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Thousands of novel translated open reading frames in humans inferred by ribosome footprint profiling

View Article: PubMed Central - PubMed

ABSTRACT

Accurate annotation of protein coding regions is essential for understanding how genetic information is translated into function. We describe riboHMM, a new method that uses ribosome footprint data to accurately infer translated sequences. Applying riboHMM to human lymphoblastoid cell lines, we identified 7273 novel coding sequences, including 2442 translated upstream open reading frames. We observed an enrichment of footprints at inferred initiation sites after drug-induced arrest of translation initiation, validating many of the novel coding sequences. The novel proteins exhibit significant selective constraint in the inferred reading frames, suggesting that many are functional. Moreover, ~40% of bicistronic transcripts showed negative correlation in the translation levels of their two coding sequences, suggesting a potential regulatory role for these novel regions. Despite known limitations of mass spectrometry to detect protein expressed at low level, we estimated a 14% validation rate. Our work significantly expands the set of known coding regions in humans.

Doi:: http://dx.doi.org/10.7554/eLife.13328.001

No MeSH data available.


Related in: MedlinePlus

Characteristics of peptides matched to novel CDS.(A) Histogram of number of independent identification of unique peptides across novel mCDS that have at least one peptide match. (B) Scatter plot of the untreated ribosome footprint density and the protein abundance (median abundance across all unique peptides) across all novel mCDS that have at least one peptide match (Spearman R = 0.24; ).DOI:http://dx.doi.org/10.7554/eLife.13328.015
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fig3s3: Characteristics of peptides matched to novel CDS.(A) Histogram of number of independent identification of unique peptides across novel mCDS that have at least one peptide match. (B) Scatter plot of the untreated ribosome footprint density and the protein abundance (median abundance across all unique peptides) across all novel mCDS that have at least one peptide match (Spearman R = 0.24; ).DOI:http://dx.doi.org/10.7554/eLife.13328.015

Mentions: We next tested whether we could detect the novel mCDS predictions using mass spectrometry data. We used a large data set of SILAC-labeled tandem mass-spectra generated by trypsin-cleavage of large, stable proteins in many of the same LCLs (Battle et al., 2015). Running MaxQuant (Cox and Mann, 2008) against the sequence database of 4831 novel mCDS, at 10% FDR, we identified 161 novel mCDS sequences that have at least one unique peptide hit -- a tryptic peptide that matches a mass-spectrum (Supplementary file 1). More than 70% of novel mCDS with a peptide hit have at least 2 distinct peptides matched to it and, in almost all cases, the unique peptides were independently identified in two or more LCLs (Figure 3—figure supplement 3).


Thousands of novel translated open reading frames in humans inferred by ribosome footprint profiling
Characteristics of peptides matched to novel CDS.(A) Histogram of number of independent identification of unique peptides across novel mCDS that have at least one peptide match. (B) Scatter plot of the untreated ribosome footprint density and the protein abundance (median abundance across all unique peptides) across all novel mCDS that have at least one peptide match (Spearman R = 0.24; ).DOI:http://dx.doi.org/10.7554/eLife.13328.015
© Copyright Policy
Related In: Results  -  Collection

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getmorefigures.php?uid=PMC4940163&req=5

fig3s3: Characteristics of peptides matched to novel CDS.(A) Histogram of number of independent identification of unique peptides across novel mCDS that have at least one peptide match. (B) Scatter plot of the untreated ribosome footprint density and the protein abundance (median abundance across all unique peptides) across all novel mCDS that have at least one peptide match (Spearman R = 0.24; ).DOI:http://dx.doi.org/10.7554/eLife.13328.015
Mentions: We next tested whether we could detect the novel mCDS predictions using mass spectrometry data. We used a large data set of SILAC-labeled tandem mass-spectra generated by trypsin-cleavage of large, stable proteins in many of the same LCLs (Battle et al., 2015). Running MaxQuant (Cox and Mann, 2008) against the sequence database of 4831 novel mCDS, at 10% FDR, we identified 161 novel mCDS sequences that have at least one unique peptide hit -- a tryptic peptide that matches a mass-spectrum (Supplementary file 1). More than 70% of novel mCDS with a peptide hit have at least 2 distinct peptides matched to it and, in almost all cases, the unique peptides were independently identified in two or more LCLs (Figure 3—figure supplement 3).

View Article: PubMed Central - PubMed

ABSTRACT

Accurate annotation of protein coding regions is essential for understanding how genetic information is translated into function. We describe riboHMM, a new method that uses ribosome footprint data to accurately infer translated sequences. Applying riboHMM to human lymphoblastoid cell lines, we identified 7273 novel coding sequences, including 2442 translated upstream open reading frames. We observed an enrichment of footprints at inferred initiation sites after drug-induced arrest of translation initiation, validating many of the novel coding sequences. The novel proteins exhibit significant selective constraint in the inferred reading frames, suggesting that many are functional. Moreover, ~40% of bicistronic transcripts showed negative correlation in the translation levels of their two coding sequences, suggesting a potential regulatory role for these novel regions. Despite known limitations of mass spectrometry to detect protein expressed at low level, we estimated a 14% validation rate. Our work significantly expands the set of known coding regions in humans.

Doi:: http://dx.doi.org/10.7554/eLife.13328.001

No MeSH data available.


Related in: MedlinePlus