mRNA-seq (NGS) with reference genome

mRNA-seq (NGS) with reference genome

RNA-seq is a standard tool across life and crop sciences, bridging the gap between genomes and proteomes. Its strength lies in discovering new transcripts and quantifying their expression in one assay. It's widely used for comparative transcriptomic studies, shedding light on genes related to various traits or phenotypes, like comparing mutants to wild-types or revealing gene expression under specific conditions. BMKCloud mRNA(Reference) APP integrates expression quantification, differential expression analysis(DEG), and sequence structure analyses into the mRNA-seq(NGS) bioinformatics pipeline and amalgamates the strengths of similar software, ensuring convenience and user-friendliness. Users can upload their RNA-seq data to the cloud, where the App offers a comprehensive, one-stop bioinformatic analysis solution. Additionally, it prioritizes customer experience, offering personalized operations tailored to users' specific needs. Users can set parameters and submit the pipeline mission by themselves, check the interactive report, view data/diagrams and complete data mining, such as: target gene selection, functional clustering, diagramming, etc.

Demo results

Data mining

Import requirement

Main analysis

Reference

Demo results

Data mining

Import requirement

Platform: Illumina, MGI
Strategy: RNA-Seq
Layout: Paried, clean-data.
Library type: fr-unstranded, fr-firststrand or fr-secondstrand
Read length: 150 bp
File type: *.fastq, *.fq, *.fastq.gz or *.fq.gz. The system will automatically pair the .fastq files according to thier file names, e.g. *_1.fastq paired with *._2.fastq.
Number of samples: There are no restrictions on the number of samples, but the analysis time will increase as the number of samples grows.
Recommended Data Amount: 6G per sample

Main analysis

The main analysis and bioinformatic tools of mRNA-seq (Reference) pipeline is as follows:

1. Rawdata Quality Control:

•Removal of low-quality sequences, adapter sequences,etc;

•Tools: in-house developed pipeline;

2. Alignment of data to a reference genome:

•Aligning reads with a splice-aware algorithm against the reference genome.

•Tools: HISAT2, samtools

3. Library quality analysis:

•Insert length analysis, sequence saturation analysis, etc;

•Tools: samtools;

4. Sequence structure analysis:

•Alternative splicing analysis, gene structure optimization, novel gene prediction, etc;

•Tools: StringTie, gffcompare, GATK, DIAMOND, InterProScan, and HMMER.

5. Differential expression analysis:

•DEG screening, co-relationship analysis, functional enrichment;

•Various visualization results;

•R with SEGseq, DESeq2, ggplot2, DEXSeq

Reference

1. Kim, Daehwan et al. “Graph-based genome alignment and genotyping with HISAT2 and HISAT-genotype.” Nature Biotechnology 37 (2019): 907 - 915.

2. McKenna, Aaron et al. “The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data.” Genome research 20 9 (2010): 1297-303 .

3. Li, Heng et al. “The Sequence Alignment/Map format and SAMtools.” Bioinformatics 25 (2009): 2078 - 2079.

4. Perțea, Mihaela et al. “StringTie enables improved reconstruction of a transcriptome from RNA-seq reads.” Nature Biotechnology 33 (2015): 290-295.

5. Love, Michael I. et al. “Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2.” Genome Biology 15 (2014): n. pag.

6. Eddy, Sean R.. “Accelerated Profile HMM Searches.” PLoS Computational Biology 7 (2011): n. pag.

mRNA-seq (NGS) with reference genome

mRNA-seq (NGS) with reference genome

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