Concepts and methods in genome assembly and annotation

Size: px

Start display at page:

Download "Concepts and methods in genome assembly and annotation"

Tobias Ellis
6 years ago
Views:

1 BCM-2002 Concepts and methods in genome assembly and annotation B. Franz LANG, Département de Biochimie Bureau: H Courrier électronique:

2 Outline 1. What is genome assembly? 2. What is genome annotation? 3. Annotating protein coding genes and introns 4. Prediction of RNA genes

3 1. What is genome assembly? Stitching together the native genome sequence (the basis for genome annotation), as well as the genome architecture, from sequence reads. These may be several tens up to thousands of nucleotides in size. Long reads or paired-end reads of long fragments are required for resolving repeat regions. Result: contigs of assembled reads that allow calculation of a coverage level. An average coverage of >10 is required for decent genome assemblies.

(chromosomes) Chromosomes may be linear, circular, directly repeated several times and linear (e.g.

4 Genome assembly at a higher level Genomes may be made from DNA or RNA (double- or single-stranded) An organism may have several genomes (foremost eukaryotes) Genomes may consist of more than one physical unit (chromosomes) Chromosomes may be linear, circular, directly repeated several times and linear (e.g., product of rolling circle replication, appears as circularmapping in sequence assembly!) Circular-mapping concatamers

5 How genome assembly of real (dirty) data works Given sequence read information (Sanger, Illumina, PacBio ) an algorithmic approach is required to: Combine reads with overlapping sequence into a genome sequence: Overlap-join procedures (slow, but sequence error/variation may be taken into account). It allows use of error-prone sequencing technologies like 454, but may introduce errors in the assembly (it does with 454 data; e.g., frame-shifts). Examples of software Phrap, Consed, Newbler, Mira. Eulerian algorithms based on graphs. Very fast, but require reads without sequence error or variation; drops reads that do not fit, up to a certain defined level. Therefore, an important feature is monitoring of the sequence coverage across contigs. Works with short sequences; these have to be highest quality. Huge datasets such as those produced by Illumina can be processed. Examples of software - Velvet, SOAPdenovo, Abyss, Allpath.

6 Graph algorithms for assembly (a) Sequence, (b) Traditional assembly, walk through Hamiltonian cycle. Variant in (c), after split of reads into short k-mers. (d) Modern de Bruijn graph finding sequence more quickly via Eulerian cycle. P. Compeau, P. Pevzner & G. Tesler (2011) NATURE BIOTECHNOLOGY 2 9:

7 How genome assembly of real (dirty) data works Given sequence read information (Sanger, Illumina, PacBio ) an algorithmic approach is required to: Discard information from contaminating DNA, primers and adapters If low level, sequence coverage cut-off values will resolve the issue Resolve repeat regions of all kinds that constitute assembly conflicts Mobile genetic elements and other short repeated DNA segments Segmental genome duplication Diploid, aneuploid genomes with sequence differences in allels ( snips ) Whole genome duplication followed by genetic drift of one copy or its partial loss. Requires sequence from large DNA fragments, chromosome size mapping, other physical or biological genome information

8 How genome assembly of real data works Resolve chromosome architecture (multiple genomes and chromosomes, linear, circular, or circular-mapping concatamers) An issue that usually needs manual input of an expert who has additional information

10 2. What is genome annotation? Finding and precise positional prediction of all genes, other genetic elements, insertion elements and repeats, on a given genome sequence Species may contain more than one genome (e.g., nuclear, mitochondrial, chloroplast, virus/phage, plasmid ) The genetic code and gene expression signals may differ from one genome to another - needs info on gene expression at the RNA and protein level Genes may be contiguous, or disrupted by introns, as well as discontinuous (trans-spliced or in pieces). Based on comparative gene/intron predictions (gene models, bioinformatics inference); information on transcript and protein sequence and other biological facts usually required List of features at the sequence level (e.g., GenBank submission file) Genetic maps

11 What is genome annotation? COMMENT Complete mitochondrial genome. FEATURES Location/Qualifiers source /organism="glomus irregulare" /organelle="mitochondrion" /mol_type="genomic DNA" /strain="daom " /type="genomic" gene /gene="rnl" rrna join( , , , , , , ) /gene="rnl" /product="large subunit ribosomal RNA" exon /gene="rnl" /number=1 intron /gene="rnl" /note="group IA3" /number=1 exon /gene="rnl" /number=2 intron /gene="rnl" /note="group IA3" /number=2 gene /gene="orf202 CDS /gene="orf202" /codon_start=1 /transl_table=4 /product="hypothetical protein" /translation="mkspnpqpalssiqreilvggllgdlsiyrakvthnarlyvqqg SVHKEYLNHLYSVFQNLCSSEPKWSLSLDKRSNTTYETLRFNSRSLPCFNYYRDVFYP EGVKIVPANIGELLTARGLAYWSMDDGYKDRGNFRLATQSFSRNDVLLLIKLLKDNFS LDCSLNTVKSTQYRIYVRANSMVQFRALVSPYFHPSMLYKLQ" exon /gene="rnl" /number=3 intron /gene="rnl" /note="group IB" /number=3 and so on Continued to the right Example: Partial GenBank annotation of a mitochondrial genome (rrna gene with introns and a predicted protein coding sequence) 11

12 What is genome annotation? Example: Genetic maps of two mitochondrial genomes 12

13 Effect of completeness, sequencing error, and assembly artifacts on genome annotation Incomplete genome assembly ( draft genome ) annotation of genes and genetic elements somewhat incomplete still works for gene identification, expression studies and comparisons Systematic sequence error (technology-specific) 454, number of nucleotides in homopolymer sequences incorrect causes difficulties in genome assembly at these sites, and potentially severe frameshifting in protein coding genes Sanger, difficulties to resolve snap-back structures; termination and/or slippage at long homopolymers - same as above but less severe Illumina, uncertain sequence at certain sequence motifs such as GGCNN seems to be less with latest technology. Error prediction and correction is possible. Ion Torrent, Pacific Biosciences overall error rate high, may to some degree be corrected by using very deep coverage (fails if polymorphic sites/snips are of interest; errors and snips are hard to distinguish)

15 3. Annotation of protein coding genes and introns First, one needs to know, or infer, the genetic code Translate Open Reading Frames (ORFs) that are not interrupted by a stop codon and that start with a know initiation codon (ATG, GTG ) ORFs may be given a functional identity, by sequence comparison to known genes. Protein sequence data can be used to confirm factual translation and identification of the genetic code.

16 3. Annotation of protein coding genes and introns Transcription data for the gene region as well as the presence of regulatory elements help to confirm the prediction (in case of bacteria, ribosomal binding site at 5 ; terminator sequence at 3 ; upstream promoters ); If these genes contain introns, exons may be identified in two ways By comparing the gene region with transcript sequences (do not contain introns) Inference of exon-intron structure based on sequence similarities of exons, intron features such as conserved splice site motifs, as well as any other feature that is known to define a gene in a given group of organisms. Gene models and intron models.

17 3. Annotation of protein coding genes and introns If genes contain introns, exon/intron boundaries (nucleus, eukaryotes) may be identified by conserved splice site motifs (intron models). For other intron types, use respective models.

18 3. Annotation of protein coding genes and introns M Yandell and D. Ence (2012) NATURE REVIEWS GENETICS 13: 329

19 3. Annotation of protein coding genes and introns M Yandell and D. Ence (2012) NATURE REVIEWS GENETICS 13: 329

21 4. Prediction of RNA genes a comparison of RNAmotif with ERPIN Features of structured RNAs: primary sequence conservation secondary structure tertiary interactions site-wise conservation is variable follow examples from catalytic introns and RNase P RNA

22 Secondary structure model of domain V of mitochondrial group II introns. The consensus structure is based on the compilation of 520 mitochondrial introns. Positional sequence conservation: R = A,G; Y = C,U; K = U,G; M = A,C; N = A,G,C,U; with prevalent nucleotides or nucleotide combinations color-coded red, 99%; magenta, 95%; blue, 80%; and green, 60%. Lower case nucleotides are alternatives that occur at frequencies of at least 10%. A few recurrent insertions of up to three nucleotides are indicated with arrows. Note that in some introns the conserved GAAA tetraloop motif (shaded grey) that interacts with a conserved structural motif in domain I is absent, that the size of the loop may vary by a few nucleotides, and that the number of base pairs in its connected helical region might be reduced.

23 Mitochondrial RNase P RNA is highly conserved in pairing P4, the reactive centre of the molecule, with respect to its bacterial counterparts. The two primary sequence motifs (red) are sufficient to identify > 50% of known homologs. However, when considering all known mitochondrial P- RNAs, the nucleotide variance (number of false positives) becomes too high for searches see following series of secondary structure models.

24 Modeling mitochondrial RNase P RNA structures has been difficult, due to their derived structures and high A+T content. Less derived mitochondrial P-RNAs have served as a starting point for RNA structure predictions.

25 By using phylogenetic-comparative principles, highly derived mitochondrial rnpb sequences were identified, leaving for most part P4 (the catalytic centre) as the universally conserved principle.

26 How to search most effectively for mitochondrial RNase P RNAs? Method 1: conserved primary sequence (using regular sequence expressions)

27 Mitochondrial primary consensus sequence most conservation is close to P4 P4 helical interaction

Corresponding regular expression: [AT]G[GA]NAA[GA]T[TC][ATC][GT][GA]... 73-386.

28 Corresponding regular expression: [AT]G[GA]NAA[GA]T[TC][ATC][GT][GA] A[CT][AU]NAAN[ATC][TC][AC][GAT][GT][CT]TTA[GAT] Apparently, primary sequence conservation is weak, just ~50% of currently known sequences are found with this information.

29 How to search most effectively for mitochondrial RNase P RNAs? Method 2: Use both conserved primary sequence plus secondary structure, united in a structural profile that is translated into an RNAmotif descriptor

30 Structured sequence profile including P4 helical region (using more sequences than in the primary sequence example)

31 Translation of this complex structural motif into an RNAmotif descriptor parms ### finds mt RNase P RNAs wc +=gu; ### permits global GU descr ss(len=20) ### 20 flanking nucleotides ss(len=5, seq="[gat][at]g[gat]a$") ### ss 5' to structure h5(len=3, seq="a[ga][ga]",mispair=1,ends='mm') ### P4-1 ss(len=1,seq="t$") ### T bulge h5(len=5,seq="[tc][atc][gat][gat].$",mispair=2,ends='mm') ### P4-2 ss(minlen=50, maxlen=1000,seq="[ac]c[atc].[ga]a$") ### P4 loop h3 (seq="[atc][atc][atc][gat][gt]$") ### P4-1' h3 (seq="[gtc][tc]t$") ### P4-2' ss(len=1,seq="a") ### universal A ss(len=20) ### 20 flanking nt It finds four false positives in a collection of 9 mtdnas with RNase P RNA, and misses one solution: lack of both sensitivity and specificity.

32 How to search most effectively for mitochondrial RNase P RNAs? Method 3: Use both conserved primary sequence plus secondary structure, united in a training set with all known sequences aligned, plus a corresponding structural line: to be used for ERPIN searches.

33 Translate the structural alignment into the ERPIN format however, it is a bit cryptic

34 The GDE editor comes to help, with color coding, coupled to a tool that translates the alignment into ERPIN format

35 ERPIN then calculates RNA primary and secondary structure profiles from the sequence alignment that are matched to the target sequence. Probabilistic search taking into account nucleotide frequencies. Much of the algorithm s efficiency stems from the use of userdefined, precisely delimited structural elements that can be searched individually or in combination, and by the option to use a defined search order ( search strategy ).

The ERPIN output of results is a bit cryptic, so we may use tools (RNAweasel) to compacts the results Note the E-values, the probability that a given structural motif occurs by chance in a target

36 The ERPIN output of results is a bit cryptic, so we may use tools (RNAweasel) to compacts the results Note the E-values, the probability that a given structural motif occurs by chance in a target database of given size and nucleotide composition. Values of 1e-2 and smaller can be already considered safe matches, although solutions close to 1e+1 might also be considered. Results are much superior to RNAmotif, no suspected false positives, more promising matches.

37 RNAweasel functions Public webserver Export of ERPIN format from GDE Automatic alignment of ERPIN results Normalization of training set sequences to increase the sensitivity of searches Reiterative mode of search A recent, even more powerful probabilistic approach covariance/hmm like inferences with Infernal (Sean Eddy development, to be watched)

38 How much structural conservation is required for meaningful ERPIN searches? Example: T-stem plus T-loop of trnas, to find matches with E-value better than 5e-2 Results: few if any false positives even in large datasets

39 This is it, folks!

Section 10.3 Outline 10.3 How Is the Base Sequence of a Messenger RNA Molecule Translated into Protein?

Section 10.3 Outline 10.3 How Is the Base Sequence of a Messenger RNA Molecule Translated into Protein? Messenger RNA Carries Information for Protein Synthesis from the DNA to Ribosomes Ribosomes Consist