics. 16, 133-151 (2015).</li><li>Pertea, M., Kim, D., Pertea, G. M., Leek, J. T., Salzberg, S. L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Transcript-level+expression+analysis+of+RNA-seq+experiments+with+HISAT,+StringTie+and+Ballgown.">Transcript-level expression analysis of RNA-seq experiments with HISAT, StringTie and Ballgown.</a> Nature Protocols. 11 (9), 1650-1667 (2016).</li><li>Conesa, 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=A+survey+of+best+practices+for+RNA-seq+data+analysis.">A survey of best practices for RNA-seq data analysis.</a> Genome Biology. 17, 13 (2016).</li></ol>

The software suite described here allows one to completely process and analyze high throughput DNA sequencing data from a DEEPN experiment. The first program used is MAPster, which takes the DNA sequence reads in standard fastq files and maps their position onto a reference DNA for downstream processing by a whole host of informatics programs including the DEEPN software. The utility of the MAPster interface and its ability to queue multiple jobs, combine input files, coveniently name output files, coupled with the speed...

One approach to discover protein interactions is the yeast 2-hybrid (Y2H) assay, which exploits engineered yeast cells that grow only when a protein of interest binds to a fragment of an interacting partner1. Detection of multiple Y2H interactions can now be done with the help of massive parallel high-throughput sequencing. Several formats have been described2,3,4,5 including one that we developed where populations are grown in batch under conditions that select for yeast containing plasmids that produce a positive Y2H interaction6. The workflow we developed, termed DEEPN (Dynamic Enrichment for Evaluation of Protein Networks), identifies differential interactomes from the same prey libraries to identify proteins that interact with one protein (or domain) vs. another protein or a conformationally distinct mutant domain. One of the major steps in this workflow is proper processing and analysis of the DNA sequencing data. Some information can be gleaned by just counting the number of reads for each gene both before and after selection of Y2H interactions in a fashion analogous to an RNA-seq experiment. However, much more in-depth information can be extracted from these datasets including information on the subdomain of a given protein that is capable of producing a Y2H interaction. In addition, whereas the DEEPN approach is valuable, analyzing many sample replicates can be cumbersome and expensive. This problem is alleviated by using a statistical model that was developed specifically for DEEPN datasets where the number of replicates is limited6. To make processing and analysis of DNA sequencing datasets reliable, complete, robust, and accessible for investigators without bioinformatics expertise, we developed a suite of software programs that cover all steps of analysis.
This suite of stand alone software programs that run on desktop computers includes MAPster, DEEPN, and Stat_Maker. MAPster is a graphic user interface that allows each fastq file queued for mapping to the genome using the HISAT2 program7, producing a standard .sam file for use in downstream applications. DEEPN has several modules. It assigns and counts reads corresponding to particular gene similar to an RNA-seq type quantification using the module &#39;Gene Count&#39;. It also extracts the sequences corresponding to the junction between the Gal4 transcriptional domain and the prey sequence and collates the position of those junctions to allow their inspection by comparative tables and graphs (using the module &#39;Junction_Make&#39;) The module &#39;Blast_Query&#39; allows easy inspection, quantitation, and comparison of the junction Gal4 junction sequences. Stat_Maker evaluates the reads per gene enrichment data statistically as a way of prioritizing likely Y2H hits. Here, we describe how to use these software programs and to fully analyze the DNA sequence data from a DEEPN Y2H experiment. Versions of DEEPN are available to run on PC, Mac, and Linux systems. Other programs, such as the mapping program MAPster and the DEEPN statistics module Stat_Maker rely on subroutines that run under Unix and are available only on Mac and linux systems.

<table>
	<tbody>
		<tr>
			<td>Mapster</td>
			<td></td>
			<td></td>
			<td>https://github.com/emptyewer/MAPster/releases</td>
		</tr>
		<tr>
			<td>DEEPN software</td>
			<td></td>
			<td></td>
			<td>https://github.com/emptyewer/DEEPN/releases</td>
		</tr>
		<tr>
			<td>Statmaker</td>
			<td></td>
			<td></td>
			<td>https://github.com/emptyewer/DEEPN/releases</td>
		</tr>
		<tr>
			<td>Minimum computer system</td>
			<td>Apple</td>
			<td></td>
			<td>Mac Intel Core i5 or better</td>
		</tr>
		<tr>
			<td></td>
			<td>-</td>
			<td></td>
			<td>4 Gb RAM or better</td>
		</tr>
		<tr>
			<td></td>
			<td>-</td>
			<td></td>
			<td>500 Gb Disk spce or better</td>
		</tr>
		<tr>
			<td></td>
			<td>-</td>
			<td></td>
			<td>OS 10.10 or higher</td>
		</tr>
		<tr>
			<td></td>
			<td>Dell</td>
			<td></td>
			<td>Intel i5-7400 or better</td>
		</tr>
		<tr>
			<td></td>
			<td>-</td>
			<td></td>
			<td>4 Gb RAM or better</td>
		</tr>
		<tr>
			<td></td>
			<td>-</td>
			<td></td>
			<td>500 Gb Disk spce or better</td>
		</tr>
		<tr>
			<td></td>
			<td>-</td>
			<td></td>
			<td>Windows 7 or higher</td>
		</tr>
	</tbody>
</table>

informatic analysis of sequence data from batch yeast 2-hybrid screens

We have adapted the yeast 2-hybrid assay to simultaneously uncover dozens of transient and static protein interactions within a single screen utilizing high-throughput short-read DNA sequencing. The resulting sequence datasets can not only track what genes in a population that are enriched during selection for positive yeast 2-hybrid interactions, but also give detailed information about the relevant subdomains of proteins sufficient for interaction. Here, we describe a full suite of stand-alone software programs that allow non-experts to perform all the bioinformatics and statistical steps to process and analyze DNA sequence fastq files from a batch yeast 2-hybrid assay. The processing steps covered by these software include: 1) mapping and counting sequence reads corresponding to each candidate protein encoded within a yeast 2-hybrid prey library; 2) a statistical analysis program that evaluates the enrichment profiles; and 3) tools to examine the translational frame and position within the coding region of each enriched plasmid that encodes the interacting proteins of interest.

Mapping fastq data: the first step 
In practically all NGS applications including DEEPN the initial output is a file of short sequence reads that must be mapped by alignment to genomic, transcriptomic, or other reference DNA8. Recently, the HISAT2 alignment program was developed that uses state-of-the-art indexing algorithms to dramatically increase the mapping speed7,9. HISAT2 runs ef...

Watch this Scientific Journal Video about Informatic Analysis of Sequence Data from Batch Yeast 2-Hybrid Screens at JoVE.com

Informatic Analysis of Sequence Data from Batch Yeast 2-Hybrid Screens

Deep sequencing of yeast populations selected for positive yeast 2-hybrid interactions potentially yields a wealth of information about interacting partner proteins. Here, we describe the operation of specific bioinformatics tools and customized updated software to analyze sequence data from such screens.

We have adapted the yeast 2-hybrid assay to simultaneously uncover dozens of transient and static protein interactions within a single screen utilizing ...

This method can help answer key questions about the nature of protein interactomes. The main advantage of this technique is that all the bioinformatics processing is operated by an easy-to-use interface. This software employs a user-friendly graphic user interface that molecular biologists, cell biologists, and biochemists that don't have a lot of expertise in bioinformatics can use to easily complete their analysis.Generally, individuals new to processing sequence data struggle because the computer programs are confusing and cumbersome. We thought making the informatics easy would make the whole technique much more accessible and useful. The first step in this procedure is to download and install the MAPster software program as described in the text protocol.Next, enter the required files and parameters through the Main tab. Select the appropriate Pairwise button to enter the files for DEEPN analysis. Turn the Pairwise option to Off to run in single read format.Load files into MAPster by drag-and-drop into the appropriate window. From the indexed genomes listed in the Genome box, select a reference DNA genome source that corresponds to the source of the Y2H prey library inserts. Since HISAT2 supports multi-threading, under the Threads box, indicate the number of computer processes to be devoted to the mapping program.Specify an output file name, a short yet descriptive name without space or special characters is recommended since this file name will be used throughout the DEEPN process. Using the Open Output Directory button, specify a folder to output the mapped files. Once the appropriate files and parameters have been selected, use the Add to Queue button to add the mapping job to the jobs queue.Once all the jobs are entered into the job queue, click the Run Queue button. Begin this analysis by opening the DEEPN software. From the main window, select the corresponding prey library information from the top selection box.Select a folder where the processed files can go by clicking the Work Folder button and navigating to the folder directory. Once a work folder is selected, DEEPN will create three subfolders with the indicated names. For successful analysis, be sure to place the correct SAM files into the correct folders and to know which files have mapped and unmapped reads.If using sam files containing both mapped and unmapped reads, such as those produced with default settings of the MAPster program, place them in the sam_files folder. Initiate processing by clicking the Gene Count plus Junction Make button. The processing time depends on the size and number of sequence data files and processing speed of the computer used.Once DEEPN has processed the data, a full set of folders will be created. The next step is to perform analysis in Stat Maker. Open Stat_Maker, and click the Verify Installation button.If running for the first time, Stat_Maker will automatically install R, JAGS, and Bioconductor by pulling these resources from the internet. Once R, JAGS, and Bioconductor are detected, Stat_Maker will become active and allow further user input. Click the Choose Folder button to navigate to the working folder that DEEPN processed.Stat_Maker will automatically find and list the files for statistical analysis in the window. Drag and drop the appropriate files from the file list window above into the file windows below for each vector and bait dataset and for each growth conditions, non-selected and selected. Importantly, Stat_Maker requires duplicate datasets for empty vector alone, two samples of non-selected populations, and two samples of selected.This gives an estimate of variability within the experiment. Click the Run button. Depending on the speed of the computer, computation will take between five to 15 minutes.The results from the Stat_Maker output are placed in a new subfolder within the main work folder labeled Stat_Maker Results. Review the results. To review the data on each potential candidate, open the DEEPN software, select the corresponding prey library information, and then select the correct working folder using the work folder.Click the Blast Query button to load a new window. In the top text box, type the gene name or GenBank NM number to select the candidate gene of interest. The gene name corresponds to the name listed in the Stat Maker output file.Type Enter or Return, which initiates retrieval of the gene of interest. Select which datasets will be used for the analysis using the Select Dataset menus. Typically, these include the vector only and bait samples grown under non-selective conditions and the bait sample grown under selection conditions.Initially, loading the data and the gene name can take a little while with the Blast Query program, but once the dataset is loaded, it'll go much faster. Blast_Query will display the fusion points along the sequence of interest and how abundant each fusion point is. This can be displayed both in a table format using the Results tab or a graphic format using the Plot tab.To export these results to a csv file, click the Save csv button in the top right. From the DEEPN window, click the Read Depth button under Analyze Data. Once the Read Depth window is open, type the NM number into the top text box.Use the pull-down menu to select the relevant dataset that contains the enriched gene of interest. Use the table on the left and the graphics display on the right to determine how many reads were found in the data that correspond to the gene of interest. The Stat Maker program produces an Excel-viewable file that summarizes the pertinent information needed to identify candidate interacting proteins.Also shown is the corresponding analysis of whether the plasmids corresponding to the prey candidate contain the proper open reading frame. The Blast Query evaluates every gene and orders the different positions according to their abundance in the dataset quantified by the ppm of the total number of junctions found within the database. In this example, the most abundant position that is In ORF and In Frame is position 867, indicating that nucleotide 867 of the GenBank NCBI Reference Sequence NM_019648 is the start of the prey fragment.This graphic shows a hypothetical sequence and how to design the five-prime oligo to capture the correct frame and fusion point between the Gal4 activation domain and the prey sequence of interest. This Read Depth window shows how many sequences were found in the data that correspond to the nucleotide positions of the sequence of interest. Once mastered, processing data can be done in 10 to 20 hours, most of the time unattended.DEEPN screens and the corresponding informatics analysis will pave the way for investigators in a broad range of fields to explore interaction networks with their protein of interest. The MAPster program is designed to facilitate mapping sequence files from DEEPN experiments. It also provides a user-friendly method for mapping sequence files to reference DNA for a variety of applications, such as RNA-Seq or ChIP-Seq.Although the DEEPN software is specifically built to process batch yeast two-hybrid data, it can also be used to process RNA-Seq data. The Stat Maker program not only predicts which genes give an authentic yeast two-hybrid interaction, but it also collates a number of other parameters, such as reading frame information, allowing investigators to quickly filter their candidate hits. Once candidate interacting proteins are identified, it is important to validate the interactions using standard yeast two-hybrid assays, biochemical pull-down assays, or related methods.

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