Bioinformatics-Review

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Bioinformatics: an interdisciplinary field that develops and applies computer and computational technologies to study biomedical questions

The -informatics in Bioinformatics

Chapter 2 Part1- Gene and RNA

+ Gene

A gene is a locus (or region) of DNA that encodes a functional protein or RNA product, and is the molecular unit of heredity.

Central dogma and splicing

Gene Finding Approaches

• Something that matches statistical patterns common to all genes
• Something that matches an already known gene (homology)
• Hybrid

Gene Measurement

• ORF (Open Reading Frame)

  • Start codon ATG
  • Stop codon TAA, TAG, TGA

• Codon Usage

  • The preference for certain synonymous codons, often measured by the Codon Adaptation Index (CAI), which reflects the efficiency of gene expression in an organism.

• Features and motifs

  • Promoters, splice sites, enhancers, untranslated regions (UTRs)

Gene Prediction

Similarity-Based Approach to gene prediction

• Genes in different organisms are similar

The similarity-based approach uses known genes in one genome to predict (unknown) genes in another genome

Problem Given a known gene and an unannotated genome sequence, find a set of substrings of the genome sequence that best fits the known gene.

Exon Chaining Algorithm

Non-coding RNA ncRNA

Transfer RNAs (tRNAs)

• the first identified RNA class.
• Function as intermediaries between DNA and amino acids in protein synthesis.
• Approximately 80 nucleotides (nt) in length.
• Have a cloverleaf-like secondary structure.
• Four short double-helical elements.
• Three loops: D loop, anticodon loop, and T loop.

microRNAs (miRNAs)

• Typically originates from non-protein-coding genomic regions or introns.
• Regulates gene expression by binding to target mRNAs, suppressing translation or promoting mRNA degradation.
• Plays a role in development and differentiation by controlling cell growth, differentiation, and apoptosis.
• Involved in disease mechanisms; miRNA dysregulation is linked to cancers, cardiovascular diseases, and neurological disorders.

Chapter 2 Part2- Genome, NGS, Transcriptome and Assembly

SNP (Single Nucleotide Polymorphism)

• Mutation of a single nucleotide (ACTG)
• Some can be associated with various phenotypic differences
• SNPs are the most common type of genetic variation among people

CNV (Copy Number Variation)

• Copy Number Variation (CNV): Refers to large segments of DNA, typically over 1 kilobase (Kb) in length, that vary in copy number compared to a reference genome.
• Types of CNVs: Includes deletions, duplications, and insertions.
• Importance of Studying CNVs:
  • Can impact gene expression and adaptation.
  • Provides insight into the complexity of phenotypic variation and disease mechanisms.

GWAS - Genome-Wide Association Studies

• Genome Wide Association Study (GWAS): A study of genetic variations across the genome to associate single nucleotide polymorphisms (SNPs) with traits or disease conditions.
• Purpose:
  • Enhances understanding of biological processes affecting health.
  • Improves disease prediction and patient care.
  • Supports the advancement of personalized medicine.

Read: a short DNA fragment that is sequenced by a sequencer.

• Contents
  • DNA sequence (represented by symbols).
  • Quality information indicating the sequencing quality of each base.
• FASTQ format: A commonly used file format for storing sequenced DNA fragments along with their quality scores. Each record includes a sequence ID, DNA sequence, separator, and quality scores.

RNA-Seq

This flowchart follows the sequence:

1. Start with the RNA sample.
2. Convert RNA to cDNA using reverse transcription.
3. Amplify the cDNA using PCR.
4. Fragment the amplified cDNA.
5. Sequence the fragments to produce reads.

Transcript Abundance

• Definition: Transcript abundance refers to the amount of mRNA molecules for a specific gene in a sample, indicating the gene's expression level.
• Importance: Higher transcript abundance reflects greater gene activity in the cell and often corresponds to genes critical for cell function.
• In RNA Sequencing: Genes with higher transcript abundance produce more reads in RNA sequencing, as they are more prevalent in the sample.

Sequencing Depth

• Definition: Sequencing depth is the total number of reads generated in a sequencing experiment, representing the coverage level of RNA in the sample.
• Importance: Higher sequencing depth increases sensitivity to detect low-abundance genes and improves accuracy in measuring gene expression.
• Normalization: When comparing gene expression across experiments, sequencing depth needs to be normalized to ensure accurate and fair comparisons of expression levels.

[!abstract]+

• Transcript Abundance affects the number of reads a gene produces; higher abundance results in more reads.
• Sequencing Depth determines the coverage and accuracy of RNA sequencing data, with higher depth allowing better detection of low-abundance genes and requiring normalization for cross-experiment comparisons.

Expression Level

RPKM (Reads Per Kilobase Million): The formula for calculating RPKM (Reads Per Kilobase of transcript, per Million mapped reads) is:

$$\text{RPKM}=\frac{{10}^9\times C}{N\times L}$$

Where:

• $C$ = the number of mapped reads for the specified transcript.
• $N$ = the total number of mapped reads in the experiment.
• $L$ = the length of the specified transcript in base pairs.

Genomic Data Mapping and Analysis Workflow

• Reads Mapping: The colorful fragments on the left represent reads obtained from sequencing data. Through a process called mapping, these reads are aligned to the reference genome (shown as a DNA structure on the right) to determine their positions within the genome.

• Mapped Alignment: Once mapped, the reads are aligned to the reference genome, forming a mapped alignment (illustrated as green bars). This step accurately locates the origin of each read within the genome.

• Analysis Applications:

  • Calling Genetic Variants: By comparing the sequencing data with the reference genome, genetic variants, such as single nucleotide polymorphisms (SNPs), can be identified.
  • Measuring Abundance: Mapped data can be used for abundance analysis, such as RNA-Seq to measure gene expression levels, and ChIP-Seq to analyze protein-DNA interactions. These analyses help in understanding gene expression and regulatory patterns under various conditions.

Mapping Reads from RNA-Seq

• Mapping RNA-Seq Reads: RNA-Seq reads are mapped to the reference genome to locate where each read aligns.
• Junction Sites: Some reads span exon-exon junctions, indicating splice sites where introns have been removed during RNA processing.
• Detection of Novel Isoforms: By analyzing these junction reads, novel splicing isoforms can be identified, revealing alternative splicing patterns and adding insight into gene expression diversity.

Chapter 2 Part3- Alignment and NGS reads Mapping - BLAST

Chapter 3 Part1- Gene Ontology

• Molecular Function elemental activity/task
• Biological Process biological goal or objective
• Cellular Component location or complex

GO Relationships

• IS A
• PART OF
• REGULATES
  • POSITIVELY REGULATES
  • NEGATIVELY REGULATES

这几种格式（FASTA、FASTQ、SAM、VCF、GFF、PDB）都是生物信息学中常用的数据文件格式，各自有不同的用途和结构。以下是每种格式的总结：

1. FASTA

• 用途：存储序列数据（如DNA、RNA或蛋白质序列）。
• 结构：
  • 每个序列由两行组成：第一行以>开头，包含序列的ID或描述；第二行是实际的序列。
• 特点：只包含序列信息，没有质量分数。适合基因组或蛋白质序列的基本存储和共享。

2. FASTQ

• 用途：存储高通量测序数据，包含序列和质量信息。
• 结构：
  • 每个条目由四行组成：第一行以@开头，表示序列标识符；第二行是序列；第三行以+开头，标记质量分数的开始；第四行是质量信息，与序列长度相同。
• 特点：包含每个碱基的测序质量分数，常用于测序数据分析，便于质量控制。

3. SAM (Sequence Alignment/Map)

• 用途：存储序列比对信息，记录reads如何映射到参考基因组上。
• 结构：
  • 文本格式，每行记录一个比对结果，包括序列名称、比对位置、匹配质量等信息。
• 特点：SAM格式文件大，但信息全面。常用于分析比对结果，可以转换成更紧凑的二进制格式（BAM）。

4. VCF (Variant Call Format)

• 用途：存储遗传变异信息，如SNPs和Indels。
• 结构：
  • 文本格式，包含头信息和每个变异的记录，每行包含染色体位置、参考碱基、变异碱基、质量分数等信息。
• 特点：标准化格式，适合基因组变异数据的存储和共享，常用于基因组学和变异分析。

5. GFF (General Feature Format) / GTF (General Transfer Format)

• 用途：存储基因组注释信息，描述基因组上各种功能元件的位置，如基因、外显子、启动子等。
• 结构：
  • 每行记录一个特征，包括染色体号、起始和结束位置、特征类型（如基因、外显子）、方向、附加信息等。
• 特点：标准化格式，常用于描述基因组的结构和功能注释。

6. PDB (Protein Data Bank)

• 用途：存储蛋白质三维结构信息，通常用于结构生物学。
• 结构：
  • 文本格式，记录每个原子的位置、原子类型、氨基酸序列等信息。
• 特点：常用于可视化蛋白质的三维结构，适合蛋白质功能和相互作用研究。

总结表格

格式	用途	包含信息	特点
FASTA	序列存储	序列	结构简单，适合基本序列存储
FASTQ	高通量测序数据	序列 + 质量信息	包含质量分数，适合测序数据分析
SAM	比对信息	序列与参考基因组的比对位置	信息全面，文件较大
VCF	遗传变异信息	变异位点和变异信息	标准格式，用于变异分析
GFF	基因组注释	基因、外显子等功能元件位置	常用于基因组功能注释
PDB	蛋白质三维结构	原子坐标和氨基酸序列	用于蛋白质结构分析和可视化

这几种格式各有用途，分别适用于序列数据存储、测序数据质量分析、比对分析、基因组变异研究、基因组注释和蛋白质结构研究等领域。