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 human genes, use the UCSC hg38 reference genome, for Saccharomyces cerevisiae genes, use the UCSC SacCer3 reference genome.</li>
	<li>Install MAPster.
	<ol>
		<li>Download MAPster software and install. The software can be found using a web browser at the following: https://github.com/emptyewer/MAPster/releases. HISAT2 runs on Unix-based systems such as an Apple Macintosh. Because of this, the MAPster program will only run on compatible systems such as Apple Macintosh and linux. 
		NOTE: System requirements for an Apple Mac are: OSX 10.10+, &#62;4 Gb RAM, &#62;500 Gb disk space, and internet access for downloading reference genomes. Users may need to consult with an institutional IT person if their enterprise has security protocols restricting administrator rights and permissions.</li>
	</ol>
	</li>
	<li>Enter required files and parameters through the &#34;Main&#34; tab (Figure 1). Select the appropriate &#34;Pairwise&#34; button to enter files either as pairs or unpaired with FASTQ as the default file format.
	<ol>
		<li>For DEEPN analysis, turn the &#34;Pairwise&#34; option to &#34;Off&#34; to run in single read format.</li>
		<li>Load files into MAPster simply by drag-and-drop into the appropriate window.</li>
		<li>Select a reference DNA/genome source that corresponds to the source of the Y2H prey library inserts. Indexed genomes from several model organisms are listed in the &#34;Genome&#34; box and can be automatically downloaded from the Johns Hopkins University Center for Computational Biology. Reference genomes will be stored locally for later use.</li>
		<li>Indicate the number of computer processes to be devoted to the mapping program under the &#34;Threads&#34; box, since HISAT2 supports multi-threading. MAPster will search the computer and suggest the maximum number of processors available as a default.</li>
		<li>Specify an output file name. This file name will be used throughout the DEEPN process so a short yet descriptive name without space or special characters is recommended. Specify a folder to output the mapped files using the &#34;Open Output Directory&#34; button.</li>
		<li>Once the appropriate files and parameters have been selected, add the mapping job to the jobs queue using the &#34;Add to Queue&#34; button. The file names in the main window can be deleted and replaced with files corresponding to a new sample and they can be added to the queue after providing a corresponding output filename.</li>
		<li>Click the &#34;Run Queue&#34; button once all the jobs are entered into the job queue. 
		NOTE: Once a mapping job has been placed in the queue, selecting that job causes the parameter settings to be displayed in the &#34;Job Parameters&#34; window and the command line statement with all arguments to be displayed in the &#34;Job Command&#34; window. The output options include directing whether to keep reads that fail to align and specifying the number of primary alignments allowed for each read. The default output file from MAPster is in SAM format (e.g. a &#39;.sam&#39; file). It will contain all the sequence reads from the fastq files specified for that sample including those that were (mapped) and were not (unmapped) successfully mapped to the specified geome.</li>
	</ol>
	</li>
</ol>
2. Bioinformatic Processing Using DEEPN Software
NOTE: DEEPN software is currently compiled for use with prey libraries containing mouse cDNA sequences, human cDNA sequences, or S. cerevisiae genomic DNA sequences. DEEPN accepts the standard .sam file format and can accept a SAM (.sam) file containing both mapped and unmapped reads or separate files for each of the unmapped and mapped reads.
<ol>
	<li>Download DEEPN software and install. The software can be found using a web browser at the following: https://github.com/emptyewer/DEEPN/releases. Select which version matches the computing platform and download. To install, open the downloaded install package. 
	NOTE: Versions of DEEPN are available for PC, Mac, and Linux sysrems. Mac and PC systems should have &#62;500 Gb hard disk space and &#62;4 Gb RAM.</li>
	<li>Open the DEEPN software. From the main window (Figure 2) select the corresponding prey library information from the top selection box. Select a folder where the processed files can go by clicking the &#34;Work Folder&#34; button and navigating to the folder/directory. One can create a new folder/directory if needed. Once a &#34;Work Folder&#34; is selected, DEEPN will create three subfolders entitled unmapped_sam_files, mapped_sam_files, and sam_files.
	<ol>
		<li>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 &#39;sam_files&#39; folder. Otherwise place .sam files into the unmapped_sam_files and mapped_sam_files accordingly.</li>
	</ol>
	</li>
	<li>Initiate processing by clicking the &#34;Gene Count+Junction Make&#34; button. 
	NOTE: Processing will begin with the Gene Count module that will use mapping positions to count how many reads correspond to each gene. Junction Make will then extract junction sequences (the sequences fused directly downstream from the Gal4-activation domain) from the reads and identify them using the Blast algorithm. This will create a full set of folders pictured in Figure 3. Processing time depends on the size and number of sequence data files and processing speed of the computer used. Typical times range from 12&#8211;30 h for an experimental dataset of ~250 million reads. The Gene Count procedure and the Junction_Make procedure can be individually started by clicking the &#34;Gene Count&#34; button or the &#34;Junction Make&#34; button.</li>
	<li>Download and install Stat_Maker (https://github.com/emptyewer/DEEPN/releases). This is a statistical analysis package designed for DEEPN datasets that currently works only on Unix Mac systems.
	<ol>
		<li>Open Stat_Maker and click the button &#34;Verify Installation&#34; (Figure 4). 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.</li>
		<li>Click the &#34;Choose Folder&#34; 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.</li>
		<li>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 (His+ media) and selected (His- media). 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.</li>
		<li>Click the &#34;Run&#34; button. Depending on the speed of the computer, computation will take between 5&#8211;15 min.</li>
	</ol>
	</li>
	<li>Review results from the Stat_Maker output, which are placed in a new subfolder within the main work folder labeled &#34;Stat_Maker Results&#34;. 
	NOTE: The results are found in a CSV (comma-separated values) file that can be opened in common spreadsheet programs. Stat_Maker will rank gene hits that are likely to be differentially enriched upon selection with the bait of interest over the empty pTEF-GBD (Figure 5). Also tabulated is the percentage of reads for each dataset where the gene insert is found upstream, downstream, or within the open reading frame and whether the gene is also found within the correct translational reading frame. Often, DEEPN will capture robust Y2H interactions of a bait with portions of a given cDNA that are out of the proper reading frame of the corresponding protein or are to a portion of the cDNA that is downstream of its corresponding open-reading frame. Scanning the combined output from Stat_Maker streamlines detection and elimination of these irrelevant hits.</li>
	<li>To review the data on each potential candidate, open the DEEPN software, select the corresponding prey library information and then the correct working folder using the &#34;Work Folder&#34;.
	<ol>
		<li>Click the &#34;Blast Query&#34; button. This loads a new window (Figure 6). In the top text box, type the gene name or GenBank NM number to select the candidate gene of interest. These gene names correspond to the names listed in the StatMaker output file. Type enter or return, which initiates retrieval of the gene of interest.</li>
		<li>Select which datasets will be used for the analysis using the &#34;Select Dataset&#34; menus. Typically, these include the Vector only and Bait samples grown under non-selective conditions and the Bait sample grown under selection conditions. Initially, the datasets will take a few moments to load, however, subsequent query of the same datasets with different genes will go rapidly. 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 &#34;Results&#34; tab or a graphic format using the &#34;Plot&#34; tab. These results can be exported to a .csv file by clicking the &#34;Save .csv&#34; button in the top right.</li>
	</ol>
	</li>
</ol>
3. Verification of Candidates Identified by DEEPN
NOTE: The purpose of DEEPN and Stat_Maker is to identify candidate genes that give a positive Y2H interaction. Verifying such Y2H interactions can be done using a traditional binary Y2H format using the bait plasmid of interest paired with the empty Gal4-activation domain &#39;prey&#39; plasmid as well as paired with the prey plasmid carrying the gene/cDNA fragment of interest. It is not feasible to isolate the actual plasmid of interest within the mixture of DNA isolated from the yeast population subjected to Y2H selection. However, one can computationally reconstruct what the gene/cDNA fragment is that produces the Y2H interaction, design primers for the 5&#39; and 3&#39; ends of that fragment, and amplify that fragment from the DNA isolated from the yeast population. This section describes how to find the 5&#39; and 3&#39; end of the candidate prey fragment.
<ol>
	<li>Open the DEEPN software and choose the parameters &#34;Select Parameter&#34; and the work folder &#34;Select Work Folder&#34; corresponding to the project. Launch the Blast_Query module by clicking the &#34;Blast Query&#34; button.</li>
	<li>Type the name of the gene of interest or its GenBank &#34;NM&#34; number in the top text box. Select from the pull-down menu the dataset that corresponds to the selected yeast population for the bait of interest to retrieve the table of junction positions under the &#39;Results&#39; tab. By default, Blast_Query will order the different positions according their abundance in the dataset, quantified by the ppm of the total number of junctions found within the database.
	<ol>
		<li>Find the most abundant position that is &#34;In ORF&#34; and &#34;In Frame&#34;. The value for position corresponds to the nucleotide position of the gene with the NCBI Reference Sequence (&#39;NM&#39; number) found in the top text box. This sequence can be retrieved from GenBank (https://www.ncbi.nlm.nih.gov/nuccore/) or copied from the lower text box in the Blast_Query window. 
		NOTE: An example can be found in Figure 6, middle panel. In the center dataset, the &#39;Results&#39; show as the most abundant junction: &#39;Position&#39;: 867; &#39;#Junctions&#39;: 20033.821; &#39;Query Start&#39;, 1; CDS: In ORF; and &#39;Frame&#39;: In Frame. Nucleotide 867 of the GenBank NCBI reference sequence NM_019648 is the start of the prey fragment.</li>
	</ol>
	</li>
	<li>If the Query Start is 1, design the 5&#39; end of the primer to include the nucleotide corresponding to the position number and extend 25 nucleotides downstream from that position (Figure 7). If Query Start is more than 1, it indicates that there are extra nucleotides between the Gal4 activation domain and the prey sequence of interest and that the primer should start further downstream according to the Query Start value.</li>
	<li>From the DEEPN window click the &#34;Read Depth&#34; button under &#34;Analyze Data&#34;. Once the Read Depth window is open, type the NCBI reference sequence (NM) number or gene name 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 (Figure 7B).</li>
	<li>Design a 3&#39; end primer that will capture the sequence of the gene fragment computed by Read Depth. If the abundance of reads goes beyond the ORF and stop codon, design the primer so that it includes the stop codon and the region just upstream of the stop codon. If the sequences for the gene do not extend to past the stop codon, use the Results Table to find the most distant 3&#39; region that can be detected and use this position as the furthest 3&#39; position to place the primer. 
	NOTE: The Read Depth program scans in intervals to find sequences that match the specified gene/cDNA of interest. This helps predict where the 5&#39; and 3&#39; end of the most abundant prey fragment is for that gene in the sample. Fluctuations in the read depth along the length of the sequence are normal, as can be seen in Figure 7. If the read depth is clearly past the stop codon, it indicates that the prey fragment extends beyond the stop codon and thus the 3&#39; primer can simply correspond the region around the stop codon.</li>
	<li>Perform a 50 &#181;L PCR reaction per gene. Each reaction contains 25 pmol of each forward and reverse primer matching the prey-library plasmid (see Table of Materials). Reactions also contain 25 &#181;L of High-fidelity 2x PCR Master Mix, 5 &#181;g of DNA sample, and water up to 50 &#181;L.
	<ol>
		<li>Amplify reactions for 25 cycles with extension times of 3 min at 72 &#176;C, annealing temperature of 55 &#176;C for 30 s, and denaturing at 98 &#176;C for 10 s. Precede cycling by a 30 s denaturation at 98 &#176;C and follow with a 5 min incubation at 72 &#176;C.</li>
	</ol>
	</li>
</ol>

<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 authors have nothing to disclose

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 ...

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

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7.1K Views.  University of Iowa. 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.

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

Conjugative Mating Assays for Sequence-specific Analysis of Transfer Proteins Involved in Bacterial Conjugation

The transfer of genetic material by bacterial conjugation is a process that takes place via complexes formed by specific transfer proteins. In Escherichia coli, these transfer proteins make up a DNA transfer machinery known as the mating pair formation, or DNA transfer complex, which facilitates conjugative plasmid transfer. The objective of this paper is to provide a method that can be used to determine the role of a specific transfer protein that is involved in conjugation using a series of deletions and/or point mutations in combination with mating assays. The target gene is knocked out on the conjugative plasmid and is then provided in trans through the use of a small recovery plasmid harboring the target gene. Mutations affecting the target gene on the recovery plasmid can reveal information about functional aspects of the target protein that result in the alteration of mating efficiency of donor cells harboring the mutated gene. Alterations in mating efficiency provide insight into the role and importance of the particular transfer protein, or a region therein, in facilitating conjugative DNA transfer. Coupling this mating assay with detailed three-dimensional structural studies will provide a comprehensive understanding of the function of the conjugative transfer protein as well as provide a means for identifying and characterizing regions of protein-protein interaction.

Here, we present a protocol to knockout a gene of interest involved in plasmid conjugation and subsequently analyze the impact of its absence using mating assays. The function of the gene is further explored to a specific region of its sequence using deletion or point mutations.

The transfer of genetic material by bacterial conjugation is a process that takes place via complexes formed by specific transfer proteins. In Escherichia ...

Transcriptomic Analysis of C. elegans RNA Sequencing Data Through the Tuxedo Suite on the Galaxy Project

Next generation sequencing (NGS) technologies have revolutionized the nature of biological investigation. Of these, RNA Sequencing (RNA-Seq) has emerged as a powerful tool for gene-expression analysis and transcriptome mapping. However, handling RNA-Seq datasets requires sophisticated computational expertise and poses inherent challenges for biology researchers. This bottleneck has been mitigated by the open access Galaxy project that allows users without bioinformatics skills to analyze RNA-Seq data, and the Database for Annotation, Visualization, and Integrated Discovery (DAVID), a Gene Ontology (GO) term analysis suite that helps derive biological meaning from large data sets. However, for first-time users and bioinformatics' amateurs, self-learning and familiarization with these platforms can be time-consuming and daunting. We describe a straightforward workflow that will help C. elegans researchers to isolate worm RNA, conduct an RNA-Seq experiment and analyze the data using Galaxy and DAVID platforms. This protocol provides stepwise instructions for using the various Galaxy modules for accessing raw NGS data, quality-control checks, alignment, and differential gene expression analysis, guiding the user with parameters at every step to generate a gene list that can be screened for enrichment of gene classes or biological processes using DAVID. Overall, we anticipate that this article will provide information to C. elegans researchers undertaking RNA-Seq experiments for the first time as well as frequent users running a small number of samples.

Transcriptomic Analysis of C. elegans RNA Sequencing Data Through the Tuxedo Suite on the Galaxy Project

Galaxy and DAVID have emerged as popular tools that allow investigators without bioinformatics training to analyze and interpret RNA-Seq data. We describe a protocol for C. elegans researchers to perform RNA-Seq experiments, access and process the dataset using Galaxy and obtain meaningful biological information from the gene lists using DAVID.

Next generation sequencing (NGS) technologies have revolutionized the nature of biological investigation. Of these, RNA Sequencing (RNA-Seq) has emerged ...

Bidirectional Retroviral Integration Site PCR Methodology and Quantitative Data Analysis Workflow

Integration Site (IS) assays are a critical component of the study of retroviral integration sites and their biological significance. In recent retroviral gene therapy studies, IS assays, in combination with next-generation sequencing, have been used as a cell-tracking tool to characterize clonal stem cell populations sharing the same IS. For the accurate comparison of repopulating stem cell clones within and across different samples, the detection sensitivity, data reproducibility, and high-throughput capacity of the assay are among the most important assay qualities. This work provides a detailed protocol and data analysis workflow for bidirectional IS analysis. The bidirectional assay can simultaneously sequence both upstream and downstream vector-host junctions. Compared to conventional unidirectional IS sequencing approaches, the bidirectional approach significantly improves IS detection rates and the characterization of integration events at both ends of the target DNA. The data analysis pipeline described here accurately identifies and enumerates identical IS sequences through multiple steps of comparison that map IS sequences onto the reference genome and determine sequencing errors. Using an optimized assay procedure, we have recently published the detailed repopulation patterns of thousands of Hematopoietic Stem Cell (HSC) clones following transplant in rhesus macaques, demonstrating for the first time the precise time point of HSC repopulation and the functional heterogeneity of HSCs in the primate system. The following protocol describes the step-by-step experimental procedure and data analysis workflow that accurately identifies and quantifies identical IS sequences.

This manuscript describes the experimental procedure and software analysis for a bidirectional integration site assay that can simultaneously analyze upstream and downstream vector-host junction DNA. Bidirectional PCR products can be used for any downstream sequencing platform. The resulting data are useful for a high-throughput, quantitative comparison of integrated DNA targets.

Integration Site (IS) assays are a critical component of the study of retroviral integration sites and their biological significance. In recent retroviral ...

Sample Preparation and Analysis of RNASeq-based Gene Expression Data from Zebrafish

The analysis of global gene expression changes is a valuable tool for identifying novel pathways underlying observed phenotypes. The zebrafish is an excellent model for rapid assessment of whole transcriptome from whole animal or individual cell populations due to the ease of isolation of RNA from large numbers of animals. Here a protocol for global gene expression analysis in zebrafish embryos using RNA sequencing (RNASeq) is presented. We describe preparation of RNA from whole embryos or from cell populations obtained using cell sorting in transgenic animals. We also describe an approach for analysis of RNASeq data to identify enriched pathways and Gene Ontology (GO) terms in global gene expression data sets. Finally, we provide a protocol for validation of gene expression changes using quantitative reverse transcriptase PCR (qRT-PCR). These protocols can be used for comparative analysis of control and experimental sets of zebrafish to identify novel gene expression changes, and provide molecular insight into phenotypes of interest.

This protocol presents an approach for whole transcriptome analysis from zebrafish embryos, larvae, or sorted cells. We include isolation of RNA, pathway analysis of RNASeq data, and qRT-PCR-based validation of gene expression changes.

The analysis of global gene expression changes is a valuable tool for identifying novel pathways underlying observed phenotypes. The zebrafish is an ...

Single Molecule Fluorescence In Situ Hybridization (smFISH) Analysis in Budding Yeast Vegetative Growth and Meiosis

Single molecule fluorescence in situ hybridization (smFISH) is a powerful technique to study gene expression in single cells due to its ability to detect and count individual RNA molecules. Complementary to deep sequencing-based methods, smFISH provides information about the cell-to-cell variation in transcript abundance and the subcellular localization of a given RNA. Recently, we have used smFISH to study the expression of the gene&#160;NDC80&#160;during meiosis in budding yeast, in which two transcript isoforms exist and the short transcript isoform has its entire sequence shared with the long isoform. To confidently identify each transcript isoform, we optimized known smFISH protocols and obtained high consistency and quality of smFISH data for the samples acquired during budding yeast meiosis. Here, we describe this optimized protocol, the criteria that we use to determine whether high quality of smFISH data is obtained, and some tips for implementing this protocol in&#160;other yeast strains and growth conditions.

Single Molecule Fluorescence In Situ Hybridization smFISH Analysis in Budding Yeast Vegetative Growth and Meiosis

This single molecule fluorescence in situ hybridization protocol is optimized to quantify the number of RNA molecules in budding yeast during vegetative growth and meiosis.

Single molecule fluorescence in situ hybridization (smFISH) is a powerful technique to study gene expression in single cells due to its ability to detect ...

Mating-based Overexpression Library Screening in Yeast

Budding yeast has been widely used as a model in studying proteins associated with human diseases. Genome-wide genetic screening is a powerful tool commonly used in yeast studies. The expression of a number of neurodegenerative disease-associated proteins in yeast causes cytotoxicity and aggregate formation, recapitulating findings seen in patients with these disorders. Here, we describe a method for screening a yeast model of the Amyotrophic Lateral Sclerosis-associated protein FUS for modifiers of its toxicity. Instead of using transformation, this new screening platform relies on the mating of yeast to introduce an arrayed library of plasmids into the yeast model. The mating method has two clear advantages: first, it is highly efficient; second, the pre-transformed arrayed library of plasmids can be stored for long-term as a glycerol stock, and quickly applied to other screens without the labor-intensive step of transformation into the yeast model each time. We demonstrate how this method can successfully be used to screen for genes that modify the toxicity of FUS.

This article presents a mating-based method to facilitate overexpression screening in budding yeast using an arrayed plasmid library.

Budding yeast has been widely used as a model in studying proteins associated with human diseases. Genome-wide genetic screening is a powerful tool ...

Novel Sequence Discovery by Subtractive Genomics

Subtractive genomics can be used in any research where the goal is to identify the sequence of a gene, protein, or general region that is embedded in a larger genomic context. Subtractive genomics enables a researcher to isolate a target sequence of interest (T) by comprehensive sequencing and subtracting out known genetic elements (reference, R). The method can be used to identify novel sequences such as mitochondria, chloroplasts, viruses, or germline restricted chromosomes, and is particularly useful when T cannot be easily isolated from R. Beginning with the comprehensive genomic data (R + T), the method uses Basic Local Alignment Search Tool (BLAST) against a reference sequence, or sequences, to remove the matching known sequences (R), leaving behind the target (T). For subtraction to work best, R should be a relatively complete draft that is missing T. Since sequences remaining after subtraction are tested through quantitative Polymerase Chain Reaction (qPCR), R does not need to be complete for the method to work. Here we link computational steps with experimental steps into a cycle that can be iterated as needed, sequentially removing multiple reference sequences and refining the search for T. The advantage of subtractive genomics is that a completely novel target sequence can be identified even in cases in which physical purification is difficult, impossible, or expensive. A drawback of the method is finding a suitable reference for subtraction and obtaining T-positive and negative samples for qPCR testing. We describe our implementation of the method in the identification of the first gene from the germline-restricted chromosome of zebra finch. In that case computational filtering involved three references (R), sequentially removed over three cycles: an incomplete genomic assembly, raw genomic data, and transcriptomic data.

The purpose of this protocol is to use a combination of computational and bench research to find novel sequences that cannot be easily separated from a co-purifying sequence, which may be only partially known.

Subtractive genomics can be used in any research where the goal is to identify the sequence of a gene, protein, or general region that is embedded in a ...

Enhanced Yeast One-hybrid Screens To Identify Transcription Factor Binding To Human DNA Sequences

Identifying the sets of transcription factors (TFs) that regulate each human gene is a daunting task that requires integrating numerous experimental and computational approaches. One such method is the yeast one-hybrid (Y1H) assay, in which interactions between TFs and DNA regions are tested in the milieu of the yeast nucleus using reporter genes. Y1H assays involve two components: a &#8216;DNA-bait&#8217; (e.g., promoters, enhancers, silencers, etc.) and a &#8216;TF-prey,&#8217; which can be screened for reporter gene activation. Most published protocols for performing Y1H screens are based on transforming TF-prey libraries or arrays into DNA-bait yeast strains. Here, we describe a pipeline, called enhanced Y1H (eY1H) assays, where TF-DNA interactions are interrogated by mating DNA-bait strains with an arrayed collection of TF-prey strains using a high density array (HDA) robotic platform that allows screening in a 1,536 colony format. This allows for a dramatic increase in throughput (60 DNA-bait sequences against &#62;1,000 TFs takes two weeks per researcher) and reproducibility. We illustrate the different types of expected results by testing human promoter sequences against an array of 1,086 human TFs, as well as examples of issues that can arise during screens and how to troubleshoot them.

Here, we present an enhanced yeast one-hybrid screening protocol to identify the transcription factors (TFs) that can bind to a human DNA region of interest. This method uses a high-throughput screening pipeline that can interrogate the binding of &#62;1,000 TFs in a single experiment.

Identifying the sets of transcription factors (TFs) that regulate each human gene is a daunting task that requires integrating numerous experimental and ...

Pooled CRISPR-Based Genetic Screens in Mammalian Cells

Genome editing using the CRISPR-Cas system has vastly advanced the ability to precisely edit the genomes of various organisms. In the context of mammalian cells, this technology represents a novel means to perform genome-wide genetic screens for functional genomics studies. Libraries of guide RNAs (sgRNA) targeting all open reading frames permit the facile generation of thousands of genetic perturbations in a single pool of cells that can be screened for specific phenotypes to implicate gene function and cellular processes in an unbiased and systematic way. CRISPR-Cas screens provide researchers with a simple, efficient, and inexpensive method to uncover the genetic blueprints for cellular phenotypes. Furthermore, differential analysis of screens performed in various cell lines and from different cancer types can identify genes that are contextually essential in tumor cells, revealing potential targets for specific anticancer therapies. Performing genome-wide screens in human cells can be daunting, as this involves the handling of tens of millions of cells and requires analysis of large sets of data. The details of these screens, such as cell line characterization, CRISPR library considerations, and understanding the limitations and capabilities of CRISPR technology during analysis, are often overlooked. Provided here is a detailed protocol for the successful performance of pooled genome-wide CRISPR-Cas9 based screens.

CRISPR-Cas9 technology provides an efficient method to precisely edit the mammalian genome in any cell type and represents a novel means to perform genome-wide genetic screens. A detailed protocol discussing the steps required for the successful performance of pooled genome-wide CRISPR-Cas9 screens is provided here.

Genome editing using the CRISPR-Cas system has vastly advanced the ability to precisely edit the genomes of various organisms. In the context of mammalian ...

Genetic Mapping of Thermotolerance Differences Between Species of Saccharomyces Yeast via Genome-Wide Reciprocal Hemizygosity Analysis

A central goal of modern genetics is to understand how and why organisms in the wild differ in phenotype. To date, the field has advanced largely on the strength of linkage and association mapping methods, which trace the relationship between DNA sequence variants and phenotype across recombinant progeny from matings between individuals of a species. These approaches, although powerful, are not well suited to trait differences between reproductively isolated species. Here we describe a new method for genome-wide dissection of natural trait variation that can be readily applied to incompatible species. Our strategy, RH-seq, is a genome-wide implementation of the reciprocal hemizygote test. We harnessed it to identify the genes responsible for the striking high temperature growth of the yeast Saccharomyces cerevisiae relative to its sister species S. paradoxus. RH-seq utilizes transposon mutagenesis to create a pool of reciprocal hemizygotes, which are then tracked through a high-temperature competition via high-throughput sequencing. Our RH-seq workflow as laid out here provides a rigorous, unbiased way to dissect ancient, complex traits in the budding yeast clade, with the caveat that resource-intensive deep sequencing is needed to ensure genomic coverage for genetic mapping. As sequencing costs drop, this approach holds great promise for future use across eukaryotes.

Genetic Mapping of Thermotolerance Differences Between Species of Saccharomyces Yeast via Genome-Wide Reciprocal Hemizygosity Analysis

Reciprocal hemizygosity via sequencing (RH-seq) is a powerful new method to map the genetic basis of a trait difference between species. Pools of hemizygotes are generated by transposon mutagenesis and their fitness is tracked through competitive growth using high-throughout sequencing. Analysis of the resulting data pinpoints genes underlying the trait.

A central goal of modern genetics is to understand how and why organisms in the wild differ in phenotype. To date, the field has advanced largely on the ...

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

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Conjugative Mating Assays for Sequence-specific Analysis of Transfer Proteins Involved in Bacterial Conjugation

Transcriptomic Analysis of C. elegans RNA Sequencing Data Through the Tuxedo Suite on the Galaxy Project

Bidirectional Retroviral Integration Site PCR Methodology and Quantitative Data Analysis Workflow

Sample Preparation and Analysis of RNASeq-based Gene Expression Data from Zebrafish

Single Molecule Fluorescence In Situ Hybridization (smFISH) Analysis in Budding Yeast Vegetative Growth and Meiosis

Mating-based Overexpression Library Screening in Yeast

Novel Sequence Discovery by Subtractive Genomics

Enhanced Yeast One-hybrid Screens To Identify Transcription Factor Binding To Human DNA Sequences

Pooled CRISPR-Based Genetic Screens in Mammalian Cells

Genetic Mapping of Thermotolerance Differences Between Species of Saccharomyces Yeast via Genome-Wide Reciprocal Hemizygosity Analysis