Isoform Sequencing (Iso-seq) using PacBio SMRT (Single Molecule, Real-Time) technology enables sequencing of full-length transcript isoforms (from 5’UTR to 3’poly-A tail) within the targeted genes. Iso-seq is a high-throughput method for characterizing fusion genes, identifying alternative splicing, annotating genomes, and discovering novel transcripts.
Iso-seq can be fully leveraged for medical and agricultural research purposes, including disease mechanism investigation, exploring drug resistance mechanisms, discovering new genes, as well as studying plant development and biotic and abiotic stresses.
*NC/QC: NanoDrop concentration/Qubit concentration.
Transcription Factor analysis
Fusion Transcript analysis*
Alternative Splicing analysis*
Alternative PolyAdenylation analysis*
*Only available when reference genome is available.
From sample and library preparation, SMRT sequencing, and data quality control, to bioinformatics analysis, Novogene provides high-quality products and professional services. Each step is performed in agreement with a high scientific standard and meticulous design to ensure high-quality research results.
Transcriptome profiling for floral development in reblooming cultivar ‘High Noon’ of Paeonia suffruticosa
Scientific Data Date: January 2019IF: 5.929DOI: https://www.nature.com/articles/s41597-019-0240-1
Hybrid sequencing-based personal full-length transcriptomic analysis implicates proteostatic stress in metastatic ovarian cancer
Oncogene Date: January 2019IF: 6.854DOI: https://www.nature.com/articles/s41388-018-0644-y
Full-length transcriptome sequences of ephemeral plant Arabidopsis pumila provides insight into gene expression dynamics during continuous salt stress
BMC Genomics Date: September 2018IF: 3.73DOI: https://bmcgenomics.biomedcentral.com/articles/10.1186/s12864-018-5106-y
The CCS (Circular Consensus Sequence), of each read can be created by correcting and aligning subreads to each other taken from a single ZMW(zero-mode waveguide).
Length distribution of CCS(Circular Consensus Sequence) reads
The x-axis represents the read length; the y-axis indicates frequency count corresponding to the read length
Distribution of isoform numbers by the characterization results. There are a significant number of isoforms for NIC or NNC (Novel isoforms) (left); Usually, one-gene-one-isoform distribution can be observed in most of the cases, especially for Novel genes (right).
Isoform numbers by structural category (left) and by genetype (right)
The x-axis shows isoform classification; the y-axis shows isoform percentage for each classification (left)；
The x-axis shows gene type; the y-axis shows genes percentage for each “isoforms per gene” classification (right)
The transcript length distribution and exon number distribution of the isoforms by the structural classification are both presented in a boxplot.
Transcript length distribution by structural classification (left) and exon numbers distribution by structural classification (right) by transcript type
The x-axis shows transcript classification; the left y-axis shows the length of transcript in each classification; the right y-axis shows the number of exons of transcript in each classification
The Gene Ontology (GO) project aims to provide reliable descriptions of gene products within several databases. GO vocabularies (ontologies) explain gene products concerning their associated biological processes, molecular functions, and cellular components in a species-independent approach. GO annotation is only available for identified novel genes and isoforms.
Gene Ontology Annotation Classification
The x-axis shows the three GO categories, and the y-axis shows the number of differential genes annotated to the term (including the sub-term of the term). The three different categories represent the three basic classifications of GO term (from left to right are biological processes, cellular components, and molecular functions)；
CNCI (Coding-Non-Coding Index) is a powerful signature tool to predict the sequences based on the intrinsic composition and offers accurate classification of transcripts assembled from whole-transcriptome sequencing data. PLEK(predictor of long non-coding RNAs and messenger RNAs based on an improved k-mer scheme) is a tool for predicting long non-coding RNAs and mRNAs in the absence of genomic sequences or annotations using a computational pipeline based on an improved k-mer scheme and a support vector machine (SVM) algorithm. The results from PLEK and CNCI are shown in the Venn diagrams.
Venn diagrams of results from PLEK and CNCI
Summary of alternative splicing events
Alt.3’: Alternative 5′ splice site; Alt.5’:Alternative 3′ splice site
*Please contact us get the full demo report.
The field is required.