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Illumina Sequencing and Transcriptomics
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Illumina Sequencing and Transcriptomics
Illumina Sequencing and Transcriptomics
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1
Question
What determines the sequence of nucleotides in Illumina sequencing and how is it represented?
Answer
The sequence of nucleotides is determined by the order of colors, each corresponding to a specific nucleotide. The resulting sequence is read out as a series of letters (e.g., CATCGT), representing the order of nucleotides in the DNA strand.
2
Question
What is the purpose of multiple clusters in the Illumina sequencing flow cell?
Answer
Multiple clusters each represent different DNA molecules, allowing simultaneous generation of multiple sequences. Each sequence corresponds to a specific cluster in the flow cell, enabling massively parallel sequencing.
3
Question
What are the key steps involved in the Illumina sequencing process starting from cDNA?
Answer
The steps include fragmentation of cDNA, attachment of Illumina adapters, size fractionation (~100-150 bp fragments), amplification with P5 and P7 sequences for flow cell binding and indexing, random binding to the flow cell surface, bridge amplification to create clusters, sequencing-by-synthesis with fluorescently labeled reversible terminators, imaging, chemical removal of terminators, and repetition of cycles to build sequence reads.
4
Question
How do single-end sequencing and paired-end sequencing differ in Illumina sequencing?
Answer
Single-end sequencing sequences from only one end of the DNA fragment (P5 or P7 end), typically generating ~50 base pair reads. Paired-end sequencing sequences both ends (P5 and P7), providing more sequence information, resolving ambiguities, and producing longer overall read lengths (e.g., 100 base pairs).
5
Question
What are the four basic steps of the Illumina sequencing workflow?
Answer
1) Sample preparation (adding adapters and indexing motifs), 2) Cluster generation (isothermal amplification on flow cell), 3) Sequencing (fluorescent base incorporation via sequencing primers), and 4) Data analysis (base calling, alignment, variant identification).
6
Question
How is paired-end sequencing performed and what role do indices play?
Answer
Paired-end sequencing involves sequencing both DNA fragment ends by generating separate reads from each end using sequencing-by-synthesis primers. Indices are short sequences included in adapters that identify which sample library a read belongs to, enabling multiplexing of samples in a single run and matching paired reads from the same fragment.
7
Question
What are the main benefits and challenges of Illumina sequencing?
Answer
Benefits include massively parallel sequencing of hundreds to millions of clusters, generating a huge amount of data at a relatively low cost. Challenges include difficulty mapping short reads in repetitive genomic regions, substitution errors increasing with longer reads, and errors associated with regions having extreme GC content.
8
Question
What is the biological question explored with the Simpsons family example?
Answer
The question is what causes decreased mental ability and baldness in male Simpson family members, hypothesizing there is a gene on the Y chromosome expressed in developing male heads starting around age eight that affects these traits.
9
Question
How is a transcriptome experiment designed to investigate gene expression related to traits like those in the Simpsons example?
Answer
The design involves sampling tissues (brain and scalp) from males and females at different developmental ages (such as 4, 8, 20 years) from both Simpson and control families, extracting RNA, converting it to cDNA, sequencing, mapping reads to the genome, and analyzing differential gene expression with statistical methods. Multiple biological replicates (ideally at least 3) are used to ensure confidence.
10
Question
Why are biological replicates important in gene expression experiments?
Answer
Biological replicates (samples from different individuals of the same group) account for natural variation and increase confidence in results. Sampling multiple replicates is more effective than multiple samples from one individual, as it better represents group-level differences and reduces bias.
11
Question
What is the purpose of normalizing RNA sequencing data within an experiment?
Answer
Normalization adjusts for differences like transcript length and sequencing depth so that gene expression levels can be compared accurately across samples and genes. It accounts for biases due to RNA fragmentation, gene size, and sequencing variability.
12
Question
What does FPKM stand for and why is it used?
Answer
FPKM stands for Fragments Per Kilobase of transcript per Million mapped reads. It normalizes gene expression data by accounting for both the length of transcripts (in kilobases) and sequencing depth (million mapped reads), allowing fair comparisons of gene expression levels within an experiment.
13
Question
Why can't traditional statistical methods like the t-test or ANOVA be directly applied to RNA sequencing data?
Answer
RNA sequencing data does not follow a normal or Poisson distribution but a negative binomial distribution, which has a longer right tail. Traditional methods assume normality, so specialized methods or modified ANOVA approaches that accommodate the negative binomial distribution are required for accurate differential expression analysis.
14
Question
How is differential gene expression analyzed in the context of the Simpsons family study?
Answer
Gene expression data are normalized and then subjected to statistical tests such as ANOVA modified for the negative binomial distribution to identify genes significantly differentially expressed between males and females at important ages. Comparing expression data from Simpson and non-Simpson families helps isolate genes uniquely responsible for observed traits.
15
Question
What is the 'guilt by association' approach in gene function analysis?
Answer
This approach identifies genes involved in a particular biological function by finding genes that are co-expressed together in the same time and place. The assumption is that genes expressed simultaneously and spatially related likely participate in shared pathways or functions.
16
Question
What is the purpose of Gene Ontology (GO) and GO enrichment analysis in gene expression studies?
Answer
Gene Ontology categorizes genes based on biological function, molecular process, and cellular component, creating a hierarchical annotation system. GO enrichment identifies which functional categories are overrepresented in differentially expressed genes, helping researchers understand biological processes underlying gene expression patterns.
17
Question
How does clustering help in analyzing gene expression data?
Answer
Clustering groups genes based on similar expression patterns without prior knowledge (unsupervised), revealing patterns and relationships among genes. This helps identify co-expressed gene groups potentially involved in shared pathways or functions and aids in interpreting complex large-scale data.