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 project, raw data were derived from a MiSeq and approximately 10 million paired reads were obtained from each sample.
<ol>
	<li>Use Trimmomatic 0.3214 to remove Illumina adaptors and low-quality bases. On the command line, enter: 
	java -jar trimmomatic-0.32.jar PE -phred33 forward.fq.gz reverse.fq.gz -baseout quality_and_adaptor_trimmed ILLUMINACLIP:TruSeq3-PE.fa:2:30:10 LEADING:3 TRAILING:3 SLIDINGWINDOW:4:20 MINLEN:40</li>
	<li>Use PEAR15 v. 0.9.6 to create high-quality merged reads from trimmomatic output paired reads, using default parameters. On the command line, enter: 
	pear -f &#60;quality_and_adaptor_trimmed_1P.fastq&#62; -r &#60;quality_and_adaptor_trimmed_2P.fastq&#62;</li>
	<li>Use Reptile v. 1.116 to error-correct the reads produced through PEAR. Follow the step-by-step protocol described in17.</li>
	<li>Use Trinity v. 2.4.018 in default mode to assemble the corrected sequences. For strand-specific libraries, use the -SS_lib_type parameter. The output is a fasta file (your_assembly.fasta). On the command line, enter: 
	Trinity --seqType fq --SS_lib_type FR &#8211;max_memory 10G &#8211;output Trinity_output --left quality_and_adaptor_trimmed_forward_paired_reads.fq &#8211;right quality_and_adaptor_trimmed_reverse_paired_reads.fq &#8211;CPU 10 
	NOTE: The output will be placed in a new directory, Trinity_output, and the assembly will be named &#39;Trinity.fasta&#39; which can be renamed as Your_assembly.fasta if desired. See the Trinity website for more details: https://github.com/trinityrnaseq/trinityrnaseq/wiki/Running-Trinity.</li>
</ol>
2. BLAST the Assembly against the Reference Sequence
NOTE: Use this step when the reference is an assembly or long reads like Sanger; if it is composed of raw Illumina reads, see step 3 below for mapping reads to the query. All BLAST steps were completed with version 2.2.29+ though the commands should work on any recent BLAST version.
<ol>
	<li>Make a BLAST database of the reference sequence (nucleotide_reference.fasta) at the command line. Enter into the command line the following: 
	makeblastdb -dbtype nucl -in nucleotide_reference.fasta -out nucleotide_reference.db</li>
	<li>BLAST-match the query assembly (generated in step 1) to the reference database. To obtain an output file, use [-out BLAST_results.txt] and to generate tabular output (required for subsequent processing steps with Python scripts), use [-outfmt 6]. These options can be combined in any order, so an example complete command is [blastn -query your_assembly.fasta -db nucleotide_reference.db -out BLAST_results.txt -outfmt 6]. If an e-value setting is desired, use the -evalue option with an appropriate number, for example [-evalue 1e-6]. Be aware however that the subtractive cycle effectively inverts the evalue setting in as described in the discussion.</li>
	<li>For increased stringency, use protein sequences from the assembly as the BLAST query with translated nucleotide BLAST (tBLASTn), which performs 6-way translation of the (nucleotide) database. This method is recommended for most non-model systems, avoiding the problem of incomplete protein annotations.
	<ol>
		<li>Ensure the correct genetic code is selected for the organism being studied, using the -db_gencode option. To obtain protein sequences for the query, run the TransDecoder.LongOrfs command (from TransDecoder package v. 3.0.1) to identify the longest open reading frames from assembled query sequences. The command is [TransDecoder.LongOrfs -t your_assembly.fasta]; the output will be placed in directory called &#39;transcripts.transdecoder_dir&#39; and will contain a file called longest_orfs.pep containing the longest predicted protein sequences from each sequence in your_assembly.fasta.</li>
		<li>To use tBLASTn, run the command [tblastn -query longest_orfs.pep -db nucleotide_reference.db -out BLAST_results.txt -outfmt 6]. If a high-quality protein reference is available, use protein-protein matching with BLASTp rather than tBLASTn.</li>
		<li>Make a BLAST database of the protein reference [makeblastdb -dbtype prot -in protein_reference.fasta -out protein_reference.db] and then [blastp -query longest_orfs.pep -db protein_reference.db -out BLAST_results.txt -outfmt 6]. Make sure to save the results as a file for downstream processing, and use tabular (outfmt 6) to ensure the Python scripts can parse them correctly.</li>
	</ol>
	</li>
</ol>
3. Map Reads onto the Assembly
NOTE: This method can be used if the reference dataset consists of raw genomic reads, rather than assembled sequences or Sanger sequences, in which case use BLAST (step 2.1).
<ol>
	<li>Using BWA -MEM v. 0.7.1219 or bowtie220, map the downloaded raw reads (raw_reads.fastq) onto the query assembly. The output will be .sam format. Commands are as follows: first index the assembly: [bwa index your_assembly.fasta], and then map the reads [bwa mem your_assembly.fasta raw_reads.fastq &#62;mapped.sam]. (Note the &#39;&#62;&#39; symbol here is not a greater-than sign; instead it instructs the output to go into the file mapped.sam).</li>
</ol>
4. Use Python Script to Remove any Matching Sequences
NOTE: Provided scripts work with Python 2.7.
<ol>
	<li>Following Step 2, use subtractive Python script by using the command [./Non-matching_sequences.py your_assembly.fasta BLAST_results.txt]. Before running the script, ensure that the BLAST output file is in format 6 (tabular). The script will output a file with non-matching sequences in fasta format named your_assembly.fasta_non-matching_sequences_BLAST_results.txt.fasta and also the matching sequences for records, as your_assembly.fasta_matching_sequences_BLAST_results.txt.fasta. The non-matching file will be the most important, as a source of potential T sequences for testing and further cycles of subtractive genomics.</li>
	<li>Following Step 3, run the Python script removeUnmapped.py to take as input the .sam from step 3.1, and identifies the names of query sequences without any matching reads and saves them to a new text file. Use the command [./removeUnmapped.py mapped.sam] and the output will be mapped.sam_contigs_with_no_reads.txt. (The program will generate a slimmed-down sam file with all unmapped reads removed; this file can be ignored for purposes of this protocol but may be useful for other analyses).</li>
	<li>As the output of the previous step is a list of sequence names in a text file called mapped.sam_contigs_with_no_reads.txt, extract a fasta file with these sequences: [./getContig.py your_assembly.fasta mapped.sam_contigs_with_no_reads.txt]. The output will be a file called mapped.sam_contigs_with_no_reads.txt.fasta.</li>
</ol>
5. Design Primers for the Sequence that Remains
NOTE: At this point there is a fasta file containing candidate T sequences. This section describes qPCR to experimentally test whether they come from T or from previously unknown regions of R. If the subtraction in step 4 removed all sequences, then either the initial assembly failed to include T, or the subtraction may have been too stringent.
<ol>
	<li>Use Geneious21 to determine optimal primer sequences manually.
	<ol>
		<li>Highlight a candidate sequence of 21-28 bp for the Forward primer. Avoid runs of 4 or more of any base. Try to target a region with a fairly uniform combination of all basepairs. A single G or C at the 3&#39; end is beneficial, helping to anchor the primer.</li>
		<li>Click on the Statistics tab on the right-hand side of the screen to view that sequence&#39;s estimated melting temperature (Tm) as the candidate region is highlighted. Look to obtain a melting temperature between 55-60 &#176;C, while avoiding repeats and long runs of G/C.</li>
		<li>Follow steps 5.1.1. and 5.1.2 to choose a reverse primer, situated 150-250 base pairs 3&#39; of the forward primer. While the primer lengths do not need to match, the predicted Tm should be as close as possible to the Tm of the forward primer. Be sure to reverse complement the sequence (if right-clicking in Geneious while the sequence is highlighted it is a menu option).</li>
	</ol>
	</li>
	<li>Use the Primer Design function, which is found in the top tool bar in the sequence window.
	<ol>
		<li>Click on the Primer Design button. Insert the region to amplify under Target Region.</li>
		<li>Under the Characteristics tab, insert desired size, melting temperature (Tm), and %GC (see step 5.1.1.).</li>
		<li>Click OK to have primers generated. Order the primers through a custom oligo service.</li>
	</ol>
	</li>
	<li>Validate primers with control DNA (encoding both T and R) to optimize Tm and extension time. Use regular Taq and gel electrophoresis to see the band size, but optimization can also be performed with qPCR following the methods in step 6.
	<ol>
		<li>Make 10X dilutions of both forward and reverse primers so that the primers have a concentration of 10 &#956;M.</li>
		<li>Use a PCR mix of 0.5 &#956;L of dNTP, 0.5 &#956;L of forward primer, 0.5 &#956;L of reverse primer, 0.1 &#956;L of Taq polymerase, 2 &#956;L of template, 0.75 &#956;L of magnesium, 2.5 &#956;L of buffer, and 18.15 &#956;L of water so that there is 25 &#956;L per template with a concentration of 5 ng/&#956;L.</li>
		<li>Test the primers at different melting temperatures in the PCR program. Usually optimal performance is observed melt temperatures slightly below the predicted Tm of the primers, but not usually above 60 &#176;C. Also test for optimal extension times using this guide: 1 min per 1000 bp (thus, usually 10-30 seconds depending on amplicon length).</li>
		<li>Perform end-point gel electrophoresis to confirm that the primers amplify the expected sequence. Run 25 &#956;L of the qPCR product mixed with 5 &#956;L of 6X glycerol dye on a 2% TAE agarose gel at 200 V for 20 min.</li>
	</ol>
	</li>
</ol>
6.&#160;qPCR Validation of the Remaining Sequence
NOTE: This step requires primers validated and PCR conditions established in step 5.
<ol>
	<li>Run each template in triplicate with the following mix; 12.5 &#956;L of PowerSYBR Green master mix, 0.5 &#956;L of forward primer with a concentration of 10 &#956;M, 0.5 &#956;L of reverse primer with a concentration of 10 &#956;M, 10.5 &#956;L of water, and 1 &#956;L of template DNA (at a concentration of 2 ng/&#956;L), so that each well contains 25 &#956;L of total volume.</li>
	<li>Run a qPCR program informed by the validated temperature and extension time from step 4. We designed and validated all primers to be compatible with a two-stage cycle, 95 &#176;C for 10 min initial melt, then 40 cycles of 95 &#176;C for 30 s and 60 &#176;C for 1 min. However, a three-stage (melt-anneal-extend) program may be more optimal for the primers and should be adapted if necessary. We recommend that final denaturing curves be generated at least the first time the primers are employed in qPCR to validate the amplification of a single DNA product.</li>
	<li>Measure qPCR/SYBR Green signals relative to actin (or any other suitable &#39;R&#39; control) by Ct. For all cases calculate the average and standard deviation of 2-(gene Ct - &#946;-actin Ct).</li>
	<li>(Optional) Perform end-point gel electrophoresis to confirm correct product size detection by qPCR. Here, run 25 &#956;L of the qPCR product mixed with 5 &#956;L of 6x glycerol dye on a 2% TAE agarose gel at 200 V for 20 min.</li>
</ol>
7.&#160;Repeat with a New Reference to Pare Down the Data.
NOTE: If step 6 validated the identified sequences from T, end the cycle here (Figure 2A). However, a variety of considerations may motivate a continuation of the cycle, for example if many R sequences remain in the file or if none of the candidate T sequences were validated by qPCR in step 6.
<ol>
	<li>Obtain a new reference. This step enables a new iteration of the cycle and may include raw genomic data, raw RNA-seq data, or other assembled datasets. Valuable resources for reference data include the Genome database at the National Center for Biotechnology Information (https://www.ncbi.nlm.nih.gov/genome) which stores assembled genomes accessible through FTP (ftp://ftp.ncbi.nlm.nih.gov/genomes/), and the Gene Expression Omnibus (https://www.ncbi.nlm.nih.gov/geo/) where raw next-generation sequence reads are stored. Genome projects may provide their raw sequence data through other project-associated websites and databases.</li>
</ol>

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The authors have nothing to disclose.

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.

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

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 ...

novel-sequence-discovery-by-subtractive-genomics

Research

JoVE Journal

Genetics

8.6K Views.  American University. Ons protocol is belangrijk omdat het kan worden gebruikt om genomische sequenties te identificeren die niet kunnen worden geïsoleerd van co-zuiverende sequenties, die zelf slechts gedeeltelijk bekend kunnen zijn. De belangrijkste voordelen van deze techniek is dat het goedkoop is, met behulp van meestal gratis software die kan worden gedownload en flexibel. Je het toepassen op veel biologische vragen.Mogelijke toepassingen zijn het identificeren van bacteriële vaccindoelen en het ide...

Video: Novel Sequence Discovery by Subtractive Genomics

A Strategy to Identify de Novo Mutations in Common Disorders such as Autism and Schizophrenia

There are several lines of evidence supporting the role of de novo mutations as a mechanism for common disorders, such as autism and schizophrenia. First, the de novo mutation rate in humans is relatively high, so new mutations are generated at a high frequency in the population. However, de novo mutations have not been reported in most common diseases. Mutations in genes leading to severe diseases where there is a strong negative selection against the phenotype, such as lethality in embryonic stages or reduced reproductive fitness, will not be transmitted to multiple family members, and therefore will not be detected by linkage gene mapping or association studies. The observation of very high concordance in monozygotic twins and very low concordance in dizygotic twins also strongly supports the hypothesis that a significant fraction of cases may result from new mutations. Such is the case for diseases such as autism and schizophrenia. Second, despite reduced reproductive fitness1 and extremely variable environmental factors, the incidence of some diseases is maintained worldwide at a relatively high and constant rate. This is the case for autism and schizophrenia, with an incidence of approximately 1% worldwide. Mutational load can be thought of as a balance between selection for or against a deleterious mutation and its production by de novo mutation. Lower rates of reproduction constitute a negative selection factor that should reduce the number of mutant alleles in the population, ultimately leading to decreased disease prevalence. These selective pressures tend to be of different intensity in different environments. Nonetheless, these severe mental disorders have been maintained at a constant relatively high prevalence in the worldwide population across a wide range of cultures and countries despite a strong negative selection against them2. This is not what one would predict in diseases with reduced reproductive fitness, unless there was a high new mutation rate. Finally, the effects of paternal age: there is a significantly increased risk of the disease with increasing paternal age, which could result from the age related increase in paternal de novo mutations. This is the case for autism and schizophrenia3. The male-to-female ratio of mutation rate is estimated at about 4&#x2013;6:1, presumably due to a higher number of germ-cell divisions with age in males. Therefore, one would predict that de novo mutations would more frequently come from males, particularly older males4. A high rate of new mutations may in part explain why genetic studies have so far failed to identify many genes predisposing to complexes diseases genes, such as autism and schizophrenia, and why diseases have been identified for a mere 3% of genes in the human genome. Identification for de novo mutations as a cause of a disease requires a targeted molecular approach, which includes studying parents and affected subjects. The process for determining if the genetic basis of a disease may result in part from de novo mutations and the molecular approach to establish this link will be illustrated, using autism and schizophrenia as examples.

Molecular genetic strategy for finding de novo mutations causing common disorders such as autism and schizophrenia.

There are several lines of evidence supporting the role of de novo mutations as a mechanism for common disorders, such as autism and schizophrenia. First, ...

Medicine

G2-seq: A High Throughput Sequencing-based Technique for Identifying Late Replicating Regions of the Genome

Numerous techniques have been developed to follow the progress of DNA replication through the S phase of the cell cycle. Most of these techniques have been directed toward elucidation of the location and timing of initiation of genome duplication rather than its completion. However, it is critical that we understand regions of the genome that are last to complete replication, because these regions suffer elevated levels of chromosomal breakage and mutation, and they have been associated with both disease and aging. Here we describe how we have extended a technique that has been used to monitor replication initiation to instead identify those regions of the genome last to complete replication. This approach is based on a combination of flow cytometry and high throughput sequencing. Although this report focuses on the application of this technique to yeast, the approach can be used with any cells that can be sorted in a flow cytometer according to DNA content.

We describe a technique for combining flow cytometry and high throughput sequencing to identify late replicating regions of the genome.

Numerous techniques have been developed to follow the progress of DNA replication through the S phase of the cell cycle. Most of these techniques have ...

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Conventional next-generation sequencing techniques (NGS) have allowed for immense genomic characterization for over a decade. Specifically, NGS has been used to analyze the spectrum of clonal mutations in malignancy. Though far more efficient than traditional Sanger methods, NGS struggles with identifying rare clonal and subclonal mutations due to its high error rate of ~0.5&#8211;2.0%. Thus, standard NGS has a limit of detection for mutations that are &#62;0.02 variant allele fraction (VAF). While the clinical significance for mutations this rare in patients without known disease remains unclear, patients treated for leukemia have significantly improved outcomes when residual disease is &#60;0.0001 by flow cytometry. In order to mitigate this artefactual background of NGS, numerous methods have been developed. Here we describe a method for Error-corrected DNA and RNA Sequencing (ECS), which involves tagging individual molecules with both a 16 bp random index for error-correction and an 8 bp patient-specific index for multiplexing. Our method can detect and track clonal mutations at variant allele fractions (VAFs) two orders of magnitude lower than the detection limit of NGS and as rare as 0.0001 VAF.

Next-generation sequencing (NGS) is a powerful tool for genomic characterization that is limited by the high error rate of the platform (~0.5&#8211;2.0%). We describe our methods of error-corrected sequencing that allow us to obviate the NGS error rate and detect mutations at variant allele fractions as rare as 0.0001.

Conventional next-generation sequencing techniques (NGS) have allowed for immense genomic characterization for over a decade. Specifically, NGS has been ...

Gene Mining and Sequence Analysis of Purine Nucleosidase Based on RNA-Seq

Caterpillar fungus (Ophiocordyceps sinensis) is one of the most valued fungal Traditional Chinese medicine (TCM), and it contains plenty of active ingredients such as adenosine. Adenosine is considered as a biologically effective ingredient that has a variety of anti-tumor and immunomodulatory activities. In order to further elucidate the mechanism of purine nucleosidase (PN) in adenosine biosynthesis, a gene encoding PN was successfully mined and further analyzed based on the RNA-Seq database of caterpillar fungus. The full-length cDNA of PN was 855 bp, which encoded 284 amino acids. BLAST analysis showed the highest homology of 85.06% with nucleoside hydrolase in NCBI. ProtProm analysis showed that the relative molecular weight was 30.69 kDa and the isoelectric point was 11.55. The secondary structure of PN was predicted by Predict Protein; the results showed that alpha helix structure accounted for 28.17%, strand structure accounted for 11.97%, and loop structure accounted for 59.86%. Moreover, PN gene was further cloned from transcriptome and detected by agarose gel electrophoresis for verification. This study provides more sufficient scientific basis and new ideas for the genetic regulation of adenosine biosynthesis in fungal TCM.

In this protocol, a method for gene mining and sequence analysis of purine nucleosidase (PN, EC:3.2.2.1) based on RNA-Seq was described. ProtProm analysis was applied to show the unique secondary and tertiary structures of PN. Furthermore, the PN gene was cloned from transcriptome to verify the reliability of RNA-Seq results.

Caterpillar fungus (Ophiocordyceps sinensis) is one of the most valued fungal Traditional Chinese medicine (TCM), and it contains plenty of active ...

Mass Spectrometry-Guided Genome Mining as a Tool to Uncover Novel Natural Products

The chemical space covered by natural products is immense and widely unrecognized. Therefore, convenient methodologies to perform wide-ranging evaluation of their functions in nature and potential human benefits (e.g., for drug discovery applications) are desired. This protocol describes the combination of genome mining (GM) and molecular networking (MN), two contemporary approaches that match gene cluster-encoded annotations in whole genome sequencing with chemical structure signatures from crude metabolic extracts. This is the first step towards the discovery of new natural entities. These concepts, when applied together, are defined here as MS-guided genome mining. In this method, the main components are previously designated (using MN), and structurally related new candidates are associated with genome sequence annotations (using GM). Combining GM and MN is a profitable strategy to target new molecule backbones or harvest metabolic profiles in order to identify analogues from already known compounds.

A mass spectrometry-guided genome mining protocol is established and described here. It is based on genome sequence information and LC-MS/MS analysis and aims to facilitate identification of molecules from complex microbial and plant extracts.

The chemical space covered by natural products is immense and widely unrecognized. Therefore, convenient methodologies to perform wide-ranging evaluation ...

Chemistry

Collection and Extraction of Saliva DNA for Next Generation Sequencing

The preferred source of DNA in human genetics research is blood, or cell lines derived from blood, as these sources yield large quantities of high quality DNA. However, DNA extraction from saliva can yield high quality DNA with little to no degradation/fragmentation that is suitable for a variety of DNA assays without the expense of a phlebotomist and can even be acquired through the mail. However, at present, no saliva DNA collection/extraction protocols for next generation sequencing have been presented in the literature. This protocol optimizes parameters of saliva collection/storage and DNA extraction to be of sufficient quality and quantity for DNA assays with the highest standards, including microarray genotyping and next generation sequencing.

DNA extraction from saliva can provide a readily available source of high molecular weight DNA, with little to no degradation/fragmentation. This protocol provides optimized parameters for saliva collection/storage and DNA extraction to be of sufficient quality and quantity for downstream DNA assays with high quality requirements.

The preferred source of DNA in human genetics research is blood, or cell lines derived from blood, as these sources yield large quantities of high quality ...

Competitive Genomic Screens of Barcoded Yeast Libraries

By virtue of advances in next generation sequencing technologies, we have access to new genome sequences almost daily. The tempo of these advances is accelerating, promising greater depth and breadth. In light of these extraordinary advances, the need for fast, parallel methods to define gene function becomes ever more important. Collections of genome-wide deletion mutants in yeasts and E. coli have served as workhorses for functional characterization of gene function, but this approach is not scalable, current gene-deletion approaches require each of the thousands of genes that comprise a genome to be deleted and verified. Only after this work is complete can we pursue high-throughput phenotyping. Over the past decade, our laboratory has refined a portfolio of competitive, miniaturized, high-throughput genome-wide assays that can be performed in parallel. This parallelization is possible because of the inclusion of DNA 'tags', or 'barcodes,' into each mutant, with the barcode serving as a proxy for the mutation and one can measure the barcode abundance to assess mutant fitness. In this study, we seek to fill the gap between DNA sequence and barcoded mutant collections. To accomplish this we introduce a combined transposon disruption-barcoding approach that opens up parallel barcode assays to newly sequenced, but poorly characterized microbes. To illustrate this approach we present a new Candida albicans barcoded disruption collection and describe how both microarray-based and next generation sequencing-based platforms can be used to collect 10,000 - 1,000,000 gene-gene and drug-gene interactions in a single experiment.

We have developed comprehensive, unbiased genome-wide screens to understand gene-drug and gene-environment interactions. Methods for screening these mutant collections are presented.

By virtue of advances in next generation sequencing technologies, we have access to new genome sequences almost daily.  The tempo of these advances is ...

Biology

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

As DNA sequencing technology has markedly advanced in recent years2, it has become increasingly evident that the amount of genetic variation between any two individuals is greater than previously thought3. In contrast, array-based genotyping has failed to identify a significant contribution of common sequence variants to the phenotypic variability of common disease4,5. Taken together, these observations have led to the evolution of the Common Disease / Rare Variant hypothesis suggesting that the majority of the "missing heritability" in common and complex phenotypes is instead due to an individual's personal profile of rare or private DNA variants6-8. However, characterizing how rare variation impacts complex phenotypes requires the analysis of many affected individuals at many genomic loci, and is ideally compared to a similar survey in an unaffected cohort. Despite the sequencing power offered by today's platforms, a population-based survey of many genomic loci and the subsequent computational analysis required remains prohibitive for many investigators.
To address this need, we have developed a pooled sequencing approach1,9 and a novel software package1 for highly accurate rare variant detection from the resulting data. The ability to pool genomes from entire populations of affected individuals and survey the degree of genetic variation at multiple targeted regions in a single sequencing library provides excellent cost and time savings to traditional single-sample sequencing methodology. With a mean sequencing coverage per allele of 25-fold, our custom algorithm, SPLINTER, uses an internal variant calling control strategy to call insertions, deletions and substitutions up to four base pairs in length with high sensitivity and specificity from pools of up to 1 mutant allele in 500 individuals. Here we describe the method for preparing the pooled sequencing library followed by step-by-step instructions on how to use the SPLINTER package for pooled sequencing analysis (<a href="http://www.ibridgenetwork.org/wustl/splinter" target="_blank">http://www.ibridgenetwork.org/wustl/splinter</a>). We show a comparison between pooled sequencing of 947 individuals, all of whom also underwent genome-wide array, at over 20kb of sequencing per person. Concordance between genotyping of tagged and novel variants called in the pooled sample were excellent. This method can be easily scaled up to any number of genomic loci and any number of individuals. By incorporating the internal positive and negative amplicon controls at ratios that mimic the population under study, the algorithm can be calibrated for optimal performance. This strategy can also be modified for use with hybridization capture or individual-specific barcodes and can be applied to the sequencing of naturally heterogeneous samples, such as tumor DNA.

Pooled DNA sequencing is a fast and cost-effective strategy to detect rare variants associated with complex phenotypes in large cohorts. Here we describe the computational analysis of pooled, next-generation sequencing of 32 cancer-related genes using the SPLINTER software package. This method is scalable, and applicable to any phenotype of interest.

As DNA sequencing technology has markedly advanced in recent years2, it has become increasingly evident that the amount of genetic variation between any ...

Next-generation Sequencing of 16S Ribosomal RNA Gene Amplicons

One of the major questions in microbial ecology is &#8220;who is there?&#8221; This question can be answered using various tools, but one of the long-lasting gold standards is to sequence 16S ribosomal RNA (rRNA) gene amplicons generated by domain-level PCR reactions amplifying from genomic DNA. Traditionally, this was performed by cloning and Sanger (capillary electrophoresis) sequencing of PCR amplicons. The advent of next-generation sequencing has tremendously simplified and increased the sequencing depth for 16S rRNA gene sequencing. The introduction of benchtop sequencers now allows small labs to perform their 16S rRNA sequencing in-house in a matter of days. Here, an approach for 16S rRNA gene amplicon sequencing using a benchtop next-generation sequencer is detailed. The environmental DNA is first amplified by PCR using primers that contain sequencing adapters and barcodes. They are then coupled to spherical particles via emulsion PCR. The particles are loaded on a disposable chip and the chip is inserted in the sequencing machine after which the sequencing is performed. The sequences are retrieved in fastq format, filtered and the barcodes are used to establish the sample membership of the reads. The filtered and binned reads are then further analyzed using publically available tools. An example analysis where the reads were classified with a taxonomy-finding algorithm within the software package Mothur is given. The method outlined here is simple, inexpensive and straightforward and should help smaller labs to take advantage from the ongoing genomic revolution.

Characterizing microbial community has been a longstanding goal in environmental microbiology. Next-generation sequencing methods now allow for the characterization of microbial communities at an unprecedented depth with minimal cost and labor. We detail here our approach to sequence bacterial 16S ribosomal RNA genes using a benchtop sequencer.

One of the major questions in microbial ecology is &#8220;who is there?&#8221; This question can be answered using various tools, but one of the ...

Transposon-insertion Sequencing as a Tool to Elucidate Bacterial Colonization Factors in a Burkholderia gladioli Symbiont of Lagria villosa Beetles

Inferring the function of genes by manipulating their activity is an essential tool for understanding the genetic underpinnings of most biological processes. Advances in molecular microbiology have seen the emergence of diverse mutagenesis techniques for the manipulation of genes. Among them, transposon-insertion sequencing (Tn-seq) is a valuable tool to simultaneously assess the functionality of many candidate genes in an untargeted way. The technique has been key to identify&#160;molecular mechanisms for the colonization of eukaryotic hosts in several pathogenic microbes and a few beneficial symbionts.
Here, Tn-seq is established as a method to identify colonization factors in a mutualistic Burkholderia gladioli symbiont of the beetle Lagria villosa. By conjugation, Tn5 transposon-mediated insertion of an antibiotic-resistance cassette is&#160;carried out at random genomic locations in B. gladioli. To identify the effect of gene disruptions on the ability of the bacteria to colonize the beetle host, the generated B. gladioli transposon-mutant library is inoculated on the beetle eggs, while a control is grown in vitro in a liquid culture medium. After allowing sufficient time for colonization, DNA is extracted from the in vivo and in vitro grown libraries. Following a DNA library preparation protocol, the DNA samples are prepared for transposon-insertion sequencing. DNA fragments that contain the transposon-insert edge and flanking bacterial DNA are selected, and the mutation sites are determined by sequencing away from the transposon-insert edge. Finally, by analyzing and comparing the frequencies of each mutant between the in vivo and in vitro libraries, the importance of specific symbiont genes during beetle colonization can be predicted.

Transposon-insertion Sequencing as a Tool to Elucidate Bacterial Colonization Factors in a Burkholderia gladioli Symbiont of Lagria villosa Beetles

This is an adapted method for identifying candidate insect colonization factors in a Burkholderia beneficial symbiont. The beetle host is infected with a random mutant library generated via transposon mutagenesis, and library complexity after colonization is compared to a control grown in vitro.

Novel Sequence Discovery by Subtractive Genomics

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