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...

<ol>
	<li>Barh, D., 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+Novel+Comparative+Genomics+Analysis+for+Common+Drug+and+Vaccine+Targets+in+Corynebacterium+pseudotuberculosis+and+other+CMN+Group+of+Human+Pathogens.">A Novel Comparative Genomics Analysis for Common Drug and Vaccine Targets in Corynebacterium pseudotuberculosis and other CMN Group of Human Pathogens.</a> Chemical Biology &amp; Drug Design. 78 (1), 73-84 (2011).</li><li>Sarangi, A. N., Aggarwal, R., Rahman, Q., Trivedi, N. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Subtractive+Genomics+Approach+for+in+Silico+Identification+and+Characterization+of+Novel+Drug+Targets+in+Neisseria+Meningitides+Serogroup+B.">Subtractive Genomics Approach for in Silico Identification and Characterization of Novel Drug Targets in Neisseria Meningitides Serogroup B.</a> Journal of Computer Science &amp; Systems Biology. 2 (5), (2009).</li><li>Kaur, 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=Identification+of+Druggable+Targets+for+Acinetobacter+baumannii+Via+Subtractive+Genomics+and+Plausible+Inhibitors+for+MurA+and+MurB.">Identification of Druggable Targets for Acinetobacter baumannii Via Subtractive Genomics and Plausible Inhibitors for MurA and MurB.</a> Applied Biochemistry and Biotechnology. 171 (2), 417-436 (2013).</li><li>Rathi, B., Sarangi, A. N., Trivedi, N. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Genome+subtraction+for+novel+target+definition+in+Salmonella+typhi.">Genome subtraction for novel target definition in Salmonella typhi.</a> Bioinformation. 4 (4), 143-150 (2009).</li><li>Epstein, J. H., 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=Identification+of+GBV-D,+a+Novel+GB-like+Flavivirus+from+Old+World+Frugivorous+Bats+(Pteropus+giganteus)+in+Bangladesh.">Identification of GBV-D, a Novel GB-like Flavivirus from Old World Frugivorous Bats (Pteropus giganteus) in Bangladesh.</a> PLoS Pathogens. 6 (7), (2010).</li><li>Kapoor, 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=Identification+of+Rodent+Homologs+of+Hepatitis+C+Virus+and+Pegiviruses.">Identification of Rodent Homologs of Hepatitis C Virus and Pegiviruses.</a> MBio. 4 (2), (2013).</li><li>Biederman, M. K., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Discovery+of+the+First+Germline-Restricted+Gene+by+Subtractive+Transcriptomic+Analysis+in+the+Zebra+Finch,+Taeniopygia+guttata.">Discovery of the First Germline-Restricted Gene by Subtractive Transcriptomic Analysis in the Zebra Finch, Taeniopygia guttata.</a> Current Biology. 28 (10), 1620-1627 (2018).</li><li>Readhead, B., 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=Multiscale+Analysis+of+Independent+Alzheimer's+Cohorts+Finds+Disruption+of+Molecular,+Genetic,+and+Clinical+Networks+by+Human+Herpesvirus.">Multiscale Analysis of Independent Alzheimer's Cohorts Finds Disruption of Molecular, Genetic, and Clinical Networks by Human Herpesvirus.</a> Neuron. 99, 1-19 (2018).</li><li>Carroll, D., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+global+virome+project.">The global virome project.</a> Science. 359 (6378), 872-874 (2016).</li><li>Pigozzi, M. I., Solari, A. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Germ+cell+restriction+and+regular+transmission+of+an+accessory+chromosome+that+mimics+a+sex+body+in+the+zebra+finch.+Taeniopygia+guttata.">Germ cell restriction and regular transmission of an accessory chromosome that mimics a sex body in the zebra finch. Taeniopygia guttata.</a> Chromosome Research. 6, 105-113 (1998).</li><li>Itoh, Y., Kampf, K., Pigozzi, M. I., Arnold, A. P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Molecular+cloning+and+characterization+of+the+germline-restricted+chromosome+sequence+in+the+zebra+finch.">Molecular cloning and characterization of the germline-restricted chromosome sequence in the zebra finch.</a> Chromosoma. 118, 527-536 (2009).</li><li>Warren, W. C., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+genome+of+a+songbird.">The genome of a songbird.</a> Nature. 464, 757-762 (2010).</li><li>Balakrishnan, C. N., Lin, Y. C., London, S. E., Clayton, D. F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=RNAseq+transcriptome+analysis+of+male+and+female+zebra+finch+cell+lines.">RNAseq transcriptome analysis of male and female zebra finch cell lines.</a> Genomics. 100, 363-369 (2012).</li><li>Bolger, A. M., Lohse, M., Usadel, B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Trimmomatic:+a+flexible+trimmer+for+Illumina+sequence+data.">Trimmomatic: a flexible trimmer for Illumina sequence data.</a> Bioinformatics. 30 (15), 2114-2120 (2014).</li><li>Zhang, J., Kobert, K., Flouri, T., Stamatakis, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=PEAR:+a+fast+and+accurate+Illumina+Paired-End+reAd+mergeR.">PEAR: a fast and accurate Illumina Paired-End reAd mergeR.</a> Bioinformatics. 30, 614-620 (2014).</li><li>Yang, X., Dorman, K. S., Aluru, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Reptile:+representative+tiling+for+short+read+error+correction.">Reptile: representative tiling for short read error correction.</a> Bioinformatics. 26, 2526-2533 (2010).</li><li>MacManes, M. D., Eisen, M. B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Improving+transcriptome+assembly+through+error+correction+of+high-throughput+sequence+reads.">Improving transcriptome assembly through error correction of high-throughput sequence reads.</a> PeerJ. 1 (113), (2013).</li><li>Grabherr, M. G., 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=Full-length+transcriptome+assembly+from+RNA-seq+data+without+a+reference+genome.">Full-length transcriptome assembly from RNA-seq data without a reference genome.</a> Nature Biotechnology. 29, 644-652 (2011).</li><li>Li, H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Aligning+sequence+reads,+clone+sequences+and+assembly+contigs+with+BWA-MEM.">Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM.</a> arXiv. , (2013).</li><li>Langmead, B., Salzberg, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Fast+gapped-read+alignment+with+Bowtie+2.">Fast gapped-read alignment with Bowtie 2.</a> Nature Methods. 9, 357-359 (2012).</li><li>Kearse, 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=Geneious+Basic:+an+integrated+and+extendable+desktop+software+platform+for+the+organization+and+analysis+of+sequence+data.">Geneious Basic: an integrated and extendable desktop software platform for the organization and analysis of sequence data.</a> Bioinformatics. 28 (12), 1647-1649 (2012).</li><li>Peirson, S. N., Butler, J. N. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Quantitative+polymerase+chain+reaction.">Quantitative polymerase chain reaction.</a> Methods in Molecular Biology. 362, 349-362 (2007).</li><li>Hunt, M., Kikuchi, T., Sanders, M., Newbold, C., Berriman, M., Otto, T. D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=REAPR+citation:+REAPR:+a+universal+tool+for+genome+assembly+evaluation.">REAPR citation: REAPR: a universal tool for genome assembly evaluation.</a> Genome Biology. 14 (5), (2013).</li><li>Meyer, 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+mitochondrial+genome+sequence+of+a+hominin+from+Sima+de+los+Huesos.">A mitochondrial genome sequence of a hominin from Sima de los Huesos.</a> Nature. 505 (7483), 403-406 (2013).</li><li>Gunnarsd&#243;ttir, E. D., Li, M., Bauchet, M., Finstermeier, K., Stoneking, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=High-throughput+sequencing+of+complete+human+mtDNA+genomes+from+the+Philippines.">High-throughput sequencing of complete human mtDNA genomes from the Philippines.</a> Genome Research. 21 (1), 1-11 (2010).</li><li>King, J. L., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=High-quality+and+high-throughput+massively+parallel+sequencing+of+the+human+mitochondrial+genome+using+the+Illumina+MiSeq.">High-quality and high-throughput massively parallel sequencing of the human mitochondrial genome using the Illumina MiSeq.</a> Forensic Science International: Genetics. 12, 128-135 (2014).</li><li>Yao, X., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+First+Complete+Chloroplast+Genome+Sequences+in+Actinidiaceae:+Genome+Structure+and+Comparative+Analysis.">The First Complete Chloroplast Genome Sequences in Actinidiaceae: Genome Structure and Comparative Analysis.</a> Plos One. 10 (6), (2015).</li><li>Zhang, Y., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+Complete+Chloroplast+Genome+Sequences+of+Five+Epimedium+Species:+Lights+into+Phylogenetic+and+Taxonomic+Analyses.">The Complete Chloroplast Genome Sequences of Five Epimedium Species: Lights into Phylogenetic and Taxonomic Analyses.</a> Frontiers in Plant Science. 7, (2016).</li><li>Swart, E. C., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+Oxytricha+trifallax+Mitochondrial+Genome.">The Oxytricha trifallax Mitochondrial Genome.</a> Genome Biologyogy and Evolution. 4 (2), 136-154 (2011).</li><li>Barth, D., Berendonk, T. U. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+mitochondrial+genome+sequence+of+the+ciliate+Paramecium+caudatum+reveals+a+shift+in+nucleotide+composition+and+codon+usage+within+the+genus+Paramecium.">The mitochondrial genome sequence of the ciliate Paramecium caudatum reveals a shift in nucleotide composition and codon usage within the genus Paramecium.</a> BMC Genomics. 12 (1), (2011).</li><li>Coombe, L., 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=Assembly+of+the+Complete+Sitka+Spruce+Chloroplast+Genome+Using+10X+Genomics'+GemCode+Sequencing+Data.">Assembly of the Complete Sitka Spruce Chloroplast Genome Using 10X Genomics' GemCode Sequencing Data.</a> Plos One. 11 (9), (2016).</li><li>Herschleb, J., Ananiev, G., Schwartz, D. C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Pulsed-field+gel+electrophoresis.">Pulsed-field gel electrophoresis.</a> Nature Protocols. 2 (3), 677-684 (2007).</li></ol>

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.

novel-sequence-discovery-by-subtractive-genomics

Research

JoVE Journal

Genetics

In Vitro Transcription Assays and Their Application in Drug Discovery

In vitro transcription assays have been developed and widely used for many years to study the molecular mechanisms involved in transcription. This process requires multi-subunit DNA-dependent RNA polymerase (RNAP) and a series of transcription factors that act to modulate the activity of RNAP during gene expression. Sequencing gel electrophoresis of radiolabeled transcripts is used to provide detailed mechanistic information on how transcription proceeds and what parameters can affect it. In this paper we describe the protocol to study how the essential elongation factor NusA regulates transcriptional pausing, as well as a method to identify an antibacterial agent targeting transcription initiation through inhibition of RNAP holoenzyme formation. These methods can be used a as platform for the development of additional approaches to explore the mechanism of action of the transcription factors which still remain unclear, as well as new antibacterial agents targeting transcription which is an underutilized drug target in antibiotic research and development.

In Vitro Transcription Assays and Their Application in Drug Discovery

In this manuscript, we describe a protocol to functionally examine transcription and the inhibitory activity of antibacterial agents targeting bacterial transcription.

In vitro transcription assays have been developed and widely used for many years to study the molecular mechanisms involved in transcription. This process ...

Novel RNA-Binding Proteins Isolation by the RaPID Methodology

RNA-binding proteins (RBPs) play important roles in every aspect of RNA metabolism and regulation. Their identification is a major challenge in modern biology. Only a few in vitro and in vivo methods enable the identification of RBPs associated with a particular target mRNA. However, their main limitations are the identification of RBPs in a non-cellular environment (in vitro) or the low efficiency isolation of RNA of interest (in vivo). An RNA-binding protein purification and identification (RaPID) methodology was designed to overcome these limitations in yeast and enable efficient isolation of proteins that are associated in vivo. To achieve this, the RNA of interest is tagged with MS2 loops, and co-expressed with a fusion protein of an MS2-binding protein and a streptavidin-binding protein (SBP). Cells are then subjected to crosslinking and lysed, and complexes are isolated through streptavidin beads. The proteins that co-purify with the tagged RNA can then be determined by mass spectrometry. We recently used this protocol to identify novel proteins associated with the ER-associated PMP1 mRNA. Here, we provide a detailed protocol of RaPID, and discuss some of its limitations and advantages.

RNA-protein interactions lie at the heart of many cellular processes. Here, we describe an in vivo method to isolate specific RNA and identify novel proteins that are associated with it. This could shed new light on how RNAs are regulated in the cell.

RNA-binding proteins (RBPs) play important roles in every aspect of RNA metabolism and regulation. Their identification is a major challenge in modern ...

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 ...

Determination of the Optimal Chromosomal Location(s) for a DNA Element in Escherichia coli Using a Novel Transposon-mediated Approach

The optimal chromosomal position(s) of a given DNA element was/were determined by transposon-mediated random insertion followed by fitness selection. In bacteria, the impact of the genetic context on the function of a genetic element can be difficult to assess. Several mechanisms, including topological effects, transcriptional interference from neighboring genes, and/or replication-associated gene dosage, may affect the function of a given genetic element. Here, we describe a method that permits the random integration of a DNA element into the chromosome of Escherichia coli and select the most favorable locations using a simple growth competition experiment. The method takes advantage of a well-described transposon-based system of random insertion, coupled with a selection of the fittest clone(s) by growth advantage, a procedure that is easily adjustable to experimental needs. The nature of the fittest clone(s) can be determined by whole-genome sequencing on a complex multi-clonal population or by easy gene walking for the rapid identification of selected clones. Here, the non-coding DNA region DARS2, which controls the initiation of chromosome replication in E. coli, was used as an example. The function of DARS2 is known to be affected by replication-associated gene dosage; the closer DARS2 gets to the origin of DNA replication, the more active it becomes. DARS2 was randomly inserted into the chromosome of a DARS2-deleted strain. The resultant clones containing individual insertions were pooled and competed against one another for hundreds of generations. Finally, the fittest clones were characterized and found to contain DARS2 inserted in close proximity to the original DARS2 location.

Determination of the Optimal Chromosomal Locations for a DNA Element in Escherichia coli Using a Novel Transposon-mediated Approach

Here, the power of a transposon-mediated random insertion of a non-coding DNA element was used to resolve its optimal chromosomal position.

The optimal chromosomal position(s) of a given DNA element was/were determined by transposon-mediated random insertion followed by fitness selection. In ...

High Fat Diet Feeding and High Throughput Triacylglyceride Assay in Drosophila Melanogaster

Heart disease is the number one cause of human death worldwide. Numerous studies have shown strong connections between obesity and cardiac malfunction in humans, but more tools and research efforts are needed to better elucidate the mechanisms involved. For over a century, the genetically highly tractable model of Drosophila has been instrumental in the discovery of key genes and molecular pathways that proved to be highly conserved across species. Many biological processes and disease mechanisms are functionally conserved in the fly, such as development (e.g., body plan, heart), cancer, and neurodegenerative disease. Recently, the study of obesity and secondary pathologies, such as heart disease in model organisms, has played a highly critical role in the identification of key regulators involved in metabolic syndrome in humans.
Here, we propose to use this model organism as an efficient tool to induce obesity, i.e., excessive fat accumulation, and develop an efficient protocol to monitor fat content in the form of TAGs accumulation. In addition to the highly conserved, but less complex genome, the fly also has a short lifespan for rapid experimentation, combined with cost-effectiveness. This paper provides a detailed protocol for High Fat Diet (HFD) feeding in Drosophila to induce obesity and a high throughput triacylglyceride (TAG) assay for measuring the associated increase in fat content, with the aim to be highly reproducible and efficient for large-scale genetic or chemical screening. These protocols offer new opportunities to efficiently investigate regulatory mechanisms involved in obesity, as well as provide a standardized platform for drug discovery research for rapid testing of the effect of drug candidates on the development or prevention of obesity, diabetes and related metabolic diseases.

High Fat Diet Feeding and High Throughput Triacylglyceride Assay in Drosophila Melanogaster

This is a high fat diet feeding protocol to induce obesity in Drosophila, a model for understanding fundamental molecular mechanisms implicated in lipotoxicity. It also provides a high throughput triacylglyceride assay for measuring fat accumulation in Drosophila and potentially other (insect) models under various dietary, environmental, genetic or physiological conditions.

Heart disease is the number one cause of human death worldwide. Numerous studies have shown strong connections between obesity and cardiac malfunction in ...

Sequence-specific and Selective Recognition of Double-stranded RNAs over Single-stranded RNAs by Chemically Modified Peptide Nucleic Acids

RNAs are emerging as important biomarkers and therapeutic targets. Thus, there is great potential in developing chemical probes and therapeutic ligands for the recognition of RNA sequence and structure. Chemically modified Peptide Nucleic Acid (PNA) oligomers have been recently developed that can recognize RNA duplexes in a sequence-specific manner. PNAs are chemically stable with a neutral peptide-like backbone. PNAs can be synthesized relatively easily by the manual Boc-chemistry solid-phase peptide synthesis method. PNAs are purified by reverse-phase HPLC, followed by molecular weight characterization by matrix-assisted laser desorption/ionization-time of flight (MALDI-TOF). Non-denaturing polyacrylamide gel electrophoresis (PAGE) technique facilitates the imaging of the triplex formation, because carefully designed free RNA duplex constructs and PNA bound triplexes often show different migration rates. Non-denaturing PAGE with ethidium bromide post staining is often an easy and informative technique for characterizing the binding affinities and specificities of PNA oligomers. Typically, multiple RNA hairpins or duplexes with single base pair mutations can be used to characterize PNA binding properties, such as binding affinities and specificities. 2-Aminopurine is an isomer of adenine (6-aminopurine); the 2-aminopurine fluorescence intensity is sensitive to local structural environment changes, and is suitable for the monitoring of triplex formation with the 2-aminopurine residue incorporated near the PNA binding site. 2-Aminopurine fluorescence titration can also be used to confirm the binding selectivity of modified PNAs towards targeted double-stranded RNAs (dsRNAs) over single-stranded RNAs (ssRNAs). UV-absorbance-detected thermal melting experiments allow the measurement of the thermal stability of PNA-RNA duplexes and PNA&#183;RNA2 triplexes. Here, we describe the synthesis and purification of PNA oligomers incorporating modified residues, and describe biochemical and biophysical methods for characterization of the recognition of RNA duplexes by the modified PNAs.

We report the protocols for the synthesis and purification of Peptide Nucleic Acid (PNA) oligomers incorporating modified residues. The biochemical and biophysical methods for the characterization of the recognition of RNA duplexes by the modified PNAs are described.

RNAs are emerging as important biomarkers and therapeutic targets. Thus, there is great potential in developing chemical probes and therapeutic ligands ...

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.

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 ...

Sequencing of mRNA from Whole Blood using Nanopore Sequencing

Sequencing in remote locations and resource-poor settings presents unique challenges. Nanopore sequencing can be successfully used under such conditions, and was deployed to West Africa during the recent Ebola virus epidemic, highlighting this possibility. In addition to its practical advantages (low cost, ease of equipment transport and use), this technology also provides fundamental advantages over second-generation sequencing approaches, particularly the very long read length, ability to directly sequence RNA, and real-time availability of data. Raw read accuracy is lower than with other sequencing platforms, which represents the main limitation of this technology; however, this can be partially mitigated by the high read depth generated. Here, we present a field-compatible protocol for sequencing of the mRNAs encoding for Niemann-Pick C1, which is the cellular receptor for ebolaviruses. This protocol encompasses extraction of RNA from animal blood samples, followed by RT-PCR for target enrichment, barcoding, library preparation, and the sequencing run itself, and can be easily adapted for use with other DNA or RNA targets.

Nanopore sequencing is a novel technology that allows cost-effective sequencing in remote locations and resource-poor settings. Here, we present a protocol for sequencing of mRNAs from whole blood that is compatible with such conditions.

Sequencing in remote locations and resource-poor settings presents unique challenges. Nanopore sequencing can be successfully used under such conditions, ...

Navigating MARRVEL, a Web-Based Tool that Integrates Human Genomics and Model Organism Genetics Information

Through whole-exome/genome sequencing, human geneticists identify rare variants that segregate with disease phenotypes. To assess if a specific variant is pathogenic, one must query many databases to determine whether the gene of interest is linked to a genetic disease, whether the specific variant has been reported before, and what functional data is available in model organism databases that may provide clues about the gene&#8217;s function in human. MARRVEL (Model organism Aggregated Resources for Rare Variant ExpLoration) is a one-stop data collection tool for human genes and variants and their orthologous genes in seven model organisms including in mouse, rat, zebrafish, fruit fly, nematode worm, fission yeast, and budding yeast. In this Protocol, we provide an overview of what MARRVEL can be used for and discuss how different datasets can be used to assess whether a variant of unknown significance (VUS) in a known disease-causing gene or a variant in a gene of uncertain significance (GUS) may be pathogenic. This protocol will guide a user through searching multiple human databases simultaneously starting with a human gene with or without a variant of interest. We also discuss how to utilize data from OMIM, ExAC/gnomAD, ClinVar, Geno2MP, DGV and DECHIPHER. Moreover, we illustrate how to interpret a list of ortholog candidate genes, expression patterns, and GO terms in model organisms associated with each human gene. Furthermore, we discuss the value protein structural domain annotations provided and explain how to use the multiple species protein alignment feature to assess whether a variant of interest affects an evolutionarily conserved domain or amino acid. Finally, we will discuss three different use-cases of this website. MARRVEL is an easily accessible open access website designed for both clinical and basic researchers and serves as a starting point to design experiments for functional studies.

Here, we present a protocol to access and analyze many human and model organism databases efficiently. This protocol demonstrates the use of MARRVEL to analyze candidate disease-causing variants identified from next-generation sequencing efforts.

Through whole-exome/genome sequencing, human geneticists identify rare variants that segregate with disease phenotypes. To assess if a specific variant is ...

Exploring Sequence Space to Identify Binding Sites for Regulatory RNA-Binding Proteins

Gene regulation plays an important role in all cells. Transcriptional, post-transcriptional (or RNA processing), translational, and post-translational steps are used to regulate specific genes. Sequence-specific nucleic acid-binding proteins target specific sequences to control spatial or temporal gene expression. The binding sites in nucleic acids are typically characterized by mutational analysis. However, numerous proteins of interest have no known binding site for such characterization. Here we describe an approach to identify previously unknown binding sites for RNA-binding proteins. It involves iterative selection and amplification of sequences starting with a randomized sequence pool. Following several rounds of these steps-transcription, binding, and amplification-the enriched sequences are sequenced to identify a preferred binding site(s). Success of this approach is monitored using in vitro binding assays. Subsequently, in vitro and in vivo functional assays can be used to assess the biological relevance of the selected sequences. This approach allows identification and characterization of a previously unknown binding site(s) for any RNA-binding protein for which an assay to separate protein-bound and unbound RNAs exists.

Sequence specificity is critical for gene regulation. Regulatory proteins that recognize specific sequences are important for gene regulation. Defining functional binding sites for such proteins is a challenging biological problem. An iterative approach for identification of a binding site for an RNA-binding protein is described here and is applicable to all RNA-binding proteins.

Gene regulation plays an important role in all cells. Transcriptional, post-transcriptional (or RNA processing), translational, and post-translational ...