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News Article | May 1, 2017
Site: www.biosciencetechnology.com

Digital imaging is another area where HPC-enabled speedups are advancing clinical care. Panelist Simon K. Warfield described innovative imaging techniques his team is applying to increase understanding of the brain’s complex circuitry. Dr. Warfield is the Thorne Griscom Professor of Radiology at Harvard Medical School and the founder and director of the Computational Radiology Lab (CRL) at Boston Children's Hospital. CRL is an Intel Parallel Computing Center that is modernizing the algorithms and data structures of medical image computing on Intel Xeon and Intel Xeon Phi processors. The lab is improving cache performance, vectorization performance and multi-threading performance, as well as creating more sophisticated imaging and modeling strategies. CRL can contribute to improved diagnosis and treatment of brain injuries, multiple sclerosis, depression, Alzheimer’s and many other conditions. Consider the novel technique CRL has developed to show more clearly water’s diffusion through the brain—and pinpoint hindrances and restrictions to its flow. In contrast to traditional image processing approaches, CRL’s diffusion-weighted imaging infers new parametric maps from data measurements. Its computational model includes tens or hundreds of 3D images—each up to 10 million pixels each—as its inputs. “This type of analysis is very computationally intensive,” Warfield said. “With the accelerated algorithm and the Intel Xeon Phi processors, we reduced the time needed from 48 hours to 15 minutes of calculations.” That speedup can translate to immediate benefits in for critically ill patients facing brain surgery. That’s because, as Warfield put it, “When you’re talking about surgical planning, life is a matter of time.” Recently, one of the hospital’s neurosurgery teams realized on a Friday that their patient’s conventional magnetic resonance scan was not clear enough to allow them to proceed with a planned brain resection. With the surgery-planning meeting scheduled for Monday, they requested emergency use of CRL’s diffusion imaging algorithm. The patient had a new scan Saturday evening, the data was processed on Sunday, and the information was ready for the team’s decision on Monday. The panel also highlighted precision medicine’s global reach—and its big data challenges. Fang Lin, Director of the Bioinformatics Center at BGI, described BGI’s use of the Lustre file system to help maintain storage performance as its data volumes grow. BGI is a global research leader as well as a provider of genetic testing products. It also operates the China National Genebank, putting it on the forefront of China’s five-year. BGI cranks 20 terabytes of sequencing data every day. The institute stores13petabytes of genomic data and uses a 10 petabyte file system comprising Intel Enterprise Edition for Lustre Software and open source technologies. Dr. David Torrents, a molecular biologist and research professor at the Barcelona Supercomputing Center, shone a spotlight on the importance of collaboration in advancing precision medicine. BSC provides resources to a variety of international centers and consortia. In addition, the institute conducts its own multidisciplinary research in computational biomedicine and related fields. BSC’s alliances also encompass a range of hospitals and medical centers, enabling it to validate and test its models and tools with data from clinical institutions. “We’re at an exciting moment,” Torrents said. “We are not just developing new solutions for personalized medicine, but now are beginning a pilot program in January 2017 to bring them together and apply them in clinical settings, beginning in Catalonia and then throughout Spain.” The panelists say continued leaps forward in precision medicine will come from faster and more sophisticated analysis of larger volumes of more varied data types. “What we want is a more holistic picture, and for that, it’s becoming absolutely critical to combine many diverse data types together for analysis,” said Lowey. To achieve that holistic picture, researchers want to use deep learning and other forms of artificial intelligence. They also want to apply those AI methods to genomic data in combination with imaging data, lifelong clinical records, population studies, environmental studies, and much more. Different aspects of the precision medicine workflow will have varying processing and storage requirements. So the push continues for faster performance with agile or heterogeneous platform architectures rather than a single “silver bullet” approach. The processors will continue as the primary workhorses, supplemented by embedded resources and FPGA accelerators for parts of the workflow. Distributed compute and storage resources will remain crucial, along with advances in applications and tools. As to the clinical impact of these holistic approaches, look no further than Boston Children’s Hospital. Noninvasive prenatal genomic testing can indicate whether a fetus has the risk factors that predispose it to be born with a malformed heart. If genetic testing shows these factors are present, data-intensive digital imaging can reveal whether the heart is actually deformed. By combining genomic with other medical data in this way, clinicians can provide peace of mind for worried parents-to-be, or help them plan for their child’s future. “We’re starting to connect the genetics that predisposes an individual to heart disease, with the imaging to see if the defect is present, and use that information to influence current treatment,” said Warfield. “That information can also help us plan for the child’s longer-term future. We can predict how they’ll do as teenagers and begin to plan accordingly.” Precision medicine is one of the most promising and meaningful applications of high-performance computing today. “It’s still early days, but we’re moving toward an exciting new era of predictive biology and personalized medicine,” said McManus. “Our panelists gave us a great taste of what’s on the horizon. With continued advances in platform technologies, artificial intelligence, and other areas, we create significant opportunities to increase the science of medicine and ultimately improve human health. Intel is excited to empower scientists and clinicians with technology innovations, resources and expertise as we collaborate to make this new era a reality.” Jan Rowell writes about technology trends in HPC, healthcare, life sciences, and other industries.


News Article | May 8, 2017
Site: www.scientificcomputing.com

Product developers talk about time to market. Web service providers measure time to first byte. For James Lowey, the key metric is time to life. Lowey is CIO at the Translational Genomics Research Institute (TGen), a nonprofit focused on turning genomics insights into faster diagnostics and treatments that are more effective. TGen’s genetics research is being applied to rare childhood diseases, cancer, neurological disorders, diabetes and others. “We’ve got patients waiting,” Lowey told the panel audience. “We need to diagnose and treat them. They need results now, not in weeks or months. We’re working to accelerate the movement of insights from the bench to the bedside.” It’s no surprise that each new generation of processors helps organizations like TGen deliver genetic results—and clinical answers—more quickly. Lowey described TGen’s farm of Intel Xeon processor E5 v3 based Dell blade servers based on Intel Scalable System Framework (Intel SSF). Using the blade servers, TGen has reduced processing time for critical genomics processing tasks from two weeks to seven hours, making it fast enough to be clinically relevant. Digital imaging is another area where HPC-enabled speedups are advancing clinical care. Panelist Simon K. Warfield described innovative imaging techniques his team is applying to increase understanding of the brain’s complex circuitry. Dr. Warfield is the Thorne Griscom Professor of Radiology at Harvard Medical School and the founder and director of the Computational Radiology Lab (CRL) at Boston Children's Hospital. CRL is an Intel Parallel Computing Center that is modernizing the algorithms and data structures of medical image computing on Intel Xeon and Intel Xeon Phi processors. The lab is improving cache performance, vectorization performance and multi-threading performance, as well as creating more sophisticated imaging and modeling strategies. CRL can contribute to improved diagnosis and treatment of brain injuries, multiple sclerosis, depression, Alzheimer’s and many other conditions. Consider the novel technique CRL has developed to show more clearly water’s diffusion through the brain—and pinpoint hindrances and restrictions to its flow. In contrast to traditional image processing approaches, CRL’s diffusion-weighted imaging infers new parametric maps from data measurements. Its computational model includes tens or hundreds of 3D images—each up to 10 million pixels each—as its inputs. “This type of analysis is very computationally intensive,” Warfield said. “With the accelerated algorithm and the Intel Xeon Phi processors, we reduced the time needed from 48 hours to 15 minutes of calculations.” That speedup can translate to immediate benefits in for critically ill patients facing brain surgery. That’s because, as Warfield put it, “When you’re talking about surgical planning, life is a matter of time.” Recently, one of the hospital’s neurosurgery teams realized on a Friday that their patient’s conventional magnetic resonance scan was not clear enough to allow them to proceed with a planned brain resection. With the surgery-planning meeting scheduled for Monday, they requested emergency use of CRL’s diffusion imaging algorithm. The patient had a new scan Saturday evening, the data was processed on Sunday, and the information was ready for the team’s decision on Monday. The panel also highlighted precision medicine’s global reach—and its big data challenges. Fang Lin, Director of the Bioinformatics Center at BGI, described BGI’s use of the Lustre file system to help maintain storage performance as its data volumes grow. BGI is a global research leader as well as a provider of genetic testing products. It also operates the China National Genebank, putting it on the forefront of China’s five-year. BGI cranks 20 terabytes of sequencing data every day. The institute stores13petabytes of genomic data and uses a 10 petabyte file system comprising Intel Enterprise Edition for Lustre Software and open source technologies. Dr. David Torrents, a molecular biologist and research professor at the Barcelona Supercomputing Center, shone a spotlight on the importance of collaboration in advancing precision medicine. BSC provides resources to a variety of international centers and consortia. In addition, the institute conducts its own multidisciplinary research in computational biomedicine and related fields. BSC’s alliances also encompass a range of hospitals and medical centers, enabling it to validate and test its models and tools with data from clinical institutions. “We’re at an exciting moment,” Torrents said. “We are not just developing new solutions for personalized medicine, but now are beginning a pilot program in January 2017 to bring them together and apply them in clinical settings, beginning in Catalonia and then throughout Spain.” The panelists say continued leaps forward in precision medicine will come from faster and more sophisticated analysis of larger volumes of more varied data types. “What we want is a more holistic picture, and for that, it’s becoming absolutely critical to combine many diverse data types together for analysis,” said Lowey. To achieve that holistic picture, researchers want to use deep learning and other forms of artificial intelligence. They also want to apply those AI methods to genomic data in combination with imaging data, lifelong clinical records, population studies, environmental studies, and much more. Different aspects of the precision medicine workflow will have varying processing and storage requirements. So the push continues for faster performance with agile or heterogeneous platform architectures rather than a single “silver bullet” approach. The processors will continue as the primary workhorses, supplemented by embedded resources and FPGA accelerators for parts of the workflow. Distributed compute and storage resources will remain crucial, along with advances in applications and tools. As to the clinical impact of these holistic approaches, look no further than Boston Children’s Hospital. Noninvasive prenatal genomic testing can indicate whether a fetus has the risk factors that predispose it to be born with a malformed heart. If genetic testing shows these factors are present, data-intensive digital imaging can reveal whether the heart is actually deformed. By combining genomic with other medical data in this way, clinicians can provide peace of mind for worried parents-to-be, or help them plan for their child’s future. “We’re starting to connect the genetics that predisposes an individual to heart disease, with the imaging to see if the defect is present, and use that information to influence current treatment,” said Warfield. “That information can also help us plan for the child’s longer-term future. We can predict how they’ll do as teenagers and begin to plan accordingly.” Precision medicine is one of the most promising and meaningful applications of high-performance computing today. “It’s still early days, but we’re moving toward an exciting new era of predictive biology and personalized medicine,” said McManus. “Our panelists gave us a great taste of what’s on the horizon. With continued advances in platform technologies, artificial intelligence, and other areas, we create significant opportunities to increase the science of medicine and ultimately improve human health. Intel is excited to empower scientists and clinicians with technology innovations, resources and expertise as we collaborate to make this new era a reality.” Jan Rowell writes about technology trends in HPC, healthcare, life sciences, and other industries.


News Article | May 8, 2017
Site: www.scientificcomputing.com

Product developers talk about time to market. Web service providers measure time to first byte. For James Lowey, the key metric is time to life. Lowey is CIO at the Translational Genomics Research Institute (TGen), a nonprofit focused on turning genomics insights into faster diagnostics and treatments that are more effective. TGen’s genetics research is being applied to rare childhood diseases, cancer, neurological disorders, diabetes and others. “We’ve got patients waiting,” Lowey told the panel audience. “We need to diagnose and treat them. They need results now, not in weeks or months. We’re working to accelerate the movement of insights from the bench to the bedside.” It’s no surprise that each new generation of processors helps organizations like TGen deliver genetic results—and clinical answers—more quickly. Lowey described TGen’s farm of Intel Xeon processor E5 v3 based Dell blade servers based on Intel Scalable System Framework (Intel SSF). Using the blade servers, TGen has reduced processing time for critical genomics processing tasks from two weeks to seven hours, making it fast enough to be clinically relevant. Digital imaging is another area where HPC-enabled speedups are advancing clinical care. Panelist Simon K. Warfield described innovative imaging techniques his team is applying to increase understanding of the brain’s complex circuitry. Dr. Warfield is the Thorne Griscom Professor of Radiology at Harvard Medical School and the founder and director of the Computational Radiology Lab (CRL) at Boston Children's Hospital. CRL is an Intel Parallel Computing Center that is modernizing the algorithms and data structures of medical image computing on Intel Xeon and Intel Xeon Phi processors. The lab is improving cache performance, vectorization performance and multi-threading performance, as well as creating more sophisticated imaging and modeling strategies. CRL can contribute to improved diagnosis and treatment of brain injuries, multiple sclerosis, depression, Alzheimer’s and many other conditions. Consider the novel technique CRL has developed to show more clearly water’s diffusion through the brain—and pinpoint hindrances and restrictions to its flow. In contrast to traditional image processing approaches, CRL’s diffusion-weighted imaging infers new parametric maps from data measurements. Its computational model includes tens or hundreds of 3D images—each up to 10 million pixels each—as its inputs. “This type of analysis is very computationally intensive,” Warfield said. “With the accelerated algorithm and the Intel Xeon Phi processors, we reduced the time needed from 48 hours to 15 minutes of calculations.” That speedup can translate to immediate benefits in for critically ill patients facing brain surgery. That’s because, as Warfield put it, “When you’re talking about surgical planning, life is a matter of time.” Recently, one of the hospital’s neurosurgery teams realized on a Friday that their patient’s conventional magnetic resonance scan was not clear enough to allow them to proceed with a planned brain resection. With the surgery-planning meeting scheduled for Monday, they requested emergency use of CRL’s diffusion imaging algorithm. The patient had a new scan Saturday evening, the data was processed on Sunday, and the information was ready for the team’s decision on Monday. The panel also highlighted precision medicine’s global reach—and its big data challenges. Fang Lin, Director of the Bioinformatics Center at BGI, described BGI’s use of the Lustre file system to help maintain storage performance as its data volumes grow. BGI is a global research leader as well as a provider of genetic testing products. It also operates the China National Genebank, putting it on the forefront of China’s five-year. BGI cranks 20 terabytes of sequencing data every day. The institute stores13petabytes of genomic data and uses a 10 petabyte file system comprising Intel Enterprise Edition for Lustre Software and open source technologies. Dr. David Torrents, a molecular biologist and research professor at the Barcelona Supercomputing Center, shone a spotlight on the importance of collaboration in advancing precision medicine. BSC provides resources to a variety of international centers and consortia. In addition, the institute conducts its own multidisciplinary research in computational biomedicine and related fields. BSC’s alliances also encompass a range of hospitals and medical centers, enabling it to validate and test its models and tools with data from clinical institutions. “We’re at an exciting moment,” Torrents said. “We are not just developing new solutions for personalized medicine, but now are beginning a pilot program in January 2017 to bring them together and apply them in clinical settings, beginning in Catalonia and then throughout Spain.” The panelists say continued leaps forward in precision medicine will come from faster and more sophisticated analysis of larger volumes of more varied data types. “What we want is a more holistic picture, and for that, it’s becoming absolutely critical to combine many diverse data types together for analysis,” said Lowey. To achieve that holistic picture, researchers want to use deep learning and other forms of artificial intelligence. They also want to apply those AI methods to genomic data in combination with imaging data, lifelong clinical records, population studies, environmental studies, and much more. Different aspects of the precision medicine workflow will have varying processing and storage requirements. So the push continues for faster performance with agile or heterogeneous platform architectures rather than a single “silver bullet” approach. The processors will continue as the primary workhorses, supplemented by embedded resources and FPGA accelerators for parts of the workflow. Distributed compute and storage resources will remain crucial, along with advances in applications and tools. As to the clinical impact of these holistic approaches, look no further than Boston Children’s Hospital. Noninvasive prenatal genomic testing can indicate whether a fetus has the risk factors that predispose it to be born with a malformed heart. If genetic testing shows these factors are present, data-intensive digital imaging can reveal whether the heart is actually deformed. By combining genomic with other medical data in this way, clinicians can provide peace of mind for worried parents-to-be, or help them plan for their child’s future. “We’re starting to connect the genetics that predisposes an individual to heart disease, with the imaging to see if the defect is present, and use that information to influence current treatment,” said Warfield. “That information can also help us plan for the child’s longer-term future. We can predict how they’ll do as teenagers and begin to plan accordingly.” Precision medicine is one of the most promising and meaningful applications of high-performance computing today. “It’s still early days, but we’re moving toward an exciting new era of predictive biology and personalized medicine,” said McManus. “Our panelists gave us a great taste of what’s on the horizon. With continued advances in platform technologies, artificial intelligence, and other areas, we create significant opportunities to increase the science of medicine and ultimately improve human health. Intel is excited to empower scientists and clinicians with technology innovations, resources and expertise as we collaborate to make this new era a reality.” Jan Rowell writes about technology trends in HPC, healthcare, life sciences, and other industries.


Tang M.,China National GeneBank | Tan M.,China National GeneBank | Tan M.,University of Chinese Academy of Sciences | Meng G.,China National GeneBank | And 10 more authors.
Nucleic Acids Research | Year: 2014

The advent in high-throughput-sequencing (HTS) technologies has revolutionized conventional biodiversity research by enabling parallel capture of DNA sequences possessing species-level diagnosis. However, polymerase chain reaction (PCR)-based implementation is biased by the efficiency of primer binding across lineages of organisms. A PCR-free HTS approach will alleviate this artefact and significantly improve upon the multi-locus method utilizing full mitogenomes. Here we developed a novel multiplex sequencing and assembly pipeline allowing for simultaneous acquisition of full mitogenomes from pooled animals without DNA enrichment or amplification. By concatenating assemblies from three de novo assemblers, we obtained high-quality mitogenomes for all 49 pooled taxa, with 36 species >15 kb and the remaining >10 kb, including 20 complete mitogenomes and nearly all protein coding genes (99.6%). The assembly quality was carefully validated with Sanger sequences, reference genomes and conservativeness of protein coding genes across taxa. The new method was effective even for closely related taxa, e.g. three Drosophila spp., demonstrating its broad utility for biodiversity research and mito-phylogenomics. Finally, the in silicosimulation showed that by recruiting multiple mito-loci, taxon detection was improved at a fixed sequencing depth. Combined, these results demonstrate the plausibility of a multi-locus mito-metagenomics approach as the next phase of the current single-locus metabarcoding method. © The Author(s) 2014. Published by Oxford University Press on behalf of Nucleic Acids Research.


Shao C.,Chinese Academy of Fishery Sciences | Li Q.,China National Genebank | Li Q.,South China University of Technology | Li Q.,Copenhagen University | And 20 more authors.
Genome Research | Year: 2014

Environmental sex determination (ESD) occurs in divergent, phylogenetically unrelated taxa, and in some species, cooccurs with genetic sex determination (GSD) mechanisms. Although epigenetic regulation in response to environmental effects has long been proposed to be associated with ESD, a systemic analysis on epigenetic regulation of ESD is still lacking. Using half-smooth tongue sole (Cynoglossus semilaevis) as a model-a marine fish that has both ZW chromosomal GSD and temperature-dependent ESD-we investigated the role of DNA methylation in transition from GSD to ESD. Comparativeanalysis of the gonadal DNA methylomes of pseudomale, female, and normal male fish revealed that genes in the sex determination pathways are the major targets of substantial methylation modification during sexual reversal. Methylation modification in pseudomales is globally inherited in their ZW offspring, which can naturally develop into pseudomales without temperature incubation. Transcriptome analysis revealed that dosage compensation occurs in a restricted, methylated cytosine enriched Z chromosomal region in pseudomale testes, achieving equal expression level in normal male testes. In contrast, female-specific W chromosomal genes are suppressed in pseudomales by methylation regulation. We conclude that epigenetic regulation plays multiple crucial roles in sexual reversal of tongue sole fish. We also offer the first clues on the mechanisms behind gene dosage balancing in an organism that undergoes sexual reversal. Finally, we suggest a causal link between the bias sex chromosome assortment in the offspring of a pseudomale family and the transgenerational epigenetic inheritance of sexual reversal in tongue sole fish. ©2014 Shao et al.


Pfenning A.R.,Howard Hughes Medical Institute | Hara E.,Howard Hughes Medical Institute | Whitney O.,Howard Hughes Medical Institute | Rivas M.V.,Howard Hughes Medical Institute | And 23 more authors.
Science | Year: 2014

Song-learning birds and humans share independently evolved similarities in brain pathways for vocal learning that are essential for song and speech and are not found in most other species. Comparisons of brain transcriptomes of song-learning birds and humans relative to vocal nonlearners identified convergent gene expression specializations in specific song and speech brain regions of avian vocal learners and humans. The strongest shared profiles relate bird motor and striatal song-learning nuclei, respectively, with human laryngeal motor cortex and parts of the striatum that control speech production and learning. Most of the associated genes function in motor control and brain connectivity. Thus, convergent behavior and neural connectivity for a complex trait are associated with convergent specialized expression of multiple genes.


Kocher S.D.,Harvard University | Kocher S.D.,CAS Kunming Institute of Zoology | Li C.,China National GeneBank | Li C.,Copenhagen University | And 11 more authors.
Genome Biology | Year: 2013

Background: Taxa that harbor natural phenotypic variation are ideal for ecological genomic approaches aimed at understanding how the interplay between genetic and environmental factors can lead to the evolution of complex traits. Lasioglossum albipes is a polymorphic halictid bee that expresses variation in social behavior among populations, and common-garden experiments have suggested that this variation is likely to have a genetic component.Results: We present the L. albipes genome assembly to characterize the genetic and ecological factors associated with the evolution of social behavior. The de novo assembly is comparable to other published social insect genomes, with an N50 scaffold length of 602 kb. Gene families unique to L. albipes are associated with integrin-mediated signaling and DNA-binding domains, and several appear to be expanded in this species, including the glutathione-s-transferases and the inositol monophosphatases. L. albipes has an intact DNA methylation system, and in silico analyses suggest that methylation occurs primarily in exons. Comparisons to other insect genomes indicate that genes associated with metabolism and nucleotide binding undergo accelerated evolution in the halictid lineage. Whole-genome resequencing data from one solitary and one social L. albipes female identify six genes that appear to be rapidly diverging between social forms, including a putative odorant receptor and a cuticular protein.Conclusions: L. albipes represents a novel genetic model system for understanding the evolution of social behavior. It represents the first published genome sequence of a primitively social insect, thereby facilitating comparative genomic studies across the Hymenoptera as a whole. © 2013 Kocher et al.; licensee BioMed Central Ltd.


Oxley P.R.,Rockefeller University | Ji L.,China National Genebank | Fetter-Pruneda I.,Rockefeller University | McKenzie S.K.,Rockefeller University | And 5 more authors.
Current Biology | Year: 2014

Social insects are important models for social evolution and behavior. However, in many species, experimental control over important factors that regulate division of labor, such as genotype and age, is limited [1, 2]. Furthermore, most species have fixed queen and worker castes, making it difficult to establish causality between the molecular mechanisms that underlie reproductive division of labor, the hallmark of insect societies [3]. Here we present the genome of the queenless clonal raider ant Cerapachys biroi, a powerful new study system that does not suffer from these constraints. Using cytology and RAD-seq, we show that C. biroi reproduces via automixis with central fusion and that heterozygosity is lost extremely slowly. As a consequence, nestmates are almost clonally related (r = 0.996). Workers in C. biroi colonies synchronously alternate between reproduction and brood care, and young workers eclose in synchronized cohorts. We show that genes associated with division of labor in other social insects are conserved in C. biroi and dynamically regulated during the colony cycle. With unparalleled experimental control over an individual's genotype and age, and the ability to induce reproduction and brood care [4, 5], C. biroi has great potential to illuminate the molecular regulation of division of labor. © 2014 Elsevier Ltd.


Nadachowska-Brzyska K.,Uppsala University | Li C.,China National GeneBank | Li C.,Copenhagen University | Smeds L.,Uppsala University | And 3 more authors.
Current Biology | Year: 2015

Global climate fluctuations have significantly influenced the distribution and abundance of biodiversity [1]. During unfavorable glacial periods, many species experienced range contraction and fragmentation, expanding again during interglacials [2-4]. An understanding of the evolutionary consequences of both historical and ongoing climate changes requires knowledge of the temporal dynamics of population numbers during such climate cycles. Variation in abundance should have left clear signatures in the patterns of intraspecific genetic variation in extant species, from which historical effective population sizes (Ne) can be estimated [3]. We analyzed whole-genome sequences of 38 avian species in a pairwise sequentially Markovian coalescent (PSMC, [5]) framework to quantitatively reveal changes in Ne from approximately 10 million to 10 thousand years ago. Significant fluctuations in Ne over time were evident for most species. The most pronounced pattern observed in many species was a severe reduction in Ne coinciding with the beginning of the last glacial period (LGP). Among species, Ne varied by at least three orders of magnitude, exceeding 1 million in the most abundant species. Several species on the IUCN Red List of Threatened Species showed long-term reduction in population size, predating recent declines. We conclude that cycles of population expansions and contractions have been a common feature of many bird species during the Quaternary period, likely coinciding with climate cycles. Population size reduction should have increased the risk of extinction but may also have promoted speciation. Species that have experienced long-term declines may be especially vulnerable to recent anthropogenic threats. © 2015 Elsevier Ltd.


Zhou Q.,University of California at Berkeley | Zhang J.,China National Genebank | Bachtrog D.,University of California at Berkeley | An N.,China National Genebank | And 6 more authors.
Science | Year: 2014

Sex-specific chromosomes, like the W of most female birds and the Y of male mammals, usually have lost most genes owing to a lack of recombination.We analyze newly available genomes of 17 bird species representing the avian phylogenetic range, and find that more than half of them do not have as fully degenerated W chromosomes as that of chicken. We show that avian sex chromosomes harbor tremendous diversity among species in their composition of pseudoautosomal regions and degree of Z/W differentiation. Punctuated events of shared or lineage-specific recombination suppression have produced a gradient of "evolutionary strata" along the Z chromosome, which initiates from the putative avian sex-determining gene DMRT1 and ends at the pseudoautosomal region.W-linked genes are subject to ongoing functional decay after recombination was suppressed, and the tempo of degeneration slows down in older strata. Overall, we unveil a complex history of avian sex chromosome evolution.

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