Ning Li and colleagues report the whole-genome sequence of the duck, Anas platyrhynchos, a natural host of avian influenza viruses. They examine host response to infection by comparing the lung transcriptomes of ducks that were infected with influenza A viruses. The duck (Anas platyrhynchos) is one of the principal natural hosts of influenza A viruses. We present the duck genome sequence and perform deep transcriptome analyses to investigate immune-related genes. Our data indicate that the duck possesses a contractive immune gene repertoire, as in chicken and zebra finch, and this repertoire has been shaped through lineage-specific duplications. We identify genes that are responsive to influenza A viruses using the lung transcriptomes of control ducks and ones that were infected with either a highly pathogenic (A/duck/Hubei/49/05) or a weakly pathogenic (A/goose/Hubei/65/05) H5N1 virus. Further, we show how the duck's defense mechanisms against influenza infection have been optimized through the diversification of its β-defensin and butyrophilin-like repertoires. These analyses, in combination with the genomic and transcriptomic data, provide a resource for characterizing the interaction between host and influenza viruses.

A synergistic combination of two next-generation sequencing platforms with a detailed comparative BAC physical contig map provided a cost-effective assembly of the genome sequence of the domestic turkey (Meleagris gallopavo). Heterozygosity of the sequenced source genome allowed discovery of more than 600,000 high quality single nucleotide variants. Despite this heterozygosity, the current genome assembly (∼1.1 Gb) includes 917 Mb of sequence assigned to specific turkey chromosomes. Annotation identified nearly 16,000 genes, with 15,093 recognized as protein coding and 611 as non-coding RNA genes. Comparative analysis of the turkey, chicken, and zebra finch genomes, and comparing avian to mammalian species, supports the characteristic stability of avian genomes and identifies genes unique to the avian lineage. Clear differences are seen in number and variety of genes of the avian immune system where expansions and novel genes are less frequent than examples of gene loss. The turkey genome sequence provides resources to further understand the evolution of vertebrate genomes and genetic variation underlying economically important quantitative traits in poultry. This integrated approach may be a model for providing both gene and chromosome level assemblies of other species with agricultural, ecological, and evolutionary interest. In contrast to the compact sequence of viruses and bacteria, determining the complete genome sequence of complex vertebrate genomes can be a daunting task. With the advent of “next-generation” sequencing platforms, it is now possible to rapidly sequence and assemble a vertebrate genome, especially for species for which genomic resources—genetic maps and markers—are currently available. We used a combination of two next-generation sequencing platforms, Roche 454 and Illumina GAII, and unique assembly tools to sequence the genome of the agriculturally important turkey, Meleagris gallopavo. Our draft assembly comprises approximately 1.1 gigabases of which 917 megabytes are assigned to specific chromosomes. Comparisons of the turkey genome sequence with those of the chicken, Gallus gallus, and the zebra finch, Taeniopygia guttata, provide insights into the evolution of the avian lineage. This genome sequence will facilitate discovery of agriculturally important genetic variants.

Macrophages are involved in many aspects of development, host defense, pathology, and homeostasis. Their normal differentiation, proliferation, and survival are controlled by CSF-1 via the activation of the CSF1R. A recently discovered cytokine, IL-34, was shown to bind the same receptor in humans. Chicken is a widely used model organism in developmental biology, but the factors that control avian myelopoiesis have not been identified previously. The CSF-1, IL-34, and CSF1R genes in chicken and zebra finch were identified from respective genomic/cDNA sequence resources. Comparative analysis of the avian CSF1R loci revealed likely orthologs of mammalian macrophage-specific promoters and enhancers, and the CSF1R gene is expressed in the developing chick embryo in a pattern consistent with macrophage-specific expression. Chicken CSF-1 and IL-34 were expressed in HEK293 cells and shown to elicit macrophage growth from chicken BM cells in culture. Comparative sequence and co-evolution analysis across all vertebrates suggests that the two ligands interact with distinct regions of the CSF1R. These studies demonstrate that there are two separate ligands for a functional CSF1R across all vertebrates.

Background: The human transcriptome annotation is regarded as one of the most complete of any eukaryotic species. However, limitations in sequencing technologies have biased the annotation toward multi-exonic protein coding genes. Accurate high-throughput long read transcript sequencing can now provide additional evidence for rare transcripts and genes such as mono-exonic and non-coding genes that were previously either undetectable or impossible to differentiate from sequencing noise. Results: We developed the Transcriptome Annotation by Modular Algorithms (TAMA) software to leverage the power of long read transcript sequencing and address the issues with current data processing pipelines. TAMA achieved high sensitivity and precision for gene and transcript model predictions in both reference guided and unguided approaches in our benchmark tests using simulated Pacific Biosciences (PacBio) and Nanopore sequencing data and real PacBio datasets. By analyzing PacBio Sequel II Iso-Seq sequencing data of the Universal Human Reference RNA (UHRR) using TAMA and other commonly used tools, we found that the convention of using alignment identity to measure error correction performance does not reflect actual gain in accuracy of predicted transcript models. In addition, inter-read error correction can cause major changes to read mapping, resulting in potentially over 6 K erroneous gene model predictions in the Iso-Seq based human genome annotation. Using TAMA’s genome assembly based error correction and gene feature evidence, we predicted 2566 putative novel non-coding genes and 1557 putative novel protein coding gene models. Conclusions: Long read transcript sequencing data has the power to identify novel genes within the highly annotated human genome. The use of parameter tuning and extensive output information of the TAMA software package allows for in depth exploration of eukaryotic transcriptomes. We have found long read data based evidence for thousands of unannotated genes within the human genome. More development in sequencing library preparation and data processing are required for differentiating sequencing noise from real genes in long read RNA sequencing data.