This work has been supported by Beijing Academy of Agriculture and Forestry Sciences (KJCX201917, KJCX20180425, and KJCX20180204) to XY and National Natural Science Foundation of China (31621001) to LL.

1. Installation and testing
<ol>
	<li>Download required dependencies: Bowtie222 and RNAfold23. Compiled packages are recommended.
	<ol>
		<li>Download Bowtie2, a read mapping tool, from its home site (<a href="http://bowtie-bio.sourceforge.net/bowtie2/index.shtml" target="_blank">http://bowtie-bio.sourceforge.net/bowtie2/index.shtml</a>).</li>
		<li>Download RNAfold, a tool of the Vienna package used to predict RNA secondary structure, from <a href="http://www.tbi.univie.ac.at/~ivo/RNA/" target="_blank">http://www.tbi.univie.ac.at/~ivo/RNA/</a>.</li>
		<li>Before installing miRDP2, ensure that these two dependencies are correctly installed, and customize the bash environment file (e.g., .bashrc) to set a correct PATH for these two dependencies. 
		NOTE: Other mapping tools such as Bowtie24 are also suitable to miRDP2; either Bowtie or Bowtie2 can be used after version 1.1.3.</li>
	</ol>
	</li>
	<li>To download the miRDP2 package, go to <a href="https://sourceforge.net/projects/mirdp2/files/latest_version/" target="_blank">https://sourceforge.net/projects/mirdp2/files/latest_version/</a> and fetch the tarball files.</li>
	<li>Before installing miRDP2, make sure that Perl is in the PATH. To install miRDP2, extract all contents of the downloaded tarball file into one folder (command lines as in 1.4.2), and then set the folder path into the PATH. 
	NOTE: A computer or computing node with at least 8 GB RAM and 100 GB storage are recommended to run miRDP2.</li>
	<li>Test the MiRDP2 pipeline.
	<ol>
		<li>To test whether miRDP2 has been correctly installed, use the test data and the expected output found in <a href="https://sourceforge.net/projects/mirdp2/files/TestData/" target="_blank">https://sourceforge.net/projects/mirdp2/files/TestData/</a>. Test data contain one formatted GSM sequencing file and one Arabidopsis thaliana genome file.</li>
		<li>Move all downloaded files to the current working directory: 
		mv miRDP2-v*.tar.gz TestData.tar.gz ncRNA_rfam.tar.gz &#60;user_selected_folder&#62; 
		cd &#60;user_selected_folder&#62;</li>
		<li>Extract the compressed tarball files: 
		tar &#8211;xvzf miRDP2-v*.tar.gz 
		tar &#8211;xvzf TestData.tar.gz 
		tar &#8211;xvzf ncRNA_rfam.tar.gz</li>
		<li>Build the Arabidopsis genome reference index: 
		bowtie2-build -f ./TestData/TAIR10_genome.fa ./TestData/TAIR10_genome</li>
		<li>Build the ncRNA reference index: 
		bowtie2-build -f ./ncRNA_rfam.fa ./1.1.3/script/index/rfam_index</li>
		<li>Run the miRDP2 pipeline: 
		bash ./1.1.3/miRDP2-v1.1.3_pipeline.bash &#8211;g ./TestData/TAIR10_genome.fa -i ./ TestData/TAIR 10_genome &#8211;f ./TestData/GSM2094927.fa &#8211;o . 
		NOTE: Linux commands used are in bold and italic fonts, with command line options in italics. *indicates the version of miRDP2 (the current version is 1.1.3). The bowtie2-build command should take roughly 10 minutes, and the miRDP2 pipeline should finish within several minutes</li>
	</ol>
	</li>
	<li>Check testing outputs.
	<ol>
		<li>Note that a folder named &#39;GSM2094927-15-0-10&#39; is automatically generated in &#60;user_selected_folder&#62;, containing all intermediate files and results.</li>
		<li>Check that the tab-delimited output file GSM2094927-15-0-10_filter_P_prediction, the final output of predicted miRNAs, contains columns that indicate chromosome id, strand direction, representative reads id, precursor id, mature miRNA location, precursor location, mature sequence, and precursor sequence. Note the additional bed file derived from this file to facilitate further analysis.</li>
		<li>Check the file &#34;progress_log&#34;, which provides information about finished steps, and the files &#34;script_log&#34; and &#34;script_err&#34;, that contain program output and warnings. 
		NOTE: Currently, we have tested miRDP2 on two Linux platforms, including CentOS release 6.5 on a cluster server, and Cygwin 2.6.0 on PC Windows system, and miRDP2 should work on similar systems that support Perl.</li>
	</ol>
	</li>
</ol>
2. Identifying novel miRNAs
<ol>
	<li>Before running the pipeline, ensure that the input reads are preprocessed into proper format. 
	NOTE: The new version 1.1.3 of miRDP2 can accept original FASTQ format files as inputs, although the process of formatting reads is carried out as in previous versions.
	<ol>
		<li>First, remove adapters from the 5&#39; and 3&#39; ends of the deep sequencing reads (if present).</li>
		<li>Second, parse the deep sequencing reads into FASTA format.</li>
		<li>Third, remove redundancy such that reads with identical sequence are represented with a single and unique FASTA entry.</li>
		<li>Finally, ensure that all of the FASTA identifiers are unique. Each sequence identifier must end with a &#39;_x&#39; and an integer, indicating the copy number of the exact sequence that was retrieved in the deep sequencing datasets. One way to ensure unique FASTA identifier is to include a running number in the ID. For reference, see the file GSM2094927.fa in the test data (<a href="https://sourceforge.net/projects/mirdp2/files/TestData/" target="_blank">https://sourceforge.net/projects/mirdp2/files/TestData/</a>).</li>
		<li>See the following for examples of correctly formatted reads: 
		 
		&#62;read0_x29909 
		TTTGGATTGAAGGGAGCTCTA 
		&#62;read1_x36974 
		TTCCACAGCTTTCTTGAACTG 
		&#62;read2_x32635 
		TTCCACAGCTTTCTTGAACTT</li>
	</ol>
	</li>
	<li>Build reference indices.
	<ol>
		<li>For the genome reference, to save time, download Bowtie2 index files from the iGenomes website (<a href="https://support.illumina.com/sequencing/sequencing_software/igenome.html" target="_blank">https://support.illumina.com/sequencing/sequencing_software/igenome.html</a>) if the genome sequences of the species of interest have been indexed. Otherwise, users index reference sequences and keep the index file for a while till the project is finished since the genome sequence might need to be re-indexed. Details on how to index a genome reference are included in bowtie2 manual (<a href="http://bowtie-bio.sourceforge.net/bowtie2/manual.shtml" target="_blank">http://bowtie-bio.sourceforge.net/bowtie2/manual.shtml</a>).</li>
		<li>Another non-miRNA ncRNA index is also needed to filter out noisy sequences from other non-coding RNA fragments. The file is a collection of main ncRNA sequences from Rfam, including rRNA, tRNA, snRNA, and snoRNA. To build this index, please refer to part 1.4, as the index should be placed and named correctly, i.e. &#60;miRDP2_version&#62;/script/index/rfam_index.</li>
	</ol>
	</li>
	<li>Run miRDP2.
	<ol>
		<li>To use miRDP2 to detect new miRNAs from deep sequencing data, run the bash script in the package to start the analysis pipeline (An example can be found in step 1.4): 
		&#60;path_to_miRDP2_folder&#62;/miRDP2-v*.*_pipeline.bash &#8211;g &#60;genome_file&#62; -i &#60;path_to_index/index_prefix&#62; -f &#60;seq_file &#62; -o &#60;output_folder&#62; 
		where * indicates the version of the pipeline bash script. There are three parameters that can be modified: 1) the number of different locations a read could be mapped to, 2) the mismatch number for running bowtie2, and 3) the threshold of RPM (Reads Per Million). Modify these using the &#8211;L, -M, and &#8211;R options, respectively. A detailed explanation is in section 3.1.</li>
	</ol>
	</li>
	<li>Check the miRDP2 outputs.
	<ol>
		<li>Note that the output folder will be automatically generated under &#60;output_folder&#62;, and named &#39;&#60;seq_file_name&#62;-15-0-10&#39;; the last 3 numbers indicate the values (default in this case) for parameters 1, 2, and 3, respectively. The file &#60;seq_file_name&#62;_filter_P_prediction contains information of the final predicted miRNAs satisfying the newly updated plant miRNA annotation criteria. Details on the format of output file are described in part 1.4.</li>
	</ol>
	</li>
</ol>
3. Modifications and caution using miRDP2
<ol>
	<li>Parameters that can be modified
	<ol>
		<li>Use the &#39;-L&#39; option to set the limit of how many locations a read could be mapped to (parameter 1). Read mapping to too many sites are possibly associated with repeat sequences, and are not likely to miRNAs. The default setting is 15. For specific species, if there are miRNA families with many members, the first parameter may be increased manually to adapt to the genome landscape.</li>
		<li>Use the &#39;-M&#39; option to set the allowed mismatches for bowtie (parameter 2). The default setting is 0.</li>
		<li>Use the &#39;-R&#39; option to set the threshold for reads potentially corresponding to mature miRNAs (parameter 3). To reduce time consumption and false-positives, filter reads by RPM. Only reads exceeding a certain RPM threshold may represent mature sequences of miRNAs rather than background noise, and would be kept for further analysis. The default setting is 10 RPM.</li>
		<li>Note that changing these parameters can potentially affect performance and time consumption. In general, an increase of parameter 1 and 2 and a decrease of parameter 3 would generate a less stringent result and longer running time and vice versa.</li>
	</ol>
	</li>
	<li>Redundancy and miRNA*
	<ol>
		<li>Note that the output miRNAs from miRDP2 may differ from the known miRNAs. We found that this is mainly due to one of two reasons: heterogeneity of the mature miRNAs or the relative abundance of miRNA and miRNA*. We found that this does not impact the optimal length selection of precursors and the profiling of known miRNA genes.</li>
	</ol>
	</li>
</ol>

<ol>
	<li>Ghildiyal, M., Zamore, P. D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Small+silencing+RNAs:+an+expanding+universe.">Small silencing RNAs: an expanding universe.</a> Nature Reviews Genetics. 10 (2), 94-108 (2009).</li><li>Bartel, D. P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=MicroRNAs:+target+recognition+and+regulatory+functions.">MicroRNAs: target recognition and regulatory functions.</a> Cell. 136 (2), 215-233 (2009).</li><li>Moran, Y., Agron, M., Praher, D., Technau, U. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+evolutionary+origin+of+plant+and+animal+microRNAs.">The evolutionary origin of plant and animal microRNAs.</a> Nature Ecology Evolution. 1 (3), 27 (2017).</li><li>Xie, Z., 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=Expression+of+Arabidopsis+MIRNA+genes.">Expression of Arabidopsis MIRNA genes.</a> Plant Physiology. 138 (4), 2145-2154 (2005).</li><li>Zhao, X., Zhang, H., Li, L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Identification+and+analysis+of+the+proximal+promoters+of+microRNA+genes+in+Arabidopsis.">Identification and analysis of the proximal promoters of microRNA genes in Arabidopsis.</a> Genomics. 101 (3), 187-194 (2013).</li><li>Bologna, N. G., Mateos, J. L., Bresso, E. G., Palatnik, J. F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+loop-to-base+processing+mechanism+underlies+the+biogenesis+of+plant+microRNAs+miR319+and+miR159.">A loop-to-base processing mechanism underlies the biogenesis of plant microRNAs miR319 and miR159.</a> EMBO JOURNAL. 28 (23), 3646-3656 (2009).</li><li>Rogers, K., Chen, X. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Biogenesis,+turnover,+and+mode+of+action+of+plant+microRNAs.">Biogenesis, turnover, and mode of action of plant microRNAs.</a> Plant Cell. 25 (7), 2383-2399 (2013).</li><li>Voinnet, O. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Origin,+biogenesis,+and+activity+of+plant+microRNAs.">Origin, biogenesis, and activity of plant microRNAs.</a> Cell. 136 (4), 669-687 (2009).</li><li>Iwakawa, H. O., Tomari, Y. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+Functions+of+MicroRNAs:+mRNA+Decay+and+Translational+Repression.">The Functions of MicroRNAs: mRNA Decay and Translational Repression.</a> Trends in Cell Biology. 25 (11), 651-665 (2015).</li><li>Lee, R. C., Feinbaum, R. L., Ambros, V. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+C.+elegans+heterochronic+gene+lin-4+encodes+small+RNAs+with+antisense+complementarity+to+lin-14.">The C. elegans heterochronic gene lin-4 encodes small RNAs with antisense complementarity to lin-14.</a> Cell. 75 (5), 843-854 (1993).</li><li>Wightman, B., Ha, I., Ruvkun, G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Posttranscriptional+regulation+of+the+heterochronic+gene+lin-14+by+lin-4+mediates+temporal+pattern+formation+in+C.+elegans.">Posttranscriptional regulation of the heterochronic gene lin-14 by lin-4 mediates temporal pattern formation in C. elegans.</a> Cell. 75 (5), 855-862 (1993).</li><li>Friedlander, M. R., 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=Discovering+microRNAs+from+deep+sequencing+data+using+miRDeep.">Discovering microRNAs from deep sequencing data using miRDeep.</a> Nature Biotechnology. 26 (4), 407-415 (2008).</li><li>Yang, X., Li, L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=miRDeep-P:+a+computational+tool+for+analyzing+the+microRNA+transcriptome+in+plants.">miRDeep-P: a computational tool for analyzing the microRNA transcriptome in plants.</a> Bioinformatics. 27 (18), 2614-2615 (2011).</li><li>Meyers, B. 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=Criteria+for+annotation+of+plant+MicroRNAs.">Criteria for annotation of plant MicroRNAs.</a> Plant Cell. 20 (12), 3186-3190 (2008).</li><li>Yang, X., Zhang, H., Li, L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Global+analysis+of+gene-level+microRNA+expression+in+Arabidopsis+using+deep+sequencing+data.">Global analysis of gene-level microRNA expression in Arabidopsis using deep sequencing data.</a> Genomics. 98 (1), 40-46 (2011).</li><li>Kuang, Z., Wang, Y., Li, L., Yang, X. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=miRDeep-P2:+accurate+and+fast+analysis+of+the+microRNA+transcriptome+in+plants.">miRDeep-P2: accurate and fast analysis of the microRNA transcriptome in plants.</a> Bioinformatics. , (2018).</li><li>Kozomara, A., Birgaoanu, M., Griffiths-Jones, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=miRBase:+from+microRNA+sequences+to+function.">miRBase: from microRNA sequences to function.</a> Nucleic Acids Research. 47 (1), 155-162 (2019).</li><li>Griffiths-Jones, S., Saini, H. K., van Dongen, S., Enright, A. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=miRBase:+tools+for+microRNA+genomics.">miRBase: tools for microRNA genomics.</a> Nucleic Acids Research. 36, 154-158 (2008).</li><li>Axtell, M. J., Meyers, B. C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Revisiting+Criteria+for+Plant+MicroRNA+Annotation+in+the+Era+of+Big+Data.">Revisiting Criteria for Plant MicroRNA Annotation in the Era of Big Data.</a> Plant Cell. 30 (2), 272-284 (2018).</li><li>Taylor, R. S., Tarver, J. E., Hiscock, S. J., Donoghue, P. C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Evolutionary+history+of+plant+microRNAs.">Evolutionary history of plant microRNAs.</a> Trends in Plant Science. 19 (3), 175-182 (2014).</li><li>Kozomara, A., Griffiths-Jones, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=miRBase:+annotating+high+confidence+microRNAs+using+deep+sequencing+data.">miRBase: annotating high confidence microRNAs using deep sequencing data.</a> Nucleic Acids Research. 42, 68-73 (2014).</li><li>Langmead, B., Salzberg, S. L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Fast+gapped-read+alignment+with+Bowtie+2.">Fast gapped-read alignment with Bowtie 2.</a> Nature Methods. 9 (4), 357-359 (2012).</li><li>Lorenz, R., 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=ViennaRNA+Package+2.0.">ViennaRNA Package 2.0.</a> Algorithms for Molecular Biology. 6, 26 (2011).</li><li>Langmead, B., Trapnell, C., Pop, M., Salzberg, S. L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Ultrafast+and+memory-efficient+alignment+of+short+DNA+sequences+to+the+human+genome.">Ultrafast and memory-efficient alignment of short DNA sequences to the human genome.</a> Genome Biology. 10 (3), 25 (2009).</li><li>An, J., Lai, J., Sajjanhar, A., Lehman, M. L., Nelson, C. C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=miRPlant:+an+integrated+tool+for+identification+of+plant+miRNA+from+RNA+sequencing+data.">miRPlant: an integrated tool for identification of plant miRNA from RNA sequencing data.</a> BMC Bioinformatics. 15, 275 (2014).</li><li>Lei, J., Sun, Y. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=miR-PREFeR:+an+accurate,+fast+and+easy-to-use+plant+miRNA+prediction+tool+using+small+RNA-Seq+data.">miR-PREFeR: an accurate, fast and easy-to-use plant miRNA prediction tool using small RNA-Seq data.</a> Bioinformatics. 30 (19), 2837-2839 (2014).</li><li>Evers, M., Huttner, M., Dueck, A., Meister, G., Engelmann, J. C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=miRA:+adaptable+novel+miRNA+identification+in+plants+using+small+RNA+sequencing+data.">miRA: adaptable novel miRNA identification in plants using small RNA sequencing data.</a> BMC Bioinformatics. 16, 370 (2015).</li><li>Mathelier, A., Carbone, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=MIReNA:+finding+microRNAs+with+high+accuracy+and+no+learning+at+genome+scale+and+from+deep+sequencing+data.">MIReNA: finding microRNAs with high accuracy and no learning at genome scale and from deep sequencing data.</a> Bioinformatics. 26 (18), 2226-2234 (2010).</li><li>Zhu, Q. H., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+diverse+set+of+microRNAs+and+microRNA-like+small+RNAs+in+developing+rice+grains.">A diverse set of microRNAs and microRNA-like small RNAs in developing rice grains.</a> Genome Research. 18 (9), 1456-1465 (2008).</li><li>Fahlgren, N., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=MicroRNA+gene+evolution+in+Arabidopsis+lyrata+and+Arabidopsis+thaliana.">MicroRNA gene evolution in Arabidopsis lyrata and Arabidopsis thaliana.</a> Plant Cell. 22 (4), 1074-1089 (2010).</li><li>Fromm, B., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+Uniform+System+for+the+Annotation+of+Vertebrate+microRNA+Genes+and+the+Evolution+of+the+Human+microRNAome.">A Uniform System for the Annotation of Vertebrate microRNA Genes and the Evolution of the Human microRNAome.</a> Annual Review of Genetics. 49, 213-242 (2015).</li><li>Blevins, 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=Identification+of+Pol+IV+and+RDR2-dependent+precursors+of+24+nt+siRNAs+guiding+de+novo+DNA+methylation+in+Arabidopsis.">Identification of Pol IV and RDR2-dependent precursors of 24 nt siRNAs guiding de novo DNA methylation in Arabidopsis.</a> Elife. 4, 09591 (2015).</li><li>Zhai, J., 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+One+Precursor+One+siRNA+Model+for+Pol+IV-Dependent+siRNA+Biogenesis.">A One Precursor One siRNA Model for Pol IV-Dependent siRNA Biogenesis.</a> Cell. 163 (2), 445-455 (2015).</li><li>Werner, S., Wollmann, H., Schneeberger, K., Weigel, D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Structure+determinants+for+accurate+processing+of+miR172a+in+Arabidopsis+thaliana.">Structure determinants for accurate processing of miR172a in Arabidopsis thaliana.</a> Current Biology. 20 (1), 42-48 (2010).</li><li>Mateos, J. L., Bologna, N. G., Chorostecki, U., Palatnik, J. F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Identification+of+microRNA+processing+determinants+by+random+mutagenesis+of+Arabidopsis+MIR172a+precursor.">Identification of microRNA processing determinants by random mutagenesis of Arabidopsis MIR172a precursor.</a> Current Biology. 20 (1), 49-54 (2010).</li><li>Vitsios, D. 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=Mirnovo:+genome-free+prediction+of+microRNAs+from+small+RNA+sequencing+data+and+single-cells+using+decision+forests.">Mirnovo: genome-free prediction of microRNAs from small RNA sequencing data and single-cells using decision forests.</a> Nucleic Acids Research. 45 (21), 177 (2017).</li></ol>

The authors have nothing to disclose.

With the advent of NGS, a large number of miRNA loci have been identified from an ever-increasing amount of sRNA sequencing data in diverse species29,30. In the centralized community database miRBase21, the deposited miRNA items have increased almost 100 times in the last decade. However, in comparison to miRNAs in animals, plant miRNAs have many unique features that make the identification/annotation more complicated13</...

One of the most exciting discoveries in the last two decades in biology is the proliferating role of sRNA species in regulating diverse functions of the genome1. In particular, miRNAs constitute an important class of 20- to 24-nt sRNAs in eukaryotes, and mainly function at post-transcriptional level as prominent gene regulators throughout life cycle development stages as well as in stimulus and stress responses2,3. In plants, miRNAs arise from primary transcripts called pri-miRNAs, which are generally transcribed by RNA polymerase II as individual transcription units4,5. Processed by evolutionarily conserved cellular machinery (Drosha RNase III in animals, DICER-like in plants), pri-miRNAs are excised into the immediate miRNA precursors, pre-miRNAs, which contain sequences forming intra-molecular stem-loop structures6,7. Pre-miRNAs are then processed into double-stranded intermediates, namely miRNA duplexes, consisting of the functional strand, mature miRNA, and the less frequently functional partner, miRNA*2,8. After loaded into the RNA-induced silencing complex (RISC), the mature miRNAs could recognize their mRNA targets based on sequence complementarity, resulting in a negative regulatory function2,8. miRNAs could either destabilize their target transcripts or prevent target translation but the former manner is dominated in plants8,9.
Since the fortuitous discovery of the first miRNA in the nematode Caenorhabditis elegans10,11, much research has been committed to miRNA identification and its functional analysis, especially after the availability of NGS method. The wide application of the NGS method has greatly promoted the utilization of computational tools that were designed to capture the unique feature of miRNAs, such as the stem-loop structure of precursors and their preferential accumulation of sequence reads on mature miRNA and miRNA*. As a result, researchers have achieved remarkable success in identifying miRNAs in diverse species. Based on a previously described probability model12, we developed miRDeep-P13, which was the first computational tool for discovering plant miRNAs from NGS data. miRDeep-P was specifically aimed at conquering the challenges of decoding plant miRNAs featuring more variable precursor length and large paralogous families13,14,15. After its release, this program has been downloaded thousands of times and used to annotate miRNA transcriptomes in more than 40 plant species16. Propelled by NGS-based tools like miRDeep-P, there has been a dramatic increase in the number of registered miRNAs in the public miRNA repository miRBase17, where over 38,000 miRNA items are currently hosted (release 22.1) in comparison to only ~500 miRNA items (release 2.0) in 200818.
However, two new challenges have arisen from plant miRNA annotation. First, high ratios of false-positives have heavily impacted the quality of plant miRNA annotations16,19 for the following reasons: 1) a deluge of endogenous short interfering RNAs (siRNAs) from NGS sRNA libraries were erroneously annotated as miRNAs due to lacking of a stringent miRNA annotation criteria; 2) for species without a priori miRNA information, false-positives predicted based on NGS data are difficult to eliminate. Using miRBase as an example, Taylor et al.20 found one third of plant miRNA entries in the public repository21 (release 21) lacked convincing supporting evidence and even three-fourths of plant miRNA families were questionable. Second, it becomes an extremely time-consuming process for predicting plant miRNAs with large and complex genomes16. To overcome these challenges, we updated miRDeep-P by adding a new filtering strategy, overhauling the scoring algorithm and integrating new criteria for plant miRNA annotation, and released the new version miRDP2. In addition, we tested miRDP2 using NGS sRNA datasets with gradually increasing genome sizes: Arabidopsis, rice, tomato, maize and wheat. Compared to other five widely used tools and its old version, miRDP2 parsed these sRNA data and analyzed miRNA transcriptomes faster with improved accuracy and sensitivity.
Contents of the miRDP2 package 
The miRDP2 package consists of six documented Perl scripts that should be run sequentially by the prepared bash script. Of the six scripts, three (convert_bowtie_to_blast.pl, filter_alignments.pl, and excise_candidate.pl) are inherited from miRDeep-P. The other scripts are modified from the original version. Functions of the six scripts are described in the following:
preprocess_reads.pl filters input reads, including reads that are too long or too short (&#60;19 nt or &#62;25 nt), and reads correlated with Rfam ncRNA sequences, as well as reads with RPM (Reads Per Million) less than 5. The script then retrieves reads correlated to known miRNA mature sequences. The input files are original reads in FASTA/FASTQ format and bowtie2 output of reads mapping to miRNA and ncRNA sequences.
The formula for calculating RPM is as the following:
<img alt="Equation 1" src="/files/ftp_upload/59864/59864equ01.jpg" />
convert_bowtie_to_blast.pl changes the bowtie format into BLAST-parsed format. BLAST-parsed format is a custom tabular separated format derived from standard NCBI BLASToutput format.
filter_alignments.pl filters the alignments of deep sequencing reads to a genome. It filters partial alignments as well as multi-aligned reads (user-specified frequency cutoff). The basic input is a file in BLAST-parsed format.
excise_candidate.pl cuts out potential precursor sequences from a reference sequence using aligned reads as guidelines. The basic input is a file in BLAST-parsed format and a FASTA file. The output is all potential precursor sequences in FASTA format.
mod-miRDP.pl needs two input files, signature file and structure file, which is modified from the core miRDeep-P algorithm by changing the scoring system with plant specific parameters. The input files are dot-bracket precursor structure file and reads distribution signature file.
mod-rm_redundant_meet_plant.pl needs three input files: chromosome_length, precursors and original_prediction generated by mod-miRDP.pl. It generates two output files, non-redundant predicted file and predicted file filtered by newly updated plant miRNA criteria. Details on the format of output file are described in section 1.4.

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Computer/computing node</td>
			<td>N/A</td>
			<td>N/A</td>
			<td>Perl is required; at least 8 GB RAM and 100 GB storage are recommended</td>
		</tr>
	</tbody>
</table>

a bioinformatics pipeline to accurately and efficiently analyze the microrna transcriptomes in plants

MicroRNAs (miRNAs) are 20- to 24-nucleotide (nt) endogenous small RNAs (sRNAs) extensively existing in plants and animals that play potent roles in regulating gene expression at the post-transcriptional level. Sequencing sRNA libraries by Next Generation Sequencing (NGS) methods has been widely employed to identify and analyze miRNA transcriptomes in the last decade, resulting in a rapid increase of miRNA discovery. However, two major challenges arise in plant miRNA annotation due to increasing depth of sequenced sRNA libraries as well as the size and complexity of plant genomes. First, many other types of sRNAs, in particular, short interfering RNAs (siRNAs) from sRNA libraries, are erroneously annotated as miRNAs by many computational tools. Second, it becomes an extremely time-consuming process for analyzing miRNA transcriptomes in plant species with large and complex genomes. To overcome these challenges, we recently upgraded miRDeep-P (a popular tool for miRNA transcriptome analyses) to miRDeep-P2 (miRDP2 for short) by employing a new filtering strategy, overhauling the scoring algorithm and incorporating newly updated plant miRNA annotation criteria. We tested miRDP2 against sequenced sRNA populations in five representative plants with increasing genomic complexity, including Arabidopsis, rice, tomato, maize and wheat. The results indicate that miRDP2 processed these tasks with very high efficiency. In addition, miRDP2 outperformed other prediction tools regarding sensitivity and accuracy. Taken together, our results demonstrate miRDP2 as a fast and accurate tool for analyzing plant miRNA transcriptomes, therefore a useful tool in helping the community better annotate miRNAs in plants.

The miRNA annotation pipeline, miRDP2, described herein is applied to 10 public sRNA-seq libraries from 5 plant species with gradually increased genome length, including Arabidopsis thaliana, Oryza sativa (rice), Solanum lycopersicum (tomato), Zea mays (maize) and Triticum aestivum (wheat) (Figure 1A). Overall, for each species, 2 representative sRNA libraries from different tissues (collapsed into unique reads, details in the pro...

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A Bioinformatics Pipeline to Accurately and Efficiently Analyze the MicroRNA Transcriptomes in Plants

A bioinformatics pipeline, namely miRDeep-P2 (miRDP2 for short), with updated plant miRNA criteria and an overhauled algorithm, could accurately and efficiently analyze microRNA transcriptomes in plants, especially for species with complex and large genomes.

MicroRNAs (miRNAs) are 20- to 24-nucleotide (nt) endogenous small RNAs (sRNAs) extensively existing in plants and animals that play potent roles in ...

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8.2K Views.  Beijing Academy of Agriculture and Forestry Sciences. A bioinformatics pipeline, namely miRDeep-P2 (miRDP2 for short), with updated plant miRNA criteria and an overhauled algorithm, could accurately and efficiently analyze microRNA transcriptomes in plants, especially for species with complex and large genomes.

Video: A Bioinformatics Pipeline to Accurately and Efficiently Analyze the MicroRNA Transcriptomes in Plants

Peptide-derived Method to Transport Genes and Proteins Across Cellular and Organellar Barriers in Plants

The capacity to introduce exogenous proteins and express (or down-regulate) specific genes in plants provides a powerful tool for fundamental research as well as new applications in the field of plant biotechnology. Viable methods that currently exist for protein or gene transfer into plant cells, namely Agrobacterium and microprojectile bombardment, have disadvantages of low transformation frequency, limited host range, or a high cost of equipment and microcarriers. The following protocol outlines a simple and versatile method, which employs rationally-designed peptides as delivery agents for a variety of nucleic acid- and protein-based cargoes into plants. Peptides are selected as tools for development of the system due to their biodegradability, reduced size, diverse and tunable properties as well as the ability to gain intracellular/organellar access. The preparation, characterization and application of optimized formulations for each type of the wide range of delivered cargoes (plasmid DNA, double-stranded DNA or RNA, and protein) are described. Critical steps within the protocol, possible modifications and existing limitations of the method are also discussed.

Existing methods for the modification of plants have limited applicability. The novel peptide-derived technology proposed here promises both simplicity and efficiency in the introduction of exogenous protein or genes into the desired intracellular compartments of intact plants.

The capacity to introduce exogenous proteins and express (or down-regulate) specific genes in plants provides a powerful tool for fundamental research as ...

Detection of Copy Number Alterations Using Single Cell Sequencing

Detection of genomic changes at single cell resolution is important for characterizing genetic heterogeneity and evolution in normal tissues, cancers, and microbial populations. Traditional methods for assessing genetic heterogeneity have been limited by low resolution, low sensitivity, and/or low specificity. Single cell sequencing has emerged as a powerful tool for detecting genetic heterogeneity with high resolution, high sensitivity and, when appropriately analyzed, high specificity. Here we provide a protocol for the isolation, whole genome amplification, sequencing, and analysis of single cells. Our approach allows for the reliable identification of megabase-scale copy number variants in single cells. However, aspects of this protocol can also be applied to investigate other types of genetic alterations in single cells.

Single cell sequencing is an increasingly popular and accessible tool for addressing genomic changes at high resolution. We provide a protocol that uses single cell sequencing to identify copy number alterations in single cells.

Detection of genomic changes at single cell resolution is important for characterizing genetic heterogeneity and evolution in normal tissues, cancers, and ...

Systemic Delivery of MicroRNA Using Recombinant Adeno-associated Virus Serotype 9 to Treat Neuromuscular Diseases in Rodents

RNA interference via the endogenous miRNA pathway regulates gene expression by controlling protein synthesis through post-transcriptional gene silencing. In recent years, miRNA-mediated gene regulation has shown potential for treatment of neurological disorders caused by a toxic gain of function mechanism. However, efficient delivery to target tissues has limited its application. Here we used a transgenic mouse model for spinal and bulbar muscular atrophy (SBMA), a neuromuscular disease caused by polyglutamine expansion in the androgen receptor (AR), to test gene silencing by a newly identified AR-targeting miRNA, miR-298. We overexpressed miR-298 using a recombinant adeno-associated virus (rAAV) serotype 9 vector to facilitate transduction of non-dividing cells. A single tail-vein injection in SBMA mice induced sustained and widespread overexpression of miR-298 in skeletal muscle and motor neurons and resulted in amelioration of the neuromuscular phenotype in the mice.

Here we describe the delivery of microRNA using a recombinant adeno-associated virus serotype 9 in a mouse model of a neuromuscular disease. A single peripheral administration in mice resulted in sustained miRNA overexpression in muscle and motor neurons, providing an opportunity to study miRNA function and therapeutic potential in vivo.

RNA interference via the endogenous miRNA pathway regulates gene expression by controlling protein synthesis through post-transcriptional gene silencing. ...

An Optogenetic Method to Control and Analyze Gene Expression Patterns in Cell-to-cell Interactions

Cells should respond properly to temporally changing environments, which are influenced by various factors from surrounding cells. The Notch signaling pathway is one of such essential molecular machinery for cell-to-cell communications, which plays key roles in normal development of embryos. This pathway involves a cell-to-cell transfer of oscillatory information with ultradian rhythms, but despite the progress in molecular biology techniques, it has been challenging to elucidate the impact of multicellular interactions on oscillatory gene dynamics. Here, we present a protocol that permits optogenetic control and live monitoring of gene expression patterns in a precise temporal manner. This method successfully revealed that intracellular and intercellular periodic inputs of Notch signaling entrain intrinsic oscillations by frequency tuning and phase shifting at the single-cell resolution. This approach is applicable to the analysis of the dynamic features of various signaling pathways, providing a unique platform to test a functional significance of dynamic gene expression programs in multicellular systems.

Here, we present a protocol to analyze cell-to-cell transfer of oscillatory information by optogenetic control and live monitoring of gene expression. This approach provides a unique platform to test a functional significance of dynamic gene expression programs in multicellular systems.

Cells should respond properly to temporally changing environments, which are influenced by various factors from surrounding cells. The Notch signaling ...

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 ...

A Rapid and Facile Pipeline for Generating Genomic Point Mutants in C. elegans Using CRISPR/Cas9 Ribonucleoproteins

The clustered regularly interspersed palindromic repeats (CRISPR)-CRISPR-associated protein 9 (Cas9) prokaryotic adaptive immune defense system has been co-opted as a powerful tool for precise eukaryotic genome engineering. Here, we present a rapid and simple method using chimeric single guide RNAs (sgRNA) and CRISPR-Cas9 Ribonucleoproteins (RNPs) for the efficient and precise generation of genomic point mutations in C. elegans. We describe a pipeline for sgRNA target selection, homology-directed repair (HDR) template design, CRISPR-Cas9-RNP complexing and delivery, and a genotyping strategy that enables the robust and rapid identification of correctly edited animals. Our approach not only permits the facile generation and identification of desired genomic point mutant animals, but also facilitates the detection of other complex indel alleles in approximately 4 - 5 days with high efficiency and a reduced screening workload.

A Rapid and Facile Pipeline for Generating Genomic Point Mutants in C. elegans Using CRISPR/Cas9 Ribonucleoproteins

Here, we present a method to engineer the genome of C. elegans using CRISPR-Cas9 ribonucleoproteins and homology dependent repair templates.

The clustered regularly interspersed palindromic repeats (CRISPR)-CRISPR-associated protein 9 (Cas9) prokaryotic adaptive immune defense system has been ...

Isolation, Characterization and MicroRNA-based Genetic Modification of Human Dental Follicle Stem Cells

To date, several stem cell types at different developmental stages are in the focus for the treatment of degenerative diseases. Yet, certain aspects, such as initial massive cell death and low therapeutic effects, impaired their broad clinical translation. Genetic engineering of stem cells prior to transplantation emerged as a promising method to optimize therapeutic stem cell effects. However, safe and efficient gene delivery systems are still lacking. Therefore, the development of suitable methods may provide an approach to resolve current challenges in stem cell-based therapies.
The present protocol describes the extraction and characterization of human dental follicle stem cells (hDFSCs) as well as their non-viral genetic modification. The postnatal dental follicle unveiled as a promising and easily accessible source for harvesting adult multipotent stem cells possessing high proliferation potential. The described isolation procedure presents a simple and reliable method to harvest hDFSCs from impacted wisdom teeth. Also this protocol comprises methods to define stem cell characteristics of isolated cells. For genetic engineering of hDFSCs, an optimized cationic lipid-based transfection strategy is presented enabling highly efficient microRNA introduction without causing cytotoxic effects. MicroRNAs are suitable candidates for transient cell manipulation, as these small translational regulators control the fate and behavior of stem cells without the hazard of stable genome integration. Thus, this protocol represents a safe and efficient procedure for engineering of hDFSCs that may become important for optimizing their therapeutic efficacy.

This protocol describes the transient genetic engineering of dental stem cells extracted from the human dental follicle. The applied non-viral modification strategy may become a basis for the improvement of therapeutic stem cell products.

To date, several stem cell types at different developmental stages are in the focus for the treatment of degenerative diseases. Yet, certain aspects, such ...

Using In Vitro and In-cell SHAPE to Investigate Small Molecule Induced Pre-mRNA Structural Changes

In the process of drug development of RNA-targeting small molecules, elucidating the structural changes upon their interactions with target RNA sequences is desired. We herein provide a detailed in vitro and in-cell selective 2&#39;-hydroxyl acylation analyzed by primer extension (SHAPE) protocol to study the RNA structural change in the presence of an experimental drug for spinal muscular atrophy (SMA), survival of motor neuron (SMN)-C2, and in exon 7 of the pre-mRNA of the SMN2 gene. In in vitro SHAPE, an RNA sequence of 140 nucleotides containing SMN2 exon 7 is transcribed by T7 RNA polymerase, folded in the presence of SMN-C2, and subsequently modified by a mild 2&#39;-OH acylation reagent, 2-methylnicotinic acid imidazolide (NAI). This 2&#39;-OH-NAI adduct is further probed by a 32P-labeled primer extension and resolved by polyacrylamide gel electrophoresis (PAGE). Conversely, 2&#39;-OH acylation in in-cell SHAPE takes place in situ with SMN-C2 bound cellular RNA in living cells. The pre-mRNA sequence of exon 7 in the SMN2 gene, along with SHAPE-induced mutations in the primer extension, was then amplified by PCR and subject to next-generation sequencing. Comparing the two methodologies, in vitro SHAPE is a more cost-effective method and does not require computational power to visualize results. However, the in vitro SHAPE-derived RNA model sometimes deviates from the secondary structure in a cellular context, likely due to the loss of all interactions with RNA-binding proteins. In-cell SHAPE does not need a radioactive material workplace and yields a more accurate RNA secondary structure in the cellular context. Furthermore, in-cell SHAPE is usually applicable for a larger range of RNA sequences (~1,000 nucleotides) by utilizing next-generation sequencing, compared to in vitro SHAPE (~200 nucleotides) that usually relies on PAGE analysis. In case of exon 7 in SMN2 pre-mRNA, the in vitro and in-cell SHAPE derived RNA models are similar to each other.

Detailed protocols for both in vitro and in-cell selective 2&#39;-hydroxyl acylation analyzed by primer extension (SHAPE) experiments to determine the secondary structure of pre-mRNA sequences of interest in the presence of an RNA-targeting small molecule are presented in this article.

In the process of drug development of RNA-targeting small molecules, elucidating the structural changes upon their interactions with target RNA sequences ...

Characterizing Histone Post-translational Modification Alterations in Yeast Neurodegenerative Proteinopathy Models

Neurodegenerative diseases, such as amyotrophic lateral sclerosis (ALS) and Parkinson&#39;s disease (PD), cause the loss of hundreds of thousands of lives each year. Effective treatment options able to halt disease progression are lacking. Despite the extensive sequencing efforts in large patient populations, the majority of ALS and PD cases remain unexplained by genetic mutations alone. Epigenetics mechanisms, such as the post-translational modification of histone proteins, may be involved in neurodegenerative disease etiology and progression and lead to new targets for pharmaceutical intervention. Mammalian in vivo and in vitro models of ALS and PD are costly and often require prolonged and laborious experimental protocols. Here, we outline a practical, fast, and cost-effective approach to determining genome-wide alterations in histone modification levels using Saccharomyces cerevisiae as a model system. This protocol allows for comprehensive investigations into epigenetic changes connected to neurodegenerative proteinopathies that corroborate previous findings in different model systems while significantly expanding our knowledge of the neurodegenerative disease epigenome.

This protocol outlines experimental procedures to characterize genome-wide changes in the levels of histone post-translational modifications (PTM) occurring in connection with the overexpression of proteins associated with ALS and Parkinson&#39;s disease in Saccharomyces cerevisiae models. After SDS-PAGE separation, individual histone PTM levels are detected with modification-specific antibodies via Western blotting.

Neurodegenerative diseases, such as amyotrophic lateral sclerosis (ALS) and Parkinson&#39;s disease (PD), cause the loss of hundreds of thousands of lives ...

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

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

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

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