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However, its analysis often needs powerful computers and advanced command\u2010line skills, which can be challenging when resources are limited. This protocol provides a simple, start\u2010to\u2010finish RNA\u2010seq data analysis method that is easy to follow, reproducible, and requires minimal local hardware. It uses free tools such as SRA Toolkit, FastQC, Trimmomatic, BWA\/HISAT2, Samtools, and Subread, along with Python and R for further analysis using Google Colab. The process includes downloading raw data from NCBI GEO\/SRA, checking data quality, trimming adapters and low\u2010quality reads, aligning sequences to reference genomes, converting file formats, counting reads, normalizing to TPM, and creating visualizations such as heatmaps, bar plots, and volcano plots. Differential gene expression is analyzed with pyDESeq2, and functional enrichment is done using g:Profiler. Troubleshooting in RNA\u2010seq generally involves configuring essential tools, resolving path and dependency issues, and ensuring proper handling of paired\u2010end reads during analysis. By running the heavy computational steps on cloud platforms, this workflow makes RNA\u2010seq analysis affordable and accessible to more researchers. \u00a9 2026 Wiley Periodicals LLC.<\/jats:p>\n                  <jats:p>\n                    <jats:bold>Basic Protocol 1<\/jats:bold>\n                    : Extracting and processing a high\u2010throughput RNA\u2010seq dataset with the command prompt and Windows Subsystem for Linux\n                  <\/jats:p>\n                  <jats:p>\n                    <jats:bold>Basic Protocol 2<\/jats:bold>\n                    : Normalization and visualization of processed RNA\u2010seq dataset with Google Colab and Python 3\n                  <\/jats:p>","DOI":"10.1002\/cpz1.70304","type":"journal-article","created":{"date-parts":[[2026,2,4]],"date-time":"2026-02-04T17:17:20Z","timestamp":1770225440000},"source":"Crossref","is-referenced-by-count":2,"title":["Streamline Protocol for Bulk\u2010RNA Sequencing: From Data Extraction to Expression Analysis"],"prefix":"10.1002","volume":"6","author":[{"given":"Abdullah Al","family":"Mohit","sequence":"first","affiliation":[{"name":"Plant Molecular Biology Laboratory, Faculty of Life Sciences and Biotechnology South Asian University  New Delhi India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Niher Ranjan","family":"Das","sequence":"additional","affiliation":[{"name":"Faculty of Mathematical Sciences South Asian University  New Delhi India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Arushi","family":"Jain","sequence":"additional","affiliation":[{"name":"Plant Molecular Biology Laboratory, Faculty of Life Sciences and Biotechnology South Asian University  New Delhi India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nazmir Binta","family":"Alam","sequence":"additional","affiliation":[{"name":"Plant Molecular Biology Laboratory, Faculty of Life Sciences and Biotechnology South Asian University  New Delhi India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5689-2269","authenticated-orcid":false,"given":"Ananda","family":"Mustafiz","sequence":"additional","affiliation":[{"name":"Plant Molecular Biology Laboratory, Faculty of Life Sciences and Biotechnology South Asian University  New Delhi India"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,2,4]]},"reference":[{"key":"e_1_2_8_2_1","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btu170"},{"key":"e_1_2_8_3_1","doi-asserted-by":"publisher","DOI":"10.1093\/gigascience\/giab008"},{"key":"e_1_2_8_4_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41586\u2010020\u20102649\u20102"},{"key":"e_1_2_8_5_1","doi-asserted-by":"publisher","DOI":"10.22074\/cellj.2019.5992"},{"key":"e_1_2_8_6_1","doi-asserted-by":"publisher","DOI":"10.3389\/fgene.2013.00288"},{"key":"e_1_2_8_7_1","doi-asserted-by":"publisher","DOI":"10.1093\/nar\/gkt214"},{"key":"e_1_2_8_8_1","doi-asserted-by":"publisher","DOI":"10.12688\/f1000research.7035.2"},{"key":"e_1_2_8_9_1","doi-asserted-by":"publisher","DOI":"10.1186\/s13059\u2010014\u20100550\u20108"},{"key":"e_1_2_8_10_1","doi-asserted-by":"publisher","DOI":"10.1038\/nmeth.1226"},{"key":"e_1_2_8_11_1","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btad547"},{"key":"e_1_2_8_12_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.devcel.2025.06.019"},{"key":"e_1_2_8_13_1","doi-asserted-by":"publisher","DOI":"10.1101\/gr.9.2.195"},{"key":"e_1_2_8_14_1","doi-asserted-by":"publisher","DOI":"10.1002\/cpz1.1054"},{"key":"e_1_2_8_15_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-13-2925-8_3"},{"key":"e_1_2_8_16_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-97-6703-8_4"},{"key":"e_1_2_8_17_1","doi-asserted-by":"publisher","DOI":"10.1093\/nar\/gkm226"},{"key":"e_1_2_8_18_1","doi-asserted-by":"publisher","DOI":"10.1109\/TCBB.2013.2297101"},{"key":"e_1_2_8_19_1","doi-asserted-by":"publisher","DOI":"10.1186\/1471\u20102164\u201010\u201022"},{"key":"e_1_2_8_20_1","unstructured":"The pandas development team. 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