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>

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<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/pubmed?term=((The+ribosome+profiling+strategy+for+monitoring+translation+in+vivo+by+deep+sequencing+of+ribosome-protected+mRNA+fragments%5BTitle%5D)+AND+%22Nature+Protocols%22%5BJournal%5D)">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>Dobin, A. 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>Elzanowski, A. 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>Li, F. 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/pubmed?term=((Ribosome+profiling%3A+global+views+of+translation%5BTitle%5D)+AND+%22Cold+Spring+Harbor+Perspectives+in+Biology%22%5BJournal%5D)">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/pubmed?term=((Global+mapping+of+translation+initiation+sites+in+mammalian+cells+at+single-nucleotide+resolution%5BTitle%5D)+AND+%22Proceedings+of+the+National+Academy+of+Sciences+of+the+United+States+of+America%22%5BJournal%5D)">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/pubmed?term=((Quantitative+profiling+of+initiating+ribosomes+in+vivo%5BTitle%5D)+AND+%22Nature+Methods%22%5BJournal%5D)">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/pubmed?term=((uORF-seqr%3A+A+Machine+Learning-Based+approach+to+the+identification+of+upstream+open+reading+frames+in+yeast%5BTitle%5D)+AND+%22Methods+in+Molecular+Biol%22%5BJournal%5D)">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>Xiao, Z. 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/pubmed?term=((Humans+and+other+commonly+used+model+organisms+are+resistant+to+cycloheximide-mediated+biases+in+ribosome+profiling+experiments%5BTitle%5D)+AND+%22Nature+Communications%22%5BJournal%5D)">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><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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Research

JoVE Journal

Biology

3.5K Views.  Xi’an Jiaotong University Health Science Center. Interpretatie van de sequencinggegevens gegenereerd door het ribosoomprofileringsexperiment is van cruciaal belang voor het kwantitatief meten van de translationele activiteiten van ribosomen op mRNA en voor het bestuderen van de mechanismen van translationele regulatie. In dit protocol beschrijven we de computationele procedure voor het gebruik van de ribosoomprofileringsgegevens en RiboCode, een opdrachtregelprogramma om mRNA-translatie op genoombrede schaal en enkele nucleotideresolutie te...

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

Genome-wide Quantification of Translation in Budding Yeast by Ribosome Profiling

Translation of mRNA into proteins is a complex process involving several layers of regulation. It is often assumed that changes in mRNA transcription reflect changes in protein synthesis, but many exceptions have been observed. Recently, a technique called ribosome profiling (or Ribo-Seq) has emerged as a powerful method that allows identification, with high accuracy, which regions of mRNA are translated into proteins and quantification of translation at the genome-wide level. Here, we present a generalized protocol for genome-wide quantification of translation using Ribo-Seq in budding yeast. In addition, combining Ribo-Seq data with mRNA abundance measurements allows us to simultaneously quantify translation efficiency of thousands of mRNA transcripts in the same sample and compare changes in these parameters in response to experimental manipulations or in different physiological states. We describe a detailed protocol for generation of ribosome footprints using nuclease digestion, isolation of intact ribosome-footprint complexes via sucrose gradient fractionation, and preparation of DNA libraries for deep sequencing along with appropriate quality controls necessary to ensure accurate analysis of in vivo translation.

Translational regulation plays an important role in the control of protein abundance. Here, we describe a high-throughput method for quantitative analysis of translation in the budding yeast Saccharomyces cerevisiae.

Translation of mRNA into proteins is a complex process involving several layers of regulation. It is often assumed that changes in mRNA transcription ...

Biochemistry

Mass Spectrometry-Based Proteomics Analyses Using the OpenProt Database to Unveil Novel Proteins Translated from Non-Canonical Open Reading Frames

Genome annotation is central to today's proteomic research as it draws the outlines of the proteomic landscape. Traditional models of open reading frame (ORF) annotation impose two arbitrary criteria: a minimum length of 100 codons and a single ORF per transcript. However, a growing number of studies report expression of proteins from allegedly non-coding regions, challenging the accuracy of current genome annotations. These novel proteins were found encoded either within non-coding RNAs, 5' or 3' untranslated regions (UTRs) of mRNAs, or overlapping a known coding sequence (CDS) in an alternative ORF. OpenProt is the first database that enforces a polycistronic model for eukaryotic genomes, allowing annotation of multiple ORFs per transcript. OpenProt is freely accessible and offers custom downloads of protein sequences across 10 species. Using OpenProt database for proteomic experiments enables novel proteins discovery and highlights the polycistronic nature of eukaryotic genes. The size of OpenProt database (all predicted proteins) is substantial and need be taken in account for the analysis. However, with appropriate false discovery rate (FDR) settings or the use of a restricted OpenProt database, users will gain a more realistic view of the proteomic landscape. Overall, OpenProt is a freely available tool that will foster proteomic discoveries.

OpenProt is a freely accessible database that enforces a polycistronic model of eukaryotic genomes. Here, we present a protocol for the use of OpenProt databases when interrogating mass spectrometry datasets. Using OpenProt database for analysis of proteomic experiments allows for discovery of novel and previously undetectable proteins.

Genome annotation is central to today's proteomic research as it draws the outlines of the proteomic landscape. Traditional models of open reading frame ...

Genetics

RIBO-seq in Bacteria: a Sample Collection and Library Preparation Protocol for NGS Sequencing

The ribosome profiling technique (RIBO-seq) is currently the most effective tool for studying the process of protein synthesis in vivo. The advantage of this method, in comparison to other approaches, is its ability to monitor translation by precisely mapping the position and number of ribosomes on a mRNA transcript.
In this article, we describe the consecutive stages of sample collection and preparation for RIBO-seq method in bacteria, highlighting the details relevant to the planning and execution of the experiment.
Since the RIBO-seq relies on intact ribosomes and related mRNAs, the key step is rapid inhibition of translation and adequate disintegration of cells. Thus, we suggest filtration and flash-freezing in liquid nitrogen for cell harvesting with an optional pretreatment with chloramphenicol to arrest translation in bacteria. For the disintegration, we propose grinding frozen cells with mortar and pestle in the presence of aluminum oxide to mechanically disrupt the cell wall. In this protocol, sucrose cushion or a sucrose gradient ultracentrifugation for monosome purification is not required. Instead, mRNA separation using polyacrylamide gel electrophoresis (PAGE) followed by the ribosomal footprint excision (28-30 nt band) is applied and provides satisfactory results. This largely simplifies the method as well as reduces the time and equipment requirements for the procedure. For library preparation, we recommend using the commercially available small RNA kit for Illumina sequencing from New England Biolabs, following manufacturer&#39;s guidelines with some degree of optimization.
The resulting cDNA libraries present appropriate quantity and quality required for next generation sequencing (NGS). Sequencing of the libraries prepared according to the described protocol results in 2 to 10 mln uniquely mapped reads per sample providing sufficient data for comprehensive bioinformatic analysis. The protocol we present is quick and relatively easy and can be performed with standard laboratory equipment.

Here we describe the stages of sample collection and preparation for RIBO-seq in bacteria. Sequencing of the libraries prepared according to these guidelines results in sufficient data for comprehensive bioinformatic analysis. The protocol we present is simple, uses standard laboratory equipment and takes seven days from lysis to obtaining libraries.

The ribosome profiling technique (RIBO-seq) is currently the most effective tool for studying the process of protein synthesis in vivo. The advantage of ...

In vivo Interrogation of Central Nervous System Translatome by Polyribosome Fractionation

Multiple processes are involved in gene expression including transcription, translation and stability of mRNAs and proteins. Each of these steps are tightly regulated, affecting the final dynamics of protein abundance. Various regulatory mechanisms exist at the translation step, rendering mRNA levels alone an unreliable indicator of gene expression. In addition, local regulation of mRNA translation has been particularly implicated in neuronal functions, shifting &#39;translatomics&#39; to the focus of attention in neurobiology. The presented method can be used to bridge transcriptomics and proteomics.
Here we describe essential modifications to the technique of polyribosome fractionation, which interrogates the translatome based on the association of actively translated mRNAs to multiple ribosomes and their differential sedimentation in sucrose gradients. Traditionally, working with in vivo samples, particularly of the central nervous system (CNS), has proven challenging due to the restricted amounts of material and the presence of fatty tissue components. In order to address this, the described protocol is specifically optimized for use with minimal amount of CNS material, as demonstrated by the use of single mouse spinal cord and brain. Briefly, CNS tissues are extracted and translating ribosomes are immobilized on mRNAs with cycloheximide. Myelin flotation is then performed to remove lipid rich components. Fractionation is performed on a sucrose gradient where mRNAs are separated according to their ribosomal loading. Isolated fractions are suitable for a range of downstream assays, including new genome wide assay technologies.

In vivo Interrogation of Central Nervous System Translatome by Polyribosome Fractionation

This protocol illustrates essential modifications of polyribosome fractionation in order to study the translatome of in vivo CNS samples. It allows global assessment of translation and transcription regulation through the isolation and comparison of total RNA to ribosome bound RNA fractions.

Multiple processes are involved in gene expression including transcription, translation and stability of mRNAs and proteins. Each of these steps are ...

Neuroscience

Isolation of Ribosome Bound Nascent Polypeptides in vitro to Identify Translational Pause Sites Along mRNA

The rate of translational elongation is non-uniform. mRNA secondary structure, codon usage and mRNA associated proteins may alter ribosome movement on the messagefor review see 1. However, it's now widely accepted that synonymous codon usage is the primary cause of non-uniform translational elongation rates1. Synonymous codons are not used with identical frequency. A bias exists in the use of synonymous codons with some codons used more frequently than others2. Codon bias is organism as well as tissue specific2,3. Moreover, frequency of codon usage is directly proportional to the concentrations of cognate tRNAs4. Thus, a frequently used codon will have higher multitude of corresponding tRNAs, which further implies that a frequent codon will be translated faster than an infrequent one. Thus, regions on mRNA enriched in rare codons (potential pause sites) will as a rule slow down ribosome movement on the message and cause accumulation of nascent peptides of the respective sizes5-8. These pause sites can have functional impact on the protein expression, mRNA stability and protein foldingfor review see 9. Indeed, it was shown that alleviation of such pause sites can alter ribosome movement on mRNA and subsequently may affect the efficiency of co-translational (in vivo) protein folding1,7,10,11. To understand the process of protein folding in vivo, in the cell, that is ultimately coupled to the process of protein synthesis it is essential to gain comprehensive insights into the impact of codon usage/tRNA content on the movement of ribosomes along mRNA during translational elongation.
Here we describe a simple technique that can be used to locate major translation pause sites for a given mRNA translated in various cell-free systems6-8. This procedure is based on isolation of nascent polypeptides accumulating on ribosomes during in vitro translation of a target mRNA. The rationale is that at low-frequency codons, the increase in the residence time of the ribosomes results in increased amounts of nascent peptides of the corresponding sizes. In vitro transcribed mRNA is used for in vitro translational reactions in the presence of radioactively labeled amino acids to allow the detection of the nascent chains. In order to isolate ribosome bound nascent polypeptide complexes the translation reaction is layered on top of 30% glycerol solution followed by centrifugation. Nascent polypeptides in polysomal pellet are further treated with ribonuclease A and resolved by SDS PAGE. This technique can be potentially used for any protein and allows analysis of ribosome movement along mRNA and the detection of the major pause sites. Additionally, this protocol can be adapted to study factors and conditions that can alter ribosome movement and thus potentially can also alter the function/conformation of the protein.

Isolation of Ribosome Bound Nascent Polypeptides in vitro to Identify Translational Pause Sites Along mRNA

A technique to identify translational pause sites on mRNA is described. This procedure is based on isolation of nascent polypeptides accumulating on ribosomes during in vitro translation of a target mRNA, followed by the size analysis of the nascent chains using a denaturing gel electrophoresis.

The rate of translational elongation is non-uniform. mRNA secondary structure, codon usage and mRNA associated proteins may alter ribosome movement on the ...

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. ...

Polysome Fractionation and Analysis of Mammalian Translatomes on a Genome-wide Scale

mRNA translation plays a central role in the regulation of gene expression and represents the most energy consuming process in mammalian cells. Accordingly, dysregulation of mRNA translation is considered to play a major role in a variety of pathological states including cancer. Ribosomes also host chaperones, which facilitate folding of nascent polypeptides, thereby modulating function and stability of newly synthesized polypeptides. In addition, emerging data indicate that ribosomes serve as a platform for a repertoire of signaling molecules, which are implicated in a variety of post-translational modifications of newly synthesized polypeptides as they emerge from the ribosome, and/or components of translational machinery. Herein, a well-established method of ribosome fractionation using sucrose density gradient centrifugation is described. In conjunction with the in-house developed &#8220;anota&#8221; algorithm this method allows direct determination of differential translation of individual mRNAs on a genome-wide scale. Moreover, this versatile protocol can be used for a variety of biochemical studies aiming to dissect the function of ribosome-associated protein complexes, including those that play a central role in folding and degradation of newly synthesized polypeptides.

Ribosomes play a central role in protein synthesis. Polyribosome (polysome) fractionation by sucrose density gradient centrifugation allows direct determination of translation efficiencies of individual mRNAs on a genome-wide scale. In addition, this method can be used for biochemical analysis of ribosome- and polysome-associated factors such as chaperones and signaling molecules.

mRNA translation plays a central role in the regulation of gene expression and represents the most energy consuming process in mammalian cells. ...

Assessment of Selective mRNA Translation in Mammalian Cells by Polysome Profiling

Regulation of protein synthesis represents a key control point in cellular response to stress. In particular, discreet RNA regulatory elements were shown to allow to selective translation of specific mRNAs, which typically encode for proteins required for a particular stress response. Identification of these mRNAs, as well as the characterization of regulatory mechanisms responsible for selective translation has been at the forefront of molecular biology for some time. Polysome profiling is a cornerstone method in these studies. The goal of polysome profiling is to capture mRNA translation by immobilizing actively translating ribosomes on different transcripts and separate the resulting polyribosomes by ultracentrifugation on a sucrose gradient, thus allowing for a distinction between highly translated transcripts and poorly translated ones. These can then be further characterized by traditional biochemical and molecular biology methods. Importantly, combining polysome profiling with high throughput genomic approaches allows for a large scale analysis of translational regulation.

The ability of cells to adapt to stress is crucial for their survival. Regulation of mRNA translation is one such adaptation strategy, providing for rapid regulation of the proteome. Here, we provide a standardized polysome profiling protocol to identify specific mRNAs that are selectively translated under stress conditions.

Regulation of protein synthesis represents a key control point in cellular response to stress. In particular, discreet RNA regulatory elements were shown ...

Rapid In Vivo Fixation and Isolation of Translational Complexes from Eukaryotic Cells

Rapid responses involving fast redistribution of messenger(m)RNA and alterations of mRNA translation are pertinent to ongoing homeostatic adjustments of the cells. These adjustments are critical to eukaryotic cell survivability and &#39;damage control&#39; during fluctuating nutrient and salinity levels, temperature, and various chemical and radiation stresses. Due to the highly dynamic nature of the RNA-level responses, and the instability of many of the RNA:RNA and RNA:protein intermediates, obtaining a meaningful snapshot of the cytoplasmic RNA state is only possible with a limited number of methods. Transcriptome-wide, RNA-seq-based ribosome profiling-type experiments are among the most informative sources of data for the control of translation. However, absence of a uniform RNA and RNA:protein intermediate stabilization can lead to different biases, particularly in the fast-paced cellular response pathways. In this article, we provide a detailed protocol of rapid fixation applicable to eukaryotic cells of different permeability, to aid in RNA and RNA:protein intermediate stabilization. We further provide examples of isolation of the stabilized RNA:protein complexes based on their co-sedimentation with ribosomal and poly(ribo)somal fractions. The separated stabilized material can be subsequently used as part of ribosome profiling-type experiments, such as in Translation Complex Profile sequencing (TCP-seq) approach and its derivatives. Versatility of the TCP-seq-style methods has now been demonstrated by the applications in a variety of organisms and cell types. The stabilized complexes can also be additionally affinity-purified and imaged using electron microscopy, separated into different poly(ribo)somal fractions and subjected to RNA sequencing, owing to the ease of the crosslink reversal. Therefore, methods based on snap-chilling and formaldehyde fixation, followed by the sedimentation-based or other type of RNA:protein complex enrichment, can be of particular interest in investigating finer details of rapid RNA:protein complex dynamics in live cells.

Rapid In Vivo Fixation and Isolation of Translational Complexes from Eukaryotic Cells

We present a technique to rapidly stabilize translational (protein biosynthesis) complexes with formaldehyde crosslinking in live yeast and mammalian cells. The approach enables dissecting transient intermediates and dynamic RNA:protein interactions. The crosslinked complexes can be used in multiple downstream applications such as in deep sequencing-based profiling methods, microscopy, and mass-spectrometry.

Rapid responses involving fast redistribution of messenger(m)RNA and alterations of mRNA translation are pertinent to ongoing homeostatic adjustments of ...

Global Identification of Co-Translational Interaction Networks by Selective Ribosome Profiling

In recent years, it has become evident that ribosomes not only decode our mRNA but also guide the emergence of the polypeptide chain into the crowded cellular environment. Ribosomes provide the platform for spatially and kinetically controlled binding of membrane-targeting factors, modifying enzymes, and folding chaperones. Even the assembly into high-order oligomeric complexes, as well as protein-protein network formation steps, were recently discovered to be coordinated with synthesis.
Here, we describe Selective Ribosome Profiling, a method developed to capture co-translational interactions in vivo. We will detail the various affinity purification steps required for capturing ribosome-nascent-chain complexes together with co-translational interactors, as well as the mRNA extraction, size exclusion, reverse transcription, deep-sequencing, and big-data analysis steps, required to decipher co-translational interactions in near-codon resolution.

Co-translational interactions play a crucial role in nascent-chain modifications, targeting, folding, and assembly pathways. Here, we describe Selective Ribosome Profiling, a method for in vivo, direct analysis of these interactions in the model eukaryote Saccharomyces cerevisiae.

In recent years, it has become evident that ribosomes not only decode our mRNA but also guide the emergence of the polypeptide chain into the crowded ...

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

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