Altklausuren

• cassette exon (exon skipping): an exon may be spliced out of the primary transcript or retained

• mutually exclusive exons: one of two exons is retained in mRNAs after splicing, but not both (are “competing”)

• competing 5’ splice site (alternative donor site): An alternative 5’ splice junction is used, changing the 3’ boundary of the upstream exon

• competing 3’ splice site (alternative acceptor site): An alternative 3’ splice junction is used, changing the 5’ boundary of the downstream exon

• retained intron: A sequence may be spliced out as an intron or simply retained. This is distinguished from exon skipping because the retained sequence is not flanked by introns.

• multiple promoters

• mutiple poly(A) sites

Two possible approaches:

• Energy minimization methods: choose complementaey sequence sets that provide the most energetically stable molecules

• take into account patterns of base-pairing that are conserved during evolution

Energy minimization method:

• every base is compared for complementarity to every other base

• The energy of each predicted structure is estimated by the nearest-neighbor rule:

  • sum the negative base-stacking energies for each pair of bases in the predicted double-stranded regions

  • add positive energies of destabilizing (unpaired) regions

• The complementary regions are evaluated by a dynamic programming algorithm to predict the most energetically stable molecule

• Programs: e.g. MFOLD, ViennaR

The similarity-based approach uses known genes in one genome to predict (unknown) genes in another genome by e.g. comparison of genomic sequence with homologous genomic sequence from close organisms. Meaning given a known gene (or a protein) and a genome sequence, find a set of substrings of the genomic sequence whose concatenation best fits the gene.

e.g. the known frog gene is aligned to different locations in the human genome. Find the “best” path to reveal the exon structure of human gene. Chaining these local alignments and look for a maximum chain of substrings.

Example programs: TWINSCAN, SGP2

Ferdis Antwort (evtl überarbeiten):

Similarity based approaches for gene prediction (TWINSCAN, in some parts: SGP2, N-SCAN, CONTRAST) uses coperative approaches, meaning homologue genomic sequences, like mouse, human and opossum, are aligns with the goal to find specific patterns in the sequence like conseration patterns or other intrincis information. Often alignment tools like BLAST or BLAT can be used. The similarity based approach focuses on homologue patterns.

GeneMark is a gene prediction tool for prokaryotes. When a gene is transcribed, the sense or anti-sense strand is used as a template. Due to the fact that genes can therefore be located on the sense or anit-sense strand, GeneMark includes the information on which strand + or - the gene is predicted. (Ferdi’s Antwort)

The FASTQ format is a common format for data exchange between different tools and an extension to the FASTA format. It can additionally store a numeric quality score of nucleotides besides other informations about the sequence and quality of the read, the quality of the read.

The FASTQ file has normally 4 lines per sequence:

• Line 1 begins with a @ character and is followed by a sequence identifier and an optional description (like a FASTA title line)

• Line 2 is the raw sequence letters

• Line 3 begins with a + character and is optionally followed by the same sequence identifier (and any description)

• Line 4 encodes the quality values for the sequence in Line 2, and must contain the same number of symbols as letters in the sequence

Sequence length normalization in next-generation sequencing (NGS) refers to ensuring that all sequenced reads are of the same length.

Used for:

• facilitate fair comparisons of gene expression, e.g.

  • compare expression of same gene between different samples

  • compare expression of different genes within a sample

• simplify data analysis

• improve alignment to a reference genome or transcriptome

• library size normalization

The Ka/Ks ratio is the ratio between the nonsynonymous rate of substitution (Ka) and the synonymous rate of substitution (Ks), to test for natural selection / evolutionary tendencies on genes or proteins.

The Ka/Ks ratio is important to predict if a mutation or rather a change is going to be fixed (evolutionary advantage) or being lost (negative selection).

The Ka/Ks ratio can indicate the following tendencies:

• Ka/Ks > 1 : positive selection

• Ka/Ks = 1 : neutral selection

• Ka/Ks < 1 : negative selection -> purifying selection

• for pseudogenes Ka/Ks = 1 expected

• 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

A Pan-genome is the entire set of genes from all strains within a clade / the union of all the genomes of a clade. Its a dispensable genome composed of genes absent from one or more strains (“accessory genes”) and genes that are unique to each strain (“unique genes”).

Ferdis Antwort:

A Pan-genome is the complete amount of genetic contet of a species incooperating all genes and genomic features. Furthermore, including genes which are present in each individual of the species (core genome) and genes present uniquely in an individual (variable genome).

Ferdis Antwort (evtl überarbeiten):

Repeat masking is the process of blocking of excluding specific potential repeats from the further analysis due to various biological aspects. One can be that the repeat might be part of a sequence being fundamental for a biological process or any other interative motif. Repeat masking therefore increases the sensitivity of the found repeats beeing a significant repeat and decreses the false positive rate.

Ferdis Antwort (evtl überarbeiten):

The main step of genome assembly is to assemble the resulting reads which are generated by method like Clone-by-clone sequencing, whole genome shotgun sequencing or NGS methods. The assembly step is the most difficult part and may include creating continuous stretches of reads (DNA / RNA) (contigs) using the overlap of those reads. The second step is often creating scaffolds by orienting the contigs to each other in a positional relationship using gap information and positional information. Often those methods require large computational capacity and are the most time consuming step. Newer methods like Oxford-Nanopore simplify the assembly step by generating larger read fragments. Generally even more and shorter read fragments are present, the assembly is more difficult.

Ferdis Antwort (evtl überarbeiten):

The major challenge of finding motifs is to interfere sequence regions which have higher diversity compared to other regions and also having a significant biological meaning, for example enabling the binding of regulatory elements or expression regulatory elements like transcription factors. Furthermore, motifs can have different size ranged meaning further complications finding an unknown sequence of unknown size in the genome. Furthermore, RNA can undergo secondary structure which is challenging to predict, but can make the potential motif inaccessible and thereforme be misleading. Even having homology information, it is difficult to distinguish motifs from other, non biological meaningful segments. The main challenges of finding motifs can therefore be explained as difficulty if finding an unknown sequence of unknown length with unknown base composition in a sequence.

Ferdis Antwort (evtl überarbeiten):

TargetScan input:

• miRNA conserved in multiple organisms

• ste of target genes (3#-UTR) of these organisms

step 1: seed match: first the miRNA is aligned to the first 3’-UTR with perfect Watson-Crick pairing. Also called “seed-match” or “miRNA seed”

step 2: seed match expansion: the seed match is expanded in both directions until mismatch occurs.

step 3: base pairing refinement: RNAfold (secondary structure of RNA prediction tool) is used to align and match the remaining 3’-miRNA and the remaining 5’-UTR.

step 4: free energy is calculated: the free energy is calculated of the binding and structure (the lower the better)

step 5: Z-score is calculated

step 6: step 1 to step 5 are repeated for each target 3’UTR

N50: The shortest contig length that needs to be included for covering 50% of the genome.

L50: The count of smallest number of contigs whose length sum makes up half of genome size.

• In Bakterien, wie z.B. M.influenzae liegt die Gendichte bei 1 Gen pro 1000 - 1400 Basen und in höheren Eukaryoten, wie z.B. H.sapiens oder Mus musculus, liegt die Gendichte bei 1 Gen pro 100000 Basen. Bakterien haben also eine höhere Gendichte als höhere Eukaryoten.

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

• In Bakterien liegt die Gendichte bei 1 Gen pro 1.000 - 1.4000 Basen und in höheren Eukaryoten liegt die Gendichte bei 1 Gen pro 100.000 Basen

• Bakterien haben also eine höhere Gendichte als höhere Eukaryoten

• EM Algorithmen, wie z.B. MEME, starten bei einer Site von mehreren Sites von Zielsequenzen und wechseln sich dann ab zwischen der Zuordnung der Site zu einem Motiv und dem Updaten des Motivmodells. Dabei werden nur die besten Treffer pro Sequenz angezeigt, obwohl niedrigere Treffer in der gleichen Sequenz auch einen Effekt haben können.

• start with initial guesses for region and size (e.g. region of a binding size 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)

• Die Höhe der Sequenzlogos gibt die relative Häufigkeit jeder Amino- oder Nukleinsäure an dieser Position in der Sequenz an.

• measure of conservation of the base at the position

• information content / entropy in bits

• 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))

• Pseudogene: nicht funktionale Sequenzen von genomischer DNA, die ursprünglich von funktionalen Genen (durch Genduplikation) abgeleitet wurden. Sie weisen degenerative Eigenschaften auf, wie vorzeitige Stopp Codons oder Mutationen des Frameshifts, die deren Expression verhindern. Pseudogene sind dennoch wichtig, da einige eine Rolle bei der Regulierung der Genakativität spielen und somit nicht funktionslos sind.

• 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

• Multiplicity: Eine miRNA kann mehr als ein Gen anzielen. Einige miRNAs scheinen sehr promiskuitiv zu sein, mit Hunderten von vorhergesagten Zielen, aber die meisten miRNAs kontrollieren nur wenige Gene.

• Co-operativity: Ein Gen kann durch mehr als eine miRNA gesteuert werden. Einige Zielgene scheinen einer sehr kooperativen Steuerung zu unterliegen, aber die meisten Gene haben nicht mehr als vier Zielorte.

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)

• Durch Verwendung einer anderen Stelle für die Translationsinitiation (alternative Initiation)

• Hinzufügen eines Stopp-Codons in der alternativen Codierungssequenz (alternative Terminierung)

• Eine andere Translationsterminationsstelle aufgrund eines Frameshift (Verkürzung oder Verlängerung)

• Ändern des inneren Bereichs aufgrund eines in-Frame Insertion oder Deletion

• 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

• Operons könnten in termophilen Organismen entstanden sein, da die Organisation von Genen in Operons die Assoziation / Verbindung von funktionell verwandten Protein-Produkten ermöglicht und diese sich somit gegenseitigen Schutz vor thermischen Verfall bieten.

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

• evtl. ausgehend von thermophilen Bakterien

Proteinkomplex: RNA-incduced splicing comple (RISC), enthält das Enzym DICER, welches die pre-miRNA in miRNA spaltet

• Die Genauigkeit der Vorhersage steigt mit der Vergrößerung des Frameshifts der ORF-Länge, da längere ORFs eher den tatsächlichen Genen entsprechen.

• vmtl bezogen auf GeneMark? -> higher sensitivity, lower specificity

• Ein starker Promoter ist der Consensus Sequenz sehr ähnlich.

• (ein schwacher Promoter unterscheidet sich stärker von der Consensus Sequenz)

• 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

• Sigma Faktor: sind Proteine die Teil des RNA Polymerase Proteinkomplexes sind, welcher an den Promoter bindet.

• Sie werden für die Initiation der Transkription benötigt.

• Häufigster Sigma Faktor: σ^70 (Housekeeping-Sigma-Faktor von E.coli; steuert die Transkription)

• 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 recognized a distinct set of promoters (promoters of house-keeping / heat-shock genes)

• sigma^70 (primary sigma factor) is expressed under normal conditions in E.coli

Clone-by-clone shotgun: BAC clones werden benötigt, clones vorher mappen

Whole genome shotgun: Mapping-Phase wird übersprungen, Assembly dauert länger

• Clone-by-Clone:

  • physical mapping: requires construction of clone-based physical map and individual clones are subcloned

  • assembly: easier to resolve complex genomic regions as position of contigs is already known (due to the physical mapping)

  • labor intensity: physical mapping is labor intensitive, but after mapping clones can be divided between different labs for sequencing (relevant as sequencing was slower at the start of the century)

• Whole Genome Shotgun:

  • physical mapping: mapping phase is skipped and subclone library is constructed from entire genome

  • assembly: order / position of contigs needs to be inferred from overlapping reads and read pairs which can be problematic for tandemly repeated DNA (incorrect overlaps)

  • labor intensity: less labor intensive, but requires more computational resources

• Ein einzelner Klon, wie das bakterielle künstliche Chromosom (BAC) wird ausgewählt

• Große Mengen von BAC-DNA wird gereinigt und durch physikalische Verfahren fragmentiert

• Die zufälligen DNA-Fragmente (typischerweise 2-5kb) werden subkloniert

• Sequenz-Reads werden aus einem oder beiden Enden von zufällig ausgewählten Subklonen erstellt (z.B. werden mehrere tausend Sequenz-Reads typischerweise aus etwa ~100-150 kb BAC erstellt)

• Die zufälligen Reads werden dann auf Basis der Sequenzüberlappungen zusammengesetzt, wodurch eine vorläufige Sequenzanordnung (vorgefertigte Sequenz) entsteht

• Eine solche Sequenz ist unvollkommen, da Lücken in der Sequenz und Teile mit schlechter Sequenzqualität vorzufinden sind

• Anschließend werden diese Lücken und schlechten Qualitätsstellen mit Erzeugung zusätzlicher Sequenzdaten ausgebessert

• Die mapping Phase wird übersprungen

• Shotgun Sequenzierung wird fortgesetzt unter Verwendung von Subklon-Bibliotheken die aus dem gesamten Genom hergestellt werden

• Typischerweise werden zig Millionen von Sequenz-Reads erstellt

• Eine Computer basiertes Anwendung wird verwendet um zusammenhängende Sequenzen der verschiedenen Größen zu erzeugen

• Entstehende Lücken werden dann durch anschließende Verfahren wieder geschlossen

• Clone-by-clone: prokaryotische Genome (assembly funktioniert hier besser)

• Whole-genome shotgun sequencing: eukaryotische Genome

• Historically 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

Durch genomweite Analyse von alternativem Splicing:

• Daten (alle von einer Spezies):

  • Genomsequenz Assemblies

  • Expressed sequence tag (EST) Sequenzen

    • EST Clustering wird von UNIGENE bereitgestellt

• BLAST-Suche mit den Konsensus EST Sequenzen gegen die Genom Assemblies:

  • identifziere Kandidaten Gen-Regionen (BLAST threshold < E10-5)

  • identifiziere vermeintliche (kurze) Exons (BLAST threshold < E10-10)

• Alignment der übereinstimmenden genomischen Regionen mit dem kompletten Satz an ESTs für ein UNIGENE Cluster mithilfe von dynamischer Programmierung

• Erkenne Splicing durch ein Computer basiertes Verfahren, dass das genomische-EST-mRNA multiple Sequenzalignment analysiert

• 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-throughput approach) with exon-exon junction probes

• Verändern der Proteinbindungseigenschaften, z.B. Rezeptor/Ligand

• Verändern der intrazellulären Lokalisierung, z.B. Membraninsertion

• Verändern der extrazellulären Lokalisation, z.B. Sekretion

• Verändern der enzymatischen Aktivität oder Signalaktivität

• Verändern der Proteinstabilität, z.B. Inklusion von cleavage sites

• Insertion von post-translationalen Modifizierungsdomänen

• Ändern der Ionenkanaleigenschaften

Roles of AS:

• Addition of new protein parts

  • multiple effects: <- Auswirkungen wenn größers / anders

  • alter protein binding properties

  • alter intracellular localization

  • alter extracellular localization

  • alter enzymatic or signaling activities

  • alter protein stability

  • …

• 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

  • …

1. Intrinsic

   a) Open reading frames (ORFs)

   b) Codon usage

   c) Anwesenheit von RBS (ribosomal binding sites)

   d) Periodizität von repeats (Wiederholungen)

2. extrinsic

   a) Expressed Sequence Tags (ESTs)

   b) cDNA-Alignments

   c) homology in known Exons

• 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

• N – intergenic region

• P – Promoter

• F – 5‘ untranslated region

• T – 3’ untranslated region

• A – poly-A

• E – Exon (sngl = single, init = initial, term = terminal, k = Phase k internal)

• Ik – Phase k Intron: 0 – zwischen Codons, 1 – nach der ersten Base eines Codons, 2 – nach der zweiten Base eines Codons

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: promoter (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

  • multiple exons:

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

    • 3 states for different phases of introns

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

  • terminal exon

  • T: three-prime UTR

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

  • reverts to N

1. i, j sind ein Basenpaar, hinzugefügt zu einer Struktur für i+1 ... j-1, add +1

2. i ist ungepaart, hinzugefügt zu einer Struktur für i+1...j

3. j ist ungepaart, hinzugefügt zu einer Struktur für i...j-1

4. i, j sind gepaart, aber nicht zu einander: die Struktur von i...j fügt Unterstrukturen zusammen für zwei Untersequenzen, i...k und k+1...j (bifurication)

• Man könnte die obige Formel noch verbessern, indem man auch Pseudoknots beachtet und mit in die Formel einbaut.

• 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 destabilizing energies for loops (size-dependend)

• 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

• TLRs (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

Segmented 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

• SNP stands for Single Nucleotide Polymorphism, it occurs when a single nucleotide replaces one of the other three nucleotide letters in a genome (or in a DNA sequence).

• is an inherited single nucleotide substitution between individuals of a species. Commonly defined as having the least frequent allele occur at a rate greater than 1 per cent in a population.

• 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. They account for 90% of the variation between individuals

• Man unterscheidet zwischen den zwei Klassen: synonyme SNPs und nicht-synonyme SNPS

• Der Unterschied liegt darin, dass bei den synonymen SNPs die Änderung der Base das Proteinprodukt nicht ändert, wohingegen sich bei den nicht-synonymen SNPs durch die Änderung der Base auch die codierte Aminosäure verändert.

Synonymous vs Nonsynonymous

• synonymous: not causing a change in the amino acid

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

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

• In kodierenden Regionen: Ein SNP mit einer nicht-synonymen Substitution, verändert die Aminosäuresequenz eines Proteins und kann somit auch dessen Funktion ändern (zur Krankheit führen)

• In nicht kodierenden Regionen: Ein SNP kann die Genexpression beeinflussen und führt somit zu einer regulatorischen Veränderung (Krankheit)

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 genes

• 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

1. Suche nach seed matches innerhalb des ersten Organismus --> perfekte Watson-Crick Komplementarität zwischen miRNA seed und UTR-Sequenz

2. Erweitere die seed matches in beide Richtungen so weit wie möglich; G-U Paare sind erlaubt, stoppe bei einem Mismatch

3. Optimiere das Base Pairing von der 3’ Region der miRNA und 35 nt upstream der Zielregion mit RNAfold (künstlicher stemloop)

4. Berechne die freie Energie G für jede miRNA: target site duplex via RNAeval

5. Berechne Z-Score für jede UTR

6. Sortiere UTRs basierend auf ihrem Z-Score und weise ihnen einen Rang zu

7. Wiederhole Schritte 1-6 für alle anderen Organismen

8. Sage als Zielgen, jedes Gen i welches Zi ≥ Zc und Ri ≤ Rc mit jeder orthologen Sequenz erfüllt, vorher

TargetScan:

• 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 R to each

• predict as targets those genes for which both Zi >= Zc and Ri <= Rc for an orthologous UTR sequence in each organism, where Zc and Rc are pre-chosen Z score and rank

• „Covariance models“ sind probabilistische RNA Strukturprofile, die ähnlich wie Sequenzprofile durch ein multiples Sequenzalignment konstruiert werden. Dabei haben Sie das Ziel, sowohl primäre Konsensus und sekundäre Strukturinformationen durch die Verwendung von stochastisch kontextfreien Grammatiken zu erfassen.

• 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.

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

• Can be applied ro several RNA anlysis 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

Kovarianz-Modelle werden automatisch konstruiert aus:

• vorhandenen RNA Sequenz-Alignments

• sogar anfänglich unalignierten Beispiel Sequenzen

Input:

• miRNA that is conserved in multiple organisms

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

Aus Vorlesung:

• Needs to be well trained

• Not suitable for searches of large RNA and for database searches

  • Structural complexity of large RNA cannot be modeled

  • Runtime

  • Memory requirements

• Can be used for scanning candidate RNAs identified by other methods

Aus Altklausuren:

• Aufgrund der 3D dynamischen Programmierung sind Sie sehr rechenintensiv (sehr hoher CPU Verbrauch)

• Dank dem allgemeinen Ansatz werden keine tRNA-spezifischen Informationen wie Anticodons oder Intron-Position im Output mit angegeben

• 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

Unterschiede:

1. Prokaryotische Genome sind im Allgemeinen wesentlich kleiner als eukaryotische Genome

2. Eukaryotische Genome besitzen einen hohen Anteil an nichtcodierender DNA (etwa 95% im Mensch) wohingegen Prokaryotische Genome nur relativ geringe Anteile nichtcodierender DNA besitzen (ca. 5-20%)

3. Das eukaryotische Genom besitzt eine Intron-Exon-Struktur der Gene, wobei das prokaryotische Genom kaum bis gar keine Introns besitzt

4. Das prokaryotische Genom ist in Operons angeordnet (polycistronisch), das eukaryotische Genom hat ein Gen pro Promoter (monocistronisch)

5. Die Gendichte in eukaryotischen Genomen ist niedriger aufgrund der vielen nicht-codierenden Bereiche, in prokaryotischen Genomen ist die Gendichte wesentlich höher

• 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

TFFM <-> Transkriptionfactor vorhersage

Kraken <-> Quality Control

Prodigal <-> prok. Genvorhersage

Augustus+ <-> euk. Genhorhersage

Annovar <-> funktional annotation database snp oder so ähnlich

• 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 alternative splices

• Alternative splices are detected when two splices are mutually exclusive

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

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

• (region around start codon)

• 5’-(gcc)gccRccAUGG-3’

• (eukaryotic equivalent to Shine-Dalgarno)

[Wir haben nur GeneMarkS; GeneMarkS-T (Gene prediction in eukaryotic transcripts) nimmt zusätzlich homology based inference (integriert transcriptome data)]

GeneMarkS (GeneMark.hmm E S):

• parallel unsupervised training and prediction

• based on eukaryotic GeneMark.hmm architecture:

  • Generalized HMM

  • models single exon genes and multiple exon 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

Struktur, die nicht vorhergesagt werden kann: Pseudoknots

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

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 possible

Sequenzbasierende Verfahren (RNA-seq)

• Vorteile:

  • identifizierung alternativer Splicevarianten / neue Transkripte

  • hohe Sensitivität

• Nachteile:

  • relatively high cost

  • high computational effort

  • prone to contamination

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

ORPHEUS: Gene prediction in bacterial genomes

SIFT: Sort intolerant from tolerant substitutions

• nicht funktionale Gene aber aus funktionalen Genen hervorgegangen

• besitzen degenerative Eigenschaften (missense / nonsense mutations), die Expression verhindern

• 2 Hauptklassen: conventional and processed pseudogenes

• Ka: Zahl der nicht synonymen Mutationen

• Ks: Zahl der synonymen Mutationen

• Ka/Ks höher mit niedrigerer Konservierung

• Ka/Ks = 1 => kein Selektionsdruck

• Ks/Ks > 1 => positiver Selektionsdruck (positive selection)

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

• Content based

  • Beispiel: ORFs, Codon usage, Repeat periodicity, Compositional complexity

• Site based

  • Beispiel: splice sites, TF binding 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

• Ablauf Splicing:

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

  • cleavage on 5’ splice site of pre-mRNA

  • reaction between 5’ splice site and branch site leads to formation of lariat-like intermediate

  • cleavage at 3’ splice site

  • ligation of exons

• Types of AS:

  • constitutive AS: more than one product is always made from transcribed gene

  • regulated AS: different forms are generated under different conditions