alignment_assembly

Question 1

What is the alignment process in bioinformatics?

Answer 1

The alignment process compares sequences (DNA, RNA, or protein) to identify regions of similarity, helping to understand functional, structural, and evolutionary relationships.

Question 2

What is a bit score?

Answer 2

A normalized score that reflects the significance of the alignment between a query sequence and a database sequence, independent of database size; higher scores indicate more significant alignments.

Question 3

What is an E-value?

Answer 3

The Expect value describes the number of hits one can expect to see by chance when searching a database of a particular size; lower E-values indicate more significant matches.

Question 4

How does the length of the query sequence affect the E-value?

Answer 4

Shorter sequences tend to have higher E-values because they are more likely to appear in the database by random chance.

Question 5

How does database size influence the E-value?

Answer 5

A larger database increases the likelihood of finding matches with the same score, affecting the E-value.

Question 6

What is BLASTN used for?

Answer 6

Comparing nucleotide sequences against nucleotide databases.

Question 7

What is BLASTP used for?

Answer 7

Comparing protein sequences against protein databases.

Question 8

What is BLASTX used for?

Answer 8

Comparing a nucleotide sequence translated into all six reading frames against a protein database.

Question 9

What is TBLASTN used for?

Answer 9

Comparing protein sequences against a nucleotide database translated in all six reading frames.

Question 10

What is TBLASTX used for?

Answer 10

Comparing nucleotide sequences translated into all six reading frames against another translated nucleotide database.

Question 11

What factors should you consider when choosing alignment software?

Answer 11

Type of sequences/experiment, sequencing platform, planned further analysis, and computational infrastructure.

Question 12

Why is it important to know your sequencing platform when choosing alignment software?

Answer 12

Different platforms (e.g., Illumina, Ion Torrent, PacBio) have unique read lengths and error types that affect compatibility with mapping tools.

Question 13

How can downstream analysis influence your choice of alignment software?

Answer 13

Ensure that subsequent tools are compatible with the reported alignment types and formats required for further analysis.

Question 14

Why is computational infrastructure important in selecting alignment software?

Answer 14

Some tools may require significant computational power or memory resources; knowing your available resources helps in making an appropriate choice.

Question 15

Aspect

Answer 15

Short Read Alignment

Question 16

Read Length and Characteristics

Answer 16

Typically range from 36 to 600 bp; generated by platforms like Illumina; cost-effective, high-quality data but can complicate assembly of complex genomes.

Question 17

Alignment Speed and Efficiency

Answer 17

Generally faster to align due to smaller size; may require more computational resources for large datasets; lower alignment rate for short reads.

Question 18

Error Rates

Answer 18

Generally lower error rates, suitable for applications requiring high accuracy, such as variant calling.

Question 19

Applications

Answer 19

Commonly used in RNA-Seq, targeted resequencing, and SNP detection due to high throughput and accuracy.

Question 20

Alignment Tools

Answer 20

Tools like BWA, Bowtie2, and STAR are designed specifically for short-read data.

Question 21

Aspect

Answer 21

Description

Question 22

Genome Assembly

Answer 22

The process of reconstructing a complete genome from short DNA sequences, known as reads.

Question 23

Reads

Answer 23

The smallest unit of sequencing data obtained from next-generation sequencing (NGS); short sequences of DNA, typically ranging from 150 to 600 base pairs.

Question 24

Contigs

Answer 24

Contiguous sequences of DNA assembled from overlapping reads; represent longer stretches of the genome without gaps, usually ranging from hundreds of bases to several kilobases.

Question 25

Scaffolds

Answer 25

Longer sequences constructed by linking contigs together using additional information about their relative positions and orientations; may contain gaps where the exact sequence is unknown.

Question 26

K-mer

Answer 26

A substring of length k from a longer DNA sequence; used in assembly algorithms to identify overlaps between reads and help construct contigs.

Question 27

De Bruijn Graph

Answer 27

A data structure representing overlaps between k-mers; each k-mer is a vertex, and edges connect vertices that overlap by k-1 bases; allows efficient assembly of contigs.

Question 28

Assembling Software

Answer 28

Tools available for genome assembly with different strengths.

Question 29

Velvet

Answer 29

A popular de novo assembler using a de Bruijn graph approach to assemble short reads into contigs.

Question 30

Edena

Answer 30

Designed for assembling short reads; can handle large datasets and produce high-quality assemblies.

Question 31

SSAKE

Answer 31

Focuses on creating contigs from paired-end reads, improving accuracy by utilizing information from both ends of the read pairs.

Question 32

K-mer Selection

Answer 32

Choosing the k in k-mer analysis is crucial; smaller k may lead to many small contigs, while larger k can produce fewer, longer contigs.

Question 33

Good Assembly Definition

Answer 33

Characterized by longer and fewer contigs, high percentage of contigs ≥ 1 kb, and high total number of bases in contigs.
High Coverage and maintain the appropriate GC content.

Question 34

Methods to Increase Assembly Quality

Answer 34

Strategies to enhance the quality of genome assembly.

Question 35

High Coverage

Answer 35

Increasing coverage depth can reduce gaps between contigs and lead to longer contigs overall.

Question 36

GC Content

Answer 36

Maintaining appropriate GC content can improve sequencing accuracy and assembly quality.

Question 37

Mate pair

Answer 37

a pair of reads from two ends of the same insert fragment

Question 38

N50 of the assembly

Answer 38

N50 (a weighted median length statistic such that 50% of the entire assembly is contained in contigs or scaffolds equal to or larger than this value).

Question 39

factors may affecting the assembly

Answer 39

• Coverage
• % GC
• Genomic content (repetitive regions, transposons, etc.)

Question 40

Effect of coverage

Answer 40

Coverage = N*L/G
N=Total Number of Reads
G=Genome Size
L is read length

The high coverage will lead to the better assembly quality

Question 40

velveth and velvetg

Answer 40

h: convert reads to k-mers
g: assemble k-mers into contigs

Velveth helps you construct the dataset for the following program, velvetg, and indicate to the system what each sequence file represents.