We would like to thank Dr. Yan Dong for the DU145 prostate tumor cells. We would like to thank Dr. Nathan Ungerleider for his guidance and advice in creating supercomputer scripts. Some of this work was funded by NIH grants R01 GM121812 to PD, R01 AG057597 to VPB, and 5TL1TR001418 to TK. We would also like to acknowledge support from the Cancer Crusaders and the Tulane Cancer Center Bioinformatics Core.

1. Cytoplasmic RNA extraction
<ol>
	<li>Obtain cells via the following methods.
	<ol>
		<li>Collect live cells from 2.75%&#8211;100% confluent, T-75 flasks.
		<ol>
			<li>Wash the flask 2 times in 5 mL of cold PBS, and in the last wash scrape off cells and transfer to a 15 mL conical tube. Centrifuge for 2 min at 1,000 x g and 4 &#176;C, and carefully remove and discard supernatant (Table of Materials).</li>
		</ol>
		</li>
		<li>Collect cells from tissue specimens.
		<ol>
			<li>Prepare tissue for cytoplasmic RNA extraction within an hour from being dissected and always keep on ice. For long-term storage, use RNA inhibitor solutions to store tissue for up to 72 hours after dissection following the manufacturer&#8217;s protocol (Table of Materials).</li>
			<li>Dice a 10 &#181;m3 sample and homogenize the fresh sample with 5 mL of cold PBS in a sterile dounce homogenizer, transfer to a 15 mL conical tube, centrifuge for 2 min at 1,000 x g at 4 &#176;C, and carefully remove and discard supernatant (Table of Materials).</li>
		</ol>
		</li>
	</ol>
	</li>
	<li>Add 2 mL of lysis buffer to cellular pellet- mix and incubate on ice for 5 min.
	<ol>
		<li>Prepare fresh lysis buffer with 150 mM NaCl, 50 mM HEPES (pH 7.4), and 25 &#956;g/mL digitonin (Table of Materials).</li>
		<li>As the minimum concentration of digitonin in the lysis buffer required to penetrate the plasma membrane may vary by cell type, microscopically confirm that cells treated with lysis buffer lose the plasma membrane and retain the intact nuclear membrane.</li>
		<li>Just before use, add 1,000 U/mL RNase inhibitor (Table of Materials).</li>
	</ol>
	</li>
	<li>Centrifuge for 1 min at 1,000 x g and 4 &#176;C, and collect the supernatant.</li>
	<li>Add supernatant to pre-chilled 7.5 mL of Trizol and 1.5 mL of chloroform. All the steps that require chloroform must be done inside a clean chemical hood (Table of Materials).</li>
	<li>Centrifuge for 35 min at 3,220 x g and 4 &#176;C.</li>
	<li>Transfer the aqueous portion (top layer) to a fresh pre-chilled 15 mL tube.</li>
	<li>Add 4.5 mL of chloroform and vortex.</li>
	<li>Centrifuge for 10 min at 3,220 x g and 4 &#176;C.</li>
	<li>Transfer the aqueous portion to fresh pre-chilled tube.</li>
	<li>Add 4.5 mL of isopropanol, shake well, and incubate at -80 &#176;C overnight (Table of Materials).</li>
	<li>Centrifuge at 3,220 x g and 4 &#176;C for 45 minutes.</li>
	<li>Remove isopropanol, add 15 mL of 100% ethanol (Table of Materials).</li>
	<li>Centrifuge at 3,220 x g for 10 min.</li>
	<li>Remove ethanol, drain and dry for approximately 1 h.
	<ol>
		<li>Use a sterile cotton swab to blot out any remaining ethanol (Table of Materials).</li>
	</ol>
	</li>
	<li>Re-suspend sample in 100 to 200 &#956;L of RNase free water depending on pellet size (Table of Materials).</li>
	<li>Fractionate samples using electrophoresis technology to determine quality and concentration of samples according to manufacturer&#8217;s intructions23 (Table of Materials).
	<ol>
		<li>Samples qualify for RNA-Seq analysis if RIN &#62; 824.</li>
	</ol>
	</li>
</ol>
2. Next-Generation sequencing
<ol>
	<li>Submit cytoplasmic RNA samples to be sequenced using next-generation sequencing platform aimed at generating at least 50 million paired-end 100 bp reads.</li>
	<li>Select for poly-adenylated RNAs and strand-specific sequencing.</li>
</ol>
3. Create annotations (optional if one has an existing annotation)
<ol>
	<li>Create full-length L1 annotation or download the full-length L1 annotation (Supplemental File 1a-b).
	<ol>
		<li>Download Repeat Masker annotations for LINE-1 elements from the UCSC genome browser with the table browser tool (https://genome.ucsc.edu/cgi-bin/hgTables). Specify the mammal clade, the human genome, the hg19 assembly (or hg38 for a more updated genome), and filter for &#8220;LINE1&#8221; under Class Name. Download as a .gtf file and label as FL-L1-BLAST.gtf.</li>
		<li>Run a local BLAST search of the first 300 bp of the L1.3 full-length L1 element encompassing the promoter region in the human genome and add 6,000 bp downstream to create an end of the L1 coordinates to the annotation file. Save in a gtf file and label as FL-L1-RM.gtf.</li>
		<li>Intersect the RepeatMasker annotation and the promoter-based L1 annotation using bedtools, and label as FL-L1-BLAST_RM.txt (Software Packages).
		<ol>
			<li>Use this command in the Linux terminal: bedtools intersect -a FL-L1-BLAST.gtf -b FL-L1-RM.gtf &#62; FL-L1-BLAST_RM.txt.</li>
		</ol>
		</li>
		<li>Separate the intersected FL-L1 annotation by the top and bottom strand.
		<ol>
			<li>Copy over the FL-L1-BLAST_RM.txt into spreadsheet software and sort by the &#8220;minus&#8221; and &#8220;plus&#8221; strand and then sort by chromosome location.</li>
			<li>Create two new spreadsheet documents, one with the intersected coordinates for full length L1s on the minus strand and one on the bottom strand, and save as FL-L1-BLAST_RM_minus.xls and FL-L1-BLAST_RM_plus.xls.</li>
			<li>Save the two new documents as .txt files.</li>
		</ol>
		</li>
		<li>Use the mac2unix program to convert the .txt files to the correct annotation files (Software Packages).
		<ol>
			<li>Use this command in the terminal: mac2unix.sh FL-L1-BLAST_RM_minus.gff.</li>
			<li>Use this command in the terminal: mac2unix.sh FL-L1-BLAST_RM_plus.gff.</li>
			<li>Save new files with the .gff extension.</li>
		</ol>
		</li>
		<li>Alternatively, use AWK to filter rows associated with the + and &#8211; strand.
		<ol>
			<li>Use the following command to get the + strand: awk &#8216;/+/&#8217; FL-L1_BLAST_RM.gtf &#62; FL-L1_BLAST_RM_plus.gtf.</li>
			<li>Use the following command line to get the - strand: awk &#8216;/-/&#8217; FL-L1_BLAST_RM.gtf &#62; FL-L1_BLAST_RM_minus.gtf.</li>
		</ol>
		</li>
	</ol>
	</li>
</ol>
4. Read alignment pipeline to identify expressed L1s
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Option</td>
			<td>Description</td>
		</tr>
		<tr>
			<td>&#8211;p</td>
			<td>This details the number of threads the computer should use running the alignment. Larger computer memory will allow more threads and should be empirically d.</td>
		</tr>
		<tr>
			<td>&#8211;m 1</td>
			<td>This tells the program to only accept reads that have one match in the genome that is better than any other genome match.</td>
		</tr>
		<tr>
			<td>&#8211; y</td>
			<td>This is the tryhard switch which makes the mapping search for all possible matches and not allow it to quit after a fixed number of matches is reached.</td>
		</tr>
		<tr>
			<td>&#8211;v 3</td>
			<td>This only allows the program to utilize memory for mapped reads with 3 or less mismatches to the genome.</td>
		</tr>
		<tr>
			<td>&#8211;X 600</td>
			<td>This only allows paired reads that map within 600 bases of one another. This makes sure the read pairs are co-linear in the genome and selects against s involving processed RNA molecules.</td>
		</tr>
		<tr>
			<td>&#8211;chunkmbs 8184</td>
			<td>This command assigns extra memory for handling the large amount of alignments possible for each L1-related read.</td>
		</tr>
	</tbody>
</table>
Table 1: Command line options for Bowtie.
<ol>
	<li>Run alignment paired-end sequencing fastq files with the RNA-Seq sample of interest using Bowtie. 
	NOTE: Bowtie1 must be used and not Bowtie2 because the parameters required for unique alignment are specifically only found in this version of bowtie (Software Packages). Bowtie is used over splice-aware aligners like STAR in order evaluate concordant, contiguous reads more relevant to L1 biology and expression.
	<ol>
		<li>Use this command line in the Linux terminal: bowtie -p 10 -m 1 -S -y -v 3 -X 600 --chunkmbs 8184 hg_X_Y_M_index -1 hg_sample_1.fq -2 hg_sample_2.fq | samtools view -hbuS - | samtools sort &#8211; hg_sample_sorted.bam. See Table 1 for a description of command line options for Bowtie.</li>
	</ol>
	</li>
	<li>Strand separate the output bam file using samtools (Software Packages) and the following Linux commands. Note that the actual flag values may vary if one is not using standard next generation sequencing protocols.
	<ol>
		<li>Use this command line to select for the top strand: samtools view -h hg_sample_sorted.bam | awk &#39;substr($0,1,1) == &#34;@&#34; || $2 == 83 || $2 == 163 {print}&#39; | samtools view -bS - &#62; hg_sample_sorted_topstrand.bam.</li>
		<li>Use this command line to select for the bottom strand: samtools view -h hg_sample_sorted.bam | awk &#39;substr($0,1,1) == &#34;@&#34; || $2 == 99 || $2 == 147 {print}&#39; | samtools view -bS - &#62; hg_sample_sorted_bottomstrand.bam.</li>
	</ol>
	</li>
	<li>Generate read counts against annotations for L1 loci using bedtools (Software Packages).
	<ol>
		<li>Use this command line to generate read counts for L1s in the sense direction on the top strand: bedtools coverage -abam FL-L1-BLAST_RM_plus.gff -b hg_sample_sorted_topstrand.bam &#62; hg_sample_sorted_bowtie_tryhard_plus_top.txt.</li>
		<li>Use this command line to generate read counts for L1s in the sense direction on the bottom strand: bedtools coverage -abam FL-L1-BLAST_RM_minus.gff -b hg_sample_sorted_bottomstrand.bam &#62; hg_sample_sorted_bowtie_tryhard_minus_bottom.txt.</li>
	</ol>
	</li>
	<li>Index bam file from step 5.1.1 to make it viewable in the Integrative Genomics Viewer (IGV)25 (Software Packages).
	<ol>
		<li>Use this command line: samtools index hg_sample_sorted.bam</li>
	</ol>
	</li>
	<li>To use a batch mode to increase the number of RNA-Seq samples piped through at a time, use a supercomputer script to complete step 4.1 called human_bowtie.sh, a script to complete steps 4.2-4.3 has been created called human_L1_pipeline.sh, and a script to complete step 4.4 has been created called bam_index.sh. These scripts may be found in Supplemental File 2 with associated supercomputer commands to run the scripts.</li>
</ol>
5. Manual curation
<ol>
	<li>Create a spreadsheet for reads mapped to each annotated L1 locus.
	<ol>
		<li>Copy over hg_sample_sorted_bowtie_tryhard_minus_bottom.txt created in step 4.3.2 and label page as &#8220;minus-bottom.&#8221;
		<ol>
			<li>Sort all columns based on highest to lowest number of reads found in column J.</li>
		</ol>
		</li>
		<li>Copy over hg_sample_sorted_bowtie_tryhard_plus_top.txt created in step 4.3.1 and label as &#8220;top-plus&#8221; in another spreadsheet.
		<ol>
			<li>Sort all columns based on highest to lowest number of reads found in column J.</li>
		</ol>
		</li>
		<li>Create a third page labeled as &#8220;combined&#8221; and add all loci with ten or more reads from &#8220;minus-bottom&#8221; and &#8220;plus-top&#8221; pages.
		<ol>
			<li>Sort all columns based on highest to lowest number of reads found in column J.</li>
		</ol>
		</li>
		<li>Load the following files into IGV25 (Software Packages): 1) reference genome of interest to visualize annotated genes, 2) FL-L1-BLAST_RM.gff to visualize the L1 annotation, 3) hg_sample_sorted.bam to visualize mapped transcripts from sample of interest, and 4) hg_genomicDNA_sorted.bam to assess mappability of genomic regions.</li>
		<li>Remove coverage and junction rows associated with each bam file.</li>
		<li>Compress hg_sample_sorted.bam and hg_genomicDNA_sorted.bam so all the IGV tracks fit on one screen.</li>
	</ol>
	</li>
	<li>Manually curate.
	<ol>
		<li>Using coordinates from loci listed on the spreadsheet &#8220;combined&#8221; page, view called loci in IGV25 (Software Packages).</li>
		<li>Curate a locus to be authentically expressed off its own if there are no reads upstream in the L1 direction up to 5 kb.
		<ol>
			<li>Label the row green in color and note why it is an authentically expressed L1. 
			NOTE: An exception to this rule exists if the region upstream of the L1 is not mappable. If this is the case, label the row red in color and note that the expression of the region upstream of the L1 promoter cannot be evaluated and therefore the L1&#8217;s expression is not able to be confidently determined.</li>
		</ol>
		</li>
		<li>Curate a locus to not be authentically expressed off its own promoter if there are reads upstream up to 5 kb.
		<ol>
			<li>Label the row red in color and note why it is not an authentically expressed L1.</li>
			<li>Curate a locus as false if it is expressed within an intron of an expressed gene in the same direction with reads upstream of the L1, if it is downstream of an expressed gene in the same direction with reads upstream of the L1, or for un-annotated expression patterns with reads upstream of the L1. 
			NOTE: An exception to this rule applies when there are minimal reads directly overlapping the L1 promoter start site, but slightly upstream of the L1. If there are no other reads upstream of an L1 case like this, consider this L1 to be authentically expressed. Label the row green color and note why it is an authentically expressed L1.</li>
		</ol>
		</li>
		<li>Curate an L1 locus as likely to be false if the pattern of mapped reads to the locus do not correlate with the specific L1&#8217;s regions of mappability. 
		NOTE: For example, if an L1 is highly mappable but only has a pile up of reads in a condensed region within the L1, it is less likely to be related to L1 expression off its own promoter and more likely to be from un-annotated sources like exons or LTRs. In cases like this, curate the loci as orange and note why the locus is suspicious. Verify sources of suspicious pile-ups by checking the L1 location in UCSC genome browser.</li>
		<li>Curate a locus to not be authentically expressed if it is within a genomic environment of sporadically expressed un-annotated regions 
		NOTE: For example, reads may be expressed 10 kb upstream of the L1, but every 10 kb or so there are mapped reads and some of those reads align with the L1. These L1s are less likely to be expressed off its own promoter, and more likely to have mapped reads due to un-annotated patterns of genomic expression. In cases like this, curate the loci as orange and note why the locus is suspicious.</li>
	</ol>
	</li>
</ol>
6. Read alignment strategy to assess mappability in reference genome (optional if one has an existing aligned genomic DNA dataset)
<ol>
	<li>Download whole genome DNA sequence files and convert to .fq files
	<ol>
		<li>Go to the NCBI website found here: https://www.ncbi.nlm.nih.gov/sra</li>
		<li>Type in WGS HeLa paired end.</li>
		<li>Select for Homo sapiens under Results by taxon.</li>
		<li>Select a sample that is paired end and has reads with 100 or more bp like the following sample: https://www.ncbi.nlm.nih.gov/sra/ERX457838[accn]</li>
		<li>Confirm read length by selecting Run and then Metadata as shown here: https://trace.ncbi.nlm.nih.gov/Traces/sra/?run=ERR492384</li>
		<li>To download the whole genome DNA sequence data, enter this command in the Linux terminal: sratoolkit.2.9.2-mac64/bin/prefetch -X 100G ERR492384 
		NOTE: The SRA toolkit prefetch function downloads the accession number &#8220;ERR492384&#8221; found in the NCBI site (Software Packages). The &#8220;100G&#8221; limits amount of downloaded data to 100 gigabytes.</li>
		<li>Enter this command in the Linux terminal: fastq-dump --split-files ERR492384 
		NOTE: This splits the downloaded genomic DNA dataset into two fastq files.</li>
	</ol>
	</li>
	<li>Run alignment using Bowtie.
	<ol>
		<li>Use this command in Linux for alignment: bowtie -p 10 -m 1 -S -y -v 3 -X 600 --chunkmbs 8184 hg_X_Y_M_index -1 hg_genomicDNA_1.fq -2 hg_genomicDNA_2.fq | samtools view -hbuS - | samtools sort &#8211; hg_genomicDNA_sorted.bam.
		<ol>
			<li>Refer to step 4.1 to understand parameters used in the Bowtie alignment (Software Packages).</li>
			<li>Download the genomically aligned bam file to assess mappability available upon author request.</li>
		</ol>
		</li>
	</ol>
	</li>
	<li>Index bam file from step 4.2.1 using samtools to make it viewable in IGV25 (Software Packages) to further inform manual curation.
	<ol>
		<li>Use this command line in Linux: samtools index hg_genomicDNA_sorted.bam</li>
	</ol>
	</li>
	<li>Assess mappability of each L1 loci
	<ol>
		<li>Determine the number of uniquely mapped reads to L1 loci using the bedtools program, the FL-L1 annotation, and the aligned genomic sequence data (Software Packages).
		<ol>
			<li>Use this command line in Linux: bedtools coverage -abam FL-L1-BLAST_RM.gtf &#8211;b hg_genomicDNA_sorted.bam &#62; L1_Mappability_hg_genomicDNA.txt.</li>
		</ol>
		</li>
		<li>Designate an L1 locus to have full coverage mappability when 400 unique reads are aligned to it.</li>
		<li>Determine the factor required to scale up or down genomic DNA aligned reads to 400 for each individual L1.</li>
		<li>To have a scaled measure of expression according to individual L1 locus mappability, multiply the factor determined in step 6.4.3 to the number of RNA transcript reads that align to authentically expressed L1s determined in sections 4&#8211;5.</li>
	</ol>
	</li>
</ol>

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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=Novel+types+of+mutation+in+the+choroideremia+(CHM)+gene:+a+full-length+L1+insertion+and+an+intronic+mutation+activating+a+cryptic+exon.">Novel types of mutation in the choroideremia (CHM) gene: a full-length L1 insertion and an intronic mutation activating a cryptic exon.</a> Human Genetics. 113 (3), 268-275 (2003).</li><li>Min&#233;, 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+large+genomic+deletion+in+the+PDHX+gene+caused+by+the+retrotranspositional+insertion+of+a+full-length+LINE-1+element.">A large genomic deletion in the PDHX gene caused by the retrotranspositional insertion of a full-length LINE-1 element.</a> Human Mutation. 28 (2), 137-142 (2007).</li><li>Solyom, S., 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=Pathogenic+orphan+transduction+created+by+a+nonreference+LINE-1+retrotransposon.">Pathogenic orphan transduction created by a nonreference LINE-1 retrotransposon.</a> Human Mutation. 33 (2), 369-371 (2012).</li><li>Hancks, D. C., Kazazian, H. H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Roles+for+retrotransposon+insertions+in+human+disease.+Mobile+DNA.">Roles for retrotransposon insertions in human disease. Mobile DNA.</a> Mobile DNA. 7, 9-9 (2016).</li><li>Tubio, J. M. 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=Mobile+DNA+in+cancer.+Extensive+transduction+of+nonrepetitive+DNA+mediated+by+L1+retrotransposition+in+cancer+genomes.">Mobile DNA in cancer. Extensive transduction of nonrepetitive DNA mediated by L1 retrotransposition in cancer genomes.</a> Science. 345 (6196), 1251343-1251343 (2014).</li><li>Ewing, A. 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=Widespread+somatic+L1+retrotransposition+occurs+early+during+gastrointestinal+cancer+evolution.">Widespread somatic L1 retrotransposition occurs early during gastrointestinal cancer evolution.</a> Genome Research. 25 (10), 1536-1545 (2015).</li><li>Beck, C. R., Garcia-Perez, J. L., Badge, R. M., Moran, J. V. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=LINE-1+elements+in+structural+variation+and+disease.">LINE-1 elements in structural variation and disease.</a> Annual Review of Genomics and Human Genetics. 12, 187-215 (2011).</li><li>Philippe, 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=Activation+of+individual+L1+retrotransposon+instances+is+restricted+to+cell-type+dependent+permissive+loci.">Activation of individual L1 retrotransposon instances is restricted to cell-type dependent permissive loci.</a> eLife. 5, e13926 (2016).</li><li>Deininger, P., 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+comprehensive+approach+to+expression+of+L1+loci.">A comprehensive approach to expression of L1 loci.</a> Nucleic Acids Research. 45 (5), e31-e31 (2017).</li><li>Jin, Y., Tam, O. H., Paniagua, E., Hammell, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=TEtranscripts:+a+package+for+including+transposable+elements+in+differential+expression+analysis+of+RNA-seq+datasets.">TEtranscripts: a package for including transposable elements in differential expression analysis of RNA-seq datasets.</a> Bioinformatics. 31 (22), 3593-3599 (2015).</li><li>. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Agilent RNA 6000 Nano Kit Guide. , (2017).</li><li>Mueller, O. L., Schroeder, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> RNA Integrity Number (RIN) &#8211;Standardization of RNA Quality Control. , (2016).</li><li>Robinson, J. T., 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=Integrative+genomics+viewer.">Integrative genomics viewer.</a> Nature Biotechnology. 29, 24 (2011).</li><li>Speek, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Antisense+promoter+of+human+L1+retrotransposon+drives+transcription+of+adjacent+cellular+genes.">Antisense promoter of human L1 retrotransposon drives transcription of adjacent cellular genes.</a> Molecular Cellular Biology. 21 (6), 1973-1985 (2001).</li><li>Belancio, V. P., Deininger, L., Roy-Engel, A. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=LINE+dancing+in+the+human+genome:+transposable+elements+and+disease.">LINE dancing in the human genome: transposable elements and disease.</a> Genome Medicine. 1 (10), 97-97 (2009).</li><li>Iskow, R. 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=Natural+Mutagenesis+of+Human+Genomes+by+Endogenous+Retrotransposons.">Natural Mutagenesis of Human Genomes by Endogenous Retrotransposons.</a> Cell. 141 (7), 1253-1261 (2010).</li><li>Scott, 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=A+hot+L1+retrotransposon+evades+somatic+repression+and+initiates+human+colorectal+cancer.">A hot L1 retrotransposon evades somatic repression and initiates human colorectal cancer.</a> Genome Research. 26 (6), 745-755 (2016).</li><li>Kines, K. J., Sokolowski, M., deHaro, D. L., Christian, C. M., Belancio, V. P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Potential+for+genomic+instability+associated+with+retrotranspositionally-incompetent+L1+loci.">Potential for genomic instability associated with retrotranspositionally-incompetent L1 loci.</a> Nucleic Acids Research. 42 (16), 10488-10502 (2014).</li></ol>

The authors have nothing to disclose.

L1 activity has been shown to cause genetic damage and instability contributing to disease27,28,29. Of the approximately 5,000 full-length L1 copies, only a few dozen evolutionarily young L1s account for the majority of retrotransposition activity2. However, there is evidence that even some older, retrotranspositionally-incompentent L1s are still able to produce DNA damaging proteins30. To fully appreciate the role of L1s in genomic instability and disease, L1 expression at the locus-specific level must be understood. However, the high background of L1-related sequences incorporated into other RNAs unrelated to L1 retrotransposition poses a significant challenge in interpreting authentic L1 expression. Another challenge in identifying and therefore understanding expression patterns of individual L1 loci occurs because of their repetitive nature that does not allow many short read sequences to map to a single unique locus. To overcome these challenges, we developed the above-described approach in identifying expression of individual L1 loci using RNA-Seq data.
Our approach filters the high level (over 99%) of transcriptional noise generated from L1 sequences that are unrelated to L1 retrotransposition by taking a number of steps. The first step involves the preparation of cytoplasmic RNA. By selecting for cytoplasmic RNA, L1-related reads found within expressed intronic mRNA in the nucleus are significantly depleted. In the sequencing library preparation, another step taken to reduce transcriptional noise unrelated to L1s include the selection of polyadenylated transcripts. This removes L1-related transcript noise found in non-mRNA species. Another step includes strand-specific sequencing in order to identify and eliminate antisense L1-related transcripts. The use of an annotation for full-length L1s with functional promoter regions when identifying the number of RNA-Seq transcripts that map to L1s also eliminates background noise that otherwise originate from truncated L1s. Finally, the last critical step in eliminating transcriptional noise of L1 sequences unrelated to L1 retrotransposition is the manual curation of full-length L1s identified to have mapped RNA-Seq transcripts. The manual curation involves the visualization of each bioinformatically identified-to-be-expressed L1 locus in the context of its surrounding genomic environment to confirm that expression originates from the L1 promoter. This approach was applied to DU145, a prostate tumor cell line. Even with all the preparation-related steps taken to reduce background noise, approximately 50% of L1 loci identified bioinformatically in DU145 were rejected as L1 background noise originating from other transcriptional sources (Figure 4), emphasizing the rigor required to produce reliable results. This approach using manual curation is labor intensive, but necessary in the development of this pipeline to evaluate and understand the genomic environment surrounding a full-length L1. The next steps include reducing the amount of necessary manual curation by automating some of the curation rules, though due to the still not completely known nature of genomic expression, un-annotated sources of expression in the reference genome, regions of low mappability, and even complicating factors involved with the construction of a reference genome it is not be possible to fully automate L1 curation at this time.
The second challenge in identifying expression of individual L1 loci with sequencing relates to the mapping of repetitive L1 transcripts. In this alignment strategy, it is required that a transcript must align uniquely and co-linearly to the reference genome in order to be mapped. By selecting for paired-end sequences that map concordantly, the amount of transcripts that uniquely align to L1 loci found in the reference genome increases. This unique-mapping strategy provides confidence in the calling of reads mapping specifically to a single L1 locus, though it potentially underestimates the expression quantity of each identified-to-be-authentically expressed, repetitive L1. To approximately correct for this underestimation, a &#8220;mappability&#8221; score for each L1 locus based on its mappability was developed and applied to the number of uniquely mapped transcript reads (Figure 6). It is of note that ideally, mappability should be scored to full coverage reads across the full-length L1 according to the matched WGS sample. Here, we use WGS of HeLa cells to determine mappability scores of each L1 loci in order to inflate or deflate reads mapping to L1 loci in DU145 prostate tumor cell lines. This mappability calculation is a crude correction score, but the chosen &#8216;complete coverage mappability&#8217; of 400 reads was determined with the dynamic nature of tumor cell lines in mind. It can be observed in Supplemental Figure 1, that there are a few L1 loci with HeLa WGS with extremely high number of mapped reads. These likely come from duplicated chromosome sequences within HeLa that are not within the reference genome, which is why those loci were not chosen to be representative of complete mappability coverage. Instead it was determined that the average of 100% read coverage occurs around 400 reads according to Supplemental Figure 1 and was then assumed that this average applies to the DU145 tumor prostate cell line as well.
This alignment strategy with 100-200 bp reads from RNA-Seq technology also preferentially selects for evolutionarily older L1s within the reference genome as older L1s have accumulated over time unique mutations that make them more mappable. This approach, therefore, has limited sensitivity when it comes to identifying the youngest of L1s as well as non-reference, polymorphic L1s. To identify the youngest of L1s, we suggest using 5&#8217; RACE selection of L1 transcripts and sequencing technology like PacBio that make use of longer reads21. This permits more unique mapping and therefore confident identification of the expressed, young L1s. Using RNA-Seq and PacBio approaches together can lead to a more comprehensive list of authentically expressed L1s. To identify authentically expressed polymorphic L1s, the first next steps include construction and insertion of polymorphic sequences into the reference genome.
The biological and technical challenges in studying repeat sequences are great, though with the above rigorous procedure to remove transcriptional noise of L1 sequences un-related to retrotransposition using RNA-sequencing technology, we begin to sift through the large levels of transcriptional background noise and being to confidently and stringently identify L1 expression patterns and quantity at the individual locus level.

Retrotransposons are repetitive DNA elements that can &#8220;jump&#8221; in the genome in a copy-and-paste mechanism via RNA intermediates. One subset of retrotransposons is known as Long INterspersed Elements-1 (LINEs/L1s) and makes up a sixth of the human genome with over 500,0000 copies1. Despite their abundance, most of these copies are defective and truncated with only an estimated 80-120 L1 elements thought to be active2. A full-length L1 is about 6 kb in length with 5&#8217; and 3&#8217; untranslated regions, an internal promoter and associated anti-sense promoter, two non-overlapping open-reading frames (ORFs), and a signal and polyA tail3,4,5. In humans, L1s are made up of subfamilies distinguished by evolutionary age with the older families having accumulated more unique sequence mutations over time compared to the youngest subfamily, L1HS6,7. L1s are the only autonomous, human retrotransposons and their ORFs encode a reverse transcriptase, endonuclease, and RNPs with RNA-binding and chaperone activities required to retrotranspose and insert in the genome in a process referred to as target-primed reverse transcription8,9,10,11,12.
Retrotransposition of L1s has been reported to cause human germline diseases by a variety of mechanisms including insertional mutagenesis, target-site deletions, and rearrangements13,14,15,16. Recently it has been hypothesized that L1s may play a role in oncogenesis and/or tumor progression as increased expression and insertion events of this mutagenic element have been observed in a variety of epithelial cancers17,18. It is estimated that there is one new L1 insertion in every 200 births19. Therefore, it is imperative to better understand the biology of the actively expressing L1s. The repetitive nature and abundance of defective copies found within transcripts of other genes have made this level of analysis challenging.
Fortunately, with the advent of high throughput sequencing technologies, strides have been made to parse out and identify authentically expressing L1s at the locus-specific level. There are differing philosophies on how to best identify expressed L1s using RNA next-generation sequencing. There have been only two reasonable approaches suggested for mapping L1 transcripts at the locus-specific level. One focuses only on the potential transcription that reads through the L1 polyadenylation signal and into flanking sequences20. Our approach takes advantage of small sequence differences between L1 elements and only maps those RNA-Seq reads that uniquely map to one locus21. Both of these methods have limitations in terms of quantitation of transcript levels. Quantitation can be improved potentially by adding a correction for the &#8216;unique mappability&#8217; of each L1 locus21, or using more complex algorithms that redistribute the multi-mapped reads that could not be uniquely mapped to a specific locus22. Here, we will detail in a step-by-step manner the RNA extraction and next-generation sequencing and bioinformatics protocol to identify expressed L1 elements at the locus-specific level. Our approach takes maximal advantage of our knowledge of the biology of functional L1 elements. This includes knowing that functional L1 elements must be generated from the L1 promoter, initiated at the beginning of the L1 element, must be translated in the cytoplasm and that their transcripts should be co-linear with the genome. Briefly, we collect fresh, cytoplasmic RNA, select for polyadenylated transcripts, and utilize strand-specific RNA-Seq analyses to uniquely map reads to L1 loci in the human reference genome. These aligned reads then still require extensive manual curation to determine if transcript reads originate from the L1 promoter before designating a locus as an authentically expressed L1. We apply this approach on the DU145 prostate tumor cell line sample to demonstrate how it identifies a relatively few actively transcribed L1 members from the mass of inactive copies.

<table><tbody><tr><td>1 M HEPES</td><td>Affymetrix</td><td>AAJ16924AE</td><td></td></tr><tr><td>5 M NaCl</td><td>Invitrogen</td><td>AM9760G</td><td></td></tr><tr><td>Agilent bioanalyzer 2100</td><td>Agilent technologies</td><td></td><td></td></tr><tr><td>Agilent RNA 6000 Nano Kit</td><td>Agilent technologies</td><td>5067-1511</td><td></td></tr><tr><td>bedtools.26.0</td><td></td><td></td><td>https://bedtools.readthedocs.io/en/latest/content/installation.html</td></tr><tr><td>bowtie-0.12.8</td><td></td><td></td><td>https://sourceforge.net/projects/bowtie-bio/files/bowtie/0.12.8/</td></tr><tr><td>Cell scraper</td><td>Olympus plastics</td><td>25-270</td><td></td></tr><tr><td>Chloroform</td><td>Fisher</td><td>C298-500</td><td></td></tr><tr><td>Digitonin</td><td>Research Products International Corp</td><td>50-488-644</td><td></td></tr><tr><td>Ethanol</td><td>Fisher</td><td>A4094</td><td></td></tr><tr><td>Gibco (Phosphate Buffered Saline)</td><td>Invitrogen</td><td>10-010-049</td><td></td></tr><tr><td>Homogenizer</td><td>Thomas Scientific</td><td>BBI-8541906</td><td></td></tr><tr><td>IGV 2.4</td><td></td><td></td><td>https://software.broadinstitute.org/software/igv/download</td></tr><tr><td>Isopropanol</td><td>Fisher</td><td>A416-500</td><td></td></tr><tr><td>mac2unix</td><td></td><td></td><td>https://sourceforge.net/projects/cs-cmdtools/files/mac2unix/</td></tr><tr><td>Q-tips</td><td>Fisher</td><td>23-400-122</td><td></td></tr><tr><td>RNAse later solution</td><td>Invitrogen</td><td>AM7022</td><td></td></tr><tr><td>RNaseZap RNase Decontamination Solution</td><td>Invitrogen</td><td>AM9780</td><td></td></tr><tr><td>samtools-1.3</td><td></td><td></td><td>https://sourceforge.net/projects/samtools/files/</td></tr><tr><td>sratoolkit.2.9.2</td><td></td><td></td><td>https://github.com/ncbi/sra-tools/wiki/Downloads</td></tr><tr><td>SUPERase&amp;middot;In RNase Inhibitor</td><td>Invitrogen</td><td>AM2694</td><td></td></tr><tr><td>Trizol</td><td>Invitrogen</td><td>15-596-018</td><td></td></tr><tr><td>Water (DNASE, RNASE free)</td><td>Fisher</td><td>BP2484100</td><td></td></tr></tbody></table>

rna next-generation sequencing and a bioinformatics pipeline to identify expressed line-1s at the locus-specific level

Long INterspersed Elements-1 (LINEs/L1s) are repetitive elements that can copy and randomly insert in the genome resulting in genomic instability and mutagenesis. Understanding the expression patterns of L1 loci at the individual level will lend to the understanding of the biology of this mutagenic element. This autonomous element makes up a significant portion of the human genome with over 500,000 copies, though 99% are truncated and defective. However, their abundance and dominant number of defective copies make it challenging to identify authentically expressed L1s from L1-related sequences expressed as part of other genes. It is also challenging to identify which specific L1 locus is expressed due to the repetitive nature of the elements. Overcoming these challenges, we present an RNA-Seq bioinformatic approach to identify L1 expression at the locus specific level. In summary, we collect cytoplasmic RNA, select for polyadenylated transcripts, and utilize strand-specific RNA-Seq analyses to uniquely map reads to L1 loci in the human reference genome. We visually curate each L1 locus with uniquely mapped reads to confirm transcription from its own promoter and adjust mapped transcript reads to account for mappability of each individual L1 locus. This approach was applied to a prostate tumor cell line, DU145, to demonstrate the ability of this protocol to detect expression from a small number of the full-length L1 elements.

The steps described above and described graphically in Figure 1 were applied to a human prostate tumor cell line DU145. The RNA sample was cytoplasmically prepped and was next-gen sequenced in a poly-A selected, strand-specific, paired-end protocol. Using Bowtie, the paired-end sequencing files were aligned allowing only unique matches in which the paired-end read matched better to one genomic location compared to any other genomic location. The DU145 sequence files were aligned to the human reference genome creating a bam file, which is available upon author request. Using bedtools, data was extracted from the DU145 strand-separated bam files on the number of reads that mapped to full length L1s. Those reads were sorted in a spreadsheet from largest to smallest and manually curated by examining the genomic environment around each L1 locus in IGV to confirm its authenticity (Supplemental Table 1). If a sample was curated to be authentically expressed, it was color-coded green with an explanation for its acceptance in the right most column. Examples of L1 loci accepted to be authentically expressed following guidelines described in the methods section are shown in Figure 2a-b. If a sample was rejected to be authentically expressed, it was color-coded as red with the reason for rejection on the right most column. Examples of L1 loci rejected because of expression from a promoter other than their own following guidelines described in the methods section are detailed in Figure 2c-e.
Here, only full-length L1s with an intact promoter region were studied. If this distinction is not made, a large source of transcriptional noise originating from truncated L1s is introduced. Examples of truncated L1s in DU145 are shown in Figure 3a-b where they were identified as having uniquely mapped RNA-Seq reads. In IGV, however, it is apparent that those transcripts were not initiated from the truncated L1, but from the inclusion of the L1 sequence in a gene or downstream from an expressed gene.
Overall in DU145, the percentage of full-length L1 loci and reads that are rejected as authentically expressed L1s after manual curation is approximately 50% (Supplemental Table 2) demonstrating the high level of L1 mapped transcript reads that would otherwise be recorded as false positives without manual curation. Specifically, in DU145 there were 114 total full-length L1 loci to have uniquely mapped reads in the sense direction with a total of 3,152 reads, but there were only 60 loci identified to be expressed off their own promoter after manual curation with 1,879 reads (Supplemental Table 1). This is the case even when steps were taken to reduce expression irrelevant to L1 biology by selecting for cytoplasmic mRNA. Note that the locus with the highest level of mapped transcripts in DU145 was rejected because it was not an authentically expressed L1 (Figure 4). Overall the number of mapped transcripts to specific L1 loci ranges similarly between the accepted and rejected L1 loci as authentically expressed after manual curation (Figure 4).
After manual curation, the number of reads that map uniquely to authentically expressed specific L1 loci in DU145 range from 175 reads to an arbitrarily chosen minimum cut off of 10 reads (Figure 5). This approach of identifying uniquely mapped transcript reads to L1s limits the ability to accurately quantify expression. To account for this, a correction factor for each locus based on its mappability was created. To create this correction factor, first bedtools was used to extract the number of uniquely mapped reads from the HeLa genomic bam file that aligned to all full-length L1 loci and graphed those loci from highest to lowest mapped transcript reads (Supplemental Figure 1). It was arbitrarily designated that L1s with 400 reads had full coverage mappability. The number of reads able to map to a L1 locus in HeLa genomic sequencing sample was scaled relative to 400 reads and that scaled number was then multiplied to the number of reads that mapped to each authentically expressed L1 loci in DU145 (Supplemental Table 2). As expected, the L1 elements that had larger correction scores for mappability came from younger subfamilies like L1PA2 (Supplemental Table 2). Once reads were adjusted for mappability scores in each locus, the quantitation for expression for most loci increased (Figure 6). The number of reads that mapped uniquely to authentically expressed specific L1 loci with mappability corrections in DU145 ranged from 612 to 4 reads and there was a re-ordering of highest to lowest expressing loci (Figure 6).
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/59771/59771fig01v2.jpg" /> 
Figure 1: Workflow schematic. 
Graphically described are the steps to identify expressed L1s in a human sample. Note that steps 1 and 2 do not need to be repeated if the appropriate files are already available. These appropriate files may be downloaded from Supplement File 1a-b and Supplement File 2. The boxes in red indicate the steps where bedtools coverage program is used to count the number of reads mapping to L1s in the same sense direction. These loci with sense oriented mapping reads are the L1s that should be manually curated. <a href="https://www.jove.com/files/ftp_upload/59771/59771fig01large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/59771/59771fig02v2.jpg" /> 
Figure 2: Examples of curated L1 loci in DU145. 
Loaded into IGV are the reference genome, the full-length L1 gff annotation file matching the reference genome version (Supplement File 1), the DU145 bam file, and lastly the genomic HeLa bam file to assess mappability, which are all available upon author request. Arrows have been added to aid in the visualization of direction of the annotated L1. Arrows and reads in red are oriented in sequence from right to left. Arrows and reads in blue are oriented in sequence from left to right. a) In IGV, this L1 locus appears to be expressed off its own promoter as there are no reads upstream of the L1 in the sense orientation for over 5 kb. This L1 has low mappability, it is not in a gene, and has evidence of expected antisense promoter activity26. b) In IGV, this L1 locus appears to be expressed off its own promoter as there are no reads upstream the L1 in the sense orientation for over 5 kb. This L1 has low mappability and is within a gene of opposite direction. c) In IGV, this L1 locus was rejected as an expressed L1 as there are upstream reads in the same orientation within 5 kb. This L1 is within a gene of the same direction so the transcript reads are most likely originating from the promoter of the expressed gene. d) In IGV, this L1 locus was rejected as an expressed L1 as there are upstream reads in the same orientation within 5 kb. This L1 is downstream of a highly expressed gene in the same direction so the transcript reads are most likely originating from the promoter of that expressed gene and extending beyond the normal gene terminator. e) In IGV, this L1 locus was rejected as an expressed L1 as there are upstream reads in the same orientation within 5 kb. This L1 is not within or near an annotated gene in the reference gene so the origin of these transcripts within and upstream of the L1 element suggest an un-annotated promoter. <a href="https://www.jove.com/files/ftp_upload/59771/59771fig02large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/59771/59771fig03v2.jpg" /> 
Figure 3: Background noise originates from truncated L1s as well. 
Our L1 annotation does not include truncated L1s as they are a major source of background noise. Arrows have been added to aid in the visualization of direction of the annotated L1. Arrows and reads in blue are oriented in sequence from left to right. a) Demonstrated is an example of a truncated L1 in the L1MB5 sufamily that is 2706 bps. In IGV it is apparent that the reads originate from downstream extension of an expressed gene. b) Shown is another example of a truncated L1. This L1 is an L1PA11 that is 4767 bps long. In IGV it is apparent that the reads mapping uniquely to the L1 originate from the expressed exon, which the L1 is within. <a href="https://www.jove.com/files/ftp_upload/59771/59771fig03large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/59771/59771fig04v2.jpg" /> 
Figure 4: Transcript reads that map uniquely to all full-length intact L1s in the human genome expressed in DU145 prostate tumor cell line. 
In black are the specific loci to be identified as authentically expressed after manual curation and in red are the specific loci to be rejected as authentically expressed reads after manual curation. In grey are loci with less than ten reads mapping to each. As these loci represent a small fraction of transcript reads, they were not manually curate. The x-axis tick marks denote every 100 full-length, intact L1s. Approximately 4,500 loci are not graphically shown as they had zero mapped reads. <a href="https://www.jove.com/files/ftp_upload/59771/59771fig04large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 5" class="xfigimg" src="/files/ftp_upload/59771/59771fig05v2.jpg" /> 
Figure 5: Transcript reads that map uniquely to authentically expressed full-length intact L1s in DU145 prostate tumor cell line. 
Shown are the numbers of transcript reads that map to specific loci in DU145 cells after manual curation. <a href="https://www.jove.com/files/ftp_upload/59771/59771fig05large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 6" class="xfigimg" src="/files/ftp_upload/59771/59771fig06v2.jpg" /> 
Figure 6: Reads mapping to authentically expressed L1 when adjusted by mappability. 
Shown are the numbers of transcript reads adjusted by loci-specific mappability scores that map to manually curated L1 loci in DU145 cells. <a href="https://www.jove.com/files/ftp_upload/59771/59771fig06large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
Supplemental File 1: Annotations for full-length, intact human L1s according to orientation. a) FL-L1-BLAST_RM_minus.gff. b) FL-L1-BLAST_RM_plus.gff. <a href="https://www.jove.com/files/ftp_upload/59771/SupplementalFile1.zip" target="_blank">Please click here to download this file.</a>
Supplemental File 2: Supercomputer scripts used to automate the bioinformatics pipeline detailed in section 4. <a href="https://www.jove.com/files/ftp_upload/59771/SupplementalFile_2.docx" target="_blank">Please click here to download this file.</a>
Supplemental Figure 1: Genomic DNA sample used to determine L1 mappability. 
Shown are the number of genomic transcript reads from HeLa cell line sample that map uniquely to all 5,000 full-length L1 loci in the genome. It was designated that an L1 has full coverage mappability when 400 reads map to the L1.&#160;<a href="https://www.jove.com/files/ftp_upload/59771/SupplementalFigure_1.pdf" target="_blank">Please click here to download this figure.</a>
Supplemental Table 1: Manual Curation of L1s in DU145. <a href="https://www.jove.com/files/ftp_upload/59771/Supplemental_Table_1.xlsx" target="_blank">Please click here to download this table.</a>
Supplemental Table 2: Curated L1s in DU145 with mappability adjustment. <a href="https://www.jove.com/files/ftp_upload/59771/Supplemental_Table_2.xlsx" target="_blank">Please click here to download this table.</a>

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RNA Next-Generation Sequencing and a Bioinformatics Pipeline to Identify Expressed LINE-1s at the Locus-Specific Level

Here, we present a bioinformatic approach and analyses to identify LINE-1 expression at the locus specific level.

Long INterspersed Elements-1 (LINEs/L1s) are repetitive elements that can copy and randomly insert in the genome resulting in genomic instability and ...

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DNA Repair and Recombination

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9.9K Views.  Tulane University. Mobiele elementen zijn een van de belangrijkste bronnen van menselijke genetische instabiliteit. Inzicht in hun expressie in verschillende weefsels en omstandigheden is van cruciaal belang om hun impact op het genoom te begrijpen. De enorme L1 transcripties zijn het resultaat van passieve opname van L1-gerelateerde sequenties in andere transcripties die geen rol spelen in de L1 levenscyclus.Onze aanpak elimineert deze irrelevante achtergrond. Dit protocol kan worden aangepast aan studi...

Video: RNA Next-Generation Sequencing and a Bioinformatics Pipeline to Identify Expressed LINE-1s at the Locus-Specific Level

Using RNA-sequencing to Detect Novel Splice Variants Related to Drug Resistance in In Vitro Cancer Models

Drug resistance remains a major problem in the treatment of cancer for both hematological malignancies and solid tumors. Intrinsic or acquired resistance can be caused by a range of mechanisms, including increased drug elimination, decreased drug uptake, drug inactivation and alterations of drug targets. Recent data showed that other than by well-known genetic (mutation, amplification) and epigenetic (DNA hypermethylation, histone post-translational modification) modifications, drug resistance mechanisms might also be regulated by splicing aberrations. This is a rapidly growing field of investigation that deserves future attention in order to plan more effective therapeutic approaches. The protocol described in this paper is aimed at investigating the impact of aberrant splicing on drug resistance in solid tumors and hematological malignancies. To this goal, we analyzed the transcriptomic profiles of several in vitro models through RNA-seq and established a qRT-PCR based method to validate candidate genes. In particular, we evaluated the differential splicing of DDX5 and PKM transcripts. The aberrant splicing detected by the computational tool MATS was validated in leukemic cells, showing that different DDX5 splice variants are expressed in the parental vs. resistant cells. In these cells, we also observed a higher PKM2/PKM1 ratio, which was not detected in the Panc-1 gemcitabine-resistant counterpart compared to parental Panc-1 cells, suggesting a different mechanism of drug-resistance induced by gemcitabine exposure.

Using RNA-sequencing to Detect Novel Splice Variants Related to Drug Resistance in In Vitro Cancer Models

Here we describe a protocol aimed at investigating the impact of aberrant splicing on drug resistance in solid tumors and hematological malignancies. To this goal, we analyzed the transcriptomic profiles of parental and resistant in vitro models through RNA-seq and established a qRT-PCR based method to validate candidate genes.

Drug resistance remains a major problem in the treatment of cancer for both hematological malignancies and solid tumors. Intrinsic or acquired resistance ...

Cancer Research

Real-time Analysis of Transcription Factor Binding, Transcription, Translation, and Turnover to Display Global Events During Cellular Activation

Upon activation, cells rapidly change their functional programs and, thereby, their gene expression profile. Massive changes in gene expression occur, for example, during cellular differentiation, morphogenesis, and functional stimulation (such as activation of immune cells), or after exposure to drugs and other factors from the local environment. Depending on the stimulus and cell type, these changes occur rapidly and at any possible level of gene regulation. Displaying all molecular processes of a responding cell to a certain type of stimulus/drug is one of the hardest tasks in molecular biology. Here, we describe a protocol that enables the simultaneous analysis of multiple layers of gene regulation. We compare, in particular, transcription factor binding (Chromatin-immunoprecipitation-sequencing (ChIP-seq)), de novo transcription (4-thiouridine-sequencing (4sU-seq)), mRNA processing, and turnover as well as translation (ribosome profiling). By combining these methods, it is possible to display a detailed and genome-wide course of action.
Sequencing newly transcribed RNA is especially recommended when analyzing rapidly adapting or changing systems, since this depicts the transcriptional activity of all genes during the time of 4sU exposure (irrespective of whether they are up- or downregulated). The combinatorial use of total RNA-seq and ribosome profiling additionally allows the calculation of RNA turnover and translation rates. Bioinformatic analysis of high-throughput sequencing results allows for many means for analysis and interpretation of the data. The generated data also enables tracking co-transcriptional and alternative splicing, just to mention a few possible outcomes.
The combined approach described here can be applied for different model organisms or cell types, including primary cells. Furthermore, we provide detailed protocols for each method used, including quality controls, and discuss potential problems and pitfalls.

This protocol describes the combinatorial use of ChIP-seq, 4sU-seq, total RNA-seq, and ribosome profiling for cell lines and primary cells. It enables tracking changes in transcription-factor binding, de novo transcription, RNA processing, turnover and translation over time, and displaying the overall course of events in activated and/or rapidly changing cells.

Upon activation, cells rapidly change their functional programs and, thereby, their gene expression profile. Massive changes in gene expression occur, for ...

Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease

Next-generation sequencing (NGS) is quickly revolutionizing how research into the genetic determinants of constitutional disease is performed. The technique is highly efficient with millions of sequencing reads being produced in a short time span and at relatively low cost. Specifically, targeted NGS is able to focus investigations to genomic regions of particular interest based on the disease of study. Not only does this further reduce costs and increase the speed of the process, but it lessens the computational burden that often accompanies NGS. Although targeted NGS is restricted to certain regions of the genome, preventing identification of potential novel loci of interest, it can be an excellent technique when faced with a phenotypically and genetically heterogeneous disease, for which there are previously known genetic associations. Because of the complex nature of the sequencing technique, it is important to closely adhere to protocols and methodologies in order to achieve sequencing reads of high coverage and quality. Further, once sequencing reads are obtained, a sophisticated bioinformatics workflow is utilized to accurately map reads to a reference genome, to call variants, and to ensure the variants pass quality metrics. Variants must also be annotated and curated based on their clinical significance, which can be standardized by applying the American College of Medical Genetics and Genomics Pathogenicity Guidelines. The methods presented herein will display the steps involved in generating and analyzing NGS data from a targeted sequencing panel, using the ONDRISeq neurodegenerative disease panel as a model, to identify variants that may be of clinical significance.

Targeted next-generation sequencing is a time- and cost-efficient approach that is becoming increasingly popular in both disease research and clinical diagnostics. The protocol described here presents the complex workflow required for sequencing and the bioinformatics process used to identify genetic variants that contribute to disease.

Next-generation sequencing (NGS) is quickly revolutionizing how research into the genetic determinants of constitutional disease is performed. The ...

Detection of Retrotransposition Activity of Hot LINE-1s by Long-Distance Inverse PCR

Long interspersed nuclear elements 1 (LINE-1s) are the only family of mobile genetic elements in the human genome that can move autonomously. They do so by a process called retrotransposition wherein they transcribe to form an mRNA intermediate which is then consequently inserted into the genome by reverse transcription. Despite being silent in normal cells, LINE-1s are highly active in different epithelial tumors. De novo LINE-1 insertions can potentially drive tumorigenesis, and hence it is important to systematically study LINE-1 retrotransposition in cancer. Out of ~150 retrotransposition-competent LINE-1s present in the human genome, only a handful of LINE-1 loci, also referred to as &#34;hot&#34; LINE-1s, account for the majority of de novo LINE-1 insertion in different cancer types. We have developed a simple polymerase chain reaction (PCR)-based method to monitor retrotransposition activity of these hot LINE-1s. This method, based on long-distance inverse (LDI)-PCR, takes advantage of 3&#180; transduction, a mechanism by which a LINE-1 mobilizes its flanking non-repetitive region, which can subsequently be used to identify de novo LINE-1 3&#180; transduction events stemming from a particular hot LINE-1.

This article outlines a simple PCR-based assay to monitor the activity of an active LINE-1 retrotransposon and to map de novo retrotranspositions in a given genome. Using the MCF7 cell line, we demonstrate herein how this method can be applied to detect activity of a LINE-1 located at 22q12.1.

Long interspersed nuclear elements 1 (LINE-1s) are the only family of mobile genetic elements in the human genome that can move autonomously. They do so ...

Mapping R-Loops and RNA:DNA Hybrids with S9.6-Based Immunoprecipitation Methods

R-loops constitute a prevalent class of transcription-driven non-B DNA structures that occur in all genomes depending on both DNA sequence and topological favorability. In recent years, R-loops have been implicated in a variety of adaptive and maladaptive roles and have been linked to genomic instability in the context of human disorders. As a consequence, the accurate mapping of these structures in genomes is of high interest to many investigators. DRIP-seq (DNA:RNA Immunoprecipitation followed by high throughput sequencing) is described here. It is a robust and reproducible technique that permits accurate and semi-quantitative mapping of R-loops. A recent iteration of the method is also described in which fragmentation is accomplished using sonication (sDRIP-seq), which allows strand-specific and high-resolution mapping of R-loops. sDRIP-seq thus addresses some of the common limitations of the DRIP-seq method in terms of resolution and strandedness, making it a method of choice for R-loop mapping.

R-loops constitute a prevalent class of transcription-driven non-B DNA structures that occur in all genomes depending on both DNA sequence and topological ...

Identification of Key Factors Regulating Self-renewal and Differentiation in EML Hematopoietic Precursor Cells by RNA-sequencing Analysis

Hematopoietic stem cells (HSCs) are used clinically for transplantation treatment to rebuild a patient&#39;s hematopoietic system in many diseases such as leukemia and lymphoma. Elucidating the mechanisms controlling HSCs self-renewal and differentiation is important for application of HSCs for research and clinical uses. However, it is not possible to obtain large quantity of HSCs due to their inability to proliferate in vitro. To overcome this hurdle, we used a mouse bone marrow derived cell line, the EML (Erythroid, Myeloid, and Lymphocytic) cell line, as a model system for this study.
RNA-sequencing (RNA-Seq) has been increasingly used to replace microarray for gene expression studies. We report here a detailed method of using RNA-Seq technology to investigate the potential key factors in regulation of EML cell self-renewal and differentiation. The protocol provided in this paper is divided into three parts. The first part explains how to culture EML cells and separate Lin-CD34+ and Lin-CD34- cells. The second part of the protocol offers detailed procedures for total RNA preparation and the subsequent library construction for high-throughput sequencing. The last part describes the method for RNA-Seq data analysis and explains how to use the data to identify differentially expressed transcription factors between Lin-CD34+ and Lin-CD34- cells. The most significantly differentially expressed transcription factors were identified to be the potential key regulators controlling EML cell self-renewal and differentiation. In the discussion section of this paper, we highlight the key steps for successful performance of this experiment.
In summary, this paper offers a method of using RNA-Seq technology to identify potential regulators of self-renewal and differentiation in EML cells. The key factors identified are subjected to downstream functional analysis in vitro and in vivo.

RNA-sequencing and bioinformatics analyses were used to identify significantly and differentially expressed transcription factors in Lin-CD34+ and Lin-CD34- subpopulations of mouse EMLcells. These transcription factors might play important roles in determining the switch between self-renewing Lin-CD34+ and partially differentiated Lin-CD34- cells.

Hematopoietic stem cells (HSCs) are used clinically for transplantation treatment to rebuild a patient&#39;s hematopoietic system in many diseases such as ...

Biology

Analysis of LINE-1 Retrotransposition at the Single Nucleus Level

Long interspersed nuclear element-1 (Line-1 or L1) accounts for approximately 17% of the DNA present in the human genome. While the majority of L1s are inactive due to 5' truncations, ~80-100 of these elements remain retrotransposition competent and propagate to different locations throughout the genome via RNA intermediates. While older L1s are believed to target AT rich regions of the genome, the chromosomal targets of newer, more active L1s remain poorly defined. Here we describe fluorescence in situ hybridization (FISH) methodology that can be used to track patterns of L1 insertion and rates of ectopic L1 incorporation at the single nucleus level. In these experiments, fluorescein isothiocyanate/cyanine-3 (FITC/CY3) labeled neomycin probes were employed to track L1 retrotransposition in vitro in HepG2 cells stably expressing ectopic L1. This methodology prevents errors in the estimation of rates of retrotransposition posed by toxicity and account for the occurrence of multiple insertions into a single nucleus.

Here we employ FISH methodology to track LINE-1 retrotransposition at the single nuclei level in chromosome spreads of HepG2 cell lines stably expressing synthetic LINE-1.

Long interspersed nuclear element-1 (Line-1 or L1) accounts for approximately 17% of the DNA present in the human genome. While the majority of L1s are ...

Targeted RNA Sequencing Assay to Characterize Gene Expression and Genomic Alterations

RNA sequencing (RNAseq) is a versatile method that can be utilized to detect and characterize gene expression, mutations, gene fusions, and noncoding RNAs. Standard RNAseq requires 30 - 100 million sequencing reads and can include multiple RNA products such as mRNA and noncoding RNAs. We demonstrate how targeted RNAseq (capture) permits a focused study on selected RNA products using a desktop sequencer. RNAseq capture can characterize unannotated, low, or transiently expressed transcripts that may otherwise be missed using traditional RNAseq methods. Here we describe the extraction of RNA from cell lines, ribosomal RNA depletion, cDNA synthesis, preparation of barcoded libraries, hybridization and capture of targeted transcripts and multiplex sequencing on a desktop sequencer. We also outline the computational analysis pipeline, which includes quality control assessment, alignment, fusion detection, gene expression quantification and identification of single nucleotide variants. This assay allows for targeted transcript sequencing to characterize gene expression, gene fusions, and mutations.

We describe a targeted RNA sequencing-based method that includes preparation of indexed cDNA libraries, hybridization and capture with custom probes and data analysis to interrogate selected transcripts for gene expression, mutations, and gene fusions. Targeted RNAseq permits cost-effective, rapid evaluation of selected transcripts on a desktop sequencer.

RNA sequencing (RNAseq) is a versatile method that can be utilized to detect and characterize gene expression, mutations, gene fusions, and noncoding ...

Visualizing Genetic Variants, Short Targets, and Point Mutations in the Morphological Tissue Context with an RNA In Situ Hybridization Assay

Because precision medicine is highly dependent on the accurate detection of biomarkers, there is an increasing need for standardized and robust technologies that measure RNA biomarkers in situ in clinical specimens. While grind-and-bind assays like RNAseq and quantitative RT-PCR enable highly sensitive gene expression measurements, they also require RNA extraction and thus prevent valuable expression analysis within the morphological tissue context. The in situ hybridization (ISH) assay described here can detect RNA target sequences as short as 50 nucleotides at single-nucleotide resolution and at the single-cell level. This assay is complementary to the previously developed commercial assay and enables sensitive and specific in situ detection of splice variants, short targets, and point mutations within the tissue. In this protocol, probes were designed to target unique exon junctions for two clinically important splice variants, EGFRvIII and MET&#916;14. The detection of short target sequences was demonstrated by the specific detection of CDR3 sequences of T-cell receptors &#945; and &#946; in the Jurkat T-cell line. Also shown is the utility of this ISH assay for the distinction of RNA target sequences at single-nucleotide resolution (point mutations) through the visualization of EGFR L858R and KRAS G12A single-nucleotide variations in cell lines using automated staining platforms. In summary, the protocol shows a specialized RNA ISH assay that enables the detection of splice variants, short sequences, and mutations in situ for manual performance and on automated stainers.

Visualizing Genetic Variants, Short Targets, and Point Mutations in the Morphological Tissue Context with an RNA In Situ Hybridization Assay

Here, we describe an in situ hybridization assay which enables sensitive and specific detection of sequences as short as 50 nucleotides with single-nucleotide resolution at the single-cell level. The assay, which can be performed manually or automatically, can enable visualization of splice variants, short sequences, and mutations within the tissue context.

Because precision medicine is highly dependent on the accurate detection of biomarkers, there is an increasing need for standardized and robust ...

A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq

Distilling and reporting large datasets, such as whole genome or transcriptome data, is often a daunting task. One way to break down results is to focus on one or more gene families that are significant to the organism and study. In this protocol, we outline bioinformatic steps to generate a phylogeny and to quantify the expression of genes of interest. Phylogenetic trees can give insight into how genes are evolving within and between species as well as reveal orthology. These results can be enhanced using RNA-seq data to compare the expression of these genes in different individuals or tissues. Studies of molecular evolution and expression can reveal modes of evolution and conservation of gene function between species. The characterization of a gene family can serve as a springboard for future studies and can highlight an important gene family in a new genome or transcriptome paper.

The purpose of this protocol is to investigate the evolution and expression of candidate genes using RNA sequencing data.

Distilling and reporting large datasets, such as whole genome or transcriptome data, is often a daunting task. One way to break down results is to focus ...

RNA Next-Generation Sequencing and a Bioinformatics Pipeline to Identify Expressed LINE-1s at the Locus-Specific Level

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