Flashcards

Question 1

Main difference between first and second generation sequencing

Answer 1

First generation (Sanger sequencing) can only sequence one fragment at a time, while second generation can perform parallel sequencing of multiple fragments

Question 2

Paired-end sequencing

Answer 2

Both ends of the same DNA fragment is sequenced

Question 3

Main difference between second and third generation sequencing

Answer 3

In third generation sequencing, sequencing is done for individual DNA molecules. There is no amplification step which it is in 2nd generation

Question 4

Duplicate (sequencing error)

Answer 4

Are caused by sequencing the same physical DNA fragment multiple times. The reads then all come from the same DNA molecule - don’t describe the true diversity in the sample. Duplicates are often caused by biases in the amplification step

Question 5

Factors contributing to errors in Illumina sequencing

Answer 5

• Read position: probability of error increases for each sequenced bp
• T has higher error rate than the other nucleotides. GC-rich patterns have high error rate
• First read has lower error rate than second (for paired-end sequencing)

Question 6

Site specific error (SSE)

Answer 6

Errors that depend on the sequence of the site where the error has occured (example: GC-rich regions)

Question 7

Steps involved in pre-processing of NGS data

Answer 7

Pre-processing is used to “clean” the data.

• Identifies erroneous reads and bp
• Cleans data by removing errors, using for example filtering or trimming

Question 8

Coverage

Answer 8

The number of times a nucleotide in the reference in “covered” by reads

Question 9

Purpose of a variant caller of SNPs

Answer 9

Variant calling aims to identify SNPs in the sequenced genome compared to the reference, and then to distinguish between true mutations and sequencing errors. A good caller should have a high sensitivity (find all true mutaitons) and a high specificity (ignore all false positives)

Question 10

GATK

Answer 10

Stands for “Genome analysis toolkit” and contains the unified genotyper, which is an advanced mutation caller

Question 11

Post-processing (genome sequencing)

Answer 11

After SNP variant calling, extra filtering might be needed. Example: sequencing errors only discovered at the end of the reads, or in one certain read direction

Question 12

Global alignment

Answer 12

Two sequences are aligned over their full length. Can use the Needleman-Wunsch algorithm

Question 13

Local alignment

Answer 13

Two sequences (often of substantially different lengths) are aligned based on their best matching subsequences. Can use the Smith-Waterman algorithm (modifies NW)

Question 14

Steps in analysis of genome sequencing data

Answer 14

Pre-processing, read mapping, quality refinement, variant calling

Question 15

Quality refinement (genome sequencing)

Answer 15

Quality refinement is the step that comes after read mapping but before variant calling in genome sequencing. The quality refinement step aims to remove errors in the data and errors introduced in the read mapping.

Question 16

Three main steps in analysis of RNA seq data

Answer 16

1. Quatification of the gene expression
2. Normalization
3. Identification of differentially expressed genes

Question 17

Splice-aware mapper

Answer 17

When mapping RNA-seq reads to a genome, the mapper needs to be able to handle splicing. In other words, the mapper should be allowed to make large gaps, corresponding to introns

Question 18

Multiple matches (RNA-seq)

Answer 18

One read matches two or more different regions. Can be explained by multiple similar regions in the genome, but also by errors.

Question 19

Semiquantitative

Answer 19

The quantitative data is relative and therefore influenced by for example one gene being substantially more expressed than others

Question 20

Which are the three statistical approaches to identify DEGs?

Answer 20

1. Methods based on normal assumptions
2. Methods based on non-parametric methods
3. Methods based on count distributions

Question 21

Family-wise error rate

Answer 21

The FWER is the probability of at least one false positive, and is equal to 1 – (1 - α)^m

Question 22

Bonferroni correction

Answer 22

Divide the significance level α by the number of performed tests m. Then use the cut-off α/m instead.

A Bonferroni adjusted p-value can be calculated by multiplying each p-value with m.

Bonferroni corrected p-values always control the FWER

Question 23

False discovery rate

Answer 23

FDR is the number of false positives in relation to the total number of rejected null hypotheses (significant tests)

Question 24

Benjamini-Hochberg correction

Answer 24

Order the p-values, then multiply each p-value with the number of tests and divide by its position.

Benjamini-Hochberg correction controls the FDR

Question 25

Difference between supervised and unsupervised data analysis methods

Answer 25

Supervised methods rely on metadata, while unsupervised methods don't. Unsupervised methods instead focus on the identification of patterns in the data.

Question 26

Distance measure (agglomerative hierarchical clustering)

Answer 26

Describes the separation between data points

Question 27

Linkage criterion (agglomerative hierarchical clustering)

Answer 27

Measures the distance between clusters

Question 28

Metagenomics

Answer 28

Metagenomics is the study of the metagenome, which is the collective genome in a microbial community

Question 29

OTU

Answer 29

Operational taxonomic unit (OTU) is a putative species formed by clustering sequences from amplicons. Sequences that are sufficiently similar are clustered together and assumed to be from the same tyoe of organism.

Question 30

Steps in amplicon sequencing

Answer 30

Reads from amplicons -> Pre-processing -> OTU identification -> OTU annotation -> Statistical analysis -> Results

Question 31

Singleton

Answer 31

Sequences that don’t cluster with any other sequence. These sequences are OTUs but are, in many cases, discarded since they’re only observed once.

Question 32

Diversity (amplicon sequencing)

Answer 32

The diversity is an estimate of how many different bacteria are present in the sample. This can reflect nutrient availability and other environmental factors

Question 33

Alpha diversity

Answer 33

The diversity on the local level

Question 34

Beta diversity

Answer 34

The diversity between habitats

Question 35

Richness (alpha diversity)

Answer 35

Unique number of OTUs

Question 36

Evenness (alpha diversity)

Answer 36

Checks the evenness of distribution of species

Question 37

Rarefaction

Answer 37

The diversity indices are dependent on sequencing depth. In order to make indices between samples comparable they need to be rarefied, i.e., subsampled to the same sequencing depth.

Question 38

Direct binning (shotgun metagenomic sequening)

Answer 38

* Search each metagenomic fragment for the presence of genes * A vast number of the microbial genes are not present in the databases. The search therefore require sensitive aligners and approximate matches are often accepted. * Requires relatively long reads and not generally possible to do for short reads * “Bins” are finally formed by counting the number of reads for each type of gene

Question 39

Reference guided binning (shotgun metagenomic sequening)

Answer 39

* Guided binning uses an annotated reference database that contains the genomes of the microorganisms present in the sample * Each metagenomic fragment is mapped against the reference database * “Bins” are formed by counting the number of reads matching each type of gene present in the genomes * Typically done for data with short reads (<500bp)