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pmid:36911695

The use of bioinformatics methods to identify the effects of SARS-CoV-2 and influenza viruses on the regulation of gene expression in patients.

Zhongyi Sun · Li Ke · Qiuyue Zhao · Jiachen Qu · Yanan Hu · Han Gao · Zhiyong Peng

Frontiers in immunology · 2023 doi:10.3389/fimmu.2023.1098688

Paper

complete

Experiment

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Data

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Analysis

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title
The use of bioinformatics methods to identify the effects of SARS-CoV-2 and influenza viruses on the regulation of gene expression in patients.
doi
10.3389/fimmu.2023.1098688
pmid
36911695
authors array · 7 items
[0]
Zhongyi Sun
[1]
Li Ke
[2]
Qiuyue Zhao
[3]
Jiachen Qu
[4]
Yanan Hu
[5]
Han Gao
[6]
Zhiyong Peng
journal
Frontiers in immunology
year
2023
abstract
Background: SARS-CoV-2 infection is a respiratory infectious disease similar to influenza virus infection. Numerous studies have reported similarities and differences in the clinical manifestations, laboratory tests, and mortality between these two infections. However, the genetic effects of coronavirus and influenza viruses on the host that lead to these characteristics have rarely been reported. Methods: COVID-19 (GSE157103) and influenza (GSE111368, GSE101702) datasets were downloaded from the Gene Expression Ominbus (GEO) database. Differential gene, gene set enrichment, protein-protein interaction (PPI) network, gene regulatory network, and immune cell infiltration analyses were performed to identify the critical impact of COVID-19 and influenza viruses on the regulation of host gene expression. Results: The number of differentially expressed genes in the COVID-19 patients was significantly higher than in the influenza patients. 22 common differentially expressed genes (DEGs) were identified between the COVID-19 and influenza datasets. The effects of the viruses on the regulation of host gene expression were determined using gene set enrichment and PPI network analyses. Five HUB genes were finally identified: IFI27, OASL, RSAD2, IFI6, and IFI44L. Conclusion: We identified five HUB genes between COVID-19 and influenza virus infection, which might be helpful in the diagnosis and treatment of COVID-19 and influenza. This knowledge may also guide future mechanistic studies that aim to identify pathogen-specific interventions.
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After normalization, the raw sequencing data were log2-transformed, and the resulting array was directly analyzed using the R package “limma” (17).
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After normalization, the raw sequencing data were log2-transformed, and the resulting array was directly analyzed using the R package “limma” (17).
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The original contributions presented in the study are included in the article/ Supplementary Material . Further inquiries can be directed to the corresponding authors.
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Five HUB genes—IFI27, OASL, RSAD2, IFI6, and IFI44L—were identified between COVID-19 and influenza virus infection.
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sections[0] "Abstract" paragraph 3
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22 common differentially expressed genes (DEGs) were identified between the COVID-19 and influenza datasets.
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sections[0] "Abstract" paragraph 3
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Five HUB genes were finally identified: IFI27, OASL, RSAD2, IFI6, and IFI44L.
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Five HUB genes—IFI27, OASL, RSAD2, IFI6, and IFI44L—were identified between COVID-19 and influenza virus infection.
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[348]
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[349]
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[350]
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[351]
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[352]
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[353]
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[354]
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[355]
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[356]
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[357]
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[358]
GSM3029691
sample_count
359
group_sizes
null
group_sizes_basis
null
platform
10558
taxon
Homo sapiens
title
Progression of whole blood transcriptional signatures from interferon-induced to neutrophil-associated patterns in patients with severe influenza
resolution_checked_at
2026-11-01T15:39:54Z
resolution_check
identifier_resolution_only_no_download_no_licence_read
downloaded
false
licence_read
false
[3] object · 15 fields
id
data:geo:GSE157103
role
raw
format
geo-series
media_type
application/vnd.ncbi.geo-series+json
uri
https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE157103
sha256
null
bytes
null
access
open
state
resolved
reader
null
reader_status
reader_required
identifier
GSE157103
version
null
note
The original contributions presented in the study are included in the article/ Supplementary Material . Further inquiries can be directed to the corresponding authors.
structure object · 15 fields
repository
GEO
access_route
GEO accession URL
access_statement_verbatim
The original contributions presented in the study are included in the article/ Supplementary Material . Further inquiries can be directed to the corresponding authors.
access_statement_kind
data_availability_statement
sample_accessions array · 126 items
[0]
GSM4753021
[1]
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[2]
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[125]
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sample_count
126
group_sizes
null
group_sizes_basis
null
platform
24676
taxon
Homo sapiens
title
Large-scale Multi-omic Analysis of COVID-19 Severity
resolution_checked_at
2026-11-01T18:06:43Z
resolution_check
identifier_resolution_only_no_download_no_licence_read
downloaded
false
licence_read
false
gaps array · 6 items
[0]
asset:data:geo:GSE101702 not downloaded
[1]
asset:data:geo:GSE101702 licence not read
[2]
asset:data:geo:GSE111368 not downloaded
[3]
asset:data:geo:GSE111368 licence not read
[4]
asset:data:geo:GSE157103 not downloaded
[5]
asset:data:geo:GSE157103 licence not read
analysis object · 4 fields
state
complete
methods array · 4 items
[0] object · 10 fields
id
method-1
family
differential_expression
specific
limma_voom
label
Differential expression analysis
dsl_operations array · 3 items
[0]
normalise_counts
[1]
differential_expression_fit
[2]
fdr_bh
missing_operations array · 0 items
parameters object · 0 fields
data_shapes array · 1 item
[0]
count_matrix_with_condition
evidence object · 3 fields
score
1
band
verbatim
quote
After normalization, the raw sequencing data were log2-transformed, and the resulting array was directly analyzed using the R package “limma” (17).
[1] object · 10 fields
id
method-2
family
correlation_analysis
specific
spearman
label
Correlation analysis
dsl_operations array · 1 item
[0]
spearman_rho
missing_operations array · 0 items
parameters object · 0 fields
data_shapes array · 1 item
[0]
paired_numeric_columns
evidence object · 3 fields
score
0.8
band
high
quote
Spearman correlation
[2] object · 10 fields
id
method-3
family
network_analysis
specific
ppi_network
label
Network / pathway analysis
dsl_operations array · 3 items
[0]
build_graph
[1]
centrality_scores
[2]
pathway_enrich
missing_operations array · 0 items
parameters object · 0 fields
data_shapes array · 1 item
[0]
edge_list
evidence object · 3 fields
score
0.7
band
medium
quote
protein-protein interaction
[3] object · 10 fields
id
method-4
family
network_analysis
specific
pathway_enrichment
label
Network / pathway analysis
dsl_operations array · 1 item
[0]
pathway_enrich
missing_operations array · 0 items
parameters object · 0 fields
data_shapes array · 1 item
[0]
edge_list
evidence object · 3 fields
score
0.8
band
high
quote
GO enrichment
targets array · 0 items
gaps array · 0 items
capability object · 3 fields
state
complete
requirements array · 1 item
[0] object · 6 fields
capability_id
capability:computational_analysis
label
Computational analysis platform
assay_families array · 9 items
[0]
meta_analysis
[1]
machine_learning
[2]
survival_analysis
[3]
statistical_regression
[4]
pca
[5]
molecular_docking
[6]
network_analysis
[7]
differential_expression
[8]
gwas
candidate
Automated Assays Laravel application and simulated adapters
gaps array · 0 items
readiness object · 8 fields
state
complete
tier
R1
blocked_on
nothing:replicable_now
axes object · 3 fields
method object · 5 fields
tier
R1
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true
required_operations array · 3 items
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normalise_counts
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differential_expression_fit
[2]
fdr_bh
missing_operations array · 0 items
basis
The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: limma_voom.
capability object · 4 fields
tier
R1
capability_id
capability:computational_analysis
availability_state
owned
basis
The computational analysis is owned. The physical bench the paper used is not, so a physical validation cannot be run here.
data object · 6 fields
tier
R1
reachable
true
accessions array · 3 items
[0]
GSE157103
[1]
GSE111368
[2]
GSE101702
resolved_accessions array · 3 items
[0]
GSE157103
[1]
GSE111368
[2]
GSE101702
statements array · 1 item
[0]
The original contributions presented in the study are included in the article/ Supplementary Material . Further inquiries can be directed to the corresponding authors.
basis
The paper names GSE157103, GSE111368, GSE101702 in Gene Expression Ominbus (GEO) database and the accession resolves in the repository. No dataset was downloaded and no licence was read.
can_run object · 3 fields
dsl_operations array · 3 items
[0]
normalise_counts
[1]
differential_expression_fit
[2]
fdr_bh
assets array · 2 items
[0]
source:pmid:36911695
[1]
data:geo:GSE101702
steps array · 1 item
[0]
run_analysis
must_build object · 5 fields
dsl_operations array · 0 items
converters array · 0 items
readers array · 0 items
capabilities array · 0 items
data_gaps array · 1 item
[0]
data:geo:GSE101702
gaps array · 0 items
review object · 5 fields
state
partial
machine_critique object · 5 fields
kind
extraction_review
reader object · 5 fields
model
MiniMax-M3.1-Flash
provider
minimax
provider_family
minimax
read_at
2026-11-01T18:33:20Z
independence
single_reader
evidence_check object · 2 fields
method
verbatim
availability
verbatim
calls
5
usage object · 3 fields
prompt_tokens
9879
completion_tokens
1223
cached_tokens
128
stages array · 1 item
[0] object · 4 fields
id
extraction
state
complete
reader
MiniMax-M3.1-Flash
provider
minimax
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review.independent_reader
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review.human_critique
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absent
status
not_run
mode
null
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gaps array · 1 item
[0]
execution.not_run
provenance object · 7 fields
entities array · 3 items
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id
pmid:36911695
type
paper
label
pmid:36911695
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id
source:pmid:36911695
type
manuscript
label
PMID 36911695 full text
[2] object · 3 fields
id
data:geo:GSE101702
type
dataset
label
GEO GSE101702
activities array · 3 items
[0] object · 4 fields
id
activity-reading-sci-2026-11-01
type
classification
used array · 3 items
[0] object · 3 fields
id
pmid:36911695
type
paper
label
pmid:36911695
[1] object · 3 fields
id
source:pmid:36911695
type
manuscript
label
PMID 36911695 full text
[2] object · 3 fields
id
data:geo:GSE101702
type
dataset
label
GEO GSE101702
agent
agent:corpus-assay-engine
[1] object · 4 fields
id
activity-read-36911695
type
extraction
used array · 1 item
[0]
source:pmid:36911695
agent
agent:minimax-reader
[2] object · 4 fields
id
activity-resolve-GSE101702
type
accession_resolution
used array · 1 item
[0]
data:geo:GSE101702
agent
agent:corpus-assay-engine
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[0] object · 3 fields
id
agent:corpus-assay-engine
type
software
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Corpus assay engine
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id
agent:minimax-reader
type
software
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MiniMax literature reader
source_run_id
reading-sci-2026-11-01
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343fb30756ba6ec3
capability_table_fingerprint
2cb3e42f047d2ecf
snapshot_id
ba34b49490d9bfaf
lineage object · 4 fields
parent_id
pmid:36911695
parent_hash
sha256:e24668009e0dbf5d3588b42d826a9753a5a8720787f2ddda02b6c2486b237bbe
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null
gaps array · 10 items
[0]
asset:data:geo:GSE101702 licence not read
[1]
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[2]
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[3]
asset:data:geo:GSE111368 not downloaded
[4]
asset:data:geo:GSE157103 licence not read
[5]
asset:data:geo:GSE157103 not downloaded
[6]
execution.not_run
[7]
review.human_critique
[8]
review.independent_reader
[9]
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[0] object · 11 fields
role
analysis
definition_id
differential_expression
definition_version
f2132e96ae6c7a4e
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[0]
normalise_counts
[1]
differential_expression_fit
[2]
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support_state
supported
source_hashes object · 1 field
dsl_catalogue
f2132e96ae6c7a4e
evidence array · 1 item
[0]
After normalization, the raw sequencing data were log2-transformed, and the resulting array was directly analyzed using the R package “limma” (17).
order_basis
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Complete file
{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:36911695", "kind": "science-record", "revision": 4, "content_hash": "sha256:06c2ece179201eedf90ba4f48480afb6ca7f6f98a420ed76acc65d7b5d3d67f0", "created_at": "2026-11-01T19:01:33Z", "updated_at": "2026-11-01T22:51:29Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "The use of bioinformatics methods to identify the effects of SARS-CoV-2 and influenza viruses on the regulation of gene expression in patients.", "doi": "10.3389/fimmu.2023.1098688", "pmid": "36911695", "authors": ["Zhongyi Sun", "Li Ke", "Qiuyue Zhao", "Jiachen Qu", "Yanan Hu", "Han Gao", "Zhiyong Peng"], "journal": "Frontiers in immunology", "year": 2023, "abstract": "Background: SARS-CoV-2 infection is a respiratory infectious disease similar to influenza virus infection. Numerous studies have reported similarities and differences in the clinical manifestations, laboratory tests, and mortality between these two infections. However, the genetic effects of coronavirus and influenza viruses on the host that lead to these characteristics have rarely been reported.\nMethods: COVID-19 (GSE157103) and influenza (GSE111368, GSE101702) datasets were downloaded from the Gene Expression Ominbus (GEO) database. Differential gene, gene set enrichment, protein-protein interaction (PPI) network, gene regulatory network, and immune cell infiltration analyses were performed to identify the critical impact of COVID-19 and influenza viruses on the regulation of host gene expression.\nResults: The number of differentially expressed genes in the COVID-19 patients was significantly higher than in the influenza patients. 22 common differentially expressed genes (DEGs) were identified between the COVID-19 and influenza datasets. The effects of the viruses on the regulation of host gene expression were determined using gene set enrichment and PPI network analyses. Five HUB genes were finally identified: IFI27, OASL, RSAD2, IFI6, and IFI44L.\nConclusion: We identified five HUB genes between COVID-19 and influenza virus infection, which might be helpful in the diagnosis and treatment of COVID-19 and influenza. This knowledge may also guide future mechanistic studies that aim to identify pathogen-specific interventions.", "claims": [{"id": "claim-method", "statement": "After normalization, the raw sequencing data were log2-transformed, and the resulting array was directly analyzed using the R package “limma” (17).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36911695", "locator": "sections[4] \"Analysis of differentially expressed genes\" paragraph 2", "quote": "After normalization, the raw sequencing data were log2-transformed, and the resulting array was directly analyzed using the R package “limma” (17)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The original contributions presented in the study are included in the article/ Supplementary Material . Further inquiries can be directed to the corresponding authors.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36911695", "locator": "sections[20] \"Data availability statement\" paragraph 2", "quote": "The original contributions presented in the study are included in the article/ Supplementary Material . Further inquiries can be directed to the corresponding authors."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Five HUB genes—IFI27, OASL, RSAD2, IFI6, and IFI44L—were identified between COVID-19 and influenza virus infection.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:36911695", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "22 common differentially expressed genes (DEGs) were identified between the COVID-19 and influenza datasets."}, {"asset": "source:pmid:36911695", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "Five HUB genes were finally identified: IFI27, OASL, RSAD2, IFI6, and IFI44L."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Five HUB genes—IFI27, OASL, RSAD2, IFI6, and IFI44L—were identified between COVID-19 and influenza virus infection.", "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:36911695"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "complete", "blocked_on": null, "description": "The GEO series accession resolves and its sample accessions are listed. 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No dataset was downloaded and no licence was read."}}, "can_run": {"dsl_operations": ["normalise_counts", "differential_expression_fit", "fdr_bh"], "assets": ["source:pmid:36911695", "data:geo:GSE101702"], "steps": ["run_analysis"]}, "must_build": {"dsl_operations": [], "converters": [], "readers": [], "capabilities": [], "data_gaps": ["data:geo:GSE101702"]}, "gaps": []}, "review": {"state": "partial", "machine_critique": {"kind": "extraction_review", "reader": {"model": "MiniMax-M3.1-Flash", "provider": "minimax", "provider_family": "minimax", "read_at": "2026-11-01T18:33:20Z", "independence": "single_reader"}, "evidence_check": {"method": "verbatim", "availability": "verbatim"}, "calls": 5, "usage": {"prompt_tokens": 9879, "completion_tokens": 1223, "cached_tokens": 128}}, "human_critique": null, "stages": [{"id": "extraction", "state": "complete", "reader": "MiniMax-M3.1-Flash", "provider": "minimax"}], "gaps": ["review.independent_reader", "review.machine_critique", "review.human_critique"]}, "execution": {"state": "absent", "status": "not_run", "mode": null, "runs": [], "gaps": ["execution.not_run"]}, "provenance": {"entities": [{"id": "pmid:36911695", "type": "paper", "label": "pmid:36911695"}, {"id": "source:pmid:36911695", "type": "manuscript", "label": "PMID 36911695 full text"}, {"id": "data:geo:GSE101702", "type": "dataset", "label": "GEO GSE101702"}], "activities": [{"id": "activity-reading-sci-2026-11-01", "type": "classification", "used": [{"id": "pmid:36911695", "type": "paper", "label": "pmid:36911695"}, {"id": "source:pmid:36911695", "type": "manuscript", "label": "PMID 36911695 full text"}, {"id": "data:geo:GSE101702", "type": "dataset", "label": "GEO GSE101702"}], "agent": "agent:corpus-assay-engine"}, {"id": "activity-read-36911695", "type": "extraction", "used": ["source:pmid:36911695"], "agent": "agent:minimax-reader"}, {"id": "activity-resolve-GSE101702", "type": "accession_resolution", "used": ["data:geo:GSE101702"], "agent": "agent:corpus-assay-engine"}], "agents": [{"id": "agent:corpus-assay-engine", "type": "software", "label": "Corpus assay engine"}, {"id": "agent:minimax-reader", "type": "software", "label": "MiniMax literature reader"}], "source_run_id": "reading-sci-2026-11-01", "dsl_catalogue_fingerprint": "343fb30756ba6ec3", "capability_table_fingerprint": "2cb3e42f047d2ecf", "snapshot_id": "ba34b49490d9bfaf"}, "lineage": {"parent_id": "pmid:36911695", "parent_hash": "sha256:e24668009e0dbf5d3588b42d826a9753a5a8720787f2ddda02b6c2486b237bbe", "fork_of": null, "generation": 3}, "gaps": ["asset:data:geo:GSE101702 licence not read", "asset:data:geo:GSE101702 not downloaded", "asset:data:geo:GSE111368 licence not read", "asset:data:geo:GSE111368 not downloaded", "asset:data:geo:GSE157103 licence not read", "asset:data:geo:GSE157103 not downloaded", "execution.not_run", "review.human_critique", "review.independent_reader", "review.machine_critique"], "assays": [{"role": "analysis", "definition_id": "differential_expression", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": ["normalise_counts", "differential_expression_fit", "fdr_bh"], "support_state": "supported", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 1.0, "evidence": ["After normalization, the raw sequencing data were log2-transformed, and the resulting array was directly analyzed using the R package “limma” (17)."], "order_basis": "analysis_only", "id": "assay:1"}]}