Five specialized sequencing assays where OmicsEdge has built bespoke pipelines from first principles - not wrapped generic tools, but purpose-built workflows tuned to each assay's unique library preparation artifacts and biological signals.
Transcriptome End Analysis by RNA-seq - quantifies 5′ end diversity and CAP status at single-nucleotide resolution
read_length: 50–150 bp
strand: stranded
input: total RNA ≥1 µg
ref: GRCh38 / mm39
Assay description
TERA-Seq (Transcriptome End RNA-seq) selectively captures and sequences the 5′ ends of RNA molecules, distinguishing between capped (7-methylguanosine or novel caps), uncapped, and phosphorylated transcripts. This allows genome-wide profiling of transcription start sites (TSS), CAP-seq analysis, and detection of cotranscriptional capping dynamics.
Key applications include characterizing mRNA capping efficiency across conditions, identifying transcription start site heterogeneity, and studying the relationship between 5′ end processing and mRNA stability.
Pipeline approach
Adapter trimming accounts for TERA-Seq-specific 5′ adapters (non-template additions). A custom CAP-score metric quantifies the fraction of reads bearing templated vs. non-templated 5′ nucleotides. TSS calling uses position-weight matrices. Differential TSS usage is tested with DESeq2 at the transcript-isoform level.
Tools used
Cutadapt 4.x - 5′ adapter trimming with TERA-Seq motif
STAR 2.7 - splice-aware alignment with --outSAMstrandField
Ribosome profiling - genome-wide quantification of ribosome-protected mRNA footprints at codon resolution
fragment: 28–32 nt (RPF)
RNase: RNase I or MNase
input: 10M–100M cells
offset: A-site (+15 nt)
Assay description
Ribosome profiling (Ribo-Seq) sequences the ~28–32 nt mRNA fragments protected from nuclease digestion by elongating ribosomes. This produces a genome-wide snapshot of translation at single-codon resolution, enabling quantification of translational efficiency (TE = Ribo-Seq RPF / RNA-Seq RPKM), detection of translated upstream ORFs (uORFs), and codon-level pausing analysis.
Paired with RNA-Seq, Ribo-Seq distinguishes transcriptional from translational regulation - a critical distinction for studies of translational control by m6A, RNA-binding proteins, or stress response.
Pipeline approach
P-site offset calibration is performed using riboWaltz. rRNA contamination is removed with Bowtie2 pre-filtering. TE (translational efficiency) scores are calculated as RPF:mRNA ratios, with anota2seq for differential TE testing. ORF discovery with RibORF or ORFquant. 3-periodicity is verified as a QC metric before proceeding.
Tools used
Bowtie2 - rRNA / tRNA depletion pre-filter
STAR 2.7 - genome alignment (--alignEndsType Extend5pOfRead1)
N6-methyladenosine mapping - transcriptome-wide profiling of the most abundant mRNA internal modification
IP: anti-m6A antibody
input: 10% of IP
read_length: 75–150 bp
motif: DRACH (RRACH)
Assay description
m6A-Seq (also called MeRIP-Seq or m6A-RIP-Seq) immunoprecipitates m6A-modified RNA fragments using anti-m6A antibodies, followed by sequencing of the enriched fragments alongside a non-IP input control. This maps N6-methyladenosine sites across the transcriptome, identifying the characteristic DRACH motif and linking m6A deposition to METTL3/14 writer and FTO/ALKBH5 eraser activity.
Key applications: differential m6A abundance across conditions, identification of METTL3 targets, correlation of m6A with translation efficiency (combined with Ribo-Seq), and mRNA stability effects.
Pipeline approach
IP and input are paired; peaks called with exomePeak2 (negative binomial model on IP/input ratio). MEME-ChIP for de novo motif discovery (DRACH confirmation). Differential methylation between conditions with Fisher exact test or DESeq2 on peak counts. Integration with RNA-Seq for transcriptional vs. m6A regulatory analysis.
Tools used
STAR 2.7 - alignment with --outSAMtype BAM SortedByCoordinate
exomePeak2 - m6A peak calling (NB model, IP vs. input)
Single-cell RNA-Seq (10x Chromium or similar droplet-based platforms) captures the transcriptome of individual cells. Unlike bulk RNA-Seq, which measures population averages, scRNA-Seq reveals cellular heterogeneity, rare cell types, and cell-state transitions. Key steps include demultiplexing barcodes, filtering low-quality cells, normalization, highly variable gene selection, dimensionality reduction, and unsupervised clustering with cell type annotation.
Extended analyses include pseudotime trajectory inference (Monocle3), RNA velocity, cell-cell communication (CellChat), and multi-sample differential abundance (Milo).
Pipeline approach
Cell Ranger (10x) or STARsolo for barcode demultiplexing and count matrix generation. Seurat v5 for QC (nFeature, nCount, pct.mt thresholds), normalization (SCTransform), integration (Harmony/CCA), clustering (Leiden), and differential expression (FindMarkers). Cell types annotated with SingleR and curated marker panels.
Oxford Nanopore Technologies (ONT) sequences DNA or RNA molecules directly through protein nanopores, measuring ionic current changes caused by each base. Unlike short-read sequencing, ONT produces reads of 10s of kilobases (or longer), enabling full-length isoform sequencing (cDNA-seq), structural variant calling (SV/CNV), and - uniquely - direct detection of base modifications (5mC, 5hmC, m6A) from the raw electrical signal without chemical conversion.
R10.4.1 chemistry with Dorado super-accurate (SUP) basecalling achieves >99% single-read accuracy in duplex mode, enabling applications previously only possible with Illumina short reads.
Pipeline approach
POD5/FAST5 → Dorado basecall (SUP model) → minimap2 splice-aware alignment → bambu isoform quantification → differential isoform usage with DESeq2. For methylation: Dorado modBAM → modkit pileup → DMR calling with DSS. SV calling with Sniffles2. NanoPlot for QC.
Tools used
Dorado 0.7 (SUP/HAC) - basecalling from POD5 signal