A Guide to the Mammalian Genome

First whole-genome sequence and assembly of the Ecuadorian brown-headed spider monkey (Ateles fusciceps fusciceps), a critically endangered species, using Oxford Nanopore Technologies

The Ecuadorian brown-headed spider monkey (Ateles fusciceps fusciceps) is currently considered one of the most endangered primates in the world and is classified as critically endangered [International union for conservation of nature (IUCN)]. It faces multiple threats, the most significant one being habitat loss due to deforestation in western Ecuador. Genomic tools are keys for the management of endangered species, but this requires a reference genome, which until now was unavailable for A. f. fusciceps. The present study reports the first whole-genome sequence and assembly of A. f. fusciceps generated using Oxford Nanopore long reads. DNA was extracted from a subadult male, and libraries were prepared for sequencing following the Ligation Sequencing Kit SQK-LSK112 workflow. Sequencing was performed using a MinION Mk1C sequencer. The sequencing reads were processed to generate a genome assembly. Two different assemblers were used to obtain draft genomes using raw reads, of which the Flye assembly was found to be superior. The final assembly has a total length of 2.63 Gb and contains 3,861 contigs, with an N50 of 7,560,531 bp. The assembly was analyzed for annotation completeness based on primate ortholog prediction using a high-resolution database, and was found to be 84.3% complete, with a low number of duplicated genes indicating a precise assembly. The annotation of the assembly predicted 31,417 protein-coding genes, comparable with other mammal assemblies. A reference genome for this critically endangered species will allow researchers to gain insight into the genetics of its populations and thus aid conservation and management efforts of this vulnerable species.

The genome sequence and insights into the immunogenetics of the bananaquit (Passeriformes: Coereba flaveola)

Avian genomics, especially of non-model species, is in its infancy relative to mammalian genomics. Here, we describe the sequencing, assembly, and annotation of a new avian genome, that of the bananaquit Coereba flaveola (Passeriformes: Thraupidae). We produced ∼30-fold coverage of the genome with an assembly size of ca. 1.2 Gb, including approximately 16,500 annotated genes. Passerine birds, such as the bananaquit, are commonly infected by avian malarial parasites (Haemosporida), which presumably drive adaptive evolution of immunogenetic loci within the host genome. In the context of our research on the distribution of avian Haemosporida, we specifically characterized immune loci, including toll-like receptor (TLR) and major histocompatibility complex (MHC) genes. Additionally, we identified novel molecular markers in the form of single nucleotide polymorphisms (SNPs), both genome-wide and within identified immune loci. We discovered nine TLR genes and four MHC genes and identified five other TLR- or MHC- associated genes. Genome-wide, over 6 million high-quality SNPs were annotated, including 568 within TLR genes and 102 in MHC genes. This newly described genome and immune characterization expands the knowledge base for avian genomics and phylogenetics and allows for immune genotyping in the bananaquit, providing tools for the investigation of host-parasite coevolution.

Paradigm shifts in genomics through the FANTOM projects

Big leaps in science happen when scientists from different backgrounds interact. In the past 15 years, the FANTOM Consortium has brought together scientists from different fields to analyze and interpret genomic data produced with novel technologies, including mouse full-length cDNAs and, more recently, expression profiling at single-nucleotide resolution by cap-analysis gene expression. The FANTOM Consortium has provided the most comprehensive mouse cDNA collection for functional studies and extensive maps of the human and mouse transcriptome comprising promoters, enhancers, as well as the network of their regulatory interactions. More importantly, serendipitous observations of the FANTOM dataset led us to realize that the mammalian genome is pervasively transcribed, even from retrotransposon elements, which were previously considered junk DNA. The majority of products from the mammalian genome are long non-coding RNAs (lncRNAs), including sense-antisense, intergenic, and enhancer RNAs. While the biological function has been elucidated for some lncRNAs, more than 98 % of them remain without a known function. We argue that large-scale studies are urgently needed to address the functional role of lncRNAs.

CPC: assess the protein-coding potential of transcripts using sequence features and support vector machine

Recent transcriptome studies have revealed that a large number of transcripts in mammals and other organisms do not encode proteins but function as noncoding RNAs (ncRNAs) instead. As millions of transcripts are generated by large-scale cDNA and EST sequencing projects every year, there is a need for automatic methods to distinguish protein-coding RNAs from noncoding RNAs accurately and quickly. We developed a support vector machine-based classifier, named Coding Potential Calculator (CPC), to assess the protein-coding potential of a transcript based on six biologically meaningful sequence features. Tenfold cross-validation on the training dataset and further testing on several large datasets showed that CPC can discriminate coding from noncoding transcripts with high accuracy. Furthermore, CPC also runs an order-of-magnitude faster than a previous state-of-the-art tool and has higher accuracy. We developed a user-friendly web-based interface of CPC at http://cpc.cbi.pku.edu.cn. In addition to predicting the coding potential of the input transcripts, the CPC web server also graphically displays detailed sequence features and additional annotations of the transcript that may facilitate users’ further investigation.

Abundant novel transcriptional units and unconventional gene pairs on human chromosome 22

Novel transcriptional units (TUs) are EST-supported transcribed features not corresponding to known genes. Unconventional gene pairs (UGPs) are pairs of genes and/or TUs sharing exon-to-exon cis-antisense overlaps or putative bidirectional promoters. Computational TU and UGP discovery followed by manual curation was performed in the entire published 34.9-Mb human chromosome 22 euchromatic sequence. Novel TUs (n = 517) were as abundant as known genes (n = 492) and typically did not have nonprimate DNA and protein homologies. One hundred seventy-one (33%) of TUs, but only 13 (3%) of genes, both lacked nonprimate conservation and localized to gaps in the human-mouse BLASTZ alignment. Novel TUs were richer in exonic primate-specific interspersed repetitive elements (P = 0.001) and were more likely to rely on splice junctions provided by them, than were known genes: 19% of spliced TUs, versus 5% of spliced genes, had a splice site within a primate-specific repeat. Hence, novel TUs and known genes may represent different portions of the transcriptome. Two hundred nine (21%) of chromosome 22 transcripts participated in 77 cis-antisense and 42 promoter-sharing UGPs. Transcripts involved simultaneously in both UGP types were more common than was expected (P = 0.01). UGPs were nonrandomly distributed along the sequence: 89 (75%) clustered in distinct regions, the sum of which equaled 4.4 Mb (<13% of the chromosome). Eighty (67%) of the UGPs possessed significant locus structure differences between primates and rodents. Since some TUs may be functional noncoding transcripts and since the cis-regulatory potential of UGPs is well recognized, TUs and UGPs specific to the primate lineage may contribute to the genomic basis for primate-specific phenotypes.

The transcriptional landscape of the mammalian genome

This study describes comprehensive polling of transcription start and termination sites and analysis of previously unidentified full-length complementary DNAs derived from the mouse genome. We identify the 5' and 3' boundaries of 181,047 transcripts with extensive variation in transcripts arising from alternative promoter usage, splicing, and polyadenylation. There are 16,247 new mouse protein-coding transcripts, including 5154 encoding previously unidentified proteins. Genomic mapping of the transcriptome reveals transcriptional forests, with overlapping transcription on both strands, separated by deserts in which few transcripts are observed. The data provide a comprehensive platform for the comparative analysis of mammalian transcriptional regulation in differentiation and development.

Construction of representative transcript and protein sets of human, mouse, and rat as a platform for their transcriptome and proteome analysis

The number of mammalian transcripts identified by full-length cDNA projects and genome sequencing projects is increasing remarkably. Clustering them into a strictly nonredundant and comprehensive set provides a platform for functional analysis of the transcriptome and proteome, but the quality of the clustering and predictive usefulness have previously required manual curation to identify truncated transcripts and inappropriate clustering of closely related sequences. A Representative Transcript and Protein Sets (RTPS) pipeline was previously designed to identify the nonredundant and comprehensive set of mouse transcripts based on clustering of a large mouse full-length cDNA set (FANTOM2). Here we propose an alternative method that is more robust, requires less manual curation, and is applicable to other organisms in addition to mouse. RTPSs of human, mouse, and rat have been produced by this method and used for validation. Their comprehensiveness and quality are discussed by comparison with other clustering approaches. The RTPSs are available at .

Vertebrate gene predictions and the problem of large genes

To find unknown protein-coding genes, annotation pipelines use a combination of ab initio gene prediction and similarity to experimentally confirmed genes or proteins. Here, we show that although the ab initio predictions have an intrinsically high false-positive rate, they also have a consistently low false-negative rate. The incorporation of similarity information is meant to reduce the false-positive rate, but in doing so it increases the false-negative rate. The crucial variable is gene size (including introns)--genes of the most extreme sizes, especially very large genes, are most likely to be incorrectly predicted.