Biomarker screening in preeclampsia
Version 1

Workflow Type: Galaxy

Objective. Biomarkers have become important for the prognosis and diagnosis of various diseases. High-throughput methods such as RNA-sequencing facilitate the detection of differentially expressed genes (DEGs), hence potential biomarker candidates. Individual studies suggest long lists of DEGs, hampering the identification of clinically relevant ones. Concerning preeclampsia, a major obstetric burden with high risk for adverse maternal and/or neonatal outcomes, limitations in diagnosis and prediction are still important issues. Therefore, we developed a workflow to facilitate the screening for biomarkers. Methods. Based on the tool DeSeq2, we established a comprehensive workflow for the identification of DEGs, analyzing data from multiple publicly available RNA-sSequencing studies. We applied it to four RNA-sSequencing datasets (one blood, three placenta) analyzing patients with preeclampsia and normotensive controls. We compared our results with other published approaches and evaluated their performance. Results. We identified 110 genes dysregulated in preeclampsia, observed in ≥3 of the analyzed studies, six even in all four studies. Among them were FLT-1, TREM-1, and FN1 which either represent established biomarkers on protein level, or promising candidates based on recent studies. In comparison, using a published meta-analysis approach we obtained 5,240 DEGs. Conclusions. We present a data analysis workflow for preeclampsia biomarker screening, capable of identifying significant biomarker candidates, while drastically decreasing the numbers of candidates. Moreover, we were also able to confirm its performance for heart failure. Our approach can be applied to additional diseases for biomarker identification and the set of identified DEGs in preeclampsia represents a resource for further studies.

Steps

ID Name Description
0 Download and Extract Reads in FASTA/Q toolshed.g2.bx.psu.edu/repos/iuc/sra_tools/fastq_dump/2.11.0+galaxy0
1 Bowtie2 toolshed.g2.bx.psu.edu/repos/devteam/bowtie2/bowtie2/2.4.2+galaxy0
2 FastQC toolshed.g2.bx.psu.edu/repos/devteam/fastqc/fastqc/0.73+galaxy0
3 featureCounts toolshed.g2.bx.psu.edu/repos/iuc/featurecounts/featurecounts/2.0.1+galaxy2
4 featureCounts toolshed.g2.bx.psu.edu/repos/iuc/featurecounts/featurecounts/2.0.1+galaxy2
5 DESeq2 toolshed.g2.bx.psu.edu/repos/iuc/deseq2/deseq2/2.11.40.7+galaxy1
6 DESeq2 toolshed.g2.bx.psu.edu/repos/iuc/deseq2/deseq2/2.11.40.7+galaxy1

Outputs

ID Name Description Type
Bowtie2 on input dataset(s): alignments Bowtie2 on input dataset(s): alignments n/a
  • File
FastQC on input dataset(s): Webpage FastQC on input dataset(s): Webpage n/a
  • File
FastQC on input dataset(s): RawData FastQC on input dataset(s): RawData n/a
  • File
featureCounts on input dataset(s): Summary featureCounts on input dataset(s): Summary n/a
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featureCounts on input dataset(s): Counts featureCounts on input dataset(s): Counts n/a
  • File
_anonymous_output_1 _anonymous_output_1 n/a
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_anonymous_output_2 _anonymous_output_2 n/a
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DESeq2 result file on input dataset(s) DESeq2 result file on input dataset(s) n/a
  • File
DESeq2 plots on input dataset(s) DESeq2 plots on input dataset(s) n/a
  • File

Version History

Version 1 (earliest) Created 3rd May 2022 at 14:05 by Marlene Rezk

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Rezk, M. (2022). Biomarker screening in preeclampsia. WorkflowHub. https://doi.org/10.48546/WORKFLOWHUB.WORKFLOW.338.1
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Created: 3rd May 2022 at 14:05

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