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Galaxy workshop self-evaluation
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What is the very first analysis you do with your FASTQ files?
Quality control
Mapping
Peak calling
Adapter trimming
What is meant by the term "mapping"?
Mapping of reads is the very first step in HTS data analysis
Mapping refers to the process of aligning short reads to a reference sequence
Mapping counting the number of reads per gene
Clear selection
Why is Stranded RNA-Seq useful?
For placing all reads onto both strands
For resolving reads that could map to overlapping genes (occoring on the sense and anti-sense strands)
For determining which strand a read maps to
Which factors could make it hard to map an NGS read to its reference genome unambiguously?
An unusually long length of the read
A high rate of sequencing errors in the read
A reference genome with lots of highly similar subsequences
An unusually short length of the read
Which of the following are true statements about SAM/BAM format?
Since BAM is a compressed format, the same type of data stored in BAM uses less space than if it's stored in SAM
BAM ans SAM do not contain read quality information
SAM and BAM can be interconverted into each other without loss of information
BAM and SAM store different types of data
How are reads from HTS are stored?
as FASTQ file
as FASTA file
as FASTREAD file
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Improving the quality of sequences can be done by
Filtering of sequences which are too short
Cutting/Trimming sequences of low quality score parts
Filtering of sequences with too many N bases
Filtering of sequences with small mean quality score
Read alignment information is stored in
FASTQ file
BAM file
GTF file
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Why paired-end sequencing may have advantages compared to single-end sequencing?
Paired-end sequencing allows users to sequence both ends of a fragment and generate high-quality data
Paired-end sequencing facilitates detection of genomic rearrangements and repetitive sequence elements
The distance between both reads is known and therefore is additional information that can improve read mapping
What is the general concept of HTS data analysis?
Raw reads -> Mapping -> Quality control -> Trimming
Mapping -> Trimming -> Quality control -> raw reads
Raw reads -> Quality control -> Trimming -> Mapping
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Which of these statements is true in the context of replicates for RNA-Seq
At least 3 technical replicates per experimental condition are needed for differential expression analysis
At least 3 biological replicates per experimental condition are needed to sensitively estimated the biological variance
A single biological replicate with the most possible depth (for my money) is enough for any analysis
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Recommended tool for mapping RNA-Seq reads
Bowtie2
STAR
BWA
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To count number of reads per gene using featureCounts, I need
A GTF file with gene annotation and a BAM/SAM file
A FASTA file of reference genome and a BAM/SAM file
A FASTQ file of reads and a GTF file with gene annotation
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DESeq2 needs the following input files
BAM files from different experimental conditions
A file with the lengths of all genes
Count files from different experimental conditions
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Tool to use for peak calling
featureCounts
computeMatrix
MACS2
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