The authors would like to acknowledge the support from the computational resources provided by the HPCC platform of Xi&#39;an Jiaotong University. Z.X. gratefully thanks the Young Topnotch Talent Support Plan of Xi&#39;an Jiaotong University.

1. Environment setup and RiboCode installation
<ol>
	<li>Open a Linux terminal window and create a conda environment: 
	conda create -n RiboCode python=3.8</li>
	<li>Switch to the created environment and install RiboCode and dependencies: 
	conda activate RiboCode 
	conda install -c bioconda ribocode ribominer sra-tools fastx_toolkit cutadapt bowtie star samtools</li>
</ol>
2. Data preparation
<ol>
	<li>Get genome reference files.
	<ol>
		<li>For the reference sequence, go to the Ensemble website at https://www.ensembl.org/index.html,&#160;click the top menu Download and left-side menu FTP Download. In the presented table, click FASTA in the column DNA (FASTA) and the row where Species is Human. In the opened page, copy the link of Homo_sapiens.GRCh38.dna.primary_assembly.fa.gz, then download and unzip it in the terminal: 
		wget -c \ 
		http://ftp.ensembl.org/pub/release-104/fasta/homo_sapiens/dna/Homo_sapiens.GRCh38.dna.primary_assembly.fa.gz 
		gzip -d Homo_sapiens.GRCh38.dna.primary_assembly.fa.gz</li>
		<li>For reference annotation, right-click GTF in the column Gene sets in the last-opened web page. Copy the link of Homo_sapiens.GRCh38.104.gtf.gz and download it using: 
		wget -c \ 
		http://ftp.ensembl.org/pub/release-104/gtf/homo_sapiens/Homo_sapiens.GRCh38.104.gtf.gz 
		gzip -d Homo_sapiens.GRCh38.104.gtf.gz 
		NOTE: It is recommended to get the GTF file from the Ensemble website as it contains genome annotations organized in a three-level hierarchy, i.e., each gene contains transcripts that contain exons and optional translations (e.g., coding sequences [CDS], translation start site, translation end site). When a gene&#39;s or transcript&#39;s annotations are missing, for instance, a GTF file obtained from UCSC or NCBI, use GTFupdate to generate an updated GTF with complete parent-child hierarchy annotations: GTFupdate original.gtf &#62; updated.gtf. For the annotation file in the .gff format, use the AGAT toolkit24 or any other tool to convert to the .gtf format.</li>
	</ol>
	</li>
	<li>Get rRNA sequences.
	<ol>
		<li>Open UCSC Genome Browser at https://genome.ucsc.edu and click&#160;Tools | Table Browser in the dropdown list.</li>
		<li>On the opened page, specify Mammal for clade, Human for genome, All Tables for group, rmask for table, and genome for region. For filter, click Create to go to a new page and set repClass as does match rRNA.</li>
		<li>Click Submit and then set the output format to sequence and output filename as hg38_rRNA.fa. Finally, click Get output | Get sequence to retrieve the rRNA sequence.</li>
	</ol>
	</li>
	<li>Get ribosome profiling datasets from Sequence Read Archive (SRA).
	<ol>
		<li>Download the replicate samples of si-eIF3e treatment group and rename them: 
		fastq-dump SRR9047190 SRR9047191 SRR9047192 
		mv SRR9047190.fastq si-eIF3e-1.fastq 
		mv SRR9047191.fastq si-eIF3e-2.fastq 
		mv SRR9047192.fastq si-eIF3e-3.fastq</li>
		<li>Download the replicate samples of control group and rename them: 
		fastq-dump SRR9047193 SRR9047194 SRR9047195 
		mv SRR9047193.fastq si-Ctrl-1.fastq 
		mv SRR9047194.fastq si-Ctrl-2.fastq 
		mv SRR9047195.fastq si-Ctrl-3.fastq 
		NOTE: The SRA accession IDs for these example datasets were obtained from Gene Expression Omnibus (GEO) website25 by searching for GSE131074.</li>
	</ol>
	</li>
</ol>
3. Trim adapters and remove rRNA contamination
<ol>
	<li>(Optional) Remove adapters from the sequencing data. Skip this step if the adapter sequences have already been trimmed, as in this case. Otherwise, use cutadapt to trim the adapters from reads. 
	for i in si-Ctrl-1 si-Ctrl-2 si-Ctrl-3 si-eIF3e-1 si-eIF3e-2 si-eIF3e-3 
	do 
	cutadapt -m 15 --match-read-wildcards -a CTGTAGGCACCATCAAT \ 
	-o ${i}_trimmed.fastq ${i}.fastq 
	done 
	NOTE: The adapter sequence after -a parameter will vary dependent on cDNA library preparation. Reads shorter than 15 (given by -m) are discarded because the ribosome-protected fragments are usually longer than this size.</li>
	<li>Remove rRNA contamination using the following steps:
	<ol>
		<li>Index rRNA reference sequences: 
		bowtie-build -f hg38_rRNA.fa hg38_rRNA</li>
		<li>Align the reads to rRNA reference to rule out the reads originating from rRNA: 
		for i in si-Ctrl-1 si-Ctrl-2 si-Ctrl-3 si-eIF3e-1 si-eIF3e-2 si-eIF3e-3 
		do 
		bowtie -n 0 -y -a --norc --best --strata -S -p 4 -l 15 \ 
		--un=./${i}_noncontam.fastq hg38_rRNA -q ${i}.fastq ${i}.aln 
		done 
		-p specifies the number of threads for parallelly running the tasks. Considering the relatively small size of the RPF reads, other arguments (e.g., -n, -y, -a, -norc, --best, --strata, and -l) should be specified to guarantee that the reported alignments are best. For more details, refer to the Bowtie website26.</li>
	</ol>
	</li>
</ol>
4. Align the clean reads to the genome
<ol>
	<li>Create a genome index. 
	mkdir STAR_hg38_genome 
	STAR --runThreadN 8 --runMode genomeGenerate --genomeDir ./STAR_hg38_genome --genomeFastaFiles Homo_sapiens.GRCh38.dna.primary_assembly.fa --sjdbGTFfile Homo_sapiens.GRCh38.104.gtf</li>
	<li>Align the clean reads (no rRNA contamination) to the created reference. 
	for i in si-Ctrl-1 si-Ctrl-2 si-Ctrl-3 si-eIF3e-1 si-eIF3e-2 si-eIF3e-3 
	do 
	STAR --runThreadN 8 --outFilterType Normal --outWigType wiggle --outWigStrand Stranded --outWigNorm RPM --outFilterMismatchNmax 1 --outFilterMultimapNmax 1 --genomeDir STAR_hg38_genome --readFilesIn ${i}_noncontam.fastq --outFileNamePrefix ${i}. --outSAMtype BAM SortedByCoordinate --quantMode TranscriptomeSAM GeneCounts --outSAMattributes All 
	done 
	NOTE: An untemplated nucleotide is frequently added to the 5&#39; end of each read by the reverse transcriptase27, which will be efficiently trimmed off by STAR as it performs soft-clipping by default. The parameters for STAR are described in STAR manual28.</li>
	<li>Sort and index alignment files. 
	for i in si-Ctrl-1 si-Ctrl-2 si-Ctrl-3 si-eIF3e-1 si-eIF3e-2 si-eIF3e-3 
	do 
	samtools sort -T ${i}.Aligned.toTranscriptome.out.sorted \ 
	-o ${i}.Aligned.toTranscriptome.out.sorted.bam \ 
	${i}.Aligned.toTranscriptome.out.bam 
	samtools index ${i}.Aligned.toTranscriptome.out.sorted.bam 
	samtools index ${i}.Aligned.sortedByCoord.out.bam 
	done</li>
</ol>
5. Size selection of RPFs and identification of their P-sites
<ol>
	<li>Prepare the transcript annotations. 
	prepare_transcripts -g Homo_sapiens.GRCh38.104.gtf \ 
	-f Homo_sapiens.GRCh38.dna.primary_assembly.fa -o RiboCode_annot 
	NOTE: This command collects required information of mRNA transcripts from the GTF file and extracts the sequences for all mRNA transcripts from the FASTA file (each transcript is assembled by merging the exons according to the structures defined in the GTF file).</li>
	<li>Select RPFs of specific lengths and identify their P-site positions. 
	for i in si-Ctrl-1 si-Ctrl-2 si-Ctrl-3 si-eIF3e-1 si-eIF3e-2 si-eIF3e-3 
	do 
	metaplots -a RiboCode_annot -r ${i}.Aligned.toTranscriptome.out.bam \ 
	-o ${i} -f0_percent 0.35 -pv1 0.001 -pv2 0.001 
	done 
	NOTE: This command plots the aggregate profiles of the 5&#39; end of the aligned reads of each length around annotated translation start (or stop) codons. The read length-dependent P-site can be manually determined by examining the distribution plots (e.g., Figure 1B) of offset distances between 5&#39; ends of the major reads and the start codon. RiboCode also generates a configuration file for each sample, in which the P-site positions of reads displaying significant 3-nt periodicity patterns are automatically determined. The parameters -f0_percent, -pv1, and -pv2 define the proportion threshold and p-value cutoffs for selecting the RPF reads enriched in the reading frame. In this example, the +12, +13, and +13 nucleotides from the 5&#39; end of the 29, 30, and 31 nt reads are manually defined in each configuration file.</li>
	<li>Edit the configuration files for each sample and merge them 
	NOTE: To generate a consensus set of unique ORFs and ensure sufficient coverage of reads to perform subsequent analysis, the selected reads of all samples in the previous step are merged. The reads of specific lengths defined in merged_config.txt file (Supplemental File 1) and their P-site information are used for evaluating the translation potential of ORFs in the next step.</li>
</ol>
6. De novo annotate translating ORFs
<ol>
	<li>Run RiboCode. 
	RiboCode -a RiboCode_annot -c merged_config.txt -l yes -g \ 
	-o RiboCode_ORFs_result -s ATG -m 5 -A CTG,GTG,TTG 
	Where the important parameters of this command are as follows: 
	-c, configuration file containing the path of input files and the information of selected reads and their P-sites. 
	-l, for transcripts having multiple start codons upstream of the stop codons, whether the longest ORFs (the region from the most distal start codon to stop codon) are used for evaluating their translation potential. If set to no, the starting codons will be automatically determined. 
	-s, the canonical start codon(s) used for ORFs identification. 
	-A, (optionally) the noncanonical start codons (e.g., CTG, GTG, and TTG for human) used for ORF identification, which may differ in mitochondria or nucleus of other species29. 
	-m, the minimum length (i.e., amino acids) of ORFs. 
	-o, the prefix of output filename containing the details of predicted ORFs (Supplemental File 2). 
	-g and -b, output the predicted ORFs to gtf or bed format, respectively.</li>
</ol>
7. (Optional) ORF quantification and statistics
<ol>
	<li>Count RPF reads in each ORF. 
	for i in si-Ctrl-1 si-Ctrl-2 si-Ctrl-3 si-eIF3e-1 si-eIF3e-2 si-eIF3e-3 
	do 
	ORFcount -g RiboCode_ORFs_result_collapsed.gtf \ 
	-r ${i}.Aligned.sortedByCoord.out.bam -f 15 -l 5 -m 25 -M 35 \ 
	-o ${i}_ORF.counts -s yes -c intersection-strict 
	done 
	NOTE: To exclude the potential accumulating ribosomes around the start and ends of ORFs, the number of reads allocated in the first 15 (specified by -f) and last 5 codons (specific by -l) are not counted. Optionally, the lengths of counted RPFs are restricted to the range from 25 to 35 nt (common sizes of RPFs).</li>
	<li>Calculate basic statistics of the detected ORFs using RiboCode: 
	Rscript RiboCode_utils.R 
	NOTE: RiboCode_utils.R (Supplemental File 3) provides a series of statistics for the RiboCode output, e.g., counting the number of identified ORFs, viewing the distribution of ORF lengths, and calculating the normalized RPF densities (i.e., RPKM, reads per kilobase per million mapped reads).</li>
</ol>
8. (Optional) Visualization of the predicted ORFs
<ol>
	<li>Obtain the relative positions of the start and stop codons for the desired ORF (e.g., ENSG00000100902_35292349_35292552_67) on its transcript from RiboCode_ORFs_result_collapsed.txt (Supplemental file 3). Then, plot the density of RPF reads in the ORF: 
	plot_orf_density -a RiboCode_annot -c merged_config.txt -t ENST00000622405 \ 
	-s 33 -e 236 --start-codon ATG -o ENSG00000100902_35292349_35292552_67 
	Where -s and -e specify the translation start and stop position of plotting ORF. --start-codon defines the start codon of the ORF, which will appear in the figure title. -o defines the prefix of the output file name.</li>
</ol>
9. (Optional) Metagene analysis using RiboMiner
NOTE: Perform the metagene analysis to assess the influence of EIF3E knockdown on the translation of identified annotated ORFs, following the steps below:
<ol>
	<li>Generate transcripts annotations for RiboMiner, which extracts the longest transcript for each gene based on the annotation file generated by RiboCode (step 5.1). 
	OutputTranscriptInfo -c RiboCode_annot/transcripts_cds.txt \ 
	-g Homo_sapiens.GRCh38.104.gtf -f RiboCode_annot/transcripts_sequence.fa \ 
	-o longest.transcripts.info.txt -O all.transcripts.info.txt</li>
	<li>Prepare the configuration file for RiboMiner. Copy the configuration file generated by the metaplots command of RiboCode (step 5.4) and rename it &#34;RiboMiner_config.txt.&#34; Then, modify it according to the format shown in Supplemental file 4.</li>
	<li>Metagene analyses using RiboMiner
	<ol>
		<li>Use MetageneAnalysis to generate an aggregate and averaged profile of RPFs&#39; densities across transcripts. 
		MetageneAnalysis -f RiboMiner_config.txt -c longest.transcripts.info.txt \ 
		-o MA_normed -U codon -M RPKM -u 100 -d 400 -l 100 -n 10 -m 1 -e 5 --norm yes \ 
		-y 100 --type UTR 
		Where important parameters are: --type, analyzing either CDS or UTR regions; --norm, whether normalized the read density; -y, the number of codons used for each transcript; -U, plot RPF density either at codon level or nt level; -u and -d, define the range of analyzing regions relative to start codon or stop codon; -l, the minimum length (i.e., the number of codons) of CDS; -M, the mode for transcripts filtering, either counts or RPKM; -n minimum counts or RPKM in CDS for analysis. -m minimum counts or RPKM of CDS in the normalized region; -e, the number of codons excluded from the normalized region.</li>
		<li>Generate a set of pdf files for comparing the ribosome occupancies on mRNA in control cells and eIF3-deficient cells. 
		PlotMetageneAnalysis -i MA_normed_dataframe.txt -o MA_normed \ 
		-g si-Ctrl,si-eIF3e -r si-Ctrl-1,si-Ctrl-2,si-Ctrl-3__si-eIF3e-1,si-eIF3e-2,si-eIF3e-3 -u 100 -d 400 --mode mean 
		NOTE: PlotMetageneAnalysis generates the set of pdf files. Details about the usage of MetageneAnalysis and PlotMetageneAnalysis are available at RiboMiner website30.</li>
	</ol>
	</li>
</ol>

<ol>
	<li>Eisenberg, A. 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=Translation+Initiation+Site+Profiling+Reveals+Widespread+Synthesis+of+Non-AUG-Initiated+Protein+Isoforms+in+Yeast.">Translation Initiation Site Profiling Reveals Widespread Synthesis of Non-AUG-Initiated Protein Isoforms in Yeast.</a> Cell Systems. 11 (2), 145-160 (2020).</li><li>Spealman, 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=Conserved+non-AUG+uORFs+revealed+by+a+novel+regression+analysis+of+ribosome+profiling+data.">Conserved non-AUG uORFs revealed by a novel regression analysis of ribosome profiling data.</a> Genome Research. 28 (2), 214-222 (2018).</li><li>Ingolia, N. T., Lareau, L. F., Weissman, J. S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Ribosome+profiling+of+mouse+embryonic+stem+cells+reveals+the+complexity+and+dynamics+of+mammalian+proteomes.">Ribosome profiling of mouse embryonic stem cells reveals the complexity and dynamics of mammalian proteomes.</a> Cell. 147 (4), 789-802 (2011).</li><li>Bazzini, A. A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Identification+of+small+ORFs+in+vertebrates+using+ribosome+footprinting+and+evolutionary+conservation.">Identification of small ORFs in vertebrates using ribosome footprinting and evolutionary conservation.</a> The EMBO Journal. 33 (9), 981-993 (2014).</li><li>Ingolia, N. 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=Ribosome+profiling+reveals+pervasive+translation+outside+of+annotated+protein-coding+genes.">Ribosome profiling reveals pervasive translation outside of annotated protein-coding genes.</a> Cell Reports. 8 (5), 1365-1379 (2014).</li><li>Chew, G. L., Pauli, A., Schier, A. F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Conservation+of+uORF+repressiveness+and+sequence+features+in+mouse,+human+and+zebrafish.">Conservation of uORF repressiveness and sequence features in mouse, human and zebrafish.</a> Nature Communications. 7, 11663 (2016).</li><li>Zhang, 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=Determinants+of+genome-wide+distribution+and+evolution+of+uORFs+in+eukaryotes.">Determinants of genome-wide distribution and evolution of uORFs in eukaryotes.</a> Nature Communications. 12 (1), 1076 (2021).</li><li>Guenther, U. 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=The+helicase+Ded1p+controls+use+of+near-cognate+translation+initiation+codons+in+5'+UTRs.">The helicase Ded1p controls use of near-cognate translation initiation codons in 5' UTRs.</a> Nature. 559 (7712), 130-134 (2018).</li><li>Goldsmith, 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=Ribosome+profiling+reveals+a+functional+role+for+autophagy+in+mRNA+translational+control.">Ribosome profiling reveals a functional role for autophagy in mRNA translational control.</a> Communications Biology. 3 (1), 388 (2020).</li><li>Magny, E. G., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Conserved+regulation+of+cardiac+calcium+uptake+by+peptides+encoded+in+small+open+reading+frames.">Conserved regulation of cardiac calcium uptake by peptides encoded in small open reading frames.</a> Science. 341 (6150), 1116-1120 (2013).</li><li>Stumpf, C. R., Moreno, M. V., Olshen, A. B., Taylor, B. S., Ruggero, D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+translational+landscape+of+the+mammalian+cell+cycle.">The translational landscape of the mammalian cell cycle.</a> Molecular Cell. 52 (4), 574-582 (2013).</li><li>Gerashchenko, M. V., Lobanov, A. V., Gladyshev, V. N. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Genome-wide+ribosome+profiling+reveals+complex+translational+regulation+in+response+to+oxidative+stress.">Genome-wide ribosome profiling reveals complex translational regulation in response to oxidative stress.</a> Proceedings of the National Academy of Sciences of the United States of America. 109 (43), 17394-17399 (2012).</li><li>Andreev, D. E., 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=Oxygen+and+glucose+deprivation+induces+widespread+alterations+in+mRNA+translation+within+20+minutes.">Oxygen and glucose deprivation induces widespread alterations in mRNA translation within 20 minutes.</a> Genome Biology. 16, 90 (2015).</li><li>Chng, S. C., Ho, L., Tian, J., Reversade, B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=ELABELA:+a+hormone+essential+for+heart+development+signals+via+the+apelin+receptor.">ELABELA: a hormone essential for heart development signals via the apelin receptor.</a> Developmental Cell. 27 (6), 672-680 (2013).</li><li>Pauli, A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Toddler:+an+embryonic+signal+that+promotes+cell+movement+via+Apelin+receptors.">Toddler: an embryonic signal that promotes cell movement via Apelin receptors.</a> Science. 343 (6172), 1248636 (2014).</li><li>Stark, A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Discovery+of+functional+elements+in+12+Drosophila+genomes+using+evolutionary+signatures.">Discovery of functional elements in 12 Drosophila genomes using evolutionary signatures.</a> Nature. 450 (7167), 219-232 (2007).</li><li>Lin, M. F., Jungreis, I., Kellis, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=PhyloCSF:+a+comparative+genomics+method+to+distinguish+protein+coding+and+non-coding+regions.">PhyloCSF: a comparative genomics method to distinguish protein coding and non-coding regions.</a> Bioinformatics. 27 (13), 275-282 (2011).</li><li>Slavoff, S. A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Peptidomic+discovery+of+short+open+reading+frame-encoded+peptides+in+human+cells.">Peptidomic discovery of short open reading frame-encoded peptides in human cells.</a> Nature Chemical Biology. 9 (1), 59-64 (2013).</li><li>Schwaid, A. G., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Chemoproteomic+discovery+of+cysteine-containing+human+short+open+reading+frames.">Chemoproteomic discovery of cysteine-containing human short open reading frames.</a> Journal of the American Chemical Society. 135 (45), 16750-16753 (2013).</li><li>Ingolia, N. T., Brar, G. A., Rouskin, S., McGeachy, A. M., Weissman, J. S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Genome-wide+annotation+and+quantitation+of+translation+by+ribosome+profiling.">Genome-wide annotation and quantitation of translation by ribosome profiling.</a> Current Protocols in Molecular Biology. , 1-19 (2013).</li><li>Ingolia, N. T., Ghaemmaghami, S., Newman, J. R., Weissman, J. S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Genome-wide+analysis+in+vivo+of+translation+with+nucleotide+resolution+using+ribosome+profiling.">Genome-wide analysis in vivo of translation with nucleotide resolution using ribosome profiling.</a> Science. 324 (5924), 218-223 (2009).</li><li>Xiao, 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=De+novo+annotation+and+characterization+of+the+translatome+with+ribosome+profiling+data.">De novo annotation and characterization of the translatome with ribosome profiling data.</a> Nucleic Acids Research. 46 (10), 61 (2018).</li><li>Lin, Y., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=eIF3+Associates+with+80S+Ribosomes+to+Promote+Translation+Elongation,+Mitochondrial+Homeostasis,+and+Muscle+Health.">eIF3 Associates with 80S Ribosomes to Promote Translation Elongation, Mitochondrial Homeostasis, and Muscle Health.</a> Molecular Cell. 79 (4), 575-587 (2020).</li><li>. AGAT: Another Gff Analysis Toolkit to handle annotations in any GTF/GFF format Available from: <a target="_blank" href="https://agat.readthedocs.io/en/latest/gff_to_gtf.html">https://agat.readthedocs.io/en/latest/gff_to_gtf.html</a> (2020)</li><li>. Gene Expression Omnibus Available from: <a target="_blank" href="https://www.ncbi.nim.nih.gov/geo">https://www.ncbi.nim.nih.gov/geo</a> (2002)</li><li>Ingolia, N. T., Brar, G. A., Rouskin, S., McGeachy, A. M., Weissman, J. S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+ribosome+profiling+strategy+for+monitoring+translation+in+vivo+by+deep+sequencing+of+ribosome-protected+mRNA+fragments.">The ribosome profiling strategy for monitoring translation in vivo by deep sequencing of ribosome-protected mRNA fragments.</a> Nature Protocols. 7 (8), 1534-1550 (2012).</li><li>. STAR manual Available from: <a target="_blank" href="https://github.com/alexdobin/STAR/blob/master/doc/STARmanual.pdf">https://github.com/alexdobin/STAR/blob/master/doc/STARmanual.pdf</a> (2022)</li><li>. The genetic codes Available from: <a target="_blank" href="https://www.ncbi.nlm.nih.gov/Taxonomy/Utils/wprintgc.cgi">https://www.ncbi.nlm.nih.gov/Taxonomy/Utils/wprintgc.cgi</a> (2019)</li><li>. RiboMiner Available from: <a target="_blank" href="https://github.com/xryanglab/RiboMiner">https://github.com/xryanglab/RiboMiner</a> (2020)</li><li>Ingolia, N. T., Hussmann, J. A., Weissman, J. S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Ribosome+profiling:+global+views+of+translation.">Ribosome profiling: global views of translation.</a> Cold Spring Harbor Perspectives in Biology. 11 (5), 032698 (2018).</li><li>Lee, 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=Global+mapping+of+translation+initiation+sites+in+mammalian+cells+at+single-nucleotide+resolution.">Global mapping of translation initiation sites in mammalian cells at single-nucleotide resolution.</a> Proceedings of the National Academy of Sciences of the United States of America. 109 (37), 2424-2432 (2012).</li><li>Gao, X., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Quantitative+profiling+of+initiating+ribosomes+in+vivo.">Quantitative profiling of initiating ribosomes in vivo.</a> Nature Methods. 12 (2), 147-153 (2015).</li><li>Spealman, P., Naik, A., McManus, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=uORF-seqr:+A+Machine+Learning-Based+approach+to+the+identification+of+upstream+open+reading+frames+in+yeast.">uORF-seqr: A Machine Learning-Based approach to the identification of upstream open reading frames in yeast.</a> Methods in Molecular Biol. 2252, 313-329 (2021).</li><li>. RiboCode Available from: <a target="_blank" href="https://github.com/xryanglab/RiboCode">https://github.com/xryanglab/RiboCode</a> (2018)</li><li>Sharma, P., Wu, J., Nilges, B. S., Leidel, S. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Humans+and+other+commonly+used+model+organisms+are+resistant+to+cycloheximide-mediated+biases+in+ribosome+profiling+experiments.">Humans and other commonly used model organisms are resistant to cycloheximide-mediated biases in ribosome profiling experiments.</a> Nature Communications. 12 (1), 5094 (2021).</li></ol>

The authors have no conflicts of interest to disclose.

Ribosome profiling offers an unprecedented opportunity to study the ribosomes' action in cells at a genome scale. Precisely deciphering the information carried by the ribosome profiling data could provide insight into which regions of genes or transcripts are actively translating. This step-by-step protocol provides guidance on how to use RiboCode to analyze ribosome profiling data in detail, including package installation, data preparation, command execution, result explanation, and data visualization. The analysis resu...

Recently, a growing body of studies has revealed widespread production of peptides translated from ORFs of coding genes and the previously annotated genes as noncoding, such as long noncoding RNAs (lncRNAs)1,2,3,4,5,6,7,8. These translated ORFs are regulated or induced by cells to respond to environmental changes, stress, and cell differentiation1,8,9,10,11,12,13. The translation products of some ORFs have been demonstrated to play important regulatory roles in diverse biological processes in development and physiology. For example, Chng et al.14 discovered a peptide hormone named Elabela (Ela, also known as Apela/Ende/Toddler), which is critical for cardiovascular development. Pauli et al. suggested that Ela also acts as a mitogen that promotes cell migration in the early fish embryo15. Magny et al. reported two micropeptides of less than 30 amino acids regulating calcium transport and affecting regular muscle contraction in the Drosophila heart10.
It remains unclear how many such peptides are encoded by the genome and whether they are biologically relevant. Therefore, systematic identification of these potentially coding ORFs is highly desirable. However, directly determining the products of these ORFs (i.e., protein or peptide) using traditional approaches such as evolutionary conservation16,17 and mass spectrometry18,19 is challenging because the detection efficiency of both approaches is dependent on the length, abundance, and amino acid composition of the produced proteins or peptides. The advent of ribosome profiling, a technique for identifying the ribosome occupancy on mRNAs at nucleotide resolution, has provided a precise way to evaluate the coding potential of different transcripts3,20,21, irrespective of their length and composition. An important and frequently used feature for identifying actively translating ORFs using ribosome profiling is the three-nucleotide (3-nt) periodicity of the ribosome&#39;s footprints on mRNA from the start codon to the stop codon. However, ribosome profiling data often have several issues, including low and sparse sequencing reads along ORFs, high sequencing noise, and ribosomal RNA (rRNA) contaminations. Thus, the distorted and ambiguous signals generated by such data weaken the 3-nt periodicity patterns of ribosomes&#39; footprints on mRNA, which ultimately makes the identification of the high-confidence translated ORFs difficult.
A package named &#34;RiboCode&#34; adapted a modified Wilcoxon-signed-rank test and P-value integration strategy to examine whether the ORF has significantly more in-frame ribosome-protected fragments (RPFs) than off-frame RPFs22. It was demonstrated to be highly efficient, sensitive, and accurate for de novo annotation of the translatome in simulated and real ribosome profiling data. Here, we describe how to use this tool to detect the potential translating ORFs from the raw ribosome profiling sequencing datasets generated by the previous study23. These datasets had been used to explore the function of EIF3 subunit &#34;E&#34; (EIF3E) in translation by comparing the ribosome occupancy profiles of MCF-10A cells transfected with control (si-Ctrl) and EIF3E (si-eIF3e) small-interfering RNAs (siRNAs). By applying RiboCode to these example datasets, we detected 5,633 novel ORFs potentially encoding small peptides or proteins. These ORFs were categorized into various types based on their locations relative to the coding regions, including upstream ORFs (uORFs), downstream ORFs (dORFs), overlapped ORFs, ORFs from novel protein-coding genes (novel PCGs), and ORFs from novel nonprotein-coding genes (novel NonPCGs). The RPF read densities on uORFs were significantly increased in EIF3E-deficient cells compared to control cells, which might be at least partially caused by the enrichment of actively translating ribosomes. The localized ribosome accumulation in the region from the 25th to 75th codon of EIF3E-deficient cells indicated a blockage of translation elongation in the early stage. This protocol also shows how to visualize the RPF density of the desired region for examining the 3-nt periodicity patterns of ribosome footprints on identified ORFs. These analyses demonstrate the powerful role of RiboCode in identifying translating ORFs and studying the regulation of translation.

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>A computer/server running Linux</td>
			<td>Any</td>
			<td>-</td>
			<td>-</td>
		</tr>
		<tr>
			<td>Anaconda or Miniconda</td>
			<td>Anaconda</td>
			<td>-</td>
			<td>Anaconda: https://www.anaconda.com; Miniconda:https://docs.conda.io/en/latest/miniconda.html</td>
		</tr>
		<tr>
			<td>R</td>
			<td>R Foundation</td>
			<td>-</td>
			<td>https://www.r-project.org/</td>
		</tr>
		<tr>
			<td>Rstudio</td>
			<td>Rstudio</td>
			<td>-</td>
			<td>https://www.rstudio.com/</td>
		</tr>
	</tbody>
</table>

de novo identification of actively translated open reading frames with ribosome profiling data

Identification of open reading frames (ORFs), especially those encoding small peptides and being actively translated under specific physiological contexts, is critical for&#160;comprehensive annotations of context-dependent translatomes. Ribosome profiling, a technique for detecting the binding locations and densities of translating ribosomes on RNA, offers an avenue to rapidly discover where translation is occurring at the genome-wide scale. However, it is not a trivial task in bioinformatics to efficiently and comprehensively identify the translating ORFs for ribosome profiling. Described here is an easy-to-use package, named RiboCode, designed to search for actively translating ORFs of any size from distorted and ambiguous signals in ribosome profiling data. Taking our previously published dataset as an example, this article provides step-by-step instructions for the entire RiboCode pipeline, from preprocessing of the raw data to interpretation of the final output result files. Furthermore, for evaluating the translation rates of the annotated ORFs, procedures for visualization and quantification of ribosome densities on each ORF are also described in detail. In summary, the present article is a useful and timely instruction for the research fields related to translation, small ORFs, and peptides.

The example ribosome profiling datasets were deposited in the GEO database under the accession number GSE131074. All the files and codes used in this protocol are available from Supplemental files 1-4. By applying RiboCode to a set of published ribosome profiling datasets23, we identified the novel ORFs actively translated in MCF-10A cells treated with control and EIF3E siRNAs. To select the RPF reads that are most likely bound by the tra...

Watch this Scientific Journal Video about De novo Identification of Actively Translated Open Reading Frames with Ribosome Profiling Data at JoVE.com

De novo Identification of Actively Translated Open Reading Frames with Ribosome Profiling Data

Translating ribosomes decode three nucleotides per codon into peptides. Their movement along mRNA, captured by ribosome profiling, produces the footprints exhibiting characteristic triplet periodicity. This protocol describes how to use RiboCode to decipher this prominent feature from ribosome profiling data to identify actively translated open reading frames at the whole-transcriptome level.

De novo Identification of Actively Translated Open Reading Frames with Ribosome Profiling Data

Identification of open reading frames (ORFs), especially those encoding small peptides and being actively translated under specific physiological ...

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3.4K Views.  Xi’an Jiaotong University Health Science Center. Translating ribosomes decode three nucleotides per codon into peptides. Their movement along mRNA, captured by ribosome profiling, produces the footprints exhibiting characteristic triplet periodicity. This protocol describes how to use RiboCode to decipher this prominent feature from ribosome profiling data to identify actively translated open reading frames at the whole-transcriptome level.

Video: De novo Identification of Actively Translated Open Reading Frames with Ribosome Profiling Data

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

The aim of de novo protein design is to find the amino acid sequences that will fold into a desired 3-dimensional structure with improvements in specific properties, such as binding affinity, agonist or antagonist behavior, or stability, relative to the native sequence. Protein design lies at the center of current advances drug design and discovery. Not only does protein design provide predictions for potentially useful drug targets, but it also enhances our understanding of the protein folding process and protein-protein interactions. Experimental methods such as directed evolution have shown success in protein design. However, such methods are restricted by the limited sequence space that can be searched tractably. In contrast, computational design strategies allow for the screening of a much larger set of sequences covering a wide variety of properties and functionality. We have developed a range of computational de novo protein design methods capable of tackling several important areas of protein design. These include the design of monomeric proteins for increased stability and complexes for increased binding affinity.
To disseminate these methods for broader use we present Protein WISDOM (<a href="http://www.proteinwisdom.org" target="_blank">http://www.proteinwisdom.org</a>), a tool that provides automated methods for a variety of protein design problems. Structural templates are submitted to initialize the design process. The first stage of design is an optimization sequence selection stage that aims at improving stability through minimization of potential energy in the sequence space. Selected sequences are then run through a fold specificity stage and a binding affinity stage. A rank-ordered list of the sequences for each step of the process, along with relevant designed structures, provides the user with a comprehensive quantitative assessment of the design. Here we provide the details of each design method, as well as several notable experimental successes attained through the use of the methods.

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

We developed computational de novo protein design methods capable of tackling several important areas of protein design. To disseminate these methods we present Protein WISDOM, an online tool for protein design (<a href="http://www.proteinwisdom.org" target="_blank">http://www.proteinwisdom.org</a>). Starting from a structural template, design of monomeric proteins for increased stability and complexes for increased binding affinity can be performed.

The aim of de novo protein design is to find the amino acid sequences that will fold into a desired 3-dimensional structure with improvements in specific ...

Analysis of Translation Initiation During Stress Conditions by Polysome Profiling

Precise control of mRNA translation is fundamental for eukaryotic cell homeostasis, particularly in response to physiological and pathological stress. Alterations of this program can lead to the growth of damaged cells, a hallmark of cancer development, or to premature cell death such as seen in neurodegenerative diseases. Much of what is known concerning the molecular basis for translational control has been obtained from polysome analysis using a density gradient fractionation system. This technique relies on ultracentrifugation of cytoplasmic extracts on a linear sucrose gradient. Once the spin is completed, the system allows fractionation and quantification of centrifuged zones corresponding to different translating ribosomes populations, thus resulting in a polysome profile. Changes in the polysome profile are indicative of changes or defects in translation initiation that occur in response to various types of stress. This technique also allows to assess the role of specific proteins on translation initiation, and to measure translational activity of specific mRNAs. Here we describe our protocol to perform polysome profiles in order to assess translation initiation of eukaryotic cells and tissues under either normal or stress growth conditions.

Here, we describe a method to analyze changes in the initiation of mRNA translation of eukaryotic cells in response to stress conditions. This method is based on the velocity separation on sucrose gradients of translating ribosomes from non-translating ribosomes.

Precise control of mRNA translation is fundamental for eukaryotic cell homeostasis, particularly in response to physiological and pathological stress. ...

Chromatin Immunoprecipitation Assay for the Identification of Arabidopsis Protein-DNA Interactions In Vivo

Intricate gene regulatory networks orchestrate biological processes and developmental transitions in plants. Selective transcriptional activation and silencing of genes mediate the response of plants to environmental signals and developmental cues. Therefore, insights into the mechanisms that control plant gene expression are essential to gain a deep understanding of how biological processes are regulated in plants. The chromatin immunoprecipitation (ChIP) technique described here is a procedure to identify the DNA-binding sites of proteins in genes or genomic regions of the model species Arabidopsis thaliana. The interactions with DNA of proteins of interest such as transcription factors, chromatin proteins or posttranslationally modified versions of histones can be efficiently analyzed with the ChIP protocol. This method is based on the fixation of protein-DNA interactions in vivo, random fragmentation of chromatin, immunoprecipitation of protein-DNA complexes with specific antibodies, and quantification of the DNA associated with the protein of interest by PCR techniques. The use of this methodology in Arabidopsis has contributed significantly to unveil transcriptional regulatory mechanisms that control a variety of plant biological processes. This approach allowed the identification of the binding sites of the Arabidopsis chromatin protein EBS to regulatory regions of the master gene of flowering FT. The impact of this protein in the accumulation of particular histone marks in the genomic region of FT was also revealed through ChIP analysis.

Chromatin Immunoprecipitation Assay for the Identification of Arabidopsis Protein-DNA Interactions In Vivo

Chromatin immunoprecipitation is a powerful technique for the identification of DNA binding sites of Arabidopsis proteins in vivo. This procedure includes chromatin cross-linking and fragmentation, immunoprecipitation with selective antibodies against the protein of interest, and qPCR analysis of bound DNA. We describe a simple ChIP assay optimized for Arabidopsis plants.

Intricate gene regulatory networks orchestrate biological processes and developmental transitions in plants. Selective transcriptional activation and ...

An Easy Method for Plant Polysome Profiling

Translation of mRNA to protein is a fundamental and highly regulated biological process. Polysome profiling is considered as a gold standard for the analysis of translational regulation. The method described here is an easy and economical way for fractionating polysomes from various plant tissues. A sucrose gradient is made without the need for a gradient maker by sequentially freezing each layer. Cytosolic extracts are then prepared in a buffer containing cycloheximide and chloramphenicol to immobilize the cytosolic and chloroplastic ribosomes to mRNA and are loaded onto the sucrose gradient. After centrifugation, six fractions are directly collected from the bottom to the top of the gradient, without piercing the ultracentrifugation tube. During collection, the absorbance at 260 nm is read continuously to generate a polysome profile that gives a snapshot of global translational activity. Fractions are then pooled to prepare three different mRNA populations: the polysomes, mRNAs bound to several ribosomes; the monosomes, mRNAs bound to one ribosome; and mRNAs that are not bound to ribosomes. mRNAs are then extracted. This protocol has been validated for different plants and tissues including Arabidopsis thaliana seedlings and adult plants, Nicotiana benthamiana, Solanum lycopersicum, and Oryza sativa leaves.

This protocol describes an easy method to extract and fractionate transcripts from plant tissues on the basis of the number of bound ribosomes. It allows a global estimate of translation activity and the determination of the translational status of specific mRNAs.

Translation of mRNA to protein is a fundamental and highly regulated biological process. Polysome profiling is considered as a gold standard for the ...

Cellular Redox Profiling Using High-content Microscopy

Reactive oxygen species (ROS) regulate essential cellular processes including gene expression, migration, differentiation and proliferation. However, excessive ROS levels induce a state of oxidative stress, which is accompanied by irreversible oxidative damage to DNA, lipids and proteins. Thus, quantification of ROS provides a direct proxy for cellular health condition. Since mitochondria are among the major cellular sources and targets of ROS, joint analysis of mitochondrial function and ROS production in the same cells is crucial for better understanding the interconnection in pathophysiological conditions. Therefore, a high-content microscopy-based strategy was developed for simultaneous quantification of intracellular ROS levels, mitochondrial membrane potential (&#916;&#936;m) and mitochondrial morphology. It is based on automated widefield fluorescence microscopy and image analysis of living adherent cells, grown in multi-well plates, and stained with the cell-permeable fluorescent reporter molecules CM-H2DCFDA (ROS) and TMRM (&#916;&#936;m and mitochondrial morphology). In contrast with fluorimetry or flow-cytometry, this strategy allows quantification of subcellular parameters at the level of the individual cell with high spatiotemporal resolution, both before and after experimental stimulation. Importantly, the image-based nature of the method allows extracting morphological parameters in addition to signal intensities. The combined feature set is used for explorative and statistical multivariate data analysis to detect differences between subpopulations, cell types and/or treatments. Here, a detailed description of the assay is provided, along with an example experiment that proves its potential for unambiguous discrimination between cellular states after chemical perturbation.

This paper presents a high-content microscopy workflow for simultaneous quantification of intracellular ROS levels, as well as mitochondrial membrane potential and morphology &#8211; jointly referred to as mitochondrial morphofunction &#8211; in living adherent cells using the cell-permeant fluorescent reporter molecules 5-(and-6)-chloromethyl-2&#39;,7&#39;-dichlorodihydrofluorescein diacetate, acetyl ester (CM-H2DCFDA) and tetramethylrhodamine methylester (TMRM).

Reactive oxygen species (ROS) regulate essential cellular processes including gene expression, migration, differentiation and proliferation. However, ...

Plant Promoter Analysis: Identification and Characterization of Root Nodule Specific Promoter in the Common Bean

The upstream sequences of gene coding sequences are termed as promoter sequences. Studying the expression patterns of promoters are very significant in understanding the gene regulation and spatiotemporal expression patterns of target genes. On the other hand, it is also critical to establish promoter evaluation tools and genetic transformation techniques that are fast, efficient, and reproducible. In this study, we investigated the spatiotemporal expression pattern of the rhizobial symbiosis-specific nodule inception (NIN) promoter of Phaseolus vulgaris in the transgenic hairy roots. Using plant genome databases and analysis tools we identified, isolated, and cloned the P. vulgaris NIN promoter in a transcriptional fusion to the chimeric reporter &#946;-glucuronidase (GUS) GUS-enhanced::GFP. Further, this protocol describes a rapid and versatile system of genetic transformation in the P. vulgaris using Agrobacterium rhizogenes induced hairy roots. This system generates &#8805;2 cm hairy roots at 10 to 12 days after transformation. Next, we assessed the spatiotemporal expression of NIN promoter in Rhizobium inoculated hairy roots at periodic intervals of post-inoculation. Our results depicted by GUS activity show that the NIN promoter was active during the process of nodulation. Together, the present protocol demonstrates how to identify, isolate, clone, and characterize a plant promoter in the common bean hairy roots. Moreover, this protocol is easy to use in non-specialized laboratories.

Promoter expression analyses are crucial to improving the understanding of gene regulation and the spatiotemporal expression of target genes. Herein we present a protocol to identify, isolate, and clone a plant promoter. Further, we describe the characterization of the nodule-specific promoter in the common bean hairy roots.

The upstream sequences of gene coding sequences are termed as promoter sequences. Studying the expression patterns of promoters are very significant in ...

De Novo Generation of Somatic Stem Cells by YAP/TAZ

Here we present protocols to isolate primary differentiated cells and turn them into stem/progenitor cells (SCs) of the same lineage by transient expression of the transcription factor YAP. With this method, luminal differentiated (LD) cells of the mouse mammary gland are converted into cells that exhibit molecular and functional properties of mammary SCs. YAP also turns fully differentiated pancreatic exocrine cells into pancreatic duct-like progenitors. Similarly, to endogenous, natural SCs, YAP-induced stem-like cells ("ySCs") can be eventually expanded as organoid cultures long term in vitro, without further need of ectopic YAP/TAZ, as ySCs are endowed with a heritable self-renewing SC-like state. 
The reprogramming procedure presented here offers the possibility to generate and expand in vitro progenitor cells of various tissue sources starting from differentiated cells. The straightforward expansion of somatic cells ex vivo has implications for regenerative medicine, for understanding mechanisms of tumor initiation and, more in general, for cell and developmental biology studies.

De Novo Generation of Somatic Stem Cells by YAP/TAZ

Availability of somatic SCs is crucial for regenerative medicine, disease modeling and to gain insight into SC properties. Here we present experimental strategies to reprogram, in vitro, differentiated adult cells into their corresponding expandable tissue-specific stem/progenitor cells by the transient expression of the single transcriptional co-activator YAP.

Here we present protocols to isolate primary differentiated cells and turn them into stem/progenitor cells (SCs) of the same lineage by transient ...

IR-TEx: An Open Source Data Integration Tool for Big Data Transcriptomics Designed for the Malaria Vector Anopheles gambiae

IR-TEx is an application written in Shiny (an R package) that allows exploration of the expression of (as well as assigning functions to) transcripts whose expression is associated with insecticide resistance phenotypes in Anopheles gambiae mosquitoes. The application can be used online or downloaded and used locally by anyone. The local application can be modified to add new insecticide resistance datasets generated from multiple -omics platforms. This guide demonstrates how to add new datasets and handle missing data. Furthermore, IR-TEx can be completely and easily recoded to use-omics datasets from any experimental data, making it a valuable resource to many researchers. The protocol illustrates the utility of IR-TEx in identifying new insecticide resistance candidates using the the microsomal glutathione transferase, GSTMS1, as an example. This transcript is upregulated in multiple pyrethroid resistant populations from C&#244;te D&#39;Ivoire and Burkina Faso. The identification of co-correlated transcripts provides further insight into the putative roles of this gene.

IR-TEx: An Open Source Data Integration Tool for Big Data Transcriptomics Designed for the Malaria Vector Anopheles gambiae

IR-TEx explores insecticide resistance-related transcriptional profiles in the species Anopheles gambiae. Provided here are full instructions for using the application, modifications for exploring multiple transcriptomic datasets, and using the framework to build an interactive database for collections of transcriptomic data from any organism, generated in any platform.

IR-TEx is an application written in Shiny (an R package) that allows exploration of the expression of (as well as assigning functions to) transcripts ...

Worm-align and Worm_CP, Two Open-Source Pipelines for Straightening and Quantification of Fluorescence Image Data Obtained from Caenorhabditis elegans

An issue often encountered when acquiring image data from fixed or anesthetized C. elegans is that worms cross and cluster with their neighbors. This problem is aggravated with increasing density of worms and creates challenges for imaging and quantification. We developed a FIJI-based workflow, Worm-align, that can be used to generate single- or multi-channel montages of user-selected, straightened and aligned worms from raw image data of C. elegans. Worm-align is a simple and user-friendly workflow that does not require prior training of either the user or the analysis algorithm. Montages generated with Worm-align can aid the visual inspection of worms, their classification and representation. In addition, the output of Worm-align can be used for subsequent quantification of fluorescence intensity in single worms, either in FIJI directly, or in other image analysis software platforms. We demonstrate this by importing the Worm-align output into Worm_CP, a pipeline that uses the open-source CellProfiler software. CellProfiler&#8217;s flexibility enables the incorporation of additional modules for high-content screening.&#160;As a practical example, we have used the pipeline on two datasets: the first dataset are images of heat shock reporter worms that express green fluorescent protein (GFP) under the control of the promoter of a heat shock inducible gene hsp-70, and the second dataset are images obtained from fixed worms, stained for fat-stores with a fluorescent dye.

Worm-align and Worm_CP, Two Open-Source Pipelines for Straightening and Quantification of Fluorescence Image Data Obtained from Caenorhabditis elegans

Worm-align/Worm_CP is a simple FIJI/CellProfiler workflow that can be used to straighten and align Caenorhabditis elegans samples and to score whole-worm image-based assays without the need for prior training steps. We have applied Worm-align/Worm_CP to the quantification of&#160;heat-shock induced expression in live animals or lipid droplets&#160;in fixed samples.

An issue often encountered when acquiring image data from fixed or anesthetized C. elegans is that worms cross and cluster with their neighbors. This ...

Assessment of de novo Protein Synthesis Rates in Caenorhabditis elegans

Maintaining a healthy proteome is essential for cell and organismal homeostasis. Perturbation of the balance between protein translational control and degradation instigates a multitude of age-related diseases. Decline of proteostasis quality control mechanisms is a hallmark of ageing. Biochemical methods to detect de novo protein synthesis are still limited, have several disadvantages and cannot be performed in live cells or animals. Caenorhabditis elegans, being transparent and easily genetically modified, is an excellent model to monitor protein synthesis rates by using imaging techniques. Here, we introduce and describe a method to measure de novo protein synthesis in vivo utilizing fluorescence recovery after photobleaching (FRAP). Transgenic animals expressing fluorescent proteins in specific cells or tissues are irradiated by a powerful light source resulting in fluorescence photobleaching. In turn, assessment of fluorescence recovery signifies new protein synthesis in cells and/or tissues of interest. Hence, the combination of transgenic nematodes, genetic and/or pharmacological interventions together with live imaging of protein synthesis rates can shed light on mechanisms mediating age-dependent proteostasis collapse.

Assessment of de novo Protein Synthesis Rates in Caenorhabditis elegans

Here, we introduce and describe a nonradioactive and noninvasive method to assess de novo protein synthesis in vivo, utilizing the nematode Caenorhabditis elegans and fluorescence recovery after photobleaching (FRAP). This method can be combined with genetic and/or pharmacological screens to identify novel modulators of protein synthesis.

Maintaining a healthy proteome is essential for cell and organismal homeostasis. Perturbation of the balance between protein translational control and ...