The NCBI Eukaryotic Genome Annotation Pipeline and Alternate Genomic Sequences
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GRC Workshop at Churchill College on Sep 21, 2014. This is Paul Kitt's talk describing the NCBI approach to annotation the full human reference assembly.
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The NCBI Eukaryotic Genome Annotation Pipeline and Alternate Genomic Sequences
- 1. GRC Assembly Analysis Workshop At Genome Informatics September 21, 2014 The NCBI Eukaryotic Genome Annotation Pipeline And Alternate Genomic Sequences Paul Kitts NCBI National Center for Biotechnology Information
- 2. Genomes Annotated By NCBI Human GRCh38 2014-02-03 Zebrafish GRCz10 in progress Mouse GRCm38.p2 2013-12-27
- 3. Outline • Overview of the NCBI Eukaryotic Genome Annotation Pipeline • What to do with alternate loci & patch scaffolds? • How we use the alt/patch/PAR alignments to inform our annotation • Examples: – Annotation only on alternate loci – Different alleles annotated on primary assembly and alternate loci – Annotation improved by patches – Pseudoautosomal Regions annotated consistently on X & Y • Recent enhancements: – Using RNA-Seq evidence for gene prediction – Gap-filling gene models using transcript sequences – Annotation reports
- 5. Ranking Alignments • Rank alignments for each query sequence – using a quality score that combines identity & coverage – Rank-1 > Rank-2 > Rank-3… • Conflicting alignments cannot have same rank – alignments of the same query sequence to an assembly conflict if they have significant overlap (>= 30%) – Insignificant – Significant • A subset of rank-1 alignments is used for annotation Span in alignment B Span in alignment A Span in alignment B Span in alignment A
- 6. mRNA-F1 Annotation Of A Simple Assembly Using Ranked Alignments mRNA-F1 mRNA-F2 Input mRNAsGenes in the assembly mRNA-F2 Unplaced scaffold1 mRNA-F1 Filter out alignments that are not rank-1 GeneF1 GeneF2Chr1 GeneF1Chr1 Resulting annotation GeneF2 Unplaced scaffold1 mRNA-F2 mRNA-F1 * * ** * * *mRNA-F1mRNA-F2 * * Rank alignments Unplaced scaffold1 GeneF2Chr1 GeneF1 Rank-1 Rank-2 Rank-3Rank-1 Rank-2 Align mRNAs Unplaced scaffold1GeneF1 GeneF2Chr1
- 7. What to do with alternate loci & patch scaffolds? 1. Omit the alternate loci & patch scaffolds 2. Include the alternate loci & patch scaffolds; no special treatment 3. Include the alternate loci & patch scaffolds; use known relationships to primary assembly
- 8. Gene1/A G2-Allele-APrimary Chr1 Resulting annotation Gene2 mRNA-3A * * * Annotation Omitting Alt-scaffolds mRNA-1A mRNA-1B mRNA-2A Input mRNAs Gene3 Primary Chr1 Alt-scaffold1 Genes/Alleles represented in the assembly Gene1/A Gene2 Gene1/B Alt-scaffold2 mRNA-3A Gene3 Scenario 1: no annotation for Gene3 no annotation for Gene1/Allele-B ✔ mRNA-1A Rank-1 mRNA alignments Gene1/A Gene2Primary Chr1 mRNA-2A ✗✔ Scenario 2: Gene3 annotated at the wrong location no annotation for Gene1/Allele-B
- 9. Gene1/A G2-Allele-A Gene3 Gene4 Primary Chr1 Alt-scaffold2 Alt-scaffold1 Resulting annotation Gene2 Annotation Using Alt-scaffolds Without Alt-to-primary Alignments mRNA-1A mRNA-1B mRNA-2A Input mRNAs Gene3 Primary Chr1 Alt-scaffold1 Genes/Alleles represented in the assembly Gene1/A Gene2 Gene1/B Alt-scaffold2 ✔ ✗ ✔ mRNA-3A mRNA-1A mRNA-1B mRNA-3A Rank-1 mRNA alignments Gene1/A Gene2 Gene3 Gene1/B Primary Chr1 Alt-scaffold2 Alt-scaffold1 mRNA-2A ✔
- 10. Gene1/A G2-Allele-A Gene3 Gene1/B Primary Chr1 Alt-scaffold2 Alt-scaffold1 Resulting annotation Gene2 Annotation Using Alt-scaffolds & Alt-to-primary Alignments mRNA-1A mRNA-1B mRNA-2A Input mRNAs Gene3 Primary Chr1 Alt-scaffold1 Genes/Alleles represented in the assembly Gene1/A Gene2 Gene1/B Alt-scaffold2 alt-to-primary alignment ✔ ✔ ✔ mRNA-3A mRNA-1A mRNA-1B mRNA-3A Rank-1 mRNA alignments Gene1/A Gene2 Gene3 Gene1/B Primary Chr1 Alt-scaffold2 Alt-scaffold1 mRNA-2A ✔
- 11. Pros & cons of different choices for dealing with alternate loci & patch scaffolds 1. Omit the alternate loci & patch scaffolds Pros: Easy to implement Cons: No representation for genes or alleles only on alts. Incorrect models for genes that have been patched. 2. Include the alternate loci & patch scaffolds; no special treatment Pros: Easy to implement Cons: Incorrectly annotate genes that have alternate alleles or patches as if they were paralogs. Wrongly penalize sequences for having multiple or ambiguous placements. 3. Include the alternate loci & patch scaffolds; use known relationships to primary assembly Pros: Genes only on alts are annotated. Correctly annotate genes with alternate alleles. Correctly annotate patched genes Cons: Requires software and pipelines changes
- 12. Eukaryotic Genome Annotation Pipeline: Steps using alt-to- primary alignments Alt-to-primary alignments Curated gene localization
- 13. Ranking Alignments Across Assembly Units • Create graph of related alignments – Alignments that are collocated or mappable – Transcript/protein to genomic – Alt or patch scaffold to primary assembly • Partition graph into clusters – Each alignment in the cluster is related to at least one other alignment in the same cluster – No alignment is related to any alignment in another cluster – Split conflicting alignments within a cluster into separate groups – Merge non-conflicting clusters into groups • Evaluate groups, sort and assign ranks – All alignments in a group get the same rank
- 14. Ranked Alignment Groups Across Assembly Units Assembly unit Assembly alignment mRNA1 alignment mRNA2 alignment Cluster Rank group Assembly1-Primary Assembly1-Alt1 Assembly1-Alt2 Rank-1 Rank-2
- 15. Ranked Alignment Groups Across Assemblies Assembly unit Assembly alignment mRNA1 alignment mRNA2 alignment Cluster Rank group Assembly1-Primary Assembly1-Alt1 Assembly1-Alt2 Rank-1 Rank-2 Assembly2-Primary Assembly3-Primary
- 16. Ranked Alignment Groups Across Pseudoautosomal Regions (PARs) Chromosome PAR alignment mRNA1 alignment mRNA2 alignment Cluster Rank group Chromosome Y Chromosome X Rank-1 PAR#1 PAR#2
- 17. NCBI> Gene> “Homo sapiens”[orgn] AND "only annotated on alternate loci in reference assembly"[Text Word] AND gene_nucleotide_pos[filter] Genes Only Annotated On GRCh38 Alternate Loci NCBI> Gene> “Homo sapiens”[orgn] AND "only annotated on alternate loci in reference assembly"[Text Word] AND gene_nucleotide_pos[filter] AND “genetype protein coding”[prop] AND srcdb_refseq_known[prop] Num. Gene Type 20 Protein Coding 40 Protein Coding (model) 21 Pseudogene 32 Pseudogene (model) 32 ncRNA (model) 5 Other 3 Other (model)
- 18. Different Alleles Annotated On GRCh38 Primary Assembly And Alternate Loci ALT_REF_LOCI_2 ALT_REF_LOCI_7 NM_001243042.1 comment: This variant represents the C*07:01:01:01 allele of the HLA-C gene. NM_002117.5 comment: This variant represents the C*07:02:01 allele of the HLA-C gene.
- 19. Annotation Of GRCh37 Improved By Patch Scaffold EPPK1 gene on primary assembly chromosome 8 has an internal deletion. EPPK1 gene on patch scaffold is complete. Primary Assembly chromosome 8 Patch scaffold HG104_HG975_PATCH
- 20. Pseudoautosomal Regions Annotated Consistently on GRCh38 chromosomes X & Y
- 21. Recent Enhancements To The Genome Annotation Pipeline: #1 Using RNA-Seq Evidence For Gene Prediction 0 10000 20000 30000 40000 50000 60000 70000 80000 Number of coding transcripts predicted +/- RNA-Seq 0 10000 20000 30000 40000 50000 60000 Number of genes predicted +/- RNA-Seq Without RNA-Seq With RNA-Seq 75 organisms annotated with RNA-Seq data
- 22. Example Of Tracks Made Using RNA-Seq Data NCBI > GENE > Xenopus (Silurana) tropicalis nbr1 [neighbor of BRCA1 gene 1]
- 23. Recent Enhancements To The Genome Annotation Pipeline: #2 Gap-filling Gene Models Using Transcript Sequences Genomic sequence Transcript alignment 1 32 4 RefSeq model Gap How gap-filling works Reporting of gap-filled regions
- 24. Recent Enhancements To The Genome Annotation Pipeline: #3 Annotation Reports RNA-Seq
- 25. Summary Including the alternate loci & patch scaffolds and using their known relationships to the primary assembly significantly improves the annotation of GRC assemblies. It is worth the extra effort!
- 26. CREDITS Genome pipeline infrastructure Alex Astashyn Nathan Bouk Rob Cohen Mike Dicuccio Eric Engelson Olga Ermoloeva Wratko Hlavina Lucian Ion Avi Kimchi Boris Kiryutin David Managadze Eyal Mozes Terence Murphy Daniel Rausch Robert Smith Sasha Souvorov Craig Wallin Alex Zasypkin Eukaryotic annotation setup & execution Françoise Thibaud-Nissen Jinna Choi Patrick Masterson Kim Pruitt and the “genome champions” from the RefSeq group Genomic Collections DB Avi Kimchi Victor Sapojnikov Charlie Xiang Andrey Zherikov Genome assemblies with alt/patch to primary alignments Genome Reference Consortium The Wellcome Trust Sanger Institute The Genome Institute at Washington University The European Bioinformatics Institute The National Center for Biotechnology Information Eukaryotic Genome Annotation at NCBI: www.ncbi.nlm.nih.gov/genome/annotation_euk/
Editor's Notes
- NCBI developed a genome annotation pipeline 14 years ago to annotate draft versions of the human genome assembly. Since then we have continued to develop and improve the annotation pipeline and to apply it to more and more genomes. So far, we have annotated the genomes for over 150 different eukaryotes, >> we last annotated the GRC’s mouse assembly in December, the GRC’s human assembly in February, and our annotation of the GRC’s new zebrafish assembly is humming along as I speak.
- Since the theme of this workshop is using the full GRC assemblies, including the alternate loci and patch scaffolds, and making use of the known relationship between these scaffolds and the primary assembly, the focus of my talk will be on how NCBI uses this information in our annotation pipeline. I will begin by giving an overview of the NCBI eukaryotic genome annotation pipeline. Then I will raise the question of what to do with alternate loci and patch scaffolds, sketching out the consequences of some different choices. I will explain how we use the alt, patch, & PAR alignments to inform our annotation, and then show some examples of the results. I will finish by briefly highlighting some recent enhancements to our annotation pipeline.
- Here is a flowchart of our genome annotation pipeline. The substrate for annotation is one or more genome assemblies (in grey). The genomic sequences are masked, and transcripts (blue), proteins (green) and RNA-Seq reads (orange) are aligned to the genome. If available for the organism being annotated, curated RefSeq genomic sequences (pink) are also aligned. The alignments of these different types of evidence go through a ranking step, which I will describe in more detail later, and a filtering step, before being fed into the gene model prediction step. The best models are selected from those known RefSeqs that aligned to the genome and from the predicted models. The selected models are then assigned to genes and named. After that, the annotation products are formatted and deployed to NCBI’s public resources: Nucleotide, Protein, Gene, BLAST & FTP. >> In order to talk about how we handle alt and patch scaffolds in our pipeline, I first need to do give you more detail on how the alignments are ranked >>
- We rank alignments for each query sequence using a quality score that combines identity & coverage. So the best alignment gets rank-1, the next best alignment gets rank-2, and so on. … Some rank-1 alignments may not be used: because even rank-1 alignments may not be good enough quality, because curated input may exclude a transcript from being placed on certain assemblies or on certain chromosomes, or because the alignments may be rejected later at the best model selection step.
- I am now going to show a carton of how we annotate a simple assembly using ranked alignments. The genomic sequence is shown in orange, with the locations of a couple of genes from a gene family shown in grey. We have mRNA sequences corresponding to these genes shown in green. >> We start by aligning the mRNAs. Alignments are shown in blue, with red stars indicating mismatches. The F1 mRNA aligns not only to the gene from which it is derived, but it also aligns to other genes in the same family, and even to less related sequences. >> the F2 mRNA also aligns to multiple locations. >> So then we rank the alignments of mRNA-F1, >> and the alignments of mRNA-F2. >> We filter out alignments that are not rank-1. >> Keeping only the rank-1 alignments allows us to correctly annotate the two genes in this family. So you see that annotating a simple assembly is pretty straightforward, at least in this cartoon! >> so then the question is…
- Here I list three choices. … I will now sketch out the consequences of each of these three choices for gene annotation.
- … >> align the mRNAs, rank them, and filter out the non-rank-1 alignments. >> The resulting annotation is good, with Gene1/Allele-A & Gene2 correctly annotated, but incomplete. Our annotation release would be missing Gene3 & Allele-B of Gene1. >> neither we nor our users would be happy. Since the true home for mRNA-3A is only present on one of the alt loci scaffolds that was omitted, one of two things could happen. In the first scenario I showed you, mRNA-3A fails to align anywhere. >> An alternative scenario is that mRNA-3A aligns imperfectly to another gene or pseudogene. Because the true home for this mRNA is missing, this alignment would get assigned rank-1. >> as a result Gene3 would be annotated at the wrong location. >> again, neither we nor or users would be happy.
- … >> after aligning the mRNAs, ranking them, and filtering out the non-rank-1 alignments, the picture would look like this… >> using these rank-1 alignments for annotation, correctly annotates Gene2, Gene3 and Gene1/Allele-A. But Allele-B of Gene1 on the alt-scaffold is incorrectly annotated as being a different gene because we had nothing to relate alt-scaffold1 to the primary assembly, and consequently failed to recognize that these were variants of the same gene. >> we would not feel good about putting out such annotation.
- … In some other pipelines, a sequence that aligns to equivalent locations on both the primary assembly and on an alt or patch scaffold may be penalized for having multiple or ambiguous placements.
- The NCBI Eukaryotic Genome Annotation pipeline has been engineered to use alt-to-primary alignments in two steps: first, in the ranking of transcript & protein alignments; and second in gene assignment & naming. >> We also use input on gene localization to particular chromosomes or assembly-units that our curators maintain. The challenge is how to do ranking across assembly-units. >>
- Here is an outline of our algorithm for doing this.
- … All the alignments in the group on the right get assigned rank-1. This is what enables us to annotate a gene consistently when it appears in equivalent locations on more than one assembly unit.
- Here I have added two additional assemblies into the picture, along with assembly-assembly alignments that relate segments of assembly 1 to assembly 2 or assembly 3. >> the algorithm used to cluster related alignments and rank them can be applied to this more complex picture. This allows us to annotate the same gene consistently across multiple assemblies. For example in our most recent human annotation run, we annotated the HuRef and CHM1 assemblies as well as GRCh38.
- The algorithm used to cluster related alignments and rank them is also applicable to pseudoautosomal regions. In this case, alignments between the pseudoautosomal regions on chromosome X and chromosome Y are used to cluster transcript alignments for ranking. Enough of the theory. How do we do in practice?
- The NCBI Gene resource tags genes only annotated on the alts with the phrase “only annotated on alternate loci in reference assembly”, which makes it easy to retrieve this set of genes. Running this query shows that there are 153 genes only annotated on the alts. >> the search can be further constrained to just the protein coding genes from known RefSeqs, by adding terms to the query. >> Doing variations of this search shows that the genes only annotated on the alts include 20 protein coding genes with known RefSeqs, another 40 protein coding gene models, and a number of pseudogenes, non-coding RNAs and genes of other types.
- My next example is different alleles… On the top we have chromosome 6 of the primary-assembly around the HLA-C gene, lower down are a scaffold from ALT_REF_LOCI_2 and a scaffold from ALT_REF_LOCI_7. Between them are grey bars showing the alt-to-primary alignments with red lines indicating mismatches, and blue hour-glasses indicating insertions or deletions. The green bar shows the extent of the gene. The blue bar shows the mRNA, with thick parts representing exons and thin parts introns. The red bar is the coding sequence. The two boxes below point out that our annotation used different RefSeq mRNAs for the HLA-C gene on primary vs ALT_REF_LOCI_2, and the comments on these mRNA records identify them as representing different HLA-C alleles. NT_113891.3 Homo sapiens chromosome 6 genomic scaffold, GRCh38 alternate locus group ALT_REF_LOCI_2 HSCHR6_MHC_COX_CTG1 NT_167249.2 Homo sapiens chromosome 6 genomic scaffold, GRCh38 alternate locus group ALT_REF_LOCI_7 HSCHR6_MHC_SSTO_CTG1 http://www.ncbi.nlm.nih.gov/nuccore/NC_000006.12?report=graph&from=31268240&to=31272643&strand=true&app_context=Gene&assm_context=GCF_000001405.26
- The GRC has not yet released any patches to GRCh38, so I went back to our annotation of GRCh37.p13 for an example of how annotation can be improved by a patch scaffold. On the top is chromosome 8 from the primary assembly in the region of the EPPK1 gene. This primary assembly has an internal deletion in this gene that was corrected in the patch scaffold shown below. The patch-to-primary alignment shown as this grey bar, helped us to annotate the EPPK1 gene at corresponding locations on the primary assembly chromosome & patch scaffold.
- My final example, is…
- I just quickly want to tell you about some recent enhancements to the genome annotation pipeline, the first of which is using RNA-Seq data as evidence for gene prediction. We have annotated 75 eukaryotes using RNA-Seq data over the last eighteen months. The chart on the left shows that adding RNA-Seq increased the number of genes predicted by about 20% on average for the organisms in this chart. The chart on the right shows that adding RNA-Seq resulted in an even bigger increase in the number of coding transcripts, as more transcript variants were annotated, an average increase of 88% for these reference assemblies. For many of the less well studies eukaryotes that we have annotated, RNA-Seq data has provided the primary source of evidence for generating gene models.
- Here is a graphical view of the Xenopus tropicalis nbr1 gene. We generated four model transcript variants for the nbr1 gene, shown in green. Below the genes track are three tracks generated using RNA-Seq data. The first shows a histogram of the RNA-Seq coverage of the exons, the second track, which looks like a mirror image of the first, shows a histogram of the intron-spanning reads, and the third track shows the intron features inferred from the RNA-Seq alignments.
- In April this year we enhanced our annotation pipeline by adding gap-filling of gene models using transcript sequences. What this means is that we can extend model RefSeqs into assembly gaps based on alignments of transcripts or Transcriptome Shotgun Assemblies. How this works is illustrated here. This genomic sequence contains a gap. A transcript aligns to the genomic sequence except for the 5’ end which falls in the gap in the assembly. We can construct a model RefSeq transcript based on the transcript sequence for exon 1 and the genomic sequence for exons 2, 3 & 4. >> Gap-filled regions are reported in the flat file record of transcript and protein models. <Example is platypus model XM_007659754.1>
- Last November we began making annotation reports for each annotation run that we do, both as a web page and as XML files. I will give you a just a quick taste of what is in each report. At the top of the report is the Annotation Release information, which includes the date the input data was frozen, and the date the annotation was made public. The assemblies that were annotated are also shown in this section. >> Gene and feature statistics, for example compare Gene or CDS counts between different assemblies or assembly units. >> RefSeq transcript alignment quality report. RefSeq alignments can be used as a metric for relative assembly quality. >> RNA-Seq alignment details
- The GRC has not released any patches to GRCh38 yet, so I went back to our annotation of GRCh37.p13 for an example of how annotation can be improved by a patch scaffold. This patch scaffold adds an component that extends the chromosome 17 sequence <point in Tiling Path track>. This is reflected in the Patch to Primary alignment which ends at the junction with the new component. The DOC2B gene on the primary assembly chromosome is partial (as indicated by the double black arrows and the grey bar). It is missing the last three exons. The extra sequence in the patch included the missing exons, hence, the DOC2B gene on the patch scaffold is complete. The patch-to-primary alignment helped us to annotate the DOC2B gene at corresponding locations on the primary assembly chromosome & patch scaffold. NW_004070872.2 Homo sapiens chromosome 17 genomic patch of type FIX, GRCh37.p13 PATCHES HG417_PATCH NC_000017.10 Homo sapiens chromosome 17, GRCh37.p13 Primary Assembly http://www.ncbi.nlm.nih.gov/nuccore/NW_004070872.2?report=graph&from=81581&to=126850&strand=true&app_context=Gene&assm_context=GCF_000001405.25