The Avian RNAseq Consortium: a community effort to annotate the chicken genome

Publication of the chicken genome sequence in 2004 (International Chicken Genome Sequencing Consortium 2004) highlighted the beginning of a revolution in avian genomics. Progression of DNA sequencing technologies and data handling capabilities has also meant that genome sequencing and assembly is now a relatively simple, fast and inexpensive procedure. The success seen with the chicken genome was soon followed by the completion of the zebra finch genome (Warren et al., 2010), an important model for neurobiology (Clayton et al., 2009), again based on Sanger sequencing. In recent years the rapid advances in Next Generation Sequencing (NGS) technologies, hardware and software have meant that many more genomes can now be sequenced faster and cheaper than ever before (Metzker, 2010). The first avian genome to be sequenced by NGS methods was the turkey (Dalloul et al., 2010), which was also integrated with genetic and physical maps thus providing an assembly of high quality, even at the chromosome level. Recently, NGS has been used to sequence the genomes of a further 42 avian species, as part of the G10K initiative (Genome 10K Community of Scientists, 2009). In addition there have also been 15 other genome assemblies recently published, each with a focus on a unique aspect of avian biology, including the Japanese Quail (domestication; Kawahara-Miki et al., 2013), Puerto Rican parrot (speciation; Oleksyk et al., 2012), Scarlet Macaw (speech, intelligence and longevity; Seabury et al., 2013), Medium and Large Ground Finches (speciation; Parker et al., 2012; Rands et al., 2013), Collared and Pied flycatchers (speciation; Ellegren et al., 2012), Peregrine and Saker Falcons (predatory lifestyle; Zhan et al., 2013), rock pigeon (domestication; Shapiro et al., 2013), the Ground tit (adaptation to high altitude; Cai et al., 2013) and the Northern Bobwhite (population history; Halley et al., 2014). Through November 2014 there are currently 57 avian genome sequences completed, either published or in press (Table 1). A new project, B10K (web.bioinfodata.org/B10K), proposes sequencing all avian genomes; this would include all 40 orders, 231 families, 2,268 genera and 10,476 species of birds. The chicken genome remains the best described genome and is used as a reference upon which the annotations of other assemblies are based. Assembly and annotation of the genome continues to improve. However, gaps and unaligned regions remain (particularly for some of the smallest micro-chromosomes), which can cause practical problems in the analysis and annotation of important loci, especially for those representing gene families. Other approaches, such as long reads generated by Pacific Biosciences (PacBio) sequencing, chromosome sorting and optical maps are being used to resolve these assembly issues (Warren and Burt, personal communications). Specific genome features also require further study; for example, non-coding RNAs, annotation of rare transcripts, confirmation of alternatively spliced transcripts, mapping of transcription start sites and identification of conserved regions. One method by which some of these goals can be achieved is through analysis of transcriptomic sequence data, or ‘RNAseq’ data.

Table 1.

Avian species with sequenced genomes

BIRD_Abbreviation	BIRD_Latin_Name	BIRD_Common_Name	BIRD_Abbreviation	BIRD_Latin_Name	BIRD_Common_Name
ACACH	Acanthisitta chloris	Rifleman	GALGA	Gallus gallus	Chicken
AMAVI	Amazona vittata	Puerto Rican parrot	GAVST	Gavia stellata	Red-throated loon
ANAPL	Anas platyrhynchos domestica	Pekin duck	GEOFO	Geospiza fortis	Medium groundfinch
ANOCA	Anolis carolinensis	Carolina anole	GEOMA	Geospiza magnirostris	Large ground finch
APAVI	Apaloderma vittatum	Bar-tailed trogon	HALAL	Haliaeetus albicilla	White-tailed eagle
APTFO	Aptenodytes forsteri	Emperor penguin	LEPDI	Leptosomus discolor	Cuckoo roller
ARAMA	Ara macao	Scarlet macaw	MANVI	Manacus vitellinus	Golden-collared manakin
BALRE	Balearica regulorum gibbericeps	Grey crowned crane	MELGA	Meleagris gallopavo	Wild turkey
BUCRH	Buceros rhinoceros silvestris	Rhinoceros hornbill	MELUN	Melopsittacus undulatus	Budgerigar
CALAN	Calypte anna	Anna's hummingbird	MERNU	Merops nubicus	Northern Carmine bee-eater
CAPCA	Caprimugus Carolinensis	Chuck-will's widow	MESUN	Mesitornis unicolor	Brown mesite
CARCR	Cariama cristata	Red-legged seriema	NESNO	Nestor notabilis	Kea
CATAU	Cathartes aura	Turkey vulture	NIPNI	Nipponia nippon	Crested ibis
CHAPE	Chaetura pelagica	Chimney swift	OPHHO	Ophisthocomus hoazin	Hoatzin
CHAVO	Charadrius vociferus	Killdeer	PELCR	Pelecanus crispus	Dalmatian pelican
CHLUN	Chlamydotis undulata	Houbara bustard	PHACA	Phalacrocorax carbo	Great cormorant
COLLI	Columba livia	Rock pigeon	PHALE	Phaethon lepturus	White-tailed tropicbird
COLST	Colius striatus	Speckled mousebird	PHORU	Phoenicopterus ruber	American flamingo
COLVI	Colinus virginianus	Northern Bobwhite	PICPU	Picoides pubescens	Downy woodpecker
CORBR	Corvus brachyrhynchos	American crow	PODCR	Podiceps cristatus	Great crested grebe
COTJA	Coturnix japonica	Japanese quail	PSEHU	Pseudopodoces humilis	Ground tit
CUCCA	Cuculus canorus	Common cuckoo	PTEGU	Pterocles guturalis	Yellow-throated sandgrouse
EGRGA	Egretta garzetta	Little egret	PYGAD	Pygoscelis adeliae	Adelie penguin
EURHE	Eurypyga helias	Sunbittern	STRCA	Struthio camelus	Ostrich
FALCH	Falco cherrug	Saker falcon	TAEGU	Taeniopygia guttata	Zebra finch
FALPE	Falco peregrinus	Peregrine falcon	TAUER	Tauraco erythrolophus	Red-crested turaco
FICAL	Ficedula albicollis	Collared flycatcher	TINMA	Tinamus major	Great tinamou
FICHY	Ficedula hypoleuca	Pied flycatcher	TYTAL	Tyto alba	Barn owl
FULGL	Fulmarus glacialis	Northern fulmar

With a view to addressing some of these issues, we decided to collect as much RNAseq data from the chicken research community as possible. This was the beginning of what we have termed ‘The Avian RNAseq Consortium’. Since the start of the Consortium at the end of 2011, it now includes 48 people from 27 different institutions (Figure 1) who have contributed to the effort to create a detailed annotation of the chicken genome by either providing RNAseq data or by helping to analyse the combined data.

Figure 1.

Worldwide locations of current RNAseq consortium members.

We currently have 21 different data sets (representing more than 1.5 Tb of data) with more data being added (Table 2 and Figure 2). These data represent transcriptome sequences from many different chicken tissues and from many different experimental conditions, including several infection/disease cases. These data were submitted to public archives, collected at The Roslin Institute and then passed on to the Ensembl team who used the information to help annotate the latest chicken genome assembly, Galgal4 as part of Ensembl release 71 (April 2013) (Table 3). This new annotation includes 15,495 protein coding genes, 1,049 micro RNAs, 456 non-codingRNAs and 42 pseudo genes. This gene_build is primarily concerned with coding genes, but there are many more non-coding genes which remain un-annotated. Consortium members have analysed the RNAseq data for long non-coding RNAs (lncRNAs) [manuscript in preparation], snoRNAs (Gardner et al., 2014) and other features of interest. Around 14,000 potential long non-coding RNA genes have thus far been identified from the RNAseq data. Ensembl release 71 marked a significant update in the annotation of the chicken genome with gene models based on experimental data. Table 4 shows how this gene_build was the first to use the Galgal4 assembly and, through the use of RNAseq data, was able to help remove assembly errors and reduce the number of predicted gene transcripts by identifying incorrectly predicted genes from previous builds and improving identification of short ncRNAs. The significance of this community effort is indicated by the fact that the current Ensembl 77 gene set has not changed since Ensembl release 71, with only difference being reflected in the total number of base pairs. This is due to the correction of one particular scaffold on the Z chromosome (which was reflected in Ensembl release 74).

Table 2.

Details of RNAseq data sets

Data set	Description of data	Reads (bp)	Sequencing*
1. Antin	Whole embryo	35	Illumina SE
2. Blackshear	LPS stimulated macrophages v control CEFs	51	Illumina PE
3. Burgess/McCarthy	miRNA from various RJF tissues (adrenal gland, adipose, cerebellum, cerebrum, testis, ovary, heart, hypothalamus, kidney, liver, lung, breast muscle, sciatic nerve, proventriculus, spleen)	50	Illumina SE
4. Burt/Smith	Spleen: Infectious Bursal Disease Virus infected v control	36	Illumina SE
5.	Lung and ileum: Avian influenza infected v control (high path H5N1 and low path H5N2 infections)	36	Illumina SE
6.	Lung short read data	25	Illumina SE
7. de Koning/Dunn/McCormack	Bone from 70wk old Leghorns	100	Illumina PE
8. Frésard/Pitel	Brain from epileptic v. non-epileptic birds	380–400	Roche 454
9.	Pooled whole embryos (stage HH26)	100	Illumina PE
10. Froman/Rhoads	Testes: roosters with high mobility sperm v low mobility sperm	35	Illumina SE
11. Garceau/Hume	Embryo, DF1 cell line and bone marrow derived macrophages	100	Illumina PE
12. Hanotte/Kemp/Noyes/Ommeh	Newcastle Disease Virus infection v control (trachea and lung epithelial cells)	50	SOLiD SE
13. Häsler/Oler/Muljo/Neuberger	DT40 cells	60	Illumina PE
14. Kaiser	Bone marrow derived dendritic cells from 6 weeks old birds (Control, DCs +LPS) Bone marrow derived macrophages from 6 weeks old birds (Control, BMDMs +LPS) Heterophils isolated from blood of day-old chicks (Control, DCs +LPS)	100	Illumina PE
15. Lagarrigue/Roux	Abdominal adipose tissue and liver tissue from 14wk old broilers	100	Illumina PE
16. Lamont	Livers of eight individual, 28-day-old broiler males - 4 control; 4 heat-stressed	100	Illumina SE
17. Munsterberg/Pais	Somites injected with anti-mir206 v. non-injected	50	Illumina PE
18. Schmidt	Tissues from heat stressed and control birds (liver, brain, spleen, thymus, bursa, kidney, ileum, jejunum, duodenum, ovary, heart, breast, monocyte	42–50	Illumina SE
19. Schwartz/Ulitsky	Whole embryo stages - HH4/5; HH11; HH14/15; HH21/22; HH25/26; HH32; HH36 - Stranded	80/100	Illumina PE
20. Skinner	Chicken embryo fibroblasts	100	Illumina PE
21. Wang/Zhou	Lung from Fayoumi and Leghorn birds - control and H5N3 infected	75	Illumina SE

^*- SE: single end; PE: paired end

Figure 2.

A comparison of the different relative amounts of RNAseq data from each tissue. Tissues from different data providers are shown separately as they have all been subject to different treatments/stimuli. Numbered data sets are as referred to in Table 2.

Table 3.

Ensembl 71 annotation statistics

Genes	Description	Biotype
15,495	Ensembl	protein coding
42	Ensembl	pseudogene
2	mt_genbank_import	Mt_rRNA
22	mt_genbank_import	Mt_tRNA
13	mt_genbank_import	protein coding
1049	ncRNA	miRNA
150	ncRNA	misc_RNA
29	ncRNA	rRNA
227	ncRNA	snoRNA
79	ncRNA	snRNA
17,108

Table 4.

Comparison of Ensembl gene_builds:

	Ensembl 70	Ensembl71	Ensembl 77
Assembly	WashUC2, May 2006	Galgal4, Nov 2011	Galgal4, Nov 2011
Base pairs	1,050,947,331	1,072,544,086	1,072,544,763
Coding genes	16,736	15,508	15,508
Short non-coding genes	1,102	1,558	1,558
Pseudogenes	96	42	42
Gene transcripts	23,392	17,954	17,954

The availability of these data will allow for the further development of a chicken expression atlas by providing the ability to analyse transcript levels across tissues (http://geneatlas.arl.arizona.edu/). It will also enable development of exon capture technology for the chicken and has already proved of great use in helping annotate the other avian genomes which have now been sequenced. On-going collection of RNAseq data will remain a valuable resource as genomic analysis of avian species continues to expand.

Methods

Ensembl gene_build

The chicken gene_build from Ensembl release 71 was done using standard Ensembl annotation procedures and pipelines, mostly focussed on protein coding sequences. Briefly, vertebrate UniProtKB proteins were downloaded and aligned to the Galgal4 (GCA_000002315.2) assembly with Genewise (http://www.ebi.ac.uk/Tools/psa/genewise/) in order to annotate protein coding models. UniProt assigns protein existence (PE) levels to each of their protein sequences. The PE level indicates the type of evidence that supports the existence of a protein sequence, and can range from PE 1 (‘Experimental evidence at protein level’) to PE 5 (‘Protein uncertain’). Only PE 1 and PE 2 proteins from UniProtKB were used for the Genewise step. RNAseq models were annotated using the Ensembl RNAseq pipeline and models from both the Genewise and the RNAseq pipelines were used as input for the final protein-coding gene set. Chicken cDNAs and also RNAseq models were also used to add UTRs in the 5’ and 3’ regions. Some missing gene models were recovered by aligning chicken, zebra finch and turkey translations from Ensembl release 65 (December 2011) to the new chicken genome assembly.

RNAseq Gene Models

Raw reads were aligned to the genome using BWA (Li & Durbin, 2009) to identify regions of the genome that are actively transcribed. The results from all tissues were used to create one set of alignment blocks roughly corresponding to exons. Read pairing information was used to group exons into approximate transcript structures called proto-transcripts. Next, partially mapped reads from both the merged (combined data from all tissue samples) and individual tissues were re-aligned to the proto-transcripts using Exonerate (Slater & Birney, 2005), to create a merged and tissue-specific sets of spliced alignments. For each gene, merged and tissue-specific transcript isoforms were computed from all observed exon-intron combinations, and only the best supported isoform was reported.

Annotation of Non-Coding RNAs

The following non-coding RNA gene types were annotated - rRNA: ribosomal RNA; snRNA: small nuclear RNA; snoRNA: small nucleolar RNA; miRNA: microRNA precursors; misc_RNA: miscellaneous other RNA. Most ncRNA genes in Ensembl are annotated by first aligning genomic sequence against RFAM (Burge et al., 2013), using BLASTN (parameters W=12 B=10000 V=10000 -hspmax 0 -gspmax 0 -kap -cpus=1), to identify likely ncRNA loci. The BLAST (Altschul et al., 1990) hits are clustered, filtered for hits above 70% coverage, and used to seed an Infernal (Nawrocki & Eddy, 2013) search with the corresponding RFAM covariance model, to measure the probability that these targets can fold into the structures required. Infernal’s cmsearch is used to build ncRNA models. MiRNAs are predicted by BLASTN (default parameters) of genomic sequence slices against miRBase (Kozomara & Griffiths-Jones, 2014) sequences. The BLAST hits are clustered, filtered to select the alignment with the lowest p-value when more than one sequence aligns at the same genomic position, and the aligned genomic sequence is checked for possible secondary structure using RNAFold (Hofacker et al., 1994). If evidence is found that the genomic sequence could form a stable hairpin structure, the locus is used to create a miRNA gene model. Transfer RNAs (tRNAs) were annotated as part of the raw compute process using tRNAscan-SE with default parameters (Schattner et al., 2005). All results for tRNAscan-SE are available through Ensembl; the results are not included in the Ensembl gene set because they are not annotated using the standard evidence-based approach (ie. by aligning biological sequences to the genome) that is used to annotate other Ensembl gene models.

Summary

The availability of this collection of chicken RNAseq data within the consortium has allowed:

• Annotation of 17,108 chicken genes, 15,495 of which are protein-coding (Ensembl 71)

• Identification of around 14,000 putative lncRNA genes (with >23,000 transcripts suggested)

• Annotation of miRNAs, snoRNAs, and other ncRNAs

• Future generation of an expression atlas which will allow comparisons of expression over many tissues

• An improved avian reference for comparative analyses with 48 other avian genomes (Zhang et al., 2014)

Future directions

The next stage in progressing annotation of the avian genomes will concentrate on the analysis of data generated by PacBio sequencing, in conjunction with stranded RNAseq data from a wide variety of tissues. PacBio technology allows for very long read lengths, producing reads with average lengths of 4,200 to 8,500 bp, with the longest reads over 30,000 base pairs. This enables sequencing of full-length transcripts. Extremely high accuracy means that de novo assembly of genomes and detection of variants with greater than 99.999% accuracy is possible. Individual molecules can also be sequenced at 99% reliability. The high sensitivity of the method also means that minor variants can be detected even when they have a frequency of less than 0.1% [http://www.pacificbiosciences.com/products/smrt-technology/smrt-sequencing-advantage/]. We currently have brain transcriptomic PacBio data generated from a female Brown Leghorn J-line chicken (Blyth and Sang 1960). This will be analyzed alongside stranded RNAseq data that has been generated from 21 different tissues. The advantage of using strand-specific sequence information is that it provides an insight into antisense transcripts and their potential role in regulation and strand information of non-coding RNAs as well as aiding in accurately quantifying overlapping transcripts. It is particularly useful for finding unannotated genes and ncRNAs. This strategy should allow us to obtain full-length transcript sequences, identify novel transcripts and low-level transcripts, map transcription start and stop sites and confirm further ncRNAs.

Avian RNAseq Consortium Members

Jacqueline Smith, Ian Dunn, Valerie Garceau, David Hume, Pete Kaiser, Richard Kuo, Heather McCormack, Dave Burt (Roslin Institute); Amanda Cooksey, Fiona McCarthy, Parker B. Antin, Shane Burgess (University of Arizona); Andrea Münsterberg, Helio Pais (University of East Anglia); Andrew Oler (NIH National Institute of Allergy and Infectious Diseases); Steve Searle (Wellcome Trust Sanger Institute); Paul Flicek, Bronwen L. Aken, Rishi Nag (European Molecular Biology Laboratory, European Bioinformatics Institute and Wellcome Trust Sanger Institute); Carl Schmidt (University of Delaware); Christophe Klopp (INRA Toulouse); Pablo Prieta Barja, Ionas Erb, Darek Kedra, Cedric Notredame (CRG, Barcelona); David Froman (Oregon State University); Dirk-Jan de Koning (Swedish University of Agricultural Sciences, Uppsala); Douglas Rhoads (University of Arkansas); Igor Ulitsky (Weizmann Institute of Science, Rehovot); Julien Häsler, Michael Neuberger (in memoriam) (MRC, Cambridge); Laure Frésard, Frédérique Pitel (INRA, Auzville); Mario Fasold, Peter Stadler (University of Leipzig); Matt Schwartz (Harvard Medical School); Michael Skinner (Imperial College London); Olivier Hanotte (University of Nottingham); Perry Blackshear (NIEHS, North Carolina); Sandrine Lagarrigue, Pierre-François Roux (INRA Agrocampus Ouest); Thomas Derrien (University of Rennes); Sheila Ommeh (Jomo-Kenyatta University of Agriculture and Technology, Kenya); Stefan Muljo (NIH NIAID, Bethesda); Steve Kemp, Harry Noyes (University of Liverpool); Susan Lamont (Iowa State University); Ying Wang, Huaijun Zhou (UC Davis).