Evidence map›Paper›PMID 40618172›Full record

ArticleBMC research notes2025

Optimising RNA extraction for paddy bulk soil samples for metatranscriptome sequencing.

Sumitra Sivaprakasam, Tan Yee Fan, Yukgehnaish Kumarasan, Erneeza Mohd Hata, Ganesan Vadamalai, Bent Petersen, Thomas Sicheritz-Pontén, Sivachandran Parimannan, Heera Rajandas

Abstract read
In one paragraph

Article in BMC research notes, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Sumitra SivaprakasamCentre of Excellence for Omics-Driven Computational Biodiscovery (COMBio), AIMST University, Bedong, Kedah, 08100, Malaysia.
Tan Yee FanCentre of Excellence for Omics-Driven Computational Biodiscovery (COMBio), AIMST University, Bedong, Kedah, 08100, Malaysia.
Yukgehnaish KumarasanCentre of Excellence for Omics-Driven Computational Biodiscovery (COMBio), AIMST University, Bedong, Kedah, 08100, Malaysia.
Erneeza Mohd HataDepartment of Plant Protection, Faculty of Agriculture, Universiti Putra Malaysia, Serdang, Selangor, 43400, Malaysia.
Ganesan VadamalaiDepartment of Plant Protection, Faculty of Agriculture, Universiti Putra Malaysia, Serdang, Selangor, 43400, Malaysia.
Bent PetersenCentre of Excellence for Omics-Driven Computational Biodiscovery (COMBio), AIMST University, Bedong, Kedah, 08100, Malaysia.
Thomas Sicheritz-PonténCentre of Excellence for Omics-Driven Computational Biodiscovery (COMBio), AIMST University, Bedong, Kedah, 08100, Malaysia.
Sivachandran ParimannanCentre of Excellence for Omics-Driven Computational Biodiscovery (COMBio), AIMST University, Bedong, Kedah, 08100, Malaysia.
Heera RajandasCentre of Excellence for Omics-Driven Computational Biodiscovery (COMBio), AIMST University, Bedong, Kedah, 08100, Malaysia. heera@aimst.edu.my.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveExtraction of high-quality RNA is crucial for understanding the molecular dynamics of microbiomes in the growth and development of paddy plants. However, paddy soil poses challenges due to contaminants such as humic substances and its clayish nature, which lead to RNA adsorption and reduced yield. This study aimed to improve existing RNA extraction methods for bulk soil samples collected from a paddy field in Perak, Malaysia. We first evaluated different published protocols, selected the best based on RNA yield and quality, and further optimized it for highly pigmented soil samples. The resulting RNA was subjected to metatranscriptome sequencing, de novo assembly and annotation.

resultsUpon evaluation, the RNA extraction protocol by Peng et al., 2018 (method B3) was optimized by incorporating 20% and 30% PEG-based precipitation to remove carry-over pigmentation. Comparative testing showed that 20% PEG produced the highest quality RNA, yielding pigment-free RNA (> 100 ng/µl, integrity > 7, and A260/A280 of 2.02 ± 0.02). Metatranscriptome sequencing and analysis with Trinity, BUSCO, and Kraken2 confirmed superior quality and higher bacterial read assignment for RNA extracted with 20% PEG, highlighting its effectiveness for downstream microbial transcriptomic applications.

Indexed as

MicrobiotaOryzaRNARNA, BacterialSoilSoil MicrobiologyTranscriptomeMalaysiaRNARNA, BacterialSoilBulk soilMetatranscriptome sequencingPaddyRNA extraction

Identifiers

PMID40618172
PMCPMC12228178

What Socratic holds

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LicenceCC BY-NC-ND
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.