Altklausurfragen

• Prokaryotes: 1 gene per 1.000-1.400 bases (~ 90%)

• Eukaryotes: 1 gene per 100.000 bases (~ 1-2%)

—> Prokaryotes > Eukaryotes

• Expectation:

  • estimate the probability of finding the site at any position of the sequences

• Maximization: update expected base distributions

• Repeat until convergence

e.g., MEME does this

• measure of conservation of the base at the position

• information content/entropy in bits

Additional answers:

• can be corrected by base frequencies of the bases

• data might include pseudocounts to overcome effects of missing data

• the maximum value for DNA bases is 2 bits. (log2(4))

• pseudogenes: Nonfunctional sequences of genomic DNA that are originally derived from functional genes, but exhibit such degenerative features as premature stop codons and frameshift mutations that prevent their expression

• might interfere with experiments

  • PCR and hybridization experiments

  • transcribed pseudogenes

  • interference with disease diagnostics and treatment

• molecular record of dynamics and evolution of genomes

  • rate of nucleotide substitutions

  • rate of DNA loss

• improvement of gene prediction and annotation efforts

• multiplicity: one miRNA can target more than one gene

• co-operativity: one gene can be controlled by more than one miRNA

Genvorhersage für D. melanogaster:

• too diverged → number of mismatches low because most of sequence can not be aligned

• too close → number of mismatches low because sequence is unchanged

• for D. melanogaster best acc. with using D. ananassae with ~1 substitution per synonymous site

• for Human mouse would be a good informant (~0.6 substitutions per synonymous site)

• exon has alternative stop codon

• alternative exon leads to frame shift → former out of frame stop codon located nearer to the start comes in frame

• Rolle des Horizontellen Gentransfers: Vorteil komplette Sets an Genen zu übertragen und dem Empfänger einen definierten Phenotyp zu übertragen

• evtl. ausgehend von thermophilen Bakterien

Increasing the Window Size

• Increased Sensitivity: More ORFs are detected.

• Increased False Positives: More random sequences are detected as ORFs.

• Balance between Sensitivity and Specificity: Can affect the positive predictive value (PPV) both positively and negatively.

Increasing the Frameshift (ORF Length)

• Increased Error Rate: Frameshift errors lead to incorrect ORF identification.

• Shortened or Lengthened ORFs: ORFs are incorrectly classified as false positives or negatives.

• Decreased Prediction Accuracy: The accuracy of ORF prediction decreases due to increased frameshift errors.

Summary

• Analysis Window: A larger window increases sensitivity and false positives.

• Frameshift: Increased frameshift errors decrease prediction accuracy.

Pseudogene:

• non-functional genes

• derived from functional genes through mutations

• degenerative properties —> inhibit expression

• Classes:

  • conventional

  • processed

• Ka - Number of non-synonymous mutations

• Ks - Number of synonymous mutations

• Higher Ka/Ks —> lower conservation

• Ka/Ks = 1 ⇒ no selection pressure

• Ka/Ks > 1 ⇒ positive selection pressure (positive selection)

• Ka/Ks < 1 ⇒ negative selection pressure (purifying selection)

—> For pseudogenes, Ka/Ks = 1 is expected.

—> Experimental < 1: underestimated Ka/Ks as genes were compared with present-day genes, not the ancestral functional gene that gave rise to the processed pseudogene.

• Content-based: Example (ORFs, codon usage, repeat periodicity, compositional complexity)

• Site-based: Example (splice sites, TF binding sites, consensus sequences, polyadenylation signals, start/stop codons)

• Comparative: Example (inference based on homology, protein sequence similarity, modular structure of proteins usually precludes finding complete gene)

DNA sequence that facilitates a high rate of transcription

• efficiently binds to the RNA polymerase and promotes robust transcription initiation

• strong promoter has a high affinity for the RNA polymerase, allowing efficient binding and initiation of transcription

• presence of specific sequence motifs within the promoter region

• Historically, clone-by-clone was used more commonly for eukaryotic genes as it allows to overcome challenges with highly repetitive and complex regions in eukaryotic genomes

• WGS is particularly suitable for organisms with smaller genomes and less complex genomic structures

• Approaches can be combined in a hybrid shotgun-sequencing approach

—> AS can be verified by analyzing RNA isoforms

• using RT-PCR with primers that flank the alternatively spliced region → different lengths of PCR product

• using microarrays (high-throughput approach) with exon-exon junction probes

Process of Splicing (two steps):

• 5 critical bases: 5’ donor SS (GU), branch point (A), 3’ acceptor SS (AG)

• first step:

  • cleavage at the 5’ SS

  • joining of the 5′ end of the intron to an A within the intron (the branch point)

  • —> lariat-like (lasso) intermediate —> intron forms a loop

• second step:

  • cleavage at the 3′ splice site and ligation of the exons

  • —> result: excision of the intron as a lariat-like structure

Effects of AS:

1. multiple isoforms of a gene —> Protein diversity

2. Tissue-specific regulation of gene expression

Larger product:

• Change protein stability

• Change enzymatic/ signaling activity

• Alignment of ESTs (expressed sequence tags) against DNA sequence

• Insertions and deletions in the ESTs relative to the mRNA are identified as potential alternative splices

• Alternative splices are detected when two splices are mutually exclusive

Requires ESTs, which are cDNA sequences derived from mRNA with reverse transcriptase

Definition operon:

• Multigene bacterial operons have one promoter and one transcriptional stop. The transcript holds more than one gene with multiple translational starts and stops.

Reason:

• genes are regulated together → faster adaptation to environmental changes

• efficient transcription and translation: genes are controlled by a single promoter region and are transcribed together; the RNA polymerase can process multiple genes in a single pass

• EcoParse: HMMs for gene prediction with different models for the intergenic region depending on operon or non-operon genes: “long intergenic region” and “short”.  (p. 52)

  • might show different distribution of base frequencies as regulatory elements are missing for genes in an operon (e.g. no RBS in (-20)...(-1) region of start codon of the second gene)

• ORPHEUS: Tool based on intrinsic and extrinsic information

  • DPS match → use as seed ORF and refine start and stop of ORF → derive codon usage → derive RBS weight matrix → full set of predicted genes

  • detects genes and RBS → can derive: operon or not

• GeneMark:

  • Fifth-order Markov model

  • uses intrinsic information about frequency of hexamers in each of the frames and background

• GeneMark.hmm:

  • HMM with states for start codons, typical/atypical (e.g. horizontal gene transfer/Class III gene) gene and stop codon for +/- strand

• GLIMMER:

  • interpolated markov models

  • detects patterns present in known gene sequences

• TESTCODE:

  • every third base tents to be the same much more often than random in coding regions (AA composition bias + codon bias)

• intrinsic information

  • exon/intron length distribution

  • promoter and polyA signals

  • conserved splice signals

  • hexamer composition of exons/introns

  • reading frame consistency of exons

  • isochore differences

• extrinsic information

  • EST (expressed sequence tag)

  • cDNA

  • protein-genome alignments

• DNA motif for protein translation initiation site in most eukaryotic mRNA transcripts

• (region arround start codon)

• 5'-(gcc)gccRccAUGG-3'

• (eukaryotic equivalent to Shine-Dalgarno)

Architecture:

• Generalized HMM (GHMM)

• models both strands at the same time; from intergenic state model can enter states for + strand genes or - strand genes

• states:

  • N: intergenic region

  • P: promotor (sensor for TATA)

  • F: five-prime UTR

  • than either single-exon gene or model for multiple exon gene

  • single-exon genes are modeled by a single state (Esngl)

  • multiple exons:

    • state for initial exon models region from translational start to donor splice site Einit

    • 3 states for different phases of introns (Ik for k: 0: between codons, 1: after first base, 2: after second base)

    • 3 states for exons between introns also for keeping the phase information Ek

  • terminal exon Eterm

  • T: three-prime UTR

  • A: poly-A signal (sensor for Cap signal)

  • reverts to N

• parallel unsupervised training and prediction

• based on GeneMark.hmm architecture:

  • non-homogeneous HMM -> coding regions

  • homogeneous HMM -> non-coding regions

  • coding capacity of sliding windows -> Bayesian decision rule

Base pair maximization: Recursive definition of the best score for a subsequence i,j → four possibilities:

• 1: i,j are a base pair, added on to a structure for i+1…j-1, add +1

• 2: i is unpaired, added on to a structure for i+1…j

• 3: j is unpaired, added on to a structure for i…j-1

• 4: i,j are paired, but not to each other: the structure for i..j adds together substructures for two sub-sequences, i..k and k+1..j (bifurcation)

Improvements:

It is more plausible that an RNA adopts a globally minimum energy structure, not the structure with the maximum number of base pairs → predict overall free energy

Additionally use thermodynamic information

• negative stacking energy for matches

• positive destabalizing energies for loops (size-dependend)

• Statistical model that captures the patterns of covariation that can be obtained from an MSA. Covariated bases tend to coevolve as this ensures that the base pair is maintained and RNA structure is conserved. RNA structure prediction can be improved by giving positions with greater covariation more weight. 

• Describes both the secondary structure and the primary sequence consensus of an RNA

• Can be applied to several RNA analysis problems: 

  • consensus secondary structure prediction

  • multiple sequence alignment

  • database similarity searching

• Iterative training procedure

• Optimal algorithm for RNA secondary structure prediction based on pairwise covariations in multiple alignments

• Covariation ensures ability to base pair is maintained and RNA structure is conserved

Can’t predict: Pseudoknots

—> violate recursive definition of the optimal score S(i,j)

• Interspersed repeats: 

  • Retroelements:

    • LINEs (Long Interspersed Nuclear Elements) [autonomous]

    • SINEs (Short Interspersed Nuclear Elements) [nonautonomous]

    • LTRs (Long Terminal Repeat Retrotransposons)

  • DNA-Transposons

• Involve RNA intermediates (Retroelements) or DNA intermediates (DNA transposons)

  • Mobility: 

    • conservative transposition

    • replicative transposition

    • retrotransposition

• Derived from biologically active ‘transposable elements’ (TEs)

• Tandemly repeated DNA (Simple sequence repeats without interruption)

  • Microsatellites

    • one to a dozen base pairs

    • may be formed by replication slippage

  • Minisatellites

    • a dozen to 500 base pairs

  • Cryptically simple repeats 

  • Low complexity repeats 

  • Satellite and telomeric repeats

• Segemented duplications

  • nearly identical copies ranging in size from 1 to >200 kb

  • originate from duplicative transpositions

• Pseudogenes

  • derived from functional genes but with deleterious mutation

• Single nucleotide polymorphisms (SNPs)

• occurs when a single nucleotide replaces one of the other three nucleotide letters. SNPs found in a coding seq are of great interest as they are more likely to alter function of a protein.

• most common type of genetic variation in humans.

• account for 90% of the variation between individuals.

• Synonymous:

  •  not causing a change in the amino acid

• Non-synonymous: 

  • A nonsynonymous or missense variant is a single base change in a coding region that causes an amino acid change in the corresponding protein

• transition: changes a purine to another purine (A ↔ G), or a pyrimidine to another pyrimidine (C ↔ T)

• transversion: change from purine (A/G) to pyrimidine (T/C) or vice versa.

SNPs may be informative with respect to disease:

• Functional variation. A SNP associated with a nonsynonymous substitution in a coding region will change the amino acid sequence of a protein.

• Regulatory variation. A SNP in a noncoding region can influence gene expression.

• Association. SNPs can be used in whole-genome association studies. SNP frequency is compared between affected and control populations.

• Number of miRNA genes present:

  • Plants: 100-200 genes 

  • Animals: 100-500

• Location within genome:

  • Plants: predominantly intergenic regions 

  • Animals: intergenic regions, introns 

• Presence of miRNA clusters: 

  • Plants: uncommon

  • Animals: common

• miRNA biosynthesis: 

  • Plants: Dicer-like

  • Animals: Drosha, Dicer 

• Mechanism of repression: 

  • Plants: mRNA-cleavage (methylation?)

  • Animals: Translational repression

• Location of miRNA-binding motifs: 

  • Plants: predominantly in the ORF 

  • Animals: predominantly in the 3’-UTR

• Number of miRNA-binding sites within target sites:

  • Plants: Generally one

  • Animals: Generally multiple

• Function of known target genes:

  • Plants: Regulatory genes - crucial for development, enzymes

  • Animals: Regulatory genes - crucial for development, structural proteins, enzymes

• TargetScan

  • thermodynamics-based RNA:RNA duplex interactions

  • comparative sequence analysis

  • Input:

    • miRNA that is conserved in multiple species

    • set of 3’UTR sequences from these species

  • Method:

    • check miRNA seed region (2-8 bases) perfect complementarity to 3’UTR

    • extend to G:U pairs but no mismatches

    • assign folding free energy G to miRNA:target

    • assign Z score to each UTR

    • sort UTRs by Z score -> assign rank

    • compare organisms -> conserved miRNAs

Input:

• miRNA that is conserved in multiple organisms 

• a set of orthologous 3‘ UTR sequences from these organisms

Disadvantages:

• Incompleteness of orthologous gene annotations

• Some targets may not meet the stringent seed matching, Z score, or rank criteria

• Some target sites may lie outside the 3‘ UTR (plants)

• Some targets may not be conserved in the complete set of organisms

⇒ The actual number of target genes regulated by each miRNA is likely to be substantially higher

• Hybridization (Microarrays):

  • Pro:

    • Relatively low cost

    • Well established in clinical use

  • Con:

    • Analysis only of pre-defined sequences

    • Dynamic range limited by scanner

    • high background-noise

    • cross-hybridization möglich

• Seqeunce-based (RNA-seq):

  • Pro:

    • identifizierung alternativer Splicevarianten/neue Transkripte

    • hohe sensitivität

  • Con:

    • relatively high cost

    • high computational effort

    • prone to contamination

RNA extraction → target enrichment → cDNA → library prep → sequencing → Transcriptome/genome mapping → data analysis 

• Experimental design: number of replicates, depth of sequencing

• Parameters: alignment rate, desired power, significance level, log-fold change

RNA-seq workflow

• Quality control

• Alignment of reads to reference genome

• Transcriptome assembly

• Differential expression

Simple extension of FASTA —> store quality of bases

1. @ = ID

2. Sequence

3. + = ID

4. Quality scores —> PHRED scores (encoded in ASCII letters, 0-93

The strand column represents the strand of DNA, i.e., forward/ reverse, where the gene is on

Normalization for sequence length:

• variations in read length across different samples or experiments

• —> adjusting/standardizing length of reads or fragments

Purpose:

• Comparability Across Samples

• Data Quality Control

• Accurate Quantification

• Alignment Efficiency

• Bias Reduction

Methods:

• Trimming —> removing bases from the end, e.g., Trimmomatic

• Subsampling -> select reads that are within common distribution

• Length Filtering

• Statistical Normalization -> TPM, etc.

• N50: length of the contig/scaffold at which 50% of the assembly is covered. Higher N50 values —> better assembly quality

• L50: number of contigs/scaffolds needed to cover 50% of the assembly. Lower L50 values —> better assembly quality

• predict the most stable secondary structure —> principle of free energy minimization

• —> Zuker algorithm (dynamic)

  1. Initialize matrix to store energy values

  2. Specify the allowable base pair rules (A-U, G-C, G-U)

  3. Fill the Matrix

     1. Energy of different types of loops (hairpin, bulge, interior, and multibranch loops)

     2. energy for stacking adjacent base pairs

  4. Traceback lowest energy —> secondary structure

• Genes in different organisms are similar

• uses known genes in one genome to predict (unknown) genes in another genome

• -> known gene and a genome sequence -> find set of substrings of the genomic sequence whose concatenation best fits the gene

• constitutive: > 1 product is always made from transcribed gene

• regulated: different forms under different conditions/times/cell types

• split genome into k-mers of length k

• count occurrences of each k-mer

• filter for heavily repeated k-mers

Pros:

• easy to implement/understand

• scalable to large genomes

Cons:

• manual k —> choosing right k is critical

• high memory usage (large genome, low k)

• identify and mask repetitive genomic DNA

• lower case = masked, upper case = not

Advantages:

• significantly reduces size of genome —> faster/more efficient analysis

• improve alignment: repeats can cause mismatches

• improve gene prediction:

  • repeats can confuse algorithms -> false positives/missing real genes

  • focus on unique sequences —> better accuracy

• Reducing False Positives in Functional Genomics

  • e.g. motif analysis

Tools: RepeatMasker

• Sequence length —> long genomes require significant computing time

• Motif Diversity —> shorter motifs = harder to detect against background

• Noise and Background —> true motifs vs random sequence

• Biological Context —> functional relevance of identified motifs

• Conservation of Functional Sites: Highly conserved regions are critical for function, and substitutions here are likely impactful

• Physicochemical Properties: Substitutions that significantly alter size, charge, hydrophobicity, or polarity are likely to affect protein function or stability.

1. Input:

   • miRNA sequence (conserved in multiple species)

   • 3’ UTR sequences from these organisms

2. search the UTRs in the first organism for segments of perfect Watson-Crick complementarity to bases 2–8 of the miRNA: “miRNA seed” and “seed matches”

3. extend each seed match with additional base pairs to the miRNA as far as possible in each direction, allowing G:U pairs, but stopping at mismatches

4. optimize base pairing of the remaining 3‘ portion of the miRNA to the 35 bases of the UTR immediately 5‘ of each seed match using the RNAfold program

5. assign a folding free energy G to each such miRNA:target site interaction

6. assign a Z score to each UTR

• entire set of genes from all strains within a clade

• more generally -> union of genomes