Comprehensive RNA-seq Data Analysis

@Nadirzaman01

Dear Client, I'm a Microbiologist with 3+ years of hands-on bioinformatics experience. I have a work history with FastQC, MultiQC, adapter/quality trimming, alignment/quantification (STAR, HISAT2, Salmon), and DESeq2 in R for differential expression, along with downstream GO/KEGG enrichment via clusterProfiler. I'm comfortable in Linux (BASH scripting, Conda environments) and can build this as a clean, reproducible pipeline on Ubuntu/CentOS. Given your reference genome and GTF/GFF are ready, I can take this straight from FASTQC through to the final volcano plots, heatmaps, and PCA visualizations, with tidy count/normalized expression tables and full DESeq2 output (log2FC, padj, etc.). I'll deliver all scripts (commented, with a reproducible Markdown or R Markdown report) alongside publication-ready figures, so you can rerun the entire pipeline independently. Happy to discuss sample size, read depth, and any specific contrasts you want tested before starting. Best regards, Nadir Zaman Microbiologist | Bioinformatics Specialist

@Kashifniaxi804

A differential-expression result is only as trustworthy as the QC, normalization, and experimental design behind it, I’ll build the pipeline so every result is reproducible and biologically defensible. I can take your FASTQ files from FastQC/MultiQC → adapter/quality trimming → STAR/Salmon quantification → gene-level counts → DESeq2 → PCA/heatmap/volcano → GO/KEGG enrichment, using your provided reference and annotation. You’ll receive publication-ready figures, tidy raw/normalized/DE tables, commented shell/R scripts, and a step-by-step reproducible report for Ubuntu/CentOS. I’ll also keep intermediate outputs and parameters documented, so every significant gene and pathway can be traced back to the underlying analysis rather than treated as a black-box result. Looking for Collaboration on this exciting Project

@hasancank2

Hi, ​I can run your full RNA-seq data pipeline end-to-end on Ubuntu and provide publication-ready figures along with a fully reproducible Jupyter/R Markdown notebook.

@anamikagiri7

I have a decade of experience as a Bioinformatician. Already have developed pipeline for bulk RNAseq analysis. I have worked at Berlin Institute of Health and Max Delbrück Center in Germany. Recently have started my startup in Berlin where we provide Bioinformatics services in Next Generation sequencing and single cell multiomics data analysis.

@azmalmahir

Hi, I can complete your RNA-seq analysis from raw FASTQ files through differential expression and biological interpretation using a fully reproducible Linux workflow. My proposed pipeline is: FastQC + MultiQC → fastp/Trim Galore → HISAT2/STAR → featureCounts → DESeq2 → PCA/heatmap/volcano plot → GO & KEGG enrichment I will provide: QC and MultiQC reports Trimmed reads and alignment statistics Raw and normalized gene count tables Complete DESeq2 results with log2FC, p-value and adjusted p-value Upregulated and downregulated gene lists Publication-quality PCA, heatmap and volcano plots GO and KEGG enrichment results Fully commented Bash/R scripts A concise step-by-step reproducibility report I have practical experience with Linux-based NGS, RNA-seq, differential expression, visualization, and functional analysis. Since you already have the reference genome and annotation files, I can start directly from the FASTQ data. I will keep the workflow clean, organized, and easy to reproduce on Ubuntu/CentOS. Best regards, Mahir

@mansisri43217

Hello, I would be glad to work on your RNA-seq analysis project. I am an M.Sc. Biotechnology graduate with hands-on experience in an end-to-end RNA-seq workflow, including FastQC, Trimmomatic, HISAT2, SAMtools, featureCounts, DESeq2 in R/RStudio, and downstream GO/KEGG and STRING analysis. My research involved paired-end RNA-seq data, where I performed quality assessment, preprocessing, reference genome alignment, gene-level quantification, differential expression analysis, and biological interpretation. I have also generated and interpreted PCA, MA plots, volcano plots, heatmaps, and enrichment visualizations. Your requested workflow closely matches my practical experience. I can provide a structured and reproducible Linux-based workflow, including processed count and normalized-expression tables, DESeq2 results, publication-quality figures, commented scripts, and a step-by-step report so the complete analysis can be reproduced on your workstation. I would first review your reference genome, annotation file, and sample information to ensure the reference and experimental design are correctly configured before beginning the analysis. I would be happy to discuss your dataset and requirements and start with the QC and experimental-design assessment. Best regards, Mansi Srivastava M.Sc. Biotechnology | RNA-seq & Bioinformatics

@rohinipd249

Hello, I would be interested in working on your RNA-seq differential expression analysis project. I have hands-on experience with end-to-end RNA-seq workflows, including raw FASTQ quality control, read preprocessing, alignment/quantification, gene-level expression analysis, differential expression, visualisation, and functional enrichment. I will provide organised output files, raw and normalised count tables, complete DESeq2 results including log2 fold changes and adjusted p-values, publication-ready figures, and all scripts used for the analysis. I will also prepare a clear step-by-step reproducibility report so that the complete workflow can be rerun on a Linux workstation. I am freelance scientific data analyst. My background combines biological research, bioinformatics, and statistical analysis, so I can focus not only on running the pipeline but also on interpreting the results in a biologically meaningful way. I would be happy to discuss the number of FASTQ samples, paired-end/single-end sequencing, organism, experimental groups, replicates, and expected turnaround time before starting. Thank you, and I look forward to collaborating with you on this project.

@nhemandh

Hi, This is exactly the kind of RNA-seq work I do regularly, and your brief is clear. Since you already have the reference and matching annotation ready, the path from FASTQ to biological insight is straightforward. How I would run it: • FastQC → Trim Galore (adapter/quality trimming) → post-trim FastQC → one aggregated MultiQC report. • Salmon for transcript-level quantification, summarised to gene level with tximport — or STAR/HISAT2 if you prefer a genome alignment. Happy either way. • DESeq2 for differential expression, with a documented design and full outputs (log2FC, padj, etc.) as tidy tables. • PCA (VST), heatmaps, and a labelled volcano plot, all at publication resolution. • GO and KEGG enrichment with clusterProfiler. • Everything in a documented conda/container environment, with commented scripts and a step-by-step R Markdown report you can re-run on your own workstation. A few things I need to scope it precisely: • How many samples, and how many conditions/groups? • Single- or paired-end, and which organism/reference build? • Is the replicate/metadata info ready for the DESeq2 design? I am comfortable on the command line and use this stack daily. Happy to start over chat or a quick call.

@litwtrain

Hi, I can deliver this RNA-seq analysis as a complete, reproducible workflow rather than a collection of disconnected outputs. I will begin by validating the experimental design and sample metadata, then run FastQC/MultiQC, adapter and quality trimming, and either STAR/HISAT2 alignment with gene-level counting or Salmon-based quantification, selecting the route best suited to your organism, annotation, and downstream goals. Differential expression will be performed in DESeq2 with appropriate filtering, normalization, contrasts, and multiple-testing correction. Deliverables will include QC reports, raw and normalized count tables, complete DESeq2 results, PCA/heatmap/volcano figures at publication resolution, GO/KEGG enrichment tables and plots, commented shell/R scripts, environment details, and a step-by-step reproducible report. Before starting, I would confirm the sample count, group/contrast design, biological replicates, paired- or single-end layout, strandedness, organism, read length, and expected data volume. These details will determine the most accurate workflow and turnaround estimate. I can also provide an initial QC checkpoint before the full analysis so that any sample-quality or design issues are identified early.

@abhijits999

Hi, I’m Abhijit, a Bioinformatician with an M.Sc. in Bioinformatics and strong experience in RNA seq and NGS data analysis. I can handle your complete RNA seq analysis from FASTQ files to biological interpretation. I will perform quality checks, trimming, alignment or Salmon quantification, count generation, DESeq2 analysis, and GO and KEGG enrichment. I will provide clear PCA, heatmap, and volcano plots along with clean result tables. I will also provide all scripts and a simple step by step report so you can reproduce the analysis on your system. I can start immediately and deliver reliable, reproducible results