黄唇鱼(Bahaba flavolabiata)的全基因组测序和基因组特征研究

黄唇鱼(Bahaba flavolabiata)为国家二级重点保护野生动物、IUCN(世界自然保护联盟)红色名录的极度濒危物种(CR)。基于其样本数量极其有限,全基因组研究可以提供大量与重要性状相关的功能基因和分子标记,从而揭示其重要生命现象的遗传机制。采用二代测序技术于2018年5月完成了黄唇鱼基因组精细图的测序,分析结果表明,测序得到约202 Gb的高质量数据,总测序深度约为317×;组装得到的基因组大小为637.43 Mb,Contig N50约为88 Kb,Scaffold N50约为4.65 Mb;重复序列约142.72 Mb,占比22.39%,预测得到23743个基因、920个t RNA、85个rRNA、176个假基因;98.46%的基因可以注释到NR、GO等数据库中;有67个基因家族是黄唇鱼所特有的。本研究从单碱基错误率、核心基因完整性及二代Reads比对分析3个方面对黄唇鱼基因组精细图的组装结果进行了评估,结果显示所组装的基因区的完整性较好。黄唇鱼基因组序列图谱的绘制完成,对于黄唇鱼自然资源的保护和种质资源挖掘具有极其重要的科学意义。     

Whole-Genome Characterization of Human and Simian Immunodeficiency Virus Intrahost Diversity by Ultradeep Pyrosequencing

Rapid evolution and high intrahost sequence diversity are hallmarks of human and simian immunodeficiency virus (HIV/SIV) infection. Minor viral variants have important implications for drug resistance, receptor tropism, and immune evasion. Here, we used ultradeep pyrosequencing to sequence complete HIV/SIV genomes, detecting variants present at a frequency as low as 1%. This approach provides a more complete characterization of the viral population than is possible with conventional methods, revealing low-level drug resistance and detecting previously hidden changes in the viral population. While this work applies pyrosequencing to immunodeficiency viruses, this approach could be applied to virtually any viral pathogen.The viral population within each human immunodeficiency virus (HIV)-infected individual is highly diverse and constantly evolving (2, 3). However, our understanding of the viral population is based largely on the consensus sequence of the dominant circulating virus because the full diversity of the viral population is extremely difficult to characterize. One recent study showed that despite viral fitness recovery in vitro, recovery was not correlated with changes observed in the consensus sequence of HIV. Instead, increased fitness correlated with general viral heterogeneity (5). This finding suggests that by limiting our studies to consensus sequences, we are missing many aspects of viral evolution that influence fitness, drug resistance, and immune evasion, among other characteristics. Studies that examined minor viral variants have provided new insights into HIV transmission and pathogenesis, with direct implications for HIV treatment (7, 13). Unfortunately, traditional techniques to identify rare variants, such as molecular cloning, single-genome amplification, or quantitative real-time (qRT)-PCR, are either labor intensive or restricted to the detection of single variants, limiting their widespread use (8, 11, 12, 14).New second-generation technologies have radically altered DNA sequencing. Recent work by our group and others has employed pyrosequencing for targeted ultradeep sequencing of short regions of the viral genome, including CD8⁺ T-lymphocyte epitopes and regions of known drug resistance mutations, demonstrating a practical method to identify extremely low-frequency viral variants (4, 15). While sequencing short regions is appropriate in certain circumstances, the region of interest must be identified in advance, and the effect of mutations in that region on the remaining genome is ignored. Studying the heterogeneity of HIV across the entire genome may provide insights into interactions between minor variants, improve our understanding of HIV evolution, and ultimately provide insights into disease pathogenesis.In this study, we combined pyrosequencing with a transposon-based fragmentation method to allow powerful ultradeep sequencing of the full-length HIV and simian immunodeficiency virus (SIV) genomes, demonstrating a new and highly practical approach to study the complexity of the viral population within a host and identify minor variants on a genome-wide scale. While this study applied pyrosequencing to immunodeficiency viruses, this approach could be applied to any viral pathogen.     

Complete Genome Sequencing of Four Geographically Diverse Strains of Batai Virus

Batai virus (BATV) is a widely distributed but poorly studied member of the Orthobunyavirus genus in the family Bunyaviridae and is of particular interest as a known participant in natural reassortment events. Both research and surveillance efforts on this and other related viruses have been hampered by the lack of available full-length sequence data covering all three genomic segments. Here, we report the complete genome sequence of four BATV strains (MM2222, Chittoor/IG-20217, UgMP-6830, and MS50) isolated from various geographical locations. Based on these data, we have determined that strain MS50 is in fact unrelated to BATV and likely represents as a novel genotype in the genus Orthobunyavirus.     

Identification of EMS-Induced Mutations in Drosophila melanogaster by Whole-Genome Sequencing

Next-generation methods for rapid whole-genome sequencing enable the identification of single-base-pair mutations in Drosophila by comparing a chromosome bearing a new mutation to the unmutagenized sequence. To validate this approach, we sought to identify the molecular lesion responsible for a recessive EMS-induced mutation affecting egg shell morphology by using Illumina next-generation sequencing. After obtaining sufficient sequence from larvae that were homozygous for either wild-type or mutant chromosomes, we obtained high-quality reads for base pairs composing ~70% of the third chromosome of both DNA samples. We verified 103 single-base-pair changes between the two chromosomes. Nine changes were nonsynonymous mutations and two were nonsense mutations. One nonsense mutation was in a gene, encore, whose mutations produce an egg shell phenotype also observed in progeny of homozygous mutant mothers. Complementation analysis revealed that the chromosome carried a new functional allele of encore, demonstrating that one round of next-generation sequencing can identify the causative lesion for a phenotype of interest. This new method of whole-genome sequencing represents great promise for mutant mapping in flies, potentially replacing conventional methods.     

CNVannotator: A Comprehensive Annotation Server for Copy Number Variation in the Human Genome

Copy number variation (CNV) is one of the most prevalent genetic variations in the genome, leading to an abnormal number of copies of moderate to large genomic regions. High-throughput technologies such as next-generation sequencing often identify thousands of CNVs involved in biological or pathological processes. Despite the growing demand to filter and classify CNVs by factors such as frequency in population, biological features, and function, surprisingly, no online web server for CNV annotations has been made available to the research community. Here, we present CNVannotator, a web server that accepts an input set of human genomic positions in a user-friendly tabular format. CNVannotator can perform genomic overlaps of the input coordinates using various functional features, including a list of the reported 356,817 common CNVs, 181,261 disease CNVs, as well as, 140,342 SNPs from genome-wide association studies. In addition, CNVannotator incorporates 2,211,468 genomic features, including ENCODE regulatory elements, cytoband, segmental duplication, genome fragile site, pseudogene, promoter, enhancer, CpG island, and methylation site. For cancer research community users, CNVannotator can apply various filters to retrieve a subgroup of CNVs pinpointed in hundreds of tumor suppressor genes and oncogenes. In total, 5,277,234 unique genomic coordinates with functional features are available to generate an output in a plain text format that is free to download. In summary, we provide a comprehensive web resource for human CNVs. The annotated results along with the server can be accessed at http://bioinfo.mc.vanderbilt.edu/CNVannotator/.     

Coverage Bias and Sensitivity of Variant Calling for Four Whole-genome Sequencing Technologies

The emergence of high-throughput, next-generation sequencing technologies has dramatically altered the way we assess genomes in population genetics and in cancer genomics. Currently, there are four commonly used whole-genome sequencing platforms on the market: Illumina’s HiSeq2000, Life Technologies’ SOLiD 4 and its completely redesigned 5500xl SOLiD, and Complete Genomics’ technology. A number of earlier studies have compared a subset of those sequencing platforms or compared those platforms with Sanger sequencing, which is prohibitively expensive for whole genome studies. Here we present a detailed comparison of the performance of all currently available whole genome sequencing platforms, especially regarding their ability to call SNVs and to evenly cover the genome and specific genomic regions. Unlike earlier studies, we base our comparison on four different samples, allowing us to assess the between-sample variation of the platforms. We find a pronounced GC bias in GC-rich regions for Life Technologies’ platforms, with Complete Genomics performing best here, while we see the least bias in GC-poor regions for HiSeq2000 and 5500xl. HiSeq2000 gives the most uniform coverage and displays the least sample-to-sample variation. In contrast, Complete Genomics exhibits by far the smallest fraction of bases not covered, while the SOLiD platforms reveal remarkable shortcomings, especially in covering CpG islands. When comparing the performance of the four platforms for calling SNPs, HiSeq2000 and Complete Genomics achieve the highest sensitivity, while the SOLiD platforms show the lowest false positive rate. Finally, we find that integrating sequencing data from different platforms offers the potential to combine the strengths of different technologies. In summary, our results detail the strengths and weaknesses of all four whole-genome sequencing platforms. It indicates application areas that call for a specific sequencing platform and disallow other platforms. This helps to identify the proper sequencing platform for whole genome studies with different application scopes.     

高等植物基因组测序回顾与展望

全基因组序列测定为揭示植物重要性状形成的分子和遗传机制提供了强大工具,基因组学研究正开始指引着农作物新品种培育向定向化和精确化转变.在新一代测序技术的带动下,植物全基因组测序的热潮已经到来.对迄今开展的高等植物基因组测序工作进行简要回顾,并对未来的研究热点进行展望.     

Analysis of Multiple Ethyl Methanesulfonate-Mutagenized Caenorhabditis elegans Strains by Whole-Genome Sequencing

Whole-genome sequencing (WGS) of organisms displaying a specific mutant phenotype is a powerful approach to identify the genetic determinants of a plethora of biological processes. We have previously validated the feasibility of this approach by identifying a point-mutated locus responsible for a specific phenotype, observed in an ethyl methanesulfonate (EMS)-mutagenized Caenorhabditis elegans strain. Here we describe the genome-wide mutational profile of 17 EMS-mutagenized genomes as assessed with a bioinformatic pipeline, called MAQGene. Surprisingly, we find that while outcrossing mutagenized strains does reduce the total number of mutations, a striking mutational load is still observed even in outcrossed strains. Such genetic complexity has to be taken into account when establishing a causative relationship between genotype and phenotype. Even though unintentional, the 17 sequenced strains described here provide a resource of allelic variants in almost 1000 genes, including 62 premature stop codons, which represent candidate knockout alleles that will be of further use for the C. elegans community to study gene function.INDUCING molecular lesions in a genome is an effective approach to interrogate the genome for its functional elements. Molecular lesions can be induced using a variety of methods. Because of their efficiency and their ability to generate alleles with various different alterations in gene activity (e.g., amorphic, antimorphic, hypomorphic, and hypermorphic), chemical mutagens, such as ethyl methanesulfonate (EMS), are frequently used in genetic mutant screens (Anderson 1995). However, due to mutagen efficiency, a mutant animal selected for a single-locus phenotype invariably contains EMS-induced “background mutations” in its genome. Experimenters try to minimize the potential impact of background mutations through outcrossing to animals with a wild-type genome. Yet no full snapshots of genome sequences right after EMS mutagenesis and after outcrossing have so far been provided to illustrate the extent of background mutations and the extent to which they can indeed be eliminated.Another caveat of using base-changing chemical mutagens is the relative difficulty associated with identifying the phenotype-causing molecular lesion. In multicellular genetic model organisms, mutant identification involves time-consuming positional cloning approaches, usually involving breeding with genetically marked strains that allow pinpointing of the location of a molecular lesion. Even with rapid, SNP-based mapping approaches in animals with short generation times, such as Caenorhabditis elegans, substantial time hurdles, particularly in the final, fine-mapping stages, still exist. Conceptually similar problems in defining the location of a molecular lesion are encountered by human geneticists who attempt to identify disease-causing genetic lesions.Whole-genome sequencing (WGS) is beginning to emerge as an efficient and cost-effective tool to shortcut time-consuming mapping and positional cloning efforts (Hobert 2010). The sequencing of an entire genome and its ensuing comparison to a wild-type reference genome can potentially directly pinpoint the molecular lesion that results in the mutant phenotype the animal has been selected for. Proof-of-concept studies in bacteria, yeast, plants, worms, and flies have validated the applicability of this approach (Sarin et al. 2008; Smith et al. 2008; Srivatsan et al. 2008; Blumenstiel et al. 2009; Irvine et al. 2009; Flowers et al. 2010).Present-day deep sequencing platforms used for WGS generate relatively short sequence reads, thereby posing the bioinformatic challenge to align those reads to a reference genome. We previously described a software pipeline, MAQGene, which is based on the standard alignment program MAQ (Li et al. 2008) and facilitates this bioinformatic step by providing the end user with an extensively curated list of sequence variants from a WGS run of a mutated genome compared to a reference genome (Bigelow et al. 2009). This pipeline can be used for well-annotated, assembled genomes, such as C. elegans or Drosophila. In this article, we describe that this pipeline can identify not only point mutations but also deletions. We then use this pipeline to analyze a total of 17 EMS-mutagenized genomes. We find that EMS-mutagenized genomes carry a significant mutational load including presumptive loss-of-function alleles in several protein-coding genes that can lead to synthetic genetic interactions, one of which we describe here in more detail. We show that outcrossing to wild-type animals can lighten the mutational load; however, a substantial number of sequence variants are also introduced during outcrossing. Even though background mutations uncovered by WGS may complicate the interpretation of mutant phenotypes, they do provide a potentially useful source for functional studies of the affected genes.     

Increasing the Uniformity of Genome Fragment Coverage for High-Throughput Sequencing of Influenza A Virus

Molecular Biology - The high variability of the influenza A virus poses a significant threat to public health, therefore monitoring viral strains and studying their genetic properties are important...     

A Strategy for Direct Mapping and Identification of Mutations by Whole-Genome Sequencing

Mutant screens have proven powerful for genetic dissection of a myriad of biological processes, but subsequent identification and isolation of the causative mutations are usually complex and time consuming. We have made the process easier by establishing a novel strategy that employs whole-genome sequencing to simultaneously map and identify mutations without the need for any prior genetic mapping.THE challenges posed by the identification of a causal mutation in a mutant of interest have in effect restricted the use of forward genetics to those organisms benefiting from a solid genetic toolbox. Whole-genome sequencing (WGS) is promising to revolutionize the way phenotypic traits are assigned to genes. However, current strategies to identify causal mutations using WGS require first the identification of an approximate genomic location containing the mutation of interest (Sarin et al. 2008; Smith et al. 2008; Srivatsan et al. 2008; Blumenstiel et al. 2009; Irvine et al. 2009). This is because genomes contain many natural sequence variations (Denver et al. 2004; Hillier et al. 2008; Sarin et al. 2010), which, along with mutagen-induced ones, complicate the identification of the causal mutation when an approximate genomic location has not been previously identified. Mapping has previously been achieved with time-consuming and laborious techniques that, in addition, rely on an organism''s single-nucleotide polymorphism (SNP) map and established variant strains. For example, traditional SNP-based mapping (Wicks et al. 2001; Davis et al. 2005) has previously been used in Caenorhabditis elegans to narrow down the genomic region containing the mutation of interest, prior to conducting WGS (Sarin et al. 2008). In Arabidopsis, simultaneous SNP mapping and mutation identification has been achieved with WGS, but this requires the generation of a mapping population of up to 500 F₂ progeny to identify only one allele (Schneeberger et al. 2009). This is a challenging prospect for many model systems. Indeed, if the mutant phenotype is subtle, the isolation of such numbers of recombinants is very tedious. Furthermore, it is not applicable in those organisms where a mapping population cannot be generated, simply because of a lack of intercrossable variants or because of life cycles (parasitic organisms, for example) that would make it extremely difficult to follow and isolate many recombinant individuals.Here, we describe a strategy to simultaneously and rapidly locate and identify multiple mutations from a mutagenesis screen with WGS that circumvents these limitations. This powerful and straightforward method directly uses mutagen-induced nucleotide changes that are linked to the causal mutation to identify its specific genomic location, thus negating the construction of genetic mapping populations and subsequent mapping.Treatment of organisms with a chemical mutagen induces nucleotide changes throughout the genome. Following mutagenesis, backcrossing or outcrossing of the mutagenized organism to unmutagenized counterparts is performed to eliminate mutagen-induced mutations (Figure 1A; supporting information, File S2). The phenotype-causing mutation remains as only backcrossed individuals showing the phenotype of interest are retained. In addition, mutagen-induced nucleotide changes that are genetically linked to the causal mutation and physically surround it on the chromosome will remain, in contrast to unlinked nucleotide changes (Figure 1A). As a result of this genetic linkage, a high-density cluster of typical mutagen-induced variants is visualized from sequence data obtained by WGS, which is positioned around the causal mutation. By locating such high-density regions, one maps the approximate genomic location of the causal mutation and subsequently identifies the affected gene within this region.Open in a separate windowFigure 1.—Mapping mutations on the basis of density of mutagen-induced DNA damage across the genome. (A) Visual representation of our WGS cloning strategy. Mutagen treatment induces point mutations throughout the genome (red asterisks). Backcrossing to the original unmutated parent strain removes much of the mutagen-induced nucleotide changes except for the causal mutation (green asterisk) and those genetically linked to it. WGS sequencing can be used to detect canonical mutagen-induced point mutations, thus revealing a physical position for the causal mutation. Shared background variants (yellow crosses) are filtered out from WGS data by comparing the sequences of mutants sequenced side-by-side, revealing a high-density variant cluster in only one genomic region. Importantly, genomic sequences of mutants derived from the same starting strain must be compared, to allow subtraction of nucleotide variants that are common to this particular strain, through sequence comparison. (B) Physical map of total nucleotide variations per megabase across the genome compared to the wild-type reference genome for each mutant (fp6, fp9, and fp12) after WGS. (C) After sequence quality filtering, subtraction of common variants between the 3 mutants, and filtering out noncanonical EMS nucleotide changes, high-density variant peaks are obtained in one genomic location for each mutant (red boxes). Steps 1 and 3 are essential for clear visualization of the high-density peaks whereas step 2 improves visualization. (D) Close-up of variants on chromosome III for fp6. Within this peak we identified only 6 candidate mutations that could potentially affect a protein sequence. We confirmed that the missense mutation in egl-5 was the causal mutation (Figure S2). For fp9 and fp12 we identified only 10 (9 missense and 1 3′-UTR) and 4 (2 premature stop and 2 missense) candidate mutations, respectively, within each mutant''s EMS-based mapped region. Thus, our method consistently allowed precise mapping in 3 different mutants to a region small enough to contain only a handful of candidate mutations.As a proof-of-principle, we simultaneously mapped and sequenced the causal mutations of multiple C. elegans mutants isolated from an EMS mutagenesis screen using this strategy. The mutagenesis screen itself was undertaken to identify genes that controlled the reprogramming of a single cell called Y into another cell called PDA during C. elegans development (Jarriault et al. 2008). After EMS treatment, three distinct mutant alleles (fp6, fp9, and fp12) were backcrossed to the original unmutagenized strain 4-6X. It is important to note that a backcrossing or outcrossing step is necessary for the analysis of mutants obtained from all mutagenesis screens, irrespective of the type of mutant identification strategy used or the type of mutagen or organism used (and, as such, does not represent an extra step introduced by our method). The mutants then underwent WGS side-by-side (Table S1, Table S2, Figure S1, and File S2). After alignment to the wild-type N2 reference genome using MAQgene software (Bigelow et al. 2009), the sequencing data obtained for each mutant were compared, and we subtracted common nucleotide variants that were shared between at least two of our three mutants (File S1). These shared variants, which are very unlikely to be either the causal mutation or EMS-induced mutations from the screen itself, represent strain differences between the N2 used to generate the reference genome and the PS3662 strain used here for mutagenesis. Note that this step eliminated ∼2000 point mutations as potential candidates for our causal mutation. This result strongly emphasizes the advantage of conducting WGS on two or more mutants side-by-side, as reference genomes may contain many nucleotide variations when compared to organisms sequenced from the laboratory (Denver et al. 2004; Hillier et al. 2008; Sarin et al. 2010; this study) and as such would confound mutation identification.To identify EMS-induced changes linked to the causal mutation and expose its location, we looked only at variants that matched the canonical EMS-induced G/C > A/T transitions (Drake and Baltz 1976), revealing localized peaks of high-density variation on a single chromosome for each mutant (Figure 1, B and C). These peaks correspond to regions of high mutagen-induced damage that were not removed during backcrossing and therefore are most likely genetically linked to the causal mutation. We therefore focused our attention on these physical regions to identify candidate mutations within them. We localized fp6 to a 4.29-Mb region on chromosome III, fp9 to a 7.11-Mb region on chromosome X, and fp12 to a 1.28-Mb region on a different part of chromosome X (Figure 1C).As a proof of principle, we further examined the nucleotide changes present in the interval to which fp6 was linked. Taking into consideration all variant types (point mutations and indels), we identified only six candidate mutations that potentially affected a gene''s function (Figure 1D and Table S3). One of these, affecting the egl-5/hox gene, lies almost perfectly in the middle of the predicted EMS-based mapped region. We confirmed the existence of the mutation in egl-5 by manual resequencing. Both egl-5 targeted RNAi and noncomplementation with the egl-5(n945) null allele confirmed that fp6 affected egl-5 and caused the Y-to-PDA reprogramming defect (Figure S2). fp9 and fp12 each map to distinct regions on chromosome X that also contain only a handful of candidate mutations (10 and 4, respectively) (Figure 1C). Thus, our method consistently allowed precise mapping in 3 different mutants to a region small enough to contain only a handful of candidate mutations and subsequent identification of the causal mutation.We calculated that comparison of WGS data for only two mutants of the same mutagenesis screen is sufficient to localize and sequence the causal mutation (Table S4). Thirteen times sequence coverage has been found to be sufficient to identify a mutation in a pre-SNP mapped C. elegans mutant (Shen et al. 2008). Here, we tested the sequence coverage necessary to perform simultaneous mapping and mutant identification using our strategy and found that 13× was more than enough (Table S4). In addition, by performing longer reads and/or paired-end sequencing, our method can be scaled up to bigger genomes or allow multiple mutant sequencing on each flow cell lane [for, e.g., using multiplex WGS (Cronn et al. 2008)]. Furthermore, because direct sequence comparison is ultimately made between two mutants sequenced side-by-side, the quality of an organism''s reference genome (which is used only for alignment purposes) does not have a bearing on the mapping or mutant identification outcome. Moreover, recent advances in de novo alignment of short reads generated from next generation sequencing platforms (Li et al. 2010; Nowrousian et al. 2010; Webb and Rosenthal 2010; Young et al. 2010) suggest that a reference genome may not even be required to perform mutagen-based mapping and mutant identification with WGS. We predict that technical advances in these areas will make it possible to perform mutagenesis screens on any nonsequenced and genetically uncharacterized organism and use our strategy to quickly identify the causal mutation of an interesting mutant.

TABLE 1

Summary of WGS cloning strategy

Complete Genome Sequencing of Mosquito and Human Isolates of Ngari Virus

Ngari virus (NRIV) is a recently described, naturally occurring reassortant between two other orthobunyaviruses, Bunyamwera virus (BUNV) and Batai virus (BATV). Intriguingly, this reassortment was associated with the acquisition of heightened virulence, although the molecular basis for this is not understood. Here we report the first complete genome sequences of Ngari virus. We include five isolates from various geographical locations, as well as samples isolated from both mosquitos and human cases. Based on an analysis of these sequence data, NRIVs are clearly genetically distinct from all known BUNV and BATV strains but are very closely related to one another regardless of their source.     

A Single Cell Level Based Method for Copy Number Variation Analysis by Low Coverage Massively Parallel Sequencing

Copy number variations (CNVs), a common genomic mutation associated with various diseases, are important in research and clinical applications. Whole genome amplification (WGA) and massively parallel sequencing have been applied to single cell CNVs analysis, which provides new insight for the fields of biology and medicine. However, the WGA-induced bias significantly limits sensitivity and specificity for CNVs detection. Addressing these limitations, we developed a practical bioinformatic methodology for CNVs detection at the single cell level using low coverage massively parallel sequencing. This method consists of GC correction for WGA-induced bias removal, binary segmentation algorithm for locating CNVs breakpoints, and dynamic threshold determination for final signals filtering. Afterwards, we evaluated our method with seven test samples using low coverage sequencing (4∼9.5%). Four single-cell samples from peripheral blood, whose karyotypes were confirmed by whole genome sequencing analysis, were acquired. Three other test samples derived from blastocysts whose karyotypes were confirmed by SNP-array analysis were also recruited. The detection results for CNVs of larger than 1 Mb were highly consistent with confirmed results reaching 99.63% sensitivity and 97.71% specificity at base-pair level. Our study demonstrates the potential to overcome WGA-bias and to detect CNVs (>1 Mb) at the single cell level through low coverage massively parallel sequencing. It highlights the potential for CNVs research on single cells or limited DNA samples and may prove as a promising tool for research and clinical applications, such as pre-implantation genetic diagnosis/screening, fetal nucleated red blood cells research and cancer heterogeneity analysis.     

中丹科学家完成古人类全基因组测序

由中国深圳华大基因研究院和丹麦哥本哈根大学联合创建的中丹基因组联合中心以Saqqaq古人为样本,完成了世界首例古人类全基因组的深度序列测定和解读.这一历史性成果以封面故事发表在2010年2月11日出版的科学期刊上.