I'm doing analysis with shotgun sequencing data, not 16S, from human stool sample sequenced by Illumina Miseq.
Usually I do BIobakery Humann pipeline, and get results of path coverage, path abundace, and gene families, but I have to narrow down my research.
Is there any other option to take deeper look - getting results of single-gene level or single-protein level abundace from my shotgun sequencing data?
Upasna Srivastava
Preprocessing by Quality control: Assess the quality of your sequencing data, including evaluating base quality scores and trimming low-quality reads if necessary using tools like Trimmomatic or Cutadapt, Remove any sequencing adapters or contaminants using tools like Trimmomatic or Cutadapt and for Read alignment you can Map your cleaned reads to a reference genome or transcriptome. Popular aligners include STAR, HISAT2, and Bowtie. for Gene Quantification:a. Count-based methods: Quantify gene expression using count-based methods like featureCounts, HTSeq, or Salmon. These tools assign reads to specific genes and generate a count matrix for downstream analysis.b. Transcript abundance estimation: If you are interested in isoform-level analysis, you can use tools like Kallisto or Salmon to estimate transcript abundances. for Differential Expression Analysis:a. Normalize counts: Normalize the count matrix to account for variations in sequencing depth and library size. Common normalization methods include TPM (Transcripts Per Million) or FPKM (Fragments Per Kilobase Million).b. Statistical analysis: Use differential expression analysis tools like DESeq2, edgeR, or limma to identify genes that are significantly differentially expressed between conditions or groups of interest. and for Visualization:a. Create plots and visualizations to explore and present your results, such as volcano plots, MA plots, heatmaps, and PCA plots. Functional Analysis:a. Interpret the results by performing functional enrichment analysis to understand the biological processes, pathways, or gene ontology terms associated with the differentially expressed genes. Tools like GOSeq, Enrichr, or gProfiler can help with this.
here is general steps for this :
Analyzing single-gene level abundance in shotgun sequencing data typically involves a series of steps to extract and quantify the expression levels of specific genes from the raw sequence data. Here's a general outline of the process:
0 votes
0 thanks
- Badges
- Science method
- Similar topics
- Mathematics
- Statistics
- Statistical Techniques
- Meta-Analysis
More Heeun Kwon's questions See All
Similar questions and discussions