This page provides information on data analysis techniques used in sequencing. Sequencing plays a crucial role in genomics, transcriptomics, and other fields, and proper data analysis is essential for meaningful insights.
Common Data Analysis Techniques
- Quality Control: Ensuring the quality of raw sequencing data is the first step in the analysis process.
- Read Mapping: Aligning reads to a reference genome or transcriptome.
- Quantification: Estimating the abundance of transcripts or sequences.
- Differential Expression Analysis: Identifying genes or transcripts that are differentially expressed between samples.
- Functional Annotation: Assigning biological functions to sequences.
Useful Tools
- FastQC: A quality control tool for high-throughput sequence data.
- Bowtie2: A fast and sensitive mapper for short sequences.
- HTSeq: A Python framework for various types of read counting.
- DESeq2: A bioconductor package for differential expression analysis.
Learn More
For further reading, you might find the following resources helpful:
Sequencing Data Analysis
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