Genome Annotation/V3.1

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This page is under construction...

It is meant to document both the annotation process and the resulting data accessible via the cosmoss.org interfaces.

You can browse V3.1 using gbrowse: http://www.cosmoss.org/fgb2/gbrowse/V3.1/

1 The Physcomitrella patens V3.1 genome annotation

The Physcomitrella patens V3.1 genome annotation

Improved Locus Definition - cosmoss.org gene ids (CGIs)

CGIs provide the unique address of a gene (protein-coding and non-protein-coding genes) on a respective assembly. CGIs also function as primary IDs/accession numbers to access genes and gene products in V3.1. On the V3 assembly CGIs can either be localized on pseudo-chromosomes or scaffolds. Locus ids are ordered from 5' to the 3' end of the reference sequence.

CGI Syntax

Like a German car licence plate, the CGI carries a lot of information about the gene product you're looking at: Comprising the fields species, assembly version, reference sequence, locus index, type and (sequence) index.

Inference of locus indices

With V3.1 we further improved the cosmoss gene id concept, adopting the locus id incrementation scheme employed by TAIR [1].

In order to allow both subsequent addition of novel loci and stable accession numbers, V3.1 CGIs increment by a dynamic window (+10 minimum), i.e. allowing the addition of a minimum of 10 genes in between to previously annotated loci. The added window size is determined for each subsequent locus independently to accommodate gene-rich as well as gene sparse genomic regions. The dynamic component is determined by the median gene density of the V3.1 assembly (see below) and a fix gap bonus (200 genes for gaps > 5000bp).

The initial CGIs were derived for all models, including those filtered as transposon-derived in subsequent steps, using the following formula:

$s=\begin{cases} w_{ij}\times D & s > 10\\ 10 & s < 10 \end{cases}$

$C_j = \begin{cases} \lVert C_i + s\lVert_{10} & g_{ij} < 5000 \\ \lVert C_i + s\lVert_{10} + 200 & g_{ij} > 5000 \end{cases}$

The next locus index for a CGI $C j$ was calculated considering the intergeneic distance $w i j$ between two genes $i$ and $j$ , the CGI locus index $C i$ of gene $i$ , the median gene density $D$ , the total gap length $g i j$ between genes $i$ and $j$ , a constant minimal CGI incrementor of 10, a constant incrementor of 200 to account for large gaps and constant defining large gaps [>=5000bp]. Resulting locus indices $C j$ were rounded to the next multiple of 10.

V3.1 Gene distances, density and gap sizes

distances [bp]
  Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
-26680     248    1618    4771    5867  129900

chromosomal distances [bp]
  Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
-26680     274    1716    4908    6130  129900

Distribution plots of gene distances

gene density [Mbp]
  Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 33.44  214.80  320.00  389.80  499.30 5051.00

gene density on chromosomes [Mbp]
  Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 137.1   145.5   148.7   149.3   152.6   166.8

Distribution plots of gene densities

gap size [bp]
  Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
   100     100     260    2264    2018   43280

size of gaps larger 5kb 
  Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  5001   10000   10000   10580   10000   43280

Distribution plots of gap sizes

CGI Examples

Normal distances

cgi	reference	start	stop	predictor tag	gene_offset	s	nmodels	is_selected	predicted
Pp3c1_10	Chr1	5216	5627	T1	0	0	1	TRUE	supported_by_EST_or_cDNA
Pp3c1_20	Chr1	8931	13135	P2	3304	0	50	TRUE	supported_by_EST_or_cDNA
Pp3c1_30	Chr1	16782	18062	J3	3647	0	51	FALSE	supported_by_EST_or_cDNA
Pp3c1_40	Chr1	16796	21548	T1	-1266	0	51	TRUE	supported_by_EST_or_cDNA
Pp3c1_50	Chr1	20198	21414	J2	-1350	0	51	TRUE	supported_by_EST_or_cDNA
Pp3c1_60	Chr1	29286	36549	P2	7872	1	210	TRUE	supported_by_EST_or_cDNA
Pp3c1_70	Chr1	39546	47674	E2	2997	0	169	TRUE	supported_by_EST_or_cDNA
Pp3c1_80	Chr1	50397	53156	P2	2723	0	13	TRUE	supported_by_EST_or_cDNA
Pp3c1_90	Chr1	50549	50710	C1	-2607	0	13	FALSE	supported_by_EST_or_cDNA
Pp3c1_100	Chr1	63062	66241	P2	12352	2	20	TRUE	supported_by_EST_or_cDNA

Larger distances

cgi	reference	start	stop	predictor tag	gene_offset	s	nmodels	is_selected	predicted
Pp3c1_15680	Chr1	11690544	11697036	E2	-1475	0	40	TRUE	supported_by_EST_or_cDNA
Pp3c1_15690	Chr1	11697105	11699028	E2	69	0	40	TRUE	supported_by_EST_or_cDNA
Pp3c1_15710	Chr1	11803514	11804120	J3	104486	13	23	FALSE	predicted_by_ab_initio_computation
Pp3c1_15720	Chr1	11804587	11805231	J2	467	0	23	FALSE	predicted_by_ab_initio_computation
Pp3c1_15730	Chr1	11804599	11809753	P2	-632	0	23	TRUE	supported_by_EST_or_cDNA

Large gaps

cgi	reference	start	stop	predictor tag	gene_offset	s	gaps_>100bp	nmodels	total_gap_length (g_ij)	is_selected	predicted
Pp3c1_17460	Chr1	13234598	13235039	C1	1012	0	0	4	0	FALSE	predicted_by_ab_initio_computation
Pp3c1_17470	Chr1	13235447	13236029	E1	408	0	0	4	0	TRUE	predicted_by_ab_initio_computation
Pp3c1_17680	Chr1	13252043	13262701	N1	16014	2	1	4	8120	TRUE	supported_by_sequence_similarity
Pp3c1_17690	Chr1	13262832	13263058	P0	131	0	0	1	0	TRUE	supported_by_EST_or_cDNA
Pp3c1_17700	Chr1	13275382	13275856	P0	12324	2	0	1	0	TRUE	supported_by_EST_or_cDNA

Multi-level Annotation Pipeline behind V3.1

Overall Aim

Guarantee continuity to existing cosmoss annotation
- Quality of structural annotations
  - including manual curations (at least >600 high-quality curations)
  - including more experimental evidences
- Being able to transfer substantial existing functional annotation
  - e.g. GO and gene names
  - Ongoing projects extending annotation (e.g. literature curation, MossCyc)
Cosmoss Gene IDs
- ID-Lookup: ensure continuity of Physcomitrella annotation

Structural annotation

Incorporate all data
- Also in-house data which couldn't be released yet
  - RNASeq
  - Proteomics (N-termini and total protein coverage)
  - microarrays
Use updated transcript data:
- train ab initio models
- evidence for gene calling
Gene prediction for all scaffold partitions
Use combiner
Incorporate/Detect non-protein-coding genes early on
Optimally handle pseudoalleles (identical (tandem-) paralogs)

Used data - short reads

84 libraries mapped with tophat-2.0.8b
- In addition to what was used by JGI
- 36 libraries SRA
- 48 internal libraries (Reski or collaborators)
Not all these libraries are suitable for gene body prediction
- SAGE tags, CAPSeq, sRNA

Training gene finders (EuGene/Augustus)

Using splice sites from short reads and EST alignments
Manual curation of “manually curated gene models”
- Reducing >3000 models → 603 models
- Only 2 of the user_model track from the old JGI browser (most were just promoted ab initio models)
Augusts was excluded due to bad performance on training set (30% sensitivity transcript level)
All EuGene sensors retrained
Multiple rounds of training/parameter settings using both w/o alternative splicing
Original V1.6 model (BMC 2013) was used as well

Evaluation of gene predictions on the training set

Using eval software
All gene predictions were reduced to representative splice variant
Performance measures (sensitivity and specificity)
- Nucleotide
- Exon
- Transcript
- Gene
- Combined score (based on Gene+Exon/sensitivity+specificity) for ranking

Results

predictor tag	source	Gene Sensitivity	Gene Specificity	Transcript Sensitivity	Transcript Specificity	Exon Sensitivity	Exon Specificity	Nucleotide Sensitivity	Nucleotide Specificity	score
E6	final_Wminmax5	0.757	0.758	0.666	0.758	0.933	0.94	0.958	0.981	3.3884
	EVM_prefinal2_Wminmax5	0.757	0.741	0.666	0.741	0.934	0.937	0.958	0.978	3.369
	EVM_prefinal1_Wminmax5	0.759	0.736	0.667	0.736	0.933	0.934	0.958	0.975	3.3612
E2	EuGene_more_data_weights1	0.731	0.74	0.644	0.74	0.918	0.948	0.958	0.985	3.3363
E3	EuGene_more_data_weights1_AS	0.734	0.71	0.647	0.71	0.918	0.939	0.959	0.983	3.3007
E4	EuGene_more_data_weights2	0.707	0.711	0.625	0.711	0.91	0.948	0.958	0.984	3.2755
E5	EuGene_more_data_weights2_AS	0.707	0.678	0.625	0.678	0.91	0.938	0.959	0.981	3.2333
	EVM.first	0.699	0.639	0.625	0.639	0.883	0.928	0.956	0.977	3.1483
C1	cosmoss_V1.6	0.722	0.589	0.68	0.605	0.894	0.898	0.947	0.953	3.1028
J2	JGI_gene	0.622	0.551	0.542	0.551	0.861	0.887	0.929	0.956	2.9208
E1	EuGene_BMC_Genomics2013	0.582	0.502	0.518	0.502	0.87	0.88	0.945	0.963	2.8335
T1	transdecoder	0.054	0.027	0.05	0.027	0.007	0.012	0.935	0.517	0.0997

Assembly of short read and EST evidences

EST and short read data cannot be used directly as evidence for gene prediction. Additionally if coverage is optimal, full-length mature transcripts and thus gene structures can be inferred directly by read assembly. This can be achieved either de-novo (e.g. Trininty) or using a genomic sequence as reference (e.g. PASA or cufflinks). Both methods have its drawbacks. De-novo assembly can result in fragmentary assemblies:

The three yellow highlighted features represent three independent Trininty short read assemblies from the same experiment. From their overlap with the V3.1 gene model below we can clearly deduce that they are transcript fragments and not full-length transcripts of three distinct genes.

Genome-guided assembly also has its problems as demonstrated in the figure below:

The different colors in the track "mapping test" represent short reads assemblies from distinct experimental samples generated by cufflinks, which was initially developed for vertebrate genomes. We can see that there are a lot of assemblies that represent fusions of at least two neighboring genes. On the moss genome, PASA is a little better in dealing with this problem, thus we have used PASA to assemble the data sets.

The cufflinks data were used to filter libraries to be used based on manual inspection to ensure:

Good coverage
Splice sites

De-novo assembly of short reads and spliced alignments

21 filtered short read libraries assembled with Trinity (r2013_08_14)
Seqclean
Stats:
- 1,702,106 transcripts total
- Mean: 77,368.45 transcripts
- Mean length: 1,219.96bp
Adding Sanger and 454 ESTs
Mapped with GenomeThreader

source	raw	seqclean	seqclean%	GenomeThreader	GenomeThreader%
Sanger reads combined	518,256	476,307	91.91%
454 reads	631,313	576,759	91.36%
Trinity assemblies combined	1,702,106	1,702,082	100.00%
Total	2,851,675	2,755,148	96.62%	2,640,714	95.85%

Genome guided assembly with PASA

The resulting spliced alignments were assembled using PASA:

Transcripts or Assemblies	Count
Total transcript seqs	2755148
Fli cDNAs	0
partial cDNAs (ESTs)	2755148
Number transcripts with any alignment	2438714
Valid custom alignments	2260452
Total Valid alignments	2260452
Valid FL-cDNA alignments	0
Valid EST alignments	2260452
Number of assemblies	266051
Number of subclusters (genes)	68382
Number of fli-containing assemblies	0
Number of non-fli-containing assemblies	266051

All Models

Predictor tags - What's E1?

predictor tag	name	algorithm	source	predicted as
C1	cosmoss_V1.6	SpliceMachine/EuGene	cosmoss	protein-coding_gene
E1	EuGene_BMC_Genomics2013	SpliceMachine/EuGene	cosmoss	protein-coding_gene
E2	EuGene_more_data_weights1	SpliceMachine/EuGene	cosmoss	protein-coding_gene
E3	EuGene_more_data_weights1_AS	SpliceMachine/EuGene	cosmoss	protein-coding_gene
E4	EuGene_more_data_weights2	SpliceMachine/EuGene	cosmoss	protein-coding_gene
E5	EuGene_more_data_weights2_AS	SpliceMachine/EuGene	cosmoss	protein-coding_gene
E6	EVM_final_Wminmax5	EvidenceModeler	cosmoss	protein-coding_gene
J1	JGI_gene_alt	JGI	JGI	protein-coding_gene
J2	JGI_gene	JGI	JGI	protein-coding_gene
J3	JGI_pasa_gene	PASA	JGI	protein-coding_gene
P1	v3_real.gene_structures_post_PASA_updates.31614	EVM/PASA 5th iteration	cosmoss	protein-coding_gene
P2	v3_real.gene_structures_post_PASA_updates.31978	EVM/PASA 6th iteration	cosmoss	protein-coding_gene
T1	transdecoder	PASA/transdecoder	cosmoss	protein-coding_gene
N1	combined ncRNA predictions	Infernal, aragorn, tRNAScan, RNAammer, mirbase, snoscan, Eugene	cosmoss	ncRNA_gene
P0	putative long ncRNA	PASA without transdecoder support and unselected ab initio models	cosmoss	ncRNA_gene

Locus Clustering

Filtering of Transposable Element-derived Gene Models

Selected Models for the V3.1 Gene Catalog

Phypa_ids

To avoid confusion and improve database cross-referencing, V3.1 does not anymore offer Phypa_ids. Phypa_ids were used as secondary accession numbers in previous versions like e.g. Genome Annotation/V1.6. They were originally based on JGI protein/transcript IDs.

If you only know the Phypa_id of your favorite gene, you can map it to a V1.6 CGI (e.g. using this excel table). V1.6 models were mapped to V3.1 and thus can be looked up using the cosmoss genome browser simply by searching with the CGI.

Splice Variants

mRNAs derived from the same locus, i.e. splice variants are children of the same gene feature (indicated by the number after the V6 in V1.6 CGIs):

gene    ID=Pp1s1_5V6;Name=Pp1s1_5V6
mRNA    ID=Pp1s1_5V6.1;Parent=Pp1s1_5V6;Alias=Phypa_422107
mRNA    ID=Pp1s1_5V6.2;Parent=Pp1s1_5V6;Alias=Phypa_422004

V3.1 does not yet include splice variants. Although some of the predictors generated splice variants (e.g. P2, T1, ...), we have decided not to incorporate them into the final release, because the data is still to noisy and in more analysis is need to guarantee reliable quality. Instead, the selected V1.1 models should always provide the major, functional isoform encoding the evolutionarily conserved gene product. This was achieved by multiple-evidence machine learning process trained to select the optimal gene model per locus. For details see section on locus clustering.

Functional Annotation, Gene Names and Gene Families

Download

The flat file distribution of V3.1 (FASTA, GFF3, GAF2 etc.) will be made available via the Downloads section as soon as the paper describing the analysis of the V3 assembly and V3.1 annotation is accepted for publication.

Contributions

This document and the V3.1 genome annotation was created by Daniel Lang with contributions from:

Andreas Zimmer: Initial user models, SpliceMachine/Augustus/Eugene traininig and prediction
Shu Shenqiang and David Goodstein(JGI): Gene models of the V3.0 [J1, J2, J3]
Nico van Gessel: non-protein coding gene annotation

None of this would be possible without the tremendous work and investments done by the JGI. The V3 assembly/scaffolding was created by Jeremy Schmutz and Jerry Jenkins. The underlying genetic map was inferred by Wellington Muchero based on the mapping populations from Andrew Cuming, Stuart McDaniel and the Reski lab with Stefan Rensing as head of the consortium and external PI at the JGI.

Retrieved from "https://www.cosmoss.org/physcome_project/wiki/Genome_Annotation/V3.1"

Categories: Annotation | Assembly | Cosmoss