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RNA sequencing read depth requirement for optimal transcriptome coverage in Hevea brasiliensis

Identifieur interne : 001B12 ( Main/Exploration ); précédent : 001B11; suivant : 001B13

RNA sequencing read depth requirement for optimal transcriptome coverage in Hevea brasiliensis

Auteurs : Keng-See Chow [Malaisie] ; Ahmad-Kamal Ghazali [Malaisie] ; Chee-Choong Hoh [Malaisie] ; Zainorlina Mohd-Zainuddin [Malaisie]

Source :

RBID : PMC:3926681

Descripteurs français

English descriptors

Abstract

Background

One of the concerns of assembling de novo transcriptomes is determining the amount of read sequences required to ensure a comprehensive coverage of genes expressed in a particular sample. In this report, we describe the use of Illumina paired-end RNA-Seq (PE RNA-Seq) reads from Hevea brasiliensis (rubber tree) bark to devise a transcript mapping approach for the estimation of the read amount needed for deep transcriptome coverage.

Findings

We optimized the assembly of a Hevea bark transcriptome based on 16 Gb Illumina PE RNA-Seq reads using the Oases assembler across a range of k-mer sizes. We then assessed assembly quality based on transcript N50 length and transcript mapping statistics in relation to (a) known Hevea cDNAs with complete open reading frames, (b) a set of core eukaryotic genes and (c) Hevea genome scaffolds. This was followed by a systematic transcript mapping process where sub-assemblies from a series of incremental amounts of bark transcripts were aligned to transcripts from the entire bark transcriptome assembly. The exercise served to relate read amounts to the degree of transcript mapping level, the latter being an indicator of the coverage of gene transcripts expressed in the sample. As read amounts or datasize increased toward 16 Gb, the number of transcripts mapped to the entire bark assembly approached saturation. A colour matrix was subsequently generated to illustrate sequencing depth requirement in relation to the degree of coverage of total sample transcripts.

Conclusions

We devised a procedure, the “transcript mapping saturation test”, to estimate the amount of RNA-Seq reads needed for deep coverage of transcriptomes. For Hevea de novo assembly, we propose generating between 5–8 Gb reads, whereby around 90% transcript coverage could be achieved with optimized k-mers and transcript N50 length. The principle behind this methodology may also be applied to other non-model plants, or with reads from other second generation sequencing platforms.


Url:
DOI: 10.1186/1756-0500-7-69
PubMed: 24484543
PubMed Central: 3926681


Affiliations:


Links toward previous steps (curation, corpus...)


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<sec>
<title>Background</title>
<p>One of the concerns of assembling
<italic>de novo</italic>
transcriptomes is determining the amount of read sequences required to ensure a comprehensive coverage of genes expressed in a particular sample. In this report, we describe the use of Illumina paired-end RNA-Seq (PE RNA-Seq) reads from
<italic>Hevea brasiliensis</italic>
(rubber tree) bark to devise a transcript mapping approach for the estimation of the read amount needed for deep transcriptome coverage.</p>
</sec>
<sec>
<title>Findings</title>
<p>We optimized the assembly of a
<italic>Hevea</italic>
bark transcriptome based on 16 Gb Illumina PE RNA-Seq reads using the Oases assembler across a range of k-mer sizes. We then assessed assembly quality based on transcript N50 length and transcript mapping statistics in relation to (a) known
<italic>Hevea</italic>
cDNAs with complete open reading frames, (b) a set of core eukaryotic genes and (c)
<italic>Hevea</italic>
genome scaffolds. This was followed by a systematic transcript mapping process where sub-assemblies from a series of incremental amounts of bark transcripts were aligned to transcripts from the entire bark transcriptome assembly. The exercise served to relate read amounts to the degree of transcript mapping level, the latter being an indicator of the coverage of gene transcripts expressed in the sample. As read amounts or datasize increased toward 16 Gb, the number of transcripts mapped to the entire bark assembly approached saturation. A colour matrix was subsequently generated to illustrate sequencing depth requirement in relation to the degree of coverage of total sample transcripts.</p>
</sec>
<sec>
<title>Conclusions</title>
<p>We devised a procedure, the “transcript mapping saturation test”, to estimate the amount of RNA-Seq reads needed for deep coverage of transcriptomes. For
<italic>Hevea de novo</italic>
assembly, we propose generating between 5–8 Gb reads, whereby around 90% transcript coverage could be achieved with optimized k-mers and transcript N50 length. The principle behind this methodology may also be applied to other non-model plants, or with reads from other second generation sequencing platforms.</p>
</sec>
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<author>
<name sortKey="Chen, X" uniqKey="Chen X">X Chen</name>
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<name sortKey="Parra, G" uniqKey="Parra G">G Parra</name>
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<name sortKey="Bradnam, K" uniqKey="Bradnam K">K Bradnam</name>
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<author>
<name sortKey="Ning, Z" uniqKey="Ning Z">Z Ning</name>
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<author>
<name sortKey="Keane, T" uniqKey="Keane T">T Keane</name>
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<name sortKey="Korf, I" uniqKey="Korf I">I Korf</name>
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<analytic>
<author>
<name sortKey="Kush, A" uniqKey="Kush A">A Kush</name>
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<author>
<name sortKey="Goyvaerts, E" uniqKey="Goyvaerts E">E Goyvaerts</name>
</author>
<author>
<name sortKey="Chye, Ml" uniqKey="Chye M">ML Chye</name>
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<author>
<name sortKey="Chua, Nh" uniqKey="Chua N">NH Chua</name>
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<author>
<name sortKey="Mukerji, S" uniqKey="Mukerji S">S Mukerji</name>
</author>
<author>
<name sortKey="Green, M" uniqKey="Green M">M Green</name>
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<author>
<name sortKey="Altschul, Sf" uniqKey="Altschul S">SF Altschul</name>
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<author>
<name sortKey="Gish, W" uniqKey="Gish W">W Gish</name>
</author>
<author>
<name sortKey="Miller, W" uniqKey="Miller W">W Miller</name>
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<author>
<name sortKey="Myers, Ew" uniqKey="Myers E">EW Myers</name>
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<author>
<name sortKey="Lipman, Dj" uniqKey="Lipman D">DJ Lipman</name>
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<name sortKey="Slater, Gs" uniqKey="Slater G">GS Slater</name>
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<name sortKey="Birney, E" uniqKey="Birney E">E Birney</name>
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<li>Malaisie</li>
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<name sortKey="Chow, Keng See" sort="Chow, Keng See" uniqKey="Chow K" first="Keng-See" last="Chow">Keng-See Chow</name>
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<name sortKey="Ghazali, Ahmad Kamal" sort="Ghazali, Ahmad Kamal" uniqKey="Ghazali A" first="Ahmad-Kamal" last="Ghazali">Ahmad-Kamal Ghazali</name>
<name sortKey="Hoh, Chee Choong" sort="Hoh, Chee Choong" uniqKey="Hoh C" first="Chee-Choong" last="Hoh">Chee-Choong Hoh</name>
<name sortKey="Mohd Zainuddin, Zainorlina" sort="Mohd Zainuddin, Zainorlina" uniqKey="Mohd Zainuddin Z" first="Zainorlina" last="Mohd-Zainuddin">Zainorlina Mohd-Zainuddin</name>
</country>
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</affiliations>
</record>

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