Name: novel sequence discovery by subtractive genomics
Uploaded: 2019-01-25T13:30:00
Description: Watch this Scientific Journal Video about Novel Sequence Discovery by Subtractive Genomics at JoVE.com

The authors acknowledge Michelle Biederman, Alyssa Pedersen, and Colin J. Saldanha for their assistance with the zebra finch genomics project at various stages. We also acknowledge Evgeny Bisk for computing cluster system administration and NIH grant 1K22CA184297 (to J.R.B.) and NIH NS 042767 (to C.J.S).

1. De novo Assemble Starting Sequence
NOTE: Any Next-Generation Sequence (NGS) data can be used, as long as an assembly can be produced from those data. Suitable input data includes Illumina, PacBio, or Oxford Nanopore reads assembled into a fasta file. For concreteness, this section describes an Illumina-based transcriptomic assembly specific to the zebra finch study we performed7; however be aware that the specifics will vary by project. For our example projec...

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While subtractive genomics is powerful, it is not a cookie-cutter approach, requiring customization at several key steps, and careful selection of reference sequences and test samples. If the query assembly is of poor quality, filtering steps might only isolate assembly artifacts. Therefore, it is important to thoroughly validate the de novo assembly using an appropriate validation protocol to the specific project. For RNA-seq, guidelines are provided on the Trinity website18 and for DNA,...

The purpose of this method is to identify a novel target (T) genomic sequence, either DNA or RNA, from a genomic context, or reference (R) (Figure 1). The method is most useful if the target cannot be physically separated, or it would be expensive to do so. Only a few organisms have perfectly finished genomes for subtraction, so a key innovation of our method is the combination of computational and bench methods into a cycle enabling researchers to isolate target sequences when the reference is imperfect, or a draft genome from a non-model organism. At the end of a cycle, qPCR testing is used to determine whether more subtraction is needed. A validated candidate T sequence will show statistically greater detection in known T-positive samples by qPCR.
Incarnations of the method have been implemented in discovery of new bacterial drug targets that do not have host homologs1,2,3,4 and identification of novel viruses from infected hosts5,6. In addition to identification of T, the method can improve R: we recently used the method to identify 936 missing genes from the zebra finch reference genome and a new gene from a germline-only chromosome (T)7. Subtractive genomics is particularly valuable when T is likely to be extremely divergent from known sequences, or when the identity of T is broadly undefined, as in the zebra finch germline-restricted chromosome7.
By not requiring positive identification of T beforehand, a key advantage of subtractive genomics is that it is unbiased. In a recent study, Readhead et al. examined the relationship between Alzheimer&#39;s disease and viral abundance in four brain regions. For viral identification, Readhead et al. created a database of 515 viruses8, severely limiting the viral agents that their study could identify. Subtractive genomics could have been used to compare the healthy and Alzheimer&#39;s genomes in order to isolate possible novel viruses associated with the disease, regardless of their similarity to known infectious agents. While there are 263 known human-targeting viruses, it has been estimated that approximately 1.67 million undiscovered viral species exist, with 631,000-827,000 of them having a potential to infect humans9.
Isolation of novel viruses is an area in which subtractive genomics is particularly effective, but some studies may not need such a stringent method. For example, studies identifying novel viruses have used unbiased high-throughput sequencing followed by reverse transcription and BLASTx for viral sequences5 or enriching of viral nucleic acids to extract and reverse transcribe viral sequences6. While these studies employed de novo sequencing and assembly, subtraction was not used because the target sequences were positively identified through BLAST. If the viruses were completely novel and not related (or distantly related) to other viruses, subtractive genomics would have been a useful technique. The benefit of subtractive genomics is that sequences that are completely new can be obtained. If the organism&#39;s genome is known, it can be subtracted out to leave any viral sequences. For example, in our published study we isolated a novel viral sequence from zebra finch through subtractive genomics, though it was not our original intent7.
Subtractive genomics has also proved useful in the identification of bacterial vaccine targets, motivated by the dramatic rise in antibiotic resistance1,2,3,4. To minimize the risk of autoimmune reaction, researchers narrowed down the potential vaccine targets by subtracting any proteins that have homologs in the human host. One particular study, looking at Corynebacterium pseudotuberculosis, performed subtraction of vertebrate host genomes from several bacterial genomes to ensure that possible drug targets would not affect proteins in the hosts leading to side effects1. The basic work flow of these studies is to download the bacterial proteome, determine vital proteins, remove redundant proteins, use BLASTp to isolate the essential proteins, and BLASTp against host proteome to remove any proteins with host homologs1,2,3,4. In this case, subtractive genomics ensure that the vaccines developed will not have any off-target effects in the host1,2,3,4.
We used subtractive genomics to identify the first protein-coding gene on a germline-restricted chromosome (GRC) (in this case, T), which is found in germlines but not somatic tissue of both sexes10. Before this study, the only genomic information that was known about the GRC was a repetitive region11. De novo assembly was performed on RNA sequenced from ovary and teste tissues (R+T) from adult zebra finches. The computational elimination of sequences was performed using published somatic (muscle) genome sequence (R1)12, its raw (Sanger) read data (R2), and a somatic (brain) transcriptome (R3)13. The sequential use of three references was driven by the qPCR testing at step 5 of each cycle (Figure 2A), showing that additional filtering was required. The discovered &#945;-SNAP gene was confirmed through qPCR from DNA and RNA, and cloning and sequencing. We show in our example that this method is flexible: it is not dependent on matching nucleic acids (DNA vs RNA) and that subtraction can be performed with references (R) that are comprised of assemblies or raw reads.

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			<td height="20" style="height:20px;width:215px;">Accustart II Taq DNA Polymerase</td>
			<td style="width:65px;">Quanta Bio</td>
			<td align="right" style="width:52px;">95141</td>
			<td style="width:167px;"></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;width:215px;">Blasic Local Alignment Search Tool (BLAST)</td>
			<td style="width:65px;"></td>
			<td style="width:52px;"></td>
			<td>https://github.com/trinityrnaseq/trinityrnaseq/wiki/Transcriptome-Assembly-Quality-Assessment</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;width:215px;">Bowtie 2</td>
			<td style="width:65px;"></td>
			<td style="width:52px;"></td>
			<td>https://www.python.org/download/releases/2.7/</td>
		</tr>
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			<td height="20" style="height:20px;width:215px;">BWA-MEM v. 0.7.12</td>
			<td style="width:65px;"></td>
			<td style="width:52px;"></td>
			<td>https://github.com/BenLangmead/bowtie2</td>
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			<td height="20" style="height:20px;width:215px;">Geneious</td>
			<td style="width:65px;"></td>
			<td style="width:52px;"></td>
			<td>https://blast.ncbi.nlm.nih.gov/Blast.cgi</td>
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			<td height="20" style="height:20px;width:215px;">PEAR v. 0.9.6</td>
			<td style="width:65px;"></td>
			<td style="width:52px;"></td>
			<td>http://www.mybiosoftware.com/reptile-1-1-short-read-error-correction.html</td>
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			<td height="20" style="height:20px;width:215px;">Personal Computer</td>
			<td style="width:65px;">Biomatters</td>
			<td style="width:52px;"></td>
			<td>http://www.geneious.com/</td>
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			<td height="31" style="height:31px;width:215px;">PowerSYBR qPCR mix</td>
			<td style="width:65px;">ThermoFisher</td>
			<td align="right" style="width:52px;">4367659</td>
			<td></td>
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			<td height="20" style="height:20px;width:215px;">Python v. 2.7</td>
			<td style="width:65px;"></td>
			<td style="width:52px;"></td>
			<td>https://sco.h-its.org/exelixis/web/software/pear/</td>
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			<td height="20" style="height:20px;width:215px;">Reptile v.1.1</td>
			<td style="width:65px;"></td>
			<td style="width:52px;"></td>
			<td>https://alurulab.cc.gatech.edu/reptile</td>
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		<tr height="46">
			<td height="46" style="height:46px;width:215px;">Stratagene Mx3005P</td>
			<td style="width:65px;">Agilent Technologies</td>
			<td align="right" style="width:52px;">401456</td>
			<td></td>
		</tr>
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			<td height="20" style="height:20px;width:215px;">TransDecoder v. 3.0.1</td>
			<td style="width:65px;"></td>
			<td style="width:52px;"></td>
			<td>https://sourceforge.net/projects/bio-bwa/files/</td>
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			<td height="20" style="height:20px;width:215px;">Trinity v. 2.4.0</td>
			<td style="width:65px;"></td>
			<td style="width:52px;"></td>
			<td>https://github.com/TransDecoder/TransDecoder/wiki</td>
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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.

After running BLAST, the output file will have a list of sequences from the query that match the database. After Python subtraction, a number of nonmatching sequences will be obtained, and tested by qPCR. The results of this, and next steps, are discussed below.
Negative result. There are two possible negative results that can be seen after BLAST to the reference sequence. There may be no BLAST results, meaning ...

Watch this Scientific Journal Video about Novel Sequence Discovery by Subtractive Genomics at JoVE.com

Novel Sequence Discovery by Subtractive Genomics

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 ...

Our protocol is significant because it can be used to identify genomic sequences that cannot be isolated from co-purifying sequences, which may themselves only be partially known. The main advantages of this technique is that it's inexpensive, using mostly free software that can be downloaded as well as flexible. You can apply it to many biological questions.Potential applications include identifying bacterial vaccine targets and identifying viruses whose sequences are extremely different from known microbes. This method can be applied to any system in which the unknown can not be experimentally separated, as long as the reference sequence without the genomic target is available. This technique takes some trial and error, so patience is important.You may need to trouble shoot the programs. You may use programs different from the ones we describe here. Use the user manuals as much as possible.Visual demonstration of this method is helpful because computational work is dependent on a basic understanding of how command line programming is structured. To begin, use Trimmomatic 0.32 to remove illumina adapters and low quality bases. Use Pear version 0.9.11 to create high quality merged reads from trimmomatic output paired reads using default parameters.Then use Reptile version 1.1 to error correct the reads produced through Pear. Finally use Trinity version 2.4.0 in default mode to assemble the corrected sequences. For strand specific libraries, use the SS_lib_type parameter.The output is a FASTA file that will be placed in a new directory called trinity_output. Make a BLAST database of the reference sequence nucleotide_reference. fasta at the command line.BLAST matched the query assembled to the reference database. To obtain an output file, use BLAST_results. txt To generate tabular output required for subsequence processing steps with Python scripts, use outfmt 6 For increased stringency, use protein sequences from the assembly as the BLAST query with translated nucleotide BLAST, which performs six-way translation of the nucleotide database.To obtain protein sequences for the query, Run the TransDecoder. Long0rfs command to identify the longest open reading frames from assembled query sequences. Now run tblastn.If necessary, ensure the correct genetic code is selected for the organism being studied using the db_gencode with appropriate code option. If a high quality protein reference is available, use protein-protein matching with blastp rather than tblastn Make a BLAST database of the protein reference. Make sure to save the result as a file for downstream processing, and use tabular output to ensure the Python scripts can parse them correctly.Now, use the subtractive Python script to remove any matching sequences. To map the reads onto the assembly, use BWA-MEM Version 0.7.12 or bowtie 2 to map the downloaded raw reads onto the query assembly. First, index the assembly, then map the reads.The output will be SAM format. Run the Python script removeUnmapped. py using the SAM file as input.This identifies the names of query sequences without any matching reads and saves them to a new text file. The output of the previous step is a list of sequences names in a txt file. Extract a FASTA file with these sequences.The output will be a fasta file. Use Genius to determine optimal primer sequences manually. Highlight a candidate sequence of 21 to 28 base pairs for the forward primer, avoiding runs of four or more of any base.Try to target a region with a fairly uniform combination of all base pairs. A single G or C at three Prime End is beneficial, helping to anchor the primer. Click on the Statistics tab on the right-hand side of the screen to view that sequence's estimated melting temperature as the candidate region is highlighted.Aim for a melting temperature between 55 and 60 degrees Celsius, while avoiding repeats, and long runs of G C.Choose a reverse primer in the same manner, situated 150 to 250 base pairs three prime of the forward primer. While the primer lengths do not need to match, the predicted melting temperature should be as close as possible to that of the forward primer. Be sure to reverse complement the sequence by right-clicking in Genius while a sequence is highlighted in a menu option.An alternative method is to use the Primer Design function, which is found in the top toolbar in the Sequence window. Insert the region to amplify under Target Region. Under the characteristics tab, insert desired size, melting temperature, and per cent G C, then click OK to have primers generated.To perform quantitative PCR validation of the remaining sequence, first prepare a reaction mixture for each template in triplicate, with our SYBR Green Master Mix, forward and reverse primers, and water, for a total volume of 25 microlitres. Run a qPCR program informed by the previously validated temperature and extension time. Final denaturing curves should be generated at least the first time the primers are employed in qPCR to validate the amplification of a single DNA product.Measure the qPCR SYBR Green signals relative to Actin by Ct For all cases, calculate the average, and standard deviation of two to the Ct relative to Actin. Perform endpoint gel electrophoresis, to confirm correct product size detection by qPCR. Here, run 25 microlitres of the qPCR product mixed with five microlitres of 6X Glitterol dye on a 2%TAE Agarose gel at 200 volts for 20 minutes.If your qPCR shows that you have not identified the target sequences, repeat the whole cycle with a new reference, which may be obtained from an online database. The subtractive project in this case started with sequencing the RNA from germ-lined tissue of male and female adult zebra finch. Ultimately, 935 somatic genes that were not previously included in the whole genome annotation were identified.After computational filtering, quantitative PCR may yield a negative result in which there was no difference in detection across bird tissues. Conversely, a positive result representing the identification of a True Target Sequence is confirmed when genomic DNA qPCR shows statistically greater detection in the tissue of interest, relative to the reference. Here, the alpha snap gene was validated to be germline-restricted because it was depleted in somatic tissue relative to testees DNA where it was present that levels equivalent to Actin.It is important to remember to use the correct inputs during each of the computational steps. You may have to go through the cycle subtraction multiple times in order to obtain the target sequence or sequences. A variety of phylogenetic, structural, and functional analysis can be performed on the discovered genes.These additional methods give insight into the evolutionary and functional roles of the genes. We identified the first gene on a germline-restricted songbird chromosome, which broadened interest in this surprising genomic element. Subsequent work showed similar chromosomes in many songbird species containing many additional genes.

Research

JoVE Journal

Genetics

In Vitro Transcription Assays and Their Application in Drug Discovery

In Vitro Transcription Assays and Their Application in Drug Discovery

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