Robust Identification of Noncoding RNA from Transcriptomes Requires Phylogenetically-Informed Sampling
Publication Date
October 30, 2014
Journal
PLOS Computational Biology
Authors
Stinus Lindgreen, Sinan Uğur Umu, Alicia Sook Wei Lai, Hisham Eldai, et al
Volume
10
Issue
10
Pages
e1003907
DOI
https://dx.plos.org/10.1371/journal.pcbi.1003907
Publisher URL
http://journals.plos.org/ploscompbiol/article?id=10.1371%2Fjournal.pcbi.1003907
PubMed
http://www.ncbi.nlm.nih.gov/pubmed/25357249
PubMed Central
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4214555
Europe PMC
http://europepmc.org/abstract/MED/25357249
Web of Science
000344547900045
Scopus
84908336343
Mendeley
http://www.mendeley.com/research/robust-identification-noncoding-rna-transcriptomes-requires-phylogeneticallyinformed-sampling
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Mendeley | Further Information

{"title"=>"Robust Identification of Noncoding RNA from Transcriptomes Requires Phylogenetically-Informed Sampling", "type"=>"journal", "authors"=>[{"first_name"=>"Stinus", "last_name"=>"Lindgreen", "scopus_author_id"=>"15128650200"}, {"first_name"=>"Sinan Uğur", "last_name"=>"Umu", "scopus_author_id"=>"56398365200"}, {"first_name"=>"Alicia Sook Wei", "last_name"=>"Lai", "scopus_author_id"=>"56397654800"}, {"first_name"=>"Hisham", "last_name"=>"Eldai", "scopus_author_id"=>"56306558200"}, {"first_name"=>"Wenting", "last_name"=>"Liu", "scopus_author_id"=>"56397268000"}, {"first_name"=>"Stephanie", "last_name"=>"McGimpsey", "scopus_author_id"=>"56398308500"}, {"first_name"=>"Nicole E.", "last_name"=>"Wheeler", "scopus_author_id"=>"56397818600"}, {"first_name"=>"Patrick J.", "last_name"=>"Biggs", "scopus_author_id"=>"55347344000"}, {"first_name"=>"Nick R.", "last_name"=>"Thomson", "scopus_author_id"=>"7201917002"}, {"first_name"=>"Lars", "last_name"=>"Barquist", "scopus_author_id"=>"24484749700"}, {"first_name"=>"Anthony M.", "last_name"=>"Poole", "scopus_author_id"=>"7201466330"}, {"first_name"=>"Paul P.", "last_name"=>"Gardner", "scopus_author_id"=>"8834594200"}], "year"=>2014, "source"=>"PLoS Computational Biology", "identifiers"=>{"scopus"=>"2-s2.0-84908336343", "pmid"=>"25357249", "sgr"=>"84908336343", "doi"=>"10.1371/journal.pcbi.1003907", "isbn"=>"1553-7358 (Electronic)\\r1553-734X (Linking)", "issn"=>"15537358", "pui"=>"600310966", "arxiv"=>"1406.7133"}, "id"=>"12bc604f-6c89-3f42-8bfb-15a693236c1d", "abstract"=>"Noncoding RNAs are integral to a wide range of biological processes, including translation, gene regulation, host-pathogen interactions and environmental sensing. While genomics is now a mature field, our capacity to identify noncoding RNA elements in bacterial and archaeal genomes is hampered by the difficulty of de novo identification. The emergence of new technologies for characterizing transcriptome outputs, notably RNA-seq, are improving noncoding RNA identification and expression quantification. However, a major challenge is to robustly distinguish functional outputs from transcriptional noise. To establish whether annotation of existing transcriptome data has effectively captured all functional outputs, we analysed over 400 publicly available RNA-seq datasets spanning 37 different Archaea and Bacteria. Using comparative tools, we identify close to a thousand highly-expressed candidate noncoding RNAs. However, our analyses reveal that capacity to identify noncoding RNA outputs is strongly dependent on phylogenetic sampling. Surprisingly, and in stark contrast to protein-coding genes, the phylogenetic window for effective use of comparative methods is perversely narrow: aggregating public datasets only produced one phylogenetic cluster where these tools could be used to robustly separate unannotated noncoding RNAs from a null hypothesis of transcriptional noise. Our results show that for the full potential of transcriptomics data to be realized, a change in experimental design is paramount: effective transcriptomics requires phylogeny-aware sampling.", "link"=>"http://www.mendeley.com/research/robust-identification-noncoding-rna-transcriptomes-requires-phylogeneticallyinformed-sampling", "reader_count"=>68, "reader_count_by_academic_status"=>{"Unspecified"=>3, "Professor > Associate Professor"=>6, "Researcher"=>14, "Student > Doctoral Student"=>2, "Student > Ph. D. Student"=>25, "Student > Postgraduate"=>3, "Student > Master"=>7, "Student > Bachelor"=>5, "Professor"=>3}, "reader_count_by_user_role"=>{"Unspecified"=>3, "Professor > Associate Professor"=>6, "Researcher"=>14, "Student > Doctoral Student"=>2, "Student > Ph. D. Student"=>25, "Student > Postgraduate"=>3, "Student > Master"=>7, "Student > Bachelor"=>5, "Professor"=>3}, "reader_count_by_subject_area"=>{"Unspecified"=>3, "Biochemistry, Genetics and Molecular Biology"=>14, "Agricultural and Biological Sciences"=>44, "Medicine and Dentistry"=>2, "Chemistry"=>1, "Computer Science"=>3, "Immunology and Microbiology"=>1}, "reader_count_by_subdiscipline"=>{"Medicine and Dentistry"=>{"Medicine and Dentistry"=>2}, "Chemistry"=>{"Chemistry"=>1}, "Immunology and Microbiology"=>{"Immunology and Microbiology"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>44}, "Computer Science"=>{"Computer Science"=>3}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>14}, "Unspecified"=>{"Unspecified"=>3}}, "reader_count_by_country"=>{"New Zealand"=>1, "Netherlands"=>1, "Belgium"=>1, "United States"=>4, "Brazil"=>1, "Denmark"=>1, "United Kingdom"=>1, "Switzerland"=>1, "Germany"=>2}, "group_count"=>5}

Scopus | Further Information

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/1772651"], "description"=>"<p>Non-coding RNA elements show high expression across transcriptomes. Both annotated Rfam families (red - core Rfam families (see <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003907#s4\" target=\"_blank\">Methods</a>) are dark red, all others are light red) and expressed RUFs (black) are among the highest expressed outputs in transcriptomes (blue - core Pfam families (see <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003907#s4\" target=\"_blank\">Methods</a>) are dark blue, all others are light blue). For each strain we generated relative rankings of expression spanning protein coding genes, RNA genes and candidate RUFs. Accurately estimating expression levels from read depths is confounded by a number of factors (e.g. sample preparation, overall sequencing depths, rRNA depletion, etc.). For consistency, we have ranked genes for each strain and compared rankings instead of comparing the read depths directly between strains. For a given strain, the annotated genes were ranked based on the median read depth of the annotated region. RUFs were manually picked by masking out annotated genes and selecting regions showing evidence of expression by inspecting read depth across the genome. This yielded 844 gene candidate sequences in Bacteria and 78 in Archaea. The plot contains the 50 most highly expressed elements for each strain/species.</p>", "links"=>[], "tags"=>["transcriptional noise", "noncoding RNA outputs", "unannotated noncoding RNAs", "phylogenetic", "noncoding RNA identification", "noncoding RNA elements"], "article_id"=>1223558, "categories"=>["Uncategorised"], "users"=>["Stinus Lindgreen", "Sinan Uğur Umu", "Alicia Sook-Wei Lai", "Hisham Eldai", "Wenting Liu", "Stephanie McGimpsey", "Nicole E. Wheeler", "Patrick J. Biggs", "Nick R. Thomson", "Lars Barquist", "Anthony M. Poole", "Paul P. Gardner"], "doi"=>["https://dx.doi.org/10.1371/journal.pcbi.1003907.g001"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Identification_of_transcribed_elements_across_publicly_available_RNA_seq_data_/1223558", "title"=>"Identification of transcribed elements across publicly-available RNA-seq data.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-10-30 03:36:13"}
  • {"files"=>["https://ndownloader.figshare.com/files/1772654"], "description"=>"<p><b>A–C:</b> We have defined the “family conservation” for Pfam, Rfam and RUFs based upon the maximum phylogenetic distance (using structural SSU rRNA alignments) between any two strains hosting the family. We have divided the highly expressed transcripts (ranks 1–204) into Low, Medium and High conservation groups based on the lower-quartile, inter-quartile range and the upper-quartile of the family conservation measure (see <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003907#s4\" target=\"_blank\">Methods</a> for further details). Both the known Rfam families and the RUFs identified in this analysis are often highly expressed transcripts. In contrast to protein-coding transcripts (blue), where highly-expressed transcripts are well-conserved, the opposite is true of many non-coding RNA elements (Rfam, red; RUFs, black). Notably, the greatest proportion of highly expressed Rfam-annotated RNA elements show a narrow evolutionary distribution. This is also reflected in the RUFs identified in this study. <b>D:</b> Venn diagram of the 568 highly expressed RUFs. Each RUF was analysed to look for evidence of secondary structure formation, level of conservation, and evidence of expression in at least one other RNA-seq dataset. All RUFs showing expression in other strains/species are conserved in at least two strains/species, so the figure also shows that 219 highly expressed RUFs are conserved across a limited phylogenetic distance only.</p>", "links"=>[], "tags"=>["transcriptional noise", "noncoding RNA outputs", "unannotated noncoding RNAs", "phylogenetic", "noncoding RNA identification", "noncoding RNA elements"], "article_id"=>1223561, "categories"=>["Uncategorised"], "users"=>["Stinus Lindgreen", "Sinan Uğur Umu", "Alicia Sook-Wei Lai", "Hisham Eldai", "Wenting Liu", "Stephanie McGimpsey", "Nicole E. Wheeler", "Patrick J. Biggs", "Nick R. Thomson", "Lars Barquist", "Anthony M. Poole", "Paul P. Gardner"], "doi"=>["https://dx.doi.org/10.1371/journal.pcbi.1003907.g002"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Many_ncRNAs_and_RUFs_are_highly_expressed_but_show_limited_conservation_across_represented_strains_species_/1223561", "title"=>"Many ncRNAs and RUFs are highly expressed but show limited conservation across represented strains/species.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-10-30 03:36:13"}
  • {"files"=>["https://ndownloader.figshare.com/files/1772658"], "description"=>"<p>All of the available full length Bacterial and Archaeal genomes were annotated using Rfam and Pfam models. For each Pfam/Rfam family, RNA-seq species or taxonomic group the “phylogenetic distance” is calculated using the maximum SSU rRNA F84 distance (see <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003907#s4\" target=\"_blank\">Methods</a> for details). <b>A.</b> For the Pfam and the Rfam families we compare the levels of conservation as a function of phylogenetic distance using annotations of 2,562 bacterial genomes. E.g. of RNA families are conserved between species from the same family, whereas of protein families are conserved within the same taxonomic range. <b>B.</b> The barplot shows the distribution of all pairwise distances between the RNA-seq datasets. Eleven pairs (boxed) are in the Goldilocks Zone (See <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003907#pcbi-1003907-g004\" target=\"_blank\">Figure 4</a> for further analysis). <b>C.</b> The ranges of phylogenetic distances for comparing species from different taxonomic groups.</p>", "links"=>[], "tags"=>["transcriptional noise", "noncoding RNA outputs", "unannotated noncoding RNAs", "phylogenetic", "noncoding RNA identification", "noncoding RNA elements"], "article_id"=>1223565, "categories"=>["Uncategorised"], "users"=>["Stinus Lindgreen", "Sinan Uğur Umu", "Alicia Sook-Wei Lai", "Hisham Eldai", "Wenting Liu", "Stephanie McGimpsey", "Nicole E. Wheeler", "Patrick J. Biggs", "Nick R. Thomson", "Lars Barquist", "Anthony M. Poole", "Paul P. Gardner"], "doi"=>["https://dx.doi.org/10.1371/journal.pcbi.1003907.g003"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Conservation_of_protein_and_RNA_families_/1223565", "title"=>"Conservation of protein and RNA families.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-10-30 03:36:13"}
  • {"files"=>["https://ndownloader.figshare.com/files/1772662"], "description"=>"<p>Ten strains with corresponding, publicly available RNA-seq data and phylogenetic distances in the Goldilocks Zone (<a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003907#pcbi-1003907-g003\" target=\"_blank\">Figure 3</a>) have been identified. The maximum likelihood tree from a SSU rRNA alignment shows the relationships between these taxa. They fall into three clades, containing members of the families: Enterobacteriaceae and Xanthomonadaceae, and the genus: <i>Pseudomonas</i>. The nodes connecting taxa within the Goldilocks Zone are coloured gold, taxa that are too close are coloured red and those that are too divergent are coloured cyan. Each strain is annotated with gold boxes where there was stranded information, or if the majority of core mRNAs and ncRNAs (see <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003907#s4\" target=\"_blank\">Methods</a>) were expressed (see <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003907#pcbi.1003907.s003\" target=\"_blank\">Table S3</a> for the raw data).</p>", "links"=>[], "tags"=>["transcriptional noise", "noncoding RNA outputs", "unannotated noncoding RNAs", "phylogenetic", "noncoding RNA identification", "noncoding RNA elements"], "article_id"=>1223567, "categories"=>["Uncategorised"], "users"=>["Stinus Lindgreen", "Sinan Uğur Umu", "Alicia Sook-Wei Lai", "Hisham Eldai", "Wenting Liu", "Stephanie McGimpsey", "Nicole E. Wheeler", "Patrick J. Biggs", "Nick R. Thomson", "Lars Barquist", "Anthony M. Poole", "Paul P. Gardner"], "doi"=>["https://dx.doi.org/10.1371/journal.pcbi.1003907.g004"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Public_RNA_seq_datasets_that_lie_in_the_Goldilocks_Zone_/1223567", "title"=>"Public RNA-seq datasets that lie in the Goldilocks Zone.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-10-30 03:36:13"}
  • {"files"=>["https://ndownloader.figshare.com/files/1772665"], "description"=>"<p>In this figure we illustrate data corresponding to 3 exemplar RUFs that show high covariation, conserved predicted secondary structures and are derived from one of the Goldilocks Zone clades shown in <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003907#pcbi-1003907-g004\" target=\"_blank\">Figure 4</a>. (<b>A–C</b>) The expression levels inferred from RNA-seq in the region encompassing each RUF. The regions contain a mix of ncRNAs (red arrows) and protein coding genes (blue arrows) and a RUF (red arrow). For each nucleotide, the total number of reads that map to that nucleotide was computed, and are presented as a heatmap; darker colours indicate high relative expression, lighter colours indicate low expression and black indicates a gap in the genomic alignment of the sequences for the locii. (<b>D–F</b>) R2R <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003907#pcbi.1003907-Weinberg1\" target=\"_blank\">[68]</a> representations of the predicted consensus secondary structures for exemplar RNAs of Unknown Function (RUFs) selected from the Enterobacteriaceae, <i>Pseudomonas</i> and Xanthomonadaceae data. Covariation is highlighted in green, structure-neutral variation is highlighted in blue, highly conserved regions are highlighted in pink. The Enterobacteriaceae RUF contains a conserved tetraloop of the GNRA or UNCG type, and there have been two independent insertions of hairpins in <i>S. enterica</i> and <i>K. pneumoniae</i> within the first hairpin. The <i>Pseudomonas</i> RUF hosts a 3′ rho independent transcription terminator.</p>", "links"=>[], "tags"=>["transcriptional noise", "noncoding RNA outputs", "unannotated noncoding RNAs", "phylogenetic", "noncoding RNA identification", "noncoding RNA elements"], "article_id"=>1223570, "categories"=>["Uncategorised"], "users"=>["Stinus Lindgreen", "Sinan Uğur Umu", "Alicia Sook-Wei Lai", "Hisham Eldai", "Wenting Liu", "Stephanie McGimpsey", "Nicole E. Wheeler", "Patrick J. Biggs", "Nick R. Thomson", "Lars Barquist", "Anthony M. Poole", "Paul P. Gardner"], "doi"=>["https://dx.doi.org/10.1371/journal.pcbi.1003907.g005"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Comparative_analysis_of_RNA_seq_datasets_in_the_Goldilocks_Zone_is_a_powerful_approach_for_identifying_RUFs_/1223570", "title"=>"Comparative analysis of RNA-seq datasets in the Goldilocks Zone is a powerful approach for identifying RUFs.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-10-30 03:36:13"}
  • {"files"=>["https://ndownloader.figshare.com/files/1772666", "https://ndownloader.figshare.com/files/1772667", "https://ndownloader.figshare.com/files/1772668"], "description"=>"<div><p>Noncoding RNAs are integral to a wide range of biological processes, including translation, gene regulation, host-pathogen interactions and environmental sensing. While genomics is now a mature field, our capacity to identify noncoding RNA elements in bacterial and archaeal genomes is hampered by the difficulty of <i>de novo</i> identification. The emergence of new technologies for characterizing transcriptome outputs, notably RNA-seq, are improving noncoding RNA identification and expression quantification. However, a major challenge is to robustly distinguish functional outputs from transcriptional noise. To establish whether annotation of existing transcriptome data has effectively captured all functional outputs, we analysed over 400 publicly available RNA-seq datasets spanning 37 different Archaea and Bacteria. Using comparative tools, we identify close to a thousand highly-expressed candidate noncoding RNAs. However, our analyses reveal that capacity to identify noncoding RNA outputs is strongly dependent on phylogenetic sampling. Surprisingly, and in stark contrast to protein-coding genes, the phylogenetic window for effective use of comparative methods is perversely narrow: aggregating public datasets only produced one phylogenetic cluster where these tools could be used to robustly separate unannotated noncoding RNAs from a null hypothesis of transcriptional noise. Our results show that for the full potential of transcriptomics data to be realized, a change in experimental design is paramount: effective transcriptomics requires phylogeny-aware sampling.</p></div>", "links"=>[], "tags"=>["transcriptional noise", "noncoding RNA outputs", "unannotated noncoding RNAs", "phylogenetic", "noncoding RNA identification", "noncoding RNA elements"], "article_id"=>1223571, "categories"=>["Uncategorised"], "users"=>["Stinus Lindgreen", "Sinan Uğur Umu", "Alicia Sook-Wei Lai", "Hisham Eldai", "Wenting Liu", "Stephanie McGimpsey", "Nicole E. Wheeler", "Patrick J. Biggs", "Nick R. Thomson", "Lars Barquist", "Anthony M. Poole", "Paul P. Gardner"], "doi"=>["https://dx.doi.org/10.1371/journal.pcbi.1003907.s001", "https://dx.doi.org/10.1371/journal.pcbi.1003907.s002", "https://dx.doi.org/10.1371/journal.pcbi.1003907.s003"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Robust_Identification_of_Noncoding_RNA_from_Transcriptomes_Requires_Phylogenetically_Informed_Sampling_/1223571", "title"=>"Robust Identification of Noncoding RNA from Transcriptomes Requires Phylogenetically-Informed Sampling", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2014-10-30 03:36:13"}

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  • {"unique-ip"=>"7", "full-text"=>"8", "pdf"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"8"}

Relative Metric

{"start_date"=>"2014-01-01T00:00:00Z", "end_date"=>"2014-12-31T00:00:00Z", "subject_areas"=>[{"subject_area"=>"/Biology and life sciences/Computational biology", "average_usage"=>[341, 529]}, {"subject_area"=>"/Biology and life sciences/Genetics", "average_usage"=>[306, 482]}, {"subject_area"=>"/Computer and information sciences", "average_usage"=>[327, 511]}, {"subject_area"=>"/Ecology and environmental sciences", "average_usage"=>[320]}, {"subject_area"=>"/Ecology and environmental sciences/Conservation genetics", "average_usage"=>[267, 496]}]}
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Net::HTTPInternalServerError

Source
Counter
Time
2019-04-06 02:41:26 UTC
Target URL
http://counter-101.soma.plos.org/api/v1.0/stats/doi/10.1371%2Fjournal.pcbi.1003907
Trace

/app/models/concerns/networkable.rb:21:in `get_result'
/app/models/source.rb:165:in `get_data'
/app/models/retrieval_status.rb:47:in `perform_get_data'
/app/jobs/source_job.rb:52:in `block (2 levels) in perform'
/app/jobs/source_job.rb:51:in `block in perform'
/app/jobs/source_job.rb:35:in `each'
/app/jobs/source_job.rb:35:in `perform'