Altklausurfragen pre 2023

• Gendichte bei Prokaryoten (~ 90%) >>> Gendichte bei Eukaryonten (~ 1-2%)

• In Bakterien liegt die Gendichte bei 1 Gen pro 1.000 - 1400 Basen und in höheren Eukaryoten liegt die Gendichte bei 1 Gen pro 100.000 Basen. 

• Bakterien haben also eine höhere Gendichte als höhere Eukaryoten.

• start with initial guesses for region and size (e.g. region of a binding site is already known from prior experiments)

• 1) expectation step:

  • position-wise composition of the site is used to estimate the probability of finding the site at any position of the seqs

  • these probabilities are used in turn to provide new information as to the expected base distribution for each column

• 2) maximization step: new counts of bases for each position in the site found in E-step are substituted for the previous set

• E- and M-steps repeated until convergence

• This is e.g. done by the MEME (Multiple EM for Motif Elucidation)

• measure of conservation of the base at the position

• information content / entropy in bits

GREY:

• 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 interfer 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

???

1. Gen-Duplikation und -Fusion: Durch Gen-Duplikation könnten mehrere Kopien eines Gens entstanden sein, die sich anschließend unterschiedlich spezialisiert haben. Diese Duplikate könnten sich dann in der Nähe zueinander angeordnet haben, um unter der Kontrolle eines einzigen Promotors reguliert zu werden, was zur Bildung eines Operons führte. Dies könnte durch genetische Rekombination oder andere chromosomale Veränderungen geschehen sein.

2. Horizontaler Gentransfer: Bakterien können Gene über horizontalen Gentransfer austauschen. Wenn mehrere funktionell verwandte Gene zusammen übertragen werden, könnten sie sich in einem neuen Wirt zu einem Operon zusammenschließen, um eine koordinierte Regulation zu ermöglichen. Diese Zusammenführung könnte durch gemeinsame Regulationselemente und Promotoren begünstigt werden.

3. Selektionsdruck: Ein starker Selektionsdruck könnte die Bildung von Operons begünstigt haben. Organismen, die in der Lage waren, mehrere Gene gemeinsam und effizient zu regulieren, hatten möglicherweise einen Vorteil in bestimmten Umgebungen, was zu einer natürlichen Selektion für solche genetischen Strukturen führte.

4. Regulatorische Effizienz: Die Nähe von funktionell verwandten Genen kann die regulatorische Effizienz erhöhen. Wenn Gene, die an demselben Stoffwechselweg beteiligt sind, nah beieinander liegen, können sie leichter und schneller durch gemeinsame Regulatorproteine kontrolliert werden. Dies könnte zur Selektion für die Bildung von Operons geführt haben.

5. Co-transkriptionale Vorteile: Die gleichzeitige Transkription von Genen, die für zusammenarbeitende Proteine kodieren, könnte den Zusammenbau von Proteinkomplexen erleichtern und sicherstellen, dass alle notwendigen Komponenten in den richtigen Verhältnissen vorhanden sind. Dies könnte einen evolutionären Vorteil bieten und die Bildung von Operons fördern.

???

[vmtl. bezogen auf GeneMark???]

• higher sensitivity

• lower specificity

???

• nicht-funktionale Gene, aus funktionalen Genen hervorgegangen besitzen degenerative Eigenschaften (missense/nonsense mutations), die Expression verhindern

• Classes:

  • conventional

  • processed pseudogenes

• Ka - Zahl der nicht synonymen Mutationen

• Ks - Zahl der synonymen Mutationen

• Ka/Ks höher mit niedrigerer Konservierung

• Ka/Ks = 1 ⇒ kein Selektionsdruck

• Ka/Ks > 1 ⇒ positiver Selektionsdruck (positive selection)

• Ka/Ks < 1 ⇒ negativer Selektionsdruck (purifying selection)

• for pseudogenes Ka/Ks = 1 expected

• The majority of human genes undergo “purifying selection,” the evolutionary process disfavors nucleotide mutations that cause detrimental amino acid substitutions in the protein thus keeps the protein as it is

• experimental < 1: underestimated Ka/Ks as genes were compared with present day genes and not the ancestral functional gene that gave rise to the processed pseudogene

Content based:

Beispiel (ORFs, Codon usage, Repeat periodicity, Compositional complexity) 

Site based: 

Beispiel (splice sites, TFbinding sites, Consensus sequences, Polyadenylation signals, start/stop codons) 

Comparative: 

Beispiel (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

???

1. Effiziente Bindung der RNA-Polymerase: Ein starker Promotor hat Sequenzen, die eine hohe Affinität zur RNA-Polymerase aufweisen, wodurch die Enzymbindung erleichtert und die Initiation der Transkription effizienter wird.

2. Starke Aktivator-Bindungsstellen: Starke Promotoren können Bindungsstellen für Transkriptionsaktivatoren enthalten, die die Transkription fördern, indem sie die RNA-Polymerase rekrutieren oder deren Aktivität erhöhen.

3. Minimale Repressoren-Bindungsstellen: Um die Transkription nicht zu hemmen, weisen starke Promotoren oft wenige oder keine Bindungsstellen für Repressoren auf, die die Transkription blockieren könnten.

4. Mangel an sekundären Strukturen: Die DNA-Sequenz eines starken Promotors hat wenige sekundäre Strukturen (wie Haarnadelstrukturen), die die Bindung der RNA-Polymerase und den Transkriptionsprozess stören könnten.

Untereinheiten der bakteriellen RNA-Polymerase, die notwendig sind, um die RNA-Polymerase an spezifische Promotorsequenzen der DNA zu binden und die Transkription zu starten

1. Promotorerkennung: Sigma-Faktoren erkennen und binden spezifische Promotorsequenzen auf der DNA.

2. Initiation der Transkription: Nachdem der Sigma-Faktor die RNA-Polymerase an den Promotor gebunden hat, hilft er bei der Entwindung der DNA-Doppelhelix, um den Einzelstrang als Matrize für die RNA-Synthese zugänglich zu machen. Dies ermöglicht den Beginn der RNA-Transkription.

3. Austauschbarkeit: Verschiedene Sigma-Faktoren können je nach Umweltbedingungen oder Wachstumsphasen unterschiedliche Gene regulieren. Bakterien verfügen über mehrere Sigma-Faktoren, die jeweils spezifische Promotoren erkennen und so die Expression verschiedener Gen-Sets steuern.

4. Regulation der Genexpression: Sigma-Faktoren ermöglichen es Bakterien, schnell auf Umweltveränderungen zu reagieren. Zum Beispiel kann ein bestimmter Sigma-Faktor aktiviert werden, wenn die Zelle Stressbedingungen ausgesetzt ist, wodurch Gene exprimiert werden, die an der Stressantwort beteiligt sind.

5. Spezifität und Funktion: Jeder Sigma-Faktor hat eine spezifische Rolle und erkennt eine bestimmte Gruppe von Promotoren:

   • Sigma-70 (σ70): Der Haupt-Sigma-Faktor, der für die Transkription der meisten Haushaltsgene unter normalen Wachstumsbedingungen verantwortlich ist.

???

• dissociable subunit of the RNA polymerase holoenzyme needed for transcription initiation from promoter elements 

• enables specific binding of promoter region

• There are multiple interchangeable sigma factors, each of which recognizes a distinct set of promoters (promoters of house-keeping/heat-shock genes)

• σ70 (primary sigma factor) is expressed under normal conditions in E. coli

???

???

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

• WGS 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 analysing RNA isoforms

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

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

Ablauf Splicing:

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

Types of AS:

• constitutive AS: gene is always spliced the same way

• regulated AS: different forms are generated under different conditions

Roles of AS:

• Addition of new protein parts

  • multiple effects:  ← Auswirkungen wenn größer/anders

    • alter protein binding properties

    • alter intracellular localization

    • alter extracellular localization

    • alter enzymatic or signaling activities

    • alter protein stability

    • … 

  • Example: Transkription factor without activation part (from one exon) is a Repressor

• Influence RNA function

  • AS alters 5’ or 3’ UTR regions → effects subcellular localization and/or RNA stability

• Coordinated Regulation of Biological Events

  • neuron development (DSCAM)

  • Channel activity associated with hearing

  • Muscle contraction

• Alignment of ESTs (expressed sequence tags) against DNA (/ pre-mRNA?) sequence

• Insertions and deletions in the ESTs relative to the [?pre-]mRNA are identified as potential altervaltive splices

• Alternative splices are detected when two splices are mutually exclusive

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

???

Defintion 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:

1. Koordinierte Genexpression: Durch die Organisation von Genen in einem Operon können alle Gene, die für eine bestimmte biochemische Funktion oder einen Stoffwechselweg benötigt werden, gleichzeitig und in einem koordinierten Muster exprimiert werden. Dies gewährleistet, dass alle benötigten Proteine in der richtigen Menge zur Verfügung stehen.

2. Effizienz in der Genregulation: Ein Operon ermöglicht die Regulierung mehrerer Gene durch eine einzige regulatorische Region (wie einen Promotor und einen Operator). Dies spart Energie und Ressourcen, da nur ein Regulatorprotein notwendig ist, um die Expression mehrerer Gene zu steuern.

3. Schnelle Anpassung an Umweltveränderungen: Bakterien können schnell auf Änderungen in ihrer Umwelt reagieren, indem sie die Expression ganzer Gencluster in einem Operon anpassen. Zum Beispiel kann ein Bakterium, das in eine Umgebung mit Laktose gelangt, schnell die Gene des Lac-Operons aktivieren, um die Laktose zu verwerten.

4. Synchronisierte Abschaltung: Ebenso wie die gleichzeitige Aktivierung ermöglicht ein Operon die gleichzeitige Abschaltung der Genexpression. Dies ist nützlich, wenn die Produkte der Gene nicht mehr benötigt werden, was Energie und Ressourcen spart.

5. Kompakte genetische Organisation: Operons tragen zur kompakten Organisation des Genoms bei, was besonders in prokaryotischen Zellen mit ihrem begrenzten Platzangebot wichtig ist.

???

prokaryotische Genvorhersagemethoden:

• 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

  • conserved splice signals

  • hexamer composition of exons/introns

  • reading frame consistency of exons

  • exon/intron length distribution

  • promoter and polyA signals

  • isochore differences

• extrinsic information

  • EST

  • cDNA

  • protein-genome alignments

???

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

• ribosomal binding site

• (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

GeneMarkS (GeneMark.hmm EucaryoticSelf-training): 

• parallel unsupervised training and prediction

• based on eukaryotik GeneMark.hmm; architecture:

  • Generalized HMM

  • models single exon genes and multiple exons genes

  • models strands at the same time

  • states:

    • initial start site

    • for single exon genes only one single exon gene state

    • for multiple exon genes:

      • initial exon

      • donor site

      • intron

      • acceptor site

      • internal exon (goes back to donor site)

      • or terminal exon

    • stop site state

    • intergenic region state

Procedure:

• all parameters of the model with reduced architecture are initialized

  • reduced architecture:

    • donor/acceptor only with two canconic dinucleotides

    • initiation/termination site: canonic start/stop codons

    • sequences emitted by non-site states: uniform length distributions

    • non-coding: zero-order Markov model, parameters estimated based on nucleotide frequencies in the genome

    • coding: different approaches, e.g. [pre?]trained on long ORFs

• GeneMark run to get coding and non-coding labels

• subset of uniformly labeled fragments are used to reestimate parameters

• repeat until convergence

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)

Verbesserung:

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)

• 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

• Covariance models are constructed automatically

  • from existing RNA sequence alignments

  • even from initially unaligned example sequences

• Iterative training procedure

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

• Statistical model that captures the patterns of covariation that can be obtained from a 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. 

  
  

A covariance model of tRNA sequences is an extremely sensitive and discriminative tool for searching for additional tRNAs and tRNA-related sequences in sequence databases.

Rfam is a database of ncRNA families represented by multiple sequence alignments and profile SCFGs

Pseudoknots

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

“Complex repeats” (interspersed repeats)

• Constitute ~45% of the human genome

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

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

• Retroelements: reproduce via reverse transcription followed by integration into the host DNA

  • long-terminal repeat transposons (LTR)

  • long interspersed elements (LINEs); these encode a reverse transcriptase

  • short interspersed elements (SINEs); these include Alu repeats

  • DIRS-like elements

  • Penelope-like elements (PLEs)

• DNA transposons: capable of integrating themselves to, and excising themselves from, the host genome, thus taking advantage of the host replication through this ‘cut-and-paste’ mechanism.

  • DNA transposons constitute 3% of the human genome

—————————————————

1. Transposable Elements (TEs):

   • Class I TEs (Retrotransposons): These elements transpose through an RNA intermediate. They are transcribed into RNA, which is then reverse transcribed back into DNA and inserted into a new location in the genome. Subtypes include Long Interspersed Nuclear Elements (LINEs) and Short Interspersed Nuclear Elements (SINEs).

     • LINEs: Autonomous elements capable of independent transposition.

     • SINEs: Non-autonomous elements that rely on the enzymatic machinery of LINEs for their transposition.

   • Class II TEs (DNA Transposons): These elements transpose directly through a DNA intermediate. They cut and paste themselves into new locations within the genome.

2. Processed Pseudogenes: These are non-functional sequences derived from functional genes through retrotransposition. They are created when mRNA transcripts are reverse transcribed and inserted back into the genome without their regulatory elements.

3. Other Retrotransposons: This category includes elements like endogenous retroviruses (ERVs), which are remnants of ancient viral infections that have integrated into the host genome.

—————————————————————

• Interspersed repeats: 

  • Retroelements:

    • LINEs (Long Interspersed Nuclear Elements) [autonomous]

    • SINEs (Short Interspersed Nuclear Elements) [nonautonomous]

    • LTRs (Long Terminal Repeat Retrotransposons)

  • DNA-Transposons

• Tandemly repeated DNA: 

  • Microsatellites 

  • Minisatellites

  • Cryptically simple repeats 

  • Low complexity repeats 

  • Satellite and telomeric repeats 

• Segmental Duplications

GREY:

• TIRs (Terminal inverted repeats)

• PLEs (Penelope-like elements)

• DIRS-like elements

• 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 interuption)

• 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 vs Nonsynonymous 

• 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

GREY:

Transition vs Transversion 

• 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: 

• Vorhersage von miRNA-Zielgenen

• thermodynamics-based modeling of RNA:RNA duplex interactions

• comparative sequence analysis

Input:

• miRNA that is conserved in multiple organisms 

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

Structures, energies, and scoring for predicted RNA-duplexes

• 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”

• 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

• optimize basepairing 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

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

• assign a Z score to each UTR

• sort the UTRs in this organism by Z score and assign a rank Ri to each

predict as targets those genes for which both Zi≥ZC and Ri≤RCfor an orthologous UTR sequence in each organism, where ZC and RC are pre-chosen Z score and rank

Nachteile:

• 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

DICER 

GREY:

micro-RNA: 

• Family of 21-25 nucleotide small RNAs 

• Function:  altering the expression levels of a diverse repertoire of genes in a sequence-dependent manner: 

  • at the transcriptional or post-transcriptional level 

  • regulate many aspects of development and physiology

???

• Hybridisierungsverfahren (Microarrays):

  • Vorteile:

    • Relatively low cost

    • Well established in clinical use

  • Nachteile:

    • Analysis only of pre-defined sequences

    • Dynamic range limited by scanner

    • high background-noise

    • cross-hybridization möglich

• Sequenzbasierdende Verfahren (RNA-seq):

  • Vorteile:

    • identifizierung alternativer Splicevarianten/neue Transkripte

    • hohe sensitivität

  • Nachteile:

    • relatively high cost

    • high computational effort

    • prone to contamination

???

GREY:

Identifies the full set of transcripts, including large and small RNAs, novel transcripts from unannotated genes, rare transcripts, splicing isoforms and gene-fusion transcripts

• Reveals the complex landscape and dynamics of the transcriptome from yeast to human at an unprecedented level of sensitivity and accuracy

• Base-pair-level resolution and a much higher dynamic range of expression levels

Overview of the experimental steps in an RNA sequencing (RNA-seq) protocol

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

???

• Size: 

  • prokaryotes between 1s and 10s of Mb 

  • eukaryotes between 1s and 1.000s of Mb

• Topology: 

  • prokaryotes: mostly circular 

  • eukyryotes: mostly linear 

• Gene number: 

  • prokaryotes: most <10.000

  • eukaryotes: often >10.000

• Pseudogenes: 

  • prokaryotes: few

  • eukaryotes: many

• Complexity:  

  • prokaryotes: low

  • eukaryotes: high

• Horizontal gene transfer:  

  • prokaryotes: frequent

  • eukaryotes: rare

• Intergenic regions:  

  • prokaryotes: short (<100kb)

  • eukaryotes: long (often >100kb)

• Genome duplication:  

  • prokaryotes: none

  • eukaryotes: frequent (especially in plants)

• Gene duplication:  

  • prokaryotes: rare

  • eukaryotes: frequent

• Repeated sequences:  

  • prokaryotes: minor components

  • eukaryotes: major components

ribosomal binding site of bacterial mRNA

• Sequences from a start codon to a stop codon

• may be a gene

Expressed Sequence Tags (ESTs) are short sub-sequences of cDNA (complementary DNA) sequences that are generated from mRNA transcripts

• Repeats are believed to play significant roles in genome evolution and disease

• Mobile elements (transposons and retrotransposons) may contain coding regions that are hard to distinguish from other types of genes

• Repeats often induce many local alignments, complicating sequence assembly, comparisons between genomes and analysis of large-scale duplications and rearrangements

Sequences are scanned for overrepresented string of certain length

• Since repeats and transposons in particular are not exactly the same, some mismatches must be allowed when oligo frequencies are calculated.

• Challenge: to determine optimal size of an oligo (k-mer) and the number of mismatches allowed

Determines all exact repetitive substrings in complete genomes

A clustering method for repeat analysis in DNA sequences

• First identify all exact repeats in the input sequence (Reputer)

• Then define repeat classes by merging and extending these short exact matches

• Best known program

• Uses precompiled representative sequence libraries to find homologous copies of known repeat families

about 1 every 100 to 300 bases

• Structural consequences of the respective non-synonymous mutations in proteins

• Goal: to obtain a lower limit estimate for the quantity of non-synonymous SNPs that might have phenotypic effects

• How many SNPs are associated with multifactorial human disorders?

• Folding

• Interaction sites

• Solubility

• Stability

• Stochastic implementation of expectation maximization motif finding

• Takes a weighted sample of subsequences (not ALL sequences)

• Given a set of putative exons, find a maximum set of non-overlapping putative exons

• Input: a set of weighted intervals (putative exons)

• Output: A maximum chain of intervals from this set