Gene Finding Methods

Outline

• Overview
• Evidence collection
• Building EST-based gene models
• Computational prediction of tRNAs and RNAs
• Training Gene Prediction Programs
• Computational Gene Models
• Transfer Annotation
• Selection of Consensus Gene Models
• Predicting Alternatively Spliced Transcript Models
• Predicting UTR
• Assigning Gene Product Names
• Reporting Annotation Accuracy
• Gene Numbering
• Overview of Reference genes
• Overview of Query Genes
• Feature Mapping
• Feature Comparisons
• Splice Analysis
• Possible Problems

Overview

This document provides a general description of our automated genome annotation for eukaryotic genomes.

Gene annotation at Broad is a multi-step process.

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Evidence collection

• Blast evidence:

Blast homology search against the Genbank's NR database produces a set of raw blast out put. Individual blast alignments are then clustered into single blast clusters by linking the blast alignments derived from the same blast hit. Several such overlapping blast clusters on the genomic axis represents what we call as blast loci on the genome assembly. Currently, all blast data with e-values greater than 1e-10 are used considered as usable blast evidence.

• EST evidence:

For most of our fungal and other eukaryotic genomes, we use species-specific ESTs sequenced here are broad as well as publicly available EST sequences from genbank to produce a set of high confidence gene models.

• Pfam domains:

We run Hmmer searches using pFAM library to find pFAM domains on six-frame translations of the genomic sequence.

• EST alignments:

First, we align ESTs to the genome using BLAT and then collapse them into distinct clusters transcripts using a Broad-developed program called CallReferenceGenes (described below). EST alignments with 90% identity over 50% of the EST length with canonical splice junctions are considered valid EST alignments suitable for building gene models.

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Building EST-based gene models

EST clusters produced in the previously described section are used as inputs for building high confidence gene models for the purposes of training and evaluating gene finding tools.

FindEstOrf is an internal tool used for constructing CDS from EST transcripts. This tool assigns start and stop codons to create a longest ORFs. We then compare these constructed ORFs to overlapping BLAST evidence and pick the ones with comparable reading frame and protein length to the best blast hits from closely related species. FL_EST gene models created by this process are fully covered by EST alignments and are within + 20% of the length of homologous proteins.

In addition, we also build EST-based gene models by a manual process. By combining blast, EST and ab initio predictions produced using a parameter file from a related genome, manual annotators carefully build gene models that are otherwise missed by our highly conservative automated findORF script.

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Computational prediction of tRNAs and RNAs

tRNA scan is used for detecting tRNAs on the genome assembly. We use RNA finding programs such as RNAmmer and RFamSearch to detect the common RNA features.

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Training Gene Prediction Programs

Commonly used gene finding programs such as Augustus, GENEID, GeneMark, FGENESH and SNAP are trained in house or by the developers of these programs using the high confidence EST gene sets.

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Computational Gene Models

Gene structures are predicted using a combination of gene models from computational gene prediction programs such as FGENESH, GENEID, GeneMark and EST-based automated and manual gene models. FGENESH is a commercial gene prediction program sold by Softberry, while GENEID, by Enrique Blanco and Roderic Guigo, is available under the GPL. GeneMark is another gene finding tool developed by the Mark Borodovsky's group (Borodovsky & McIninch, Comp. Chem., 1993). Both GENEID and FGENESH are usually trained using a set of high confidence EST-based gene models generated by clustering blat-aligned species-specific ESTs. In addition, we also use other commonly used gene finding programs such as SNAP (Ian Korf) and Augustus (Mario Stanke) if and when genome-specific parameter files are available.

After training the gene prediction programs, we run each of them on the assembly and evaluate their performance by comparing the gene models with EST and blast evidence. Those that perform adequately are used in the automated gene calling pipeline. The modular architecture of our automated pipeline makes it easy to incorporate new gene prediction programs and customize the pipeline to suit the genomes annotated.

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Transfer Annotation

We use well-curated annotations from other sources and reference genomes to improve our automated annotations. Broad's in-house synteny-based gene transfer process has two main steps:

First, we generate collinear block alignments between the two genomes by creating pair-wise alignments between the two genomes. Further, a global alignment is generated for the entire region the collinear block covers.

Second, an in-house gene mapping program then transfers genes from reference onto the target genome within the specific syntenic blocks. We use genewise to further refine a gene model at each locus.

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Selection of Consensus Gene Models

Broad's automated gene calling process uses a rule-based selection process to evaluate the evidence and build consensus gene models.

Ab initio predictions, blast and EST alignments, reference gene models, and manual and automated EST-gene models are clustered into potential gene loci. We select the most likely non-conflicting gene models based on the best evidence available at each locus. Our method uses heuristics such as splice agreement with ESTs and relative overlap with the BLAST hits to choose the prediction most in accord with the evidence. It does not have an internal model of gene structure and thus runs on a wide variety of eukaryotic and prokaryotic organisms without training.

Our gene selection process offers several useful options to exclude calling genes at certain loci. For example, we can exclude genes in regions with tRNA, rRNA and known repeat elements using a conservative overlap criterion.

Gene models with problems are tagged appropriately with curation flags and notes in the gene report to indicate potential problems. Despite all the progress in the field of gene finding, accurate gene finding on draft genomes is still a challenge. We make an effort to track easily identifiable problematic gene models and tag them with appropriate curation flags to alert the users of the nature of the problems. These tags are also used by manual annotators to specifically target manual editing and fine-tuning of bad gene models.

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Predicting Alternatively Spliced Transcript Models

We do not predict alternatively spliced transcript unless there is manual or full-length EST evidence in support of their existence. Transcript models that differ only with respect to un-spliced 3' and 5' ends with the reference gene models are not considered evidence for alternative splicing. We include only canonically spliced and uniquely aligned ESTs with alternate splice junctions as valid alternatively spliced transcripts.

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Predicting UTR

When an EST alignment uniquely aligns with >= 95% identity and overlaps a gene prediction, and the region of overlap has absolute labeling agreement (e.g., every nucleotide in the region of overlap is exonic in both the prediction and the alignment, or is intronic in both), we consider the prediction and alignment to be compatible. UTR predictions are generated by walking out along these compatible EST alignments from the end(s) of each prediction. A chain of one or more overlapping, compatible EST alignments that begin at the 5' or 3' end forms each UTR extension. When an EST aligns to more than one location on the genome, or touches more than one gene prediction (on either strand) it is ignored.

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Assigning Gene Product Names

We do not assign gene symbols. Instead, our gene naming protocol currently relies on high confidence blast homology and in some cases, community inputs to assign gene product names. We hope to improve the gene naming process in the future based on other functional annotation protocols and tools.

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Reporting Annotation Accuracy

We run in-depth reports on each annotation we produce to get a measure of our annotation accuracy. A host of accuracy statistics is compiled for each prediction that touches an EST; following Guigo's Evaluation of gene structure prediction programs (Genomics, 1996 Jun 15; 34(3):353-67). We calculate specificity, sensitivity, correlation coefficient and simple matching coefficient on the levels of nucleotides, splice sites, introns and exons.

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Gene Numbering

Every annotated gene is given a Locus Number of the form MGG_##### that should be considered the only guaranteed way to identify a gene uniquely. Each locus number is guaranteed to identify a unique gene even over different assemblies. Loci are simply identifiers and are not guaranteed to have any particular order or internal structure.

The mapping of gene accessions from the previous release to the current version is available here.

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Overview of Reference genes

8358 genes (4531 on '+' strand, 3827 on '–')
9217 transcripts (3374 spliced, 5843 unspliced)
14855 exons, 5638 introns

	len	%cov	%gc	%at
genic	5328453	12.78	53.15	46.85
intergenic	36367004	87.22	51.34	48.66
exonic	4811617	11.54	53.77	46.23
intronic	470769	1.13	47.08	52.92
coding	4129984	9.91	54.78	45.22
5′ UTR	318331	0.76	50.25	49.75
3′ UTR	480571	1.15	46.49	53.51
alt. spliced	46067	0.11	50.90	49.10
genomic	41617957	99.81	51.57	48.43
	min	median	mean	n50	max
total length (incl. UTR + introns)	22	565	650	734	3486
coding length	22	453	488	567	2421
exons per transcript	1	1	1.61	2	13
exons per spliced transcript	2	2	2.67	5	13
nt per exon	4	312	361	502	2885
nt per intron	23	87	112	108	978
intergenic nt	1	2118	4353	9506	183972
5′ UTR nt	1	17	67	160	1212
3′ UTR nt	1	50	102	237	2216

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Overview of Query Genes

11043 genes (5580 on '+' strand, 5463 on '–')
11054 transcripts (8800 spliced, 2254 unspliced)
30705 exons, 19651 introns

	len	%cov	%gc	%at
genic	19361282	46.43	55.98	44.02
intergenic	22334175	53.57	47.72	52.28
exonic	16968750	40.70	57.23	42.77
intronic	2390421	5.73	47.12	52.88
coding	15977408	38.32	57.92	42.08
5′ UTR	399584	0.96	47.89	52.11
3′ UTR	602820	1.45	45.02	54.98
alt. spliced	2111	0.01	51.83	48.17
genomic	41617957	99.81	51.57	48.43
	min	median	mean	n50	max
total length (incl. UTR + introns)	132	1503	1754	2073	19707
coding length	99	1194	1446	1791	19500
exons per transcript	1	2	2.78	3	18
exons per spliced transcript	2	3	3.23	3	18
nt per exon	3	316	553	1056	12935
nt per intron	4	88	122	122	1029
intergenic nt	1	1023	2028	3838	167583
5′ UTR nt	2	136	169	225	1077
3′ UTR nt	2	196	240	323	1465

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Feature Mapping

Searched for mappings from 11043 query features to 8358 reference features.

Reference genes farther than 200 nt from any query gene: 1452
Containing 1507 transcripts: 1330 unspliced (713 with CDS), 0 with non-canonical splices (0 with CDS), 177 with canonical splices (71 with CDS).
These may indicate missed genes.

379 query genes (3.4%) hit 555 reference genes (6.6%) on the opposing strand. Hits to opposing strands are considered misses in the following comparisions.

Strand mismatches such as these may be orientation problems in the reference set, or incorrect predictions in the query set. Unspliced reference transcripts are difficult to orient correctly. 522 reference gene(s) were unspliced. These may easily have been placed on the wrong strand; queries hitting these genes are likely to be oriented correctly. 33 reference gene(s) had at least one canonically-spliced transcript; everything else being equal, their orientation is more likely to be correct than the queries hitting them.

The table below shows how query genes (rows, roman type) map to reference genes (columns, italic type). We cluster genes into overlapping groups on the same strand and record eight types of relationship. To the left of each arrow is the number of query genes in each type of cluster; the number of reference genes is to the right.

		none			reference one			many
	none		-		0	↔	2546	0	↔	0
query	one	6349	↔	0	3659	↔	3659	995	↔	2125
	many	0	↔	0	30	↔	15	10	↔	13
		counts per cluster type
		none			reference one			many
	none		-		0	↔	30.5	0	↔	0
query	one	57.5	↔	0	33.1	↔	43.8	9.0	↔	25.4
	many	0	↔	0	0.3	↔	0.2	0.1	↔	0.2
		percentages per cluster type

one↔one clear, unambiguous mappings from one query gene to one reference gene. none↔one reference genes that do not map to any query gene (misses). none↔many reference genes that overlap other reference genes but do not map to query genes. None↔X mappings may represent underpredictions. one↔none query genes that do not map to any reference gene. many↔none query genes that overlap other query genes but do not map to reference genes. X↔none mappings may represent overpredictions or deficiencies in the reference gene set. one↔many single query genes overlapped by multiple reference genes (merges). These may occur if the reference set contains partial genes. many↔one single reference genes spanning multiple query genes (splits). These are often annotation errors. many↔many complex clusters where multiple query genes map to multiple reference genes.

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Feature Comparisons

Predicted genes that hit reference genes on the wrong strand, or hit reference genes containing only non–canonically-spliced transcripts, are ignored in the following calculations. When scoring genes with multiple transcripts, we select the single best transcript↔transcript comparison. For clusters in the one↔many and many↔many categories above, each query is compared to as many references as possible. The best non-overlapping comparisons are used to score these loci.

	#comp	TP	TN	FP	FN
nucleotides	4054284	3554853	457456	4293	37682
splice sites	14311	13335	-	551	425
	sn	sp	smc	acp	ac	cc
nucleotides	0.9895	0.9988	0.9896	0.9757	0.9515	0.9510
splice sites	0.9691	0.9603	0.9318	-	-	-

	#comp	TP	TN	FP	FN
introns	4583	4154	-	113	13
exons	8725	7906	-	4	41
	sn	sp	sn_sp	wrong	miss
introns	0.9611	0.9064	0.9338	0.0030	0.0247
exons	0.8466	0.9061	0.8764	0.0044	0.0005

#comp the number of subfeatures compared. TP true positives: nucleotides predicted as exonic in both query and reference; or, splice junctions with exact agreement, in position and type (donor:donor, acceptor:acceptor), between query and reference; or, introns or exons with both splice sites in exact agreement with the reference. TN true negatives: nucleotides predicted as intronic in both query and reference; not defined for splice sites, introns or exons. FP false positives (overpredictions): nucleotides marked as exonic in the query but intronic in the reference; or, splice junctions identified only in the query*; or query introns/exons that do not overlap a reference intron/exon. FN false negatives (underpredictions): nucleotides marked as intronic in the query but exonic in the reference; or, splice junctions identified only in the reference*; or reference introns/exons that do not overlap a query intron/exon. sn sensitivity, TP ÷ (TP+FN). sp specificity, TP ÷ (TP+FP). smc simple matching coefficient, (TP+TN) ÷ (TP+FN+FP+TN). acp average conditional probability. ac approximate correlation. cc correlation coefficient. These last three terms are described in detail in the citation below, and are not well defined for splices, introns or exons. *In the very rare cases where the query predicts a donor site exactly where the reference has an acceptor, or vice versa, we count it as both FP and FN. Terminology is from Burset M and Guigo R, "Evaluation of gene structure prediction programs." Genomics, 1996 Jun 15;34(3):353-67.

The table below briefly summarizes the relative coverage of overlapping query and reference genes. The first column shows the amount of sequence (in nt and percent) in each gene set that is overlapped by sequence in the other gene set. The next three columns show the overhanging sequence from transcripts that overlap but are offset. Overhangs are categorized as 5′ (one transcript begins upstream of the other), 3′ (one transcript extends downstream past the other), and mixed (indeterminate). Finally, the last column, "complete miss", counts the number of nt in queries that did not hit a reference at all, and vice versa.

A high degree of overlap can indicate a good match between the query and reference sets. A high "missed" score may simply indicate a small reference set.

	overlap			overhang, 5′			overhang, 3′			overhang, mixed			complete miss
query	4054284 nt	/	20.9%	2364563 nt	/	12.2%	1214752 nt	/	6.3%	1066835 nt	/	5.5%	10661781 nt	/	55.1%
reference	4054284 nt	/	76.3%	30403 nt	/	0.6%	33953 nt	/	0.6%	26294 nt	/	0.5%	1166359 nt	/	22.0%

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Splice Analysis

13335 splice agreements, 976 disagreements.
6624 ignored: 216 due to EST misalignment, 6408 due to partial initial/terminal exon coverage.
perfect exon:exon/intron:intron matches: 4709/4154
0 query transcripts contained noncanonical splices.

transcripts with no splice problems	4307	91.8%
... with complete reference coverage	901	19.2%
explainable by alternate splicing	162	3.5%
... with spliced reference	113	2.4%
clashes	225	4.8%
... with spliced reference	102	2.2%

Transcripts that have a splice site dis­agree­ment with an over­lap­ping ref­er­ence gene are placed into two cate­gor­ies, de­pend­ing on the se­ver­ity of the clash. If all splice dis­agree­ments could be ex­plain­ed by well-known types of al­ter­nate splic­ing, we call the trans­cript a "pos­sible al­ter­nate splice." If the two trans­cripts cannot be rec­on­ciled in this way, we label the query a "clash." In par­ti­tio­ning splice dis­agree­ments into two cat­e­gor­ies, we are not as­sert­ing that 3.5% of this ge­nome shows al­ter­nate splic­ing. We do this as a form of triage: genes in the "clash" cat­e­go­ry are man­ually in­spect­ed before release.

		in ref.	in query
cassette exons		4	0
retained introns		88	12
early 3′ splices		15	41
late 5′ splices		12	20

cassette exon an exon that falls completely with an intron of a variant transcript. Such exons may represent alternative splice forms but are more likely instances of exonic over- and under-prediction. retained intron an intron that falls within the exon of a variant transcript. These introns may indicate alternative splicing but usually are over- and under-predicted introns. early 3′ splices two introns agree on their 5′ splice site but differ on the 3′ side, relative to the affected intron. In other words, differing 3′ splice sites lie on the leading edge of an exon. late 5′ splices two introns agree on their 3′ splice site but differ on the 5′ side, again relative to the affected intron. Most ter­mi­nol­ogy is from Mat­lin AJ, et. al. Under­stand­ing al­ter­na­tive splic­ing: to­wards a cel­lular code. Nat Rev Mol Cell Biol. 2005 May;6(5):386-98.

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Possible Problems

		multiple sources										5538
			no evidence feature									876
				MG6_FGENESH_3								1067
					MG6_FL_EST_GENES							93
						MG6_GENEID_2						589
							MG6_GENEMARK					1389
								MG6_GENEWISE_1				96
									MG6_MANUAL_1			953
										MG6_SNAP_7		453
short protein, < 50 aa	15+0	4	0	1	0	5	4	0	1	0	← not tallied in problems
shorter protein, < 30 aa	0+0	-	-	-	-	-	-	-	-	-
very short protein, < 10 aa	0+0	-	-	-	-	-	-	-	-	-
initial exon ≤ 6 nt	18+1	4	4	4	0	2	3	0	1	1
internal exon ≤ 6 nt	5+0	1	2	1	0	0	0	0	1	0
terminal exon ≤ 6 nt	12+0	0	6	2	0	2	0	0	2	0
≥ 15 exons	2+0	0	1	0	0	0	0	0	1	0
intron ≥ 1000 nt	1+0	0	0	0	0	0	0	0	1	0
intron ≤ 20 nt	16+1	0	0	0	0	0	0	0	3	14
first codon not Met	1+0	0	0	0	0	0	0	1	0	0	← not tallied in problems
first codon not xTG	0+0	-	-	-	-	-	-	-	-	-
first codon not known START	0+0	-	-	-	-	-	-	-	-	-
last codon not known STOP	1+1	0	0	0	0	0	0	0	2	0
contains in-frame STOP	0+0	-	-	-	-	-	-	-	-	-
coding length not modulo 3	0+1	0	0	0	0	0	0	0	1	0
non-canonical splicing	0+0	-	-	-	-	-	-	-	-	-
has ≥1 tagged BLAST hit	8551+118	4455	860	756	52	505	740	96	764	441	← not tallied in problems
≤1/3 as long as BLAST tags	72+5	20	1	8	7	7	19	1	11	3
≥3× longer than BLAST tags	16+1	0	3	0	0	1	1	0	12	0
contains ≥1 N in exon	57+74	7	27	54	6	4	0	0	26	7
low-quality exonic sequence	0+142	20	23	42	4	5	18	0	25	5	← not tallied in problems
touches gap(s)	7+8	2	0	5	0	1	3	0	4	0
spans contigs	7+8	2	0	5	0	1	3	0	4	0
within 1 kb of contig edge	61+19	28	2	19	0	4	10	2	13	2	← not tallied in problems
any overlap (UTR or CDS)	121+0	67	4	7	4	1	7	2	22	7	← in 60 clusters
CDS overlap only	7+0	0	0	0	1	0	0	0	6	0	← in 3 clusters
CDS overlap > 50 nt	7+0	0	0	0	1	0	0	0	6	0	← in 3 clusters
CDS overlap > 100 nt	7+0	0	0	0	1	0	0	0	6	0	← in 3 clusters
CDS overlap > 200 nt	7+0	0	0	0	1	0	0	0	6	0	← in 3 clusters
has predicted UTR	3509+44	2211	184	223	91	160	233	33	324	94	← not tallied in problems
UTR ≥ CDS length	346+3	245	5	17	25	13	9	2	29	4	← not tallied in problems
UTR is spliced	196+3	120	7	13	7	13	10	2	20	7	← not tallied in problems
one or more problems	319+82	100	47	78	15	17	33	3	77	31

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