Evidence map›Paper›PMID 35725382›Full record

ArticleBMC genomics2022

A transcriptome software comparison for the analyses of treatments expected to give subtle gene expression responses.

Cung Nawl Thawng, Geoffrey Battle Smith

Open access · goldAbstract read
In one paragraph

Article in BMC genomics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
1.2field-weighted citation impact, top 22% of its field
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

8 citing papers in PubMed, 14 citations in OpenAlex.

  1. Exploring Molecular and Genetic Differences inInternational journal of molecular sciences · 2025
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  2. Horticulture research · 2024
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  3. Review
  4. Article
  5. Article
  6. Article
  7. Review
  8. Gene Expression Analysis inInternational journal of molecular sciences · 2022
    Article
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

2 authors at 1 institution in 1 country.

Cung Nawl ThawngBiology Department and Molecular Biology Program, New Mexico State University, Las Cruces, NM, USA.
Geoffrey Battle SmithBiology Department and Molecular Biology Program, New Mexico State University, Las Cruces, NM, USA. gsmith@nmsu.edu.
New Mexico State University · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundIn this comparative study we evaluate the performance of four software tools: DNAstar-D (DESeq2), DNAstar-E (edgeR), CLC Genomics and Partek Flow for identification of differentially expressed genes (DEGs) using a transcriptome of E. coli. The RNA-seq data are from the effect of below-background radiation 5.5 nGy total dose (0.2nGy/hr) on E. coli grown shielded from natural radiation 655 m below ground in a pre-World War II steel vault. The gene expression response to three supplemented sources of radiation designed to mimic natural background, 1952 - 5720 nGy in total dose (71-208 nGy/hr), are compared to this "radiation-deprived" treatment. In addition, RNA-seq data of Caenorhabditis elegans nematode from similar radiation treatments was analyzed by three of the software packages.

resultsIn E. coli, the four software programs identified one of the supplementary sources of radiation (KCl) to evoke about 5 times more transcribed genes than the minus-radiation treatment (69-114 differentially expressed genes, DEGs), and so the rest of the analyses used this KCl vs "Minus" comparison. After imposing a 30-read minimum cutoff, one of the DNAStar options shared two of the three steps (mapping, normalization, and statistic) with Partek Flow (they both used median of ratios to normalize and the DESeq2 statistical package), and these two programs identified the highest number of DEGs in common with each other (53). In contrast, when the programs used different approaches in each of the three steps, between 31 and 40 DEGs were found in common. Regarding the extent of expression differences, three of the four programs gave high fold-change results (15-178 fold), but one (DNAstar's DESeq2) resulted in more conservative fold-changes (1.5-3.5). In a parallel study comparing three qPCR commercial validation software programs, these programs also gave variable results as to which genes were significantly regulated. Similarly, the C. elegans analysis showed exaggerated fold-changes in CLC and DNAstar's edgeR while DNAstar-D was more conservative.

conclusionsRegarding the extent of expression (fold-change), and considering the subtlety of the very low level radiation treatments, in E. coli three of the four programs gave what we consider exaggerated fold-change results (15 - 178 fold), but one (DNAstar's DESeq2) gave more realistic fold-changes (1.5-3.5). When RT-qPCR validation comparisons to transcriptome results were carried out, they supported the more conservative DNAstar-D's expression results. When another model organism's (nematode) response to these radiation differences was similarly analyzed, DNAstar-D also resulted in the most conservative expression patterns. Therefore, we would propose DESeq2 ("DNAstar-D") as an appropriate software tool for differential gene expression studies for treatments expected to give subtle transcriptome responses.

Indexed as

Escherichia coliTranscriptomeAnimalsCaenorhabditis elegansGene Expression ProfilingSequence AnalysisSequence Analysis, RNASoftwareFold-changesLow radiationRNA-Seq softwareSmall treatment differencesTranscriptome software

Identifiers

PMID35725382
PMCPMC9208185
OpenAlexW4283166605

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

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 OpenQuestion graph.