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Bioinformatics Assignment Help & Genomics

RNA-Seq • Sequence Alignment • Biopython / R Bioconductor • Turnitin Report Included

Get verified computational biology solutions for differential gene expression, Needleman-Wunsch global alignment, phylogenetic trees, and molecular docking tailored to your university rubric.

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rna_seq_deseq2.R — Bioconductor 3.19 FDR < 0.01
# RNA-Seq Differential Expression Pipeline (DESeq2)
dds <- DESeqDataSetFromMatrix(countData = counts, colData = metadata, design = ~ condition)
dds <- DESeq(dds)
res <- results(dds, contrast = c("condition", "Treated", "Control"))

# Filter Significant DEGs: |log2FC| > 2.0 & padj < 0.05
Total Analyzed: 18,450 Genes | Upregulated: 428 | Downregulated: 312
Figure 1: Volcano Plot (log2FC vs -log10 p-value) 740 Significant DEGs
Downregulated (312) Upregulated (428) log2 Fold Change
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Every script and genomic analysis pipeline is engineered from scratch by PhD-qualified computational biologists.

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Tight deadline? We fast-track sequence alignment, differential expression calculations, and documentation on-time.

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High-resolution volcano plots, clustered heatmaps, PCA score plots, phylogenetic trees, and protein docking poses.

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Unlimited adjustments to FDR cutoff thresholds, pathway database queries (KEGG/GO), or documentation until full approval.

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1

Data Ingestion & QC

FastQC evaluation, adapter trimming (Trimmomatic), low-quality read filtering, and normalization.

2

Alignment & Modeling

Genome indexing, read alignment via Bowtie2/HISAT2, or dynamic programming Needleman-Wunsch matrices.

3

Statistics & Pathways

DESeq2 / EdgeR statistical modeling, Benjamini-Hochberg FDR correction, and GO/KEGG pathway enrichment.

4

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Coursework Level: Graduate Functional Genomics

Cancer Transcriptome Bulk RNA-Seq Differential Expression & GSEA Enrichment

Task: Process raw Illumina paired-end FASTQ reads, quantify transcript abundance with Salmon, identify differentially expressed genes in DESeq2 (padj < 0.01), and run Gene Set Enrichment Analysis (GSEA) across Hallmark cancer pathways.

  • Deliverables: rnaseq_deseq2.R, Volcano plot, clustered heatmap, GSEA enrichment plot.
  • Result: Identification of 740 significant DEGs with p53 and hypoxia pathway enrichment.
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# DESeq2 Analysis Summary
Design Formula: ~ Batch + Condition
Significant DEGs: 740 (padj < 0.01, |log2FC| > 2.0)
Top Upregulated: CDKN1A (log2FC = +4.82)
GSEA NES: +2.18 (Apoptosis Pathway)
Coursework Level: Algorithms in Computational Biology

Custom Needleman-Wunsch & Smith-Waterman Dynamic Matrix in MATLAB

Task: Implement dynamic programming global and local sequence alignment algorithms in pure MATLAB without built-in functions, incorporating BLOSUM62 scoring matrix, affine gap penalties, and traceback pointers.

  • Deliverables: needleman_wunsch.m, scoring matrix heatmap, alignment ASCII output.
  • Result: Optimal global score matching NCBI BLAST baseline with 100% identity match.
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// Dynamic Programming Matrix Output
Matrix Size: 248 x 262 (Protein Sequences)
Optimal Alignment Score: +842 (BLOSUM62)
Sequence Identity: 84.6% | Gaps: 2.4%
Coursework Level: Structural Bioinformatics & Drug Design

Small Molecule Ligand-Receptor Molecular Docking & Binding Affinity in AutoDock Vina

Task: Prepare target protein PDB structure (remove water molecules, add Kollman charges), define 3D grid bounding box around catalytic active site, perform blind docking of 10 drug candidates, and extract binding affinities.

  • Deliverables: vina_docking.py, PDBQT files, PyMOL 3D interaction diagrams.
  • Result: Lead compound identified with ΔG = -9.4 kcal/mol and 3 key hydrogen bonds.
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// AutoDock Vina Binding Energetics
Target: EGFR Kinase Domain (PDB: 1M17)
Best Pose Affinity: -9.4 kcal/mol (RMSD = 0.0)
H-Bond Donors: Met793, Thr854, Asp855
Coursework Level: Metagenomics & Microbial Evolution

16S rRNA Maximum Likelihood Phylogenetic Tree & Bootstrapping in Biopython

Task: Perform multiple sequence alignment using MUSCLE across 30 bacterial isolates, select best-fit nucleotide substitution model (GTR+G+I), build Maximum Likelihood tree with 1,000 bootstrap replicates, and format tree visualization.

  • Deliverables: phylo_tree.py, Newick tree file, annotated cladogram image.
  • Result: Robust monophyletic clades with > 95% bootstrap support values.
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# Biopython Phylo Run Log
Model: General Time Reversible (GTR + Γ)
Bootstrap Replicates: 1,000 Iterations
Mean Branch Support: 94.8%
The Truth About AI Code

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Evaluation Criteria MATLABSolutions Raw AI (ChatGPT) Generic Freelancers
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MATLABSolutions: Tested Pipelines
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Pricing is based purely on dataset size, algorithm complexity, and turnaround urgency.

Standard Alignment & Trees

Needleman-Wunsch/Smith-Waterman, BLAST querying & phylogenetic trees.

Starting from $35 / assignment
  • Executable MATLAB / Biopython script
  • Dynamic programming alignment matrix
  • Phylogenetic tree image & Newick file
  • Turnitin Plagiarism Report
  • 24–48h Turnaround
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RNA-Seq & Docking

DESeq2 bulk RNA-Seq differential expression, GSEA pathways & AutoDock Vina.

Starting from $70 / project
  • Complete R Bioconductor / Python pipeline
  • Publication Volcano, Heatmap & PCA plots
  • GO / KEGG pathway enrichment analysis
  • Turnitin Plagiarism Certificate
  • Urgent 12–24h Delivery Available
Get Free Quote →

Single-Cell / Capstone

scRNA-Seq (Seurat/Scanpy), Multi-omics & Master's / PhD Thesis.

Custom Scope Custom / project
  • Full Single-Cell UMAP clustering pipeline
  • Comprehensive biological dissertation report
  • Milestone payment split (50/50)
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Frequently Asked Questions

Everything computational biology, genomics, and biotechnology students ask before getting started with our service.

Pricing starts from $35 for standard sequence alignment algorithms (Needleman-Wunsch/Smith-Waterman) and phylogenetic trees, and from $70 for complete RNA-Seq DESeq2 pipelines, GSEA pathway enrichment, and molecular docking in AutoDock Vina. Get an immediate free quote before paying.

Yes. We deliver clean, commented `.R`, `.py`, or `.m` scripts along with processed data tables (DEG tables, count matrices) and high-resolution plots.

Yes. All differential expression and enrichment results use rigorous multiple testing corrections (Benjamini-Hochberg FDR) with verified biological databases (Ensembl, NCBI, Gene Ontology, KEGG).

Yes. We offer urgent fast-track completion from 3 to 24 hours with verified pipeline executions and on-time delivery.

Yes. All code pipelines, statistical workflows, and biological discussions are written from scratch. We attach an official Turnitin Anti-Plagiarism Report to certify 0% similarity.

Yes. We provide 7 days of unlimited free revisions to adjust log2FC thresholds, re-cluster heatmaps, or include additional pathway terms until full satisfaction.

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